Automatic annotation of eukaryotic genes, pseudogenes and promoters

By Victor V. Solovyev, P. Kosarev, Igor Seledsov, Denis Vorobyev. Genome Biology, 2006

773 citations recorded by the source corpus.

BackgroundThe ENCODE gene prediction workshop (EGASP) has been organized to evaluate how well state-of-the-art automatic gene finding methods are able to reproduce the manual and experimental gene annotation of the human genome. We have used Softberry gene finding software to predict genes, pseudogenes and promoters in 44 selected ENCODE sequences representing approximately 1% (30 Mb) of the human genome. Predictions of gene finding programs were evaluated in terms of their ability to reproduce the ENCODE-HAVANA annotation.ResultsThe Fgenesh++ gene prediction pipeline can identify 91% of coding nucleotides with a specificity of 90%. Our automatic pseudogene finder (PSF program) found 90% of the manually annotated pseudogenes and some new ones. The Fprom promoter prediction program identifies 80% of TATA promoters sequences with one false positive prediction per 2,000 base-pairs (bp) and 50% of TATA-less promoters with one false positive prediction per 650 bp. It can be used to identify transcription start sites upstream of annotated coding parts of genes found by gene prediction software.ConclusionWe review our software and underlying methods for identifying these three important structural and functional genome components and discuss the accuracy of predictions, recent advances and open problems in annotating genomic sequences. We have demonstrated that our methods can be effectively used for initial automatic annotation of the eukaryotic genome.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/340439c495dff8aaf55510d5bd5c5310656343d2

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Citations in the corpus

A High Quality Draft Consensus Sequence of the Genome of a Heterozygous Grapevine Variety (2007, methodology): “Methods used were FgenesH [114], homology-based FgenesH + [114], Twinscan [101], GlimmerHMM [115] and Tentative Consensus [94] transcripts derived from 320,000 ESTs deposited in databases.”
The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update (2024, neutral): “In rare cases where open-source development cannot keep pace with commercial solutions or user demands, Galaxy has increasingly turned to offering licenced options, for example: CellRanger and FGeneSH++ ( 12 ,13 ).”
EGASP: the human ENCODE Genome Annotation Assessment Project (2006, methodology): “Of these the FGENESH++ pipeline [56], the PAIRAGON-any pipeline [57], and AUGUSTUS-any [58] are conceptually similar.”
Rice SNP-seek database update: new SNPs, indels, and queries (2016, methodology): “First, we merged the Nipponbare MSUv7 (16) and RAP (17) gene models, plus in-house FGenesh++ (18) annotation into a single set of gene models and named them using our convention, of the form OsNippo{YY}g{NNNNNN}.”
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, background): “…transcriptome studies (Trapnell et al., 2010; Pertea et al., 2015), ab-initio gene prediction based on general features of (protein-coding) genes (Solovyev et al., 2006; Stanke et al., 2008), and homology-based gene prediction relying on gene models of (closely) related, well-annotated species…”