Address of the bookmark: https://github.com/sc932/ALE

This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:

> install.packages("ggplot2")

In theory the package should just install, however:

• if you are using Linux and don’t have root access, this command won’t work.
• you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:

> install.packages("ggplot2", lib="/data/Rpackages/")
> library(ggplot2, lib.loc="/data/Rpackages/")


It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden,  we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2")
> library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

• create a file .Rprofile in your home area.
• Add the following piece of code to it:


cat(".Rprofile: Setting UK repositoryn")
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)


I found this tip in a stackoverflow answer .

To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option[0]. Here's a step-by-step guide:

Qualification : Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application/ Life Science/ Biotechnology/ Agricultural Science or equivalent. Desirable: Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics OR Post-Graduation in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application/ Life Science/ Biotechnology/ Agricultural Science or equivalent with 1st Division or 60% marks or equivalent with at least two years of research experience. Desirable:Expertise on use of various software/ tool.

No.of Post: 2

Emoluments for RA: Consolidated Rs. 24000/- per month + 30% HRA for Ph.D holders and consolidated Rs. 23000/- per month + 30% HRA for Master Degree.

Age Limit : Age should not be more than 40 years for the post of Research associate (5 years relaxation for SC/ST/ women candidates) and 3 years for OBC candidates as on date of interview.
How to apply

Interested candidates are invited to appear for Walk-In interview at Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi -110012, along with filled in application form , all the original certificates from matriculation onwards, Ph.D. or M.Sc. certificate (as the case may be) must be produced at the time of interview in either original or provisional, Bio-Data, attested copies of all experience certificates, testimonials etc., one passport size photograph and one set of the self-attested photocopies of all the required certificates from matriculation onwards and an attested copy of recent passport size photograph pasted onto the application form. Walk-in interview will be held on 16th April, 2015 at 10:30 a.m.

Click Here for Job Details & Application Form http://www.iasri.res.in/employment/employment.htm

Bioinformatics is full of opportunities. The sector is poised to open new avenues for the other related sectors also. But the biggest opportunity area in the Bioinformatics market will be in the drug discovery sector. Reduction of both the cost and time taken to discover a new drug due to fast development in the Bioinformatics tools and software zone is also making drug discovery an attractive field to venture in. Malaysian bioinformatics growth and future are discuss in this https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723929/ paper. Keeping all such inportance in mind, following universities in Malaysia offering bioinformatics cources:

3 program(s) at AIMST University, Malaysia

Master of Science in Biotechnology (MSc) - Bioinformatics by Research

Master of Science (M.Sc) in Medical Microbiology (Bioinformatics) by Research

Doctor of Philosophy in Biotechnology (PhD) - Bioinformatics by Research

1 program(s) at INTI International University and Colleges, Malaysia

American Degree Transfer Program (Biosciences) in Bioinformatics

3 program(s) at Management and Science University (MSU), Malaysia

Master in Bioinformatics (By Research)

PhD in Bioinformatics

Bachelor in Bioinformatics (Hons)

1 program(s) at Multimedia University (MMU), Malaysia

Bachelor of Science (Honours) Bioinformatics

1 program(s) at Universiti Industri Selangor (UNISEL) Bestari Jaya Campus, Malaysia

Bachelor of Bioinformatics (Hons)

2 program(s) at Universiti Malaysia Sabah (UMS), Malaysia

PhD - Doctor of Philosophy in Bioinformatics (By Research)

MSc - Master of Science in Bioinformatics (By Research)

6 program(s) at Universiti Putra Malaysia (UPM), Malaysia

MSc - Master of Science in Bioinformatics by Research

Master of Science in Bioinformatics and System Biology by Research

Master of Science (M.Sc) in Bioinformatics and Systems Biology (With Thesis)

PhD - Doctor of Philosophy in Bioinformatics by Research

PhD - Doctor of Philosophy in Bioinformatics and Systems Biology (With Thesis)

PhD - Doctor of Philosophy in Bioinformatics and System Biology by Research

1 program(s) at Universiti Selangor (UNISEL), Malaysia

Bachelor of Bioinformatics (Hons)

3 program(s) at Universiti Teknologi Malaysia (UTM), Malaysia

M.Sc - Master of Science (Bioscience) in Bioinformatics Research Group (BIRG) By Research

PhD - Doctor of Philosophy (Bioscience) in Bioinformatics Research Group (BIRG) By Research

Bachelor of Computer Science (BioInformatics)

4 program(s) at University of Malaya (UM), Malaysia

MSc - Master of Science in Bioinformatics by Research

Master in Bioinformatics by Coursework

PhD - Doctor of Philosophy in Bioinformatics by Research

Bachelor of Science (BSc) in Bioinformatics

3 program(s) at Perdana University, Malaysia

Master in Bioinformatics (By Research)

PhD in Bioinformatics

Bachelor in Bioinformatics (Hons)

3 program(s) at Monash University, Malaysia

Master in Bioinformatics (By Research)

PhD in Bioinformatics

Bachelor in Bioinformatics (Hons)

The real bioinformatics scope lies if there are research labs which work in this field. One has to take account of that. If so then try to get information of those labs and visit them to get a hang of the work they pursue. For detail Bioinformatics in Malaysia: Hope, Initiative, Effort, Reality, and Challenges are discussed in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723929/ paper.

