Search Results for "28"

...Buckles and M. Lybanon and "Fast additions on masked integers" by Michael D. Adams and David S. Wise. On 2009's computers it works quite well up to graph sizes of about 28 nodes. Runtime and memory usage are exponential in the graph size.

...The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields.

Counts NGS read alignments against GTF annotations in a multithreaded and scalable fashion. Benchmark: 8 core 1M annotations for 2Gb sorted reads ~30 seconds compared to ~28 minutes for bedtools multicov. Files include: fast_count_multi - reports all counts and RPKM, multithreading support fast_count_deseq - reports gene counts in deseq compatible format, multithreading support fast_count - reports all counts with no multithreading support. usage ./fast_count_multi num_threads gtf_file bam_file(s) > output Requires bamtools API library at run time, and c++0x for compile. ...

...For a longer high-level description of Hadoop-BAM, refer to the article "Hadoop-BAM: directly manipulating next generation sequencing data in the cloud" in Bioinformatics Volume 28 Issue 6 pp. 876-877, available online at: http://dx.doi.org/10.1093/bioinformatics/bts054 Note that the library part of Hadoop-BAM is mainly for developers with experience in using Hadoop. The command line tools of Hadoop-BAM should be understandable to all users, but they are limited in scope. See the SeqPig project for a higher-level interface to the file formats supported by Hadoop-BAM: http://seqpig.sourceforge.net See Seal for Hadoop-based read alignment tools, Seal: http://biodoop-seal.sourceforge.net

*************************************************************************** Updated 06/28/2018 The ezSift project has moved to https://github.com/robertwgh/ezSIFT *************************************************************************** The SIFT (scale-invariant feature transform) algorithm is considered to be one of the most robust local feature detector and description methods. Most of the open-source SIFT implementations rely on some 3rd-party libraries.