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IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy
BACKGROUND: Gene 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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345001/ https://www.ncbi.nlm.nih.gov/pubmed/25766582 http://dx.doi.org/10.1186/s12864-015-1315-9 |
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author | Zickmann, Franziska Renard, Bernhard Y |
author_facet | Zickmann, Franziska Renard, Bernhard Y |
author_sort | Zickmann, Franziska |
collection | PubMed |
description | BACKGROUND: Gene 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. RESULTS: We 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. CONCLUSION: We 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1315-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4345001 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43450012015-03-02 IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy Zickmann, Franziska Renard, Bernhard Y BMC Genomics Software BACKGROUND: Gene 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. RESULTS: We 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. CONCLUSION: We 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-1315-9) contains supplementary material, which is available to authorized users. BioMed Central 2015-02-26 /pmc/articles/PMC4345001/ /pubmed/25766582 http://dx.doi.org/10.1186/s12864-015-1315-9 Text en © Zickmann and Renard; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Software Zickmann, Franziska Renard, Bernhard Y IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
title | IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
title_full | IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
title_fullStr | IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
title_full_unstemmed | IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
title_short | IPred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
title_sort | ipred - integrating ab initio and evidence based gene predictions to improve prediction accuracy |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345001/ https://www.ncbi.nlm.nih.gov/pubmed/25766582 http://dx.doi.org/10.1186/s12864-015-1315-9 |
work_keys_str_mv | AT zickmannfranziska ipredintegratingabinitioandevidencebasedgenepredictionstoimprovepredictionaccuracy AT renardbernhardy ipredintegratingabinitioandevidencebasedgenepredictionstoimprovepredictionaccuracy |