BOL: Related items

Which are the best statistical programming languages to study for a bioinformatician?

Jitendra Narayan — Wed, 10 Jul 2013 14:35:34 -0500

In Bio-informatics based genome sequencing and predicting metabolic pathways research jobs I used Matlab, SAS, SPSS, R and several Bioconductor packages. Matlab had a lot of powerful tools and was easy to use, whereas SPSS is for non-programmers and R need programming skills. I am wondering what other people think is best? or there might not be one specific language but a few that lend themselves best to Bio-informatics work that is math heavy and deals with a large amount of data.

Assistant Professor - Computational Biology and Bioinformatics in Navi Mumbai

Fri, 04 Dec 2015 20:40:59 -0600

No. ACTREC / ADVT-A/2/2015
Pay in Pay band and Grade Pay : PB-3 (Rs 15,600-39,100) Pay in pay band Rs 21,900+ G.P. of Rs 7,600
Total emoluments = 82,000/- p.m. & nbsp
Educational Qualification : Ph.D. or MD/Ph.D.
Experience : Post MD / Ph.D. Research experience of 5 years The last date of application submission is January 15th, 2016.
Interested candidates shall send the applications through email: office.sao(at)actrec.gov.in.
For More Details : www.actrec.gov.in/data%20files/Vacancies/2015/Faculty-Positions-SOE-24-11-15.pdf

Upcoming R Webinar

Jitendra Narayan — Wed, 11 Sep 2013 10:30:16 -0500

This webinar will describe an R based approach to considerably speed GWAS computation time on a notebook book computer.

More http://www.extension.org/pages/68354/upcoming-webinar:-fast-semi-parallel-linear-and-logistic-regression-for-genome-wide-association-studi#.UjCL9azyPqV

ArrayGen Next Generation Genome Browser Coming Soon !!!

ArrayGen Technologies — Thu, 03 Dec 2015 05:52:09 -0600

The ANG genome browser is a visualization tool, developed by ArrayGen Technologies. This is a fast and an efficient genome browser, built with Javafx and Java swing. ANG genome browser was built for latest next generation sequencing data analysis. It is platform independent and much simpler to use.

The main features are, it supports many standard file formats such as GFF, BED, GTF, FASTA, VCF, BAM and it can be integrated with other browsers or tools for analysis of genome.

translate2R

Rahul Nayak — Fri, 21 Nov 2014 01:16:06 -0600

After their presentation at the international “user!” conference, data analysis specialist eoda starts the public alpha testing of translate2R. With the start of alpha testing the innovative migration solution by the company hailing from Kassel discards the working title “translateR” and takes on the final product brand name “translate2R”. translate2R is a service for the automated translation of SPSS® syntax to R code, therefore supporting data analysts with a quick and low-risk migration to R.

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

Import R Data

Abhimanyu Singh — Wed, 12 Jul 2017 08:30:46 -0500

It is often necessary to import sample textbook data into R before you start working on your homework.

Excel File

Quite frequently, the sample data is in Excel format, and needs to be imported into R prior to use. For this, we can use the function read.xls from the gdata package. It reads from an Excel spreadsheet and returns a data frame. The following shows how to load an Excel spreadsheet named "mydata.xls". This method requires Perl runtime to be present in the system.

> library(gdata)                   # load gdata package
> help(read.xls)                   # documentation
> mydata = read.xls("mydata.xls")  # read from first sheet

Alternatively, we can use the function loadWorkbook from the XLConnect package to read the entire workbook, and then load the worksheets with readWorksheet. The XLConnect package requires Java to be pre-installed.

> library(XLConnect) # load XLConnect package
> wk = loadWorkbook("mydata.xls")
> df = readWorksheet(wk, sheet="Sheet1")

Minitab File

If the data file is in Minitab Portable Worksheet format, it can be opened with the function read.mtp from the foreign package. It returns a list of components in the Minitab worksheet.

> library(foreign)                 # load the foreign package
> help(read.mtp)                   # documentation
> mydata = read.mtp("mydata.mtp")  # read from .mtp file

SPSS File

For the data files in SPSS format, it can be opened with the function read.spss also from the foreign package. There is a "to.data.frame" option for choosing whether a data frame is to be returned. By default, it returns a list of components instead.

> library(foreign) # load the foreign package
> help(read.spss) # documentation
> mydata = read.spss("myfile", to.data.frame=TRUE)

Table File

A data table can resides in a text file. The cells inside the table are separated by blank characters. Here is an example of a table with 4 rows and 3 columns.

100   a1   b1
200   a2   b2
300   a3   b3
400   a4   b4

Now copy and paste the table above in a file named "mydata.txt" with a text editor. Then load the data into the workspace with the function read.table.

> mydata = read.table("mydata.txt")  # read text file
> mydata                             # print data frame
   V1 V2 V3
1 100 a1 b1
2 200 a2 b2
3 300 a3 b3
4 400 a4 b4

For further detail of the function read.table, please consult the R documentation.

> help(read.table)

CSV File

The sample data can also be in comma separated values (CSV) format. Each cell inside such data file is separated by a special character, which usually is a comma, although other characters can be used as well.

The first row of the data file should contain the column names instead of the actual data. Here is a sample of the expected format.

Col1,Col2,Col3
100,a1,b1
200,a2,b2
300,a3,b3

After we copy and paste the data above in a file named "mydata.csv" with a text editor, we can read the data with the function read.csv.

