Address of the bookmark: http://www.rdatamining.com/

- Budapest Reference Connectome: View a parametrizable connectome (brain graph).
- AmphoraNet: The webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data.
- AmphoraVizu: Chart visualization for metagenomics analysis tools AMPHORA2 and AmphoraNet.
- SCARF: Free online association rule mining tool.

More at: http://pitgroup.org

Awk is a programming language that is specifically designed for quickly manipulating space delimited data. Although you can achieve all its functionality with Perl, awk is simpler in many practical cases.

Why awk? You can replace a pipeline of 'stuff | grep | sed | cut...' with a single call to awk. For a simple script, most of the timelag is in loading these apps into memory, and it's much faster to do it all with one. This is ideal for something like an openbox pipe menu where you want to generate something on the fly. You can use awk to make a neat one-liner for some quick job in the terminal, or build an awk section into a shell script. You can find a lot of online tutorials, but here I will only show a few examples which cover most of bioinformatician daily uses of awk.

choose rows where column 3 is larger than column 5:

awk '$3>$5' input.txt > output.txt

extract column 2,4,5:

awk '{print $2,$4,$5}' input.txt > output.txt

awk 'BEGIN{OFS="\t"}{print $2,$4,$5}' input.txt

show rows between 20th and 80th:

awk 'NR>=20&&NR<=80' input.txt > output.txt

calculate the average of column 2:

awk '{x+=$2}END{print x/NR}' input.txt

regex (egrep):

awk '/^test[0-9]+/' input.txt

calculate the sum of column 2 and 3 and put it at the end of a row or replace the first column:

awk '{print $0,$2+$3}' input.txt

awk '{$1=$2+$3;print}' input.txt

join two files on column 1:

awk 'BEGIN{while((getline<"file1.txt")>0)l[$1]=$0}$1 in l{print $0"\t"l[$1]}' file2.txt > output.txt

count number of occurrence of column 2 (uniq -c):

awk '{l[$2]++}END{for (x in l) print x,l[x]}' input.txt

apply "uniq" on column 2, only printing the first occurrence (uniq):

awk '!($2 in l){print;l[$2]=1}' input.txt

count different words (wc):

awk '{for(i=1;i!=NF;++i)c[$i]++}END{for (x in c) print x,c[x]}' input.txt

deal with simple CSV:

awk -F, '{print $1,$2}'

substitution (sed is simpler in this case):

awk '{sub(/test/, "no", $0);print}' input.txt

OK now here's where to read this stuff properly explained. roll

Two thorough tutorials:

http://www.gnu.org/software/gawk/manual/gawk.html

http://www.grymoire.com/Unix/Awk.html

A famous list of useful one-liners - though they're short, many are quite tricky:

http://www.pement.org/awk/awk1line.txt

And some nice explanations of those one-liners. After reading this you'll have a pretty good grasp!

http://www.catonmat.net/blog/awk-one-li … -part-one/

http://www.catonmat.net/blog/ten-awk-ti … -pitfalls/

Source : https://www.coursera.org/course/ads1

Address of the bookmark: https://www.coursera.org/course/ads1

(Indian Council of Medical Research )
Jehangir Merwanji Street, Parel, Mumbai 400 012

Advertisement No. 1/NIRRH/Ph.D. 2013
Admission to Ph.D. Programme – 2013

National Institute for Research in Reproductive Health, Mumbai, a premier institute of the Indian Council of Medical Research, conducts basic, clinical and operational research in different areas of reproductive health. The thrust areas of research include: Fertility Regulation, Infertility and Reproductive Disorders, Reproductive Tract Infections, Maternal and Child Health, Osteoporosis, Genetic Disorders, Stem Cell Biology, Structural Biology, Bioinformatics and Reproductive Toxicology. Institute is affiliated to the University of Mumbai for the award of Ph.D. degree in Applied Biology, Biochemistry, Life Sciences and Biotechnology. The institute invites applications from young and bright students for enrollment in Ph.D. programme.

More at http://www.nirrh.res.in/announcements/phd_program_2013.htm

Dr. Rawal and his US based collaborators have recently secured grant for development of vaccine against an infectious disease agent. For this project, applications are invited for the posts of Post Doctoral Fellow/Research Scientist (One Position) for the following time-bound sponsored projects as per the details given below:

PI: Dr. Kamal Rawal, Biotechnology Department, JIIT, Noida.

Essential Qualification(s) for Post Doctoral Fellow/ Research Scientist:

We are seeking an individual with expertise in analyzing literature information, text mining, network biology, data integration, and modeling. Competitive candidates would also have programming experience in scripting languages with perl, C, C++, and R programming. This position requires a PhD in Computational Biology, Bioinformatics, Biostatistics, Physics or related fields, and evidence of scientific productivity through publications in international journals. Motivation to gain an in-depth understanding of biological phenomena is required. Applications should include a current CV and names of at least three references. Application packages and inquiries regarding this position can be sent to Dr. Kamal Rawal (bioinfocvatgmaildotcom and kamaldotrawalatgmaildotcom). Screening of applications will commence immediately and the position will remain open until filled. Candidates having master’s degree with extensive experience in IT industry or research can also be considered for this post.

Salary: Rs 50000 per month.

Duration: 2 years or upto the project duration.

