gapFinisher can fill gaps in draft genomes quickly and reliably.

Address of the bookmark: https://github.com/kammoji/gapFinisher

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

Human genetic variation
Inference of human evolutionary history from genetic markers
Statistical analysis of population-genetic data
Mathematical models of gene genealogies
Theoretical population genetics
Combinatorics of evolutionary trees
The relationship between gene trees and species trees
The role of human evolutionary genetics in the search for genes that contribute to disease-susceptibility
More at https://web.stanford.edu/group/rosenberglab/index.html

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

Pakyong

F.No:NRCO/Admn/DBT /136 /

Walk-in-Interviews will be held at 737106, Sikkim for the post of 01 (One Project ‘DBT’s Twinning programme for the NE’ titled “Assessment of some fragrant orchids of north-east India for sustainable improvement of community livelihood”, indicated below. The appointment will be on contractual basis and the incumbents shall not have any regular appointment in ICAR.

‘DBT’s Twinning programme for the NE’ titled “Assessment of chemical and genetic divergence of some fragrant orchids of north-east India for sustainable improvement of community livelihood”

Junior Research Fellow (One post)

Essential Qualification : a. MSc (with NET qualification) / M.Tech degree (with or without NET) with minimum 55% marks in Biotechnology/ Bioinformatics/ Molecular Biology or any other related field.

Desirable Qualification: Computer Skills (Linux, Perl, Java, MySQL) with experience in advanced molecular Biology techniques

2nd June 2015

Advertisement: www.nrcorchids.nic.in/Employments/Vacancy%20-%20JRF.pdf

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.

Solved with perl http://rosalind.info/problems/1a/

#Find the most frequent k-mers in a string.
#Given: A DNA string Text and an integer k.
#Return: All most frequent k-mers in Text (in any order).

use strict;
use warnings;

my $string="ACGTTGCATGTCGCATGATGCATGAGAGCT";
my $kmer=4;
my %myHash;
my $max=0;

for (my $aa=0; $aa<=(length($string)-4); $aa++) {
    my $myStr=substr  $string, $aa,$kmer;
    #print "$myStr\n";
    my $km=kmerMatch ($string, $myStr, $kmer);
    if ($km > $max) { $max = $km;}
    #print "$km\t$myStr\n";
    $myHash{$myStr}=$km;
    
}

#Print all key which have matching values
foreach my $name (keys %myHash){
    print "$name " if $myHash{$name} == $max;
}

sub kmerMatch { #Check the exact matching kmers with sliding window
my ($string, $myStr, $kmer)=@_;
my $count=0;
for (my $aa=0; $aa<=(length($string)-4); $aa++) {
    my $myWin=substr  $string, $aa,$kmer;
    if ($myWin eq $myStr) {
        #print "$myWin eq $myStr\n";
        $count++;
    }
}
return $count;
}

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

Script are moved to http://bioinformaticsonline.com/snippets/view/34633/clump-finding-problem-solved-with-perl