However, the ancient invasions and migrations to slavery and trade, history is embroidered with events that led to interactions between previously separate populations. Early humans migrated due to many factors such as changing climate and landscape and inadequate food supply. Historical migration of human populations begins with the movement of Homo erectus out of Africa across Eurasia about a million years ago. Homo sapiens appear to have occupied all of Africa about 150,000 years ago, moved out of Africa 70,000 years ago, and had spread across Australia, Asia and Europe by 40,000 years BC. Indo-Aryan migration from the Indus Valley to the plain of the River Ganges in Northern India is presumed to have taken place in the Middle to Late Bronze Age, contemporary to the Late Harappan phase in India (ca. 1700 to 1300 BC). From 180 BC, a series of invasions from Central Asia followed, including those led by the Indo-Greeks, Indo-Scythians, Indo-Parthians and Kushans in the northwestern Indian subcontinent.

Using the recent advance technologies researchers have created a historical atlas of instances of such mixing. They use a sophisticated statistical method for making inferences about human history and infer populations interbredings ( happen over the past 4,000 years) with an ease.

The study published the findings and presented with an interactive map. http://admixturemap.paintmychromosomes.com/

These sort of genomic study have some limilation. It is hard to precisely define sources of mixing when it occurred between genetically similar groups, and scenarios involving multiple waves of mixing over time or between multiple groups can be difficult to tease apart. But it is believed that larger sample sizes will improve resolution. These high resolution will provide a deeper understanding of human history.

Reference:

http://www.sciencemag.org/content/early/2014/01/28/science.1245938

http://www.ncbi.nlm.nih.gov/pubmed/21390129?dopt=Abstract&holding=npg

http://www.csulb.edu/~kmacd/paper-ethnicity.html

Image: Wikipedia

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

Candidate Requirements: A PhD in computer science, computational biology, Bioinformatics, or a related field, along with sufficient programming skills for prototyping. Experience with next generation sequencing data analysis is required. Candidates with MS degree but with relevant work experience can also be considered. The successful candidate must be motivated and capable of working independently as well as in team environment.

Eligible and interested candidates can email your resumes to rohit at strandls dot com

About Strand Life Sciences: Strand was founded in 2000 by computer science and mathematics professors who recognized the need to automate and integrate life science data analysis through an algorithmic and computational approach. Strand’s solutions for life sciences research are robust and easy to use by the most novice user while powerful and configurable for the bioinformatician. Using its award-winning application development platform, AVADIS®, Strand builds innovative products that enable fast and cutting-edge analysis for basic and clinical research, drug discovery and development.

http://www.avadis-ngs.com/careers

Address of the bookmark: https://github.com/imgag/ClinCNV

Current computational interests:

Systematic analysis of genetic epistasis to identify redundant or compensatory systems and to reveal order of action in genetic pathways.
Using knockout, knockdown, or overexpression, or other perturbation experiments in combinations of genes in S. cerevisiae, C. elegans or mouse.
Using genome-scale genotyping of natural polymorphisms in S. cerevisiae and human populations.
Alternative splicing and its relationship to protein interaction networks.
Integrating large-scale studies including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns to quantitatively assign function to genes and guide experimentation.

More at http://llama.mshri.on.ca/index.html

https://github.com/CSB5/lofreq

http://csb5.github.io/lofreq/installation/

https://github.com/CSB5/lofreq/tree/master/dist

Address of the bookmark: http://csb5.github.io/lofreq/

1) Statistical machine learning methods for the analysis of medical
epigenomics data.

2) Sequence analysis algorithms for detection of RNA-DNA interactions.

Applicants should hold a M.Sc . or PhD in Computer Science or related
areas. Experience in the analysis of biological sequences, gene
expression and gene regulation is desirable. The candidate should have
solid programming skills (C, Python and/or R) and acquaintance with
Linux. Experience with high performance computing is a plus. The
working language of the group is English.

The position is based on the German TV-L 13 salary scale, including
all German social benefits (health insurance and pension scheme). The
expected starting date is September 2014. Interested candidates should
send a CV, statement of research interests and the names of three
references to jobs@costalab.org.

