There are several jobs opening in bioinformatics all around the world, but many of them do not get proper attention due to lack of advertisements, or social connectivity. This bookmark is created for an academic, non-academic, scientists and budding researchers to help and updates the bioinformatics/computational biology jobs links of all know websites around the world.

I also love to stream the live bioinformatics or Computational biology jobs updates using Twitter https://twitter.com/search?q=bioinformatics%20jobs&src=typd

Find out here about exciting job opportunities in the life sciences.

Please add well known bioinformatics jobs websites below in comment section.

Address of the bookmark: http://www.nature.com/naturejobs/science/jobs?utf8=%E2%9C%93&q=bioinformatics&where=&commit=Find+Jobs

Following are the list of Ancestral /sequence/ reconstruction (ASR) tools: 

inferCars

Reconstructs contiguous regions of an ancestral genome. Given information about adjacencies between conserved segments in each modern species, our goal is to infer segment order in the ancestral genome. To get a clean and precise statement of the problem, we formalize it using graph theory. We develop an algorithm that identifies a most parsimonious scenario for the history of each individual adjacency, although the whole-genome prediction is not guaranteed to optimize traditional measures like the number of breakpoints. We introduce weights to the graph edges to model the reliability of each adjacency.

ANGES:reconstructing ANcestral GEnomeS maps

A suite of Python programs that allows reconstructing ancestral genome maps from the comparison of the organization of extant-related genomes. ANGES can reconstruct ancestral genome maps for multichromosomal linear genomes and unichromosomal circular genomes. It implements methods inspired from techniques developed to compute physical maps of extant genomes.

Cocos

Constructs phylogenies of multi-domain proteins. With a given species tree and domain phylogenies, the procedure infers the composition of ancestral multi-domain proteins. Cocos implements and extend a suggested algorithmic approach by Behzadi and Vingron in an easy-to-use program. Such method could be applied to reconstruction of partial homologous units such as bacterial operons or protein complexes.

MySSP

Constructs an initial DNA sequence at the root of the tree and simulates evolution across the tree using a variety of common models of DNA evolution. MySSP is a program for the simulation of DNA sequence evolution across a phylogenetic tree. It is designed for large-scale studies, including simulation of multiple replicates and outputs sequences into NEXUS, MEGA, or FASTA formats. MySSP has a fairly simple graphical user interface (GUI) for basic use, but also has a specialized batch script interpreter to allow for more complicated or large-scale simulations.

PARANA: Parsimonious Ancestral Reconstruction And Network Analysis

Performs parsimony based inference of ancestral biological networks. Given multiple extant networks and phylogenetic information relating extant nodes, PARANA finds a parsimonious set of ancestral interaction events (edge gains and losses) which explain the extant networks. The framework adopted by PARANA is able to represent network evolution under models that support gene duplication and loss and independent interaction gain and loss. The method works on both directed and undirected networks and can incorporate asymmetric interaction gain and loss costs. In contrast to previous approaches, PARANA does not require knowing the relative ordering of unrelated duplication events and thus, works on phylogenetic trees even where branch lengths are not provided.

GapAdj: Gapped Adjacencies

A synteny-based method that is flexible enough to handle a model of evolution involving whole genome duplication events, in addition to rearrangements, gene insertions, and losses. Ancestral relationships between markers are defined in term of Gapped Adjacencies, i.e. pairs of markers separated by up to a given number of markers. It improves on a previous restricted to direct adjacencies, which revealed a high accuracy for adjacency prediction, but with the drawback of being overly conservative, i.e. of generating a large number of contiguous ancestral regions (CARs).

ANCESTOR

A web server allowing one to easily and quickly perform the last three steps of the ancestral genome reconstruction procedure. Ancestors implements several alignment algorithms, an indel maximum likelihood solver and a context-dependent maximum likelihood substitution inference algorithm. The results presented by the server include the posterior probabilities for the last two steps of the ancestral genome reconstruction and the expected error rate of each ancestral base prediction.

ProCARs

Reconstructs ancestral gene orders as contiguous ancestral regions (CARs) with a progressive homology-based method. ProCARs runs from a phylogeny tree (without branch lengths needed) with a marked ancestor and a block file. This homology-based method is based on iteratively detecting and assembling ancestral adjacencies, while allowing some micro-rearrangements of synteny blocks at the extremities of the progressively assembled CARs. The method starts with a set of blocks as the initial set of CARs, and detects iteratively the potential ancestral adjacencies between extremities of CARs, while building up the CARs progressively by adding, at each step, new non-conflicting adjacencies that induce the less homoplasy phenomenon. The species tree is used, in some additional internal steps, to compute a score for the remaining conflicting adjacencies, and to detect other reliable adjacencies, in order to reach completely assembled ancestral genomes.

