RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

1. Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
2. Intact ordering and orienting of contigs.
3. Misassembly correction
4. GFF lift-over
5. Structural variant calling with and integrated version of Assemblytics
6. Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

About 8,000 years ago, the gene EGLN1 changed by a single DNA base pair. Today, a relatively short time later on the scale of human history, 88 percent of Tibetans have the genetic variation, and it was virtually absent from closely related lowland Asians. The findings indicate the genetic variation endows its carriers with an advantage.

In those without the adaptation, low oxygen caused their blood to become thick with oxygen-carrying red blood cells, an attempt to feed starved tissues, which could cause long-term complications such as heart failure. The researchers found that the newly identified genetic variation protected Tibetans by decreasing the over-response to low oxygen.

Reference: http://www.nature.com/nature/journal/v512/n7513/abs/nature13408.html

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

Responsibilities

Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.
Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.
Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.
Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.
Create scientifically rigorous visualizations, communications, and presentations of results.

Reference @ https://www.google.com/about/careers/search#!t=jo&jid=62095001

Address of the bookmark: http://www.vital-it.ch/

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/

There are two options to run ShRec3D (on linuX only so far): the first one uses the Matlab complier runtime environment (MCR), the second one doesn't need any other library to be installed but only works with the latest versions of Linux (equivalent to Fedora 19 and above).

Address of the bookmark: https://sites.google.com/site/julienmozziconacci/#TOC-Downloads

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/

Post: Junior Research Fellow (1 Position)

Duration: Three years

Qualification: First class in M.Sc/M.tech in Bioinformatics/Life Sciences/Biophysics/ Biostatistics/Bioengineering. Experience in Database development, NGS data analysis, Systems Biology and Structural Bioinformatics is desired. Preference will be given to the candidates with good computer programming skills in C, C++, R, Perl, PHP, Unix Scripting etc.

Selected candidates will be paid fellowship as per existing DST norms.

How to apply:

Candidates are requested to apply through one of the two modes given below
1. Online application – Click here to submit the online application https://docs.google.com/forms/d/16h2GLnQ-Ny-tLtlgfY3Bx3sCjeHJE30cfhJaDqW_uRs/viewform?c=0&w=1
2. Application forms can be downloaded from here.https://docs.google.com/file/d/0BwwJEudQStxFWXdNWXl4NWtDaWc/edit
Filled in application form should be sent by post to Dr. D. Bharanidharan, Department of Bioinformatics, Aravind Medical Research Foundation No 1, Anna Nagar Madurai – 625 020,

Candidates should apply by online or submit their applications by post on or before 15th June, 2015. Only Short listed candidates will be called for an interview. No TA/DA will be paid.

#Find all occurrences of a pattern in a string.
#Given: Strings Pattern and Genome.
#Return: All starting positions in Genome where Pattern appears as a substring. Use 0-based indexing.

use strict;
use warnings;

my $string="GATATATGCATATACTT";
my $subStr="ATAT";
my $kmer=length($subStr);

kmerMatch ($string, $subStr, $kmer);

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