The AS is regulated by large number of proteins/non-coding RNAs/DNA and large complex network interactions among them provide the perfect capacity of cell regulation. In addition, the posttranscriptional regulation is orchestrated so finely that the cells have capacity to response rapidly before a stimulus and the cell adjust their proteome. Additionally, the cell is exposed daily to several toxics agents, UV radiation, promoting vulnerability to mutations and misregulation. Particularly, the mutations plays an important role in aberrant AS that cause diseases especially neuromuscular, neurodegenerative and multifactorial diseases as cancer [20]. Three sequences are extremely important for RNA processing and mature, the 5′, 3′ splice site; 5′, 3′ introns end and the branch point sequence, which is usually located at ~40 upstream of 3'splice site, because contain the specific sequences of recognition by spliceosome for precise exon joining [21]. However, mutations in those sites disrupt the correct spliceosome assembly. Approximately 10% of genetic diseases are cause by point mutations that disrupt the

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The class mutation and locations in the genome, contributing to different variants of AS such as: exon skipping, exon retention, alternative 5′ and 3′, among others. The severity of the disease could be represented by intensity of expression of the mutate gene, for example: In spinal muscular atrophy (SMA) the SMN2 gene has C → T change in the exon 7, this change promotes an exon skipping (SMN∆7) and their expression is proportionally mayor ~80% than ~ 20% in the healthy. Other case is in Duchenne muscular dystrophy (DND), in the dystrophin gene there is a substitution of T →A in the exon 31, promoting this exon skipping. In cystic fibrosis, the exclusion of the exon 9 in CFTR modifying the severity of the disease. In the Peutz-Jeghers syndrome the alternative transcript of LBK1 is expressed as consequence of change IVS2 + 1A > G [24].

In cancer, several alterations are involved to cell transformation, recently studies have showed that the AS plays an important role in cancer development, because change the transcriptomics and consequently the proteome; contributing to cell transformation [19, 25]. However, there are few studies focused on the identification of transcripts variants in cancer. Computational studies in cancer derived of expression sequence tags has showed that the AS in cancer was slightly lower in tumors than normal tissues [26]. The question is what is the difference between AS in cancer tissues and normal tissues? The aberrant transcripts expressed in cancer have shown a great potential as biomarkers or therapeutics targets. In breast cancer, CD44 gene can to transcribe seven alternate transcripts; the transcript variants five and seven have been involved in diverse pathologies, but the transcript six only is expressed in metastatic cancer and tumorigenic cell lines. These finding suggesting that an alternative transcript six of CD44 could play role in metastasis process [27, 28]. The BRCA1 has been involved in diverse types of cancer, in breast malignancies the mutation c.591C > T is implicated in skipping of exon 18 in BRCA1 transcript, the mutation constitute an important prognostic factor in familiar breast and ovarian cancer [29]. In gastric cancer, the KIT gene has a deletion of ~40 nucleotides, this cleavage promotes aberrant AS and loss of functional protein resulting [30].

The RNA splicing involves several steps and includes specific signals that delimited intronic and exonic sequences (splice site, SS), and sequences that help to exon skipping such as: intronic splicing enhancer (ISE), intronic splicing silencer (ISS), exonic splicing enhancer (ESE) and exonic splicing silencer (ESS) [1]. In addition, five small nuclear ribonocleoproteins (snRNP; U1, U2, U4, U5 y U6) and more than 150 additional co-factors that contributing to splicing [2, 3].

Basically, four signals that include: branch point, polypyrimidine tract, splice site 5′ and splice site 3′ [4–6]. Moreover, sequential steps that confer topological changes between RNA and snRNPs forming E complex, A complex (ATP dependent), B complex and finally C complex or spliceosome, which is the catalytic complex [7]. Additionally, the RNA could be subjected to alternative exon skipping by means of alternative splicing AS. The AS is processed using the basic machinery of splicing, and SR and hnRNPs plays an essential role for alternative exon skipping.

The coding RNA is represented by ~25,000 genes, however, more than 300,000 transcripts have been reported [8, 9]. The difference between genes and transcripts is probability by alternative splicing (AS) regulation. Actually, we know that more than 80% of RNA coding are subjected to AS, promoting a great diversity of mRNA and consequently proteins. For example, in Drosophila melanogaster, the gene Down Syndrome Cell Adhesion Molecule (Dscam) could generate more than 38,000 different mRNAs by means of alternative splicing [10]. These findings showed the importance of AS for the biology of the cell.

On the other hand, the long non-coding RNAs (lncRNAs) also could be subject to alternative splicing. However, the diversity of lncRNAs transcripts has been poorly studied. It is thought that AS in lncRNAs could be implicated in several regulatory processes, mainly mediated RNA-RNA, RNA-DNA and RNA-Proteins interaction. All possibility interaction, probability could increase the complex regulatory process.

We will focus in coding RNA. The coding RNA only is representing for ~2% of total RNA. It is known that in eukaryotic cell there are more events of AS than another organism [11, 12]. Among AS patters we found alternative promoters, exon skipping, intron retention, mutually exclusive exons, exon scrambling, Alternative 5′ splice site, alternative 3'splice site, alternative polyadenylation [4]. Interestingly, the proteins product of AS could change their native functions. In several human diseases, the AS contributes to diverse cellular process including: cell proliferation, migration, adhesion, metastasis, among others [9, 13–16]. The transcripts subject to AS and their product could be used as molecular markers and therapeutics targets, because only it is expressed in the disease or its expression is increase [11, 17–19].

## **3. Alternative splicing and diseases**

**2. RNA splicing**

56 Bioinformatics in the Era of Post Genomics and Big Data

splicing [2, 3].

The gene expression is orchestrated by means of great interaction of molecules including: SR proteins, hnRNPS, RNA, DNA, histones methylation, among others. The RNA is a fundamental molecule for the life. Recent studies have shown that the human transcriptome is fractionated in coding and non-coding RNA. Interestingly, the coding RNA is representing for only 2% of the human transcriptome and the remains is non-coding RNA, suggesting large versatility to generate protein diversity. The pre-RNA is matured by several events include, addition of a poly (A) tail in the 3′, 5′m7G cap in endings and RNA splicing; those modifica-

The RNA splicing involves several steps and includes specific signals that delimited intronic and exonic sequences (splice site, SS), and sequences that help to exon skipping such as: intronic splicing enhancer (ISE), intronic splicing silencer (ISS), exonic splicing enhancer (ESE) and exonic splicing silencer (ESS) [1]. In addition, five small nuclear ribonocleoproteins (snRNP; U1, U2, U4, U5 y U6) and more than 150 additional co-factors that contributing to

Basically, four signals that include: branch point, polypyrimidine tract, splice site 5′ and splice site 3′ [4–6]. Moreover, sequential steps that confer topological changes between RNA and snRNPs forming E complex, A complex (ATP dependent), B complex and finally C complex or spliceosome, which is the catalytic complex [7]. Additionally, the RNA could be subjected to alternative exon skipping by means of alternative splicing AS. The AS is processed using the basic machin-

The coding RNA is represented by ~25,000 genes, however, more than 300,000 transcripts have been reported [8, 9]. The difference between genes and transcripts is probability by alternative splicing (AS) regulation. Actually, we know that more than 80% of RNA coding are subjected to AS, promoting a great diversity of mRNA and consequently proteins. For example, in Drosophila melanogaster, the gene Down Syndrome Cell Adhesion Molecule (Dscam) could generate more than 38,000 different mRNAs by means of alternative splicing [10]. These

On the other hand, the long non-coding RNAs (lncRNAs) also could be subject to alternative splicing. However, the diversity of lncRNAs transcripts has been poorly studied. It is thought that AS in lncRNAs could be implicated in several regulatory processes, mainly mediated RNA-RNA, RNA-DNA and RNA-Proteins interaction. All possibility interaction, probability

We will focus in coding RNA. The coding RNA only is representing for ~2% of total RNA. It is known that in eukaryotic cell there are more events of AS than another organism [11, 12]. Among AS patters we found alternative promoters, exon skipping, intron retention, mutually exclusive exons, exon scrambling, Alternative 5′ splice site, alternative 3'splice site, alternative polyadenylation [4]. Interestingly, the proteins product of AS could change their native functions. In several human diseases, the AS contributes to diverse cellular process including: cell proliferation, migration, adhesion, metastasis, among others [9, 13–16]. The transcripts subject to AS and their product could be used as molecular markers and therapeutics targets,

because only it is expressed in the disease or its expression is increase [11, 17–19].

ery of splicing, and SR and hnRNPs plays an essential role for alternative exon skipping.

findings showed the importance of AS for the biology of the cell.

could increase the complex regulatory process.

tions conferring RNA stability, transport efficiency to the cytoplasm, among others.

The AS is regulated by large number of proteins/non-coding RNAs/DNA and large complex network interactions among them provide the perfect capacity of cell regulation. In addition, the posttranscriptional regulation is orchestrated so finely that the cells have capacity to response rapidly before a stimulus and the cell adjust their proteome. Additionally, the cell is exposed daily to several toxics agents, UV radiation, promoting vulnerability to mutations and misregulation. Particularly, the mutations plays an important role in aberrant AS that cause diseases especially neuromuscular, neurodegenerative and multifactorial diseases as cancer [20].

Three sequences are extremely important for RNA processing and mature, the 5′, 3′ splice site; 5′, 3′ introns end and the branch point sequence, which is usually located at ~40 upstream of 3'splice site, because contain the specific sequences of recognition by spliceosome for precise exon joining [21]. However, mutations in those sites disrupt the correct spliceosome assembly. Approximately 10% of genetic diseases are cause by point mutations that disrupt the interaction between RNA and spliceosome [22, 23].

The class mutation and locations in the genome, contributing to different variants of AS such as: exon skipping, exon retention, alternative 5′ and 3′, among others. The severity of the disease could be represented by intensity of expression of the mutate gene, for example: In spinal muscular atrophy (SMA) the SMN2 gene has C → T change in the exon 7, this change promotes an exon skipping (SMN∆7) and their expression is proportionally mayor ~80% than ~ 20% in the healthy. Other case is in Duchenne muscular dystrophy (DND), in the dystrophin gene there is a substitution of T →A in the exon 31, promoting this exon skipping. In cystic fibrosis, the exclusion of the exon 9 in CFTR modifying the severity of the disease. In the Peutz-Jeghers syndrome the alternative transcript of LBK1 is expressed as consequence of change IVS2 + 1A > G [24].

## **4. Alternative splicing and cancer**

In cancer, several alterations are involved to cell transformation, recently studies have showed that the AS plays an important role in cancer development, because change the transcriptomics and consequently the proteome; contributing to cell transformation [19, 25]. However, there are few studies focused on the identification of transcripts variants in cancer. Computational studies in cancer derived of expression sequence tags has showed that the AS in cancer was slightly lower in tumors than normal tissues [26]. The question is what is the difference between AS in cancer tissues and normal tissues? The aberrant transcripts expressed in cancer have shown a great potential as biomarkers or therapeutics targets. In breast cancer, CD44 gene can to transcribe seven alternate transcripts; the transcript variants five and seven have been involved in diverse pathologies, but the transcript six only is expressed in metastatic cancer and tumorigenic cell lines. These finding suggesting that an alternative transcript six of CD44 could play role in metastasis process [27, 28]. The BRCA1 has been involved in diverse types of cancer, in breast malignancies the mutation c.591C > T is implicated in skipping of exon 18 in BRCA1 transcript, the mutation constitute an important prognostic factor in familiar breast and ovarian cancer [29]. In gastric cancer, the KIT gene has a deletion of ~40 nucleotides, this cleavage promotes aberrant AS and loss of functional protein resulting [30].

