Pyicoteo

Pyicoteo analyzes high-throughput sequencing (HTS) datasets, including ChIP-Seq (punctuated and broad signals), CLIP-Seq, and RNA-Seq, to quantify differential enrichment between conditions and model chromatin-mediated gene regulation.


Key Features:

  • Multi-HTS Data Support: Processes ChIP-Seq, CLIP-Seq, and RNA-Seq datasets to analyze protein–DNA/RNA interactions and RNA expression levels.
  • Differential Enrichment Analysis: Calculates differential enrichment between two conditions for CLIP-Seq and ChIP-Seq data, with or without replicates, to detect significant changes in binding or signal intensity.
  • Epigenetic Data Integration: Integrates HTS epigenetic datasets to construct quantitative models of gene regulation and compare chromatin signals across cellular conditions.
  • Alternative Splicing Analysis: Uses machine learning to identify chromatin signals associated with alternative splicing events involving CTCF, AGO1, and HP1.
  • Enhancer Prediction: Applies a semi-supervised method to predict active transcriptional enhancers, including intragenic enhancers, based on relative chromatin signal enrichment between conditions.

Scientific Applications:

  • Regulatory Network Analysis: Characterizes protein–DNA/RNA interactions and RNA expression to elucidate gene regulatory networks.
  • Epigenetic Code Investigation: Integrates chromatin signals to study epigenetic mechanisms underlying gene regulation, cellular differentiation, and disease.
  • Splicing Regulation Studies: Examines chromatin-associated regulation of alternative splicing and its relevance to splicing-related disorders.
  • Enhancer Activity Profiling: Identifies differentially activated enhancers between cell types and evaluates their impact on host gene expression and splicing.

Methodology:

Employs memory-efficient computational algorithms for HTS signal selection and integration, differential enrichment analysis, and quantitative modeling of regulatory genomics, and incorporates machine learning and semi-supervised approaches to detect chromatin patterns associated with splicing and enhancer activity.

Topics

Details

License:
GPL-2.0
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Linux, Mac
Programming Languages:
Python
Added:
4/15/2016
Last Updated:
11/25/2024

Operations

Publications

Althammer S, González-Vallinas J, Ballaré C, Beato M, Eyras E. Pyicos: a versatile toolkit for the analysis of high-throughput sequencing data. Bioinformatics. 2011;27(24):3333-3340. doi:10.1093/bioinformatics/btr570. PMID:21994224. PMCID:PMC3232367.

Althammer S, Pagès A, Eyras E. Predictive Models of Gene Regulation from High-Throughput Epigenomics Data. Comparative and Functional Genomics. 2012;2012:1-13. doi:10.1155/2012/284786. PMID:22924024. PMCID:PMC3424690.

PMID: 22924024
PMCID: PMC3424690
Funding: - Spanish Ministry of Science: BIO2011-23920, CSD2009-00080 - Sandra Ibarra Foundation: BIO2011-23920, CSD2009-00080 - Generalitat de Catalunya: BIO2011-23920, CSD2009-00080

Agirre E, Bellora N, Alló M, Pagès A, Bertucci P, Kornblihtt AR, Eyras E. A chromatin code for alternative splicing involving a putative association between CTCF and HP1α proteins. BMC Biology. 2015;13(1). doi:10.1186/s12915-015-0141-5. PMID:25934638. PMCID:PMC4446157.

González-Vallinas J, Pagès A, Singh B, Eyras E. A semi-supervised approach uncovers thousands of intragenic enhancers differentially activated in human cells. BMC Genomics. 2015;16(1). doi:10.1186/s12864-015-1704-0. PMID:26169177. PMCID:PMC4501197.

Documentation