Search Results for "stream"

An innovative Open Source CEP (Complex Event Processing) engine. It implements the event stream processing as a library embeddable in C++ and Perl. You can think of the Complex Event Processing engine as an in-memory database driven by triggers, or a data-flow machine, or a spreadsheet on steroids (and without the GUI part).

...CSBB is implemented in Perl, while it also leverages the use of R, java and ruby in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX and Windows platforms. Currently CSBB provides 17 modules focused on analytical tasks like normalization, visualization, statistics, RNA-SEQ etc.

...CSBB is implemented in Perl, while it also leverages the use of R, java, python and ruby in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX. Currently CSBB provides 18 modules focused on analytical tasks like performing upper-quantile normalization, interactive visualization and next generation sequencing pipelines. CSBB now also has capability to process public data. ...

...CSBB is implemented in Perl, while it also leverages the use of R, java and ruby in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX and Windows platforms. Currently CSBB provides 16 modules focused on analytical tasks like normalization, visualization, statistics, RNA-SEQ etc.

It is often necessary to smooth high frequency fluctuations out of data streams sequenced by time, position, etc. The 'smooth' utility applies such smoothing using the wavelet algorithm. This implementation of wavelet smoothing was optimized for use in a data stream. It was adapted from HMMSeg Wavelet.Java by Thomas E. Wilson, University of Michigan. HMMSeg Wavelet.Java was written by Andrew Hemmaplardh, University of Washington. http://noble.gs.washington.edu/proj/hmmseg/ Unsupervised segmentation of continuous genomic data, Bioinformatics 2007 23:1424-1426 See the above references for a more thorough description of the principles behind wavelet smoothing.

It is often necessary to assign a series of discrete values to continuosly variable data sequenced by time, position, etc., thereby parsing the data into fewer and larger segments of variable width. The 'segment' utility takes an input data stream as a Hidden Markov Model and applies the Viterbi algorithm to find the most likely segmentation path through the data.

'slurp' and 'glurp' are related utilites that help with buffering of file input and output (IO) in parallel environments with large files. They read and write files using a much larger buffer than typical system and program defaults.

HiC is high-throughput sequencing technology applied to chromatin conformation techniques. This collection of tools stream-lines the processing of HiC data from raw sequence to contact matrices and beyond.

An experimental CEP (Complex Event Processing) engine. It implements the event stream processing as a library embeddable in C++ and Perl. Since then it has been renamed to Triceps, so please look at the new location https://sourceforge.net/projects/t