BOL

http://shinyheatmap.com/ - Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in...

github.com - HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order...

github.com - SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads. More at https://github.com/jts/sga SGA...

evomics.org - The objective of this activity is to help you understand how to run Velvet in general, how to accurately estimate the insert size of a paired-end library through the use of Bowtie, the primary parameters of velvet, and the process...

www.topcoder.com - Learning greedy algo for biologist.  https://www.topcoder.com/community/data-science/data-science-tutorials/greedy-is-good/ This webpage is also useful for the...

http://last.cbrc.jp/ - LAST can: Handle big sequence data, e.g: Compare two vertebrate genomes Align billions of DNA reads to a genome Indicate the reliability of each aligned column. Use sequence quality data properly. Compare DNA...

The genome assemblers generally take a file of short sequence reads and a file of quality-value as the input. Since the quality-value file for the high throughput short reads is usually highly memory-intensive, only a few assemblers, best suited for...

www.mgc.ac.cn - GenomeComp is a tool for summarizing, parsing and visualizing the genome wide sequence comparison results derived from voluminous BLAST textual output, so as to locate the rearrangements, insertions or deletions of genome segments between species or...

In a lot of my work in bioinformatics, I have been using hidden Markov models (HMMs). As a postdoc with David Haussler at UCSC we developed the so-called profile HMMs (refs). Since then I have applied HMMs to membrane proteins (refs) and gene...

Mike Ritchie Lab primary research focus is the detection of susceptibility genes for common diseases such as cancer, diabetes, hypertension, and cardiovascular disease, among others. The approaches will involve the development and application of new...