Address of the bookmark: http://pbil.univ-lyon1.fr/software/DeCoSTAR/

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data

Today, refreshing my old memories I decided to blog about the basic knowledge of biochemistry and computational proteomics skills, but after I found several article on internet saying exactly what I had wanted to say I thought I might as well just redirect BOL's blog readers there instead:

Here is the list of website and videos links which provide a good resource for you basic chemistry need:

http://tecreativ.blogspot.co.uk/2012/09/funny-shortcut-remember-periodic-table.html

This blog have some specific hindi word to remember entire periodic table. I really like

Group 14 (C Si Ge Sn Pb) -> Sentence “Chemistry Sir Gives Sanki Problems”

Sanki is a hindi word which mean crazy :P

I found this link useful as well http://www.wikihow.com/Memorise-the-Periodic-Table

The eagle genomics group provide an element of bioinformatics in periodic tables. Yes you got it, this is not periodic table rather bioinformatics tools with periodicals

http://elements.eaglegenomics.com/

You can also try this video links, which provide you an overview with tricks on periodic tables:

http://www.youtube.com/watch?v=fLSfgNxoVGk

http://www.youtube.com/user/periodicvideos

For drug design educational material, software, tools, databses, viewer, file format and many more stuff at one place http://www.allfordrugs.com/drug-design/ I highly recommend you all computational drug designer to bookmark this page for future studies as well.

I just remember one of my mini project in which I use my flash knowledge (flash .. oh ya flash) to explain amino acids in interactive and user friendly manner. I can’t provide It right now, but promise you to provide a link in near future. I hope that you will enjoy my flashy creative skills :).

Moreover, I found some of very interesting tricks to remember all amino acids chemical formulae on youtube at

http://www.youtube.com/watch?v=gqrWb0fmzQ&list=PL6132651E70BB5575

http://www.youtube.com/watch?v=C2GfoGXfySQ&list=PL6132651E70BB5575

Key points for computer added drug designers?
1. A shortage of biochemistry skills means that you absolutely nowhere in understanding the key concept and do research.
2. Keep handy with complex mathematical formula, before merely running tools or software.
3. Dig it better and deeper guys .. design it.

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. You can find a more detailed description of the new version of the pipeline in NCBI Handbook chapter. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Kindly log on to conference website http://rgcb.res.in/IACR2014 for further details and timely updates and registration. We shall truly appreciate if the same be circulated among your friends, scholars and students encouraging them to participate in the meet.

http://210.212.237.38/iacrconference/

Software; Creation of Public Domain Resources in Biology
Present Status::

Developing prediction methods for gene, beta-turn, secondary structure and MHC-binding sites.
Area of Interest ::

Comparison of force field simulations. Analysis of DNA-protein interactions using molecular mechanics methods.Drug Target Identification using in silico biology.

More @ http://www.imtech.res.in/bic/index.php?option=com_content&view=article&id=65

PIs: http://www.imtech.res.in/bic/index.php?option=com_content&view=article&id=69

sed ':a;N;$!ba;s/\n//g' filename.txt > newfilename_oneline.txt

To get average for a column of numbers (here the second column $2):

awk '{ sum += $2; n++ } END { if (n > 0) print sum / n; }'

To get sequence length for all sequences in a fasta file:

awk '/^>/ {if (seqlen){print seqlen}; print ;seqlen=0;next; } { seqlen = seqlen +length($0)}END{print seqlen}' \
filename.fasta

To copy (move, rename, etc) files based on their list in a text file:

cat file_list.txt | while read line; do cp "$line" complete_dataset/"$line"; done

To split bam files into sets with mapped and unmapped reads:

samtools view -F4 sample.bam > sample.mapped.sam
samtools view -f4 sample.bam > sample.unmapped.sam

