Evidence map›Paper›PMID 28789639›Full record

ArticleBMC bioinformatics2017

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

Carlos Guzman, Iván D'Orso

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

17 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
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  8. Global Analyses to Identify Direct Transcriptional Targets of p53.Methods in molecular biology (Clifton, N.J.) · 2021
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  11. Analytical Approaches for ATAC-seq Data Analysis.Current protocols in human genetics · 2020
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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

2 authors at 1 institution in 1 country.

Carlos GuzmanDepartment of Microbiology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. cag104@ucsd.edu.
Iván D'OrsoDepartment of Microbiology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. Ivan.Dorso@utsouthwestern.edu.
The University of Texas Southwestern Medical Center · US

Funding

Transcriptional regulatory mechanisms shaping HIV proviral fateR01AI114362 · NIAID · UT SOUTHWESTERN MEDICAL CENTER · PI D'ORSO, IVAN · 2015 to 2024
$4.6M
Transcriptional regulatory mechanisms shaping HIV proviral fateR37AI114362 · NIAID · UT SOUTHWESTERN MEDICAL CENTER · PI Ivan D'Orso · 2025 to 2026
$1.0M
NIAID NIH HHS R01 AI114362NIAID NIH HHS R37 AI114362
6 · The paper itself

Abstract

backgroundNext-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.

resultsWe 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.

conclusionsUsing 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.

Indexed as

High-Throughput Nucleotide SequencingSoftwareChromatin ImmunoprecipitationChromosome MappingDatabases, GeneticEnhancer Elements, GeneticGene Expression RegulationRegulatory Sequences, Nucleic AcidSequence Analysis, RNAATAC-seqChIP-seqChromatin statesDNase-seqEnhancersGene regulationGRO-seqMachine-learningMNase-seqNext-generation sequencingPipelinePredictionRNA-seqTranscriptionWorkflow

Identifiers

PMID28789639
PMCPMC5549294
OpenAlexW2744947612

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.