Reference-based compression of short-read sequences using path encoding
نویسندگان
چکیده
منابع مشابه
Reference-based compression of short-read sequences using path encoding
MOTIVATION Storing, transmitting and archiving data produced by next-generation sequencing is a significant computational burden. New compression techniques tailored to short-read sequence data are needed. RESULTS We present here an approach to compression that reduces the difficulty of managing large-scale sequencing data. Our novel approach sits between pure reference-based compression and ...
متن کاملCompression of short-read sequences using path encoding
Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale t...
متن کاملCompression of short-read sequences using path encoding
Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale t...
متن کاملCompression of short-read sequences using path encoding
Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale t...
متن کاملCompression of short-read sequences using path encoding
Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale t...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2015
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btv071