#Find the reverse complement of a DNA string.
#Given: A DNA string Pattern.
#Return: Pattern, the reverse complement of Pattern.

use strict;
use warnings;

my $string="AAAACCCGGT";
my $finalString="";
my %hash = (
    "C" => "G",
    "A" => "T",
    "T" => "A",
    "G" => "C",
);

for (my $aa=0; $aa<=(length($string)-1); $aa++) {
    my $char=substr $string, $aa, 1;
    #print $hash{$char};
    $finalString="$hash{$char}"."$finalString";
}

print $finalString;
print "\n";

The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

In fact, there are huge demands for expert biological data analyst. It’s a fairly new  (not exactly) hot area, these bioinformatician are invaluable because they know and understand the significance of biological data for your research and how you can use it for better understanding of biological problems.

The bioinformatics can discover biological patterns and stories in genomic and proteomics data. They can develop the pipeline needed to properly collect, store and analyse it.

Once your research group is ready to make a larger investment and hire a bioinformatician to gain a competitive edge, there are several key traits to seek out in potential candidates. The best bioinformatician are:

1. Highly Skilled - programming skills, experience with the biological software and tools.

The biological data won’t illuminate much if the scientist analysing it doesn’t possess practical programming skills, experience with the biological software and tools and a thorough understanding of basic biological stuff. A solid background in mathematics and statistics is also an indispensable trait.

2. Insight - Real vision, robust understanding and deep insight.

In order to hire the best bioinformatics and computational biologist scientist for your needs, it is always recommended and mostly practiced by the recruiters, to ask each contender to write and develop a sample script/presentation based on a specific set of data you provide. Then, explore the approaches used to deal with data provided and pick up those candidates who convey real vision, robust understanding and deep insight.

3. Energetic – Curiosity to explore

Mostly natural curiosity and enthusiasm for solving big biological problems coupled with an ability to transform data into a scientific stories may place one candidate above the rest. In addition to achieve that, the bioinformatician should be agile enough to quickly modify their methods to suit changes within a particular research.

4. Researcher – Publications

Look for someone who has a keen sense and understanding of concern biological problems. You can judge it by looking at previously published papers and data. It is always recommended to have a look at GitHub and other repository for codes written by her/him.

5. Impressive communicator - Insight that can’t be expressed is worthless.

Good bioinformatics scientists are able to uncover biological patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. In other words, they should must have good scientific writing skills. A computational biologis/bioinformatician  should know how to present the data and tell a scientific story through numbers/images.

The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.

Database URL: http://www.ensembl.org.

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/

More at https://github.com/neufeld/pandaseq

Address of the bookmark: https://github.com/neufeld/pandaseq

The software requires as input an assembly in FASTA format and paired reads mapped to the assembly in a BAM file. Mapping information such as the fragment coverage and insert size distribution is analysed to locate mis-assemblies. REAPR works best using mapped read pairs from a large insert library (at least 1000bp). Additionally, if a short insert Illumina library is also available, REAPR can combine this with the large insert library in order to score each base of the assembly.

http://www.sanger.ac.uk/science/tools/reapr

Address of the bookmark: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-5-r47

Address of the bookmark: http://busco.ezlab.org/