BOL: Related items

Bioinformatics JRF/SRF position at NATIONAL RESEARCH CENTRE ON PLANT BIOTECHNOLOGY

Sun, 11 May 2014 22:29:12 -0500

NATIONAL RESEARCH CENTRE ON PLANT BIOTECHNOLOGY
LBS, CENTRE, PUSA CAMPUS, IARI NEW DELHI
NEW DELHI – 110 012

WALK- IN –INTERVIEWS

Eligible candidates may appear in Walk-in-Interview on May 23, 2014 at 10 AM for the posts of Research Associates & Senior Research Fellows (SRF) in the following DST/DBT/ICAR funded projects.

1 NPTC Project on Bioinformatics and Comparative Genomics

Research Associate (One)

Rs. 24000/- + 30% HRA for masters degree holder with more than 4 years experience

Essential: Ph D in Plant Molecular Biology & Biotechnology/Genetics 0r Candidates who have already submitted their Ph D thesis in above subjects

Desirable: Research experience in Genomics, Molecular biology, Microarrays analysis, Gene cloning, transgenic Techniques , and computational analysis.

Senior Research Fellow ( UGCCSIR/ DBT/ ICAR Net qualified only): (One)

Rs. 16000/- + 30% HRA and Rs. 18000+30 HRA from 3rd year onwards

Essential:

1. ICAR/ UGCCSIR/DBT Net qualified only

2. M. Sc. (with thesis) in Biotechnology, Life Sciences, Biosciences/ Bioinformatics, Genetics/ Plant Pathology with experience in molecular biology.

Or M.Sc with more than 3 years research experiences

3. B.Sc. Agriculture or Biology

Desirable:
1. M. Sc. with thesis
2. Experience in molecular biology, plant tissue culture
3. Bioinformatics knowledge is important

2 DST JC Bose National Fellowship

Research Associate (Bioinformatics) : One

Rs.22000/- + 30% HRA for 1 & 2nd Yr., Rs. 23000+ 30% HRA for 3rd year and Rs. 24000+30% HRA for 4th &5th yr

Essential: M Ph D in Plant Molecular Biology & Biotechnology/Genetics

Desirable: Research experience in Genomics, Molecular biology, Microarrays analysis, Gene cloning, transgenic Techniques , and computational analysis.

Age limit: Max.35 years (Age relaxation of 5 years for SC/ST & women and 3 years for OBC)

The posts are purely temporary in nature and are co-terminus with the project. Initially the offer will be made for one year only and may be further extendable based on performance of the candidate. The interview will be held on May 23 , 2014 at 10:00 AM at NRCPB, LBS Building, Pusa Campus, IARI, New Delhi- 110012. The candidates must bring four copies of biodata (in the prescribed proforma), original certificates, attested photocopies of each of the certificates and an attested copy of recent passport size photograph. No. TA/DA would be given for the appearance in interview. Only the candidates having essential qualification would be entertained for the interviews. Short-listing of candidates based on academic merit and experience will be done in case of large number of applicants.

Advertisement: http://www.nrcpb.org/sites/default/files/Advertisement%20for%20RA%20and%20SRF%20Position.pdf

genoPlotR - plot gene and genome maps project!

Abhimanyu Singh — Wed, 12 Dec 2018 08:33:41 -0600

genoPlotR is a R package to produce reproducible, publication-grade graphics of gene and genome maps. It allows the user to read from usual format such as protein table files and blast results, as well as home-made tabular files.

Features

Linear representation of several segments of DNA
Comparisons represented by areas between the segments (like Artemis, for example)
Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
Plot several subsegments of the same segment on the same line, separated by a //
Automatic or manual placement of the segments on the plot
Add annotations to all the lines
Create smart, automatic annotations for genomes, based on gene names
Add a user-generated tree
Add a global scale or a scale to each line
Use user-defined graphical functions to represent genes

Address of the bookmark: http://genoplotr.r-forge.r-project.org/

A History of Bioinformatics (in the Year 2039)

Wed, 23 Jul 2014 06:37:51 -0500

C. Titus Brown http://video.open-bio.org/video/1/a-history-of-bioinformatics-in-the-year-2039

tinycov: standalone command line utility written in python to plot coverage from a BAM file

Jit — Mon, 23 Mar 2020 06:22:08 -0500

Tinycov is a small standalone command line utility written in python to plot the coverage of a BAM file quickly. This software was inspired by Matt Edwards' genome coverage plotter.

To install the stable version: pip3 install --user tinycov

To install the development version:

git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .

Address of the bookmark: https://github.com/cmdoret/tinycov

Scientists map 17,294 proteins produced in human body

Jit — Thu, 29 May 2014 01:57:55 -0500

Indian scientists missed the genomic profiling bus, but they've more than made up for it by creating the first human proteome map which is an extension of the genomic study. Till now, here is no direct equivalent for the human proteome. But recently two groups present mass spectrometry-based analysis of human tissues, body fluids and cells mapping the large majority of the human proteome.

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB

Circos visualization !

BioStar — Mon, 06 Mar 2023 06:01:32 -0600

Circos visualization

https://wlcb.oit.uci.edu/modules/index.html

Address of the bookmark: https://wlcb.oit.uci.edu/NG-Circos

How to sequence the human genome - Mark J. Kiel

Fri, 30 May 2014 13:24:11 -0500

View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence. Lesson by Mark J. Kiel, animation by Marc Christoforidis.

Grinder / Biogrinder - A versatile omics shotgun and amplicon sequencing read simulator

Jit — Wed, 24 May 2017 08:41:41 -0500

Grinder is a versatile program to create random shotgun and amplicon sequence libraries based on DNA, RNA or proteic reference sequences provided in a FASTA file.

Grinder can produce genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, metaproteomic shotgun and amplicon datasets from current sequencing technologies such as Sanger, 454, Illumina. These simulated datasets can be used to test the accuracy of bioinformatic tools under specific hypothesis, e.g. with or without sequencing errors, or with low or high community diversity. Grinder may also be used to help decide between alternative sequencing methods for a sequence-based project, e.g. should the library be paired-end or not, how many reads should be sequenced.

Address of the bookmark: https://sourceforge.net/projects/biogrinder/files/biogrinder/

Genomics and Personalized Medicine

Sun, 01 Jun 2014 23:38:42 -0500

(October 20, 2009) Michael Snyder, Professor of Genetics and Chair of the Department of Genetics at Stanford, discusses advances in gene sequencing, the impact of genomics on medicine, the potential for personalized medicine. and efforts at Stanford to further study these issues. Stanford Mini Med School is a series arranged and directed by Stanford's School of Medicine, and presented by the Stanford Continuing Studies program. Featuring more than thirty distinguished, faculty, scientists and physicians from Stanford's medical school, the series offers students a dynamic introduction to the world of human biology, health and disease, and the groundbreaking changes taking place in medical research and health care. Stanford University http://www.stanford.edu Stanford University School of Medicine http://med.stanford.edu Stanford Continuing Studies http://continuingstudies.stanford.edu Stanford University Channel on YouTube: http://www.youtube.com/stanford

LRCstats: Long Read Correction Statistics

Jit — Fri, 05 Jan 2018 04:04:20 -0600

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.

https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: https://github.com/cchauve/lrcstats