The University of Idaho is in Moscow, a small college town located in
Northern Idaho on the Washington border. Moscow is widely considered to
be a great place to live, and it's known for a pleasant downtown, active
farmer's market, and nearby recreational opportunities. All of Moscow
is within biking or walking distance of the University of Idaho. For
more information about Moscow, see https://visitmoscowid.com/.

The University of Idaho has very strong faculty in evolution and
genomics in multiple departments and interdisciplinary programs. Of
particular note are the Bioinformatics and Computational Biology
Program (BCB: https://www.uidaho.edu/sci/bcb/people/faculty) and
the Institute for Bioinformatics and Evolutionary Studies (IBEST:
https://www.ibest.uidaho.edu/index.php). In addition, the University of
Idaho is only eight miles from Washington State University in Pullman, and
faculty from the two institutions interact and collaborate extensively.

Minimum qualifications include: a Ph.D. in biological sciences,
bioinformatics, or a related discipline; experience conducting research
in genomics or evolutionary biology, as evidenced by publications
in peer-reviewed journals; and evidence of strong written and oral
communication skills. Experience analyzing next-generation sequence
data and familiarity with the genomics of marine fishes are desirable
but not required.

Apply at: https://uidaho.peopleadmin.com/postings/30003

Review of applications will begin January 15, 2021. The start date
is flexible.

The University of Idaho is an equal opportunity/Affirmative Action/equal
access employer.

Informal inquiries are encouraged and can be directed to Adam Jones
(adamjones@uidaho.edu).

"adamjones@uidaho.edu"

The work will focus on the functional studies of small RNAs by using next-generation sequencing approaches.

Candidates should hold a Ph.D. degree and have strong background in bioinformatics.
The University of Nice Sophia-Antipolis provides a wide range of facilities and training essential for biomedical research.
Interested applicants should send a PDF with a cover letter stating research interests and qualifications, an updated CV, a summary of previous research experience and contact information for two references to Michele Trabucchi ( mtrabucchi@unice.fr )

https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Address of the bookmark: https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Our research centres on the use of genetics/genomics and phenotypic studies to address complex questions in the ecology, epidemiology and evolution of microbes. Our most recent interest focuses upon comparative genome analysis to describe the core and flexible genome of pathogenic bacteria (Campylobacter, Acinetobacter, Escherichia coli, Helicobacter, Staphylococcus and Streptococcus suis) and how this is related to population genetic structuring, the maintenance of species, and the evolution of host/niche adaptation and virulence.

More at https://sheppardlab.com/research/

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 http://www.cmdn.org.np/main/index.php

"Robert Sapolsky is an American neuroendocrinologist, professor of biology, neuroscience, and neurosurgery at Stanford University, researcher and author" ----Wikipedia

Address of the bookmark: http://www.youtube.com/watch?v=_dRXA1_e30o

https://github.com/iansealy/coursera-comparinggenomes

Address of the bookmark: https://github.com/iansealy/coursera-comparinggenomes