AliCo: A New Efficient Representation for SAM Files

Abstract

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. AliCo can be accessed at: https://github.com/iochoa/alico