Overview of SLiM and msprime

SLiM: Forward Genetic Simulator

SLiM is a free, open-source tool designed for forward genetic simulations. It allows researchers to model complex evolutionary scenarios, including selection, recombination, and demographic events, making it particularly useful for studying adaptation and selection in populations.

Key Features of SLiM:

• Simulates population evolution forward in time

• Supports custom evolutionary models using an embedded scripting language

• Allows modeling of spatial and ecological dynamics

• Provides high flexibility and extensibility for user-defined scenarios

• Available on GitHub as an open-source project

msprime: Ancestry and Mutation Simulator

msprime is an efficient, open-source tool that simulates ancestry and mutations using a coalescent framework. It is known for its high-speed performance and low memory requirements, making it a popular choice for large-scale genomic simulations.

Key Features of msprime:

• Implements coalescent simulations for ancestry modeling

• Efficiently simulates large population histories

• Supports the addition of mutations to genealogies

• Developed using an open-source community model

• Often faster and more memory-efficient than alternative simulators

Using SLiM and msprime with slendr

Both SLiM and msprime can be integrated with slendr, a framework that facilitates structured population genetic simulations. This integration allows for seamless comparison of simulation outputs.

How They Work Together:

• SLiM and msprime simulations can be analyzed within slendr.

• The ts_read() function in slendr enables loading and comparing tree sequence outputs from both simulators.

• This integration allows researchers to validate simulation results and gain deeper insights into evolutionary processes.

Performance Considerations

While SLiM offers powerful forward simulations with extensive customization, msprime is often preferred for its speed and memory efficiency when simulating ancestry and mutations. The choice between the two depends on the research goals:

• For detailed evolutionary modeling with selection and recombination: Use SLiM.

• For large-scale coalescent simulations with mutations: Use msprime.

• For comparing different simulation models and their outputs: Use slendr to integrate SLiM and msprime results.

Conclusion

SLiM and msprime are valuable tools for genome simulation, each serving distinct but complementary purposes in population genetics research. By leveraging the strengths of both simulators with slendr, researchers can conduct robust and efficient evolutionary simulations, enhancing our understanding of genetic diversity and adaptation.

For more information, check out the official GitHub repositories for SLiM and msprime, and explore the slendr framework for streamlined simulation workflow

Candidates desirous of doing a short-term training / final year dissertation project for MSc (Life Sciences/Bioinformatics/Biotechnology or any science discipline) at UIAR Biophysics and Bioinformatics department may please drop an email atanju@iiar.res.in along with their resume.

Selected candidates will be further intimated. There will be a fees charged for doing the project at UIAR. The projects will be experimental or computational or involve both.

The training scope will be in the following areas but not limited to:

Bioinformatics analysis, Docking and Virtual screening, Molecular Dynamics simulation, Cloning, expression and purification of proteins, Biophysical and Biochemical characterisation of proteins, Crystallization and Structural Studies.

Advertisement: www.iiar.res.in/?q=node/450

Is this available, or still under trial mode? 

https://www.nanoporetech.com/

https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system

Job Description

• Work within a fast-paced, collaborative environment with small project teams working on a variety of tasks ranging from new product development to DNA data processing
• Collaborate with Centrillion research scientists in order to bridge the gap between the laboratory and the digital world
• Develop tools to enable research projects to cope with the enormous amounts of data produced by modern DNA sequencing experiments
• Build simulation algorithms to help guide and analyze research done in the lab
• Solve challenging engineering problems that require the development of innovative algorithms

Requirements

• Strong background in mathematics/statistics with a degree in a related field
• Strong analytical, coding, communication, and organizational skills
• Experience with algorithm development, simulations, and data analysis
• Proficiency in at least one modern programming language (like Python or Perl)
• Experience analyzing genetic and biological data sets (e.g., DNA data analysis and image analysis)
• Experience with machine learning and pattern recognition is preferred

Please submit your resume at https://www.centrillionbio.com/career/ to apply for this position.