BOL: Related items

Snippy: Rapid haploid variant calling and core SNP phylogeny

Neel — Sat, 11 Aug 2018 11:06:56 -0500

Snippy finds SNPs between a haploid reference genome and your NGS sequence reads. It will find both substitutions (snps) and insertions/deletions (indels). It will use as many CPUs as you can give it on a single computer (tested to 64 cores). It is designed with speed in mind, and produces a consistent set of output files in a single folder. It can then take a set of Snippy results using the same reference and generate a core SNP alignment (and ultimately a phylogenomic tree).

snippy --cpus 16 --outdir mysnps --ref Listeria.gbk --R1 FDA_R1.fastq.gz --R2 FDA_R2.fastq.gz

Address of the bookmark: https://github.com/tseemann/snippy

The 8000 years old Tibetian gene mutation !!!

Neel — Wed, 20 Aug 2014 21:57:44 -0500

A new study has provided insight into how gene mutation around 8,000 years ago helped Tibetans' to survive in the thin air on the Tibetan Plateau, where an average elevation is of 14,800 feet.

A study led by University of Utah scientists is the first to find a genetic cause for the adaptation, a single DNA base pair change that dates back 8,000 years and demonstrate how it contributes to the Tibetans' ability to live in low oxygen conditions.

About 8,000 years ago, the gene EGLN1 changed by a single DNA base pair. Today, a relatively short time later on the scale of human history, 88 percent of Tibetans have the genetic variation, and it was virtually absent from closely related lowland Asians. The findings indicate the genetic variation endows its carriers with an advantage.

In those without the adaptation, low oxygen caused their blood to become thick with oxygen-carrying red blood cells, an attempt to feed starved tissues, which could cause long-term complications such as heart failure. The researchers found that the newly identified genetic variation protected Tibetans by decreasing the over-response to low oxygen.

Reference: http://www.nature.com/nature/journal/v512/n7513/abs/nature13408.html

Assistant Professor - Medical Bioinformatics

Wed, 23 Jul 2014 05:00:38 -0500

Advt. No : ME-I/A-IV/03/14

No.of Posts:01 (SC)

Pay Scale:

Pay Band of Rs.15600-39100 + Rs.6000/- GP +NPA @ 25% of Basic Pay +Learning Resource Allowance @ Rs.20,000/-P.A.+ Conveyance Allowance @ Rs. 1650/-P.M.+ Academic Allowance @ Rs.2500/- P.M. and other admissible allowances.

Qualifications:

Area of Specialization:-

Bioinformatics/Computational/Biology/Genomics/ Proteomics/ Structural Biology

1. Postgraduate qualification, e.g. Master’s Degree in Biotechnology/Bioinformatics/ Biophysics.

2. A Doctorate Degree of recognized University/Institute in a basic or allied Medical Science subject e.g. Medical Biotechnology/Biophysics. Bioinformatics/X-ray Crystallography/

Immunology/Structural Biology etc

Experience:

1.Minimum three years teaching and/or research experience in a recognized medical/research Institution in an allied medical subject after obtaining doctorate degree and preferably in Medical

Molecular Biology/ Biophysics/Structural Biology/Genomics and Clinical Proteomics/Computational Biology.

2. Minimum two publication with atleast one in international journal and atleast one as first author

Desirable:-

Consistently excellent scholastic/academic record, demonstrated ability to write grant proposal/(s) successfully, Post Doctoral training in a frontier area of medical Bioinformatics Research and of direct relevance to clinical diagnosis or patient care (preferably from a recognized top-ranking medical institution abroad)

Send your applications to O/O, Deputy Registrar, Recruitment & Establishment Cell, University of Health Sciences, Rohtak by 08.7.2014

For more details,please visit website:http://pgimsrohtak.nic.in/2014%20AP%20Advt.pdf

Explore taxdump files !

Jit — Sat, 08 Feb 2020 04:44:55 -0600

This is an extract of taxdump-readme.txt to be found at 
ftp://ftp.ncbi.nih.gov/pub/taxonomy/

The content of the archive
--------------------------

It may look like this:

delnodes.dmp
division.dmp
gencode.dmp
merged.dmp
names.dmp
nodes.dmp
readme.txt

The readme.txt file gives a brief description of *.dmp files. These files
contain taxonomic information and are briefly described below. Each of the
files store one record in the single line that are delimited by "\t|\n"
(tab, vertical bar, and newline) characters. Each record consists of one 
or more fields delimited by "\t|\t" (tab, vertical bar, and tab) characters.
The brief description of field position and meaning for each file follows.

nodes.dmp
---------

This file represents taxonomy nodes. The description for each node includes 
the following fields:

	tax_id					-- node id in GenBank taxonomy database
 	parent tax_id				-- parent node id in GenBank taxonomy database
 	rank					-- rank of this node (superkingdom, kingdom, ...) 
 	embl code				-- locus-name prefix; not unique
 	division id				-- see division.dmp file
 	inherited div flag  (1 or 0)		-- 1 if node inherits division from parent
 	genetic code id				-- see gencode.dmp file
 	inherited GC  flag  (1 or 0)		-- 1 if node inherits genetic code from parent
 	mitochondrial genetic code id		-- see gencode.dmp file
 	inherited MGC flag  (1 or 0)		-- 1 if node inherits mitochondrial gencode from parent
 	GenBank hidden flag (1 or 0)            -- 1 if name is suppressed in GenBank entry lineage
 	hidden subtree root flag (1 or 0)       -- 1 if this subtree has no sequence data yet
 	comments				-- free-text comments and citations

names.dmp
---------
Taxonomy names file has these fields:

	tax_id					-- the id of node associated with this name
	name_txt				-- name itself
	unique name				-- the unique variant of this name if name not unique
	name class				-- (synonym, common name, ...)

