Search Results for "fastq-join"

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. Instead, MarDRe takes advantage of the MapReduce programming model to significantly improve ParDRe performance on distributed systems, especially on cloud-based infrastructures. ...

This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to...

...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 distributed computing framework for scalable Big Data processing. HSRA currently supports single-end and paired-end read alignments from FASTQ/FASTA datasets. Moreover, our tool uses the Hadoop Sequence Parser (HSP) library (link above) to efficiently read the input datasets stored on the Hadoop Distributed File System (HDFS), being able to process datasets compressed with Gzip and BZip2 codecs.

...This model introduces two key concepts -- places and asynchronous tasks -- and a few mechanisms for coordination. With these, APGAS can express both regular and irregular parallelism, message-passing-style and active-message-style computations, fork-join and bulk-synchronous parallelism. Both its modern, type-safe sequential core and simple programming model for concurrency and distribution contribute to making X10 a high-productivity language in the HPC and Big Data spaces. User productivity is further enhanced by providing tools such as an Eclipse-based IDE (X10DT). Implementations of X10 are available for a wide variety of hardware and software platforms ranging from laptops, to commodity clusters, to supercomputers.