Evidence map›Paper›PMID 29843602›Full record

ArticleBMC bioinformatics2018

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi.

Jens Keilwagen, Frank Hartung, Michael Paulini, Sven O Twardziok, Jan Grau

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 232 papers.

0numbers the graph read from it
0cells of the map it votes in
232citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

232 citing papers in PubMed.

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172 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jens KeilwagenInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, D-06484, Germany. jens.keilwagen@julius-kuehn.de.
Frank HartungInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, D-06484, Germany.
Michael PauliniEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Sven O TwardziokPlant Genome and Systems Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, D-85764, Germany.
Jan GrauInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), D-06120, Germany.ORCID 0000-0003-2081-6405

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.

resultsHere, we present an extension of the gene prediction program GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.

conclusionsGeMoMa might be of great utility for annotating newly sequenced genomes but also for finding homologs of a specific gene or gene family. GeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa .

Indexed as

Gene Expression ProfilingGenes, FungalGenes, PlantSequence Analysis, RNASequence Homology, Amino AcidSoftwareAnimalsGenomicsHordeumIntronsMolecular Sequence AnnotationNematodaGenome annotationHomology-based gene predictionRNA-seq

Identifiers

PMID29843602
PMCPMC5975413

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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.