Foreign nationals intending to have certification are eligible to appear for BINC examination.
Minimum qualification includes a degree from a recognized university/institute in the areas listed in FAQ.
Formal training in the area of Bioinformatics is not a prerequisite.
Note that the foreign students will only be certified by DBT and are not eligible for the cash award as well as junior research fellowship.

Address of the bookmark: http://binc.scisjnu.ernet.in/

http://www.biospectrumindia.com/biospecindia/features/203849/supercomputing-indian-agriculture-fast-track-mode/page/1

Position Paper Submission Extension: October 9, 2014 
Regular Paper Authors Notification: November 3, 2014 
Position Paper Authors Notification: November 6, 2014 
Regular and Position Paper Camera Ready and Registration: November 17, 2014 

Address of the bookmark: http://www.bioinformatics.biostec.org/

Address of the bookmark: http://www.rdatamining.com/

• Bioconductor – A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [ paper-2004 | web ]
• Biopython – Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the Open Bioinformatics Foundation. Contains the very useful Entrez package for API access to the NCBI databases. [ paper-2009 | web ]
• Bioconda – A channel for the conda package manager specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with conda install) bioinformatics packages. [ paper-2018 | web ]
• BioJulia – Bioinformatics and computational biology infastructure for the Julia programming language. [ web ]
• Rust-Bio – Rust implementations of algorithms and data structures useful for bioinformatics. [ paper-2016 ]
• SeqAn – The modern C++ library for sequence analysis.

Awk is a programming language that is specifically designed for quickly manipulating space delimited data. Although you can achieve all its functionality with Perl, awk is simpler in many practical cases.

Why awk? You can replace a pipeline of 'stuff | grep | sed | cut...' with a single call to awk. For a simple script, most of the timelag is in loading these apps into memory, and it's much faster to do it all with one. This is ideal for something like an openbox pipe menu where you want to generate something on the fly. You can use awk to make a neat one-liner for some quick job in the terminal, or build an awk section into a shell script. You can find a lot of online tutorials, but here I will only show a few examples which cover most of bioinformatician daily uses of awk.

choose rows where column 3 is larger than column 5:

awk '$3>$5' input.txt > output.txt

extract column 2,4,5:

awk '{print $2,$4,$5}' input.txt > output.txt

awk 'BEGIN{OFS="\t"}{print $2,$4,$5}' input.txt

show rows between 20th and 80th:

awk 'NR>=20&&NR<=80' input.txt > output.txt

calculate the average of column 2:

awk '{x+=$2}END{print x/NR}' input.txt

regex (egrep):

awk '/^test[0-9]+/' input.txt

calculate the sum of column 2 and 3 and put it at the end of a row or replace the first column:

awk '{print $0,$2+$3}' input.txt

awk '{$1=$2+$3;print}' input.txt

join two files on column 1:

awk 'BEGIN{while((getline<"file1.txt")>0)l[$1]=$0}$1 in l{print $0"\t"l[$1]}' file2.txt > output.txt

count number of occurrence of column 2 (uniq -c):

awk '{l[$2]++}END{for (x in l) print x,l[x]}' input.txt

apply "uniq" on column 2, only printing the first occurrence (uniq):

awk '!($2 in l){print;l[$2]=1}' input.txt

count different words (wc):

awk '{for(i=1;i!=NF;++i)c[$i]++}END{for (x in c) print x,c[x]}' input.txt

deal with simple CSV:

awk -F, '{print $1,$2}'

substitution (sed is simpler in this case):

awk '{sub(/test/, "no", $0);print}' input.txt

OK now here's where to read this stuff properly explained. roll

Two thorough tutorials:

http://www.gnu.org/software/gawk/manual/gawk.html

http://www.grymoire.com/Unix/Awk.html

A famous list of useful one-liners - though they're short, many are quite tricky:

http://www.pement.org/awk/awk1line.txt

And some nice explanations of those one-liners. After reading this you'll have a pretty good grasp!

http://www.catonmat.net/blog/awk-one-li … -part-one/

http://www.catonmat.net/blog/ten-awk-ti … -pitfalls/

(Indian Council of Medical Research )
Jehangir Merwanji Street, Parel, Mumbai 400 012

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• the Damerau–Levenshtein distance allows insertion, deletion, substitution, and the transposition of two adjacent characters;
• the longest common subsequence (LCS) distance allows only insertion and deletion, not substitution;
• the Hamming distance allows only substitution, hence, it only applies to strings of the same length.
• the Jaro distance allows only transposition.

use Text::Levenshtein qw(distance);

 print distance("foo","four");
 # prints "2"

 my @words     = qw/ four foo bar /;
 my @distances = distance("foo",@words);

 print "@distances";
 # prints "2 0 3"

use Algorithm::LCSS qw( LCSS CSS CSS_Sorted );
    my $lcss_ary_ref = LCSS( \@SEQ1, \@SEQ2 );  # ref to array
    my $lcss_string  = LCSS( $STR1, $STR2 );    # string
    my $css_ary_ref = CSS( \@SEQ1, \@SEQ2 );    # ref to array of arrays
    my $css_str_ref = CSS( $STR1, $STR2 );      # ref to array of strings
    my $css_ary_ref = CSS_Sorted( \@SEQ1, \@SEQ2 );  # ref to array of arrays
    my $css_str_ref = CSS_Sorted( $STR1, $STR2 );    # ref to array of strings

There are many different modules on CPAN for calculating the edit distance between two strings. Here's just a selection.

Text::LevenshteinXS and Text::Levenshtein::XS are both versions of the Levenshtein algorithm that require a C compiler, but will be a lot faster than this module.

The Damerau-Levenshtein edit distance is like the Levenshtein distance, but in addition to insertion, deletion and substitution, it also considers the transposition of two adjacent characters to be a single edit. The module Text::Levenshtein::Damerau defaults to using a pure perl implementation, but if you've installed Text::Levenshtein::Damerau::XS then it will be a lot quicker.

Text::WagnerFischer is an implementation of the Wagner-Fischer edit distance, which is similar to the Levenshtein, but applies different weights to each edit type.

Text::Brew is an implementation of the Brew edit distance, which is another algorithm based on edit weights.

Text::Fuzzy provides a number of operations for partial or fuzzy matching of text based on edit distance. Text::Fuzzy::PP is a pure perl implementation of the same interface.

String::Similarity takes two strings and returns a value between 0 (meaning entirely different) and 1 (meaning identical). Apparently based on edit distance.

Text::Dice calculates Dice's coefficient for two strings. This formula was originally developed to measure the similarity of two different populations in ecological research.

More at >> http://www.baumbachlab.net/