BOL: Related items

PhD student / Bio-informatician in computational protein modeling

Sun, 23 Feb 2020 03:46:46 -0600

PhD student / Bio-informatician in computational protein modeling
Job Profile
You will perform research on drug/protein interaction analysis in the context of lung cancer, using computational protein modeling. You will implement existing models predicting drug efficacy, related to EGFR-driven cancer. You will translate these models to novel oncogenes, including ROS1. You will validate these models against experimental data from a parallel project, with the final goal of deployment of your methods into clinical decision making. Your work will be embedded in an international network consisting of both academic partners and ROS1-NSCLC patient organizations.

Requirements

You are (or soon will be) a master in bio-informatics. You have strong ICT skills and you are eager to fully submerge into the world of protein modeling. You have good experience with Linux and one or more programming languages as well as knowledge of tertiary structure analysis. Candidates with a Master degree in one of the life sciences (Biomedical sciences, Biochemistry, Bio-engineering, Biostatistics, …), with relevant interest and extended experience in this field are also welcome. A general background cancer biology and genetics is needed. You are willing and eligible to apply for a personal PhD fellowship with the Flemish FWO (FWO.be). Therefore, it is required that you hold a master degree from a European university, and have not obtained your master diploma more than three years ago (see FWO website for detailed conditions). Proficiency in English, and good communication skills, both oral and written, are required. You are highly motivated, and you like to work in an interactive research team. You are willing to work on a 4-year PhD project starting beginning of 2020.

What we offer

We offer a one year position, as a PhD student, which can be extended up to 4 year upon positive evaluation, even if a personal fellowship application is not successful. Wages are according to the standard Flemish bursary levels for PhD students.

Interested?
For additional information please contact dr. Geert Vandeweyer. To apply, send a copy of your CV including details of your relevant skills and a motivation letter by e-mail to dr. Geert Vandeweyer (geert.vandeweyer@uantwerpen.be) before March 15, 2020.

Source:https://academicpositions.be/ad/university-of-antwerp/2020/phd-student-bio-informatician-in-computational-protein-modeling/141252?utm_source=jooble&utm_medium=cpc&utm_campaign=jooble

HybPiper

Jit — Fri, 04 Nov 2016 05:02:10 -0500

HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.

Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.

HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information, please see our wiki.

HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.

Address of the bookmark: https://github.com/mossmatters/HybPiper

Nigerian Bioinformatics and Genomics Network (NBGN)

Tue, 31 Aug 2021 08:29:40 -0500

This is to announce the second official conference of the Nigerian Bioinformatics and Genomics Network (NBGN). October 11-13,2021 at Landmark University, Omu-Aran, Kwara State and Zoom ( conference link to be announced soon

#NBGN21

www.nbgn21conference.com

visNetwork: an R package for network visualization, using vis.js javascript library

Jit — Wed, 09 Jan 2019 11:00:32 -0600

visNetwork is an R package for network visualization, using vis.js javascript library (http://visjs.org/). All remarks and bugs are welcome on github : https://github.com/datastorm-open/visNetwork.

Features

Based on htmlwidgets, so :

compatible with shiny, R Markdown documents, and RStudio viewer

The package proposes all the features available in vis.js API, and even more with special features for R :

easy to use
custom shapes, styles, colors, sizes, …
works smooth on any modern browser for up to a few thousand nodes and edges
interactivity controls (highlight, collapsed nodes, selection, zoom, physics, movement of nodes, tooltip, events, …)
visualize rpart tree

Address of the bookmark: https://datastorm-open.github.io/visNetwork/

netGO: R-Shiny package for network-integrated pathway enrichment analysis

Jit — Wed, 12 Feb 2020 12:40:54 -0600

netGO is an R/Shiny package for network-integrated pathway enrichment analysis.
netGO provides user-interactive visualization of enrichment analysis results and related networks.

Currently, netGO supports analysis for four species (Human, Mouse, Arabidopsis thaliana,and Yeast)
These data are available from netGO-Data repository.

https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa077/5728635

Address of the bookmark: https://github.com/unistbig/netGO

Coronavirus Resources !

Neel — Wed, 25 Mar 2020 17:11:33 -0500

2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

Bioinformatics Tutorial !

Neel — Mon, 22 Aug 2022 23:56:22 -0500

This site aims to be a useful resource for bioinformatics beginners. Feel free to jump right in with the section most relevant to you, and if you're not sure, then the place to start is definitely Unix

Address of the bookmark: https://astrobiomike.github.io/

scikit-bio™ is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics.

Jit — Fri, 02 Mar 2018 04:29:47 -0600

scikit-bio is currently in beta. We are very actively developing it, and backward-incompatible interface changes can and will arise. To avoid these types of changes being a surprise to our users, our public APIs are decorated to make it clear to users when an API can be relied upon (stable) and when it may be subject to change (experimental). See the API stability docs for more details, including what we mean by stable and experimental in this context.

Address of the bookmark: http://scikit-bio.org/

Finding a mimicry game for teaching on-line and mentioned general resources

Shruti Paniwala — Tue, 28 Jun 2022 07:32:05 -0500

Mimicry and other resources
Mimicry games:
Great Heliconius game:
http://heliconius.org/evolving_butterflies/
(See also 
https://royalsocietypublishing.org/doi/10.1098/rspb.2020.0014)
Other one, a bit less friendly:
https://ccl.northwestern.edu/netlogo/models/Mimicry
Camouflage practical
https://alexis-catherine.github.io/publication/natural-selection-and-camouflage/
(NetLogo also has one: 
https://ccl.northwestern.edu/netlogo/models/BugHuntCamouflage)
Peppered moth game:
https://askabiologist.asu.edu/peppered-moths-game/play.html

General resources
The always popular Populus:
https://cbs.umn.edu/populus/overview
Drift & Gene Flow 
https://cartwrig.ht/apps/genie/
(Cock van Oosterhout has a great ppt to lead students through this)
See also https://cartwrig.ht/apps/redlynx/
https://demonstrations.wolfram.com/ReplicatorMutatorDynamicsWithThreeStrategies/
NetLogo:
http://ccl.northwestern.edu/netlogo/models/index.cgi
Population Genetics:
https://www.radford.edu/~rsheehy/Gen_flash/popgen/
Evolution in general
https://evolution.berkeley.edu/evolibrary/home.php
Mitochondrial Eve:
https://projects.ncsu.edu/cals/gn/ex/mit-eve.html
Y chromosomes:
https://projects.ncsu.edu/cals/gn/ex/y-chrom.html
A professional online package from Michael Kasumovic:
https://arludo.com/
a compilation of resources:
https://planted.botany.org/index.php?P=Home
Finally, Donald Forsdyke has some great on-line videos explaining
evolutionary principles (occasionally in a fake Scottish accent):
http://post.queensu.ca/~forsdyke/videolectures.htm

RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data

Jit — Mon, 27 Nov 2017 08:25:33 -0600

RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.

This work has been accepted for publication:

MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to Nucleic Acids Research April 4, 2012.

If you have any questions or bug reports, please let us know at .

Address of the bookmark: http://kiwi.cs.dal.ca/Software/RITA