Address of the bookmark: https://github.com/marbl/Krona/wiki

TMAP is a fast and accurate alignment software for short and long nucleotide sequences produced by next-generation sequencing technologies.

• The latest TMAP is unsupported. To use a supported version, please see the TMAP version associated with a Torrent Suite release below.

• Get the latest source code:

  git clone git://github.com/iontorrent/TMAP.git
   cd TMAP
   git submodule init
   git submodule update

https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

Address of the bookmark: https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

 All the nodes in the tree are clickable. This displays various information and options:

• The species name (and the associated common name if there is one)
• The rank (kingdom, family, class, species...)
• Ability to go to the corresponding node/species on NCBI web site (displayed in a new window)
• Possibility to download the corresponding subtree in newick extended format
• Possibilty to get the whole lineage from the current node/tip to the root of the tree.

Address of the bookmark: http://lifemap-ncbi.univ-lyon1.fr/

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

It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer.^[1]

Address of the bookmark: https://en.wikipedia.org/wiki/FASTQ_format

https://bienkocrosettolabs.org/

https://salzberg-lab.org/

We describe a new method for predicting the ancestral order and orientation of those intervals from their observed adjacencies in modern species. We combine the results from this method with data from chromosome painting experiments to produce a map of an early mammalian genome that accounts for 96.8% of the available human genome sequence data. The precision is further increased by mapping inversions as small as 31 bp. Analysis of the predicted evolutionary breakpoints in the human lineage confirms certain published observations but disagrees with others. Although only a few mammalian genomes are currently sequenced to high precision, our theoretical analyses and computer simulations indicate that our results are reasonably accurate and that they will become highly accurate in the foreseeable future. Our methods were developed as part of a project to reconstruct the genome sequence of the last ancestor of human, dogs, and most other placental mammals;

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

We are now seeking a postdoc scholarship holder with strong background in transcriptomics to use this large collection of data for integrative studies. Focus will be on advanced bioinformatics and statistical analysis of data from high-throughput sequencing including integration with the other platforms.

The scholarship holder must have a PhD with an outstanding research and publication record and will be selected based on her/his excellence and her/his skills. A PhD should have been awarded less than five years before the deadline of the application. The scholarship holder must have a strong background in bioinformatics, computer science, computational biology or equivalent with a profound knowledge about biology and biostatistics.

Your complete application must be received at KTH no later than 2018-01-15.

https://www.kth.se/en/om/work-at-kth/stipendier/postdoctoral-scholarship-in-bioinformatics-with-focus-on-transcriptomics-and-data-integration-1.779571

The new features include

(i) building gene-based and supermatrix-based phylogenies using a single command,

(ii) testing and visualizing evolutionary models,

(iii) calculating distances between trees of different size or including duplications, and

(iv) providing seamless integration with the NCBI taxonomy database.

ETE is freely available at http://etetoolkit.org

Address of the bookmark: http://etetoolkit.org