BOL: Related items

You and your friend have similar DNA !!!

Jit — Sun, 27 Jul 2014 20:44:05 -0500

New research out of Massachusetts claims that people often choose friends that are similar to them in genetics and they are more accurate than you might suppose. A study published on PNAS http://www.pnas.org/content/111/Supplement_3/10796.full found that people are apt to pick friends who are genetically similar to themselves - so much so that friends tend to be as alike at the genetic level as a person's fourth cousin.

Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes — a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population.

The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.

Reference:

http://www.pnas.org/content/111/Supplement_3/10796.full

Image : http://i.kinja-img.com

Kevler: Reference-free variant discovery in large eukaryotic genomes

Jit — Tue, 28 Jan 2020 03:21:53 -0600

Welcome to kevlar, software for predicting de novo genetic variants without mapping reads to a reference genome! kevlar's k-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar

Protein function annotation and machine learning - UPMC - Paris, France

Sat, 02 Aug 2014 01:22:52 -0500

Protein function annotation and machine learning - UPMC - Paris, France

Job Description: We are interested in finding an excellent postdoc with interests in protein functional annotation, machine learning and computer grids. The position is open for 3.5 years at the Université Pierre et Marie Curie, in the heart of paris.

Research topic: Protein function annotation, multiple probabilistic models, domain architecture, machine learning, combinatorial optimization, computer grid.

Title: A novel integrative platform for large scale protein annotation that exploits a multitude of diversified probabilistic models in several protein signature databases.

We propose a novel integrated approach for large scale protein annotation that will exploit an unprecedented amount of genomic data as well as sophisticated machine learning techniques and combinatorial optimization approaches taking advantages of High Performance Computing (HPC) environments. The idea is to uncover as much as possible the evolutionary processes of protein sequences that took place throughout the whole tree of life and that affected the evolution of a protein family. We have already demonstrated in a previous work that the problem of functional annotation is inherent to the ability of uncovering such paths. Now, we shall extend this approach to large scale genome annotation by considering 11 different protein databases, constituted by about 10^9 protein sequences, and by producing a large pool of diversified probabilistic models coding for about 10^7 evolutionary protein pathways. Such models will be used to search for specific domains in genomes to be annotated. Our previous methodology needs to be fundamentally improved to deal with this large amount of biological data. In this project, we shall work on the algorithms to reduce the space of models and the search complexity, and we shall implement some important algorithmic changes towards the realization of a powerful integrated annotation tool.

Where: This project is run on the Laboratoire de Biologie Computationnelle et Quantitative UMR7238 CNRS-UPMC – Analytical Genomics team, headed by A.Carbone. It is co-advised with Pierre-Henri Wuillemin, Laboratoire d’Informatique de Paris 6 – Equipe DECISION.

Start date: September 1st, 2014
Contact Person: Alessandra Carbone
Contact: alessandra.carbone@lip6.fr

CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs)

Jit — Mon, 18 May 2020 10:53:32 -0500

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies. The core algorithm of both programs involves gene calling, mapping of predicted ORFs against the nr protein database, and voting-based classification of the entire contig / MAG based on classification of the individual ORFs. CAT and BAT can be run from intermediate steps if files are formated appropriately (see Usage).

Address of the bookmark: https://github.com/dutilh/CAT

Swabs to Genomes: A Comprehensive Workflow

Rahul Nayak — Sun, 10 Aug 2014 03:01:21 -0500

The sequencing, assembly, and basic analysis of microbial genomes, once a painstaking and expensive undertaking, has become almost trivial for research labs with access to standard molecular biology and computational tools. However, there are a wide variety of options available for DNA library preparation and sequencing, and inexperience with bioinformatics can pose a significant barrier to entry for many who may be interested in microbial genomics. The objective of the present study was to design, test, troubleshoot, and publish a simple, comprehensive workflow from the collection of an environmental sample (a swab) to a published microbial genome; empowering even a lab or classroom with limited resources and bioinformatics experience to perform it.

Address of the bookmark: https://peerj.com/preprints/453.pdf

Published a dataset of 363 genomes from approximately 92 percent of bird families

Jit — Thu, 19 Nov 2020 07:04:41 -0600

A research team published a dataset of 363 genomes from approximately 92 percent of bird families and showed the significance of sampling dense organisms for biodiversity research. The study was jointly conducted by Chinese and international institutions and museums and was led by researchers from the Kunming Institute of Zoology (KIZ) of the Chinese Academy of Sciences (CAS). Total of 267 were newly published among the 363 sequenced genomes. They were mainly taken from samples of avian tissue kept in museums around the world, enabling researchers to sequence rare and endangered birds' genomes.

Its descendants have adapted to a wide variety of ecological niches since the first bird formed more than 150 million years ago, giving rise to small, hovering hummingbirds, plunge-diving pelicans and showy paradise birds. More than 10,000 bird species live on the planet today - and now scientists are well on their way to capturing a full genetic image of that diversity.

B10K is expanding its efforts to encompass the next stage of avian classification with 363 genomes complete. The team will sequence thousands of extra genomes in this process, attempting to represent each of the approximately 2,300 bird genera.