To gzip all your fastq files using gnu parallel and gzip:

parallel gzip ::: *.fastq

To gzip all your fastq files using pigz:

pigz *.fastq

To count all sequences in a fasta file:

grep "^>" yourfile.fasta -c

To count all sequences in all fasta files in your current directory:

for a in *.fasta; do ls $a; grep "^>" -c $a; done

To keep only one copy of duplicated lines:

awk '!seen[$0]++'

To sum assembly size from SPAdes contigs.fasta or scaffolds.fasta file:

grep "^>" scaffolds.fasta | cut -f 4 -d '_' | paste -sd+ | bc

To remove everything after the first space at each line, e.g. to to simplify fasta headers:

cut -d' ' -f1 < your_file

To count reads in a all .fastq.gz files in your current folder (fast, using gnu parallel):

parallel "echo {} && gunzip -c {} | wc -l | awk '{d=\$1; print d/4;}'" ::: *.gz

To count reads in a all .fastq.gz files in your current folder:

zcat *.gz | echo $((`wc -l`/4))

To count reads in a all .fastq files in your current folder:

cat *.fastq | echo $((`wc -l`/4))

To count base pairs in a all .fastq.gz files in your current folder:

zcat *.fastq.gz | paste - - - - | cut -f 2 | tr -d '\n' | wc -c 

To split multifasta file into many fasta files:

awk '/^>/ {OUT=substr($0,2) ".fa"}; {print >> OUT; close(OUT)}' Input_File

To convert Illumina FASTQ 1.3 to 1.8:

sed -e '4~4y/@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghi/!"#$%&'\''()*+,-.\/0123456789:;<=>?@ABCDEFGHIJ/' f.fastq

To convert FASTQ to FASTA:

sed -n '1~4s/^@/>/p;2~4p' 

To get fastq read length distribution:

cat reads.fastq | awk '{if(NR%4==2) print length($1)}' | sort | uniq -c

To deinterleave interleaved fastq file:

cat myf.fq | paste - - - - - - - - | tee >(cut -f 1-4 | tr "\t" "\n" > myfile_1.fq) | cut -f 5-8 | \
tr "\t" "\n" > myf2.fq 

To filter and sort contig identifiers from SPAdes assembly (e.g. here lenght >= 4000 + coverage >=100):

grep "^>" scaffolds.fasta | sed s"/_/ /"g | awk '{ if ($4 >= 4000 && $6 >= 100) print $0 }' | sort -k 4 -n | \
sed s"/ /_/"g

To append something to all headers of your fasta files:

sed 's/>.*/&YOURSTRING/' filename.fasta > new_filename.fasta

To replace/squeeze multiple adjacent spaces by only one space: 

tr -s " " < file

To filter fastq based on length (here larger than or equal to 21, but smaller than or equal to 25.

cat your.fastq | paste - - - - | awk 'length($2)  >= 21 && length($2) <= 25' | sed 's/\t/\n/g' > filtered.fastq

To print difference between the last and first row in 5th column:

awk '{if (!first){first=$5;}; last=$5;} END {print last-first}' myfile.txt

To sample only 200 first bases from all sequences in a multifasta file (e.g. from assembly scaffolds.fasta file here):

awk '/^>/{ seqlen=0; print; next; } seqlen < 200 { if (seqlen + length($0) > 200) $0 = substr($0, 1, 200-seqlen);\
 seqlen += length($0); print }' scaffolds.fasta > 200bp_scaffolds.fasta

 To pipe a compressed fasta file directly into makeblastdb.

gunzip -c fasta.gz | makeblastdb -in -

To remove sequences with duplicate fasta headers from a fasta file.

awk '/^>/{f=!d[$1];d[$1]=1}f' in.fasta > out.fasta

Dated : 22 Nov 2013 -23 Nov 2013

http://www.ngsasia-congress.com/

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.

The Taverna suite is written in Java and includes the Taverna Engine(used for enacting workflows) that powers both the Taverna Workbench(the desktop client application) and the Taverna Server (which allows remote execution of workflows). Taverna is also available as a Command Line Tool for a quick execution of workflows from a terminal.

Address of the bookmark: http://www.taverna.org.uk/