Population genetics: A concise guide. John Gillespie.The Johns Hopkins University Press (1997)•

Population genetics. J. S. Gale. Wiley (1980)•

Evolutionary genetics. John Maynard-Smith. Oxford University Press (1998)•

The growth of biological thought. Ernst Mayr. Harvard University Press (1985)•

Guns, germs and steel. Jared Diamond. W. W. Norton (2007)•

Evolutionary theory: Mathematical and conceptual foundations. Sean Rice. Oxford University Press (2004)

Few places in Europe where good (and @some places no tution fees) master bioinformatics courses and recommended are:

KU Leuven, Belgium

http://onderwijsaanbod.kuleuven.be/opleidingen/e/CQ_50269018.htm

ETH, Zurich

http://www.cbb.ethz.ch/

University of Copenhagen, Denmark

http://studies.ku.dk/masters/bioinformatics/

University of Helsinki, Finland

http://www.cs.helsinki.fi/en/mbi/

Stockholm University, Sweden

http://www.sbc.su.se/masters/

Universities in Netherlands

http://www.nbic.nl/education/msc-programmes/

TUM , Munich Germany

http://www.mastersportal.eu/studies/865/bioinformatics.html

University of Bergen, Norway

http://www.uib.no/en/studieprogram/MAMN-INF/BI/plan

Goethe-University in Frankfurt am Main

http://www.uni-frankfurt.de/58444439/mabi

Other links:

http://www.masterstudies.com/Masters-Degree/Bioinformatics/Europe/

https://studyinsweden.se/programmes/?query=bioinformatics&period=ht-2016&level=ma&subject=natural-science&university=#search

WALK -IN- TEST CUM INTERVIEW

Walk- in- Test cum Interview (based on test) for the selection of Research Associate (Bioinformatics) & Bioinformatic Trainees under the scheme ‘Distributed Information Sub Centre- DISC’ will be held at this Institute as per details indicated below.

Research Associate

Date of Interview : 21 -01-2014 at 10.00 A.M

Qualifications : a) Essential: Doctorate degree in Bioinformatics or Biotechnology/Life Sciences/Biochemistry with expertise in Bioinformatics as evidenced by publications.

OR

Three years research experience after MVSc/MPharm/ME/MTech with Bioinformatics Specialization.

b Desirable: Experience in handling NGS data Programming skills in Python/Bioperl

Emoluments : Rs:22000/- per month + HRA (higher pay upto Rs.24000/- can be paid depending on the qualifications and experience.

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till the closure of the project.

General Terms and conditions

1. The above positions are purely on temporary basis and is co-terminus with the closure of the project. There is no provision of re-employment after termination of project. The selected candidate will not have any right for claiming pay scale or absorption against any regular post being vacant on a later date at this Institute.
2 . No TA/DA will be paid for attending the Interview.
3. Canvassing in any form will lead to cancellation of candidate.
4. The decision of Director, IISR would be final and binding in all aspects.
5. Candidates will not be permitted to enter the Examination Hall after 10.00 A.M.
6. Candidates who secure the minimum marks prescribed by the Institute in written test only will be eligible for calling for the interview. The number of candidates to be called for the interview will be decided by the Director of the Institute.
7 Those who do not possess original Degree/PG certificate or Provisional certificate will not be allowed to attend the Test/Interview.

Note: All relevant certificates (in original) and bio data
No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.
Location of IISR Kozhikode Main Campus - Pallithazham bus stop between Moozhikkal East and Chelavoor on the NH 212 ”Kozhikode - Kollegal” Road.

Advertisement: www.spices.res.in/pdf/DISC-Website.pdf

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/

• 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.

• 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.

awk '/pattern1/ {Actions}
/pattern2/ {Actions}' file

The working of Awk is as follows
Awk reads the input files one line at a time.
For each line, it matches with given pattern in the given order, if matches performs the corresponding action.
If no pattern matches, no action will be performed.
In the above syntax, either search pattern or action are optional, But not both.
If the search pattern is not given, then Awk performs the given actions for each line of the input.
If the action is not given, print all that lines that matches with the given patterns which is the default action.
Empty braces with out any action does nothing. It wont perform default printing operation.
Each statement in Actions should be delimited by semicolon.
Say you have data.tsv with the following contents:

$ cat data/test.tsv
contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2 ACTTTATATATT
contig3 ACTTATATATATATA
contig4 ACTTATATATATATA
contig5 ACTTTATATATT
By default Awk prints every line from the file.

