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. 2012 Jul 15;28(14):1919-20.
doi: 10.1093/bioinformatics/bts277. Epub 2012 May 9.

BEDOPS: high-performance genomic feature operations

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BEDOPS: high-performance genomic feature operations

Shane Neph et al. Bioinformatics. .

Abstract

The large and growing number of genome-wide datasets highlights the need for high-performance feature analysis and data comparison methods, in addition to efficient data storage and retrieval techniques. We introduce BEDOPS, a software suite for common genomic analysis tasks which offers improved flexibility, scalability and execution time characteristics over previously published packages. The suite includes a utility to compress large inputs into a lossless format that can provide greater space savings and faster data extractions than alternatives.

Availability: http://code.google.com/p/bedops/ includes binaries, source and documentation.

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Figures

Fig. 1.
Fig. 1.
Performance results using subsets of 46-way phyloP (Pollard et al., 2010) human conservation data as inputs. (a) The time and memory resources used to merge overlapping and adjoining genomic segments as a function of input size. (b) Compressed file sizes on disk as a percentage of original uncompressed sizes. (c) Single-chromosome data extraction times from files containing >2.8×109 sorted records. BEDOPS accessed target chromosome data only, whereas conventional approaches processed inputs sequentially

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