More at http://bioinfo.vanderbilt.edu/zhanglab/

Address of the bookmark: https://github.com/dhlbh/BASE

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.

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

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

http://www.stratosgenomics.com/stratos-genomics-technology

Bioinformatics supports to researchers
Customized training in Bioinformatics for researchers and faculty members
Support in Installing, implementing and maintaining software on computer.
Create awareness for taking preventive measure against data security
Organize workshops on thrust ares of Bioinformatics
Research Training to students of Biotechnology and Bioinformatics

More at http://bioinfo.bisr.res.in/index.php

Rajiv Gandhi Centre for Biotechnology (RGCB) invites applications for the following three faculty scientist positions:

Scientist E-II or F in Bioinformatics & Computational Biology

SCIENTIST E-II OR F IN COMPUTATIONAL BIOLOGY & BIOINFORMATICS

Highly motivated and innovative individual who will pursue basic research, solve biological problems with emphasis on computational and quantitative experimental methods and build active bridges to translational research. The scientist will also provide computational biology support to ongoing research programs in disease biology, provide assistance to analyze complex data sets generated by RGCB scientists and collaborators inclusive of including high dimensional “omics” data and next generation sequencing data, such as whole genome, exome, RNA-seq and ChIP-seq as well as provide leadership for high quality training for junior scientists and regular teaching programs of the institute. Areas of research of interest to RGCB include but are not limited to computational, systems, or quantitative biology with applications to cell biology, developmental biology, metabolism, genomics, proteomics, biophysics, biological information systems, network pharmacology, drug design and cancer research. The scientist’s responsibilities include efforts for the integration of DNA variant annotation with statistical genetic analysis methods including linkage, imputation and association methods, adopting novel and innovative methodologies to analyze, integrate and interpret high dimensional data sets, provision of annotation to robust genetics and genomics findings using several data sources and methods, data management of exploratory clinical and R&D studies in partnership with other lines of genetic data generated from internal and external studies, delivery and documentation of genomic information to support genetic studies, ensuring high-quality genetic and genomic data is incorporated into exploratory- clinical research programs, developing tools that make maximum use of multiple data sources to support annotation of DNA variation and contributes to systems biology initiatives within RGCB

More at http://rgcb.res.in/scientist-positions/

Application Form http://rgcb.res.in/wp-content/uploads/2015/11/APPLICATION-FORMAT-FOR-SCIENTISTS.docx

More at https://github.com/aaronwolen/liftover

https://www.bioconductor.org/help/workflows/liftOver/

Address of the bookmark: https://github.com/aaronwolen/liftover

More at http://hgdownload.cse.ucsc.edu/admin/exe/

Address of the bookmark: http://hgdownload.cse.ucsc.edu/admin/exe/