$ awk '{print;}' data/test.tsv
contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2 ACTTTATATATT
contig3 ACTTATATATATATA
contig4 ACTTATATATATATA
contig5 ACTTTATATATT
We print the line which matches the pattern contig3

$ awk '/contig3/' data/test.tsv
contig3 ACTTATATATATATA
Awk has number of builtin variables. For each record i.e line, it splits the record delimited by whitespace character by default and stores it in the $n variables. If the line has 5 words, it will be stored in $1, $2, $3, $4 and $5. $0 represents the whole line. NF is a builtin variable which represents the total number of fields in a record.

$ awk '{print $1","$2;}' data/test.tsv
contig1,ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2,ACTTTATATATT
contig3,ACTTATATATATATA
contig4,ACTTATATATATATA
contig5,ACTTTATATATT

$ awk '{print $1","$NF;}' data/test.tsv
contig1,ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2,ACTTTATATATT
contig3,ACTTATATATATATA
contig4,ACTTATATATATATA
contig5,ACTTTATATATT

Awk has two important patterns which are specified by the keyword called BEGIN and END. The syntax is as follows:

BEGIN { Actions before reading the file}
{Actions for everyline in the file}
END { Actions after reading the file }

For example,
$ awk 'BEGIN{print "Header,Sequence"}{print $1","$2;}END{print "-------"}' data/test.tsv
Header,Sequence
contig1,ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2,ACTTTATATATT
contig3,ACTTATATATATATA
contig4,ACTTATATATATATA
contig5,ACTTTATATATT
-------
We can also use the concept of a conditional operator in print statement of the form print CONDITION ? PRINT_IF_TRUE_TEXT : PRINT_IF_FALSE_TEXT. For example, in the code below, we identify sequences with lengths > 14:

$ awk '{print (length($2)>14) ? $0">14" : $0"<=14";}' data/test.tsv
contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG>14
contig2 ACTTTATATATT<=14
contig3 ACTTATATATATATA>14
contig4 ACTTATATATATATA>14
contig5 ACTTTATATATT<=14
We can also use 1 after the last block {} to print everything (1 is a shorthand notation for {print $0} which becomes {print} as without any argument print will print $0 by default), and within this block, we can change $0, for example to assign the first field to $0 for third line (NR==3), we can use:

$ awk 'NR==3{$0=$1}1' data/test.tsv
contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2 ACTTTATATATT
contig3
contig4 ACTTATATATATATA
contig5 ACTTTATATATT
You can have as many blocks as you want and they will be executed on each line in the order they appear, for example, if we want to print $1 three times (here we are using printf instead of print as the former doesn't put end-of-line character),

$ awk '{printf $1"\t"}{printf $1"\t"}{print $1}' data/test.tsv
contig1 contig1 contig1
contig2 contig2 contig2
contig3 contig3 contig3
contig4 contig4 contig4
contig5 contig5 contig5
Although, we can also skip executing later blocks for a given line by using next keyword:

$ awk '{printf $1"\t"}NR==3{print "";next}{print $1}' data/test.tsv
contig1 contig1
contig2 contig2
contig3
contig4 contig4
contig5 contig5

$ awk 'NR==3{print "";next}{printf $1"\t"}{print $1}' data/test.tsv
contig1 contig1
contig2 contig2

contig4 contig4
contig5 contig5
You can also use getline to load the contents of another file in addition to the one you are reading, for example, in the statement given below, the while loop will load each line from test.tsv into k until no more lines are to be read:

$ awk 'BEGIN{while((getline k <"data/test.tsv")>0) print "BEGIN:"k}{print}' data/test.tsv
BEGIN:contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
BEGIN:contig2 ACTTTATATATT
BEGIN:contig3 ACTTATATATATATA
BEGIN:contig4 ACTTATATATATATA
BEGIN:contig5 ACTTTATATATT
contig1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
contig2 ACTTTATATATT
contig3 ACTTATATATATATA
contig4 ACTTATATATATATA
contig5 ACTTTATATATT
You can also store data in the memory with the syntax VARIABLE_NAME[KEY]=VALUE which you can later use through for (INDEX in VARIABLE_NAME) command:

$ awk '{i[$1]=1}END{for (j in i) print j"<="i[j]}' data/test.tsv
contig1<=1
contig2<=1
contig3<=1
contig4<=1
contig5<=1

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