2015
DOI: 10.1186/s13040-014-0034-0
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Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks

Abstract: BackgroundGenetic studies are increasingly based on short noisy next generation scanners. Typically complete DNA sequences are assembled by matching short NextGen sequences against reference genomes. Despite considerable algorithmic gains since the turn of the millennium, matching both single ended and paired end strings to a reference remains computationally demanding. Further tailoring Bioinformatics tools to each new task or scanner remains highly skilled and labour intensive. With this in mind, we recently… Show more

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Cited by 403 publications
(308 citation statements)
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References 19 publications
(15 reference statements)
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“…Subsequent bioinformatics analyses were performed with clean reads according to the following pipeline: clean reads were aligned to the A. thaliana reference genome by Tophat56, the mapped reads were manipulated to BAM files by SAMtools57, then calculated the gene expression level by HTseq58. Differentially expressed genes were acquired by DESeq259; the unmapped BAM files were converted to Fastq files via bedtools and aligned to virus reference genome by Bowtie 260.…”
Section: Methodsmentioning
confidence: 99%
“…Subsequent bioinformatics analyses were performed with clean reads according to the following pipeline: clean reads were aligned to the A. thaliana reference genome by Tophat56, the mapped reads were manipulated to BAM files by SAMtools57, then calculated the gene expression level by HTseq58. Differentially expressed genes were acquired by DESeq259; the unmapped BAM files were converted to Fastq files via bedtools and aligned to virus reference genome by Bowtie 260.…”
Section: Methodsmentioning
confidence: 99%
“…If we do not encourage diversity, we may end up with a population where the majority of programs are very similar to the seed. New generations of individuals are Approach Representation Improvement Fitness Metric locoGP Java (AST) Performance Bytecode Operations Langdon [17], Petke [28] C++ (Statement) Performance, Specialisation Line Count Arcuri [2], White [41] Java-like (AST) Performance Simulated CPU Cycle Walsh & Ryan (Paragen) [30,31], Parallelisation Instructions Parallel Programs Functionality Chennupati (MCGE) [4] Orlov (FINCH) [25] Java (Byecode) Functionality Error Count Castle [3] Java-like (AST) Functionality Error Count O'Cinnéide [5], Simons [33] Java (Refactoring Patterns) Quality (e.g. elegance) Software Metrics Table 1: Feature Comparison of Improvement Approaches.…”
Section: Locogpmentioning
confidence: 99%
“…As Java is a widely used general purpose language, the ability to automatically improve existing Java programs is of wide interest. In this context, GP is a good approach for exploring the implicit effects of source code changes on performance [17,42].…”
Section: Introductionmentioning
confidence: 99%
“…However, optimizing attributes like execution time, memory consumption and power consumption is generally considered an improvement of a non-functional property which spans another big part of the GI literature. Of those attributes, execution time seems to be very popular, with Langdon's work on the 50k line DNA sequencing tool Bowtie [18,20] possibly the best known. Langdon has also reported 100 fold speed-up of another DNA sequencing tool BarraCUDA [17,19,[21][22][23] and the GI improvements have now been included in the official release.…”
Section: Related Workmentioning
confidence: 99%