Exploratory analysis of genomic segmentations with Segtools

<p>Abstract</p> <p>Background</p> <p>As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale. Many functional genomics experiments involve partitioning the genome into labeled segments, such that segments sharing the same label exhibit one or more biochemical or functional traits. For example, a collection of ChlP-seq experiments yields a compendium of peaks, each labeled with one or more associated DNA-binding proteins. Similarly, manually or automatically generated annotations of functional genomic elements, including <it>cis</it>-regulatory modules and protein-coding or RNA genes, can also be summarized as genomic segmentations.</p> <p>Results</p> <p>We present a software toolkit called <it>Segtools </it>that simplifies and automates the exploration of genomic segmentations. The software operates as a series of interacting tools, each of which provides one mode of summarization. These various tools can be pipelined and summarized in a single HTML page. We describe the Segtools toolkit and demonstrate its use in interpreting a collection of human histone modification data sets and <it>Plasmodium falciparum </it>local chromatin structure data sets.</p> <p>Conclusions</p> <p>Segtools provides a convenient, powerful means of interpreting a genomic segmentation.</p

SigTools: An exploratory visualization tool for genomic signals

With the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, with genomic coordinates associated with a score or probability indicating some form of biological activity. An example of genomic signals isEpigenomicmarkswhich represent short-read coverage measurements over the genome, and are utilized to locate functional and nonfunctional elements in genome annotation studies. To understand and evaluate the results of such studies, one needs to explore and analyze the characteristics of the input data. Information visualization is an effective approach that leverages human visual ability in data analysis. Several visualization applications have been deployed for this purpose such as the UCSC genome browser, Deeptools, and Segtools. However, we believe there is room for improvement in terms of programming skills requirements and proposed visualizations. Sigtools is an R-based exploratory visualization package, designed to enable the users with limited programming experience to produce statistical plots of continuous genomic data. It consists of several statistical visualizations such as value distribution, correlation, and autocorrelation that provide insights regarding the behavior of a group of signals in larger regions – such as a chromosome or the whole genome – as well as visualizing them around a specific point or short region. To demonstrate Sigtools utilization, first, we visualize five histone modifications downloaded from Roadmap Epigenomics data portal and show that Sigtools accurately captures their characteristics. Then, we visualize five chromatin state features, probabilistic generated genome annotations, to display how sigtools can assist in the interpretation of new and unknown signals

System for turnkey analysis of semi-automated genome annotations

Un software ha sigut desenvolupat per ajudar en l'anàlisi de segmentacions del genoma, descarregant dades, executant anàlisis i resumint els resultats en una sola visualització que és fàcil d'interpretar i de la qual els investigadors poden extreure hipòtesis biològiques.A software has been developed which helps in the analysis of genome segmentations by automatically downloading data, running analysis and summarizing all results in one single visualization that is easy to read so that researchers can easily conclude biological hypothesis from it

ChIPnorm: a statistical method for normalizing and identifying differential regions in histone modification ChIP-seq libraries

The advent of high-throughput technologies such as ChIP-seq has made possible the study of histone modifications. A problem of particular interest is the identification of regions of the genome where different cell types from the same organism exhibit different patterns of histone enrichment. This problem turns out to be surprisingly difficult, even in simple pairwise comparisons, because of the significant level of noise in ChIP-seq data. In this paper we propose a two-stage statistical method, called ChIPnorm, to normalize ChIP-seq data, and to find differential regions in the genome, given two libraries of histone modifications of different cell types. We show that the ChIPnorm method removes most of the noise and bias in the data and outperforms other normalization methods. We correlate the histone marks with gene expression data and confirm that histone modifications H3K27me3 and H3K4me3 act as respectively a repressor and an activator of genes. Compared to what was previously reported in the literature, we find that a substantially higher fraction of bivalent marks in ES cells for H3K27me3 and H3K4me3 move into a K27-only state. We find that most of the promoter regions in protein-coding genes have differential histone-modification sites. The software for this work can be downloaded from http://lcbb.epfl.ch/software.html