Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data.

ABSTRACT:

Motivation

Cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, local or regional epigenetic silencing is a common mechanism in cancer for silencing expression of tumor suppressor genes. Though several tools have been created to enable detection of histone marks in ChIP-seq data from normal samples, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. Indeed, cancer genomes are often characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. Copy number alterations may create a substantial statistical bias in the evaluation of histone mark signal enrichment and result in underdetection of the signal in the regions of loss and overdetection of the signal in the regions of gain.

Results

We present HMCan (Histone modifications in cancer), a tool specially designed to analyze histone modification ChIP-seq data produced from cancer genomes. HMCan corrects for the GC-content and copy number bias and then applies Hidden Markov Models to detect the signal from the corrected data. On simulated data, HMCan outperformed several commonly used tools developed to analyze histone modification data produced from genomes without copy number alterations. HMCan also showed superior results on a ChIP-seq dataset generated for the repressive histone mark H3K27me3 in a bladder cancer cell line. HMCan predictions matched well with experimental data (qPCR validated regions) and included, for example, the previously detected H3K27me3 mark in the promoter of the DLEC1 gene, missed by other tools we tested.

SUBMITTER: Ashoor H

PROVIDER: S-EPMC3834794 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

diffReps: detecting differential chromatin modification sites from ChIP-seq data with biological replicates.

Project description:ChIP-seq is increasingly being used for genome-wide profiling of histone modification marks. It is of particular importance to compare ChIP-seq data of two different conditions, such as disease vs. control, and identify regions that show differences in ChIP enrichment. We have developed a powerful and easy to use program, called diffReps, to detect those differential sites from ChIP-seq data, with or without biological replicates. In addition, we have developed two useful tools for ChIP-seq analysis in the diffReps package: one for the annotation of the differential sites and the other for finding chromatin modification "hotspots". diffReps is developed in PERL programming language and runs on all platforms as a command line script. We tested diffReps on two different datasets. One is the comparison of H3K4me3 between two human cell lines from the ENCODE project. The other is the comparison of H3K9me3 in a discrete region of mouse brain between cocaine- and saline-treated conditions. The results indicated that diffReps is a highly sensitive program in detecting differential sites from ChIP-seq data.

| S-EPMC3677880 | biostudies-literature

Inference of interactions between chromatin modifiers and histone modifications: from ChIP-Seq data to chromatin-signaling.

Project description:Chromatin modifiers and histone modifications are components of a chromatin-signaling network involved in transcription and its regulation. The interactions between chromatin modifiers and histone modifications are often unknown, are based on the analysis of few genes or are studied in vitro. Here, we apply computational methods to recover interactions between chromatin modifiers and histone modifications from genome-wide ChIP-Seq data. These interactions provide a high-confidence backbone of the chromatin-signaling network. Many recovered interactions have literature support; others provide hypotheses about yet unknown interactions. We experimentally verified two of these predicted interactions, leading to a link between H4K20me1 and members of the Polycomb Repressive Complexes 1 and 2. Our results suggest that our computationally derived interactions are likely to lead to novel biological insights required to establish the connectivity of the chromatin-signaling network involved in transcription and its regulation.

| S-EPMC4267652 | biostudies-literature

A carrier-assisted ChIP-seq method for estrogen receptor-chromatin interactions from breast cancer core needle biopsy samples.

Project description:BACKGROUND:The Estrogen Receptor alpha (ER?) is the key transcriptional regulator in luminal breast cancer and is therefore the main target for adjuvant treatment of this subtype. Luminal gene signatures are dictated by the transcriptional capacities of ER?, which are a direct consequence of the receptors binding preference at specific sites on the chromatin. The identification of ER? binding signatures on a genome-wide level has greatly enhanced our understanding of Estrogen Receptor biology in cell lines and tumours, but the technique has its limitations with respect to its applicability in limited amounts of tumour tissue. RESULTS:Here, we present a refinement of the ChIP-seq procedures to enable transcription factor mapping on limited amounts of tissue culture cells as well as from a limited amount of tumor tissue derived from core needle biopsies. Our approach uses a carrier that can be removed prior to DNA amplification and sequencing. CONCLUSION:We illustrate the applicability of this refined technology by mapping the ER? genome-wide chromatin binding landscape in core needle biopsy material from primary breast tumours. With this, our refined technology permits for a high-resolution transcription factor mapping even from clinical samples.

| S-EPMC3637562 | biostudies-literature

A comparison of control samples for ChIP-seq of histone modifications.

