Add to ORKGDescription Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation
Motivation: ChIP-seq technology enables investigators to study genome-wide binding of transcription ...
Summary: RNA sequencing data are becoming a major method of choice to study transcriptomes, includin...
Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to ...
Background In vivo detection of protein-bound genomic regions can be achieved by combining chromatin...
ChIP-Seq experiments combine the recently developed next-generation sequencing technology with the e...
Motivation: ChIP-seq has become an important tool for identifying genome-wide protein-DNA interactio...
We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read coun...
Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental t...
Description This package detects statistically significant difference between read enrichment profil...
Abstract ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions an...
Three statistical models are developed to address problems in Next-Generation Sequencing data. The f...
Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for...
<p>We see how we can get the ChIP-seq library, input DNA control, and the random distribution (null ...
Chromatin-immunoprecipitation and sequencing (ChIP-seq) is a rapidly maturing technology that draws ...
The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revol...
Motivation: ChIP-seq technology enables investigators to study genome-wide binding of transcription ...
Summary: RNA sequencing data are becoming a major method of choice to study transcriptomes, includin...
Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to ...
Background In vivo detection of protein-bound genomic regions can be achieved by combining chromatin...
ChIP-Seq experiments combine the recently developed next-generation sequencing technology with the e...
Motivation: ChIP-seq has become an important tool for identifying genome-wide protein-DNA interactio...
We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read coun...
Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental t...
Description This package detects statistically significant difference between read enrichment profil...
Abstract ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions an...
Three statistical models are developed to address problems in Next-Generation Sequencing data. The f...
Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for...
<p>We see how we can get the ChIP-seq library, input DNA control, and the random distribution (null ...
Chromatin-immunoprecipitation and sequencing (ChIP-seq) is a rapidly maturing technology that draws ...
The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revol...
Motivation: ChIP-seq technology enables investigators to study genome-wide binding of transcription ...
Summary: RNA sequencing data are becoming a major method of choice to study transcriptomes, includin...
Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to ...