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Innumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing (NGS). However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis. Many error correction methods have been developed to correct erroneous NGS reads before further analysis, but independent evaluation of the impact of such dataset features as read length, genome size, and coverage depth on their performance is lacking. This comparative study aims to investigate the strength and weakness as well as limitations of some newest k-spectrum-based methods and to provide recommendations for users in selecting suitable methods with respect to specific NGS datasets.
Pubmed ID: 27461106 RIS Download
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Software tool for Bloom-filter-based error correction for next-generation sequencing (NGS) reads. The algorithm produces accurate correction results with much less memory.
View all literature mentionsError correction algorithm designed for short-reads from next-generation sequencing platforms such as Illumina''s Genome Analyzer II.
View all literature mentionsAn error correction module for Illumina sequencing reads, which is based on the k-mer spectrum approach.
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