Correcting short reads with high error rates for improved sequencing result.
ABSTRACT In the sequencing process, reads of the sequence are generated, then assembled to form contigs. New technologies can produce reads faster with lower cost and higher coverage. However, these reads are shorter. With errors, short reads make the assembly step more difficult. Chaisson et al. (2004) proposed an algorithm to correct the reads prior to the assembly step. The result is not satisfactory when the error rate is high (e.g., >or=3%). We improve their approach to handle reads of higher error rates. Experimental results show that our approach is much more effective in correcting errors, producing contigs of higher quality.
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ABSTRACT: BACKGROUND: The emergence of Next Generation Sequencing technologies has made it possible for individual investigators to generate gigabases of sequencing data per week. Effective analysis and manipulation of these data is limited due to large file sizes, so even simple tasks such as data filtration and quality assessment have to be performed in several steps. This requires (potentially problematic) interaction between the investigator and a bioinformatics/computational service provider. Furthermore, such services are often performed using specialized computational facilities. RESULTS: We present a windows-based application, Slim-Filter designed to interactively examine the statistical properties of sequencing reads produced by Illumina Genome Analyzer and to perform a broad spectrum of data manipulation tasks including: filtration of low quality and low complexity reads; filtration of reads containing undesired subsequences (such as parts of adapters and PCR primers used during the sample and sequencing libraries preparation steps); excluding duplicated reads (while keeping each read's copy number information in a specialized data format); and sorting reads by copy numbers allowing for easy access and manual editing of the resulting files. Slim-Filter is organized as a sequence of windows summarizing the statistical properties of the reads. Each data manipulation step has roll-back abilities, allowing for return to previous steps of the data analysis process. Slim-Filter is written in C++ and is compatible with fasta, fastq, and specialized AS file formats presented in this manuscript. Setup files and a user's manual are available for download at the supplementary web site (https://www.bioinfo.uh.edu/Slim_Filter/). CONCLUSION: The presented windows-based application has been developed with the goal to provide individual investigators with integrated sequencing reads analysis, curation, and manipulation capabilities.BMC Bioinformatics 07/2012; 13(1):166. · 3.02 Impact Factor