HiLive: real-time mapping of illumina reads while sequencing
| Autor: | Martin Lindner, Bernhard Y. Renard, Andreas Nitsche, Piotr Wojtek Dabrowski, Simon H. Tausch, Benjamin Strauch, Jakob M. Schulze |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Statistics and Probability FASTQ format Source code Sequence analysis Computer science media_common.quotation_subject Biochemistry Genome DNA sequencing Deep sequencing Transcriptome 03 medical and health sciences Humans Real time mapping Molecular Biology Illumina dye sequencing media_common business.industry Genome Human Sequence Analysis RNA High-Throughput Nucleotide Sequencing Sample (graphics) Computer Science Applications Computational Mathematics 030104 developmental biology Computational Theory and Mathematics business Computer hardware Algorithms Software |
| Zdroj: | Bioinformatics (Oxford, England). 33(6) |
| ISSN: | 1367-4811 |
| Popis: | Motivation Next Generation Sequencing is increasingly used in time critical, clinical applications. While read mapping algorithms have always been optimized for speed, they follow a sequential paradigm and only start after finishing of the sequencing run and conversion of files. Since Illumina machines write intermediate output results, HiLive performs read mapping while still sequencing and thereby drastically reduces crucial overall sample analysis time, e.g. in precision medicine. Methods We present HiLive as a novel real time read mapper that implements a k-mer based alignment strategy. HiLive continuously reads intermediate BCL files produced by Illumina sequencers and then extends initial k-mer matches by increasingly produced data from the sequencer. Results We applied HiLive on real human transcriptome data to show that final read alignments are reported within few minutes after the end of a full Illumina HiSeq 1500 run, while already the necessary conversion to FASTQ files as the standard input to current read mapping methods takes roughly five times as long. Further, we show on simulated and real data that HiLive has comparable accuracy to recent read mappers. Availability and Implementation HiLive and its source code are freely available from https://gitlab.com/SimonHTausch/HiLive. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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