Following are the weblink for different available browsers:

http://www.ensembl.org/index.html

http://ensemblgenomes.org/

http://genome.ucsc.edu/

http://www.ncbi.nlm.nih.gov/genome

http://www.ebi.ac.uk/genomes/

http://flybase.org/

http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi

http://www.sanger.ac.uk/resources/databases/

http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/

Address of the bookmark: http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/

Pondicherry University, Puducherry has been identified as a nodal agency by the Department of Biotechnology, Govt. of India to coordinate this examination along with nine centres namely,

Pune University, Pune;

Anna University, Chennai;

Bose Institute, Kolkata;

Institute of Bioinformatics & Applied Biotechnology, Bangalore;

North-Eastern Hill University, Shillong, University of Hyderabad, Hyderabad;

University of Kerala, Thiruvananthapuram;

Jawaharlal Nehru University, New Delhi and

Assam Agricultural University, Guwahati. 

In the BINC 2015 and 2016 examination, 23 candidates and five candidates were certified respectively. DBT has agreed to fund Research fellowships for all the BINC qualified Indian nationals to pursue Ph.D. in Indian Institutes/Universities.

Note that the candidate must possess a postgraduate degree(or equivalent) & meet the criteria of the institutes/universities in order to avail research fellowship.

In addition, cash prize of Rs. 10,000/- will be awarded to the top 10 BINC qualifiers.

More at http://www.pondiuni.edu.in/exams/binc/

• 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

• optimized to work with vast amount of data and very large file sets (~ 10,000 - 100,000).
• allows the selective retrieval of only the required files (file masks, ls-lR parsing, recursive search, updates)
• has a built-in repository containing the settings for the most common bioinformatics download needs
• built-in wizard for batch post-processing of downloaded files (archive extraction, file conversion, etc.)
• capable of performing multiple download or update tasks simultaneously

BioDownloader has a built-in repository containing the settings for common bioinformatics file-synchronization needs, including the Protein Data Bank (PDB) and National Center for Biotechnology Information (NCBI) databases. It can post-process downloaded files, including archive extraction and file conversions.

http://dunbrack.fccc.edu/BioDownloader/

Address of the bookmark: http://dunbrack.fccc.edu/BioDownloader/

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html

This software distribution includes both the DAGchainer utility and a Java-based graphical interface that allows the inputs and outputs to be navigated and interrogated dynamically.

Address of the bookmark: http://dagchainer.sourceforge.net/

Ref https://genomicsaotearoa.github.io/metagenomics_summer_school/day4/ex15_gene_annotation_part3/#overview-of-drampy-annotate-output 

Address of the bookmark: https://github.com/WrightonLabCSU/DRAM