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

Requirement of Senior Research Fellow Applications are invited for the following vacancy under Research project entitled “Exploiting temporal transcription profile, computational analysis and post-transcriptional gene silencing to identify and intercept interactions between host and dormant and actively replicating Mycobacterium tuberculosis”

Senior Research Fellow One

M.Sc. Degree in Biotechnology/ /Microbiology/Bioinformatics and 2 years research experience in the relevant field NET/GATE Qualification with at least 2 years research experience in M. tuberculosis culture and related techniques as well as transcriptome data generation and analysis.

1 year

1. Age relaxation for SC/ST/OBC/PH Candidates will be as per the government rules.

2. Qualification/degree should be from a reputed Institution/University.

3. Mere fulfilling the essential qualification/experience does not guarantee selection.

4. Canvassing in any form will be a disqualification.

5. No TA/DA will be paid either for attending the interview or joining the post.

Interested candidates are to submit their Curriculum Vitae by 5 PM on 17th August, 2015 to Room No.107, Department of Biotechnology, AIIMS, New Delhi-29.

Only shortlisted candidates will be notified by email for interview

Advertisement:

www.aiims.edu/images/pdf/recruitment/advertisement/advertisement%20of%20SRF_N1306.pdf

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

Experts from India and Japan in this fields will deliver lectures and contribute in discussions. This will provide an opportunity to the participants to interact and learn.

Topics:

Algorithms for biomolecular Sequences / Structures
Bioinformatics databases and tools
High performance computing
Large scale data analysis
Protein function
Structure based drug design
Applications to specific diseases

More at http://www.iitm.ac.in/bioinfo/Symposium-2015/index.html

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.

A: He was getting too Sassy.

What did the bioinformatician say when he found out his team stopped using version control?

A: Y’all better Git!

Why did the computational biologist stay home from work?

A: He had a code!

Why was the bioinformatician's paper was rejected?

A: Journal thought it seemed scripted.

How can you tell that a Bioinformatics is working?

A: You can hear him Grunting!

Why bioinformatician always silence?

A: Because bioinformatician calmly whisper, “SSH”

Why was the bioinformatician always so sleepy?

A: He/She wasn’t given any Java.

Why did the program/software hanged?

A: Because genome float.

Why was the class upset that its parent died?

A: Because it wouldn’t be getting the inheritance!

Why did bioinformatician always works on the command line?

A: Because they don't want to scare you with huge amount of data!

Why did the bioinformatician attend the gay pride parade?

A: They supported polymorphism.

Why did bioinformatician prefer awk, PerlOneliner?

A: Because even computer can't handle to load the data.

Why don’t bioinformatician get along with others?

A: They’re too MEAN.

Why computational biologist are cool?

A: Because they are scripted!!

Why they talk $ unzip; strip; touch; finger; grep; mount; fsck; more; yes; fsck; fsck; umount; clean; sleep;

A: Ah, Ohhh, dude, these are *NIX commands

Did they really hack genome?

A: Yes, I guess so.

• Whole-genome sequencing
• Whole-exome sequencing
• RNA-seq (speical mode available)
• ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

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.

Address of the bookmark: https://github.com/fenderglass/Flye

https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Address of the bookmark: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html