Evidence map›Paper›PMID 31703556›Full record

ArticleBMC bioinformatics2019

VARUS: sampling complementary RNA reads from the sequence read archive.

Mario Stanke, Willy Bruhn, Felix Becker, Katharina J Hoff

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. AccuratebioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
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  8. GALBA: Genome Annotation with Miniprot and AUGUSTUS.bioRxiv : the preprint server for biology · 2023
    Article
  9. TSEBRA: transcript selector for BRAKER.BMC bioinformatics · 2021
    Article
  10. Article
  11. Article
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.

Mario StankeInstitute for Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, Greifswald, 17489, Germany. mario.stanke@uni-greifswald.de.ORCID http://orcid.org/0000-0001-8696-0384
Willy BruhnInstitute for Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, Greifswald, 17489, Germany.
Felix BeckerInstitute for Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, Greifswald, 17489, Germany.
Katharina J HoffInstitute for Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, Greifswald, 17489, Germany.

Funding

NIGMS Administrative Supplements to Support Undergraduate Summer ResearchR01GM128145 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI BORODOVSKY, MARK · 2018 to 2021
$1.1M
NIGMS NIH HHS R01 GM128145
6 · The paper itself

Abstract

backgroundVast amounts of next generation sequencing RNA data has been deposited in archives, accompanying very diverse original studies. The data is readily available also for other purposes such as genome annotation or transcriptome assembly. However, selecting a subset of available experiments, sequencing runs and reads for this purpose is a nontrivial task and complicated by the inhomogeneity of the data.

resultsThis article presents the software VARUS that selects, downloads and aligns reads from NCBI's Sequence Read Archive, given only the species' binomial name and genome. VARUS automatically chooses runs from among all archived runs to randomly select subsets of reads. The objective of its online algorithm is to cover a large number of transcripts adequately when network bandwidth and computing resources are limited. For most tested species VARUS achieved both a higher sensitivity and specificity with a lower number of downloaded reads than when runs were manually selected. At the example of twelve eukaryotic genomes, we show that RNA-Seq that was sampled with VARUS is well-suited for fully-automatic genome annotation with BRAKER.

conclusionsWith VARUS, genome annotation can be automatized to the extent that not even the selection and quality control of RNA-Seq has to be done manually. This introduces the possibility to have fully automatized genome annotation loops over potentially many species without incurring a loss of accuracy over a manually supervised annotation process.

Indexed as

Databases, GeneticSoftwareAlgorithmsAnimalsDrosophila melanogasterEukaryotaHigh-Throughput Nucleotide SequencingIntronsMolecular Sequence AnnotationRNA, ComplementarySequence Analysis, RNATranscriptomeRNA, ComplementaryGenome annotationOnline algorithmRNA-SeqSample

Identifiers

PMID31703556
PMCPMC6842140

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.