MutaBind guides you through this process, step by step, starting with selecting a protein complex and inputting PDB code or uploading PDB files. You can also retrieve results with a job ID number, view help documents, and review the MutaBind method and references.

More at http://www.ncbi.nlm.nih.gov/projects/mutabind/index.fcgi/

More at http://www.pondiuni.edu.in/news?quicktabs_2=5#quicktabs-2

Distribution package for the Prgressive Cactus multiple genome aligner. Dependencies are linked as submodules

https://github.com/glennhickey/progressiveCactus

Address of the bookmark: https://github.com/glennhickey/progressiveCactus

Anvi’o is an analysis and visualization platform for ‘omics data.

Please find the methods paper here: https://peerj.com/articles/1319/

Anvi’o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper

An analysis and visualization platform for 'omics data http://merenlab.org/projects/anvio

Paper https://peerj.com/articles/1839/

Address of the bookmark: https://github.com/meren/anvio

The corresponding publication can be found here:

B. Voss*, M. Hanselmann*, B.Y. Renard, M.S. Lindner, U. Köthe, M. Kirchner, F.A. Hamprecht (2011). SIMA: Simultaneous Muliple Alignment of LC/MS Peak Lists, Bioinformatics 27(7):987-993. [doi] [techreport]

Address of the bookmark: https://hciweb.iwr.uni-heidelberg.de/hci/softwares/sima

LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scalede novo predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.

One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.

DarkHorse is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the references cited below for a complete explanation of parameter selection and result interpretation. A brief tutorial page is also available on-line.

Address of the bookmark: http://darkhorse.ucsd.edu/download.html

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set.

When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.

Availability: The method is publicly available athttp://research.haifa.ac.il/~ssagi/software/nearHGT.zip

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408

Address of the bookmark: https://github.com/martinjvickers/KAST

Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim