De novo ChIP-seq analysis.

Xin He,Ziv Bar-Joseph,Hai-Son Le,A Ercument Cicek,Marcel H Schulz,Yuhao Wang

doi:10.1186/s13059-015-0756-4

Abstract

Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0756-4) contains supplementary material, which is available to authorized users.

Highlights

Over the last few years, generation sequencing (NGS) technologies have revolutionized our ability to study genomic data
To date, the analysis of ChIP-Seq data has relied on peak calling based on the alignment to a reference genome
While several of the methods developed for this task have been highly successful, the requirements for a reference genome prevented the use of this technology for non-sequenced species

Summary

Introduction

Over the last few years, generation sequencing (NGS) technologies have revolutionized our ability to study genomic data. While these techniques have initially been used to study DNA sequence data [1], they are widely used to study additional types of dynamic and condition-specific biological data. Several methods have been proposed to perform such peak detection and for quantifying peak enrichment [6] While these methods differ in important aspects (including the type of distribution they assume, the method that they assign reads to genomic regions, the way in which enrichment is calculated, and so on), all current ChIP-Seq analysis methods rely on the first step mentioned above: Read alignment to the genome

Methods

Results

Conclusion