Gargi believes in its mission statement that every student who passes through the portals of the college emerges as a wholly developed individual symbolizing the spirit of enterprise and inquiry that characterizes Gargi.

Bioinformatics Infrastructure Facility (BIF), Gargi College, University of Delhi invites candidates for filling up the following purely temporary positions sponsored by DBT, New Delhi.

1. Name of the post: Traineeship
Essential Qualification: Post Graduate degree in Bioinformatics or any other branch of Life Sciences preferably with dissertation in Bioinformatics.
Desirable Qualification: Prior knowledge of programming languages such as C, VB, SQL etc. and software/database development.

2. Name of the post: Research Associate
Essential Qualification: PhD in Bioinformatics/Biological Sciences/Computer Science or allied sciences with proven experience in bioinformatics.

3. Name of the post: Studentship
Essential Qualifications: Final year Post Graduate students pursuing a degree in Bioinformatics or any branch of Life Science with knowledge of bioinformatics.

How to apply:
Interested candidates are required to appear for the walk in interview on 29th Aug, 2013 at 10.00 AM in Principal’s Office, Gargi College, Sirifort Road, N. Delhi-110049, with their CVs, original documents and a set of Photostat copies of all original documents.

http://www.du.ac.in/fileadmin/DU/students/Pdf/du/advt/2013/16082013_Gargi_RAplus2_Advt.pdf

• 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.

POSITIONS OPEN FOR TEMPORARY RESEARCH PROJECT POSTS

(Date of interview 23rd September 2013 at 10:30 AM)

CSIR-Institute of Genomics & Integrative Biology (IGIB), desires to engage qualified incumbents on purely temporary basis as detailed below:

Project Code/Title (Project Code BSC0123)

Genome dynamics in Cellular Organization, Differentiation and Enantiostasis (GENCODE)

Project Fellow

First Class M.Sc./M.Tech in bioinformatics/Human Genetics/Genomics

Rs. 16,000/- + 30 % HRA per month

Sr. Project Fellow

First Class M.Sc./M.Tech in bioinformatics/Human Genetics/Genomics

With two years of experience in NGS data analysis.

Rs. 18,000/- + 30 % HRA per month

Age relaxation as per Govt. of India instructions.

Engagement is for the project and on behalf of the funding agency and the tenure shall be as mentioned above. The duration of the post is initially for One year or till the closing date of the project, whichever is earlier. Tenure may be extendable up to project duration. Contract may be terminated at any time by giving one-month notice by either side. The applicants will have no claim implicit or explicit for consideration against any CSIR/IGIB post.

How to Apply:

It is mandatory for eligible applicants to apply by both the processes as given below:

1. Sending the resume in MS Word format directly to hrd@igib.res.in (Mentioning the Project Code-Post Code in the Subject Line of the email example:GAP0059-1)

2. They also need to fill up proforma by clicking on the following link HR Online Form.

3. Candidate cannot apply for more than two posts.

Last date of receiving application is 02-09-2013.

No application would be entertained with result awaited status or after due date.

The email will be sent to the short listed candidates.

No TA/DA will be paid to the candidates to attend the interview. The engagement shall be as per guidelines of CSIR/Funding agency. Candidates will have an option to give reply in Hindi.

Note: The shortlisted candidates, who will receive the email for interview, have to report at 09:00 AM on the day of interview along with any Photo ID card and original certificates for entry purpose. Entry will be closed by 10:00 AM.

More @ http://www.igib.res.in/sites/default/files/23092013.htm

https://edu.t-bio.info/collaborative-model-bioinformatics-education-combining-biologically-inspired-bioinformatics-project-based-learning/

Address of the bookmark: http://www.biotnet.org/training-materials/python-programmers

http://www.csir.res.in/External/Heads/aboutcsir/announcements/ProjectPost_130813.pdf

The bioinforamtics for precision oncology online course provides an opportunity to learn about bioinformatics methods used in precision oncology research and practice. As a subset of precision medicine, precision oncology deals with molecular factors involved in the biological rpocesses that lead to cancer and can help diagnose, treat or prevent this disease. Oncology is driven by data, often times generated using Next Generation Sequencing (NGS) that helps us study the genomic and transcriptomic sub-cellular processes. Learn more and register: https://edu.t-bio.info/bioinformatics-training-precision-oncology/

This project presents demonstrations of selected computer science algorithms important in bioinformatics, implemented in the spreadsheet program Microsoft Excel. Spreadsheets provide an interesting platform for demonstration of algorithms, since various steps of the calculations can be exposed in a manner that is easily comprehensible to users with little programming experience. The algorithms demonstrated include two approaches to approximate string matching (dynamic programming and Shift-AND numeric approximate matching), Hierarchical Clustering (used in phylogenetic studies and microarray analysis of gene expression), a Naive Bayes Classifier for simulated microarray gene expression data, and a simple Neural Network. These demonstrations are designed to serve as instructional aids in bioinformatics courses.

Tutorial @ http://www.cybertory.org/downloads/bae/BioinformaticsAlgorithmsInExcel.zip

One of the best resource for online bioinformatics learning is https://stepic.org/Bioinformatics-Algorithms-2 Enjoy the online learning.

Reference : cybertory

" Please add your favourite bioinformatics algorithms and tutorial links below in the comment section, for the benefit of bioinformatics and computational biology community ". 

Address of the bookmark: http://www.cybertory.org/downloads/bae/BioinformaticsAlgorithmsExcelDoc.pdf