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

Please visit our site at www.arraygen.com

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

JRF (Maximum Tenure‐ Five Years: 2yrs as JRF and 3yrs as SRF)
A first class Master’s Degree in Bioinformatics (likely 2 posts)
Around Rs 16,000/ Plus 30% HRA (as per rules of funding agency)

Applications are invited from candidates possessing the above qualifications. The upper age limit is as on the last date for receipt of application. (5 years relaxation to SC/ST candidates, 3 years to OBC candidates, and other entitled categories as per Govt rules). Actual No. of vacancies may vary.

Application form can be download from the website www.drdo.gov.in and E Mailed to inmashrd@gmail.com.
Last date to apply by email is 1700 hrs on 15 Oct 2014
Incomplete applications are liable to be rejected.
Confirmation will be sent to short-listed candidates through email only
Antecedents of selected candidates will be verified.
Written Test will be conducted from 0930-1030 hrs. Latecomers will not be considered.
Candidates will be required to produce certificates/testimonials in original at the time of interview.
It may please be noted that offer of Fellowship does not confer on fellows any right for absorption in DRDO.
Candidates should carry photocopy of Application form sent by email with them.
No TA/DA will be paid for attending interview & on joining.
Last date to apply by email is 1700 hrs on 15 Oct 2014

More at http://drdo.gov.in/drdo/English/jrf29092014.pdf
http://drdo.gov.in/drdo/English/index.jsp?pg=inmas29092014.jsp

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.

minimap -t 24 assembly.fasta long_reads.fastq.gz | racon -t 24 long_reads.fastq.gz - assembly.fasta racon_assembly.fasta
nucmer -p nucmer assembly.fasta racon_assembly.fasta
show-snps -C -T -r nucmer.delta

This reports Racon's changes in a table. You can exclude indels with the -I option in show-snps. 

This process (Racon -> MUMmer -> SNP table) solves the problem I originally raised in this issue. So as far as I'm concerned, you can close this issue (or keep it open if you still want to implement some kind of variant table).

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

Current projects in the Morota lab include developing kernel-based whole-genome prediction and kernel-based genome-wide association models, polygenic modeling of binary traits, reexamining the results from quantitative genetics analysis in light of functional annotation, and extending kernel methods (such as GBLUP and RKHS) specifically tailored for diverse types of emerging omics data.

In addition, candidates will be expected to leverage opportunities to interact with faculty in animal genetics and biometrics at the UNL in the areas of bioinformatics, breeding, functional genomics, quantitative genetics, and molecular genetics.

Candidates should have a B.S. or M.S. degree in quantitative disciplines with strong background and interest in statistical computing.
The starting date is Fall 2015.
For more information about research in the Morota lab at the UNL, visit: http://www.morotalab.org

A letter of interest in the position, C.V., and contact information for
three references should be emailed to Gota Morota at .
Review of applications will begin immediately, and continue until the
positions are filled. Informal inquiries are also welcome.

Also, please see: http://animalscience.unl.edu/anscprospectivegraduatestudents