• -e Makes the line of code be executed instead of a script
• -n Forces your line to be called in a loop. Allows you to take lines from the diamond operator (or stdin)
• -p Forces your line to be called in a loop. Prints $_ at the end

• This counts the number of quotation marks in each line and prints it

  perl -ne '$cnt = tr/"//;print "$cnt\n"' inputFileName.txt

• Adds string to each line, followed by tab

  perl -pe 's/(.*)/string\t$1/' inFile > outFile

• Append a new line to each line

  perl -pe 's//\n/' all.sent.classOnly > all.sent.classOnly.sep

• Replace all occurrences of pattern1 (e.g. [0-9]) with pattern2

  perl -p -i.bak -w -e 's/pattern1/pattern2/g' inputFile

• Go through file and only print words that do not have any uppercase letters.

  perl -ne 'print unless m/[A-Z]/' allWords.txt > allWordsOnlyLowercase.txt

• Go through file, split line at each space and print words one per line.

  perl -ne 'print join("\n", split(/ /,$_));print("\n")' someText.txt > wordsPerLine.txt

• or in other words, delete every character that is not a letter, white space or line end (replace with nothing)

  perl -pne 's/[^a-zA-Z\s]*//g' text_withSpecial.txt > text_lettersOnly.txt

• perl -pne 'tr/[A-Z]/[a-z]/' textWithUpperCase.txt > textwithoutuppercase.txt;

• Print only the second column of the data when using tabular as a separator

  perl -ne '@F = split("\t", $_); print "$F[1]";' columnFileWithTabs.txt > justSecondColumn.txt

• One-Liner: Sort lines by their length

  perl -e 'print sort {length $a <=> length $b} <>' textFile

• One-Liner: Print second column, unless it contains a number

  perl">perl -lane 'print $F[1] unless $F[1] =~ m/[0-9]/' wordCounts.txt

To create single-genome plot by aligning genome data along all chromosomes of a single genome, go to the Single-genome plot menu.

To cretae two-genome plot for comparison of data across two genomes, go to the Two-genome plot menu.

For the detail format of input data, check the Input data format submenu of the Help menu.

shinyChromosome is deployed at http://150.109.59.144:3838/shinyChromosome/, http://shinyChromosome.ncpgr.cn, and https://yimingyu.shinyapps.io/shinyChromosome for online use. The source code and manual of shinyChromosome are freely available at https://github.com/venyao/shinyChromosome.

https://yimingyu.shinyapps.io/shinychromosome/

https://www.sciencedirect.com/science/article/pii/S1672022919301883

Address of the bookmark: https://yimingyu.shinyapps.io/shinychromosome/

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

http://ritg.med.harvard.edu/training/perl/RC_Perl_Intro.pdf

Address of the bookmark: http://cbb.sjtu.edu.cn/course/database/beginning.pdf

Perl one-liners are small and awesome Perl programs that fit in a single line of code and they do one thing really well. These things include changing line spacing, numbering lines, doing calculations, converting and substituting text, deleting and printing certain lines, parsing logs, editing files in-place, doing statistics, carrying out system administration tasks, updating a bunch of files at once, and many more. Perl one-liners will make you the shell warrior. Anything that took you minutes to solve, will now take you seconds!

perl -pe '$\="\n"'   
#double space a file

perl -pe '$_ .= "\n" unless /^$/'
#double space a file except blank lines

perl -pe '$_.="\n"x7'
#7 space in a line.

perl -ne 'print unless /^$/'
#remove all blank lines

perl -lne 'print if length($_) < 20'
#print all lines with length less than 20.

perl -00 -pe ''
#If there are multiple spaces, delete all leaving one(make the file a single spaced file).

perl -00 -pe '$_.="\n"x4'
#Expand single blank lines into 4 consecutive blank lines

perl -pe '$_ = "$. $_"'
#Number all lines in a file

perl -pe '$_ = ++$a." $_" if /./'
#Number only non-empty lines in a file

perl -ne 'print ++$a." $_" if /./'
#Number and print only non-empty lines in a file

perl -pe '$_ = ++$a." $_" if /regex/'
#Number only lines that match a pattern

perl -ne 'print ++$a." $_" if /regex/'
#Number and print only lines that match a pattern

perl -ne 'printf "%-5d %s", $., $_ if /regex/'
#Left align lines with 5 white spaces if matches a pattern (perl -ne 'printf "%-5d %s", $., $_' : for all the lines)

perl -le 'print scalar(grep{/./}<>)'
#prints the total number of non-empty lines in a file

perl -lne '$a++ if /regex/; END {print $a+0}'
#print the total number of lines that matches the pattern

perl -alne 'print scalar @F'
#print the total number fields(words) in each line.

perl -alne '$t += @F; END { print $t}'
#Find total number of words in the file

perl -alne 'map { /regex/ && $t++ } @F; END { print $t }'
#find total number of fields that match the pattern

perl -lne '/regex/ && $t++; END { print $t }'
#Find total number of lines that match a pattern

perl -le '$n = 20; $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $m'
#will calculate the GCD of two numbers.

perl -le '$a = $n = 20; $b = $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $a*$b/$m'
#will calculate lcd of 20 and 35.

perl -le '$n=10; $min=5; $max=15; $, = " "; print map { int(rand($max-$min))+$min } 1..$n'
#Generates 10 random numbers between 5 and 15.

perl -le 'print map { ("a".."z",”0”..”9”)[rand 36] } 1..8'
#Generates a 8 character password from a to z and number 0 – 9.

perl -le 'print map { ("a",”t”,”g”,”c”)[rand 4] } 1..20'
#Generates a 20 nucleotide long random residue.

perl -le 'print "a"x50'
#generate a string of ‘x’ 50 character long

perl -le 'print join ", ", map { ord } split //, "hello world"'
#Will print the ascii value of the string hello world.

perl -le '@ascii = (99, 111, 100, 105, 110, 103); print pack("C*", @ascii)'
#converts ascii values into character strings.

perl -le '@odd = grep {$_ % 2 == 1} 1..100; print "@odd"'
#Generates an array of odd numbers.

perl -le '@even = grep {$_ % 2 == 0} 1..100; print "@even"'
#Generate an array of even numbers

perl -lpe 'y/A-Za-z/N-ZA-Mn-za-m/' file
#Convert the entire file into 13 characters offset(ROT13)

perl -nle 'print uc'
#Convert all text to uppercase:

perl -nle 'print lc'
#Convert text to lowercase:

perl -nle 'print ucfirst lc'
#Convert only first letter of first word to uppercas

perl -ple 'y/A-Za-z/a-zA-Z/'
#Convert upper case to lower case and vice versa

perl -ple 's/(\w+)/\u$1/g'
#Camel Casing

perl -pe 's|\n|\r\n|'
#Convert unix new lines into DOS new lines:

perl -pe 's|\r\n|\n|'
#Convert DOS newlines into unix new line

perl -pe 's|\n|\r|'
#Convert unix newlines into MAC newlines:

perl -pe '/regexp/ && s/foo/bar/'
#Substitute a foo with a bar in a line with a regexp.

Reference/Sources:

http://genomics-array.blogspot.in/2010/11/some-unixperl-oneliners-for.html

http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html

http://biowize.wordpress.com/2012/06/15/command-line-magic-for-your-gene-annotations/

http://genomics-array.blogspot.com/2010/11/some-unixperl-oneliners-for.html

http://bioexpressblog.wordpress.com/2013/04/05/split-multi-fasta-sequence-file/

#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;
    }
}
}

The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.

https://code.google.com/p/biowiki/

https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk

NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&projectsearch=Search+projects

NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&projectsearch=Search+projects

Address of the bookmark: https://code.google.com/hosting/search?q=bioinformatics&sa=Search

You see, for the past couple of years, I’ve been a bit frustrated because OS X does not come with a whole lot of Perl modules pre-installed, and for all I googled, I couldn’t find an “idiot’s” guide for moderately-savvy-but-not-expert users like myself to install modules and dependencies on demand.

