Many times in bioinformatics we need to deal with huge datasets which  are more than 100GB size. The traditional way to analysis a file is using the while loop

while (FILE){

Do something;

}

This is very slow(since we are using only one processor) and if we have 500 million lines in the dataset it takes more than a day to iterate through the whole dataset. So how do we make best use of all our processors and get the work done quickly?

Here is a very simple and efficient technique with perl which i have been using. I am  more inclined towards using perl fork than perl threads.

One of the oldest way to fork is

my $fork = fork();
if($fork){   
push (@childs,$fork); 
}
elseif($fork==0){
your code here;
exit(0);
}
else{die “Couldnt fork : $!”;}

## wait for the child process to finish
foreach(@childs){
my $tmp=waitid($_,0);
}

what a fork does is it creates a child process and takes the variables and code with it to analyze it separately (detached from the parent process) and thus a separate process is created( which usually runs on a separate processor). Thats it!! One big disadvantage of forking is its very difficult to share variables among the different processes. I will show you how to do it easily but still it has its own drawbacks.

Okie, now if you really do not want to use fork in your code, that’s okie too..There are many useful modules which do it for you very efficiently. One really useful module is Parallel::ForkManager. You can use Parallel::ForkManager to manage the number of forks you want to generate (number of processors you want to use).

Simple usage:
use Parallel::ForkManager;
my $max_processors=8;
my $fork= new Parallel::ForkManager($max_processors);
foreach (@dna) {
$fork->start and next; # do the fork
you code here;
$fork->finish; # do the exit in the child process
}
$pm->wait_all_children;

so you will be generating 8 forks which do the same thing for your each element of array. when one child finishes, Parallel::ForkManager generates a new one and thus you will be using all your processors to analyze the data. Now, if you have generated 8 child processes and want to write the data to one file. You need to lock the file to do this, because you will have problems with the buffering. You can lock the file using flock command.

open (my $QUAL, “myfile.txt”);
flock $QUAL, LOCK_EX or die “cant lock file $!”;
print $QUAL “$output”;
flock $QUAL, LOCK_UN or die “$!”;
close $QUAL;

I would not suggest using flock when dealing with multiple processes because it will decrease the processing efficiency( each child process must wait for the lock to be released by the other child process). Instead, I would suggest each fork writing to a separate file and after the processing just concatenating them.

Putting it all together, If you have 100GB data you can do this

step 1 : split the dataset equally according to number of processors you have. this may take a few hours(about 2-3 hrs for 100GB file)
You can use unix “split” command for this
for example:
my $number_split=int($number_of_entries_in_your_dataset/$max_processors);
my $split_Files=`split -l $number_split “your_file.fasta” “file_name”`;

step2: open you directory comtaining you split files and start Parallel::ForkManager.
For example:
opendir(DIRECTORY, $split_files_directory) or die $!; ### open the directory
my $fork= new Parallel::ForkManager($max_processors);
while (my $file = readdir(DIRECTORY)) { ### read the directory
if($file=~/^\./){next;}
print $file,”\n”;
########## Start fork ##########
my $pid= $super_fork->start and next;
Whatever you want to do with the split file ;
analyze my piece of $file;
######### end fork ###############
$super_fork->finish;
}
$super_fork->wait_all_children;

So basically each processor will be active with its piece of data (split file) and thus you have created 8 processes at one time which run without interfering with the other process. I again will not suggest writing output from each child process to one file(for reasons above). Write output from each fork to a separate file and finally concatenate them. Thats it, you have just increased your program speed by 8 times!! Isnt it easy?

Note:
You may worry about concatenation of the output each child generates, since it does take some time(remember 100GB). I think now you can use a mysql database LOAD DATA LOCAL INFILE command to load all the files into a single table(Should take about 3hrs for 100Gb dataset) and then export the whole table into one file. This should be faster than just concatenating them using “cat” command.(correct me if I am wrong)

Or much simpler way is to use pipes

cat output_dir/* | my_pipe or my_pipe <(file1) final_file;

Thats it guys!! Enjoy programming and please do comment. I am not a computer scientist so forgive me for any mistakes and if any please report them. Thank you.

Requirements
- 3-5 years of relevant bioinformatics experience such as public data mining,
processing, analysing and visualising omics data, etc.
- Ph.D., Masters or Bachelors in Bioinformatics, Biotechnology,
Computational Biology, or related field.
- Understanding of molecular biology and biochemistry.
- Comfort and experience with biological research and data.
- Proficient in a programming language used for bioinformatics such as R or
python.
- Excellent communication skills.
- Ability to summarise and simplify complex analyses for a non-technical
audience.
- Strong analytical skills, curiosity and a knack to solve difficult problems.
- Work well in multi-disciplinary teams with people of vastly different
backgrounds.
- Demonstrated success in collaboration and independent work.

More at https://angel.co/elucidata/jobs/460104-senior-bioinformatics-scientist

Also, Bioinformatics Industrial Training Program (BIITP) is merged with the HRD Biotechnology Industrial Training Programme (BITP).

While this comes as a surprise for a lot of participants. We believe this is a good attempt to unify and create a national benchmark for talent. And we appreciate this endeavor from Department of biotechnology.

However, such last-minute announcements can create confusion. Thus candidates are advised to go through the complete notification DBT-BET JRF 2019 via the link below.If you have any kind of doubts, you must contact DBT JRF or Biotecnika for any kind of help & assistance.

Attention:-Bioinformatics Programs (BINC and BIITP)

1. Bioinformatics National Certification (BINC) has been merged with DBT-Junior
Research Fellow (BET Exam)

2. Bioinformatics Industrial Training Program (BIITP) is merged with HRDBiotechnology Industrial Training Programme (BITP).

Students of Bioinformatics, who are interested to apply for Fellowship or Industrial
Training may keep track of the advertisement of DBT-JRF (BET Exam) and BITP
of DBT.

 More at http://www.bcil.nic.in/files/Attention_Bioinformatics_Programs_(BINC_and_BIITP).pdf

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 position is a full-time position for a fixed term of two years. The salary depends on the successful candidate’s qualifications and experience.

For more information, please contact Prof. Dirk Repsilber,This is an email address, Prof. Hugo Hesser, This is an email addressor Prof. Allan Sirsjö, allan.This is an email address, or Prof. Robert Brummer,This is an email address.

Örebro University actively pursues an equal work environment and values the qualities that diversity adds to our operations.

More detail at https://www.oru.se/english/working-at-orebro-university/jobs-and-vacancies/job/?jid=20200286/

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

4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

Descriptive paper @ http://www.biomedcentral.com/1471-2105/14/243

Image source: BMC Bioinformatics

Post I

Name of the Post: Scientist-E (Non-Medical)

Number of positions: One

Upper Age limit: 50 years

Post II

Name of the Post: Scientist-D (Non-Medical)

Number of positions: Six

Upper Age limit: 45 years

Fee:

Application Fee of Rs. 1500/- (Rupees one thousand five hundred only) is needed. SC / ST / Women/ PWD/ EWS applicants are exempted from application fee. Application Fee is to be paid by candidates through online web link given up the application. Application fees when paid will certainly not be reimbursed under any situations.

How to apply:

i) Candidates should apply online on https://recruit.icmr.org.in. A separate application needs to be submitted for every post, with the required application fee.

ii) Following self-attested documents are required to be uploaded together with the application:
a) Proof of Date of Birth.
b) Educational qualifications.
c) Experience.

More at https://recruit.icmr.org.in/assets/uploads/advertisement/ICMR_Advertisement_06112020.pdf