Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

PeakRegressor identifies composite sequence motifs responsible for STAT1 binding sites and their potential rSNPs.

PloS one - 27 Aug 2010

Pessiot Jean-François, Chiba Hirokazu, Hyakkoku Hiroto, Taniguchi Takeaki, Fujibuchi Wataru

Abstract excerpt

How to identify true transcription factor binding sites on the basis of sequence motif information (e.g., motif pattern, location, combination, etc.) is an important question in bioinformatics. We present "PeakRegressor," a system that identifies binding motifs by combining DNA-sequence data and ChIP-Seq data. PeakRegressor uses L1-norm log linear regression in order to predict peak values from binding motif...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Binding Sites
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Linear Models

Share to Principal Component Analysis

Share to Regulatory Sequences, Nucleic Acid

Share to Repetitive Sequences, Nucleic Acid

Share to RNA Polymerase II

Share to STAT1 Transcription Factor

Source

Open in PubMed Open full text

Identifiers

PMID: 20806061
DOI: 10.1371/journal.pone.0011881
PMCID: PMC2929187

Genes

STAT1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

PeakRegressor identifies composite sequence motifs responsible for STAT1 binding sites and their potential rSNPs.PMID 20806061Read ↗

Comparative Analysis of DNA Structural Parameters and the Corresponding Computational Tools to Differentiate Regulatory DNA Motifs and PromotersSemantic proximity Read ↗

Discovery and validation of information theory-based transcription factor and cofactor binding site motifs.Semantic proximity Read ↗

Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy MinimizationSemantic proximity Read ↗

SARNAclust: Semi-automatic detection of RNA protein binding motifs from immunoprecipitation dataSemantic proximity Read ↗

Classic machine learning on top of multiple position weight matrices improves genomic prediction of transcription factor binding sitesSemantic proximity Read ↗

Identification of functional cis-regulatory polymorphisms in the human genome.Semantic proximity Read ↗

Identification of upstream transcription factor binding sites in orthologous genes using mixed Student’s <i>t</i> -test statisticsSemantic proximity Read ↗

BART: a transcription factor prediction tool with query gene sets or epigenomic profilesSemantic proximity Read ↗

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning FrameworkSemantic proximity Read ↗

SEMPLR: an R package for transcription factor binding prediction.Semantic proximity Read ↗

A novel k-mer set memory (KSM) motif representation improves regulatory variant predictionSemantic proximity Read ↗

MotifMap: a human genome-wide map of candidate regulatory motif sites.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.