Number of position: 1

Candidate may also fill the following form:

https://docs.google.com/…/1FAIpQLSdZoZ21ZoNRStEeL5…/viewform

http://tinyurl.com/bioinfocv2017

More at >> http://www.baumbachlab.net/

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

• 20 Python Libraries You Aren't Using (But Should) (Just fill the fields with any values)
• A Beginner's Python Tutorial
• A Byte of Python (3.x) (HTML, PDF, EPUB, Mobi)
• A Guide to Python's Magic Methods - Rafe Kettler
• A Whirlwind Tour of Python - Jake VanderPlas (PDF) (EPUB, MOBI)
• Automate the Boring Stuff - Al Sweigart
• Biopython (PDF)
• Build applications in Python the antitextbook (3.x) (HTML, PDF, EPUB, Mobi)
• Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho, Packt. (Just fill the fields with any values)
• Building Skills in Object-Oriented Design (Python) (PDF) (2.1.1)
• Building Skills in Python (PDF) (2.6)
• Code Like a Pythonista: Idiomatic Python
• CodeCademy Python
• Composing Programs (3.x)
• Data Structures and Algorithms in Python - B. R. Preiss (PDF)
• Dive into Python 3 - Mark Pilgrim (3.0)
  • Dive into Python - Mark Pilgrim (2.3)
• From Python to NumPy
• Full Stack Python
• Functional Programming in Python (Just fill the fields with any values)
• Fundamentals of Python Programming - Richard L. Halterman (PDF) (3.2)
• Google's Python Class (2.4 - 2.x)
• Google's Python Style Guide
• Hacking Secret Cyphers with Python - Al Sweigart (3.3)
• Hadoop with Python (Just fill the fields with any values)
• High Performance Python (PDF)
• Hitchhiker's Guide to Python! (2.6)
• How to Make Mistakes in Python - Mike Pirnat (PDF) (1st edition)
• How to Think Like a Computer Scientist: Learning with Python, Interactive Edition (3.2)
  • How to Think Like a Computer Scientist: Learning with Python - Allen B. Downey, Jeff Elkner and Chris Meyers (2.4)
  • Think Python - Allen B. Downey (2.x & 3.0)
• Intermediate Python - Muhammad Yasoob Ullah Khalid (1st edition)
• Introduction to Programming with Python (3.3)
  • Introduction to Programming Using Python - Cody Jackson (1st edition) (2.3)
• Introduction to Python - Kracekumar (2.7.3)
• Invent Your Own Computer Games With Python - Al Sweigart (3.1)
• Learn Python, Break Python
• Learn Python in Y minutes
• Learn Python The Hard Way (2.5 - 2.6)
• Learn to Program Using Python - Cody Jackson (PDF)
• Learning Python - Fabrizio Romano, Packt. (Just fill the fields with any values)
• Learning to Program
• Lectures on scientific computing with python - J.R. Johansson (2.7)
• Making Games with Python & Pygame - Al Sweigart (2.7)
• Modeling Creativity: Case Studies in Python - Tom D. De Smedt (PDF)
• Natural Language Processing with Python (3.x)
• Non-Programmer's Tutorial for Python 3 (3.3)
  • Non-Programmer's Tutorial for Python 2.6 (2.6)
• Picking a Python Version: A Manifesto (Just fill the fields with any values)
• Porting to Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
• Practical Programming in Python - Jeffrey Elkner (PDF)
• Problem Solving with Algorithms and Data Structures using Python - Bradley N. Miller and David L. Ranum
• Program Arcade Games With Python And Pygame (3.3)
• Programming Computer Vision with Python (PDF)
• Python 2 Official Documentation (PDF, HTML, TEXT) (2.x)
• Python 2.7 quick reference - New Mexico Tech (2.7)
• Python 3 Official Documentation (PDF, EPUB, HTML, TEXT) (3.x)
• Python Cookbook - David Beazley
• Python Data Science Handbook - Jake VanderPlas (HTML, Jupyter Notebooks)
• Python for Econometrics - Kevin Sheppard (PDF) (2.7.5)
• Python for Everybody Exploring Data Using Python 3 - Charles Severance (PDF, EPUB, HTML)
  • Python for Informatics: Exploring Information (2.7.5)
• Python for you and me (2.7.3)
• Python for you and me (3.x)
• Python Idioms (PDF)
• Python in Education (Just fill the fields with any values)
• Python in Hydrology - Sat Kumar Tomer
• Python Koans (2.7 or 3.x)
• Python Module of the Week (3.x)
  • Python Module of the Week (2.x)
• Python Notes for Professionals - Compiled from StackOverflow documentation (3.x)
• Python Practice Book (2.7.1)
• Python Practice Projects
• Python Programming (PDF) (2.6)
• Scipy Lecture Notes
• SICP in Python (3.2)
• Snake Wrangling For Kids (3.x)
• Suporting Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
• Test-Driven Web Development with Python (3.3 - 3.x)
• Text Processing in Python - David Mertz (2.3 - 2.x)
• The Coder's Apprentice: Learning Programming with Python 3 - Pieter Spronck (PDF) (3.x)
• The Definitive Guide to Jython, Python for the Java Platform - Josh Juneau, Jim Baker, Victor Ng, Leo Soto, Frank Wierzbicki (2.5)
• The Little Book of Python Anti-Patterns (Source)
• The Programming Historian - William J. Turkel, Adam Crymble and Alan MacEachern
• The Python Ecosystem: An Introduction
• The Python GTK+ 3 Tutorial
• The Standard Python Library - Fredrik Lundh
• Think Complexity - Allen B. Downey (2nd Edition) (PDF, HTML)
• Web2py: Complete Reference Manual, 6th Edition (pre-release) (2.5 - 2.x)
• Wikibooks: Python Programming (2.7)

To cite BioNumbers please refer to: Milo et al. Nucl. Acids Res. (2010) 38: D750-D753. When using a specific entry from the database it is highly recommended that you also specify the BioNumbers 6 digit ID, e.g. "BNID 100986, Milo et al 2010". 

Address of the bookmark: http://bionumbers.hms.harvard.edu/

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.