↓ Skip to main content

CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction

Overview of attention for article published in BMC Bioinformatics, August 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
14 X users

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
115 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction
Published in
BMC Bioinformatics, August 2017
DOI 10.1186/s12859-017-1770-1
Pubmed ID
Authors

Carlos Guzman, Iván D’Orso

Abstract

Next-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs. Although these technologies are frequently used in biological studies, NGS data analysis remains a challenging, time-consuming, and often irreproducible process. Therefore, there is a need for a comprehensive and flexible workflow platform that can accelerate data processing and analysis so more time can be spent on functional studies. We have developed an integrative, stand-alone workflow platform, named CIPHER, for the systematic analysis of several commonly used NGS datasets including ChIP-seq, RNA-seq, MNase-seq, DNase-seq, GRO-seq, and ATAC-seq data. CIPHER implements various open source software packages, in-house scripts, and Docker containers to analyze and process single-ended and pair-ended datasets. CIPHER's pipelines conduct extensive quality and contamination control checks, as well as comprehensive downstream analysis. A typical CIPHER workflow includes: (1) raw sequence evaluation, (2) read trimming and adapter removal, (3) read mapping and quality filtering, (4) visualization track generation, and (5) extensive quality control assessment. Furthermore, CIPHER conducts downstream analysis such as: narrow and broad peak calling, peak annotation, and motif identification for ChIP-seq, differential gene expression analysis for RNA-seq, nucleosome positioning for MNase-seq, DNase hypersensitive site mapping, site annotation and motif identification for DNase-seq, analysis of nascent transcription from Global-Run On (GRO-seq) data, and characterization of chromatin accessibility from ATAC-seq datasets. In addition, CIPHER contains an "analysis" mode that completes complex bioinformatics tasks such as enhancer discovery and provides functions to integrate various datasets together. Using public and simulated data, we demonstrate that CIPHER is an efficient and comprehensive workflow platform that can analyze several NGS datasets commonly used in genome biology studies. Additionally, CIPHER's integrative "analysis" mode allows researchers to elicit important biological information from the combined dataset analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 115 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 23%
Researcher 26 23%
Student > Master 15 13%
Student > Bachelor 12 10%
Other 8 7%
Other 11 10%
Unknown 17 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 33%
Agricultural and Biological Sciences 25 22%
Computer Science 10 9%
Engineering 6 5%
Medicine and Dentistry 6 5%
Other 10 9%
Unknown 20 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 January 2018.
All research outputs
#4,991,241
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#1,854
of 7,454 outputs
Outputs of similar age
#84,094
of 319,590 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 89 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 319,590 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.