FastML

A user-friendly tool for the reconstruction of ancestral sequences. FastML implements various novel features that differentiate it from existing tools: (i) FastML uses an indel-coding method, in which each gap, possibly spanning multiples sites, is coded as binary data. FastML then reconstructs ancestral indel states assuming a continuous time Markov process. FastML provides the most likely ancestral sequences, integrating both indels and characters; (ii) FastML accounts for uncertainty in ancestral states: it provides not only the posterior probabilities for each character and indel at each sequence position, but also a sample of ancestral sequences from this posterior distribution, and a list of the k-most likely ancestral sequences; (iii) FastML implements a large array of evolutionary models, which makes it generic and applicable for nucleotide, protein and codon sequences; and (iv) a graphical representation of the results is provided, including, for example, a graphical logo of the inferred ancestral sequences.

maxAlike

Reconstructs a genomic sequence for a specific taxon based on sequence homologs in other species. The input is a multiple sequence alignment and a phylogenetic tree that also contains the target species. For this target species, the algorithm computes nucleotide probabilities at each sequence position. Consensus sequences are then reconstructed based on a certain confidence level.

MLGO: Maximum Likelihood for Gene Order Analysis

A web tool for the reconstruction of phylogeny and/or ancestral genomes from gene-order data. MLGO was designed for analysis of large-scale genomic changes including not only rearrangements but also gene insertions, deletions and duplications. MLGO can be used to infer a phylogeny from genome rearrangement and gene order data, and can also obtain an estimation of ancestral genomes, given an input tree. MLGO takes the advantage of binary encoding on gene-order data, supports a fairly general model of genomic evolution (rearrangements plus duplications, insertions, and losses of genomic regions), and successfully accommodates itself into the framework of maximized likelihood.

Image Reference : Wiki

http://www.sidowlab.org/

At the Repository for Tomato Genomics Resources, we are working on Tomato Functional Genomics, using TILLING, Insertional Mutagenesis, proteomics, metabolomics approaches to study fruit ripening in tomato. The current aims of the group include using reverse and forward genetics strategies to isolate tomato mutants delayed in ripening, having high lycopene and folate content in tomato fruits and analysis of light and hormonal signal transduction pathways. For recent publications of the group see (Plant Physiol 161: 2085–2101, Plant Physiol 156: 1424-1438; Molecular Plant 3: 854-869; Plant Methods 6: 3; Plant Methods 5:18; Plant Signaling and Behavior 5:11.).

Currently we have one position available in the projects awarded to Prof. R.P. Sharma funded by Dept of Biotechnology. The qualification for this Position is as follows:

Research Assistant: Applicants should have experience in networking using R language and should be able to develop networks using the transcriptome, proteome and metabolite data sets. M.Tech. in Bioinformatics is required. The selected candidate would be paid Rs. 13,000/-pm- consolidated.

Candidates interested in above positions should send a one page statement clearly explaining how their skills are relevant to the position. The candidates should also enclose detailed CV and the name/email id for three referees. The candidates can send their application by email at rameshwar.sharma@uohyd.ac.in and y.sreelakshmi@uohyd.ac.in on or before December 10th, 2013. The position is purely temporary in nature. Shortlisted candidates would be called for interview. No TA/DA would be provided for attending the interview. We also have openings for CSIR-NET JRF candidates for pursuing PhD in above research areas.

Interested candidates with CSIR-NET JRF can send their CV to the above email
addresses.

Advertisement:

http://www.uohyd.ac.in/images/recruitment/tomanet_positions_221113.pdf

Postdoctoral position is available for three years to work on development of Bioinformatics resources for the analysis and visualization of ribosome profiling data. Ribosome profiling (ribo-seq) is a technology that allows mapping positions of the ribosomes on the whole transcriptome level with a nucleotide precision. The technology allows obtaining high resolution digital snapshots of gene expression in cells. The position is available starting on the 1st of October, 2013.

Candidate is expected to have Ph.D. in Bioinformatics or Computational Biology. Candidates with the degree in non-Biological disciplines such as Computer Science, Statistics, Applied Mathematics, Physics or Electrical Engineering will also be considered.

The position is available at LAPTI (http://lapti.ucc.ie) that is located in the Western Gate Building (http://www.stwarchitects.com/project-information.php?c=1&p=09993) at University College Cork. Western Gate Building Research Complex hosts several UCC departments and provides ideal environment for interdisciplinary research. Cork (sometimes referenced as “Venice of Ireland”) is the second most populous city in the Republic. It has friendly cosmopolitan atmosphere and vibrant culture. A number of American industrial giants such as Apple , EMC and Pfizer have chosen Cork as a home for their European headquarters.

The details of the application process are given at http://lapti.ucc.ie/jobs.html. To ensure prompt processing of your application use the subject line: ‘Postdoc computational’. All applications received prior to August the 1st are guaranteed equal consideration. However, applications at the later dates will also be considered until the position is filled.

For more info visit http://lapti.ucc.ie

Research Area:
Linear models for microarray data
Digital gene expression technologies
Detection of molecular pathways
Bioinformatics resources for medical research

Link @ http://www.wehi.edu.au/faculty_members/professor_gordon_smyth/