In the healthy cell a several proteins are key for DNA repair, transcript regulators, among others. The BCL protein is very important in programed cell death. However, the cancer cell up regulates the expression and AS of BCL-xL, promoting the expression of long protein involved in anti-apoptotic process. In contrast, with short protein BCL-xS is involved in the apoptosis [31]. In ovarian cancer was found a new alternative transcript of p53 (TP53INP2) its expression is strongly associated to migration and cell invasion [32] its expression is associated to adverse prognosis [33]. The leukemia is the most frequency malignance in childhood, this neoplasia is not the exception also has been identifying AS in several transcripts including: CCAR1 promote the complex Par-4/THAP1 y Notch3 [34] and confer unfavorable prognosis as well as hMLH1Delta6 [35]. The Ikaros is a suppressor tumor gene, the variant IK11 is associated to proliferation and anti-apoptotic process [36].

## **5. Alternative splice transcript/proteins as molecular markers and therapeutic targets**

design. Usually the primers are flanking the exon skipping; however, this method not could detect novel splice transcripts. The in situ hybridization is other method that is used for AS detection as well as PCR this method no could detect novel splice sites. In the last 20 years has been developed massive method for detection gene expression. These tools have provided quickly gene expression profiles diseases-associated. Nowadays, gene expression microarrays and next generation sequencing are being used for detection novel molecules expressed in diverse diseases, include alternative mRNA spliced. Actually, the microarray gene expression (MGE) can measure exon expression, in this context, the low expression or suppression in particular probe set could be indicating AS **Figure 1**. Up to day, there are 25,252 assays performed with Affymetrix Human Exon 1.0 ST; 39,836 assays using Affymetrix GeneChip Human Gene 1.0 ST; and the most recently version 2422 assays with Affymetrix GeneChip Human Gene 2.0 ST, the experiments were performed between 8/7/07 and 8/8/16, 8/12/08 and 12/1/17 and 8/1/13 to 12/19/17, respectively each version array. Moreover, the microarrays data are available for data mining provides extraordinary information about profiles in human diseases including cancer. Additionally, the microarray analysis can be driving to

**Figure 1.** Representative probe set signal in microarray and alternative splicing detection. The figure showed in the top the probe set, the markers that inspect exon level expression. In the middle part depicted signal intensity in microarray

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hybridization. On the bottom the alternative splicing by low signal intensity is shown.

The most common type of cancer is the breast cancer with more than 255,000 new cases expected in the United States in 2017, followed lung and prostate cancer according to National Cancer Institute. The question is Which are the transcripts alternatively spliced between normal and cancerous tissues? The major difficulty has been to determine whether the splicing changes detected in cancer are pathogenic [26]. Then we showed different analysis using high density microarrays to identify AS in three models of cancer. We performed data mining of Affymetrix microarrays. The data were download of ArrayExpress (https://www.ebi.ac.uk/ arrayexpress/) [44] or Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) [45]. The microarray analysis was performed using Partek Genomics Suite v7.17 according to

**7. Alternative splicing in the most common cancer types**

explorer the AS.

previous reports [46].

The great challenge in cancer is the identification of the molecular markers and therapeutic targets. The proteins and transcripts products of AS are a magnify molecules because open some new opportunities in cancer. The aberrant AS is a consequence of malignant transformation, the mutations and gene expression modulation promote the expression of new molecules that confers advantage to cancer cell, such as: cell proliferation, migration, invasion, evading programed death, among others. In this context, the identification of molecules expressed in cancer could be a best molecular marker as well as treatment targets, because only are expressed in pathological tissue. There is a little information about of AS profiles in cancer, nevertheless, some molecules have been used such as molecular markers. The CD44 isoforms be predictive to anti CD44 treatment in many types of cancer [37]. The androgen receptor AR-V7 has been used as a predictive marker [38], patients who expressed V7 isoforms are resistant to therapy using enzalutamide and abiraterone [39]. The isoforms of SLC39A14 are used to detection of non- invasive colorectal cancer and the isoform is specific of the colon and rectum [40, 41]. The new transcript variant of VNN1 also be specific of cancer colon cancer and is used detection by their specificity [42].

The prospect for treatment of cancer is based on antibodies specifics for isoform expressed exclusively in the disease. However, there are other strategies that also could be used with RNA target, such as: using stable antisense RNAs, this approach could be used in different types of RNAs (coding and non-coding RNA) inclusively pre-RNA. The interference RNA is other strategy used in the elimination of aberrant expressed transcripts or even splicing variant [43].

## **6. Alternative splicing methods for detection**

The mRNA splice is easily visualized using several tools for molecular biology; the most used is the RT-PCR. The implications for AS detection using the PCR, is based on primers

Alternative RNA Splicing: New Approaches for Molecular Marker Discovery in Cancer http://dx.doi.org/10.5772/intechopen.74415 59

In the healthy cell a several proteins are key for DNA repair, transcript regulators, among others. The BCL protein is very important in programed cell death. However, the cancer cell up regulates the expression and AS of BCL-xL, promoting the expression of long protein involved in anti-apoptotic process. In contrast, with short protein BCL-xS is involved in the apoptosis [31]. In ovarian cancer was found a new alternative transcript of p53 (TP53INP2) its expression is strongly associated to migration and cell invasion [32] its expression is associated to adverse prognosis [33]. The leukemia is the most frequency malignance in childhood, this neoplasia is not the exception also has been identifying AS in several transcripts including: CCAR1 promote the complex Par-4/THAP1 y Notch3 [34] and confer unfavorable prognosis as well as hMLH1Delta6 [35]. The Ikaros is a suppressor tumor gene, the variant IK11 is

**5. Alternative splice transcript/proteins as molecular markers and**

The great challenge in cancer is the identification of the molecular markers and therapeutic targets. The proteins and transcripts products of AS are a magnify molecules because open some new opportunities in cancer. The aberrant AS is a consequence of malignant transformation, the mutations and gene expression modulation promote the expression of new molecules that confers advantage to cancer cell, such as: cell proliferation, migration, invasion, evading programed death, among others. In this context, the identification of molecules expressed in cancer could be a best molecular marker as well as treatment targets, because only are expressed in pathological tissue. There is a little information about of AS profiles in cancer, nevertheless, some molecules have been used such as molecular markers. The CD44 isoforms be predictive to anti CD44 treatment in many types of cancer [37]. The androgen receptor AR-V7 has been used as a predictive marker [38], patients who expressed V7 isoforms are resistant to therapy using enzalutamide and abiraterone [39]. The isoforms of SLC39A14 are used to detection of non- invasive colorectal cancer and the isoform is specific of the colon and rectum [40, 41]. The new transcript variant of VNN1 also be specific of cancer colon cancer

The prospect for treatment of cancer is based on antibodies specifics for isoform expressed exclusively in the disease. However, there are other strategies that also could be used with RNA target, such as: using stable antisense RNAs, this approach could be used in different types of RNAs (coding and non-coding RNA) inclusively pre-RNA. The interference RNA is other strategy used in the elimination of aberrant expressed transcripts or even splicing

The mRNA splice is easily visualized using several tools for molecular biology; the most used is the RT-PCR. The implications for AS detection using the PCR, is based on primers

associated to proliferation and anti-apoptotic process [36].

58 Bioinformatics in the Era of Post Genomics and Big Data

and is used detection by their specificity [42].

**6. Alternative splicing methods for detection**

**therapeutic targets**

variant [43].

**Figure 1.** Representative probe set signal in microarray and alternative splicing detection. The figure showed in the top the probe set, the markers that inspect exon level expression. In the middle part depicted signal intensity in microarray hybridization. On the bottom the alternative splicing by low signal intensity is shown.

design. Usually the primers are flanking the exon skipping; however, this method not could detect novel splice transcripts. The in situ hybridization is other method that is used for AS detection as well as PCR this method no could detect novel splice sites. In the last 20 years has been developed massive method for detection gene expression. These tools have provided quickly gene expression profiles diseases-associated. Nowadays, gene expression microarrays and next generation sequencing are being used for detection novel molecules expressed in diverse diseases, include alternative mRNA spliced. Actually, the microarray gene expression (MGE) can measure exon expression, in this context, the low expression or suppression in particular probe set could be indicating AS **Figure 1**. Up to day, there are 25,252 assays performed with Affymetrix Human Exon 1.0 ST; 39,836 assays using Affymetrix GeneChip Human Gene 1.0 ST; and the most recently version 2422 assays with Affymetrix GeneChip Human Gene 2.0 ST, the experiments were performed between 8/7/07 and 8/8/16, 8/12/08 and 12/1/17 and 8/1/13 to 12/19/17, respectively each version array. Moreover, the microarrays data are available for data mining provides extraordinary information about profiles in human diseases including cancer. Additionally, the microarray analysis can be driving to explorer the AS.

## **7. Alternative splicing in the most common cancer types**

The most common type of cancer is the breast cancer with more than 255,000 new cases expected in the United States in 2017, followed lung and prostate cancer according to National Cancer Institute. The question is Which are the transcripts alternatively spliced between normal and cancerous tissues? The major difficulty has been to determine whether the splicing changes detected in cancer are pathogenic [26]. Then we showed different analysis using high density microarrays to identify AS in three models of cancer. We performed data mining of Affymetrix microarrays. The data were download of ArrayExpress (https://www.ebi.ac.uk/ arrayexpress/) [44] or Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) [45]. The microarray analysis was performed using Partek Genomics Suite v7.17 according to previous reports [46].

## **8. Breast cancer**

We performed analysis using the data set E-GEOD-81838 available in ArrayExpress web page or GEO-GSE81838 available in GEO, the data that we used was published by Lehmann et al. [47]. The data set was established by 10 breast tumors and 10 stromal cells. Our analysis showed 605 differential and alternatively spliced transcripts, the top 10 overexpress and suppressed are showed in the **Table 1**. We showed the most significant over and down expressed. The DTL transcript have 15 exons, is over expressed in tumor cell and DTL showed a potential alternative cap site **Figure 2** The FGF7 transcript have four exons, the heat map showed two apparent alternative site; cap and polyadenylation **Figure 3**.

**Figure 2.** Differential exon expression of DTL gene. The figure showed in the top tree transcripts variants reported. The middle part sowed the level expression, the line red indicates tumor samples and blue indicate stroma samples. The heat

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**Figure 3.** Differential exon expression of FGF7 gene. The figure showed in the top one transcript variant reported, in the middle indicates level expression; the blue line indicates stroma samples and the line red indicates tumor samples. The heat map showed exon level expression on the far left and right, the exons are supressed suggesting that there are two

new potential alternative transcripts.

map showed exon level expression on the far left, the exon is supressed suggesting an alternative splicing.

**Table 1.** Main genes whit potential alternative splicing in breast cancer.

Alternative RNA Splicing: New Approaches for Molecular Marker Discovery in Cancer http://dx.doi.org/10.5772/intechopen.74415 61

**8. Breast cancer**

60 Bioinformatics in the Era of Post Genomics and Big Data

**Figure 3**.

We performed analysis using the data set E-GEOD-81838 available in ArrayExpress web page or GEO-GSE81838 available in GEO, the data that we used was published by Lehmann et al. [47]. The data set was established by 10 breast tumors and 10 stromal cells. Our analysis showed 605 differential and alternatively spliced transcripts, the top 10 overexpress and suppressed are showed in the **Table 1**. We showed the most significant over and down expressed. The DTL transcript have 15 exons, is over expressed in tumor cell and DTL showed a potential alternative cap site **Figure 2** The FGF7 transcript have four exons, the heat map showed two apparent alternative site; cap and polyadenylation

**Gene Symbol RefSeq p-value Fold-Change** *DTL* NM\_001286229 1.45E-46 4.97616 *ESRP1* NM\_001034915 3.48E-77 3.86032 *HOOK1* NM\_015888 1.36E-71 3.69894 *TTK* NM\_001166691 3.31E-42 3.6733 *GRHL1* NM\_198182 4.13E-44 3.5722 *ASPM* NM\_001206846 7.69E-67 3.53763 *DLGAP5* NM\_001146015 9.97E-41 3.51451 *OCLN* NM\_001205254 1.02E-19 3.4842 *ELF5* NM\_001243080 1.35E-12 3.41892 *TDRD5* NM\_001199085 1.07E-42 3.33299 *INHBA* NM\_002192 6.07E-12 −2.99379 *TSHZ2* NM\_001193421 1.53E-05 −3.01074 *FGF10* NM\_001142556 1.27E-13 −3.01537 *PDZRN4* NM\_001164595 4.88E-21 −3.0344 *NEXN* NM\_001172309 6.90E-53 −3.03644 *CXCL12* NM\_000609 7.58E-23 −3.06879 *IGF1* NM\_000618 7.66E-16 −3.46073 *COL8A1* NM\_001850 6.71E-20 −3.50649 *FGF7* NM\_002009 3.49E-19 −4.07332 *FGF7P2* OTTHUMT00000157659 6.90E-12 −4.13171

**Table 1.** Main genes whit potential alternative splicing in breast cancer.

**Figure 2.** Differential exon expression of DTL gene. The figure showed in the top tree transcripts variants reported. The middle part sowed the level expression, the line red indicates tumor samples and blue indicate stroma samples. The heat map showed exon level expression on the far left, the exon is supressed suggesting an alternative splicing.

