For more information visit: http://www.snubi.org/TBC2013/

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

http://biopython.org/DIST/docs/tutorial/Tutorial.html

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)

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.

http://www.interpretomics.co/

Address of the bookmark: https://github.com/carolzhou/multiPhATE

Center for Integrative Genomics, University of Lausanne, Switzerland

Two postdoctoral positions (2 years with possible extensions up to 5 years) are available immediately in the evolutionary genomics group of Henrik Kaessmann.

We are seeking highly qualified and enthusiastic applicants with strong skills in computational biology/bioinformatics, preferably also with experience in data mining and comparative or evolutionary genome analysis.

We have been interested in a range of topics related to the functional evolution of genomes from primates (e.g., the emergence of new genes and their functions) and other mammals (e.g., the origin and evolution of mammalian sex chromosomes). In the framework of a recently launched series of projects, a large amount of transcriptome and genome (e.g., epigenome) data are being produced by the wet lab unit of the group using next generation sequencing technologies for a unique collection of tissues from representative mammals and outgroup species (e.g., birds). Topics of current projects based on these data include the origins and/or evolution of protein-coding genes, alternative splicing, microRNAs, long noncoding RNAs, and dosage compensation.

The postdoctoral fellow will perform integrated evolutionary/bioinformatics analyses based on data produced in the lab and available genomic data. The specific project will be developed together with the candidate.

The language of the institute is English, and its members form an international group that is rapidly expanding. The institute is located in Lausanne, a beautiful city at Lake Geneva.

For more information on the group and our institute more generally, please refer to our website: http://www.unil.ch/cig/page7858_en.html

Please submit a CV, statement of research interest, and names of three references to: Henrik Kaessmann (Henrik.Kaessmann@unil.ch).

Webpage : http://www.unil.ch/cig/page7858.html