WACS: improving ChIP-seq peak calling by optimally weighting controls

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1471-2105

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: WACS: improving ChIP-seq peak calling by optimally weighting controls ; volume:22 ; number:1 ; day:15 ; month:2 ; year:2021 ; pages:1-21 ; date:12.2021
BMC bioinformatics ; 22, Heft 1 (15.2.2021), 1-21, 12.2021

Creator: Awdeh, Aseel
Turcotte, Marcel
Perkins, Theodore J.

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12859-020-03927-2

URN: urn:nbn:de:101:1-2021040421142575744849

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:00 AM CEST

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Awdeh, Aseel
Turcotte, Marcel
Perkins, Theodore J.
SpringerLink (Online service)

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Solving the differential peak calling problem in ChIP-seq data

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Solving the differential peak calling problem in ChIP-seq data

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Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

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PeakRanger: A cloud-enabled peak caller for ChIP-seq data

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peaksat: an R package for ChIP-seq peak saturation analysis

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The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

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WACS: improving ChIP-seq peak calling by optimally weighting controls

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Other Objects (12)

Solving the differential peak calling problem in ChIP-seq data

Differential peak calling of ChIP-seq signals with replicates with THOR

Robust algorithms for improved reproducible ChIP-seq and ChIP-nexus peak calling

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

Shape-based peak identification for ChIP-Seq

PeakRanger: A cloud-enabled peak caller for ChIP-seq data

BayesPeak: Bayesian analysis of ChIP-seq data

peaksat: an R package for ChIP-seq peak saturation analysis

Combining multiple ChIP-seq peak detection systems using combinatorial fusion

GoPeaks: histone modification peak calling for CUT&Tag

Characterising ChIP-seq binding patterns by model-based peak shape deconvolution

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

Solving the differential peak calling problem in ChIP-seq data

Differential peak calling of ChIP-seq signals with replicates with THOR

Robust algorithms for improved reproducible ChIP-seq and ChIP-nexus peak calling

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

Shape-based peak identification for ChIP-Seq

PeakRanger: A cloud-enabled peak caller for ChIP-seq data

BayesPeak: Bayesian analysis of ChIP-seq data

peaksat: an R package for ChIP-seq peak saturation analysis

Combining multiple ChIP-seq peak detection systems using combinatorial fusion

GoPeaks: histone modification peak calling for CUT&Tag

Characterising ChIP-seq binding patterns by model-based peak shape deconvolution

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

Solving the differential peak calling problem in ChIP-seq data

Differential peak calling of ChIP-seq signals with replicates with THOR

Robust algorithms for improved reproducible ChIP-seq and ChIP-nexus peak calling

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

Shape-based peak identification for ChIP-Seq

PeakRanger: A cloud-enabled peak caller for ChIP-seq data

BayesPeak: Bayesian analysis of ChIP-seq data

peaksat: an R package for ChIP-seq peak saturation analysis

Combining multiple ChIP-seq peak detection systems using combinatorial fusion

GoPeaks: histone modification peak calling for CUT&Tag

Characterising ChIP-seq binding patterns by model-based peak shape deconvolution

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

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