Comparison of high-throughput sequencing data compression tools.

Ibrahim NumanagićJames K BonfieldFaraz HachJan VogesJörn OstermannClaudio AlbertiMarco MattavelliS Cenk Sahinalp

Published in: Nature methods (2016)

High-throughput sequencing (HTS) data are commonly stored as raw sequencing reads in FASTQ format or as reads mapped to a reference, in SAM format, both with large memory footprints. Worldwide growth of HTS data has prompted the development of compression methods that aim to significantly reduce HTS data size. Here we report on a benchmarking study of available compression methods on a comprehensive set of HTS data using an automated framework.

Keyphrases

electronic health record
high throughput sequencing
big data
machine learning
data analysis
working memory
artificial intelligence
deep learning