Evidence map›Paper›PMID 26578583›Full record

ArticleNucleic acids research2016

csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows.

Aaron T L Lun, Gordon K Smyth

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 293 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
293citing 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

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

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233 more citing papers are in PubMed but not listed here.

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.

Aaron T L LunThe Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia.
Gordon K SmythThe Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia smyth@wehi.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify binding sites for a target protein in the genome. An important scientific application is to identify changes in protein binding between different treatment conditions, i.e. to detect differential binding. This can reveal potential mechanisms through which changes in binding may contribute to the treatment effect. The csaw package provides a framework for the de novo detection of differentially bound genomic regions. It uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data. csaw is implemented as a R software package and is freely available from the open-source Bioconductor project.

Indexed as

GenomeSoftwareAnimalsBinding SitesChromatin ImmunoprecipitationDatasets as TopicGenomicsHigh-Throughput Nucleotide SequencingHistonesMiceMouse Embryonic Stem CellsProtein BindingTranscription FactorsHistonesTranscription Factors

Identifiers

PMID26578583
PMCPMC4797262

What OpenQuestion holds

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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.