**Figure 3.** Differential exon expression of FGF7 gene. The figure showed in the top one transcript variant reported, in the middle indicates level expression; the blue line indicates stroma samples and the line red indicates tumor samples. The heat map showed exon level expression on the far left and right, the exons are supressed suggesting that there are two new potential alternative transcripts.

## **9. Lung cancer**

The analysis in lung cancer was performed using data set E-GEOD-30979 available in ArrayExpress web page or GEO-GSE30979, the data was published by Leithner et al. [48]. The model was hypoxic-based in lung cancer. Our analysis revealed 101 transcripts expressed differentially also could have potentially alternative splicing, in the **Table 2** we showed the top 10 over and suppressed transcript identifies in this analysis. One of the most significant AS transcripts was LOX, this transcript has six exons. Our results showed a potential alternative site in cap **Figure 4**. The CEACAM6 was the supressed in hypoxic condition, also apparently showed an alternative cap site **Figure 5**.

**Figure 4.** Differential exon expression of LOX gene. The figure showed in the top two alternative transcripts reported, the middle part the blue line indicates hypoxic model and the red line indicates normoxic model. The heat map showed exon level expressions on the far right two probe set are supressed, both markers inspection one exon. Our results could

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**Figure 5.** Differential exon expression of CEACAM6 gene. The figure showed in the top one transcripts, in the middle parte the blue line indicates hypoxic model and the red line indicates normoxic model. The heat map showed exon level expression, on the far left one marker is supressed indicating a potential fractioned exon, consequently alternative cap site.

indicate the expression is the LOX NM\_001178102 transcript variant.

**Table 2.** Main genes whit potential alternative splicing in lung cancer.

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**9. Lung cancer**

showed an alternative cap site **Figure 5**.

62 Bioinformatics in the Era of Post Genomics and Big Data

**Table 2.** Main genes whit potential alternative splicing in lung cancer.

The analysis in lung cancer was performed using data set E-GEOD-30979 available in ArrayExpress web page or GEO-GSE30979, the data was published by Leithner et al. [48]. The model was hypoxic-based in lung cancer. Our analysis revealed 101 transcripts expressed differentially also could have potentially alternative splicing, in the **Table 2** we showed the top 10 over and suppressed transcript identifies in this analysis. One of the most significant AS transcripts was LOX, this transcript has six exons. Our results showed a potential alternative site in cap **Figure 4**. The CEACAM6 was the supressed in hypoxic condition, also apparently

**Gene symbol RefSeq p-value Fold-Change** *MROH9* NM\_001163629 8.67E-26 2.95561 *LOX* NM\_001178102 1.53E-21 2.66476 *CLGN* NM\_001130675 4.13E-18 2.49403 *MME* NM\_000902 1.73E-34 2.47655 *DDIT3* NM\_001195053 9.74E-10 2.46406 *NUCB2* NM\_005013 5.49E-34 2.41954 *FICD* NM\_007076 8.59E-07 2.36754 *DNAJB9* NM\_012328 6.35E-15 2.35193 *GBE1* NM\_000158 2.04E-60 2.24392 *ADM* NM\_001124 2.06E-08 2.20975 *BPIFA1* NM\_001243193 2.45E-05 −2.91878 *IGKC* AF113887 9.63E-15 −2.93589 *TOP2A* NM\_001067 3.08E-75 −3.05223 *SFTPB* NM\_000542 2.40E-09 −3.0864 *HP* NM\_001126102 0.00045976 −3.12945 *PI15* NM\_015886 8.33E-14 −3.13972 *IGKV3OR2–268* OTTHUMT00000330418 0.000711524 −3.52251 *IGKV2D-30* OTTHUMT00000323285 0.00240377 −3.55542 *CEACAM6* NM\_002483 6.87E-11 −3.68686 *CEACAM5* NM\_001291484 2.69E-11 −3.8858

**Figure 4.** Differential exon expression of LOX gene. The figure showed in the top two alternative transcripts reported, the middle part the blue line indicates hypoxic model and the red line indicates normoxic model. The heat map showed exon level expressions on the far right two probe set are supressed, both markers inspection one exon. Our results could

indicate the expression is the LOX NM\_001178102 transcript variant.

**Figure 5.** Differential exon expression of CEACAM6 gene. The figure showed in the top one transcripts, in the middle parte the blue line indicates hypoxic model and the red line indicates normoxic model. The heat map showed exon level expression, on the far left one marker is supressed indicating a potential fractioned exon, consequently alternative cap site.

## **10. Prostate cancer**

The prostate cancer is one of the most common malignance in the worldwide. For this chapter we performed data mining using the data set E-GEOD-66852 available in ArrayExpress web page or GEO-GSE66852, the data was published by Nouri et al. [49]. Our results showed 777 transcripts that have significant differential exon expression, the most significant over and down expressed are shown in the **Table 3**. Our results showed the over expression in the CCDC80 transcript also showed an alternative spliced site in the exon six **Figure 6**. The down regulate transcript was DLGAP5, this transcript showed two potential sites of splicing; in the exon four and eight **Figure 7**.

**Figure 6.** Differential exon expression of CCDC80 gene. The figure showed in the top two alternative transcripts, the middle part the blue line indicates parental cells model and the red line indicates transdifferentiated cells model. The heat map showed exon level expression, on the middle transcript one marker is supressed indicated by blue color in

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**Figure 7.** Differential exon expression of DLGAP5 gene. The figure showed in the top two alternative transcripts. The middle the blue line indicates parental cells model and the red line indicates transdifferentiated cells. The heat map showed exon level expression on the right side two markers were supressed in the parental model. Our results suggest

two additional transcript variants non-reported are expressed in this model.

transdifferentiated model.

**Table 3.** Main genes whit potential alternative splicing in prostate cancer.

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**10. Prostate cancer**

64 Bioinformatics in the Era of Post Genomics and Big Data

exon four and eight **Figure 7**.

The prostate cancer is one of the most common malignance in the worldwide. For this chapter we performed data mining using the data set E-GEOD-66852 available in ArrayExpress web page or GEO-GSE66852, the data was published by Nouri et al. [49]. Our results showed 777 transcripts that have significant differential exon expression, the most significant over and down expressed are shown in the **Table 3**. Our results showed the over expression in the CCDC80 transcript also showed an alternative spliced site in the exon six **Figure 6**. The down regulate transcript was DLGAP5, this transcript showed two potential sites of splicing; in the

**Gene symbol RefSeq p-value Fold-Change** *CCDC80* NM\_199511 2.98E-52 12.3906 *PLA2G2A* NM\_000300 1.48E-25 9.88943 *PCDH11X* NM\_001168360 6.22E-45 8.56495 *RIMS1* NM\_001168407 2.78E-104 7.77511 *SI* NM\_001041 1.17E-118 7.63493 *IGFBP3* NM\_000598 5.86E-35 7.56274 *NLGN1* NM\_014932 2.60E-22 7.29711 *PCDH11X* NM\_001168360 3.23E-36 6.91453 *LRRN1* NM\_020873 4.82E-16 6.45031 *EPB41L4A* NM\_022140 8.22E-55 6.22688 *SHCBP1* NM\_024745 1.26E-42 −14.8909 *KIF20A* NM\_005733 4.31E-69 −15.7441 *HMMR* NM\_001142556 5.84E-65 −16.2617 *FAM111B* NM\_001142703 3.52E-18 −17.0768 *MELK* NM\_001256685 3.51E-64 −17.2272 *HIST1H3I* NM\_003533 1.58E-10 −19.3335 *TOP2A* NM\_001067 5.06E-126 −23.1938 *PBK* NM\_001278945 4.11E-38 −24.005 *NCAPG* NM\_022346 2.24E-64 −24.1474 *DLGAP5* NM\_001146015 4.89E-73 −32.3098

**Table 3.** Main genes whit potential alternative splicing in prostate cancer.

**Figure 6.** Differential exon expression of CCDC80 gene. The figure showed in the top two alternative transcripts, the middle part the blue line indicates parental cells model and the red line indicates transdifferentiated cells model. The heat map showed exon level expression, on the middle transcript one marker is supressed indicated by blue color in transdifferentiated model.

**Figure 7.** Differential exon expression of DLGAP5 gene. The figure showed in the top two alternative transcripts. The middle the blue line indicates parental cells model and the red line indicates transdifferentiated cells. The heat map showed exon level expression on the right side two markers were supressed in the parental model. Our results suggest two additional transcript variants non-reported are expressed in this model.

## **11. Conclusions**

The alternative splicing is an important transcriptional mechanism that promote protein diversity. In cancer, several alterations in AS has been reported. In this chapter, we showed the generalities of alternative splicing process, the implications of AS in human diseases. The potential use of alternative transcript expressed in cancer as molecular markers and therapeutic targets. Finally, a simple method for identification of alterative transcripts expressed in three models of cancer using available dataset of Affymetrix.

[4] Chen J, Weiss WA. Alternative splicing in cancer: Implications for biology and therapy. Oncogene. 2015;**34**(1):1-14. Epub 2014/01/21. DOI: 10.1038/onc.2013.570. PubMed PMID:

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67

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[7] Li Q, Lee JA, Black DL. Neuronal regulation of alternative pre-mRNA splicing. Nature Reviews. Neuroscience. 2007;**8**(11):819-831. Epub 2007/09/27. DOI: 10.1038/nrn2237.

[8] Carninci P. Constructing the landscape of the mammalian transcriptome. The Journal of Experimental Biology. 2007;**210**(Pt 9):1497-1506. DOI: 10.1242/jeb.000406. PubMed

[9] Mironov AA, Fickett JW, Gelfand MS. Frequent alternative splicing of human genes. Genome Research. 1999;**9**(12):1288-1293. PubMed PMID: 10613851; PubMed Central

[10] Celotto AM, Graveley BR. Alternative splicing of the drosophila Dscam pre-mRNA is both temporally and spatially regulated. Genetics. 2001;**159**(2):599-608. Epub 2001/10/19.

[11] Modrek B, Lee C.A genomic view of alternative splicing. Nature Genetics. 2002;**30**(1):13-19.

[12] Graveley BR. Alternative splicing: Increasing diversity in the proteomic world. Trends

[13] Modrek B, Resch A, Grasso C, Lee C. Genome-wide detection of alternative splicing in expressed sequences of human genes. Nucleic Acids Research. 2001;**29**(13):2850-2859.

