CDSA is pleased to announce a 4 days hands-on training program on “Essentials of Statistics and Data Analysis using R” at ICGEB, Aruna Asaf Ali Road, New Delhi on December 1 – 4, 2015. This will involve developing and enhancing skills to understand basic principles of statistics for summarizing data and use of appropriate statistical tests as well as providing an understanding of data analysis using R. Didactic lectures with practical sessions will be delivered by experienced faculties from AIIMS and Novartis. Live classroom with power point presentations, case studies, mock exercise, practical sessions on R, group work with time for discussion and Q&A sessions are added advantages of this workshop.

Please contact gayatrivishwakarma.cdsa@thsti.res.in or vineetabaloni.cdsa@thsti.res.in for program and registration details.

Please nominate personage or register yourself on or before November 6, 2015 along with the electronic transfer of registration fee.

gapFinisher can fill gaps in draft genomes quickly and reliably.

Address of the bookmark: https://github.com/kammoji/gapFinisher

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

 http://metapred2cs.ibers.aber.ac.uk/

Address of the bookmark: https://github.com/martinjvickers/MetaPred2CS

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

(Reaccredited by NAAC with ‘A’ grade)

TIRUCHIRAPPALLI - 620 024

I N V I T E S

APPLICATIONS FOR FACULTY POSITIONS UNDER UGC XII PLAN CREATED POSTS ON CONTRACTUAL BASIS TILL 31.03.2017 IN THE FOLLOWING UNIVERSITY DEPARTMENTS. (Notification No. 31300 /H2/2015, Dt.: 18.09.2015)

ASSISTANT PROFESSOR

1. Bio-Informatics ONE (MBC)

2. Bio-Chemistry ONE G.T.(W)DW

Consolidated Pay of Rs.45,000/- p.m.

Qualification : As per UGC/AICTE/NCTE norms

Application along with other details including specialization can be downloaded from the University website www.bdu.ac.in

LAST DATE FOR RECEIPT OF FILLED IN APPLICATION IS 07.10.2015 BY 5.00 P.M.

More Info : www.bdu.ac.in/adv/UGC_XII_Plan_Contract_Positions.pdf

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.

Project Title : "Structural and functional characterization of serine biosynthetic pathway enzymes from Entamoeba histolytica"

No. of Post : 01

Qualification : Ph.D in any computational biology / bioinformatics

Desirable : 1. Experience with docking, simulations and structural biology with proof of publications is preferred. 2. National Eligibility Test cleared [CSIR/UGC] candidate is preferred.

Salary : According to CSIR rules
How to apply

The application on plain paper indicating name, date of birth/age, address, essential/technical/professional qualifications. experience, list of publications should reach The Prof. S. Gourinath, Principal Investigator, [Project Director], School of Life Sciences, Jawaharlal Nehru University, New Delhi-110067, on or before 23rd May 2016.

More at http://www.jnu.ac.in/Career/currentjobs.htm

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

Eligibility : Pursuing final year of M.Sc./M.Tech.

Subject category for training : I. Agricultural biotechnology II. Computational biology and bioinformatics III. Food Science & Technology IV. Nutrition Science & Technology

Duration of Training : 6 months (January to June 2016)

Selection Procedure : Through screening and selection based on merit, experience, and expression of interest submitted by candidates
How to apply

The last date of application to reach NABI latest by November 30, 2015 by 5:30 PM

More at http://www.nabi.res.in/Training.aspx