TY - GEN

T1 - AliCo

T2 - 2019 Data Compression Conference, DCC 2019

AU - Ochoa, Idoia

AU - Li, Hongyi

AU - Baumgarte, Florian

AU - Hergenrother, Charles

AU - Voges, Jan

AU - Hernaez, Mikel

N1 - Funding Information: This work was partially funded by grant numbers 2018-182798 and 2018-182799 from the Chan Zuckerberg Initiative DAF, and an SRI grant from UIUC.

PY - 2019/5

Y1 - 2019/5

N2 - As genome sequencing continues to become more cost-effective and affordable, more raw and aligned genomic files are expected to be generated in future years. In addition, due to the increase in the throughput of sequencing machines, the size of these files is significantly growing. In particular, aligned files (e.g., SAM/BAM) are used for further processing of the data, and hence efficient representation of these files is a pressing need. In this work we present AliCo, a new compression method tailored to the aligned data represented in the SAM format. We demonstrate through simulations on existing datasets that AliCo outperforms in compression ratio, on average, the state-of-the-art compressors for SAM files, achieving more than 85% reduction in size when operating in its lossless mode. AliCo also supports a variety of modes for lossy compression of the quality scores, including for the first time the recently proposed lossy compressor CALQ, which uses information from the aligned reads to adjust the level of quantization for each location of the genome (achieving more than 10× compression gains in high-coverage datasets). AliCo also supports optional compression of the reference sequence used for compression, hence guaranteeing exact reconstruction of the compressed data. Finally, AliCo allows to stream the data as it is being compressed, as well as to decompress the data as it is being received, potentially providing significant time savings.

AB - As genome sequencing continues to become more cost-effective and affordable, more raw and aligned genomic files are expected to be generated in future years. In addition, due to the increase in the throughput of sequencing machines, the size of these files is significantly growing. In particular, aligned files (e.g., SAM/BAM) are used for further processing of the data, and hence efficient representation of these files is a pressing need. In this work we present AliCo, a new compression method tailored to the aligned data represented in the SAM format. We demonstrate through simulations on existing datasets that AliCo outperforms in compression ratio, on average, the state-of-the-art compressors for SAM files, achieving more than 85% reduction in size when operating in its lossless mode. AliCo also supports a variety of modes for lossy compression of the quality scores, including for the first time the recently proposed lossy compressor CALQ, which uses information from the aligned reads to adjust the level of quantization for each location of the genome (achieving more than 10× compression gains in high-coverage datasets). AliCo also supports optional compression of the reference sequence used for compression, hence guaranteeing exact reconstruction of the compressed data. Finally, AliCo allows to stream the data as it is being compressed, as well as to decompress the data as it is being received, potentially providing significant time savings.

KW - Aligned data

KW - Compression

KW - Genomic data

KW - SAM file

UR - http://www.scopus.com/inward/record.url?scp=85066315861&partnerID=8YFLogxK

U2 - 10.1109/DCC.2019.00017

DO - 10.1109/DCC.2019.00017

M3 - Conference contribution

AN - SCOPUS:85066315861

SN - 978-1-7281-0658-8

T3 - Data Compression Conference Proceedings

SP - 93

EP - 102

BT - 2019 Data Compression Conference (DCC)

A2 - Storer, James A.

A2 - Bilgin, Ali

A2 - Serra-Sagrista, Joan

A2 - Marcellin, Michael W.

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 26 March 2019 through 29 March 2019

ER -