[14] Kan Z, Rouchka EC, Gish WR, States DJ. Gene structure prediction and alternative splicing analysis using genomically aligned ESTs. Genome Research. 2001;**11**(5):889-900. DOI: 10.1101/gr.155001. PubMed PMID: 11337482; PubMed Central PMCID: PMC311065 [15] Cartegni L, Chew SL, Krainer AR. Listening to silence and understanding nonsense: Exonic mutations that affect splicing. Nature Reviews. Genetics. 2002;**3**(4):285-298. DOI:

[16] Pick M, Flores-Flores C, Soreq H. From brain to blood: Alternative splicing evidence for the cholinergic basis of mammalian stress responses. Annals of the New York Academy of Sciences. 2004;**1018**:85-98. DOI: 10.1196/annals.1296.010. PubMed PMID: 15240356

PubMed PMID: 11606537; PubMed Central PMCID: PMCPMC1461822

in Genetics: TIG. 2001;**17**(2):100-107. PubMed PMID: 11173120

PubMed PMID: 11433032; PubMed Central PMCID: PMC55780

DOI: 10.1038/ng0102-13. PubMed PMID: 11753382

10.1038/nrg775. PubMed PMID: 11967553

24441040

PMCID: PMCPMC2958924.

PubMed PMID: 23385723

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PMID: 17449815

PMCID: PMC310997

## **Acknowledgements**

This work was supported by SEP-CONACyT Basic Science grant 243233, Sectorial Fund for Research in Health and Social Security grant 272633 and Fiscal Resources, INP 2017.

## **Competing interests**

The authors declare that they have no competing interests.

## **Author details**

Vanessa Villegas-Ruíz and Sergio Juárez-Méndez\*

\*Address all correspondence to<EMAIL_ADDRESS>
Experimental Oncology Laboratory, Research Department, National Institute of Pediatrics, Mexico City, Mexico

## **References**

[4] Chen J, Weiss WA. Alternative splicing in cancer: Implications for biology and therapy. Oncogene. 2015;**34**(1):1-14. Epub 2014/01/21. DOI: 10.1038/onc.2013.570. PubMed PMID: 24441040

**11. Conclusions**

66 Bioinformatics in the Era of Post Genomics and Big Data

**Acknowledgements**

**Competing interests**

**Author details**

Mexico City, Mexico

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The alternative splicing is an important transcriptional mechanism that promote protein diversity. In cancer, several alterations in AS has been reported. In this chapter, we showed the generalities of alternative splicing process, the implications of AS in human diseases. The potential use of alternative transcript expressed in cancer as molecular markers and therapeutic targets. Finally, a simple method for identification of alterative transcripts expressed in

This work was supported by SEP-CONACyT Basic Science grant 243233, Sectorial Fund for

Experimental Oncology Laboratory, Research Department, National Institute of Pediatrics,

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three models of cancer using available dataset of Affymetrix.

The authors declare that they have no competing interests.

Vanessa Villegas-Ruíz and Sergio Juárez-Méndez\*

\*Address all correspondence to<EMAIL_ADDRESS>
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**Chapter 5**

Provisional chapter

**A Novel Approach to Mine for Genetic Markers via**

A Novel Approach to Mine for Genetic Markers via

**Repeats Extracted from Tagged Whole Genomic**

Repeats Extracted from Tagged Whole Genomic

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75113

**Sequences**

Sequences

Jing-Doo Wang

Abstract

post genomics.

1. Introduction

repeat, MapReduce programming

Jing-Doo Wang

**Comparing Class Frequency Distributions of Maximal**

DOI: 10.5772/intechopen.75113

The cost to extract one new biomarker within genomic sequences is very huge. This chapter adopts a scalable approach, developed previously and based on MapReduce programming model, to extract maximal repeats from a huge amount of tagged whole genomic sequences and meanwhile computing the similarities of sequences within the same class and the differences among the other classes, where the types of classes are derived from those tags. The work can be extended to any kind of genomic sequential data if one can have the organisms into several disjoint classes according to one specific phenotype, and then collect the whole genomes of those organisms. Those patterns, for example, biomarkers, if exist in only one class, with distinctive class frequency distribution can provide hints to biologists to dig out the relationship between that phenotype and those genomic patterns. It is expected that this approach may provide a novel direction in the research of biomarker extraction via whole genomic sequence comparison in the era of

Keywords: biomarker, comparative genomics, class frequency distribution, maximal

It is very attractive and challenging to discover markers [1] from genomic sequences and then to use these markers for genetic tests [2] to diagnose diseases and for personalized medicine to adverse drug responses [3, 4]. Nowadays, genome-wide association studies (GWASs) [5] have

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Comparing Class Frequency Distributions of Maximal

**A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats Extracted from Tagged Whole Genomic Sequences** A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats Extracted from Tagged Whole Genomic Sequences

DOI: 10.5772/intechopen.75113

Jing-Doo Wang Jing-Doo Wang

International Journal of Cancer. 2012;**131**(6):1479-1485. Epub 2011/12/17. DOI: 10.1002/

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[49] Nouri M, Caradec J, Lubik AA, Li N, Hollier BG, Takhar M, et al. Therapy-induced developmental reprogramming of prostate cancer cells and acquired therapy resistance. Oncotarget. 2017;**8**(12):18949-18967. Epub 2017/02/02. DOI: 10.18632/oncotarget.14850

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PubMed PMID: 24007497; PubMed Central PMCID: PMC3847372

ijc.27399. PubMed PMID: 22173985

70 Bioinformatics in the Era of Post Genomics and Big Data

PMCPMC2096424

2017-10-29]; 2017

PMCPMC3905926

2017

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75113

#### Abstract

The cost to extract one new biomarker within genomic sequences is very huge. This chapter adopts a scalable approach, developed previously and based on MapReduce programming model, to extract maximal repeats from a huge amount of tagged whole genomic sequences and meanwhile computing the similarities of sequences within the same class and the differences among the other classes, where the types of classes are derived from those tags. The work can be extended to any kind of genomic sequential data if one can have the organisms into several disjoint classes according to one specific phenotype, and then collect the whole genomes of those organisms. Those patterns, for example, biomarkers, if exist in only one class, with distinctive class frequency distribution can provide hints to biologists to dig out the relationship between that phenotype and those genomic patterns. It is expected that this approach may provide a novel direction in the research of biomarker extraction via whole genomic sequence comparison in the era of post genomics.

Keywords: biomarker, comparative genomics, class frequency distribution, maximal repeat, MapReduce programming

## 1. Introduction

It is very attractive and challenging to discover markers [1] from genomic sequences and then to use these markers for genetic tests [2] to diagnose diseases and for personalized medicine to adverse drug responses [3, 4]. Nowadays, genome-wide association studies (GWASs) [5] have

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

already examined single-nucleotide polymorphisms (SNPs) across human genomes to identify specific SNPs related to some diseases, for example, diabetes, heart abnormalities, Parkinson disease, and Crohn disease [6]. Furthermore, GWAS is also used to predict cancer [7] and to influence human intelligence [8].

Most of GWASs are achieved with SNP arrays [9]. The "Illumina" [10] uses the "wholegenome genotyping" to interrogate SNPs across the entire genome to obtain the most comprehensive view of genomic variation; the Affymetrix Genome-Wide Human SNP Array 6.0 features 1.8 million genetic markers which includes more than 906,600 single-nucleotide polymorphisms (SNPs) [11]. The majority of these SNPs are designed to investigate the coding regions of genes in genomic sequences. However, some of the non-coding regions, once being mistaken as "junk DNA," are believed to contain functions to regulate gene transcription and to account for the genetic differences between individuals [12]. Although, on the one hand, the number of SNPs on one chip may be several hundreds of thousands, on the other hand, its coverage is still not enough [13] to figure out the relationship between genotypes and phenotypes in humans as given in the database of "dbGaP" [14].

As the era of post genomics with Next-Generation Sequencing (NGS) is coming, it is expected that the cost of genomic sequencing is decreasing and the availability of complete whole genomes of individual creatures is becoming popular. After using NGS for DNA sequencing [15], as shown on the right side in Figure 1, for example, one creature, for example, a virus, is supposed to contain three chromosomes with eight genotypes. On the other side of Figure 1, there are three phenotypes, for example, "Drug Resistance" "Envelope," and "Contents," inspected and detected by three domain experts, respectively. Under the assumption that these three phenotypes are totally dominated by those eight genotypes, represented as different icons, without considering the epigenetics [16], as shown in Figure 2, it is difficult for biologists in wet laboratory to analyze aimlessly the relationships among these phenotypes and those

genotypes without further bioinformatics information or techniques such as comparative

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats…

http://dx.doi.org/10.5772/intechopen.75113

73

Figure 2. Example: how to identify the relationships among genotypes and phenotypes as described in Figure 1.

With more and more complete whole genomes of distinctive creatures being available and popular in the coming days, it is very interesting and desired to extract common significant subsequences as candidate genomic markers as genotypes via comparing these creatures' whole DNA sequences according to the classes (or types) of their phenotypes observed and specified by domain experts. Figure 3 shows the conceptual diagram of the corresponding classes for each of these three phenotypes given in Figure 1. With precise observations or experiments (phenotypes), biologists or experts can divide these creatures with complete whole genomes into disjoint classes if possible. Then, it is highly expected for biologists that some distinctive patterns (genotypes) hidden within their DNA sequences can be extracted as the candidates of class markers (phenotypes) if the frequency distributions of these patterns among classes are extremely biased, or some patterns are just in one class solely and appear in all instances belonging to that class ideally. To achieve the earlier-mentioned goal, one needs to extract repeats and to compute class frequency distributions of these repeats from a huge amount of tagged genomic sequences, where the types of classes are derived from the tags.

Due to the availability of genomic sequences in National Center for Biotechnology Information (NCBI) [18], The Cancer Genome Altas (TCGA) [19], it is interesting to have class frequency distribution of maximal repeats from these tagged genomic sequences for mining the biomarker or specific patterns. As the age of Next-Generation Sequencing (NGS) is going to be introduced for the project "Cancer Moonshot" in the National Cancer Institute [20], it is very

genomics [17].

Figure 1. An example of one creature with three phenotypes and eight genotypes.

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats… http://dx.doi.org/10.5772/intechopen.75113 73

already examined single-nucleotide polymorphisms (SNPs) across human genomes to identify specific SNPs related to some diseases, for example, diabetes, heart abnormalities, Parkinson disease, and Crohn disease [6]. Furthermore, GWAS is also used to predict cancer [7] and to

Most of GWASs are achieved with SNP arrays [9]. The "Illumina" [10] uses the "wholegenome genotyping" to interrogate SNPs across the entire genome to obtain the most comprehensive view of genomic variation; the Affymetrix Genome-Wide Human SNP Array 6.0 features 1.8 million genetic markers which includes more than 906,600 single-nucleotide polymorphisms (SNPs) [11]. The majority of these SNPs are designed to investigate the coding regions of genes in genomic sequences. However, some of the non-coding regions, once being mistaken as "junk DNA," are believed to contain functions to regulate gene transcription and to account for the genetic differences between individuals [12]. Although, on the one hand, the number of SNPs on one chip may be several hundreds of thousands, on the other hand, its coverage is still not enough [13] to figure out the relationship between genotypes and pheno-

As the era of post genomics with Next-Generation Sequencing (NGS) is coming, it is expected that the cost of genomic sequencing is decreasing and the availability of complete whole genomes of individual creatures is becoming popular. After using NGS for DNA sequencing [15], as shown on the right side in Figure 1, for example, one creature, for example, a virus, is supposed to contain three chromosomes with eight genotypes. On the other side of Figure 1, there are three phenotypes, for example, "Drug Resistance" "Envelope," and "Contents," inspected and detected by three domain experts, respectively. Under the assumption that these three phenotypes are totally dominated by those eight genotypes, represented as different icons, without considering the epigenetics [16], as shown in Figure 2, it is difficult for biologists in wet laboratory to analyze aimlessly the relationships among these phenotypes and those

influence human intelligence [8].

72 Bioinformatics in the Era of Post Genomics and Big Data

types in humans as given in the database of "dbGaP" [14].

Figure 1. An example of one creature with three phenotypes and eight genotypes.

