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.

Applications are invited for the STUDENTSHIP and TRAINEESHIP vacancies to carry out project/research work in the DBT - Bioinformatics Infrastructure Facility with consolidated stipend of Rs.5,000/- per month.

Essential Qualification

Student Trainee: Those who have completed M.Sc., Bioinformatics/Biophysics/Life sciences or Pursuing M.Tech., Bioinformatics/Biotechnology

Duration : 3-4 Months

Student Trainee: Those who are pursuing M.Sc Bioinformatics/Biophysics/ Life sciences/others

Duration : 2-3 Months

Mail your CV on or before 25th November 2013 to shirai2011@gmail.com and hard copy to "Dr. D. Velmurugan, Professor & Head, CAS in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600 025". Also, the applicants are requested to attend the interview on 29th November, 2013 at 11 A.M.

Advertisement:

www.unom.ac.in/uploads/announcements/bifadvertisement_20131114080003_23240.pdf

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data

Data mining commonly involves four classes of tasks:

• Classification - Arranges the data into predefined groups. For example, an email program might attempt to classify an email as legitimate or spam. Common algorithms include decision tree learning, nearest neighbor, naive Bayesian classification and neural networks.
• Clustering - Is like classification but the groups are not predefined, so the algorithm will try to group similar items together.
• Regression - Attempts to find a function which models the data with the least error.
• Association rule learning - Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
• From experience, I can say that is one of the most frustrating positions to be in. Data mining is a huge field and can easily be bewildering for a beginner. However, high through-put techniques in molecular biology require, more and more, that bioinformatics is required to interpret the data. Furthermore, people working in bioinformatics generally come from computer science, or biology backgrounds. Data mining, however, involves statistics to one degree or another, which means entering a field that is may not be your strong point.
• Excel is fine for creating graphs. If you’re serious about data mining though, you’ll need something more heavy weight. I use R, free, and with good data mining packages such as vegan and labdsv. For beginners R can be impenetrable, I recommend this book an introduction to R as well as the underlying statistics.
• Any of us can rush head on into a land of support vector machines, hidden markov models and neural networks. But coming back to the first point, what are you trying to prove? Always question what are you doing, how does it fit in to the wider picture? Try to regularly review, and keep track of where you are going? This will prevent you from falling into data mining despair.

Data Mining Resources on the net:

A laboratory of data mining and bioinformatics is headed by Prof. Ambuj Singh. There are currently seven graduate students in the research group. Our research focuses on image informatics and scalable querying and mining of graphs.For more detail visit: http://www.cs.ucsb.edu/~dbl/

Here are the materials (Lecture notes) from several past courses on data mining and/or Web mining by Stanford: For detail visit: http://infolab.stanford.edu/~ullman/mining/mining.html
Statistical Data Mining Tutorial Slides by Andrew Moore The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms. For detail visit: http://www.autonlab.org/tutorials/

A tutorial on Introduction to Data Mining for Discovering hidden value in your data warehouse:http://www.thearling.com/text/dmwhite/dmwhite.htm 
Wiki Links: http://en.wikipedia.org/wiki/Data_mining
Bioinformatics with Clementine http://www.spss.ch/upload/1051192224_inseratClemBio.pdf 
Causal Data Mining in Bioinformatics by Ioannis Tsamardinos: http://www.forth.gr/ics/bmi/In_the_News/2007/EN69-4.pdf

Report on ACM Text Mining in Bioinformatics (TMBIO 006) http://www.sigir.org/forum/2007J/2007j_sigirforum_song.pdf 
BIOKDD 2002: Recent Advances in Data Mining for 
Bioinformatics: http://www.acm.org/sigs/sigkdd/explorations/issue4-2/zaki.pdf

Bioinformatics and Medical Informatics: 

Tools for Mining and Applying Genetic Information in Patient Care:http://www.biomedtechalliance.org/pdfs/03_03_05/03_03_05.pdf

DATA MINING OF MICROARRAY DATABASES FOR HUMAN LUNG CANCER: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.385&rep=rep1&type=pdf

Towards knowledge-based gene expression data mining: http://www.ailab.si/blaz/papers/2007-JBI-BellazziZupan.pdf

DRAFT Accepted for publication in 'Data Mining in Bioinformatics'
Jason Wang, Mohammed Zaki, Hannu Toivonen, and Dennis Shasha (Eds.), Springer:http://www.cs.helsinki.fi/u/htoivone/pubs/gene_mapping_by_pattern_discovery.pdf

Data Mining and Text Mining for Bioinformatics: Proceedings of the European Workshop: http://www.rok.informatik.hu-berlin.de/wbi/research/publications/2003/proceedings_ws_mining.pdf

Biological Network Analysis:

Graph Mining in Bioinformatics: http://agbs.kyb.tuebingen.mpg.de/wikis/bg/BNA-5.pdf.

Text mining in bioinformatics: http://agbs.kyb.tuebingen.mpg.de/wikis/bg/4.pdf

Some datamining books that are available on google books:

Data mining and bioinformatics: first international workshop, VDMB 2006 By Mehmet M. Dalkilic

Data mining: concepts and techniques By Jiawei Han, Micheline Kamber

Script https://sourceforge.net/projects/c-l-authenticator/files/

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155459

Research with the help of bioinformatics with a trans-disciplinary approach is yielding good results.

http://www.thehindu.com/features/education/research/research-with-help-of-bioinformatics-helpful/article2295629.ece

https://salzberg-lab.org/

The most common newbie questions are:

Should I try to use these free open source programs?  Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.  

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. You can find a more detailed description of the new version of the pipeline in NCBI Handbook chapter. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Job Qualifications
Education and Experience
• Master Degree in Bioinformatics, Computational biology, Scientific Computing or related field
• 3-5 years of Post-Master’s experience in Bioinformatics software development
• Proven experience developing high throughput bioinformatics applications
Required Competencies
• Strong proven experience in Python programming language in Linux environment
• Proven High Performance computing experience (LSF/SGE/OGE)
• Exposure in code versioning and repository management (GIT/SVN)
• Proven experience in Bioinformatics algorithm development
• Deep understanding in Bioinformatics tools, data types
Desired Competencies
• Familiarity working in a scientific computing environment (NumPy, SciPy, Pandas etc.)
• Familiarity working with Cloud technologies (AWS, Azure)
• Ability to demonstrate solid analytical skills and exceptional attention to detail.
• Experience in relational databases and data structures
• Proven experience working with teams using agile software development methodologies and processes
• Familiarity with Service Oriented Architecture (SOA)
• Familiarity with build tools (Jenkins, make, ANT, Maven)
• Exposure to project management tools (JIRA, Confluence, RED MINE, etc.)

More at http://careers.dupont.com/jobsearch/job-details/senior-bioinformatics-software-developer/012939W-01/