PT  - JOURNAL ARTICLE
AU  - Vida Ravanmehr
AU  - Minji Kim
AU  - Zhiying Wang
AU  - Olgica Milenković
TI  - ChIPWig: A Random Access-Enabling Lossless and Lossy Compression Method for ChIP-seq Data
AID  - 10.1101/127464
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 127464
4099  - http://biorxiv.org/content/early/2017/04/14/127464.short
4100  - http://biorxiv.org/content/early/2017/04/14/127464.full
AB  - Motivation The past decade has witnessed a rapid development of data acquisition technologies that enable integrative genomic and proteomic analysis. One such technology is chromatin immunoprecipitation sequencing (ChIP-seq), developed for analyzing interactions between proteins and DNA via next-generation sequencing technologies. As ChIP-seq experiments are inexpensive and time-efficient, massive datasets from this domain have been acquired, introducing significant storage and maintenance challenges. To address the resulting Big Data problems, we propose a state-of-the-art lossless and lossy compression framework specifically designed for ChIP-seq Wig data, termed ChIPWig. Wig is a standard file format, which in this setting contains relevant read density information crucial for visualization and downstream processing. ChIPWig may be executed in two different modes: lossless and lossy. Lossless ChIPWig compression allows for random access and fast queries in the file through careful variable-length block-wise encoding. ChIPWig also stores the summary statistics of each block needed for guided access. Lossy ChIPWig, in contrast, performs quantization of the read density values before feeding them into the lossless ChIPWig compressor. Nonuniform lossy quantization leads to further reductions in the file size, while maintaining the same accuracy of the ChIP-seq peak calling and motif discovery pipeline based on the NarrowPeaks method tailor-made for Wig files. The compressors are designed using new statistical modeling approaches coupled with delta and arithmetic encoding.Results We tested the ChIPWig compressor on a number of ChIP-seq datasets generated by the ENCODE project. Lossless ChIPWig reduces the file sizes to merely 6% of the original, and offers an average 6-fold compression rate improvement compared to bigWig. The running times for compression and decompression are comparable to those of bigWig. The compression and decompression speed rates are of the order of 0.2 MB/sec using general purpose computers. ChIPWig with random access only slightly degrades the performance and running time when compared to the standard mode. In the lossy mode, the average file sizes reduce by 2-fold compared to the lossless mode. Most importantly, near-optimal nonuniform quantization with respect to mean-square distortion does not affect peak calling and motif discovery results on the data tested.Availability and Implementation Source code and binaries freely available for download at https://github.com/vidarmehr/ChIPWigContact milenkov{at}illinois.eduSupplementary information Is available on bioRxiv.