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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Feb 19, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

ChIPWig: a random access-enabling lossless and lossy compression method for ChIP-seq data.

Vida Ravanmehr¹, Minji Kim¹, Zhiying Wang²

¹Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Bioinformatics (Oxford, England)

|November 1, 2017

Summary

This summary is machine-generated.

ChIPWig offers efficient lossless and lossy compression for ChIP-seq Wig data, significantly reducing file sizes and addressing Big Data challenges in genomics. This framework provides fast access and statistics, aiding in storage and analysis.

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Last Updated: Feb 19, 2026

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Published on: June 23, 2012

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Chromatin immunoprecipitation sequencing (ChIP-seq) generates massive datasets, posing significant storage and maintenance challenges.
Existing data formats and compression methods may not optimally address the unique characteristics of ChIP-seq Wig data.
Efficient data handling is crucial for the advancement of large-scale genomics projects.

Purpose of the Study:

To develop a novel compression framework, ChIPWig, specifically designed for ChIP-seq Wig data.
To provide both lossless and lossy compression options to balance file size reduction and data integrity.
To enable efficient data access, including random access and summary statistics lookups.

Main Methods:

ChIPWig employs a compression framework based on the asymptotic theory of optimal point density design for nonuniform quantizers.
The framework supports both lossless and lossy compression strategies tailored for ChIP-seq Wig data.
Implementation in C++ allows for efficient compression and decompression speeds.

Main Results:

Lossless ChIPWig reduced file sizes to an average of 6% of the original, a 6-fold improvement over bigWig.
Lossy compression further reduced file sizes by a factor of two compared to lossless mode.
Peak calling and motif discovery using NarrowPeaks methods showed minimal impact from lossy compression.
Compression and decompression speeds averaged 0.2 sec/MB on general-purpose computers.

Conclusions:

ChIPWig effectively addresses the Big Data challenges associated with ChIP-seq experiments by providing significant file size reduction.
The framework offers a practical solution for managing large ChIP-seq datasets, facilitating storage, maintenance, and analysis.
ChIPWig's performance in terms of compression ratio and speed makes it a valuable tool for the genomics community.