Figure 2. Example: how to identify the relationships among genotypes and phenotypes as described in Figure 1.

genotypes without further bioinformatics information or techniques such as comparative genomics [17].

With more and more complete whole genomes of distinctive creatures being available and popular in the coming days, it is very interesting and desired to extract common significant subsequences as candidate genomic markers as genotypes via comparing these creatures' whole DNA sequences according to the classes (or types) of their phenotypes observed and specified by domain experts. Figure 3 shows the conceptual diagram of the corresponding classes for each of these three phenotypes given in Figure 1. With precise observations or experiments (phenotypes), biologists or experts can divide these creatures with complete whole genomes into disjoint classes if possible. Then, it is highly expected for biologists that some distinctive patterns (genotypes) hidden within their DNA sequences can be extracted as the candidates of class markers (phenotypes) if the frequency distributions of these patterns among classes are extremely biased, or some patterns are just in one class solely and appear in all instances belonging to that class ideally. To achieve the earlier-mentioned goal, one needs to extract repeats and to compute class frequency distributions of these repeats from a huge amount of tagged genomic sequences, where the types of classes are derived from the tags.

Due to the availability of genomic sequences in National Center for Biotechnology Information (NCBI) [18], The Cancer Genome Altas (TCGA) [19], it is interesting to have class frequency distribution of maximal repeats from these tagged genomic sequences for mining the biomarker or specific patterns. As the age of Next-Generation Sequencing (NGS) is going to be introduced for the project "Cancer Moonshot" in the National Cancer Institute [20], it is very attractive to identify specific biomarkers from these genomic sequences with tags, such as cancer types or distinctive genotypes. Figure 4 gives the conceptual diagram of how to reduce the gap between phenotypes and genotypes by using the phenotypes as classes to identify those subsequences that appear in unique class only as biomarkers.

Figure 3. Mining the relationship of phenotypes and genotypes via classes comparison.

The remainder of this chapter is organized as follows. Section 2 gives the review of potential applications with class frequency distributions of maximal repeats. Section 3 shows the scalable approach to extract maximal repeat from tagged sequential data. Section 4 describes the most recent work [21] that compute co-occurrences of DNA maximal repeat patterns appearing in both humans and viruses. Section 5 concludes and discusses on future works.

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2. Potential applications with class frequency distribution of maximal

The previous work in [22] was a scalable approach based on Hadoop MapReduce programming model to overcome the computational bottleneck of using single computer with external memory [23, 24]. Furthermore, it had been applied for a USA patent (US-2017-0255634-A1) [25] whose publication data is as "Sep. 7, 2017" [25]. Recently, in these 2 years, many novel and potential applications, derived from that work, were launched in diverse fields successfully, due to its scalability being able to handle a huge amount of sequential data. There were many experiments in diverse applications with a huge amount of tagged sequential data, such as textual data for trend analysis [26–28], genomic sequences for biomarker identification [21, 29, 30], time-stamped gantry sequences for significant travel time intervals [31] and, most recently,

The scalable approach of maximal repeat extraction adopted in this chapter is based on Hadoop MapReduce programming model, and the details can be found in [22]. To illustrate the concept of the earlier approach clearly, as shown in Figure 5, there are 20 creatures generated manually. Each of them is with three phenotypes, "Drug Resistance," "Envelope," and "Contents," as given in Figure 3, and all of its chromosomes are concatenated into one line which may contain genotypes including motifs, domains, or unknown DNA segments that are represented as icons for simplicity. Even though with the conceptual diagram as shown in Figure 5, it is still very difficult for users to catch the hidden connection (or relationship) among these three phenotypes and those icons (genotypes) at first glance, let alone each of these icons (genotypes) presents one continuous subsequence whose length is not fixed and its

To reveal the possible mapping of phenotype "Drug Resistance," for example, to genotypes on purpose, Figure 6 presents the rearrangement in the order of these 20 chromosomes which may contain icons as hidden or unknown DNA segments. The mapping of different types of phenotype "Drug Resistance" to the corresponding genotypes (icons) can be observed. Similarly, one can have the mapping of different types of phenotype "Envelope"

repeats extracted from tagged sequential data

the sequences of product traceability for quality control [32].

location is unknown within chromosomes.

3. Methods

Figure 4. The conceptual diagram of reducing the gap between phenotypes and genotypes.

The remainder of this chapter is organized as follows. Section 2 gives the review of potential applications with class frequency distributions of maximal repeats. Section 3 shows the scalable approach to extract maximal repeat from tagged sequential data. Section 4 describes the most recent work [21] that compute co-occurrences of DNA maximal repeat patterns appearing in both humans and viruses. Section 5 concludes and discusses on future works.

## 2. Potential applications with class frequency distribution of maximal repeats extracted from tagged sequential data

The previous work in [22] was a scalable approach based on Hadoop MapReduce programming model to overcome the computational bottleneck of using single computer with external memory [23, 24]. Furthermore, it had been applied for a USA patent (US-2017-0255634-A1) [25] whose publication data is as "Sep. 7, 2017" [25]. Recently, in these 2 years, many novel and potential applications, derived from that work, were launched in diverse fields successfully, due to its scalability being able to handle a huge amount of sequential data. There were many experiments in diverse applications with a huge amount of tagged sequential data, such as textual data for trend analysis [26–28], genomic sequences for biomarker identification [21, 29, 30], time-stamped gantry sequences for significant travel time intervals [31] and, most recently, the sequences of product traceability for quality control [32].

## 3. Methods

attractive to identify specific biomarkers from these genomic sequences with tags, such as cancer types or distinctive genotypes. Figure 4 gives the conceptual diagram of how to reduce the gap between phenotypes and genotypes by using the phenotypes as classes to identify

those subsequences that appear in unique class only as biomarkers.

74 Bioinformatics in the Era of Post Genomics and Big Data

Figure 3. Mining the relationship of phenotypes and genotypes via classes comparison.

Figure 4. The conceptual diagram of reducing the gap between phenotypes and genotypes.

The scalable approach of maximal repeat extraction adopted in this chapter is based on Hadoop MapReduce programming model, and the details can be found in [22]. To illustrate the concept of the earlier approach clearly, as shown in Figure 5, there are 20 creatures generated manually. Each of them is with three phenotypes, "Drug Resistance," "Envelope," and "Contents," as given in Figure 3, and all of its chromosomes are concatenated into one line which may contain genotypes including motifs, domains, or unknown DNA segments that are represented as icons for simplicity. Even though with the conceptual diagram as shown in Figure 5, it is still very difficult for users to catch the hidden connection (or relationship) among these three phenotypes and those icons (genotypes) at first glance, let alone each of these icons (genotypes) presents one continuous subsequence whose length is not fixed and its location is unknown within chromosomes.

To reveal the possible mapping of phenotype "Drug Resistance," for example, to genotypes on purpose, Figure 6 presents the rearrangement in the order of these 20 chromosomes which may contain icons as hidden or unknown DNA segments. The mapping of different types of phenotype "Drug Resistance" to the corresponding genotypes (icons) can be observed. Similarly, one can have the mapping of different types of phenotype "Envelope"

Figure 5. Each of 20 creatures is with three kinds of phenotypes as given in Figure 3 and all of its chromosomes are concatenated as one line containing several icons as motif, domain, or unknown patterns.

and "Contents" to the corresponding genotypes (icons). Due to the page limitation, the corresponding mapping of figures for "Envelope" and "Contents" are given in the supplements. Focusing on the repeats whose class frequency distributions are biased, as shown in

Figure 7, one can estimate these repeats as candidate class markers which can be the clues for further experiments of analyzing the mapping of phenotypes and genotypes derived from 20

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Figure 6. The mapping of different types of phenotype "Drug Resistance" to the corresponding genotypes (icons).

creatures in Figure 5.

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Figure 6. The mapping of different types of phenotype "Drug Resistance" to the corresponding genotypes (icons).

and "Contents" to the corresponding genotypes (icons). Due to the page limitation, the corresponding mapping of figures for "Envelope" and "Contents" are given in the supplements. Focusing on the repeats whose class frequency distributions are biased, as shown in

Figure 5. Each of 20 creatures is with three kinds of phenotypes as given in Figure 3 and all of its chromosomes are

concatenated as one line containing several icons as motif, domain, or unknown patterns.

76 Bioinformatics in the Era of Post Genomics and Big Data

Figure 7, one can estimate these repeats as candidate class markers which can be the clues for further experiments of analyzing the mapping of phenotypes and genotypes derived from 20 creatures in Figure 5.

the classes (tags) for further experiments. Experimental resources included the complete whole genomes of humans (GRCh38.p7 Primary Assembly) downloaded from the NCBI FTP [33] and that of 559 virus genuses, including 2712 viruses that had genus name and were selected from the total of 4388 viruses download from in NCBI FTP [34] on January 14, 2017. Table 1 shows the partial statistics of 560 classes, including 559 virus genuses and the humans as "C248." Note that each of the 24 human chromosomes is estimated as one individual instance for observing the frequency distribution among human chromosomes. This chapter, for

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"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by

Table 1. The partial statistics of 559 virus genuses and human genomes (C248).

IEEE, 2017." [21].

Class ID Human and virus genuses No of Instances

C1 Alfamovirus 1 C2 Allexivirus 6 C3 Allolevivirus 3 C4 Alpha3microvirus 2 C5 Alphabaculovirus 40 C6 Alphacarmotetravirus 1 C7 Alphabaculovirus 7 … … … C247 Human mastadenovirus E 1 C248 HumanGenomes\_23\_Assembled 24 C249 Hunnivirus 1 C250 Hypovirus 4 C478 Sobemovirus 15 C479 Solendovirus 1 C480 Soymovirus 4 … … … C553 Xipapillomavirus 1 C554 Xp10virus 5 C555 Yatapoxvirus 2 C556 Yatapoxvirus 3 C557 Zeavirus 1 C558 Zetapapillomavirus 1 C559 Zetatorqueviurs 1 C560 primate papillomaviruses 1

Figure 7. The mapping of phenotypes and genotypes derived from 20 creatures in Figure 5.

## 4. Case study: mining for the co-occurrences of DNA maximal repeat patterns in both human and viruses

There were three studies with a huge amount of genomic sequences [21, 29, 30] based on the scalable approach of maximal repeat extraction with class frequency distribution mentioned in this chapter. This chapter only describes the most recent work [21] that the co-occurrences of DNA maximal repeat patterns appearing in both humans and viruses are extracted via a scalable approach that is based on Hadoop distributed computing [22]; that work aimed to mine for specific DNA patterns within human genomes via observing class frequency distribution of DNA maximal repeats extracted from the whole genomic DNA sequences of humans and 559 virus genuses. The detail in [21] is described for reference in the following.

#### 4.1. Genome resources

In [21], Wang et al. extracted significant DNA sequences appearing in both the genomes of humans and viruses. In this study, the taxonomic level of viruses is "genus" and is selected as the classes (tags) for further experiments. Experimental resources included the complete whole genomes of humans (GRCh38.p7 Primary Assembly) downloaded from the NCBI FTP [33] and that of 559 virus genuses, including 2712 viruses that had genus name and were selected from the total of 4388 viruses download from in NCBI FTP [34] on January 14, 2017. Table 1 shows the partial statistics of 560 classes, including 559 virus genuses and the humans as "C248." Note that each of the 24 human chromosomes is estimated as one individual instance for observing the frequency distribution among human chromosomes. This chapter, for

"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017." [21].

Table 1. The partial statistics of 559 virus genuses and human genomes (C248).

4. Case study: mining for the co-occurrences of DNA maximal repeat

Figure 7. The mapping of phenotypes and genotypes derived from 20 creatures in Figure 5.

and 559 virus genuses. The detail in [21] is described for reference in the following.

