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.

Sr Research Fellow & Technical Asst Posts: Indian Institute of Maize Research (IIMR), New Delhi has advertised a notification for the recruitment of Senior Research Fellow & Technical Assistant vacancies for the project titled “Genetic modifications to improve biological nitrogen fixation for augmenting nitrogen needs of cereals” on contractual basis. Eligible candidates may apply in prescribed application format on or before 25-06-2015. Other details like age, educational qualification, selection process, how to apply are given below…

Indian Institute of Maize Research Vacancy Details:
Total No. of Posts: 03
Name of the Posts :
1. Senior Research Fellow: 02 Posts
2. Technical Assistant: 01 Post

Age Limit: Candidates age should be 35 years for Senior Research Fellow, Minimum 21 years and maximum 45 years for Technical Assistant as on the closing date of the application. Age relaxation is 5 years to SC/ST and women candidates and 3 years to OBC candidates as per ICAR rules.

Educational Qualification: Candidates should possess Post graduate degree in Biotechnology/ Molecular Biology/ Bioinformatics/ Plant physiology for Senior Research fellow, Graduate degree in Agriculture/ Biotechnology/ any discipline of life sciences for Technical Assistant with relevant experience.

Selection Process: Candidates will be selected based on their performance in interview.

How to Apply: Eligible candidates can send their application in prescribed format along with bio-data to Dr. Pranjal Yadava, Principal Investigator, ICAR Indian Institute of Maize Research, Pusa Campus, New Delhi-110012 or mail to pranjal.yadava@gmail.com on or before 25-06-2015 & attend the interview along with application in the attached format at the time of interview, one passport size photograph, self attested copies of certificates for age, and qualifications, reprints of publications and ‘No Objection Certificate’, original documents on 01-07-2015. Venue details are mentioned below.

Important Dates:
Last Date for Receipt of Application: 25-06-2015
Date & Time of Interview : 01-07-2015.
Venue: IIMR, Pusa Campus, New Delhi

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

Eligibility : BE/B.Tech(CSE, ECE, IT)

Location : Pondicherry

Last Date : 26 Jun 2015

Hiring Process : Face to Face Interview
Pondicherry University - Job DetailsDate of posting:19 May 15

Senior Technical Assistant Job position in Pondicherry University on temporary basis

Project Title : "Bioinformatics National Certification (BINC) for certifying quality human resource in Bioinformatics"

Qualification : i) B.E/ B.Tech Computer Science/ Electronics & Communication Engineering/ Information Technology with 55% or equivalent marks. ii) One year Experience in relevant field in Government/ Public Sector or reputed Private Organizations. Desirable : Working experience in JSP and ASP/PHP

No. of Post : 01

Department : Biotechnology

Pay : Rs. 27,800/-

Age Limit : 30 Yrs
How to apply

Both above-mentioned posts are purely on temporary basis, extended by one year based on performance and will be terminated with the completion of BINC Program at Pondicherry University. Interested Candidates may send their application in the prescribed format with self-attested copies of all mark sheets and certificates to Dr. Basant K. Tiwary, Coordinator (BINC), Centre for Bioinformatics, Pondicherry University, Puducherry-605 014 before June 26, 2015.

Click Here http://www.pondiuni.edu.in/news/requirement-post-one-senior-technical-assistant-one-computer-assistant-–-dept-biotechnologygove

• Binaries for Linux/macOS
• From sources for Linux/macOS
• From sources for Windows

Address of the bookmark: https://github.com/imgag/ngs-bits

Address of the bookmark: https://seeing-theory.brown.edu/

Starting 15th July 2015, the position relates to a project specifically for in silico drug docking, screening, design, optimisation and linkage with active chemists.

Experience in many docking softwares and operating systems is essential.

Additional experience in bioinformatics and computational biology tools will be useful.

Submit curriculum vitae to: sb.icgeb@gmail.com

Closing date: 5 July 2015

Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you. 

Address of the bookmark: http://revigo.irb.hr/

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