Applications are invited for the post of Senior Research Fellow- SRF (one) and Junior Research Fellow- JRF (one) to be appointed in a SERB-funded major research project entitled “Biochemical and functional properties of Synechocystis Glutathione S-transferase(s)” sanctioned to Dr. Timir Tripathi, Molecular and Structural Biophysics Laboratory, Department of Biochemistry, NEHU, Shillong.

Essential Qualifications: For both positions M.Sc. or equivalent with a good academic record is a prerequisite.

For Project-SRF, experience in bioinformatics/computational biology is required, which should be evident by publications.

Students waiting for their last semester result can apply for JRF position.

Stipend: As per SERB norms.

Interested students can email their detailed bio-data including mobile number and recent photograph to msb.biochem@gmail.com, latest by 01.08.15. The hard copy is not required at this stage.

The date of interview will be informed after primary scrutiny of the applications. No TA/DA will be paid if called for interview. P.S: Students applied for the same post as per date 01.06.15, need not to apply again as their application will be considered in this advertisement also.

For details of the research work of the PI’s group kindly visit www.ttripathi.webs.com

Dr. Timir Tripathi Principal Investigator DST-SERB Project Department of Biochemistry NEHU, Shillong

Advertisement: www.nehu.ac.in/Advertisements/BiochemPVAdvtTT_200715.pdf

• DNA-mapping*
• ChIP-seq*
• RNA-seq*
• ATAC-seq*
• scRNA-seq
• Hi-C
• Whole Genome Bisulfite Seq/WGBS

(*Also available in "allele-specific" mode)

snakePipes can be installed via conda :

'conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'.

Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.

Address of the bookmark: https://github.com/maxplanck-ie/snakepipes

Eligibility : MSc(Bio-Chemistry, Bio-Informatics, Bio-Tech)

Location : Chennai

Last Date : 20 Aug 2015

Hiring Process : Walk - In
Alagappa University - Job Details

RA Bioinformatics Job position in Alagappa University

Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/ Life Sciences

No.of Post: One

Salary : Rs. 11000
How to apply

A walk-in Interview will be held at the Department of Biotechnology, Alagappa University, Science Campus, Karaikudi 630 004 on 20.08.2015 (Thursday) at 10.30 AM.

Interested candidates are encouraged to send their Curriculum Vitae by email in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview.

Click Here for more http://alagappauniversity.ac.in/files/news_files/Notification.pdf

gapFinisher can fill gaps in draft genomes quickly and reliably.

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

With InsideDNA, you can upload and store your own genomic/genetic datasets in a limitless cloud space, and instantly analyze it with a powerful compute instance, without any tool installation or set up hassle.

More at https://insidedna.me/

Address of the bookmark: https://insidedna.me/

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

Center for Computational Natural Sciences and Bioinformatics

Junior Research Fellowship Position

Applications are invited for one JRF position in the following DAE sponsored project.

Title of the project: Insight into the Structure–Function Relationships of Chemically Modified Nucleic Acids: A Molecular Dynamics Simulations Study.

The above project involves theoretical modelling and simulations on chemically modified nucleic acids to investigate their structures, dynamics and thermodynamic stabilities.

Desired qualification: M.Sc. in Chemistry/ Bioinformatics; GATE/UGC-CSIR NET qualification.

To apply: Send detailed curriculum vitae by e-mail to the following address on or before 31 August 2015: Prof. U. Deva Priyakumar (devalab@iiit.ac.in), CCNSB, IIIT-H, Gachibowli, Hyderabad 500 032.

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

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.