More at http://costalab.org/wp/phd-and-postdoc-position-in-computational-biology/

• Filters SNVs from any variant caller to remove false positives
• Calculates metrics based on BAM files and provides filtering not possible with other tools
• Fully user-configurable filtering (including which filters to use and their thresholds)
• Option to use filters identical to ICGC recommendations

FiNGS provides researchers with a tool to reproducibly filter somatic variants that is simple to both deploy and use, with filters and thresholds that are fully configurable by the user. It ingests and emits standard variant call format (VCF) files and will slot into existing sequencing pipelines. It allows users to develop and implement their own filtering strategies and simple sharing of these with others.

FiNGS reliably improves upon the precision of default variant caller outputs and performs better than other tools designed for the same task.

Address of the bookmark: https://github.com/cpwardell/FiNGS

2. In some perl script I found this
 . . . . . .
 . . . . . .
# It works for me, only God understood how it is working
while (/(<\/[^>]+>)|(<[^>]+>)|(<[^>]+>)$|([^><]+)/go) {
            $startGene=$1;
            $beginChromosome=$2;
    
. . . . . .
 .. . . . . .
}

3. One more interesting message in Perl found …. It will must tickle you bone :)
open(my $fh, "<", "gene.txt")    or kill " Me if you think this is a mistake :$!";

4. From the Perl

  while () {  # "The Mothership Connection is here!"
    print “$_\n”; # Printing the offspring :)

5. Perl message
if ($1) { print “Just found a the error in chromosome !!!, yahoo…”; else { “That is not error, but mutation you moron!”;

6. One genome database curator walk in wine bar asked the bartender:
CREATE TABLE gene IF NOT EXISTS SexOnTheBeach;

Variant calling and/or genotyping

• DiscoSNP++ and discoSnpRAD: Reference-free small variant discovery (SNPs and indels)
• MindTheGap: Detection and assembly of large insertion variants
• TakeABreak: reference-free inversion discovery tool
• SVJedi: Structural Variant genotyper with long read data
• SVJedi-graph: Structural Variant genotyper with long read data using a variation graph

Sequence assembly

• MinYS: reference-guided genome assembly in metagenomics data
• MTG-link: local assembly tool for linked-read data
• Minia: De novo short read assembler
• de-novo pipeline: de-novo assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes
• Mapsembler2: Targeted assembly (not maintained)

Managing k-mers & indexation

• findere: simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
  • fimpera extends findere adding the abundance information.
• kmtricks: modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.
• kmindex is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.
• back to sequences: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.
• Backpack Quotient Filter: k-mer indexing data structure with abundance
• short read connector: Detect similar reads from potentially large read set
• DSK: Count K-mer in sequences

Pangenome graph manipulation

• Pancat: Pangenome Comparison and Analysis Toolkit
• GFAGraphs: a Python library to handle pangenome graph files in GFA format.

Comparative metagenomics with k-mers

• Simka and SimkaMin: Comparative metagenomics for large-scale datasets
• Comparead & Commet: comparison of metagenomic datasets

Species and bacterial strains identification

• ORI: software using long nanopore reads to identify bacteria present in a sample at the strain level
• StrainFLAIR: STRAIN-level proFiLing using vArIation gRaph

General-purpose sequencing data manipulation

• GASSST: long read mapper
• Leon: short read compressor (now included in GATB-core)
• Bloocoo: short read corrector
• BCALM: Construct compacted de Bruijn graphs (unitigs)

 Protein Structure

• A_Purva: Contact Map Overlap solver
• MD-Jeep: Distance Geometry solver
• CSA: Comparative Structural Alignment

Workflow

• SLICEE: parallel execution of bioinformatics workflows

Comparative Genomics

• CASSIS: detection of rearrangement breakpoints
• PLAST: intensive bank-to-bank sequence comparison
• DRJBreakpointFinder: detection and precise localization of excision sites in proviral segments