Date : June 10-13

Organizers: The Society for Bioinformatics in Northern European countries (SocBiN) and the Norwegian Bioinformatics Platform / ELIXIR.NO

Venue: Department of Informatics, University of Oslo, Norway

Topics:
Tools and technologies for integrative bioinformatics
Metagenomics
Comparative genomics and phylogeny
Post-ENCODE bioinformatics
Gene regulation
Cancer genomes
Marine genomics

CGHub
https://cghub.ucsc.edu/
Comprehensive data repository; huge data size

EGA
https://www.ebi.ac.uk/ega/
Comprehensive data repository; huge data size

COSMIC
http://cancer.sanger.ac.uk
Largest somatic mutation database; genome sequencing paper curation

CPRG
http://www.broadinstitute.org/software/cprg
Interface for cancer program resources

GDAC
http://gdac.broadinstitute.org/
Data analysis; automatic pipelines; user-friendly reports

SNP500Cancer
http://snp500cancer.nci.nih.gov
Sequence and genotype verification of SNPs

canEvolve
www.canevolve.org/
Comprehensive analysis of tumor profile; Data from 90 studies involving more than 10,000 patients

MethyCancer
http://methycancer.psych.ac.cn
Relationship among DNA methylation, gene expression and cancer

SomamiR
http://compbio.uthsc.edu/SomamiR/
Correlation between somatic mutation and microRNA; genome-wide displaying

cBioPortal
http://www.cbioportal.org/public-portal/
Graphical summaries; gene alteration; processed data; visualization

UCSC Cancer Genomics Browser
https://genome-cancer.soe.ucsc.edu/
Clinical information; gene expression; copy number variation; visualization

CGWB
https://cgwb.nci.nih.gov/
Visualization; gene mutation and variation; automated analysis pipeline

GDSC
http://www.cancerrxgene.org
Drug sensitivity information; drug response information

canSAR
https://cansar.icr.ac.uk/
Multidisciplinary information; drug discovery

NONCODE
http://www.noncode.org/ ncRNAs;
lncRNAs; up-to-date and comprehensive resource

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

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

• It's widely used in the scientific community
• It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
• It lend itself well to being integrated with other, existing tools
• It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

• Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
• Python is dynamically typed, so the variables types are defined automatically.
• Python is strongly typed. So the developers need to cast the type manually.
• Less code and more use makes it more acceptable.
• Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

More at http://www.mathivananlab.org/index.html

http://scholar.google.com/citations?user=U6PyEdYAAAAJ&hl=en

Future projects and goals

Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.
DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.
Cancer genomics, genomes as biomarkers, cancer phylogeny.
Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).

More @ http://www.embl.de/research/units/genome_biology/huber/index.html