Genomics, Epigenetics and Transcriptomics data integration
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Genomics, Epigenetics and Transcriptomics data integration
bibliography in the area of methods for integrating genomics, epigenetics and transcriptomics data
Curated by Fabrice Legeai
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Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation

An international, peer-reviewed genome sciences journal featuring outstanding original research that offers novel insights into the biology of all organisms
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Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

An international, peer-reviewed genome sciences journal featuring outstanding original research that offers novel insights into the biology of all organisms...
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Genome Biology | Full text | Modeling gene expression using chromatin features in various cellular contexts

Previous work has demonstrated that chromatin feature levels correlate with gene expression. The ENCODE project enables us to further explore this relationship using an unprecedented volume of data.
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BMC Bioinformatics | Full text | Exploratory analysis of genomic segmentations with Segtools

As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale.
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Integrative annotation of chromatin elements from ENCODE data

The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape.

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Segway: a way to segment the genome

The free Segway software package contains a novel method for analyzing multiple tracks of functional genomics data. Our method uses a dynamic Bayesian network (DBN) model, which enables it to analyze the entire genome at 1-bp resolution even in the face of heterogeneous patterns of missing data. This method is the first application of DBN techniques to genome-scale data and the first genomic segmentation method designed for use with the maximum resolution data available from ChIP-seq experiments without downsampling. Our software has extensive documentation and was designed from the outset with external users in mind. Researchers at other universities and institutes have already installed and used Segway for their own projects.

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Genome Biology | Full text | A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs.
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ChromHMM: Chromatin state discovery and characterization

http://compbio.mit.edu/ChromHMM/

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