BOL

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...

github.com - pyScaf orders contigs from genome assemblies utilising several types of information: paired-end (PE) and/or mate-pair libraries (NGS-based mode) long reads (NGS-based mode) synteny to the genome of some related species (reference-based...

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...

neufeldserver.uwaterloo.ca - PANDASEQ assembles paired-end Illumina reads into sequences, trying to correct for errors and uncalled bases. The assembler reads two files in FASTQ format with quality information. If amplification primers were used (e.g., to isolate a variable...

www.khanacademy.org - Topics Displaying and describing data Modeling distributions of data Describing relationships in quantitative data Designing studies Probability Random variables Sampling distributions Confidence intervals (one sample) Significance tests...

www.bcgsc.ca - This sockeye software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects...

www.csd.uwo.ca - E-MEM is a C++/OpenMP program designed to efficiently compute MEMs between large genomes. See the README file for instructions on how to use E-MEM. E-MEM source code The source code can be downloaded here. If you use E-MEM, please...

sco.h-its.org - PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory. PEAR evaluates all possible paired-end read overlaps and without requiring the...

Structural variation caller tools using long reads

sfg.stanford.edu - This website and accompaning documents are intended as a tool to help researchers dealing with non-model organisms acquire and process transcriptomic high-throughput sequencing data without having to learn extensive bioinformatics skills. It covers...