Address of the bookmark: http://greengenes.lbl.gov/cgi-bin/nph-index.cgi

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC430967/

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: http://www.cmbb.arizona.edu/?page_id=250

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

For further details about the underlying algorithm see the original publication:
MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons.
Vincent Ranwez, Sébastien Harispe, Frédéric Delsuc, Emmanuel JP Douzery
PLoS One 2011, 6(9): e22594.

Address of the bookmark: https://bioweb.supagro.inra.fr/macse/index.php?menu=releases

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/

1. ElasticBLAST can handle much LARGER queries! 

ElasticBLAST can search query sets that have hundreds to millions of sequences and against BLAST databases of all sizes.

2. ElasticBLAST is FASTER

ElasticBLAST distributes your searches across multiple cloud instances to process them simultaneously. The ability to scale resources in this way allows you to process large numbers of queries in a shorter time than you could with BLAST+.

3. ElasticBLAST is EASY to run on the cloud

ElasticBLAST is easy to set up using our step-by-step instructions (Amazon Web Services (AWS), Google Cloud Platform (GCP)) and allows you to leverage the power of the cloud. Once configured, it manages the software and database installation, handles partitioning of the BLAST workload among the various instances, and deallocates cloud resources when the searches are done.

ElasticBLAST also selects the instance (i.e., machine) type for you based on database size. Of course, you can also choose the instance type manually if you prefer. 

Address of the bookmark: https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/

Applications are invited for a post in DST, India funded Project entitled: "Positive and negative impacts of macromolecular crowding agents during target site location by DNA binding proteins – origin of optimal search at physiological ionic concentration (Reference Number: ECR/2016/000188) ''. The selected candidate will be appointed purely on temporary basis, initially for two years as a JRF that may be extended to one year of SRF based on the performance.

Position: Junior Research Fellow (1)

Qualifications & Experience: Candidate must have a consistently good academic record with at least 60% marks in all throughout and must have qualified NET/GATE.

Desirable: Basic knowledge in the field of biophysics, molecular simulations and computational biology are desirable.

Salary: Consolidated Rs. 25,000 per month.

Tenure: The project duration is for three years and the selected candidate would be appointed after an interview. Appointment will be purely on temporary basis as stipulated by the existing rules of the University.

Interested candidates need to send an application to the address mentioned below mentioning the name of the project and post applied for (on the cover of the envelope).

The applications along with CV should be mailed at the address given below. Name, address, contact number and e. mail address of two referees must be enclosed with the application. The last date for the application is July 31st 2016.

Dr. Arnab Bhattacharjee (Principal Investigator)
Assistant Professor
School of Computational and Integrative Sciences
Jawaharlal Nehru University
New Delhi-110067
E-mail: arnab@jnu.ac.in

Note: 1. Only shortlisted candidates will be communicated to appear in the interview at SCIS, JNU and no other communications in this regard will be entertained.

2. No TA/DA will be paid for appearing in interview.

https://prakharg24.github.io/papers/401851.full.pdf

Address of the bookmark: https://prakharg24.github.io/papers/401851.full.pdf