Evidence map›Paper›PMID 21106489›Full record

ArticleHuman genomics2010

A survey of statistical software for analysing RNA-seq data.

Dexiang Gao, Jihye Kim, Hyunmin Kim, Tzu L Phang, Heather Selby, Aik Choon Tan, Tiejun Tong

Abstract readEvaluation Study
In one paragraph

Article in Human genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Transcriptional Profiling of Exosomes Derived fromOxidative medicine and cellular longevity · 2021
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018
    Review
  12. Article
  13. Article
  14. Review
  15. 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

7 authors.

Dexiang GaoDepartment of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA. Dexiang.Gao@UCDenver.edu
Jihye Kim
Hyunmin Kim
Tzu L Phang
Heather Selby
Aik Choon Tan
Tiejun Tong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput RNA sequencing is rapidly emerging as a favourite method for gene expression studies. We review three software packages - edgeR, DEGseq and baySeq - from Bioconductor http://bioconductor.org for analysing RNA-sequencing data. We focus on three aspects: normalisation, statistical models and the testing employed on these methods. We also discuss the advantages and limitations of these software packages.

Indexed as

Databases, Nucleic AcidGene Expression RegulationModels, StatisticalSoftwareGene Expression Profiling

Identifiers

PMID21106489
PMCPMC3500157

What OpenQuestion holds

Textmetadata
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.