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1.  Using several pair-wise informant sequences for de novo prediction of alternatively spliced transcripts 
Genome Biology  2006;7(Suppl 1):S8.
As part of the ENCODE Genome Annotation Assessment Project (EGASP), we developed the MARS extension to the Twinscan algorithm. MARS is designed to find human alternatively spliced transcripts that are conserved in only one or a limited number of extant species. MARS is able to use an arbitrary number of informant sequences and predicts a number of alternative transcripts at each gene locus.
MARS uses the mouse, rat, dog, opossum, chicken, and frog genome sequences as pairwise informant sources for Twinscan and combines the resulting transcript predictions into genes based on coding (CDS) region overlap. Based on the EGASP assessment, MARS is one of the more accurate dual-genome prediction programs. Compared to the GENCODE annotation, we find that predictive sensitivity increases, while specificity decreases, as more informant species are used. MARS correctly predicts alternatively spliced transcripts for 11 of the 236 multi-exon GENCODE genes that are alternatively spliced in the coding region of their transcripts. For these genes a total of 24 correct transcripts are predicted.
The MARS algorithm is able to predict alternatively spliced transcripts without the use of expressed sequence information, although the number of loci in which multiple predicted transcripts match multiple alternatively spliced transcripts in the GENCODE annotation is relatively small.
PMCID: PMC1810557  PMID: 16925842
2.  AceView: a comprehensive cDNA-supported gene and transcripts annotation 
Genome Biology  2006;7(Suppl 1):S12.
PMCID: PMC1810549  PMID: 16925834
3.  Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment 
Genome Biology  2006;7(Suppl 1):S3.
PMCID: PMC1810552  PMID: 16925837
Genome Biology  2006;7(Suppl 1):S11.
PMCID: PMC1810548  PMID: 16925833
5.  Automatic annotation of eukaryotic genes, pseudogenes and promoters 
Genome Biology  2006;7(Suppl 1):S10.
PMCID: PMC1810547  PMID: 16925832
6.  Exogean: a framework for annotating protein-coding genes in eukaryotic genomic DNA 
Genome Biology  2006;7(Suppl 1):S7.
PMCID: PMC1810556  PMID: 16925841
7.  AnnoTrack - a tracking system for genome annotation 
BMC Genomics  2010;11:538.
PMCID: PMC3091687  PMID: 20923551
8.  nGASP – the nematode genome annotation assessment project 
BMC Bioinformatics  2008;9:549.
PMCID: PMC2651883  PMID: 19099578
9.  GENCODE: producing a reference annotation for ENCODE 
Genome Biology  2006;7(Suppl 1):S4.
The GENCODE consortium was formed to identify and map all protein-coding genes within the ENCODE regions. This was achieved by a combination of initial manual annotation by the HAVANA team, experimental validation by the GENCODE consortium and a refinement of the annotation based on these experimental results.
The GENCODE gene features are divided into eight different categories of which only the first two (known and novel coding sequence) are confidently predicted to be protein-coding genes. 5' rapid amplification of cDNA ends (RACE) and RT-PCR were used to experimentally verify the initial annotation. Of the 420 coding loci tested, 229 RACE products have been sequenced. They supported 5' extensions of 30 loci and new splice variants in 50 loci. In addition, 46 loci without evidence for a coding sequence were validated, consisting of 31 novel and 15 putative transcripts. We assessed the comprehensiveness of the GENCODE annotation by attempting to validate all the predicted exon boundaries outside the GENCODE annotation. Out of 1,215 tested in a subset of the ENCODE regions, 14 novel exon pairs were validated, only two of them in intergenic regions.
In total, 487 loci, of which 434 are coding, have been annotated as part of the GENCODE reference set available from the UCSC browser. Comparison of GENCODE annotation with RefSeq and ENSEMBL show only 40% of GENCODE exons are contained within the two sets, which is a reflection of the high number of alternative splice forms with unique exons annotated. Over 50% of coding loci have been experimentally verified by 5' RACE for EGASP and the GENCODE collaboration is continuing to refine its annotation of 1% human genome with the aid of experimental validation.
PMCID: PMC1810553  PMID: 16925838
10.  Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions 
BMC Genomics  2013;14:141.
PMCID: PMC3607840  PMID: 23448259
Proteogenomic mapping; MS/MS spectra; Genome annotation; Proteomics; Genomics
11.  EuCAP, a Eukaryotic Community Annotation Package, and its application to the rice genome 
BMC Genomics  2007;8:388.
PMCID: PMC2151081  PMID: 17961238
12.  Comparative Proteomics Reveals a Significant Bias Toward Alternative Protein Isoforms with Conserved Structure and Function 
Molecular Biology and Evolution  2012;29(9):2265-2283.
PMCID: PMC3424414  PMID: 22446687
alternative splicing; shotgun proteomics; genome annotation; heterogeneous nuclear ribonucleoproteins; NAGNAG splicing; mutually exclusive exons
13.  The RAST Server: Rapid Annotations using Subsystems Technology 
BMC Genomics  2008;9:75.
