We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

Main research topics:
- Comparative and Population Genomics of Plant Symbionts
- Parasite Genome Evolution
- Experimental Evolution of Microbial Symbionts and Parasites
- Phylogenomics of Early Branching Fungi

More at http://corradilab.weebly.com/

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

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.

More at https://software.broadinstitute.org/gatk/download/bundle

ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/.

ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/

Address of the bookmark: https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1

This readme covers all necessary instructions for the impatient to get andi up and running. For extensive instructions please consult the manual.

More at https://github.com/evolbioinf/andi/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full

Tutorial https://github.com/andyrimmer/Platypus/blob/master/misc/README.txt

Address of the bookmark: http://www.well.ox.ac.uk/platypus

https://www.researchgate.net/job/939073_Senior_Scientist_Bioinformatics-Multi_Omics_Integration

Senior_Scientist_Bioinformatics-Transcriptomics_Analysis

https://www.researchgate.net/job/939075_Senior_Scientist_Bioinformatics-Transcriptomics_Analysis-Belgium_France_Switzerland_The_Netherlands

Senior Scientist Bioinformatics - Network Analytics

https://www.researchgate.net/job/939070_Senior_Scientist_Bioinformatics-Network_Analytics_Belgium_France_Switzerland_the_Netherlands

Team Leader Bioinformatics Data Sciences - Mechelen, Belgium

https://www.researchgate.net/job/938787_Team_Leader_Bioinformatics_Data_Sciences-Mechelen_Belgium

AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:https://github.com/UcarLab/QuIN/.

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809

Address of the bookmark: http://userweb.eng.gla.ac.uk/umer.ijaz/bioinformatics/oneliners.html