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Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome.

Mammana A, Chung HR - Genome Biol. (2015)

Bottom Line: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome.Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes.By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg.

View Article: PubMed Central - PubMed

Affiliation: Otto-Warburg-Laboratory, Epigenomics, Max Planck Institute for Molecular Genetics, D-14195, Berlin, Germany. mammana@molgen.mpg.de.

ABSTRACT
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg.

No MeSH data available.


State distribution around the average transcript in the K562_1 dataset. The plot shows how often a particular state occurs at a particular position of the transcript. In each transcript, the state sequence between TSS and TES has been stretched or shrunk proportionally so that all transcripts have the same length
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Fig9: State distribution around the average transcript in the K562_1 dataset. The plot shows how often a particular state occurs at a particular position of the transcript. In each transcript, the state sequence between TSS and TES has been stretched or shrunk proportionally so that all transcripts have the same length

Mentions: In order to show the salient differences between the two algorithms without focusing on single regions, we collapsed the segmentation data into genome-wide summary statistics. The first summary statistic (Fig. 8) is a bar plot where each bar corresponds to a chromatin state and where its length is proportional to the state frequency. Additionally edges between states of the two segmentations have been drawn with widths proportional to the number of overlapping bins. Another statistic (Fig. 9) shows where each state tends to localize with respect to genes. More precisely, for each annotated transcript in the GENCODE database [16] and for a given segmentation we considered a region comprising the transcript, 5,000 bps upstream the TSS and 5,000 bps downstream the TES, we labeled each base pair with its inferred state, and we rescaled the region between TSS and TES to a reference length. Finally, taking into account all transcripts, we counted how many regions are annotated with a given state at a given position. The third summary statistic (Fig. 10) is a heatmap showing the log-transformed average histone modification levels per state.Fig. 8


Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome.

Mammana A, Chung HR - Genome Biol. (2015)

State distribution around the average transcript in the K562_1 dataset. The plot shows how often a particular state occurs at a particular position of the transcript. In each transcript, the state sequence between TSS and TES has been stretched or shrunk proportionally so that all transcripts have the same length
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4514447&req=5

Fig9: State distribution around the average transcript in the K562_1 dataset. The plot shows how often a particular state occurs at a particular position of the transcript. In each transcript, the state sequence between TSS and TES has been stretched or shrunk proportionally so that all transcripts have the same length
Mentions: In order to show the salient differences between the two algorithms without focusing on single regions, we collapsed the segmentation data into genome-wide summary statistics. The first summary statistic (Fig. 8) is a bar plot where each bar corresponds to a chromatin state and where its length is proportional to the state frequency. Additionally edges between states of the two segmentations have been drawn with widths proportional to the number of overlapping bins. Another statistic (Fig. 9) shows where each state tends to localize with respect to genes. More precisely, for each annotated transcript in the GENCODE database [16] and for a given segmentation we considered a region comprising the transcript, 5,000 bps upstream the TSS and 5,000 bps downstream the TES, we labeled each base pair with its inferred state, and we rescaled the region between TSS and TES to a reference length. Finally, taking into account all transcripts, we counted how many regions are annotated with a given state at a given position. The third summary statistic (Fig. 10) is a heatmap showing the log-transformed average histone modification levels per state.Fig. 8

Bottom Line: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome.Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes.By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg.

View Article: PubMed Central - PubMed

Affiliation: Otto-Warburg-Laboratory, Epigenomics, Max Planck Institute for Molecular Genetics, D-14195, Berlin, Germany. mammana@molgen.mpg.de.

ABSTRACT
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg.

No MeSH data available.