Grinder can produce genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, metaproteomic shotgun and amplicon datasets from current sequencing technologies such as Sanger, 454, Illumina. These simulated datasets can be used to test the accuracy of bioinformatic tools under specific hypothesis, e.g. with or without sequencing errors, or with low or high community diversity. Grinder may also be used to help decide between alternative sequencing methods for a sequence-based project, e.g. should the library be paired-end or not, how many reads should be sequenced.

Address of the bookmark: https://sourceforge.net/projects/biogrinder/files/biogrinder/

https://genome.sph.umich.edu/wiki/Vt

https://github.com/atks/vt

Address of the bookmark: https://genome.sph.umich.edu/wiki/Vt

Uni Computing (formerly Uni BCCS) is a department of Uni Research, affiliated with the University of Bergen.

5 groups in this lab works on computational resources, methods, algorithms, and software.

Following two bioinformatics groups are:

The Computational Biology Unit (CBU) provides education and research in bioinformatics focused on functional genomics.

The Computational Ecology Unit (CEU) is basically deal with population fluctuations, behavioural patterns and the ways life cycles emerge.

Address of the bookmark: http://www.genengnews.com/keywordsandtools/print/3/32115/

Average read length is around 8,500 base pairs in release dataset. Best thing about this, there is not much effect on cost and quality of data.

You can also check following pages for publications on long reads and more:

http://www.illumina.com/services/long-read-sequencing-service.ilmn

http://blog.basespace.illumina.com/2013/07/22/first-data-set-from-fasttrack-long-reads-early-access-service/

genomic scale studies
endogenous mechanisms of mutations, germ line and somatic
computational aspects of immunology in cancer
signalling networks
three-dimensional organization of information in the nucleus
gene silencing
metastatic cross-talk
kinase signaling
personalized medicine
detection of biomarkers in cancer
historical DNA variation

From : http://www.ous-research.no/hovig/

Group address:
Eivind Hovig, The Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway
Email: ehovig@radium.uio.no

Address of the bookmark: http://www.genome.gov/27532724

1. rs1426654 SNP of SLC24A5 gene is decider of skin pigmentation variation in South Asia
2. rs1426654-A allele is widely spread throughout the Indian subcontinent 
3. Skin pigmentation is also account by the combination of processes like selection and demographic history of populations affected by their language and origin
4. Sign of positive selection in Europeans, Middle East, Pakistan, Central Asia and North India but not in South India
5. In European , A-allele is almost reached to fixation

Paper:

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003912

Source:

http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049

Some of GeneProf's highlights include:

• Easy-to-use web-based interface:Access your data at any time from any computer with a working internet connection -- no need to install software! (cp. Section 'System Requirements').
• Analysis wizards make your life easy:Step-by-step workflows make it easy to analyse high-throughput data within a minimum of hands-on time. (cp. SubConcept 'Analysis Wizards').
• Versatile modules:Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely into sophisticated workflows (cp. Concept 'Workflows').
• Integrated Analysis:Analysis of ChIP-seq and RNA-seq data in one place, plus support for the integration of other external data (e.g. from microarrays).
• Comprehensive Resource:GeneProf provides a comprehensive resource of fully analyzed next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent (cp. Tutorial 'Examining Public Next-Gen Data..').
• Extensibility:Algorithm developers and computer programmers can develop their own modules and extend GeneProf. Existing software can be easily wrapped in the workflow framework (cp. Section 'Module Development: Adding new..') and data from GeneProf may be used externally (cp. Section 'Web API: Retrieving Data from ..').

GeneProf is academic software developed at the Centre for Regenerative Medicine / Institute for Stem Cell Research, University of Edinburgh and has benefited from funding by the Medical Research Council and the EU Framework 7 Project "EuroSyStem".

Address of the bookmark: https://www.geneprof.org/GeneProf/index.jsp