Date: April 24th. 2017 Last Date: May 6th 2017
PROJECT ID: 632

The following posts are urgently required to be filled for the Department of Biotechnology, Government of India funded project jointly running with IIIT-Hyderabad & JNU, entitled "Computational Core for Plant Metabolomics" administrated by Prof Indira Ghosh, School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi-110 067.
NB: For all the posts, preference will be given to candidates with a good knowledge of Python and/or R in UNIX platform , knowledge of JAVA will also get a special consideration.

1. RA / Research Associate (Metabolic engineering/Computational Biologist)

Salary: Rs. 36000/- + HRA

Vacancy: 1

Essential Qualifications: PhD in Bioinformatics /Mathematics/Computer Science with experience in analyzing high throughput omics-based data/Analysis of Network Biology/Chemoinformatics/Computational Biology related Software development. Published paper in the field is a must to prove the experience. Special consideration will be given if have experience in Industry, teaching & product development.

Desired Skills: Prior experience in handling and guiding bioinformatics, metabolomics data, planning of new research area in metabolic driven network , collaborating with industry , preparing and filing reports etc. Will be expected to communicate with user groups and coordinate with LIMS group in Hyderabad and the Cheminformatics group in Delhi.

2. Project SRF (Network model building/Systems biology integration)

Salary*: Rs.18000/- + HRA

Vacancy: 1

Essential Qualifications: M.Tech in Computational Biology with project experience or Masters / B.Tech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Mathematical Model building using Computational Biology tools & related Database / Network analysis etc. For M.Sc/B.Tech, Published paper in peer-reviewed Journal whereas for M.Tech, the degree obtained in computational biology is a must.

Desired Skills: Will be expected to manage ongoing research activities in LIMS, interact with LIMS group, build network model using data compiled by experimentalist, prepare and file reports and associated project work etc. Familiarity with plant systems biology and genomics /metabolite resources related to plant metabolomics is desirable.

More at http://www.jnu.ac.in/Career/currentjobs.htm

Link @ http://bioinf.boku.ac.at/

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

for applicants interested in performing high-quality research on the connection between software engineering, database systems, and theoretical computer science as well as their applications in bioinformatics and business informatics. The research programme will start on October 1, 2013 until 30.09.2016. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz – WissZeitVG).

This research programme is a joint activity of Professors Lehner, Assmann, Baader, Baier, Schill, Schlegel, Schroeder, and Strahringer at TU Dresden. Alongside their research, an individual mentoring and qualification approach are arranged with specialized courses that prepare them optimally for their research, a research seminar where they can meet internationally renowned researchers in the field, and soft skills and language courses.

Requirements: Applicants should have an excellent academic record, and hold a MSc (or an equivalent university degree) in computer science or related disciplines (such as mathematics, bioinformatics or business informatics). Fluency in spoken and written English is required. Applicants with a good knowledge of software engineering or one of the application areas mentioned above are preferred. TU Dresden is committed to increase the proportion of women in research.

Applications from women are particularly welcome. The same applies to disabled people.

Please send enquiries to: wolfgang.lehner@tu-dresden.de

Applications consist of a CV, the names of two referees, transcipts of documents summarizing their academic performance, and a statement of interest. Application by email in pdf format is preferred, and should be submitted to wolfgang.lehner@tu-dresden.de in an electronically signed and encrypted form by July 30, 2013 (stamped arrival date of the university central mail service applies). Alternatively, applications can be sent to: TU Dresden, Fakultät Informatik, Institut für Systemarchitektur, Prof.  Dr.-Ing.  Wolfgang Lehner, 01062 Dresden, Germany.

Shortlisted candidates will be invited to Dresden in the middle of August to give a presentation on their Master’s thesis and discuss their research interest with the participating professors. Candidates that have not yet finished their degree when they send in their application should send preliminary transcripts of their academic records as well as a letter by the thesis adviser that comments on their progress so far and on the expected date of completion of their MSc or equivalent degree.

https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md

Address of the bookmark: http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/

Link @ http://lgfus.ca/public/

It takes FASTA DNA sequence as input, and write GFF3 as output. It uses the new NHMMER tool that comes with HMMER 3.1 for HMM searching in RNA:DNA style. NHMMER binaries for 64-bit Linux and Mac OS X are included and will be auto-detected. Multithreading is supported and one can expect roughly linear speed-ups with more CPUs. 

Address of the bookmark: https://github.com/tseemann/barrnap

Faculty facilitator/Founder Director for two start-up companies (Leadinvent incubated at IIT, Delhi from 2006-2009 & Novoinformatics, under incubation at IIT Delhi since 2011).

Research Interest
Genome Analysis, Protein Structure Prediction and Drug Design.

Link @ http://www.scfbio-iitd.res.in/

JRF/SRF / Project Assistant-II recruitment in National Agri-Food Biotechnology Institute (NABI)

Essential Qualification: According to the DST (DST OM No.SR/S9/Z-09/2012 dated 21.10.2014) Post Graduate degree in basic science(M.Sc) in Bioinformatics/Computational Biology/Systems Biology/Information Technology with NET or Graduate degree in professional course with NET or Post Graduate Degree (M.Tech) in professional course in Bioinformatics/Computational Biology/Systems Biology/Information Technology. Desirable qualification/skills: 1) Should be proficient in programming in Perl/Python/R language etc. 2) Should have knowledge and skills for data mining in biological sequence database . sequence analysis tools/packages, NGS Analysis . 3) Should have knowledge and skills to work in linux environment and write shell scripts.

Age : 28 years

Hiring Process : Written-test
Job Role : Research/JRF/SRF
How to apply

Application should be sent to Administrative officer, National Agri-Food Biotechnology Institute, Knowledge City, Sector-81, Mohali so as to reach latest by 30.05.2017 before 5:30 pm.

More at http://www.nabi.res.in/Vacancies/NABI/ResearchFellowships/JRFSRFRA/2017/ADVT.%20No%202017Researcher%20(02)/ApplicationForm.pdf

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