More at https://sourceforge.net/projects/sb2nhri/files/MyPro/

Address of the bookmark: http://www.sciencedirect.com/science/article/pii/S0167701215001207

• AssemblyMapper : reference-guided genome assembly
• GapFiller : long-reads based gap filling
• TeloExplorer : telomere identification
• CentroMiner : centromere candidate prediction

https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false 

Address of the bookmark: http://www.atcgn.com:8080/quarTeT/home.html

This project is part of a larger Genome Canada project on genetics and physiology of adaptation to climate change in rainbow trout, and the population genomics component is in the labs of Eric Taylor and Michael Whitlock. The landscape genomics component primarily involves whole genome sequencing approaches to understanding the genomic basis of adaptation to features of habitat, but also to provide insights into phylogeography and the influence of watershed structure on population subdivision in rainbow trout. A PhD in a related field with expertise in basic theory and bioinformatic analysis of population genomics data is required.

The position is available for one year with renewal for up to three additional years. Salary is $55,000 per year plus benefits. To apply, please send a brief cover letter summarizing your qualifications for the position, a CV, and the names, addresses, phone numbers and emails of three references.

Review of applications will begin January 16, 2017. Address application materials to etaylor@zoology.ubc.ca to whom any questions can also be addressed. etaylor@zoology.ubc.ca

Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the lengths of sequences being aligned. For example, a pair of Y. pestis genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours. However, the current algorithm’s compute time (progressiveMauve) scales cubically in the number of genomes to align, making it unsuitable for datasets containing more than 50-100 bacterial genomes.

Address of the bookmark: http://darlinglab.org/mauve/mauve.html

Positions available

Applications were invited from for the following posts in an industry sponsored project. The project entitled "OsHK3b technology and Know How", valid for a period upto February, 2018.

Post 3: Senior Research Fellow (Computational Biologist / Metabolic engineering)

Salary: As per DBT rule.

Duration: All the above posts are purely temporary and liable to be terminated at any time without prior notice or ceased/withdrawn by the funding agency.

Age limit: The upper age limit for SRF shall be 32 years, which is relaxed upto 5 years in the case of candidates belonging to Schedule Castes/Schedule Tribes, Women, Physically Handicapped and OBC applicants.

Essential Qualifications: Masters/B Tech/Mtech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Computational Biology related to Database /portal building & maintenance, high throughput data handling and analysis etc. For M.Sc/B.Tech, Published paper in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must. Selection preference will be given to candidates with a good knowledge of Python and/or R. Knowledge of JAVA will also get a special consideration.

Desired Skills: Will be expected to manage ongoing research activities in the project, interact with Experimental group, manage the project data analysis, prepare file reports and associated project work etc. Familiarity with plant systems biology and genomics /metabolite resources related to plant metabolomics is desirable.

1. The post applied for must be clearly written on the Envelope containing the application
2. Applications received after last date shall not be entertained, School will not be responsible for any postal delay.
3. No application will be accepted via hand delivery or via e-mail. Please send printed & signed applications with detailed CV on or before 31st January, 2017 by post to the following address:

Prof. Ashwani Pareek
(Project Investigator)
Stress Physiology and Molecular Biology Laboratory (Room No-413),
School of Life Sciences,
Jawaharlal Nehru University,
New Delhi, India – 110067
Email: ashwanipareek@gmail.com

There are several pre-computed trees available for display, including the main Tree Of Life, described in Ciccarelli, et al., 2006. In addition to the precomputed trees, users can upload and display personal trees and data, using the 'Data upload' page or through a personal user account.

Address of the bookmark: http://itol.embl.de/

• For each genome, obtain a set of exon annotations. These annotations can be a combination of both exon predictions (e.g. Genscan) and annotations that have been experimentally verified (e.g. RefSeq). Ideally, we would like to have these annotations be as sensitive as possible. Specificity is not a concern, as incorrect annotations are not likely not have significant alignments with other gene annotations.
• Compare all exons against all exons in other genomes and record significant alignments between exons. Currently, we use BLAT to do this all-vs-all comparison with alignments being performed in protein space.
• Construct a graph with each vertex corresponding to a exon and edges between vertices whose corresponding exons have significant alignments.
• Identify cliques in this graph. These cliques are potential anchors to be used in the map.
• Starting with the largest cliques (those that have exons in all or most of the genomes), join neighboring (adjacent in genomic coordinates, in each genome) cliques to form runs. Smaller cliques that are inconsistent with runs formed by larger cliques are filtered out. After the smallest cliques have been considered, cliques that are not part of a run are discarded.
• The extents of each run in each genome are outputted as orthologous segments. The cliques from each run are used to output the exact genomic coordinates of anchors within each orthologous segment. These anchors can be used by genomic alignment programs (such as MAVID) to do a detailed alignment of each orthologous segment.

https://www.biostat.wisc.edu/~cdewey/mercator/

Address of the bookmark: https://www.biostat.wisc.edu/~cdewey/mercator/

Candidates interested in the position should contact me in advance by email to: g.stracquadanio@essex.ac.uk

The deadline for the application is 28/02/2017; info about the application can be found on the Essex CSEE website.

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. The Cancer Genome Atlas Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The resulting data consisting of genetic variants is stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.

maftools is on bioRxiv

Please cite the below if you find this tool useful for you.

Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: http://dx.doi.org/10.1101/052662

Address of the bookmark: https://github.com/PoisonAlien/maftools

http://www.bx.psu.edu/~ratan/

Address of the bookmark: http://www.bx.psu.edu/miller_lab/