BOL: Related items

JCVI:Python utility libraries on genome assembly, annotation and comparative genomics

Jit — Tue, 17 Mar 2020 06:19:06 -0500

Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

https://github.com/tanghaibao/jcvi

More at https://github.com/tanghaibao/jcvi/wiki

Address of the bookmark: https://github.com/tanghaibao/jcvi

gggenomes: A grammar of graphics for comparative genomics

Surabhi Chaudhary — Mon, 01 Feb 2021 14:47:32 -0600

gggenomes is a versatile graphics package for comparative genomics. It extends the popular R visualization packageggplot2 by adding dedicated plot functions for genes, syntenic regions, etc. and verbs to manipulate the plot to, for example, quickly zoom in into gene neighborhoods.

Address of the bookmark: https://github.com/thackl/gggenomes

Pevzner Lab !

Thu, 02 Nov 2023 05:39:26 -0500

The laboratory works on genome sequencing, immunoproteogenomics, antibiotics sequencing, and comparative genomics - computational technologies that enabled new applications and allowed scientists to attack biological problems that remained beyond the reach of previous techniques.

https://bioalgorithms.ucsd.edu/research4.html

DIYA: a bacterial annotation pipeline for any genomics lab

Jit — Fri, 30 Jun 2017 08:48:26 -0500

DIY Genomics is an open source bioinformatics consortium intended to bring a collection of tools and libraries into the hands of small scale genomics labs for the process of sequence assembly and annotation. Projects include DIYA, MGAP, CRISPR, and DIYGV

http://gmod.org/wiki/Diya

Address of the bookmark: https://sourceforge.net/projects/diyg/

ShendureLab

Thu, 28 Dec 2017 09:57:50 -0600

The mission of our lab is to develop and apply new technologies and methods for genetics, genomics and molecular biology. Most of our work exploits next-generation DNA sequencing which is effectively emerging as a broadly enabling microscope for the measurement of biological phenomena. Our ongoing work generally falls into six areas. These are listed below as links to representative publications in each area.

Developing New Molecular Methods

Genomic Approaches to Developmental Biology

Massively Parallel Functional Genomics

Translating Genomics to the Clinic

Genetic Basis of Human Disease

Genome Sequencing Technologies

http://krishna.gs.washington.edu/index.html
http://www.gs.washington.edu/faculty/shendure.htm

ASAR: Advanced metagenomic Sequence Analysis in R

Jit — Mon, 09 Jul 2018 05:20:50 -0500

An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

The Global Alliance for Genomics and Health (GA4GH)

Jit — Sat, 08 Feb 2020 07:37:31 -0600

The Global Alliance for Genomics and Health (GA4GH) is a policy-framing and technical standards-setting organization, seeking to enable responsible genomic data sharing within a human rights framework.

GA4GH core funders and sponsors enable our work and allow us to convene the international genomic data sharing community.

https://www.ga4gh.org/

Address of the bookmark: https://www.ga4gh.org/

Bioinformatics Scientist – ICMR Computational Genomics Centre

Sat, 26 Dec 2020 10:18:29 -0600

ICMR invites online applications, from Indian Citizens, up to 8th January 2020 till 5:30 PM to fill up the following post to be filled purely on a temporary basis under “ICMR Computational Genomics Centre” under Dr. Harpreet Singh, Head, Division of Biomedical Informatics (BMI), ICMR HQRS, New Delhi 110029.
The Terms & Conditions for the post are as follows:

a) Scientist-B – UR (2 posts-Bioinformatics) on consolidated salary of Rs.48,000/- pm + HRA

b) Scientist C – UR (1 post -Bioinformatics) on consolidated salary of Rs. 51,000 pm+ HRA

c) Scientist B- UR (2 post-Statistics) on a consolidated salary of Rs.48,000/- pm +HRA

d) Computer Programmer 1 post UR & 1 post SC on a consolidated salary of Rs. 32,500/- pm

e) Research Assistant -UR 1 post on a consolidated salary of Rs. 31,000/- pm

More at https://projectjobs.icmr.org.in/sccbioinformatics/uploads/recruitment/Adv_BMI_24122020.pdf

Machine Learning for Genomics

Jit — Thu, 09 Sep 2021 11:26:32 -0500

Module 1: Statistics for genomics (2-8 August 2021)

A simple intro to statistical distributions
hypothesis testing
linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

Understanding basic intuition behind machine learning approaches.
Using unsupervised learning to cluster and visualise data points
Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

Understanding and using supervised learning methods for predictive purposes
How to measure prediction performance
Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

Manthey Research Group – Evolutionary Genomics

Thu, 22 Aug 2024 06:25:55 -0500

We focus on fundamental questions in genomics, ecology, and evolution. Our methods include fieldwork and labwork, but most of our time is spent analyzing genomics data using computational biology approaches.

Ant / bacteria co-evolution, landscape genomics, and population genomics
Vertebrate and/or invertebrate genome evolution

If you might be interested in joining our research group, send an email with your intent and why this group would potentially be a good fit for your future goals along with a CV / Resume to jdmanthey (at) gmail (dot) com

More at https://mantheylab.org/