List of analysis tools developed for Oxford Nanopore data

BWA
Fast nanopore data tuned alignment tool
https://github.com/lh3/bwa

GraphMap
Mapper for long and error-prone reads
https://github.com/isovic/graphmap

LAST
Nanopore tuned alignment tool
http://last.cbrc.jp/

LINKS
Software tool for long read scaffolding
https://github.com/warrenlr/LINKS/

marginAlign
Tools to align nanopore reads to a reference
https://github.com/benedictpaten/marginAlign

minoTour
Real time analysis tools
http://minotour.nottingham.ac.uk/

nanoCORR
Error-correction tool for nanopore sequence data
https://github.com/jgurtowski/nanocorr

NanoOK
Software for nanopore data, quality and error profiles
https://documentation.tgac.ac.uk/display/NANOOK/NanoOK

Nanopolish
Nanopore analysis and genome assembly software
https://github.com/jts/nanopolish

nanopore
Variant-detection tool for nanopore sequence data
https://github.com/mitenjain/nanopore

Nanocorrect
Error-correction tool for nanopore sequence data
https://github.com/jts/nanocorrect/

npReader
Real-time conversion and analysis of nanopore reads
https://github.com/mdcao/npReader

poRe
Tool for analyzing and visualizing nanopore data
https://sourceforge.net/p/rpore/wiki/Home/

PoreSeq
Error-correction and variant-calling software
https://github.com/tszalay/poreseq

Poretools
Nanopore sequence analysis and visualization software
https://github.com/arq5x/poretools

SSPACE-LongRead
Genome scaffolding tool
http://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE-longread

SMIS
Genome scaffolding tool
https://sourceforge.net/projects/phusion2/files/smis/

List of assemblers for Oxford Nanopore MinION long reads

LQS
DALIGNER, Celera OLC Nanocorrect,
Nanopolish corrector
https://github.com/jts/nanopolish

PBcR
HGAP or BLASR, Celera OLC
PBcR corrector
http://wgs-assembler.sourceforge.net/wiki/index.php/PBcR
–
Canu
MHAP, Celera OLC
Canu corrector
https://github.com/marbl/canu

Falcon
String graph, Celera OLC
Falcon corrector
https://github.com/PacificBiosciences/falcon

Miniasm
OLC
https://github.com/lh3/miniasm

ra-integrate
OLC
https://github.com/mariokostelac/ra-integrate/

ALLPATHS-LG
de Bruijn graph
ALLPATHS-L corrector
https://www.broadinstitute.org/software/allpaths-lg/blog/?page_id=12

SPAdes
de Bruijn graph
SPAdes corrector
http://bioinf.spbau.ru/spades

1. Accelrys Software Solution Pvt Ltd.
12th Floor, Discover, ITPL, White Field, Bangalore-65.
www.accelrys.com

2. Apticraft Systems (P) Ltd.
142, Electronics Complex, Pardeshipura, Indore – 452010 (M.P.), India
www.apticraft.com

3. Aptuit Informatics
Plot No. 100-103, Export Promotion Industrial Park, White Field, Bangalore-560066
www.aptiuit.com

4. Bigtec
J. K. Towers, 8th Block, Sangam Circle,46th Cross, Bangalore-560082.
www.bigtec.org

5. Bijam Biosciences Private Limited
Nagarjuna Hills, Hyderabad 500 082, India
www.nagarjunagroup.com

6. Bio Base Databases India Pvt Ltd.
Crescent Towers, 4th Floor, No : 32/1, Crescent Road, Bnagalore – 560 001
www.biobase-international.com

7. BioImagene India Pvt. Ltd.
4th floor, C-Wing, Godrej Eternia, Shivajinagar, Pune-411005
www.bioimagene.com

8. BioInformatics Institute Of India – Noida
C-56 A/28, Sector -62, Noida – 201 301
www.bii.in

9. CLC bio India Pvt Ltd
#Plot No. 51, H.No. 8-3-214/51, Srinivasa Nagar (West) Ameerpet Hyderabad – 500 038
www.clcbio.com/india

