1 Asst. Professor (on Pre-Ph.D. Contract)

2 Asst. Professor (on Contract)

3 Asst. Professor

4 Associate Professor

5 Professor

Life Sciences:-

i)Biochemistry and Molecular Biology; ii)Cell and Developmental Biology; iii)Immunology and Molecular Medicine; (iv) Microbiology and Ecology (v)Bioinformatics Group; vi)Biophysical Sciences

HOW TO APPLY:-

a. Candidates willing to apply for one or more posts are requested to apply online at “http://www.nitrkl.ac.in/ JOBS & TENDERS /Faculty Position” .
b. Persons employed in Government and Semi-Government organizations may apply directly against the standing advertisement. For this the application should be completed online. The printout of the application generated online should be submitted through employer if shortlisted for interview.
c. The online application can be filled in multiple sessions.
d. Candidates are required to check the Institute website from time to time for latest information, application status call for interview, change of dates and final results.
e. Applications shall be received online only. Please do not send application or CV against this advertisement by email or letter mail.

More Info: http://nitrkl.ac.in/Jobs_Tenders/1FacultyPosition/Default.aspx

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

1. Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
2. Intact ordering and orienting of contigs.
3. Chimeric contig correction
4. GFF lift-over
5. Structural variant calling with and integrated version of Assemblytics
6. Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

Address of the bookmark: http://pevsnerlab.kennedykrieger.org/php/?q=software

Address of the bookmark: https://github.com/bpucker/MGSE

Name of the Post: Project Fellow

No. of the Post: 02 Two

Salary: Rs. 12000/- per month or Rs. 14000/- per month

Age Limit: Max. 28 years as on 10.06.2015 and relaxation as per rules

Required Job Profile:

Candidate must possess first class B.Tech. in bioinformatics or computational biology OR M.Sc. in bioinformatics or computational biology with fifty five percent marks OR M.Tech. in bioinformatics or computational biology with fifty five percent marks.

How to apply:

Eligible and interested candidates should need to appear for walk-in interview on 10.06.2015 at 9:00 am at the above mentioned address.

Refer to http://www.ihbt.res.in/recruit/AdvtNo7_2015.pdf

Address of the bookmark: https://schneebergerlab.github.io/syri/

What is de novo genome assembly?
The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either “single ended” as described above or “paired end.”

Why genome assembly?
In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.

Raw NGS data
Reads can be saved as a Fasta file as text or in a FastQ file with their attributes. FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.

In a nutshell the protocol:
Get the sequence file(s) read from the sequencing machine (s).
Look at the readings - have an idea of what you have and what the standard is like.
If required, raw data cleanup/quality trimming.
Choose an adequate parameter set for assembly.
Assemble the data into scaffolds/contigs.
Examine the assembly performance and determine the efficiency of the assembly.

Read Quality Control:
Check the qualiy with fastQC.
Script
https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda

Quality trimming/cleanup of read files.
This function trims adapters, barcodes and other contaminants from the reads.
Script
https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command

Genome Assembly:
The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina

A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own.
Some of the assemblers available include:
Velvet
SOAP-denovo
MIRA
ALLPATHS

Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now. Few stuff you can note about the contigs you just created: They're the draft Contigs. Any mis-assemblies can occur.

Mis-assembly checking and assembly metric tools:
QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast
Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve
InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/
inGAP is also useful for finding structural variants between genomes from read mappings.

Genome finishing tools:
Semi-automated gap fillers:
Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/

IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page

Genome visualisers and editors:
Artemis - http://www.sanger.ac.uk/resources/software/artemis/
IGV - http://www.broadinstitute.org/igv/

Automated and semi automated annotation tools:
Prokka - https://github.com/tseemann/prokka
RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer
JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/

Frequent command use for the analysis are at:

https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly
https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools

Applications are invited for pre-selection of candidates for Ministry of Science and Technology, Department of Science and Technology INSPIRE Faculty Scheme: a component of “Assured Opportunity for Research Career (AORC)” under INSPIRE in the area of computational Biology/Systems Biology/Bioinformatics.

Candidates having done their B.Tech/B.E. and or M.Sc./M.Tech in Computer Science or Biotechnology and Ph.D. in Systems/ Computational Biology or Bioinformatics may apply in the following format prescribed by DST to the Director, Cluster Innovation Center, University Stadium, GC Narang Marg, University of Delhi, Delhi -11107. Detials of other qualification, age limits etc., please visit www.inspire-dst.gov.in.

Application on the prescribed format may be submitted by email to director@cic.du.ac.in before October 25, 2014. Selected candidates shall be called for an interview. The date, time and venue of the interview shall be informed by email/telephone. For more information about Cluster Innovation Center, please visit https://ducic.ac.in.

More at https://ucdavis-bioinformatics-training.github.io/

Address of the bookmark: https://ucdavis-bioinformatics-training.github.io/2020-Genome_Assembly_Workshop/snakemake/snakemake_intro