Project description:The advent of high-throughput sequencing has allowed genome wide profiling of histone modifications by Chromatin ImmunoPrecipitation (ChIP) followed by sequencing (ChIP-seq). In this assay the histone mark of interest is enriched through a chromatin pull-down assay using an antibody for the mark. Due to imperfect antibodies and other factors, many of the sequenced fragments do not originate from the histone mark of interest, and are referred to as background reads. Background reads are not uniformly distributed and therefore control samples are usually used to estimate the background distribution at any given genomic position. The Encyclopedia of DNA Elements (ENCODE) Consortium guidelines suggest sequencing a whole cell extract (WCE, or "input") sample, or a mock ChIP reaction such as an IgG control, as a background sample. However, for a histone modification ChIP-seq investigation it is also possible to use a Histone H3 (H3) pull-down to map the underlying distribution of histones. In this paper we generated data from a hematopoietic stem and progenitor cell population isolated from mouse fetal liver to compare WCE and H3 ChIP-seq as control samples. The quality of the control samples is estimated by a comparison to pull-downs of histone modifications and to expression data. We find minor differences between WCE and H3 ChIP-seq, such as coverage in mitochondria and behavior close to transcription start sites. Where the two controls differ, the H3 pull-down is generally more similar to the ChIP-seq of histone modifications. However, the differences between H3 and WCE have a negligible impact on the quality of a standard analysis.

| S-EPMC4174756 | biostudies-literature

A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.

Project description:Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.

| S-EPMC3326300 | biostudies-literature

Standardizing chromatin research: a simple and universal method for ChIP-seq.

Project description:Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) is a key technique in chromatin research. Although heavily applied, existing ChIP-seq protocols are often highly fine-tuned workflows, optimized for specific experimental requirements. Especially the initial steps of ChIP-seq, particularly chromatin shearing, are deemed to be exceedingly cell-type-specific, thus impeding any protocol standardization efforts. Here we demonstrate that harmonization of ChIP-seq workflows across cell types and conditions is possible when obtaining chromatin from properly isolated nuclei. We established an ultrasound-based nuclei extraction method (NEXSON: Nuclei EXtraction by SONication) that is highly effective across various organisms, cell types and cell numbers. The described method has the potential to replace complex cell-type-specific, but largely ineffective, nuclei isolation protocols. By including NEXSON in ChIP-seq workflows, we completely eliminate the need for extensive optimization and sample-dependent adjustments. Apart from this significant simplification, our approach also provides the basis for a fully standardized ChIP-seq and yields highly reproducible transcription factor and histone modifications maps for a wide range of different cell types. Even small cell numbers (?10,000 cells per ChIP) can be easily processed without application of modified chromatin or library preparation protocols.

| S-EPMC4838356 | biostudies-literature

Detecting broad domains and narrow peaks in ChIP-seq data with hiddenDomains.

Project description:BackgroundCorrectly identifying genomic regions enriched with histone modifications and transcription factors is key to understanding their regulatory and developmental roles. Conceptually, these regions are divided into two categories, narrow peaks and broad domains, and different algorithms are used to identify each one. Datasets that span these two categories are often analyzed with a single program for peak calling combined with an ad hoc method for domains.ResultsWe developed hiddenDomains, which identifies both peaks and domains, and compare it to the leading algorithms using H3K27me3, H3K36me3, GABP, ESR1 and FOXA ChIP-seq datasets. The output from the programs was compared to qPCR-validated enriched and depleted sites, predicted transcription factor binding sites, and highly-transcribed gene bodies. With every method, hiddenDomains, performed as well as, if not better than algorithms dedicated to a specific type of analysis.ConclusionshiddenDomains performs as well as the best domain and peak calling algorithms, making it ideal for analyzing ChIP-seq datasets, especially those that contain a mixture of peaks and domains.

| S-EPMC4806451 | biostudies-literature

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures.

Project description:Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein-chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.

| S-EPMC3763565 | biostudies-literature

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.

Project description:A cell's epigenome arises from interactions among regulatory factors-transcription factors and histone modifications-co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC-HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu.

| S-EPMC4852466 | biostudies-literature

A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples.

Project description:Deep sequencing approaches, such as chromatin immunoprecipitation by sequencing (ChIP-seq), have been successful in detecting transcription factor-binding sites and histone modification in the whole genome. An approach for comparing two different ChIP-seq data would be beneficial for predicting unknown functions of a factor. We propose a model to represent co-localization of two different ChIP-seq data. We showed that a meaningful overlapping signal and a meaningless background signal can be separated by this model. We applied this model to compare ChIP-seq data of RNA polymerase II C-terminal domain (CTD) serine 2 phosphorylation with a large amount of peak-called data, including ChIP-seq and other deep sequencing data in the Encyclopedia of DNA Elements (ENCODE) project, and then extracted factors that were related to RNA polymerase II CTD serine 2 in HeLa cells. We further analyzed RNA polymerase II CTD serine 7 phosphorylation, of which their function is still unclear in HeLa cells. Our results were characterized by the similarity of localization for transcription factor/histone modification in the ENCODE data set, and this suggests that our model is appropriate for understanding ChIP-seq data for factors where their function is unknown.

| S-EPMC3592427 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data