Evidence map›Paper›PMID 26747388›Full record

ArticleBMC genomics2016

QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.

Shanrong Zhao, Li Xi, Jie Quan, Hualin Xi, Ying Zhang, David von Schack, Michael Vincent, Baohong Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
5.0field-weighted citation impact, top 4% of its field
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

20 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Life science alliance · 2019
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

8 authors at 1 institution in 1 country.

Shanrong ZhaoPharmaTherapeutics Clinical R&D, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Shanrong.Zhao@pfizer.com.
Li XiPharmaTherapeutics Clinical R&D, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Li.Xi@pfizer.com.
Jie QuanComputational Sciences Center of Emphasis, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Jie.Quan@pfizer.com.
Hualin XiComputational Sciences Center of Emphasis, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Hualin.Xi@pfizer.com.
Ying ZhangPharmaTherapeutics Clinical R&D, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Ying.Zhang1@pfizer.com.
David von SchackPharmaTherapeutics Clinical R&D, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. David.VonSchack@pfizer.com.
Michael VincentPharmaTherapeutics Clinical R&D, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Michael.Vincent@pfizer.com.
Baohong ZhangPharmaTherapeutics Clinical R&D, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA. Baohong.Zhang@pfizer.com.
Pfizer (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRNA sequencing (RNA-seq), a next-generation sequencing technique for transcriptome profiling, is being increasingly used, in part driven by the decreasing cost of sequencing. Nevertheless, the analysis of the massive amounts of data generated by large-scale RNA-seq remains a challenge. Multiple algorithms pertinent to basic analyses have been developed, and there is an increasing need to automate the use of these tools so as to obtain results in an efficient and user friendly manner. Increased automation and improved visualization of the results will help make the results and findings of the analyses readily available to experimental scientists.

resultsBy combing the best open source tools developed for RNA-seq data analyses and the most advanced web 2.0 technologies, we have implemented QuickRNASeq, a pipeline for large-scale RNA-seq data analyses and visualization. The QuickRNASeq workflow consists of three main steps. In Step #1, each individual sample is processed, including mapping RNA-seq reads to a reference genome, counting the numbers of mapped reads, quality control of the aligned reads, and SNP (single nucleotide polymorphism) calling. Step #1 is computationally intensive, and can be processed in parallel. In Step #2, the results from individual samples are merged, and an integrated and interactive project report is generated. All analyses results in the report are accessible via a single HTML entry webpage. Step #3 is the data interpretation and presentation step. The rich visualization features implemented here allow end users to interactively explore the results of RNA-seq data analyses, and to gain more insights into RNA-seq datasets. In addition, we used a real world dataset to demonstrate the simplicity and efficiency of QuickRNASeq in RNA-seq data analyses and interactive visualizations. The seamless integration of automated capabilites with interactive visualizations in QuickRNASeq is not available in other published RNA-seq pipelines.

conclusionThe high degree of automation and interactivity in QuickRNASeq leads to a substantial reduction in the time and effort required prior to further downstream analyses and interpretation of the analyses findings. QuickRNASeq advances primary RNA-seq data analyses to the next level of automation, and is mature for public release and adoption.

Indexed as

SoftwareAlgorithmsBase SequenceHigh-Throughput Nucleotide SequencingRNASequence Analysis, RNATranscriptomeRNA

Identifiers

PMID26747388
PMCPMC4706714
OpenAlexW2222859485

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.