Abstract
Data storage is a major and growing part of IT budgets for research since manyyears. Especially in biology, the amount of raw data products is growing continuously,and the advent of the so-called "next-generation" sequencers has made things worse.Affordable prices have pushed scientists to massively sequence whole genomes and to screenlarge cohort of patients, thereby producing tons of data as a side effect. The need formaximally fitting data into the available storage volumes has encouraged and welcomednew compression algorithms and tools. We focus here on state-of-the-art compression toolsand measure their compression performance on ABI SOLiD data.
Highlights
Since 1953, when the engineers at IBM’s San Jose California laboratory invented the first “random access file” [1], storage performance has continuously grown both in terms of capacity and speed data access
We correctly retrieved more than 95% of the original variants after decompression of the BAM files compressed by CRAMtools, NGC and Quip
Given the dramatic expansion of Next Generation Sequencing (NGS) technology in biomedical research and the consequent production of huge amount of data, data compression has become of great importance
Summary
Since 1953, when the engineers at IBM’s San Jose California laboratory invented the first “random access file” [1], storage performance has continuously grown both in terms of capacity and speed data access. The space/cost ratio has dropped drastically with time. The cost was estimated to follow the equation: cost = 100.2502 × (year−1980) + 6.304 , with an extraordinarily high coefficient of correlation (r = 0.9916) [2]. This positive trend has only mitigated the economical impact of the highly elevated data production rates of today’s biomedical instruments. Storage costs have largely exceeded reagent costs, leading sometimes to the extreme decision of carrying out the experiment again rather than retain raw data for long time
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