BOL: Related items

Galaxy Training Resources !

Jit — Sun, 27 Dec 2020 05:28:07 -0600

Welcome to Galaxy Training!

Collection of tutorials developed and maintained by the worldwide Galaxy community

Topic	Tutorials
Introduction to Galaxy Analyses	10
Assembly	6
Climate	3
Computational chemistry	6
Ecology	6
Epigenetics	6
Genome Annotation	3
Imaging	3
Metabolomics	4
Metagenomics	7
Proteomics	18
Sequence analysis	2
Statistics and machine learning	8
Transcriptomics	23
Variant Analysis	8
Visualisation	2

Address of the bookmark: https://training.galaxyproject.org/training-material/

Scientist position in Structural Bioinformatics at Lonza

Wed, 03 Feb 2021 21:38:06 -0600

Lonza (https://www.lonza.com/) are seeking a highly motivated and skilled (Senior) Scientist with experience in Structure-based Protein Engineering and Bioinformatics to join Lonza's Applied Protein Services (APS) Bioinformatics team based in Cambridge, UK.

More at https://instruct-eric.eu/jobs/scientist-position-in-structural-bioinformatics-at-lonza-cambridge-uk/

vcfR: a package to manipulate and visualize VCF data in R

Jit — Thu, 25 Oct 2018 09:05:59 -0500

VcfR is an R package intended to allow easy manipulation and visualization of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices from the VCF data for use with typical R functions. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file or converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and the R environment connecting familiar software with genomic data.

Address of the bookmark: https://github.com/knausb/vcfR

Bioinformatics in Africa: Part6 - Sudan

BioStar — Sat, 06 Feb 2021 21:20:59 -0600

Commission for Biotechnology & Genetic Engineering Khartoum: The Commission for Biotechnology and Genetic Engineering was established in 9/2/1993 as research unit. In addition to research activities it acts as focal point for the International Center for Biotechnology and Genetic Engineering. The commission conducts researches in order to play a part in solving economical, environmental, health and nutritional problems using modern research techniques with an emphasis on the applied researches in these areas. The laboratories were well furnished with the essential equipments and the catalyst infrastructure to facilitate emergence of a successful for research. The Commission equipped with a computer center and information to serve as informatics and Digital library.

Research Interest and Activities: 1. Plant Genetic Transformations
2. Molecular Population Genetics 3. Detection of human and Animals diseases 4. Breast Cancerspecific protein marker 5. Phytochemical 6. Genomic map 7. Bioremediation 8. Tissue Culture.

Web site and links: www.geocity.cbge.com

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

Bioinformatics Research Scientist @ Oklahoma State University (OSU)

Tue, 17 Aug 2021 13:24:39 -0500

This position is an early career research scientist in the area of Bioinformatics to support research projects involving faculty and staff, at Oklahoma State University (OSU). This is a highly technical position that requires a strong research background in biomedical or life sciences, including a high level of expertise with bioinformatics algorithms, databases, and analyses with a focus on next-generation sequence data. Although most of the projects will deal directly with the analysis of DNA and RNA sequence data the individual should be well versed in other types of data sources as well (i.e., microarrays) and handling of large datasets (using data analytics, machine learning, and deep learning techniques).

More at https://okstate.csod.com/ats/careersite/JobDetails.aspx?site=8&id=9874

Getting Started with Nextflow

LEGE — Sat, 18 Sep 2021 01:28:41 -0500

Introduction to Bioinformatics workflows with Nextflow and nf-core

Getting Started with Nextflow

Objectives Understand

What a workflow management system is.

Understand the benefits of using a workflow management system.

Explain the benefits of using Nextflow as part of your bioinformatics workflow.

Explain the components of a Nextflow script.

Run a Nextflow script.

Introduction to Bioinformatics workflows with Nextflow and nf-core

Getting Started with Nextflow

Address of the bookmark: https://carpentries-incubator.github.io/workflows-nextflow/aio/index.html

Genomic Impact

Jitendra Narayan — Wed, 10 Jul 2013 01:33:50 -0500

The ongoing genomic research in USA contributed $31 billion to the U.S. gross national product and helped support 152,000 jobs.

Reference: http://www.unitedformedicalresearch.com/wp-content/uploads/2013/06/The-Impact-of-Genomics-on-the-US-Economy.pdf

Bioinfo Lab

Fri, 04 Mar 2022 00:17:00 -0600

The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.

Contact:
Computer Science Building (Science Park 3)
Altenberger Str. 69, A-4040 Linz, Austria
Tel. +43 732 2468 4520 / Fax +43 732 2468 4539
E-mail secretary@bioinf.jku.at

http://www.bioinf.jku.at/

Five unique traits of effective computational biologist

Jitendra Narayan — Thu, 11 Jul 2013 13:12:51 -0500

Bioinformatics research is driven by large set of software, scripts, and tools to analyse gigantic biological data. Being a great biological programmer or bioinformatician involves more than writing code that works. The biological programmers who rise to the top ranks of their profession are not only good programmer but also expert in biological stuff. Moreover, In order to be a good and effective biological programmer, you need to possess a combination of traits that allow your computational as well as biological skill, experience, and knowledge to produce working code. There are some technically skilled biological programmers who will never be effective because they lack the other important traits needed. Here are top five traits that are necessary to become a great biological programmer.

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.