There were three studies with a huge amount of genomic sequences [21, 29, 30] based on the scalable approach of maximal repeat extraction with class frequency distribution mentioned in this chapter. This chapter only describes the most recent work [21] that the co-occurrences of DNA maximal repeat patterns appearing in both humans and viruses are extracted via a scalable approach that is based on Hadoop distributed computing [22]; that work aimed to mine for specific DNA patterns within human genomes via observing class frequency distribution of DNA maximal repeats extracted from the whole genomic DNA sequences of humans

In [21], Wang et al. extracted significant DNA sequences appearing in both the genomes of humans and viruses. In this study, the taxonomic level of viruses is "genus" and is selected as

patterns in both human and viruses

78 Bioinformatics in the Era of Post Genomics and Big Data

4.1. Genome resources

simplicity, only takes the positive-strand DNA sequences of humans and viruses for further experiments.

Length Virus (only) Human (only) Human and virus

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<PHONE_NUMBER><PHONE_NUMBER> 84 48 16,454 29 26 65,556 5 11 262,154 1 9 1,048,579 956 4093 4,189,216 95,386 1,198,404 15,310,125 547,437 23,069,913 34,360,563 788,030 110,159,534 42,567,207 547,766 273,869,697 36,497,761 305,641 322,333,237 22,317,495 206,969 209,993,387 10,170,128 86,585 103,569,439 3,920,359 47,417 48,474,700 1,407,005 66,719 25,284,157 493,326 25,068 15,882,880 175,934 18,507 11,902,168 67,700 39,947 9,921,624 29,793 14,802 8,649,670 14,795 12,227 7,794,361 8749 …… … … 165 107,159 15 710 102,830 15 707 99,579 13 1607 96,326 13 608 93,630 12 638 92,129 11 …… … … 19 1933 1 27 2000 1 22 1812 1 26 1936 1

464 23 1993 465 19 1817

…… … …

#### 4.2. Computational time and environment

To show the scalability of this approach from a practical view of point, as shown in Figure 8, the computational platform was the Hadoop cluster with eight computing nodes, two name (master) nodes, and six data (slave) nodes; Table 2 showed the specifications of hardware and software of one computing node; the computational time was about 37.5 h when the maximum length of maximal repeat patterns was limited to 500 bp (base pair).

Figure 8. The conceptual diagram of a Hadoop cluster with two name (master) nodes, and six data (worker) nodes; "Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017" [41].

"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017." [21].

Table 2. The hardware and software of one computing node in Hadoop cluster.

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats… http://dx.doi.org/10.5772/intechopen.75113 81

simplicity, only takes the positive-strand DNA sequences of humans and viruses for further

To show the scalability of this approach from a practical view of point, as shown in Figure 8, the computational platform was the Hadoop cluster with eight computing nodes, two name (master) nodes, and six data (slave) nodes; Table 2 showed the specifications of hardware and software of one computing node; the computational time was about 37.5 h when the maximum

Figure 8. The conceptual diagram of a Hadoop cluster with two name (master) nodes, and six data (worker) nodes; "Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by

> RAM 128 GB (16GB\*8, ECC/REG DDR4 2133) Hard Disk 6 TB (SATA3 2 TB\*3, 7200 rpm 3.5 inch)

Hadoop Hadoop 2.6 ("Cloudera Express 5.4.5") "Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by

Network Card Intel Ethernet X540 10GBASE-T RJ45 DualPort \*4

Hardware CPU Intel® Xeon® Processor E5-2630 v3 (8 cores)

Software OS CentOS 6.7

Table 2. The hardware and software of one computing node in Hadoop cluster.

length of maximal repeat patterns was limited to 500 bp (base pair).

experiments.

IEEE, 2017" [41].

IEEE, 2017." [21].

4.2. Computational time and environment

80 Bioinformatics in the Era of Post Genomics and Big Data

Maximal repeat patterns DF TF Length Class frequency

ctaaccctaaccctaaccctaaccctaac 2 111 463 (C248#1#109)

ccctaaccctaaccctaaccctaacccta accctaaccctaaccctaaccctaaccct aaccctaaccctaaccctaaccctaaccc taaccctaaccctaaccctaaccctaacc ctaaccctaaccctaaccctaaccctaac cctaaccctaaccctaaccctaaccctaa ccctaaccctaaccctaaccctaacccta accctaaccctaaccctaaccctaaccct aaccctaaccctaaccctaaccctaaccc taaccctaaccctaaccctaaccctaacc Ctaaccctaaccctaaccctaaccctaac Cctaaccctaaccctaaccctaaccctaa Ccctaaccctaaccctaaccctaacccta Accctaaccctaaccctaaccctaaccc

IEEE, 2017." [21].

distribution (ClassID#DF#TF)

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats…

(C442#1#2)

"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by

Figure 9. BLAST: "Homo sapiens chromosome 5" versus "human herpesvirus 6B"; "Reproduced with permission from

International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017" [21].

Table 4. The longest DNA maximal repeat patterns (Length = 463 bp) appearing in both humans and viruses.

6B cctaaccctaaccctaaccctaaccctaa

Regular expression Human chromosome (GRCh38.p7 Primary assembly)

(TAACCC)n 5 Human

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Viruses

83

herpesvirus

"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017." [21].

Table 3. The partial of frequency distribution of DNA maximal repeats (length 5–500 bp).

## 4.3. The length distribution of DNA maximal repeats in both the genomes of human and 559 virus genuses

Comparing the maximal repeats that appear only in virus (Virus only), only in humans (Human only) or in both human and virus (Human and virus), Table 3 shows the partial frequency distribution of maximal repeats whose lengths are from 5 to 500 bp. It is observed that the majority of those maximal repeats whose length range from 7 to 11 almost belong to the "Human and Virus." Note that there may exist extra nucleic acid codes, for example, "N," within these DNA sequences such that the number of maximal repeat (length = 5) appearing in both humans and viruses in Table 3 is 1; 341 and that is great than 45 (= 1024).

## 4.4. The longest DNA maximal repeat (length = 463 bp) appearing in both the genomes of human and 559 virus genuses

Table 3 shows the length of the longest maximal repeat extracted in both the genomes of humans and selected viruses of 559 virus genuses is 463 bp. In [21], the result of blasting two sequences, "Homo sapiens chromosome 5" (NC\_000005.10) and "Human herpesvirus 6B" (NC\_000898.1), as shown in Table 4, that longest repeat appears 109 times within human chromosome 5 and two times within virus "Human herpesvirus 6B." To further inspect the longest maximal repeat, as show in Figure 9, one can find that the longest one is a tandem repeat (TAACCC) and appears within virus "Human herpesvirus 6B" at two intervals, the front (8249–8711 bp) and tail (161570– 162,032 bp), that are located within the regions of direct repeats (DR) [35]. Figure 10 gives one of two longest patterns aligned within "Human herpesvirus 6B" (8249–8711 bp) in Figure 9.

## 4.5. The statistics of DNA maximal repeat patterns (length = 100 bp) appearing in both human and 559 virus genuses

Table 5, for example, shows the statistics of 13 DNA maximal repeat patterns (length = 100 bp) appearing in both human and 559 virus genuses. It is observed that the three repeats as the A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats… http://dx.doi.org/10.5772/intechopen.75113 83

4.3. The length distribution of DNA maximal repeats in both the genomes of human and

Table 3. The partial of frequency distribution of DNA maximal repeats (length 5–500 bp).

"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by

Length Virus (only) Human (only) Human and virus

495 7 1542 496 16 1564 497 27 1408 498 14 1451 499 16 1494 500 22 1542

82 Bioinformatics in the Era of Post Genomics and Big Data

Comparing the maximal repeats that appear only in virus (Virus only), only in humans (Human only) or in both human and virus (Human and virus), Table 3 shows the partial frequency distribution of maximal repeats whose lengths are from 5 to 500 bp. It is observed that the majority of those maximal repeats whose length range from 7 to 11 almost belong to the "Human and Virus." Note that there may exist extra nucleic acid codes, for example, "N," within these DNA sequences such that the number of maximal repeat (length = 5) appearing in both humans and viruses in Table 3 is 1; 341 and that is

4.4. The longest DNA maximal repeat (length = 463 bp) appearing in both the genomes of

Table 3 shows the length of the longest maximal repeat extracted in both the genomes of humans and selected viruses of 559 virus genuses is 463 bp. In [21], the result of blasting two sequences, "Homo sapiens chromosome 5" (NC\_000005.10) and "Human herpesvirus 6B" (NC\_000898.1), as shown in Table 4, that longest repeat appears 109 times within human chromosome 5 and two times within virus "Human herpesvirus 6B." To further inspect the longest maximal repeat, as show in Figure 9, one can find that the longest one is a tandem repeat (TAACCC) and appears within virus "Human herpesvirus 6B" at two intervals, the front (8249–8711 bp) and tail (161570– 162,032 bp), that are located within the regions of direct repeats (DR) [35]. Figure 10 gives one of

two longest patterns aligned within "Human herpesvirus 6B" (8249–8711 bp) in Figure 9.

4.5. The statistics of DNA maximal repeat patterns (length = 100 bp) appearing in both

Table 5, for example, shows the statistics of 13 DNA maximal repeat patterns (length = 100 bp) appearing in both human and 559 virus genuses. It is observed that the three repeats as the

559 virus genuses

IEEE, 2017." [21].

great than 45 (= 1024).

human and 559 virus genuses

human and 559 virus genuses

Table 4. The longest DNA maximal repeat patterns (Length = 463 bp) appearing in both humans and viruses.

Figure 9. BLAST: "Homo sapiens chromosome 5" versus "human herpesvirus 6B"; "Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017" [21].

<sup>&</sup>quot;Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017." [21].

Figure 10. One of two aligned patterns (8249–8711 bp) in Figure 9 "Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017" [41].

1st, the 6th, and the11th, for example, have the similar regular expression as "(AACCCT)n", "(CTAACC)n," and "(TAACCC)n", respectively, and all of them appear in human chromosomes "1," "5," "10" and "12"; all of these three repeats appear in these viruses, "Cyprinid herpesvirus 1," "Falconid herpesvirus 1," "Gallid herpesvirus 2," "Human herpesvirus 6A," "Human herpesvirus 6B," and "Equid herpesvirus 3." It is very interesting to investigate the relationship between these human chromosomes and those viruses for further research. On the other hand, from the biological viewpoint, furthermore, (AACCCT)n, (CCCTAA)n, and (CTAACC)n may comprise the same maximal repeat pattern with different repeat frame; (GGGTTA)n, and (AGGGTT)n can also comprise the same maximal repeat pattern in complementary sequence. Moreover, the (GGGTTA)n is expected to be targeted by cisplatin [36].

