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Open Access Software

Slim-Filter: an interactive windows-based application for illumina genome analyzer data assessment and manipulation

Georgiy Golovko12*, Kamil Khanipov1, Mark Rojas12, Antonio Martinez-Alcántara1, Jesse J Howard1, Efren Ballesteros1, Sharu Gupta1, William Widger13 and Yuriy Fofanov123

Author Affiliations

1 Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA

2 Department of Computer Science, University of Houston, Houston, TX, 77204, USA

3 Department of Biology and Biochemistry, University of Houston, Houston, TX, 77204, USA

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BMC Bioinformatics 2012, 13:166  doi:10.1186/1471-2105-13-166

Published: 16 July 2012

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/ webcite).

Conclusion

The presented Windows-based application has been developed with the goal of providing individual investigators with integrated sequencing reads analysis, curation, and manipulation capabilities.