PMCID: PMC2265698  PMID: 18261238
14.  Using computational predictions to improve literature-based Gene Ontology annotations: a feasibility study 
Database URL:
PMCID: PMC3067894  PMID: 21411447
15.  Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning 
PLoS Computational Biology  2007;3(2):e20.
Author Summary
PMCID: PMC1808025  PMID: 17319737
16.  APPRIS: annotation of principal and alternative splice isoforms 
Nucleic Acids Research  2012;41(D1):D110-D117.
PMCID: PMC3531113  PMID: 23161672
17.  Metagenomic Guilt by Association: An Operonic Perspective 
PLoS ONE  2013;8(8):e71484.
Next-generation sequencing projects continue to drive a vast accumulation of metagenomic sequence data. Given the growth rate of this data, automated approaches to functional annotation are indispensable and a cornerstone heuristic of many computational protocols is the concept of guilt by association. The guilt by association paradigm has been heavily exploited by genomic context methods that offer functional predictions that are complementary to homology-based annotations, thereby offering a means to extend functional annotation. In particular, operon methods that exploit co-directional intergenic distances can provide homology-free functional annotation through the transfer of functions among co-operonic genes, under the assumption that guilt by association is indeed applicable. Although guilt by association is a well-accepted annotative device, its applicability to metagenomic functional annotation has not been definitively demonstrated. Here a large-scale assessment of metagenomic guilt by association is undertaken where functional associations are predicted on the basis of co-directional intergenic distances. Specifically, functional annotations are compared within pairs of adjacent co-directional genes, as well as operons of various lengths (i.e. number of member genes), in order to reveal new information about annotative cohesion versus operon length. The results suggests that co-directional gene pairs offer reduced confidence for metagenomic guilt by association due to difficulty in resolving the existence of functional associations when intergenic distance is the sole predictor of pairwise gene interactions. However, metagenomic operons, particularly those with substantial lengths, appear to be capable of providing a superior basis for metagenomic guilt by association due to increased annotative stability. The need for improved recognition of metagenomic operons is discussed, as well as the limitations of the present work.
PMCID: PMC3735515  PMID: 23940763
18.  Next generation models for storage and representation of microbial biological annotation 
BMC Bioinformatics  2010;11(Suppl 6):S15.
Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation.
Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software system to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable.
The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way.
Here, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files.
The power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is understandable to researchers. In this way, all researchers, experimental and computational, will more easily understand the informatics processes constructing genome annotation and ultimately be able to help improve the systems that produce them.
PMCID: PMC3026362  PMID: 20946598
19.  ParsEval: parallel comparison and analysis of gene structure annotations 
BMC Bioinformatics  2012;13:187.
PMCID: PMC3439248  PMID: 22852583
20.  Gene Ontology annotations at SGD: new data sources and annotation methods 
Nucleic Acids Research  2007;36(Database issue):D577-D581.
The Saccharomyces Genome Database (SGD; collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current.
PMCID: PMC2238894  PMID: 17982175
21.  GIFtS: annotation landscape analysis with GeneCards 
BMC Bioinformatics  2009;10:348.
PMCID: PMC2774327  PMID: 19852797
22.  The GENCODE pseudogene resource 
Genome Biology  2012;13(9):R51.
PMCID: PMC3491395  PMID: 22951037
23.  A computational approach for identifying pseudogenes in the ENCODE regions 
Genome Biology  2006;7(Suppl 1):S13.
PMCID: PMC1810550  PMID: 16925835
24.  The UCSC genome browser and associated tools 
Briefings in Bioinformatics  2012;14(2):144-161.
PMCID: PMC3603215  PMID: 22908213
UCSC genome browser; bioinformatics; genetics; human genome; genomics; sequencing
Nucleic Acids Research  2000;28(17):3278-3288.
Four years after the original sequence submission, we have re-annotated the genome of Mycoplasma pneumoniae to incorporate novel data. The total number of ORFss has been increased from 677 to 688 (10 new proteins were predicted in intergenic regions, two further were newly identified by mass spectrometry and one protein ORF was dismissed) and the number of RNAs from 39 to 42 genes. For 19 of the now 35 tRNAs and for six other functional RNAs the exact genome positions were re-annotated and two new tRNALeu and a small 200 nt RNA were identified. Sixteen protein reading frames were extended and eight shortened. For each ORF a consistent annotation vocabulary has been introduced. Annotation reasoning, annotation categories and comparisons to other published data on M.pneumoniae functional assignments are given. Experimental evidence includes 2-dimensional gel electrophoresis in combination with mass spectrometry as well as gene expression data from this study. Compared to the original annotation, we increased the number of proteins with predicted functional features from 349 to 458. The increase includes 36 new predictions and 73 protein assignments confirmed by the published literature. Furthermore, there are 23 reductions and 30 additions with respect to the previous annotation. mRNA expression data support transcription of 184 of the functionally unassigned reading frames.
PMCID: PMC110705  PMID: 10954595

Results 1-25 (874103)