ArticleBMC genomics2015
IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions.Bioinformatics advances · 2025Review
- GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes.Genome research · 2024Article
- A new gene finding tool GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes.bioRxiv : the preprint server for biology · 2024Article
- TSEBRA: transcript selector for BRAKER.BMC bioinformatics · 2021Article
- Ancient evolutionary signals of protein-coding sequences allow the discovery of new genes in the Drosophila melanogaster genome.BMC genomics · 2020Article
- Physico-chemical fingerprinting of RNA genes.Nucleic acids research · 2017Article
- Using intron position conservation for homology-based gene prediction.Nucleic acids research · 2016Article
- MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms.Bioinformatics (Oxford, England) · 2015Article
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2 authors.
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Abstract
backgroundGene prediction is a challenging but crucial part in most genome analysis pipelines. Various methods have evolved that predict genes ab initio on reference sequences or evidence based with the help of additional information, such as RNA-Seq reads or EST libraries. However, none of these strategies is bias-free and one method alone does not necessarily provide a complete set of accurate predictions.
resultsWe present IPred (Integrative gene Prediction), a method to integrate ab initio and evidence based gene identifications to complement the advantages of different prediction strategies. IPred builds on the output of gene finders and generates a new combined set of gene identifications, representing the integrated evidence of the single method predictions.
conclusionWe evaluate IPred in simulations and real data experiments on Escherichia Coli and human data. We show that IPred improves the prediction accuracy in comparison to single method predictions and to existing methods for prediction combination.
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