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

Phone No. 0495 2731 410

WALK -IN- TEST CUM INTERVIEW
Walk- in- Test cum Interview (based on test) for the selection of Research Associate (Bioinformatics) & Bioinformatic Trainees under the scheme ‘Distributed Information Sub Centre- DISC’ will be held at this Institute as per details indicated below.

Research Associate
Date of Interview : 21 -01-2014 at 10.00 A.M
Qualifications :
a) Essential: Doctorate degree in Bioinformatics or Biotechnology/Life Sciences/Biochemistry with expertise in Bioinformatics as evidenced by publications.
OR Three years research experience after MVSc/MPharm/ME/MTech with Bioinformatics Specialization.
b Desirable: Experience in handling NGS data Programming skills in Python/Bioperl
Emoluments : Rs:22000/- per month + HRA (higher pay upto Rs.24000/- can be paid depending on the qualifications and experience.
Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)
Duration of Project : Till the closure of the project.

General Terms and conditions
1. The above positions are purely on temporary basis and is co-terminus with the closure of the project. There is no provision of re-employment after termination of project.
The selected candidate will not have any right for claiming pay scale or absorption
against any regular post being vacant on a later date at this Institute.
2 . No TA/DA will be paid for attending the Interview.
3. Canvassing in any form will lead to cancellation of candidate.
4. The decision of Director, IISR would be final and binding in all aspects.
5. Candidates will not be permitted to enter the Examination Hall after 10.00 A.M.
6. Candidates who secure the minimum marks prescribed by the Institute in written test only will be eligible for calling for the interview. The number of candidates to be called for the interview will be decided by the Director of the Institute.
7 Those who do not possess original Degree/PG certificate or Provisional certificate will not be allowed to attend the Test/Interview.

Note: All relevant certificates (in original) and bio data, No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.
Advertisement: http://spicebioinfo.res.in/downloads/DISC-Website.pdf

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)

Salary: £27,854 - £29,541, with progression to £36,298

You will perform cutting edge computational biology within the Faculty of Medical Sciences, with a particular focus on the Northern Institute for Cancer Research (NICR), and contribute to the delivery of Faculty wide programmes of training, analytical services and skill transfer between Faculty Institutes.

You will have a relevant first degree or equivalent qualifications and/or experience in a relevant scientific/technical role, together with previous specialist experience at a senior level in bioinformatics. A PhD is desirable.

This position is part of the Bioinformatics Support Unit but physically located for the majority of the time in the NICR buildings.

Tenable for three years.

Informal enquiries to unit head Dr Simon Cockell: 0191 222 7253; simon.cockell@ncl.ac.uk

For more information visit @ https://www15.i-grasp.com/fe/tpl_newcastle02.asp?s=4A515F4E5A565B1A&jobid=50667,2552984041&key=70203469&c=725434237887&pagestamp=sepghtjhowdqpsxuyn

You can also find several other jobs @http://bsu.ncl.ac.uk/support/recruitment/

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.

Functioning of efflux systems in Gram-negative bacteria
Determinants of the compound-efflux system interactions
Action of inhibitors on efflux systems
Structural and dynamical features of the efflux systems

TatA
Assembly of the TatA system
Study of the dynamical features of the charge zipper

Methods
Setup of a kinetic Monte Carlo (KMC) scheme to study the flux of antibiotics through porins and efflux systems
Setup of protocol to integrate MD results in a ligand-based approach

Viral inhibitors
Interactions of selected compounds with RNA-dependent RNA polymerases (RdRps) of HCV and BVDV
Assessment of the role of mutations in RdRps
Antimicrobial peptides

Interactions of antimicrobial peptides with membranes: structure and dynamics
Interactions between antimicrobial peptides in the presence of different membranes
Protein-protein interactions
Effects of mutations

Lab Page
http://www.dsf.unica.it/~paolo/Site/Home.html

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

Genome Annotation and Functional Genomics

Bergman Lab is actively engaged in the development and application of computational methods to improve the annotation of functional biological features in genome sequences. Bergman Lab work focuses on improving annotation of non-protein-coding regions of the genome including conserved noncoding sequences (CNSs), cis-regulatory modules (CRMs), transcription factor binding sites (TFBSs), transposable elements (TEs) and noncoding RNA (ncRNA) genes. Current projects include improving the (i) annotation of TEs in the fly and yeast genomes, (ii) annotation of CRMs and TFBSs in the fly genome, and (iii) analysis of transposon knockout collections in flies. Research in this area is supported by the EC FP7 programme.

Genome and Molecular Evolution
Text and Data Mining

More @ http://bergmanlab.smith.man.ac.uk/

Nature Biotechnology spoke with Altschul and several other originators of computational biology software programs widely used today (Table 1). The conversations explored what makes certain software tools successful, the unique challenges of developing them for biological research and how the field of computational biology, as a whole, can move research agendas forward. What follows is an edited compilation of interviews.

Detail @ http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html

News Source @ Nature