Dataset Information

HiHMM: Bayesian non-parametric joint inference of chromatin state maps.

ABSTRACT: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications.Here, we present a new Bayesian non-parametric method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in multiple genomes (different species, cell types and developmental stages) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them. We demonstrate the utility of this method using synthetic data as well as multiple modENCODE ChIP-seq datasets.The hierarchical and Bayesian non-parametric formulation in our approach is an important extension to the current set of methodologies for comparative chromatin landscape analysis.Source codes are available at https://github.com/kasohn/hiHMM. Chromatin data are available at http://encode-x.med.harvard.edu/data_sets/chromatin/.

SUBMITTER: Sohn KA

PROVIDER: S-EPMC4481846 | biostudies-other | 2015 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

hiHMM: Bayesian non-parametric joint inference of chromatin state maps.

Sohn Kyung-Ah KA Ho Joshua W K JW Djordjevic Djordje D Jeong Hyun-Hwan HH Park Peter J PJ Kim Ju Han JH

Bioinformatics (Oxford, England) 20150227 13

<h4>Motivation</h4>Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal e ...[more]

PMID: 25725496

Similar Datasets

Project description:BackgroundFlow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way.ResultsWe demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively.ConclusionsThese results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.

Dataset Information

HiHMM: Bayesian non-parametric joint inference of chromatin state maps.

Publications

hiHMM: Bayesian non-parametric joint inference of chromatin state maps.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets