Integration of ENCODE RNAseq and eCLIP Data Sets.

Methods Mol Biol

Bioinformatics Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany.

Published: July 2018

AI Article Synopsis

  • The study focuses on advancements in mRNA decay research facilitated by high-throughput assays, particularly from next generation sequencing and mass spectrometry.
  • Researchers face challenges in integrating diverse data formats from various assays, creating a need for comprehensive analysis methods.
  • The authors utilize Python, R, and bash to analyze RNAseq and eCLIP data from ENCODE, employing tools like biomart for annotation, MEME for motif analysis, and DAVID for functional enrichment analysis specifically on KHSRP data from K562 cells.

Article Abstract

During the last decade, the study of mRNA decay has largely benefited from an increasing number of high-throughput assays that emerged from developments in next generation sequencing (NGS) technologies as well as mass spectrometry. While assay-specific data analysis is often reported and software made available many researchers struggle with the overwhelming challenge of integrating data from diverse assays, different sources, and of different formats.We here use Python, R, and bash to analyze and integrate RNAseq and eCLIP data publicly available from ENCODE. Annotation is performed with biomart, motif analysis with MEME and finally a functional enrichment analysis using DAVID. This analysis is centered on KHSRP eCLIP data from K562 cell as well as RNAseq data from KHSRP knockdown and respective mock controls.

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Source
http://dx.doi.org/10.1007/978-1-4939-7540-2_8DOI Listing

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