CloudRS: An error correction algorithm of high-throughput sequencing data based on scalable framework

Next-generation sequencing (NGS) technologies produce huge amounts of data. These sequencing data unavoidably are accompanied by the occurrence of sequencing errors which constitutes one of the major problems of further analyses. Error correction is indeed one of the critical steps to the success of...

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Bibliographic Details
Published in2013 IEEE International Conference on Big Data pp. 717 - 722
Main Authors Chien-Chih Chen, Yu-Jung Chang, Wei-Chun Chung, Der-Tsai Lee, Jan-Ming Ho
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2013
Subjects
Algorithm design and analysis
Assembly
Benchmark testing
Bioinformatics
Error correction
genome assembly
Genomics
mapreduce
next-generation sequencing
Sequential analysis
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Summary:Next-generation sequencing (NGS) technologies produce huge amounts of data. These sequencing data unavoidably are accompanied by the occurrence of sequencing errors which constitutes one of the major problems of further analyses. Error correction is indeed one of the critical steps to the success of NGS applications such as de novo genome assembly and DNA resequencing as illustrated in literature. However, it requires computing time and memory space heavily. To design an algorithm to improve data quality by efficiently utilizing on-demand computing resources in the cloud is a challenge for biologists and computer scientists. In this study, we present an error-correction algorithm, called the CloudRS algorithm, for correcting errors in NGS data. The CloudRS algorithm aims at emulating the notion of error correction algorithm of ALLPATHS-LG on the Hadoop/ MapReduce framework. It is conservative in correcting sequencing errors to avoid introducing false decisions, e.g., when dealing with reads from repetitive regions. We also illustrate several probabilistic measures we introduce into CloudRS to make the algorithm more efficient without sacrificing its effectiveness. Running time of using up to 80 instances each with 8 computing units shows satisfactory speedup. Experiments of comparing with other error correction programs show that CloudRS algorithm performs lower false positive rate for most evaluation benchmarks and higher sensitivity on genome S. cerevisiae. We demonstrate that CloudRS algorithm provides significant improvements in the quality of the resulting contigs on benchmarks of NGS de novo assembly.
DOI:10.1109/BigData.2013.6691642