Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.

Address of the bookmark: https://github.com/dnanexus/parliament2

Title of the Project
Research Associate
(One position)
DBTs Biotechnology/Bioinformatics training centre
PI Dr. Ashok Varma

Duration of the Project Six Months from the date of appointment, can be extended further for six month.

Date & Time: 17th February, 2015 at 10.00 a.m.

Venue: Meeting Room, 3rd floor, Khanolkar Shodhika, ACTREC

Essential Qualifications and Experience:

Ph.D. Degree in Basic Sciences from recognized University. Research experience in Bioinformatics or on gene cloning, protein purification, and crystallization.

*M.Sc. degree obtained after a one year course will not be considered.

Selected candidate will have to join at the earliest.

Consolidated Salary: Rs.28,600/- p.m. {Rs.22,000/- + 30% HRA}

The work progress of the candidate will be monitored and extension after 6 months will depend on satisfactory progress of the work.

Candidates fulfilling these requirements should pre-register by sending their application in the prescribed format with recent CV and contact details of 2 referees by e-mail to ‘program.office@actrec.gov.in’ latest by 17.00 hrs on 12-02-2015.
The interviews would be held on 17th February, 2015 and will be only for the pre-registered candidates. Candidates should report between 09.30 to 10.00 a.m. in Steno Pool, 3rd floor, Khanolkar Shodhika, ACTREC, Kharghar, Navi Mumbai.
No T.A./D.A. will be admissible for attending the interview.

At the time of Interview the candidate should bring original certificates along with CV with contact details of 2 referees and submit the photocopies (attested) of the certificates, with a recent passport size photograph.

All correspondence should be strictly made only to ‘program.office@actrec.gov.in’ as indicated.

Advertisement: www.actrec.gov.in/data%20files/2015/AV-RA-DBT-28-1-15.docx

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

Who you are
You’re someone who wants to influence your own development. You’re looking for a company where you have the opportunity to pursue your interests across functions and geographies. Where a job title is not considered the final definition of who you are, but the starting point.

Qualifications:
Major: Bioinformatcis or biology major who is interested and wants to learn Biocomputing, At least 2 years of college.
Basic knowledge of LINUX and programming, e.g., perl, python, XML.

More at http://www.roche.com/careers/jobs/jobsearch/job.htm?id=E-00437679&locale=en&title=Summer%20Intern%20-%20Research%20Bioinformatics

The "Ifs" of NGS QC and Trimming

1. Ensures Data Integrity
   If you want to minimize errors in downstream analyses, QC and trimming remove low-quality reads and bases, ensuring high-confidence data. This step is essential for reliable variant calling, assembly, and other applications.

2. Removes Contaminants
   If adapter sequences or contaminants are present in the raw reads, trimming can eliminate them. This prevents issues like misalignment or incorrect biological interpretations, ensuring cleaner data for analysis.

3. Improves Mapping and Assembly
   If your goal is better alignment to a reference genome or improved de novo assembly, trimming low-quality bases and adapters is critical. High-quality reads map more efficiently and generate more accurate assemblies.

4. Reduces Computational Load
   If you want to save computational resources, trimming reduces the dataset size, which speeds up processing and analysis. Clean datasets mean less computational time spent on processing low-quality data.

5. Prepares for Standardized Analyses
   If your project involves multiple datasets, QC and trimming ensure uniformity across them. This standardization makes comparisons valid and reproducible, particularly in large collaborative studies.

The "Buts" of NGS QC and Trimming

1. Risk of Over-Trimming
   But excessive trimming can lead to the loss of informative sequences, reducing read depth and potentially discarding biologically relevant data. This is especially critical in studies with limited sequencing depth.

2. Bias Introduction
   But trimming algorithms might introduce biases, especially if they inadvertently remove sequences with specific biological patterns. This can skew results and compromise biological insights.

3. Loss of Context in Paired-End Reads
   But trimming one read in a pair more than the other can lead to loss of pairing information. This complicates downstream analyses that rely on paired-end data, such as structural variant detection.

