**2.7. Visualization**

Analyzed RNASeq data can be visualized in many different ways. Several tools such as Cummerbund (an R package), RNAseqViewer for single and multiple sample visualization [122], HeatmapGenerator for heatmap visualization, GOexpress for GO term enrichment visualization (http://www.bioconductor.org/packages/devel/bioc/html/GOexpress.html), RNASeq-specific genome viewers such as RNASeqExpressionBrowse [123], and RNASeq‐ Browser [124] are available for RNASeq data visualization.

We have recently developed SanGeniX (www.sangenix.com), an easy-to-use client-serverbased NGS data analysis application with a highly intuitive user interface (manuscript under preparation). SanGeniX supports primary, secondary, and tertiary analysis of sequence data from Illumina, Ion Torrent, SOliD, and PacBio RS. SanGeniX integrates multiple robust and validated algorithms in the form of predefined workflows and offers flexibility to construct custom workflows for RNASeq (reference-based as well as *de novo*), genome assembly, ChIPSeq and DNASeq (for SNP and CNV calling). For example, in the case of RNASeq workflow, the analysis starts with quality check (using tool FastQC), contaminant/adapter trimming and removal (using Cutadapt and in-house scripts), read mapping using splice aware aligners (using STAR, TopHat2), transcript quantification, differential expression analysis (using Cufflinks packages and DESeq2), and gene ontology, as well as pathway enrichment analysis (using GoMiner) (Figure 2). Further, graphically enriched visuals such as heatmap based on clustering, scatter plot, and volcano plot for differentially expressed genes, pie chart on gene-ontology-based annotation, visualization of read data in the genome viewer, etc., are generated for easy interpretation of the data (Figure 3). These figures and underlying data can be downloaded in svg, png, and tsv formats. Moreover, the raw output files such as output of mapping in SAM and BAM formats can also be downloaded. The executed work‐ flows can be shared with peers, rerun after changing parameters or tools. SanGeniX is available as cloud-hosted as well as on premise solution and supported on multiple Linux platforms such as Ubuntu, CentOS, and RedHat.

This approach addresses few limitations of ORA. It considers all the genes and their expression for pathway enrichment, so as to take into consideration the coordinated changes (irrespective of the magnitude) unlike ORA where only differentially expressed genes were considered and that too without considering their expression levels. Example of such tools include global test

But this approach too has some limitations. Similar to ORA, this approach ignores the dependency between the pathways and the interaction between gene products in a given

To overcome the limitations of ORA and FCS, the Pathway-Topology-based approach has been devised. It uses pathway knowledgebase to include pathway topology information for enrichment analysis [112]. This information includes genes that are interacting, their mode of interaction (e.g, activation, inhibition), and their location of interaction (e.g, cytoplasm, nucleus). SPIA [120], an R package, is an example of this category of pathway analysis approach, which combines evidence of pathway overrepresentation and unusual signaling perturbations. NetGSA [121] is another method in this category that takes into consideration the change in correlation as well as the change in network structure as experimental condition changes. However, in the absence of high-resolution knowledge databases that can provide knowledge for all conditions, tissue- and cell-specific functions of a gene product; the true pathway topology is rarely inferred. And hence this restricts a researcher to investigate the

Analyzed RNASeq data can be visualized in many different ways. Several tools such as Cummerbund (an R package), RNAseqViewer for single and multiple sample visualization [122], HeatmapGenerator for heatmap visualization, GOexpress for GO term enrichment visualization (http://www.bioconductor.org/packages/devel/bioc/html/GOexpress.html), RNASeq-specific genome viewers such as RNASeqExpressionBrowse [123], and RNASeq‐

We have recently developed SanGeniX (www.sangenix.com), an easy-to-use client-serverbased NGS data analysis application with a highly intuitive user interface (manuscript under preparation). SanGeniX supports primary, secondary, and tertiary analysis of sequence data from Illumina, Ion Torrent, SOliD, and PacBio RS. SanGeniX integrates multiple robust and validated algorithms in the form of predefined workflows and offers flexibility to construct custom workflows for RNASeq (reference-based as well as *de novo*), genome assembly, ChIPSeq and DNASeq (for SNP and CNV calling). For example, in the case of RNASeq workflow, the analysis starts with quality check (using tool FastQC), contaminant/adapter trimming and removal (using Cutadapt and in-house scripts), read mapping using splice aware aligners (using STAR, TopHat2), transcript quantification, differential expression analysis (using Cufflinks packages and DESeq2), and gene ontology, as well as pathway enrichment analysis (using GoMiner) (Figure 2). Further, graphically enriched visuals such as heatmap based on clustering, scatter plot, and volcano plot for differentially expressed genes,

**c.** Third Generation: Pathway Topology (PT)-based approach

128 Next Generation Sequencing - Advances, Applications and Challenges

Browser [124] are available for RNASeq data visualization.

[118], GSEA [119].

dynamic states of a system [112].

**2.7. Visualization**

pathway.

Cufflinks package and (B) HTSeq and DESeq2 are shown.

58

**Figure 3.** Snapshots from RNASeq results dashboard from SanGeniX for an experiment consisting of four groups (or samples). (A) Boxplot: It displays distribution of normalized expression values among different groups. Similar distri‐ bution of normalized expression values among the different groups of interest indicates that any technical biases due to difference in sequencing depth have been taken care of. (B) Heatmap is a convenient way to visualize cluster of genes based upon their expression. Here, log2 fold change of genes in three groups with respect to a reference group, Group1 has been plotted. The color-code helps to infer gene expression level. Scatter plot (C), MA plot (D), and Volca‐ no plot (E) present visual investigation of differentially expressed genes between two conditions, for example, here Group 4 and Group 1. Scatter plot helps to quickly compare the expression of a gene between the two conditions, while MA plot depicts trends of difference in expression over the average expression, and Volcano plot helps to spot genes by considering both fold change and test statistic.