10. CytoGenomics India (P) Ltd.
#3004, 12A Main HAL 2nd Stage, Bangalore 560008
www.silicocyte.com

11. Genotypic Technology
211, 6th Cross, 80ft Road, RMV II Stage, Bangalore 560094
www.genotypic.co.in

12. Genvea Biosciences
Dr. D. T. Singh, CSO, 53, Craig Rd. #04-01, Singapore-089691
www.genvea.com

13. Helix Info Systems
132 A, II Floor, Sterling Towers, IV Cross Street, Sterling Road, Nungambakkam, Chennai.
www.helixinfosystems.com

14. Jalaja Technologies Pvt. Ltd.,
21/1,Victoria Layout, Victoria Road, Bangalore-47
www.jalaja.com

15. Jubilant Biosys Ltd
#96, Industrial Subrub, 2nd Stage, Yeshwanthpur, Bangalore- 560022
Jubilant Organosys Ltd.
1A, Sector 16A, Noida – 201 301 (India)
www.jubl.com

16. Kshema Technologies
#1, Global Village, Mylasandra, Mysore Road, Bangalore-560 059.
www.mphasis.com

17. LabNetworx
B-704, Gitanjali Apartments, Vikas Marg Extension, New Delhi – 110 092
www.labnetworx.com

18. LabVantage Solutions Pvt. Ltd.
Bengal Intelligent Park, Building C, 2nd Floor, Sector V, Salt Lake Electronics Complex, Kolkata – 700 091
www.labvantage.com

19. LeadInvent, 
2nd Floor, Biotech Centre, University of Delhi, South Campus, Benito Juarez Road, New Delhi 110021, India
Contact no: +91 11 24119241
Email: contact@leadinvent.com
www.leadinvent.com

20. Mascon Life Sciences
B – 8/ 10, Vasant Vihar, New Delhi 110057, India
www.masconlifesciences.com

21. Molecular Connections P Ltd
Kandala Mansion, 2/2 Kariappa Road, Near Krishna Rao Park, Basavangudi, Bangalore – 4
www.molecularconnections.com

22.Novo Informatics Pvt. Ltd.
TBIU, 2nd Floor, Synergy Building, Indian Institute of Technology, Hauz Khas, New Delhi-16.
Contact: 91-11-26581524, 91-11-26581766(Extension: 28)
Email: info@novoinformatics.com
www.novoinformatics.com

23. Ocimum Biosolutions (India) Ltd
6th Floor, Reliance Classic, Road No.1 Banjara Hills, Hyderabad 500 034, India.
www.ocimumbio.com

24. Scube Scientific Software Solutions
613, Hemkunt Chambers, 89, Nehru Place, New Delhi -110 019
www.scribeindia.com

25. Siri Technologies Pvt Ltd.
38/C -23, South End Road, Basavanagudi, Bangalore-56004.
www.siritech.com

26. Strand Life Sciences Pvt. Ltd.
#237, Sir C. V. Raman Avenue, Raj Mahal Vilas, Bangalore 560 080 INDIA
www.strandls.com

27. SooryaKiran Bioinformatics (P) Ltd
TBIC-13, Tejaswini Building, Technopark, Thriruvananthapuram- 695 584, Keralam, India

Ph: +91 471 4060979,+91 9895404104
Email: reachus@sooryakiran.com
http://www.sooryakiran.com

28. Systat Software Asia Pacific
4th Floor, Block 1, Shankar Narayan Building, No.25, MG Road, Bangalore – 560001
www.systat.com

29. ABC Genomics (India) Pvt. Ltd.
Biotech Park, Sector G, Jankipuram, Kursi Road, Lucknow-226021, U.P., INDIA
Tel +91-522-4068579, Email: director@abcgenomics.com
www.abcgenomics.com

30. en-GENE-ier's Core Technology Services,
1/340, Virat Khand, Gomtinagar, 
(Near Maharaja Agrasen Public School)
lucknow-226010, U.P., India.
http://www.bio.egicore.com/

Best of luck for your job hunts :).

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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

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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.

More @ http://www.au-kbc.org/