Evidence map›Paper›PMID 32272892›Full record

ArticleBMC genomics2020

A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms.

Nicolas Scalzitti, Anne Jeannin-Girardon, Pierre Collet, Olivier Poch, Julie D Thompson

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In one paragraph

Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Nicolas ScalzittiDepartment of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France.
Anne Jeannin-GirardonDepartment of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France.
Pierre ColletDepartment of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France.
Olivier PochDepartment of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France.
Julie D ThompsonDepartment of Computer Science, ICube, CNRS, University of Strasbourg, Strasbourg, France. thompson@unistra.fr.ORCID http://orcid.org/0000-0003-4893-3478

Funding

Agence Nationale de la Recherche ANR-18-RAR3-0006-02Agence Nationale de la Recherche GA-676559BIONIRIA GB
6 · The paper itself

Abstract

backgroundThe draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations.

resultsWe describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools.

conclusionsThe experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.

Indexed as

AnimalsComputational BiologyData CurationEukaryotaEvolution, MolecularHumansMolecular Sequence AnnotationPhylogenyBenchmark studyGene predictionGenome annotationProtein prediction

Identifiers

PMID32272892
PMCPMC7147072

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.