Total

 Length Class frequency

distribution

Regular

Human

Viruses

chromosome

(GRCh38.p7

primary

assembly)

C248

C5,C14,C149,C284,C305,C357,

C442C541

expression

(ClassID#DF#TF)

Maximal Repeat Patterns

1 2 3 4 5 6

ctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaa

11 591 100

 (C149#1#18)

(CCCTAA)n

 1, 5,10,12

 Cyprinid herpesvirus

herpesvirus

2, Human herpesvirus

Human herpesvirus

herpesvirus

3

 7, Equid herpesvirus

 1, Gallid herpesvirus

 6A,

 6B, Human

 1, Falconid

(C248#4#298)

(C305#2#41)

(C442#3#210)

(C541#l#24)

ccctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaa

ccctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaa

7

gagagagagagagagagagagagagagagagagagagagagagag

3 55 100

 (C149#1#32)

(GA)n

 6, 11

Cyprinid herpesvirus

 3

http://dx.doi.org/10.5772/intechopen.75113

(C248#2#23)

agagagagagagagagagagagagagagagagagagagagagaga

gagagagaga

8 9

ggggttagggttagggttagggttagggttagggttagggttagggttagg

3 3 100

 (C248#2#2)

G(GGGTTA)n

 13, 18

Meleleagrid

 herpesvirus

 1

(C305#1#1)

gttagggttagggttagggttagggtagggttagggttagggttaggg

gggttagggttagggttagggttagggttagggttagggttagggttaggg

9 188 100

(C248#7#152)

(GGGTTA)n

 2, 12,13,18,22,

Falconid herpesvirus

herpesvirus

 2 85

 1, Gallid

> X, Y

(C305#2#36)

ttagggttagggttagggttagggttagggttagggttagggttagggt

agggttagggttagggttagggttagggttagggttagggttagggttagg

9 188 100

(C248#7#152)

(AGGGTT)n

Y

2,12,13,18,22,

 X,

Falconid herpesvirus

herpesvirus

 2

 1, Gallid

(C305#2#36)

gttagggttagggttagggttagggttagggttagggttagggttaggg

atatatatatatatatatatatatatatatatatatatatatatatatatatatatata

6 533 100

 (C14#1#34)

(AT)n

 2, 3, 7, 19, X

 Gryllus

bimaculatus

 nudivirus

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats…

(C248#5#499)

tatatatatatatatatatatatatatatatatatatatat

ccctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaaccct

3 4 100

 (C248#1#1)

(CCCTAA)n

 5

Meleleagrid

herpesvirus

 3

 herpesvirus

 1, Equid

(C305#1#2)

(C541#1#1)

aaccctaaccctaaccctaaccctaaccctaaccctaaccctaacccc

aatagaatagaatagaatagaatagaatagaatagaatagaatagaataga

3 42 100

 (C248#1#18)

(AATAG)n

 X

(C284#1#11)

(C357#1#13)

atagaatagaatagaatagaatagaatagaatagaatagaatagaatag

aaccctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaac

10 591 100

 (Cl 49# 1 # 18)

(AACCCT)n

 1, 5, 10, 12

 Cyprinid herpesvirus

herpesvirus

2, Human herpesvirus

Human herpesvirus

herpesvirus

Rabbit fibroma virus, Taterapox

virus,

 3

 1, Gallid herpesvirus

 6A,

 6B, Equid

 1, Falconid

(C248#4#299)

(C305#2#41)

(C442#2#208)

(C541#l#25)

cctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaacc

DF TF

#### 4.6. Phenotypes: "Group I(dsDNS)" in Baltimore virus classification

It is observed that all of these viruses in Table 5 belong to the "Group I(dsDNS)" of Baltimore classification [37], as shown in Table 6, and most of them are from the family "Herpesviridae" and order "Herpesvirales." Indeed, it is very interesting and attractive to have all the viruses compared with human whole genome and to inspect these co-occurrences of repeats for virus prevention from the genomic point of view in the future.

1st, the 6th, and the11th, for example, have the similar regular expression as "(AACCCT)n", "(CTAACC)n," and "(TAACCC)n", respectively, and all of them appear in human chromosomes "1," "5," "10" and "12"; all of these three repeats appear in these viruses, "Cyprinid herpesvirus 1," "Falconid herpesvirus 1," "Gallid herpesvirus 2," "Human herpesvirus 6A," "Human herpesvirus 6B," and "Equid herpesvirus 3." It is very interesting to investigate the relationship between these human chromosomes and those viruses for further research. On the other hand, from the biological viewpoint, furthermore, (AACCCT)n, (CCCTAA)n, and (CTAACC)n may comprise the same maximal repeat pattern with different repeat frame; (GGGTTA)n, and (AGGGTT)n can also comprise the same maximal repeat pattern in complementary sequence. Moreover, the (GGGTTA)n is expected to be targeted

Figure 10. One of two aligned patterns (8249–8711 bp) in Figure 9 "Reproduced with permission from International

It is observed that all of these viruses in Table 5 belong to the "Group I(dsDNS)" of Baltimore classification [37], as shown in Table 6, and most of them are from the family "Herpesviridae" and order "Herpesvirales." Indeed, it is very interesting and attractive to have all the viruses compared with human whole genome and to inspect these co-occurrences of repeats for virus

4.6. Phenotypes: "Group I(dsDNS)" in Baltimore virus classification

Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017" [41].

84 Bioinformatics in the Era of Post Genomics and Big Data

prevention from the genomic point of view in the future.

by cisplatin [36].

Table 5. The statistics of 13 DNA maximal repeat patterns (length = 100 bp) appearing in both humans and viruses.

5. Conclusions and future works

Table 6. The taxonomy of 12 viruses selected in Table 5

IEEE, 2017.

" [21].

their corresponding features (phenotypes).

mentioned in this chapter is somewhat like

Except considering the phenotypes that result from the epigenetics [38], it is believed that some of the phenotypes of creatures (or organisms) are determined by their genotypes as they are born in the beginning. This chapter proposes a novel approach to mine for genetic markers via comparing class frequency distributions of maximal repeats extracted from tagged genomic sequences of creatures, where the classes are derived from the tags given by domain experts. Once domain experts can divide the creatures into disjoint classes as precisely as possible according to their features (phenotypes), then they can adopt the scalable approach developed in [22] to extract the maximal repeats and compute class frequency distributions of these repeats via comparing the whole genomic sequences of these creatures. The repeats or the combination of some repeats that are with extremely biased class frequency distribution can be seen as class markers (genotypes) and can provide clues to biologists to analyze the relationship among these class markers (genotypes) and

.

Viruses Class ID The International Committee on Taxonomy of Viruses (ICTV)

Genus Family Order

Orgvia pseudotsugata MNPV C5 Alphabaculovirus Baculoviridae N Group I(dsDNA) Gryllus bimaculatus nudiviras C14 Alphanudivirus Nudiviridae N Group I(dsDNA) Cyprinid herpesvirus 1 C149 Cyprinivirus Alloherpesviridae Herpesvirales Group I (dsDNA) Rabbit fibroma virys C284 Leporipoxvirus Poxviridae N Group I (dsDNA) Falconid herpesvirus 1 C305 Mardivirus Herpesviridae Herpesvirales Group I(dsDNA) Gallid herpesvirus 2 C305 Mardivirus Herpesviridae Herpesvirales Group I(dsDNA) Meleagrid herpesvirus 1 C305 Mardivirus Herpesviridae Herpesvirales Group I(dsDNA) Taterapox virus C357 Orthopoxvirus Poxviridae N Group I(dsDNA) Human herpesvirus 6A C442 Roseolovirus Herpesviridae Herpesvirales Group I(dsDNA) Human herpesvirus 6B C442 Roseolovirus Herpesviridae Herpesvirales Group I(dsDNA) Human herpesvirus 7 C442 Roseolovirus Herpesviridae Herpesvirales Group I(dsDNA) Equid herpesvirus 3 C541 Varicellovirus Herpesviridae Herpesvirales Group I(dsDNA) "Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats…

Due to the availability of cloud computing with flexible infrastructure, nowadays, it becomes possible to compute class frequency distributions of maximal repeats from a huge amount of tagged whole genomic sequences of many creatures across species via the scalable maximal repeat extraction approach [22] with Hadoop MapReduce programming model. The function

"Archimedes

' Law of the Lever,

" as shown in

Baltimore classification 87

http://dx.doi.org/10.5772/intechopen.75113

"Reproduced with permission from International Conference on BioInformatics and BioEngineering (BIBE); published by IEEE, 2017." [21].

Table 6. The taxonomy of 12 viruses selected in Table 5.

## 5. Conclusions and future works

Total

 Length Class frequency

distribution

Regular

Human

Viruses

chromosome

(GRCh38.p7

primary

assembly)

expression

(ClassID#DF#TF)

10 11

gtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtg

5 78 100

 (C248#4#38)

(GT)n

 2,10, 16, 19

 Orgyia

pseudotsugata

 MNPV

(C5#1#40)

tgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtg

taaccctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaac

10 588 100

 (C149#1#16)

(TAACCC)n

 1, 5,10,12

 Cyprinid herpesvirus

herpesvirus

herpesvirus

6A, Human herpesvirus

herpesvirus

 3

 1 Human herpesvirus

 6B, Rquid

 2, Falconid

 1, Gallid

86 Bioinformatics in the Era of Post Genomics and Big Data

(C248#4#298)

(C305#2#41)

(C442#2#208)

(C541#1#25)

cctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaac

cctaaccctaaccctaaccctaaccctaaccctaaccctaaccctaac

12 13 Table 5. The statistics of 13 DNA maximal repeat patterns (length = 100 bp) appearing in both humans and viruses.

tgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgt

5 76 100

 (C248#4#35)

(GT)n

 2,10, 16, 19

 Orgyia

pseudotsugata

 MNPV

(C5#1#41)

gtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgtgt

ttagggttagggttagggttagggttagggttagggttagggttagggttag

9 186 100

(C248#7#150)

(AGGGTT)n

Y

2,12,13,18,22,

 X,

Falconid herpesvirus

herpesvirus

 2

 1, Gallid

(C305#2#36)

ggttagggttagggttagggttagggttagggttagggttagggttag

Except considering the phenotypes that result from the epigenetics [38], it is believed that some of the phenotypes of creatures (or organisms) are determined by their genotypes as they are born in the beginning. This chapter proposes a novel approach to mine for genetic markers via comparing class frequency distributions of maximal repeats extracted from tagged genomic sequences of creatures, where the classes are derived from the tags given by domain experts. Once domain experts can divide the creatures into disjoint classes as precisely as possible according to their features (phenotypes), then they can adopt the scalable approach developed in [22] to extract the maximal repeats and compute class frequency distributions of these repeats via comparing the whole genomic sequences of these creatures. The repeats or the combination of some repeats that are with extremely biased class frequency distribution can be seen as class markers (genotypes) and can provide clues to biologists to analyze the relationship among these class markers (genotypes) and their corresponding features (phenotypes).

Due to the availability of cloud computing with flexible infrastructure, nowadays, it becomes possible to compute class frequency distributions of maximal repeats from a huge amount of tagged whole genomic sequences of many creatures across species via the scalable maximal repeat extraction approach [22] with Hadoop MapReduce programming model. The function mentioned in this chapter is somewhat like "Archimedes' Law of the Lever," as shown in

and finical support. I also thank Prof. Yi-Chun Wang for collecting the human chromosomes and inspecting experimental results about viruses and humans. I also thank Prof. Rouh-Mei Hu who helped in explaining the SNPs and the relationship between genotypes and phenotypes; Prof. Jan-Gowth Chang and Charles C.N. Wang had valuable discussions; I thank Jazz Wang for providing valuable Hadoop programming discussions. Finally, I thank Ling-Yu Ji for offering her drawing to show the conceptual diagram of "Archimedes' Law of the Lever."

A Novel Approach to Mine for Genetic Markers via Comparing Class Frequency Distributions of Maximal Repeats…

http://dx.doi.org/10.5772/intechopen.75113

89

1 Department of Computer Science and Information Engineering, Asia University, Taichung,

[1] Azuaje F. Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personal-

[2] Novelli G, Ciccacci C, Borgiani P, Amati MP, Abadie E. Genetic tests and genomic biomarkers: Regulation, qualification and validation. Clinical Cases in Mineral and Bone

[3] Glauser TA. Biomarkers for antiepileptic drug response. Biomarkers in Medicine. 2011;

[4] Sun W et al. Common genetic polymorphisms influence blood biomarker measurements

[5] What are genome-wide association studies? https://ghr.nlm.nih.gov/primer/genomicresearch/

[6] Genome-wide association studies. https://www.yourgenome.org/stories/genome-wide--

[7] Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: Current

[8] Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, Taskesen E, Hammerschlag AR, Okbay A, Zabaneh D, Amin N, Breen G, Cesarini D, Chabris CF, Iacono WG, Arfan Ikram M, Johannesson M, Koellinger P, Lee JJ, Magnusson PKE,

insights and future perspectives. Nature Reviews Cancer. 2017;17:692704

2 Department of Medical Research, China Medical University Hospital, China Medical

Author details

Jing-Doo Wang1,2\*

University, Taichung, Taiwan

ized Medicine. Wiley; 2011

Metabolism. 2008;5(2):149154

in COPD. PLoS Genetics. 2016;12(8):e1006011

Taiwan

References

5(5):635641

gwastudies.

association-studies.

