Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

By Mario Stanke, Oliver Schöffmann, Burkhard Morgenstern, Stephan Waack. BMC Bioinformatics, 2006

1,271 citations recorded by the source corpus.

BackgroundIn order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence.ResultsWe present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly.ConclusionSensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/53137eba105d2b7653994e0d2bf2ff7c3893a17a

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Citations in the corpus

AUGUSTUS: ab initio prediction of alternative transcripts (2006, background): “An extended version of the program is able to use additional extrinsic information, for example matches to protein databases or alignments of genomic sequences, to improve the prediction accuracy (4).”
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models (2006, methodology): “In the case of predicting eukaryotic genes, for example, the programs GENSCAN [16,17], HMMGene [18,19], GenomeScan [20], AUGUSTUS [21], and AUGUSTUS+ [22] are HMM-based.”
Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes (2011, methodology): “We conducted de novo gene annotation with AUGUSTU”
AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome (2006, methodology): “A recent extension of the program is also able to integrate extrinsic information from arbitrary sources for improved prediction accuracy [3]. gene prediction were systematically evaluated and compared, including some of the most widely used gene-finding tools [4,5].”
BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database (2020, neutral): “The accuracy of BRAKER2 was determined at exon and gene level (Figures 3 and 4).”
Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments (2007, methodology): “Other tools, including Augustus [23], Genie [24], and ExonHunter [25] include mechanisms to incorporate extrinsic data into the ab initio gene prediction framework to improve accuracy further.”
Genome Annotation in Plants and Fungi: EuGene as a Model Platform (2008, methodology): “Regarding the above-mentioned examples, it is worth noting that these software tools are independent and that none of the GHMM-based gene finders yet has the possibility to exploit simultaneously more than one of these types of evidence (AUGUSTUS+, one of the most advanced software in this category,”