Improved transcript isoform discovery using ORF graphs.

William H Majoros, Niel Lebeck, Uwe Ohler, Song Li

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

Computational Identification of Novel Genes: Current and Future Perspectives.Bioinformatics and biology insights · 2016
Review

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

4 authors.

William H MajorosProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, GermanyProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, Germany.

Niel LebeckProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, Germany.

Uwe OhlerProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, GermanyProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, GermanyProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, GermanyProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicin

Song LiProgram in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27705, USA, Department of Computer Science, Duke University, Durham, NC 27708, USA, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA, Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin 13125, Germany and Department of Biology, Humboldt University of Berlin, Berlin 10115, Germany.

Funding

Computational Biology and Bioinformatics Training ProgramT32GM071340 · NIGMS · DUKE UNIVERSITY · PI MAGWENE, PAUL MITAARI · 2005 to 2020

$3.3M

NIGMS NIH HHS T32 GM071340

6 · The paper itself

Abstract

motivationHigh-throughput sequencing of RNA in vivo facilitates many applications, not the least of which is the cataloging of variant splice isoforms of protein-coding messenger RNAs. Although many solutions have been proposed for reconstructing putative isoforms from deep sequencing data, these generally take as their substrate the collective alignment structure of RNA-seq reads and ignore the biological signals present in the actual nucleotide sequence. The majority of these solutions are graph-theoretic, relying on a splice graph representing the splicing patterns and exon expression levels indicated by the spliced-alignment process.

resultsWe show how to augment splice graphs with additional information reflecting the biology of transcription, splicing and translation, to produce what we call an ORF (open reading frame) graph. We then show how ORF graphs can be used to produce isoform predictions with higher accuracy than current state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: RSVP is available as C++ source code under an open-source licence: http://ohlerlab.mdc-berlin.de/software/RSVP/.

Indexed as

Open Reading FramesArabidopsisExonsHigh-Throughput Nucleotide SequencingHumansRNA IsoformsRNA SplicingSequence Analysis, RNASoftwareRNA Isoforms

Identifiers

PMID24659106

DOI10.1093/bioinformatics/btu160

PMCPMC4288128

What OpenQuestion holds

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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.