The program is written in Java in order to provide a graphical user interface that is portable across a variety of computer platforms; indeed its name stands for "Local Alignments with Java". Currently it exists in two forms, a stand-alone application and a web-based applet, with slightly different capabilities.

Address of the bookmark: http://www.bx.psu.edu/~ratan/

http://www.bx.psu.edu/~ratan/

Address of the bookmark: http://www.bx.psu.edu/miller_lab/

Using the reference sequence dictionary (*.dict), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.

Usage

java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)

Options

• -h help; This screen.
• -R (indexed reference file) REQUIRED.
• -u (unmapped chromosome name): default:Unmapped
• -e | --empty : generate EMPTY bams for chromosome having no read mapped
• -m | --mock : if option '-e', add a mock pair of sam records to the empty bam
• -p (output file/bam pattern) REQUIRED. MUST contain __CHROM__ and end with .bam
• -s assume input is sorted.
• -x | --index create index.
• -t | --tmp (dir) tmp file directory
• -G (file) chrom-group file (see below)

Address of the bookmark: https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki

To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics). Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers >40,000 CLG files of publicly available Bacterial and Archaeal genomes.

Address of the bookmark: https://chunlab.wordpress.com/clgenomics-software/

https://academic.oup.com/bioinformatics/article/32/4/605/1744462/MaxBin-2-0-an-automated-binning-algorithm-to

The most recent version of MaxBin is 2.2, which supports the analysis of coassemblies of multiple samples. It is available at this JBEI downloads sites as well as MaxBin and MaxBin 2.0 sourceforge sites.

Address of the bookmark: http://downloads.jbei.org/data/microbial_communities/MaxBin/MaxBin.html

We currently access an unprecedented knowledge on the basic components making up living systems. An ever growing number of new technologies are being developed with the capacity to systematically measure the behaviour of multiple cellular components, such as biochemical activities, biophysical properties, subcellular localization and interaction. These technologies produce large amounts of raw data that can be transformed into information by software and tools developed both in academia and industry. However, this biological information does not equal biological knowledge. As biology becomes large-scale, new methods have to be developed to integrate, visualize and query the large amounts of information available. This is usually beyond the expertise of experimental labs. The goal of this young computational systems biology group is to generate biological sense from the growing amount of biological data becoming available and turn biological information into biological knowledge. http://www.biw.kuleuven.be/m2s/cmpg/research/CSB

We seek a PhD student who will work as a computational biologist in the interdisciplinary project NATAR. Understanding the relationship between an organism’s genotype and phenotype remains one of biology’s fundamental challenges. In the project two model organisms, budding yeast and E. coli, will be exploited to better understand two complex genetic traits: ethanol and high temperature tolerance. For both organisms both natural variation and the outcomes of experimental evolution will be correlated with phenotypes. The first challenge in the project will be to identify which of the many variants in these genomes have functional effects. Then we will try to facilitate unravelling complex interactions between alleles, for example through functional gene networks that are based on a combination of published data. An important part of the generated data that underlies this project consists of high-throughput sequencing of DNA and mRNA of populations and isolates. For answering specific questions data integration with other data sources will be needed as well as the application of specific machine learning techniques. Basic molecular biology knowledge and basic programming skills are a prerequisite for this position. Experience with high throughput sequencing analysis methods, machine learning, protein interaction databases, perl/python scripting or BioconductoR are preferred. For more information contact prof. Rob Jelier (r.jelier@gmail.com) or prof. Vera van Noort (vera.vannoort@gmail.com). In your application please include a CV describing coursework, grades and previous research experience as well as a minimum of two letters of recommendation.

Starting date: from 1st of October 2013

GroopM is largely parameter-free. Use: groopm -h for more info.

For installation and usage instructions see : http://ecogenomics.github.io/GroopM/

Address of the bookmark: https://github.com/ecogenomics/GroopM

Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes

Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, Kåre L. Nielsen and Per .H. Nielsen

Nature Biotechnology 2013, doi: 10.1038/nbt.2579

See the associated online guide for detailed information.

https://github.com/MadsAlbertsen/multi-metagenome

Address of the bookmark: https://github.com/MadsAlbertsen/multi-metagenome