\*Address all correspondence to<EMAIL_ADDRESS>
Figure 11. "Mining for biomarkers via observing class frequency distributions of maximal repeats from tagged genomic sequences" is somewhat like "Archimedes' Law of the Lever".

Figure 11, the Archimedes, an outstanding ancient Greek scientist, said that "Give me a place to stand on, and I will move the Earth." With scalable computing power and enough tagged genomic sequences, in other words, a domain expert can figure out the relationship among phenotypes and genotypes if the classes are properly and precisely defined. It is desired to have further cooperation with domain experts, especially who have collected the whole genomes of diverse organisms and desire to find or identify the relationship between genomic signatures and the features they concern in the future.

From a practical point of view, it is inconvenient for general users to have experiments of maximal repeat extraction by themselves in the beginning because there are a lot of preprocessing works and need considerable hardware infrastructure to support such a bigdata computing. Furthermore, it might be a bottleneck or nightmare for general users, for example, biologists, to implement Hadoop MapReduce programming as described in [22]. Therefore, it is highly desired if maximal repeat extraction can be provided in public cloud services, such as Amazon Elastic Container Service (AWS ECS) [39], Google Cloud Platform [40], and Azure Container Service (AKS) [41]. It is highly expected that one will develop novel comparative genome with tagged genomic sequences and bring users with novel cloud services of computing class frequency distribution of maximal repeats in the future.

## Acknowledgements

This study is supported by Ministry of Science and Technology, Taiwan, under project MOST 106-2632-E-468-002. I thank Prof. Jeffrey J.P. Tsai who provided computational environments and finical support. I also thank Prof. Yi-Chun Wang for collecting the human chromosomes and inspecting experimental results about viruses and humans. I also thank Prof. Rouh-Mei Hu who helped in explaining the SNPs and the relationship between genotypes and phenotypes; Prof. Jan-Gowth Chang and Charles C.N. Wang had valuable discussions; I thank Jazz Wang for providing valuable Hadoop programming discussions. Finally, I thank Ling-Yu Ji for offering her drawing to show the conceptual diagram of "Archimedes' Law of the Lever."

## Author details

Jing-Doo Wang1,2\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

2 Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

## References

Figure 11, the Archimedes, an outstanding ancient Greek scientist, said that "Give me a place to stand on, and I will move the Earth." With scalable computing power and enough tagged genomic sequences, in other words, a domain expert can figure out the relationship among phenotypes and genotypes if the classes are properly and precisely defined. It is desired to have further cooperation with domain experts, especially who have collected the whole genomes of diverse organisms and desire to find or identify the relationship between genomic

Figure 11. "Mining for biomarkers via observing class frequency distributions of maximal repeats from tagged genomic

From a practical point of view, it is inconvenient for general users to have experiments of maximal repeat extraction by themselves in the beginning because there are a lot of preprocessing works and need considerable hardware infrastructure to support such a bigdata computing. Furthermore, it might be a bottleneck or nightmare for general users, for example, biologists, to implement Hadoop MapReduce programming as described in [22]. Therefore, it is highly desired if maximal repeat extraction can be provided in public cloud services, such as Amazon Elastic Container Service (AWS ECS) [39], Google Cloud Platform [40], and Azure Container Service (AKS) [41]. It is highly expected that one will develop novel comparative genome with tagged genomic sequences and bring users with novel cloud ser-

This study is supported by Ministry of Science and Technology, Taiwan, under project MOST 106-2632-E-468-002. I thank Prof. Jeffrey J.P. Tsai who provided computational environments

vices of computing class frequency distribution of maximal repeats in the future.

signatures and the features they concern in the future.

sequences" is somewhat like "Archimedes' Law of the Lever".

88 Bioinformatics in the Era of Post Genomics and Big Data

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**Chapter 6**

**Provisional chapter**

**Mining for Structural Variations in Next-Generation**

**Mining for Structural Variations in Next-Generation**

Genomic structural variations (SVs) are genetic alterations that result in duplications, insertions, deletions, inversions, and translocations of segments of DNA covering 50 or more base pairs. By changing the organization of DNA, SVs can contribute to phenotypic variation or cause pathological consequences as neurobehavioral disorders, autoimmune diseases, obesity, and cancers. SVs were first examined using classic cytogenetic methods, revealing changes down to 3 Mb. Later techniques for SV detection were based on array comparative genome hybridization (aCGH) and single-nucleotide polymorphism (SNP) arrays. Next-generation sequencing (NGS) approaches enabled precise characterization of breakpoints of SVs of various types and sizes at a genome-wide scale. Dissecting SVs from NGS presents substantial challenge due to the relatively short sequence reads and the large volume of the data. Benign variants and reference errors in the genome present another dimension of problem complexity. Even though a wide range of tools is available, the usage of SV callers in routine molecular diagnostic is still limited. SV detection algorithms relay on different properties of the underlying data and vary in accuracy and sensitivity; therefore, SV detection process usually utilizes multiple variant callers. This chapter summarizes strengths and limitations of different tools in effective NGS SV calling.

**Keywords:** bioinformatics, genome organization, next-generation sequencing, structural

First, efforts in exploring genetic variations were focused on single-nucleotide polymorphisms (SNPs) which were initially considered the main source of genetic and phenotypic human variation [1], while larger variations were thought to be rare events. However, in 2004

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.76568

**Sequencing Data**

**Abstract**

**Sequencing Data**

Minja Zorc, Jernej Ogorevc and Peter Dovč

Minja Zorc, Jernej Ogorevc and Peter Dovč

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76568

variation, variant calling

**1. Introduction**

#### **Mining for Structural Variations in Next-Generation Sequencing Data Mining for Structural Variations in Next-Generation Sequencing Data**

DOI: 10.5772/intechopen.76568

Minja Zorc, Jernej Ogorevc and Peter Dovč Minja Zorc, Jernej Ogorevc and Peter Dovč

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76568

#### **Abstract**

Genomic structural variations (SVs) are genetic alterations that result in duplications, insertions, deletions, inversions, and translocations of segments of DNA covering 50 or more base pairs. By changing the organization of DNA, SVs can contribute to phenotypic variation or cause pathological consequences as neurobehavioral disorders, autoimmune diseases, obesity, and cancers. SVs were first examined using classic cytogenetic methods, revealing changes down to 3 Mb. Later techniques for SV detection were based on array comparative genome hybridization (aCGH) and single-nucleotide polymorphism (SNP) arrays. Next-generation sequencing (NGS) approaches enabled precise characterization of breakpoints of SVs of various types and sizes at a genome-wide scale. Dissecting SVs from NGS presents substantial challenge due to the relatively short sequence reads and the large volume of the data. Benign variants and reference errors in the genome present another dimension of problem complexity. Even though a wide range of tools is available, the usage of SV callers in routine molecular diagnostic is still limited. SV detection algorithms relay on different properties of the underlying data and vary in accuracy and sensitivity; therefore, SV detection process usually utilizes multiple variant callers. This chapter summarizes strengths and limitations of different tools in effective NGS SV calling.

**Keywords:** bioinformatics, genome organization, next-generation sequencing, structural variation, variant calling

## **1. Introduction**

First, efforts in exploring genetic variations were focused on single-nucleotide polymorphisms (SNPs) which were initially considered the main source of genetic and phenotypic human variation [1], while larger variations were thought to be rare events. However, in 2004

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

two studies [2, 3] revealed an unexpectedly large amount of large-scale variations (several kb to hundreds of kb) in the human genome. The evidence for the prevalence of structural variants (SVs), such as deletions, duplications, and inversions, began to accumulate. By changing the organization of the DNA, SVs can contribute to the phenotypic differences among healthy individuals or cause severe phenotypic consequences. SVs are involved in a wide range of diseases and conditions, such as autism spectrum disorders [4–6], schizophrenia [7], Crohn's disease [8], rheumatoid arthritis [9], lupus erythematosus [10], psoriasis [11], obesity [12], and cancers [13, 14]. Among the different classes of genetic variations, SVs have remained the most challenging to detect and characterize. SVs were examined since the identification of chromosomal abnormalities using classic cytogenetic methods, revealing changes down to 3 Mb. Later techniques for SVs detection are based on array comparative genome hybridization (aCGH) and single-nucleotide polymorphism arrays. Next-generation sequencing (NGS) has enabled methods for precise definition of breakpoints of SVs of different sizes and types. Characterization of SVs from high-throughput sequencing data presents complex task due to the volume of the data and short sequence reads.

chromosome that is usually lost without phenotype effect. In case three or more chromosomal breakpoints are involved, we speak of complex chromosome rearrangements, which

Mining for Structural Variations in Next-Generation Sequencing Data

http://dx.doi.org/10.5772/intechopen.76568

95

The first commercially available next-generation sequencing platform was released in 2005 [21]. The technology has been continuously upgraded and has fundamentally changed the field of genetics studies. Next-generation sequencing (NGS), also known as high-throughput sequencing, parallelizes the sequencing process and produces millions of short reads (50–400 bp each) in a single experimental run. It has contributed to rapid progress in singlenucleotide polymorphisms detection. Due to the nature of the NGS short-read sequences, the category of longer variants remained poorly characterized. Variants in range 10–100 kb are small for detection by cytogenetic methods [22] but too large for reliable detection with short-read sequencing. SVs affect more bases than single-nucleotide polymorphisms [23] and present an important class of genetic variation. Moreover, many SVs have been shown to play

Once the samples are sequenced, the NGS data analysis becomes the task in bioinformatics field. The computational analysis and interpretation of the data generated remains one of the major bottlenecks in NGS projects. The basic steps for analyzing NGS data are quality assessment, reads alignment (mapping) to a reference sequence, and variant identification. The second stage of analysis comprises variant analysis, visualization, and interpretation of the variants in relation to phenotypes. Commercial packages such as CLCBio Genomic Workbench, CASAVA, and SeqNext often provide all-in-one solutions, while academic pipe-

The output from the sequencing machines are reads, which are usually stored in text-based FASTQ files. The data obtained from NGS are compromised by sequence artifacts, including read errors, poor-quality reads, and primer contamination [25]. To avoid erroneous conclusions, the artifacts should be removed. A number of bioinformatics tools for sequencing quality assessment, such as FastQC, FASTX-Toolkit, PRINSEQ [26], TagDust [27], and NGS QC Toolkit [28] are designed. Next step in NGS data analysis is alignment of short reads to corresponding positions on a reference sequence. A variety of algorithms have been developed for this task. Representative read mappers are Bowtie2 [29], BWA [30], and Novoalign. The typical output from the read mapper is BAM file which contains information about qualities and positions of aligned sequences. Variant analysis consists of genotyping, variant calling, annotation, and prioritization. Genomic variants, such as SNPs and short-scale insertions and deletions are identified by variant callers. Widely used tools for variant calling are Genome Analysis Tool Kit HaplotypeCaller (GATK-HC) [14], Samtools mpileup [31], Freebayes and Torrent Variant Caller (TVC). Variant callers take in a BAM file and return a list of variants. To annotate

lines typically consist of sequential tools for specific steps in the analysis.

may result in balanced or unbalanced state [20].

relevant roles in phenotypic variability and disease [24].

**3. Next-generation sequencing**

**3.1. NGS data analysis pipeline**

## **2. Structural variations**

Genomic structural variations (SVs) are genetic alterations that result in amplifications, losses, inversions, and translocations of segments of DNA greater than 50 bp. SVs are a normal part of genomic variation but can also cause disorders. Standard detection methods include chromosome banding, fluorescent in situ hybridization (FISH), and array comparative genome hybridization (aCGH) that is very useful to detect copy number variations (CNVs) but cannot detect copy-neutral SVs (inversions, balanced translocations) [15]. Recent methods include employment of NGS to identify SVs, which are not detectable by cytogenetic methods.