The only instructions I could find point to Fink, which basically installs modules in a path that isn’t included in the Perl @INC variable, meaning you have to manually specify the full path to the modules in every script — which is not a lot of fun if you’re developing on OS X and deploying on Red Hat, for instance.

Moreover, Fink doesn’t seem to make every module available, and it’s not very easy to determine which Fink package you need to install if you need a particular module.

So, with a script that called on several apparently unavailable modules, and a deadline looming, I finally decided to suck it up and figure out how to use CPAN to install them:

1) Make sure you have the Apple Developer Tools (XCode) installed.

These are on one of your install discs, or available as a huge but free download from the Apple Developer Connection [free registration required] or the Mac App Store. I thought I had them, but apparently when we upgraded that computer to Tiger, they went missing.

If you don’t have this stuff installed, your installation will fail with errors about unavailable commands.

1.5) Install Command Line Tools (Recent XCode versions only)

(Thank you to Tom Marchioro for informing me about this step.)

Older versions of XCode installed the command line tools (which are required to properly install CPAN modules) by default, but apparently newer ones do not. To check whether you have the command line tools already installed, run the following from the Terminal:

$ which make

This command checks the system for the “make” tool. If it spits out something like /usr/bin/make you’re golden and can skip ahead to Step 2. If you just get a new prompt and no output, you’ll need to install the tools:

1. Launch XCode and bring up the Preferences panel.
2. Click on the Downloads tab
3. Click to install the Command Line Tools

If you like, you can run which make again to confirm that everything’s installed correctly.

2) Configure CPAN.

$ sudo perl -MCPAN -e shell

perl> o conf init

This will prompt you for some settings. You can accept the defaults for almost everything (just hit “return”). The two things you must fill in are the path to make (which should be /usr/bin/make or the value returned when you run which make from the command line) and your choice of CPAN mirrors (which you actually choose don’t really matter, but it won’t let you finish until you select at least one). If you use a proxy or a very restrictive firewall, you may have to configure those settings as well.

If you skip Step 2, you may get errors about make being unavailable.

3) Upgrade CPAN

$ sudo perl -MCPAN -e 'install Bundle::CPAN'

Don’t forget the sudo, or it’ll fail with permissions errors, probably when doing something relatively unimportant like installing man files.

This will spend a long time downloading, testing, and compiling various files and dependencies. Bear with it. It will prompt you a few times about dependencies. You probably want to enter “yes”. I agreed to everything it asked me, and everything turned out fine. YMMV of course. If everything installs properly, it’ll give you an “OK” at the end.

4) Install your modules. For each module….

$ sudo perl -MCPAN -e 'install Bundle::Name'

or

$ sudo perl -MCPAN -e 'install Module::Name'

This will install the module and its dependencies. Nice, eh? Again, don’t forget the sudo.

The first time you run this after upgrading CPAN, it may prompt you to configure again (see Step 2). If you accept its offer to try to configure itself automatically, it may just run through everything without a problem.

There are a couple of potential pitfalls with specific modules (such as theLWP::UserAgent / HEAD issue), but most have workarounds, and I haven’t run into anything that wasn’t easily recoverable.

And that’s it!

Did you find this useful? Is there anything I missed?

Perl script for creating a publication-ready vectorial genetic/linkage map in Scalable Vector Graphics (SVG) format. The resulting file can either be submitted for publication and edited with any vectorial drawing software like Inkscape and Abobe Illustrator(R).

The input file must be a text file with at least the marker name (ID), linkage group (LG) and the position (POS) separeted by tabulations. Additionally a logarithm of odds (LOD score) can be provided. Any extra parameter will be ignored.

map.tsv

ID<tab>LG<tab>POS<tab>LOD
13519  12     0       0.250840894
2718   12     1.0     0.250840893
11040  12     1.6     0.252843341
...

https://github.com/pseudogene/genetic-mapper

Address of the bookmark: https://github.com/pseudogene/genetic-mapper