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/

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

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.

Find out more on our blog:
http://www.ensembl.info/…/find-predicted-crispr-sites-usin…/

August 17, 2016

RGCB invites applications for the following positions from Indian citizens with prescribed qualifications. Full details including job description, additional desirable qualifications, etc. are described below.

Code No. 1

Senior Manager (Bioinformatics Operations)

(To download application format, click here )

Scale of Pay

PB-3 Rs.15600-39100 + Grade Pay Rs.6600/-

Number of Positions

1 (General)

Minimum Qualifications

PhD in Bioinformatics, Biotechnology, Life Sciences or Computer Science applied to biological questions.
A minimum of 5 years documented experience in national or state government R&D centers or state and central universities.
Track record of research funding and peer reviewed publications.
Proficiency using statistical analysis software or libraries such as R or Matlab.
Experience with a general scripting language such as Python, Ruby, or Pearl
Experience working with Next Generation Sequencing data
Proficiency with data visualization tools (Spotfire, Tableau, R, Python, etc.)
Experience with an object-oriented language such as Java, C++ or C# and familiarity with standard software development best practices: source code control, unit testing, in-code documentation and automated build environments.
Excellent listening, time management, organizational and interpersonal skills
Excellent communication skills, including the ability to illustrate problems and generate solutions
Management skills – demonstrated through the successful management of a team or large projects.
Broad and deep knowledge of computational methods for high-throughput sequence analysis and interpretation.
Extensive experience in delivering bioinformatics as a service and conducting training programs.
Experience of working with a production, customer-focused environment and business development projects.
Experience with management of funding and financial sustainability.
Demonstrated ability to work in a team environment and ability to lead and motivate an effective team, and also work as a good team player.
Good problem solver, able to logically identify solutions to technical problems.
Able to see the bigger picture and contribute towards strategic direction of Platforms and Pipelines teams.
Responsibilities

This position will involve cross-functional teamwork to build and develop bioinformatics tools and provide analysis for ongoing clinical trials.
Collaborate with biomarker scientists, clinical investigators and pipeline teams to build analytical tools.
Implement and evaluate new algorithms for R&D.
Support Research and Development teams by analyzing NGS data to identify predictive response markers
Lead training programs in Computational Biology and Bioinformatics.

More at http://rgcb.res.in/positions.php

Address of the bookmark: http://software.broadinstitute.org/software/genomestrip/

Address of the bookmark: http://omega.omicsbio.org/

WALK-IN INTERVIEW

JIP/PSM/INDO-US TB/ 2016/

Walk-in-interview for the following vacant posts funded by Department of Biotechnology, Govt.of India for the project entitled “Biomarkers for Risk of Tuberculosis and for Tuberculosis Treatment Failure and Relapse” in the Department of Preventive & Social Medicine, JIPMER, Puducherry.

3. Technical Assistant

MCA/ MSc in Biostatistics/ MSc in Computational Biology from any recognized University @ Rs.23,220 1

Interested candidates may attend the walk-in interview with written screening test on 07, September 2016 at 9.30 A.M in the Dept. of Preventive and Social Medicine, IV Floor, Administrative Block, JIPMER.

The applicants are requested to bring the filled in application form and bio-data with original certificates for verification.

More Info: http://jipmer.edu.in/wp-content/uploads/2016/09/RECRUITEMENTsite-protocol-7.9.2016.pdf

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

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