Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1474-760X

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome ; volume:23 ; number:1 ; day:10 ; month:6 ; year:2022 ; pages:1-23 ; date:12.2022
Genome biology ; 23, Heft 1 (10.6.2022), 1-23, 12.2022

Creator: Karimzadeh, Mehran
Hoffman, Michael M.

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13059-022-02690-2

URN: urn:nbn:de:101:1-2022082020232995858206

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

Last update: 15.08.2025, 7:30 AM CEST

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Karimzadeh, Mehran
Hoffman, Michael M.
SpringerLink (Online service)

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Shape-based peak identification for ChIP-Seq

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

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ChIPseqR: analysis of ChIP-seq experiments

BayesPeak: Bayesian analysis of ChIP-seq data

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Principles of ChIP-seq Data Analysis Illustrated with Examples

zweidimensionales bewegtes Bild

Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

Model-based Analysis of ChIP-Seq (MACS)

Normalization of ChIP-seq data with control

Shape-based peak identification for ChIP-Seq

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

RELACS nuclei barcoding enables high-throughput ChIP-seq

ChIPseqR: analysis of ChIP-seq experiments

BayesPeak: Bayesian analysis of ChIP-seq data

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Principles of ChIP-seq Data Analysis Illustrated with Examples

zweidimensionales bewegtes Bild

Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

Model-based Analysis of ChIP-Seq (MACS)

Normalization of ChIP-seq data with control

Shape-based peak identification for ChIP-Seq

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

RELACS nuclei barcoding enables high-throughput ChIP-seq

ChIPseqR: analysis of ChIP-seq experiments

BayesPeak: Bayesian analysis of ChIP-seq data

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Principles of ChIP-seq Data Analysis Illustrated with Examples

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Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

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Other Objects (12)

Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

Model-based Analysis of ChIP-Seq (MACS)

Normalization of ChIP-seq data with control

Shape-based peak identification for ChIP-Seq

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

RELACS nuclei barcoding enables high-throughput ChIP-seq

ChIPseqR: analysis of ChIP-seq experiments

BayesPeak: Bayesian analysis of ChIP-seq data

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Principles of ChIP-seq Data Analysis Illustrated with Examples

Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

Model-based Analysis of ChIP-Seq (MACS)

Normalization of ChIP-seq data with control

Shape-based peak identification for ChIP-Seq

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

RELACS nuclei barcoding enables high-throughput ChIP-seq

ChIPseqR: analysis of ChIP-seq experiments

BayesPeak: Bayesian analysis of ChIP-seq data

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Principles of ChIP-seq Data Analysis Illustrated with Examples

Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

Model-based Analysis of ChIP-Seq (MACS)

Normalization of ChIP-seq data with control

Shape-based peak identification for ChIP-Seq

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

RELACS nuclei barcoding enables high-throughput ChIP-seq

ChIPseqR: analysis of ChIP-seq experiments

BayesPeak: Bayesian analysis of ChIP-seq data

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Principles of ChIP-seq Data Analysis Illustrated with Examples

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