The genomic resource is expected to provide new insights on evolutionary processes in cross-species comparative studies and assist in efforts to protect species, according to the research findings reported as a cover story in the journal Nature.

Ref at Dense sampling of bird diversity increases power of comparative genomics https://www.nature.com/articles/s41586-020-2873-9

Assistant Professor in Bioinformatics at Indian Institute of Technology Delhi

Fri, 15 Aug 2014 06:16:06 -0500

Indian Institute of Technology Delhi Hauz Khas ,New Delhi – 110016

ROLLING ADVERTISEMENT NO. 01/2014(E-1)
ADVERTISEMENT FOR THE POSITIONS OF ASSISTANT PROFESSOR CANDIDATES CAN APPLY ANY TIME DURING THE YEAR.

IIT Delhi invites applications from qualified Indian Nationals, Persons of Indian Origin (PIOs) and Overseas Citizens of India (OCIs) for the following positions in the various Departments/Centres/Schools (in the fields
mentioned alongwith them):
Post Pay Band Assistant Professor and Assistant Professor (on Contract) Rs.15600-39100 (PB-3) (Minimum pay of Rs.30000/-)+ AGP Rs.8000/-

The following norms will be followed for fixing the basic pay + AGP for Assistant Professors appointed on
contract with Ph.D but experience of 3 years or less:-
Type Qualification & Experience on the date of joining
Assistant Professor (Contract) PB3 (Rs. 15,600-39,100).

MINIMUM QUALIFICATIONS AND EXPERIENCE:
Ph.D. with First class at the preceding degree or equivalent in the appropriate branch with very good academic record throughout. A minimum of three years industrial/research/teaching experience, excluding however, the experience gained while Pursuing Ph. D. The candidates should preferably be below
35 years of age for male and 38 years for female ( to be relaxed by 5 years in case of persons with physical disability, SC/ST and 3 years in case of OBC-NCL).

Qualified persons include:
(a) Indian Nationals,
(b) Foreign Nationals who are “Persons of Indian Origin” (PIO) or Overseas
Citizens of India (OCI), in whose case, if selected, permission will be sought from Govt. of India
before he/she can join IIT Delhi, or
(c) Other Foreign Nationals, in whose case, if selected, appointment will be on a contract basis for up to 5 (five) years subject to permission from the Govt. of India before he/she can join IIT Delhi.
(d) Institute specifically encourages applicants from SC/ST/OBC category as well as persons
with disability to apply for these positions.

AMAR NATH & SHASHI KHOSLA SCHOOL OF INFORMATION TECHNOLOGY:
Computational Neuroscience, Medical Applications of Information Technologies, Computational & Systems Biology, Machine to Machine (M2M) Technologies, Embedded Systems & Sensors, Computer Security.
KUSUMA SCHOOL OF BIOLOGICAL SCIENCES:
In-silico Biology Applications, Systems Biology, Infection Biology, Neurodegeneration.

More at http://www.iitd.ac.in/sites/default/files/jobs/faculty/spl-areas-rolling-advt.pdf

http://www.iitd.ac.in/content/faculty-positions

OrthoVenn3: an integrated platform for exploring and visualizing orthologous data across genomes

Abhi — Tue, 02 May 2023 00:48:28 -0500

OrthoVenn3 is a powerful tool for comparative genomics analysis, used as a web server for full genome comparisons, annotation, and evolutionary analysis of orthologous clusters across multiple species. It has already been used by thousands of users from over 60 countries.

Address of the bookmark: https://orthovenn3.bioinfotoolkits.net/

Lecturer/Senior Lecturer (Level B/C) in Bioinformatics

Fri, 22 Aug 2014 12:45:52 -0500

Lecturer/Senior Lecturer (Level B/C) in Synthetic Biology, Research Fellow (Level B) in Synthetic Biology & Lecturer/Senior Lecturer (Level B/C) in Bioinformatics

Apply now Job no: 494553
Work type: Continuing full time
Vacancy type: External Vacancy, Internal Vacancy
Categories: Academic - Teaching and Research

The Faculty of Science is launching a new and innovative branch of biological science at Macquarie University – Synthetic Biology. Synthetic biology combines engineering principles with molecular biological approaches to design and construct biological devices and systems. Recent highlights in this field include the design and synthesis of a functional bacterial genome and a yeast chromosome, and generation of synthetic bacterial cells. The rational synthesis of "designer" organisms yield important insights into how organisms work and has the potential to revolutionise biotechnological applications in areas such as bioenergy and biomanufacturing.

Find more at http://jobs.mq.edu.au/cw/en/job/494553/lecturersenior-lecturer-level-bc-in-synthetic-biology-research-fellow-level-b-in-synthetic-biology-lecturersenior-lecturer-level-bc-in-bioinformatics

PhD opportunity at Université de Liège - Belgium

Mon, 01 Sep 2014 17:16:22 -0500

The Bioinformatics and Systems Biology Unit of Université de Liège (Belgium) is looking for a highly motivated master student with programming skills for a PhD thesis project (4 years, fully funded) with the goal of designing computational tools that use literature, genomic and structural data in order to infer regulatory and metabolic networks.

Applicants are invited to send their resume and a recommendation letter to Prof. Patrick Meyer (more details at www.biosys.ulg.ac.be )