Job description
We are looking for a motivated, self-driven post-doctoral researcher to work with large-scale sequence data analysis. The position is for 24 months and located at Mathematical Statistics, Department of Mathematical Sciences in Erik Kristiansson’s research group. We are focused on methods development for and analysis of next generation DNA sequencing, in particular comparative metagenomics and gene expression analysis (RNA-seq). We have strong interdisciplinary profile and are actively collaborating with several experimental groups, especially within the environmental sciences, ecology, infectious diseases and cancer genomics. More information is available at http://bioinformatics.math.chalmers.se.

The Post-doctoral position is an appointment that offers an opportunity to qualify for further research positions within academia or industry. The majority of your working time is devoted to your own research, normally as a member of a research group. Included in your work is also to take part in supervision of Ph.D. students and M.Sc thesis students. Teaching of undergraduate students may also be included to a small extent.

The employment is limited to a maximum of 2 years (1+1).

Qualifications
The applicant should have Ph.D. degree preferably in bioinformatics, mathematics, statistics, computer science or equivalent by the start of the appointment. Experience from analysis of large-scale data, in particular from next generation DNA sequencing, is highly valued. The applicant should also be proficient in programming (e.g. Python/Java/C) and comfortable with Unix/Linux systems. Interaction with experimental biologists is central and good collaborative skills are therefore important. Fluency in written and spoken English is a strong requirement. As a post-doctoral researcher you are expected to work independently and to be able to supervise/co-supervise PhD and Master’s students.

Application procedure
The application should be marked with Ref 20130126 and written in English. The application should be sent electronically via Chalmers webpage.

Application deadline: September 8, 2013.

For questions, please contact:
Ass prof. Erik Kristiansson, Matematiska Vetenskaper, erik.kristiansson@chalmers.se, +46 31-772 3521, +46 70-5259751.

Chalmers continuously strive to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.

More at http://pythonhosted.org/pybedtools/

A powerful toolset for genome arithmetic.http://code.google.com/p/bedtools/

The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.

https://code.google.com/p/biowiki/

https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk

NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&projectsearch=Search+projects

NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&projectsearch=Search+projects

Address of the bookmark: https://code.google.com/hosting/search?q=bioinformatics&sa=Search

Address of the bookmark: https://github.com/mattharrison/Tiny-Python-3.6-Notebook/blob/master/python.rst

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.

Other tools

https://github.com/shenwei356/taxonkit

• ETE, version: 3.1.1
• BioPython, version: 1.73
• taxadb, version: 0.10.1
• TaxonKit, version: 0.5.0

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

https://github.com/tanghaibao/jcvi

More at https://github.com/tanghaibao/jcvi/wiki

Address of the bookmark: https://github.com/tanghaibao/jcvi

Example:

if you want to run fastqc for QC of fastq file:

from subprocess import Popen,PIPE,call

p=Popen(["fastqc","-f","fastq","-o", "/home/name/result/","/dev/stdin"],stdin=fopen("read.fastq","r") ,stdout=PIPE,stderr=PIPE)

print p.stderr

p.stdout.close()

More:

http://pymotw.com/2/subprocess/

• DNA-mapping*
• ChIP-seq*
• RNA-seq*
• ATAC-seq*
• scRNA-seq
• Hi-C
• Whole Genome Bisulfite Seq/WGBS

(*Also available in "allele-specific" mode)

snakePipes can be installed via conda :

'conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'.

Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.

Address of the bookmark: https://github.com/maxplanck-ie/snakepipes