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Abstract
BackgroundNearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for a more efficient compression tool. Although numerous compressors exist, both specialized and general-purpose, choosing one of them was difficult because no comprehensive analysis of their comparative advantages for sequence compression was available.FindingsWe systematically benchmarked 430 settings of 48 compressors (including 29 specialized sequence compressors and 19 general-purpose compressors) on representative FASTA-formatted datasets of DNA, RNA, and protein sequences. Each compressor was evaluated on 17 performance measures, including compression strength, as well as time and memory required for compression and decompression. We used 27 test datasets including individual genomes of various sizes, DNA and RNA datasets, and standard protein datasets. We summarized the results as the Sequence Compression Benchmark database (SCB database, http://kirr.dyndns.org/sequence-compression-benchmark/), which allows custom visualizations to be built for selected subsets of benchmark results.ConclusionWe found that modern compressors offer a large improvement in compactness and speed compared to gzip. Our benchmark allows compressors and their settings to be compared using a variety of performance measures, offering the opportunity to select the optimal compressor on the basis of the data type and usage scenario specific to a particular application.
Highlights
Most molecular sequence databases currently use gzip for data compression
A lively field emerged that produced a stream of methods, algorithms, and software tools for sequence compression [3,4]
Despite this activity, currently most databases universally depend on gzip for compressing FASTA-formatted sequence data
Summary
Molecular sequence databases store and distribute DNA, RNA and protein sequences as compressed FASTA-formatted files. These measures can be shown in a table and visualized in column charts and scatterplots This allows tailoring the output to answer specific questions, such as what compressor is better at compressing particular kind of data, or which setting of each compressor performs best at particular task. We can compare compressors to gzip as shown on Fig.3 In this example, we compare only best settings of each compressor, selected using specific measures (transfer+decompression speed and compression+transfer+decompression speed on Figs.3A and 3B, respectively).
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