division.dmp
------------
Divisions file has these fields:
	division id				-- taxonomy database division id
	division cde				-- GenBank division code (three characters)
	division name				-- e.g. BCT, PLN, VRT, MAM, PRI...
	comments

gencode.dmp
-----------
Genetic codes file:

	genetic code id				-- GenBank genetic code id
	abbreviation				-- genetic code name abbreviation
	name					-- genetic code name
	cde					-- translation table for this genetic code
	starts					-- start codons for this genetic code

delnodes.dmp
------------
Deleted nodes (nodes that existed but were deleted) file field:

	tax_id					-- deleted node id

merged.dmp
----------
Merged nodes file fields:

	old_tax_id                              -- id of nodes which has been merged
	new_tax_id                              -- id of nodes which is result of merging

The 5 reasons to mistakes at bioinformatics work !!!

Jit — Thu, 24 Jul 2014 02:51:41 -0500

When you're just starting out with biological programming, it's easy to run into complex problems that make you wonder how anyone has ever managed to write a program. There are some problems that trip up nearly every bioinformatician--everything from getting started understanding the biological problems to dealing with program design. Some random mistakes are so prominent that even experienced biological programmers do it. The 8 years in bioinformatics and my few random observations, most of them are snarky. These reasons will always take longer than expected and compel you to postpone your project deadline.

1.Stupid for biologist: Biology is so complex that it will make bioinformatician feel stupid. There are no any universal fixed rules; it can surprise you any time. So be nice to biologists who ask questions and resolve your biological puzzles. Sometime you will have no idea what the hell you were doing either.

2.Puzzling why: Do not hesitate to ask question. Especially. at the beginning of project you will have to ask a lot of questions. Instead of puzzling it out at end check out and clear your doubt even for a single error. It may can leads to wrong conclusion.

3.Running marathon: The most of the biological software’s documentation is always incomplete. In other word they are no more than 95 percent complete. Sometime a single problem can halt your entire project for months. Compilation and running the pipelines in tedious because almost all are interdependent and need proper configuration. I face the same kind of problem with Evolver :( …

4.Folders missing: The pipelines generate lots of data, and we keep them in several folders for future use. But sometime we delete them by mistake and move to recovery…

5.Digging deeper: Digging deeper is fruitful, but some time it can be catastrophic. You may get frustrated or direction less. So keep a biologist with you for rescue …. Sometime an expert computer programmer to handle your server. Remember, the server will always go down when you need it the most.

The most common frustrating common line: Why do we do this again?

alienness : Rapid Detection of Candidate Horizontal Gene Transfers across the Tree of Life

Jit — Mon, 12 Mar 2018 09:24:40 -0500

Horizontal gene transfer (HGT) is the transmission of genes between organisms by other means than parental to offspring inheritance. While it is prevalent in prokaryotes, HGT is less frequent in eukaryotes and particularly in Metazoa. Here, we propose Alienness, a taxonomy-aware web application available at http://alienness.sophia.inra.fr

http://www.mdpi.com/2073-4425/8/10/248

Address of the bookmark: http://alienness.sophia.inra.fr/cgi/index.cgi

PhD opportunity at Université de Liège - Belgium

Sat, 02 Aug 2014 01:12:43 -0500

PhD opportunity at Université de Liège - Belgium

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 )

For more information : www.biosys.ulg.ac.be

Blaxter Lab

Thu, 19 Nov 2020 08:05:28 -0600

Using these high quality genomes we explore

the evolutionary history of genes and species, building phylogenetic trees of life
the contrasting roles of horizontal gene transfer and introgression in shaping evolution
the biology of symbioses, especially symbioses between eukaryotes and bacteria, and between parasites and their hosts
the processes that drive the evolution of pattern in the structure of chromosomes
the diversity of meiofauna, particularly tardigrades, nematodes and other Ecdysozoa
the genomics of extremophilia

More at https://www.sanger.ac.uk/group/blaxter-group/

MEGADOCK 4.0

Suleman Khan — Thu, 07 Aug 2014 18:08:54 -0500

An ultra–high-performance protein–protein docking software for heterogeneous supercomputers

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of over 97% strong scaling.

Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.

Contact: akiyama@cs.titech.ac.jp

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/08/06/bioinformatics.btu532.short

TMRCA Calculator

BioStar — Wed, 03 Feb 2021 05:07:30 -0600

This program calculates the probability that two people have a certain number of generations between them, based on the standard infinite alleles formula of Walsh. It calculates both the probability of being at an exact number of generations back to the Most Recent Common Ancestor (MRCA) of a certain pair of people and the cumulative probability that the actual number of generations is less than a certain value. Note that the convention using generations is changed from an earlier version of this calculator which used "transmission events". It can list both result types in a table or graph. In either case the horizontal axis stops at the point where the cumulative probability reaches 95% or 10 generations, whichever is longer, or an absolute max of 50,000. Beyond 90% the calculation becomes inaccurate.

https://clandonaldusa.org/index.php/tmrca-calculator

Address of the bookmark: https://clandonaldusa.org/index.php/tmrca-calculator