Evidence map›Paper›PMID 31861980›Full record

ArticleBMC bioinformatics2019

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

Venkat Sundar Gadepalli, Hatice Gulcin Ozer, Ayse Selen Yilmaz, Maciej Pietrzak, Amy Webb

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 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

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

  1. Pooled it
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  12. Caspase-4/11 exacerbates disease severity in SARS-CoV-2 infection by promoting inflammation and immunothrombosis.Proceedings of the National Academy of Sciences of the United States of America · 2022
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  14. Frontiers in cell and developmental biology · 2022
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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.

Venkat Sundar GadepalliBiomedical Informatics, The Ohio State University, Columbus, OH, USA.
Hatice Gulcin OzerBiomedical Informatics, The Ohio State University, Columbus, OH, USA.
Ayse Selen YilmazBiomedical Informatics, The Ohio State University, Columbus, OH, USA.
Maciej PietrzakBiomedical Informatics, The Ohio State University, Columbus, OH, USA.
Amy WebbBiomedical Informatics, The Ohio State University, Columbus, OH, USA. amy.hite@osumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment.

resultsDeveloped as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/.

conclusionThe workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.

Indexed as

Gene ExpressionHigh-Throughput Nucleotide SequencingHumansRNASequence Analysis, RNASoftwareWorkflowRNARNAseqTranscriptomeVisualizationWorkflow

Identifiers

PMID31861980
PMCPMC6923898

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.