4. Time and Resource Intensive
   But running QC and trimming for large datasets can be computationally expensive and time-consuming. As sequencing depth increases, preprocessing becomes a bottleneck in the analysis pipeline.

5. Variable Standards
   But the criteria for trimming (e.g., quality threshold, minimum read length) can vary between tools and datasets. This variability may affect reproducibility and comparability of results across studies.

Balancing the "Ifs" and "Buts"

To maximize the benefits of QC and trimming while mitigating the challenges, consider the following best practices:

• Use QC Tools Wisely: Start with tools like FastQC to identify quality issues in your raw data. Visualizing quality metrics helps tailor your trimming parameters.

• Choose Reliable Trimming Tools: Tools like Trimmomatic, Cutadapt, and BBduk offer adaptive and customizable trimming options. Select one that aligns with your dataset and project goals.

• Set Reasonable Parameters: Avoid over-trimming by setting quality thresholds and minimum read lengths that balance data retention and quality improvement.

• Test Downstream Effects: Validate the impact of QC and trimming on downstream analyses, such as alignment efficiency, variant calling accuracy, or assembly quality.

• Document Your Workflow: Maintain detailed records of the parameters and tools used for QC and trimming. This ensures reproducibility and enables better troubleshooting.

Conclusion

NGS quality control and trimming are essential steps to ensure reliable and accurate data for analysis. While the "ifs" highlight the clear benefits of these steps, the "buts" remind us of the potential pitfalls. By adopting best practices and carefully balancing these considerations, you can optimize your preprocessing workflow and unlock the full potential of your sequencing data.

Name of the Scheme : Distributed Information Sub Centre – DISC

Qualifications : M.Sc/ B Tech in Bioinformatics with NET/GATE or M Tech in Bioinformatics

Number of posts : One

Emoluments : Rs. 25,000/-

Upper age limit : 35 years for Men & 40 years for Women as on date of Interview
How to apply

Date of Interview : 12-03-2015 at 10.00 AM. All relevant certificates (in original) and bio data, No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.

More at http://spices.res.in/index.php?option=com_content&view=article&id=263

Address of the bookmark: https://github.com/broadinstitute/VariantBam

This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:

> install.packages("ggplot2")

In theory the package should just install, however:

• if you are using Linux and don’t have root access, this command won’t work.
• you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:

> install.packages("ggplot2", lib="/data/Rpackages/")
> library(ggplot2, lib.loc="/data/Rpackages/")


It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden,  we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2")
> library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

• create a file .Rprofile in your home area.
• Add the following piece of code to it:


cat(".Rprofile: Setting UK repositoryn")
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)


I found this tip in a stackoverflow answer .

A swift, versatile phylogenomic and high-throughput sequencing simulator https://jackalope.lucasnell.com

Address of the bookmark: https://github.com/lucasnell/jackalope

Qualification : Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application/ Life Science/ Biotechnology/ Agricultural Science or equivalent. Desirable: Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics OR Post-Graduation in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application/ Life Science/ Biotechnology/ Agricultural Science or equivalent with 1st Division or 60% marks or equivalent with at least two years of research experience. Desirable:Expertise on use of various software/ tool.

No.of Post: 2

Emoluments for RA: Consolidated Rs. 24000/- per month + 30% HRA for Ph.D holders and consolidated Rs. 23000/- per month + 30% HRA for Master Degree.

Age Limit : Age should not be more than 40 years for the post of Research associate (5 years relaxation for SC/ST/ women candidates) and 3 years for OBC candidates as on date of interview.
How to apply

Interested candidates are invited to appear for Walk-In interview at Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi -110012, along with filled in application form , all the original certificates from matriculation onwards, Ph.D. or M.Sc. certificate (as the case may be) must be produced at the time of interview in either original or provisional, Bio-Data, attested copies of all experience certificates, testimonials etc., one passport size photograph and one set of the self-attested photocopies of all the required certificates from matriculation onwards and an attested copy of recent passport size photograph pasted onto the application form. Walk-in interview will be held on 16th April, 2015 at 10:30 a.m.

Click Here for Job Details & Application Form http://www.iasri.res.in/employment/employment.htm