118,440 research outputs found

    MaxSSmap: A GPU program for mapping divergent short reads to genomes with the maximum scoring subsequence

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    Programs based on hash tables and Burrows-Wheeler are very fast for mapping short reads to genomes but have low accuracy in the presence of mismatches and gaps. Such reads can be aligned accurately with the Smith-Waterman algorithm but it can take hours and days to map millions of reads even for bacteria genomes. We introduce a GPU program called MaxSSmap with the aim of achieving comparable accuracy to Smith-Waterman but with faster runtimes. Similar to most programs MaxSSmap identifies a local region of the genome followed by exact alignment. Instead of using hash tables or Burrows-Wheeler in the first part, MaxSSmap calculates maximum scoring subsequence score between the read and disjoint fragments of the genome in parallel on a GPU and selects the highest scoring fragment for exact alignment. We evaluate MaxSSmap's accuracy and runtime when mapping simulated Illumina E.coli and human chromosome one reads of different lengths and 10\% to 30\% mismatches with gaps to the E.coli genome and human chromosome one. We also demonstrate applications on real data by mapping ancient horse DNA reads to modern genomes and unmapped paired reads from NA12878 in 1000 genomes. We show that MaxSSmap attains comparable high accuracy and low error to fast Smith-Waterman programs yet has much lower runtimes. We show that MaxSSmap can map reads rejected by BWA and NextGenMap with high accuracy and low error much faster than if Smith-Waterman were used. On short read lengths of 36 and 51 both MaxSSmap and Smith-Waterman have lower accuracy compared to at higher lengths. On real data MaxSSmap produces many alignments with high score and mapping quality that are not given by NextGenMap and BWA. The MaxSSmap source code is freely available from http://www.cs.njit.edu/usman/MaxSSmap

    Community Engagement as a Student-Athlete

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    Hannah Waterman discusses student engagement at Linfield College with regard to her involvement with the volleyball team.https://digitalcommons.linfield.edu/inauguration2019_students/1020/thumbnail.jp

    SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications

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    Summary: The Smith Waterman (SW) algorithm, which produces the optimal pairwise alignment between two sequences, is frequently used as a key component of fast heuristic read mapping and variation detection tools, but current implementations are either designed as monolithic protein database searching tools or are embedded into other tools. To facilitate easy integration of the fast Single Instruction Multiple Data (SIMD) SW algorithm into third party software, we wrote a C/C++ library, which extends Farrars Striped SW (SSW) to return alignment information in addition to the optimal SW score. Availability: SSW is available both as a C/C++ software library, as well as a stand alone alignment tool wrapping the librarys functionality at https://github.com/mengyao/Complete- Striped-Smith-Waterman-Library Contact: [email protected]: 3 pages, 2 figure

    Inclusions of Waterman-Shiba spaces into generalized Wiener classes

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    The characterization of the inclusion of Waterman-Shiba spaces ΛBV(p)\Lambda BV^{(p)} into generalized Wiener classes of functions BV(q; δ)BV(q;\,\delta) is given. It uses a new and shorter proof and extends an earlier result of U. Goginava.Comment: 5 page

    Parallel Smith-Waterman Algorithm for Gene Sequencing

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    Smith-Waterman Algorithm represents a highly robust and efficient parallel computing system development for biological gene sequence. The research work here gives a deep understanding and knowledge transfer about exiting approach for gene sequencing and alignment using Smith-waterman their strength and weaknesses. Smith-Waterman algorithm calculates the local alignment of two given sequences used to identify similar RNA, DNA and protein segments. To identify the enhanced local alignments of biological gene pairs Smith-Waterman algorithm uses dynamic programming approach. It is proficient in finding the optimal local alignment considering the given scoring system. DOI: 10.17762/ijritcc2321-8169.150515
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