• In a GitHub or GitLab repository
• Free to use
• Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

WALK IN INTERVIEW

A walk-in Interview for the following position tenable at the Bioinformatics Infrastructure Facility (BIF), Department of Biotechnology, Alagappa University will be held at the Department of Biotechnology, Alagappa University, Karaikudi 630 003 on 15.05.2014 (Thursday) at 01:00 PM. This national facility is funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi. The main objectives of the Centre involve teaching and research activities in bioinformatics/biotechnology.

RA (One Post):

Salary : Rs. 11000 p.m. plus admissible HRA

Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/ Life Sciences

Interested candidates are encouraged to send their Curriculum Vitae by email to “sk_pandian@rediffmail.com” in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview. No TA&DA will be given for attending the interview.

Advertisement: http://www.alagappabiotech.org/Walk%20in%20interview.pdf

1. NCBI (National Center for Biotechnology Information):

   • NCBI provides various databases related to genetic information, including 16S rRNA sequences.
   • You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/).
   • Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.
   • You can download sequences individually or in batches using the provided tools.

2. GreenGenes:

   • GreenGenes is a widely used 16S rRNA gene sequence database.
   • You can access it at http://greengenes.secondgenome.com/.
   • GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.

3. SILVA:

   • SILVA (https://www.arb-silva.de/) is another comprehensive database for ribosomal RNA (rRNA) sequences.
   • It covers not only 16S rRNA but also other ribosomal RNA sequences.
   • SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.

4. Ribosomal Database Project (RDP):

   • RDP (http://rdp.cme.msu.edu/) is a curated database that offers 16S rRNA sequences.
   • It provides tools for sequence analysis and classification.
   • You can download sequences and taxonomy information from their website.

5. QIIME (Quantitative Insights Into Microbial Ecology):

   • QIIME (https://qiime2.org/) is a widely used bioinformatics platform for microbiome analysis.
   • It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.
   • QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.

Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.

NCBI 16s RNA database location ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz

Pay scale: Pay Band of Rs. 37400-67000 with AGP of Rs. 9000.

i. Educational Qualification: Good academic record with a Ph.D. Degree in the concerned/allied/relevant disciplines.

ii. A Master's Degree with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed).

iii. A minimum of eight years of experience of teaching and/or research in an academic/research position equivalent to that of Assistant Professor in a University, College or Accredited Research Institution/industry excluding the period of Ph.D. research with evidence of published work and a minimum of 5 publications as books and/or research/policy papers.

iv. Contribution to educational innovation, design of new curricula and courses, and technology - mediated teaching learning process with evidence of having guided doctoral candidates and research students.

v. A minimum score as stipulated in the Academic Performance Indicator (API) based Performance Based Appraisal System (PBAS), set out in UGC Regulation.

Download application form from website: http://www.allduniv.ac.in/

Send your application to the Registrar, University of Allahabad, Allahabad-211002 (U.P.) on or before 30th April 2014

For more details: http://www.allduniv.ac.in/images/adv/backlog/advt-details.pdf OR http://www.allduniv.ac.in/images/news/extension-notice.pdf

Last Apply Date: 30 May 2014

Address of the bookmark: http://teacheng.illinois.edu/

• Project name: SeQuiLa-cov

• Project home page: http://biodatageeks.org/sequila/

• Source code repository: https://github.com/ZSI-Bio/bdg-sequila

• Operating system: Platform independent

• Programming language: Scala

• Other requirements: Docker

• License: Apache License 2.0

Address of the bookmark: https://academic.oup.com/gigascience/article/8/8/giz094/5543653

1. Cancer Genomics

2. Microbial Genetics and Metagenomics

3. Human Infective Diseases

4. Computational Drug Design

Interested candidates may apply to Dr. Ranjith N. Kumavath, Assistant Professor & Head, Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Padannakad (PO), Nileshwar, Kasaragod-671328,Kerala. Email: RNkumavath@gmail.com

The tutorial is divided in three main parts:

• In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
• The Structure part is about molecular visualization, homology modeling and structural domain prediction.
• In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

• data = mock data for the snakefile to use
• Snakefile = name of the snakemake “formula” file
  • Note: The default file that snakemake looks for in the current working directory is the Snakefile. If you would like to override that you can specify it following the -s
    • snakemake -s snakefile.py
• envs = directory for storing the conda environments that the workflow will use.
• scripts = directory for storing python scripts called by the snakemake formula.
• config.json = json format file with extra parameters for our snakemake file to use.
• cluster.json = json format file with specification for running on the HPC
• samples.txt = file we will use later relating to the config.json file.

Run the snakemake file as a dry run (the example workflow shown above).

• This will build a DAG of the jobs to be run without actually executing them.
• snakemake --dry-run

User can execute rules of interest.

• snakemake --dry-run all VS. snakemake --dry-run call VS. snakemake --dry-run bwa

Run the snakemake file in order to produce an image of the DAG of jobs to be run.

• snakemake --dag | dot -Tsvg > dag.svg OR snakemake --dag | dot -Tsvg > dag.svg

Run the snakemake (this time not as a dry run)

1. snakemake --use-conda