Showing 4 open source projects for "bio-bwa"

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  • 1
    PANDA

    PANDA

    A comprehensive and flexible quantification tool for proteomics data

    PANDA is a comprehensive and flexib tool for quantitative proteomics data analysis, which is developed based on our solid foundations in quantitative proteomics for years. Several novelties have been implemented in it. First, we implement the advantage algorithms of LFQuant (Proteomics 2012, 12, (23-24), 3475-84) and SILVER (Bioinformatics 2014, 30, (4), 586-7) into PANDA. Second, we consider the state-of-art concept of quantification reliability in this quantitative workflow. On the levels...
    Downloads: 2 This Week
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  • 2

    HSRA

    Hadoop spliced read aligner for RNA-seq data

    HSRA is a MapReduce-based parallel tool for mapping reads from RNA sequencing (RNA-seq) experiments. RNA-seq analyses typically begin by mapping reads to a reference genome in order to determine the location from which the reads were originated, which is a very time-consuming step. This tool allows bioinformatics researchers to efficiently distribute their mapping tasks over the nodes of a cluster by combining a fast multithreaded spliced aligner (HISAT2) with Apache Hadoop, which is a...
    Downloads: 0 This Week
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  • 3

    MarDRe

    MapReduce-based tool to remove duplicate DNA reads

    MarDRe is a de novo MapReduce-based parallel tool to remove duplicate and near-duplicate DNA reads through the clustering of single-end and paired-end sequences from FASTQ/FASTA datasets. This tool allows bioinformatics to avoid the analysis of not necessary reads, reducing the time of subsequent procedures with the dataset. MarDRe is the Big Data counterpart of ParDRe (link above), which employs HPC technologies (i.e., hybrid MPI/multithreading) to reduce runtime on multicore systems....
    Downloads: 0 This Week
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  • 4
    Boon Rules

    Boon Rules

    Study the computational basis of human learning and inference

    The main problem of the machine learning is to find a scientific regularity in experimental observations. Many methods may be served - from baseless guesses to an accurate investigation of models - to create a possible scientific interpretation. This approach consider on searching for underlying laws. Laws can be evoked from the massive data sets (genetics, robot sensors, social networks, adverisement) available now (so called the Big Data).
    Downloads: 0 This Week
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