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/

This utility makes it easy to identify what are the properties of a read based on its SAM flag value, or conversely, to find what the SAM Flag value would be for a given combination of properties.

To decode a given SAM flag value, just enter the number in the field below. The encoded properties will be listed under Summary below, to the right.

Address of the bookmark: https://broadinstitute.github.io/picard/explain-flags.html

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

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

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/

Tools

• Parsnp - Core-genome alignment and analysis
• Gingr - Interactive visualization of alignments, trees and variants
• HarvestTools - Archiving and postprocessing

Address of the bookmark: https://harvest.readthedocs.io/en/latest/

ICGEB NEW DELHI, INDIA

Biotechnology research positions

Projects include:

a) protein structure determination
b) malaria parasite biology
c) genomics and metagenomics
d) molecular and cellular biology
e) bioinformatics and computational biology

Minimum eligibility for students who have already obtained a MSc:

1) INSPIRE award for PhD
2) SPM award for PhD
3) CSIR/DBT/DST JRF for PhD

Applicants should submit their curriculum vitae by email to: sb.icgeb@gmail.com by 30 August 2016

http://www.bcgsc.ca/platform/bioinfo/software/xmatchview

Address of the bookmark: https://github.com/warrenlr/xmatchview

See the user manual or the paper for more information about Scarpa. Click here for the supplementary material.

Address of the bookmark: http://compbio.cs.toronto.edu/hapsembler/scarpa.html

The BINC is a nationwide examination aimed at certifying professionals in bioinformatics and tests their theoretical and practical knowledge across three phases of examination. He is entitled to receive a DBT research fellowship leading to a Ph.D. from any premier research institute in India.