> mydata = read.csv("mydata.csv")  # read csv file
> mydata
  Col1 Col2 Col3
1  100   a1   b1
2  200   a2   b2
3  300   a3   b3

In various European locales, as the comma character serves as the decimal point, the function read.csv2 should be used instead. For further detail of the read.csv and read.csv2 functions, please consult the R documentation.

> help(read.csv)

Working Directory

Finally, the code samples above assume the data files are located in the R working directory, which can be found with the function getwd.

> getwd() # get current working directory

You can select a different working directory with the function setwd(), and thus avoid entering the full path of the data files.

> setwd("") # set working directory

Note that the forward slash should be used as the path separator even on Windows platform.

> setwd("C:/MyDoc")

Fellowship (Doctoral Research In Biomedical Genomics, Including Statistical Genomics)

Sun, 20 Dec 2015 06:03:43 -0600

Fellowship (Doctoral Research In Biomedical Genomics, Including Statistical Genomics)
Eligibility : MSc(Bio-Chemistry, Bio-Informatics, Bio-Tech, Mathematics / Applied Mathematics, Stati, Zoology)
Location : Kolkata
Last Date : 31 Dec 2015
Hiring Process : Written-test

NO: 340/ESTB/ADMN/NIBMG/2015-16
Doctoral Research In Biomedical Genomics, Including Statistical Genomics conduct National Institute of Biomedical Genomics (NIBMG)
Information For Students Interested To Pursue Doctoral Research In Biomedical Genomics, Including Statistical Genomics, At The National Institute Of Biomedical Genomics (Nibmg), Kalyan
Eligibility conditions for specific areas of research are :
Statistical Genomics : An applicant who wishes to pursue research in Statistical Genomics should hold a Master's degree (First class or equivalent) in a relevant discipline (Statistics, Mathematics, Bioinformatics, or a related discipline)
Biomedical Genomics : An applicant who wishes to pursue research in any area of biomedical genomics, other than statistical genomics, should hold a Master's degree (First class or equivalent) in a relevant discipline (Biochemistry, Biotechnology, Molecular Biology, Genetics, Zoology, Physiology, or a related discipline)
Fellowship : An applicant should have passed the NET conducted by CSIR/UGC/ICMR/DBT within the past ONE year AND should have been awarded a valid Junior Research Fellowship from CSIR, UGC, ICMR, DBT (Category-I only), DST (INSPIRE), NBHM. Preference will be given to candidates with demonstrable research training in the form of summer training or short-term courses in established research laboratories in preparation for a research career in biomedical sciences
How to apply
Online application will be accepted until 5 PM of December 31, 2015. A formal interview of the short-listed candidates will be held on January 12, 2016

More at http://www.nibmg.ac.in/?q=Career%20Opportunities

R googleVis examples

Rahul Nayak — Sun, 10 Dec 2017 06:13:42 -0600

It may take a little while to load all charts. Please be patient. All charts require an Internet connection.

These examples are taken from the googleVis demo. You can execute the demo via

library(googleVis)
demo(googleVis)

For more details about the charts and further examples see the helpfiles of the individual googleVis function and review the Google Charts API documentation and Terms of Service.

Address of the bookmark: https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html

Julia Programming Language, a Python and R rival

Radha Agarkar — Sat, 25 Aug 2018 04:46:39 -0500

Big data has grown to become one of the most lucrative fields. In fact, data scientists are some of the most sought people. They are usually hired to analyze, control and parse large chunks of data. Implementing these actions using traditional techniques is not a walk in the park. This is why most data scientists prefer using programming languages such as R and Python. However, there is one more programming language that can do the job. That is Julia programming language.

What Is Julia Language?

Julia is a programming language that came into the limelight in 2012. It is a general-purpose programming language that was designed for solving scientific computations. Julia was meant to be an alternative to Python, R and other programming languages that were mainly used for manipulating data. This is because it has numerous features that can minimize the complexities of numerical computations.

Julia optimizes on the best features of Python and R while at the same time overlooks their weaknesses. This explains why it is viewed as an alternative to these programming languages. For instance, it utilizes the readability and simplicity of Python then performs faster.

Julia is the most preferred programming language for data scientists and mathematicians. This is because its core features are similar to the ones that are used on most data software. Also, the language is ideal for these two subjects because its syntax is similar to the standard mathematical formulas.

Key Features Of Julia Language
Uses JIT Compilation
Parallelism
Dynamic Typing
Simple Syntax
Allows Metaprogramming
Accessible to Libraries
-1-Array Indexing

Julia Vs Python And R Programming Languages
1. Speed
Julia is faster than both Python and R. This is a very critical aspect that is given special attention in the big data programming. The high speed of Julia is because of JIT compilers. You will need to install external libraries on Python to achieve similar speed.

2. Syntax
Julia has a math-friendly syntax. The syntax of this programming language is similar to the mathematical formulas hence can be used to perform mathematical and scientific computations. This syntax makes it easier to learn than Python.

3. Parallelism
Although both Python and R use parallelism, Julia uses a top-level parallelism. Julia allows the processor to perform to the optimum level than what Python and R can achieve.

4. Versatility
Julia programming language is more versatile than Python and R. It allows a programmer to move from different codes and functions with ease.

The only area that Python and R are superior to Julia is in terms of community. Given that Julia is a new programming language, it has a small community as compared to others which have been around for years.

In overall Julia programming language is a better alternative that you can use to handle Big data projects. Despite having a small community, it is one of those programming languages that you can easily learn.

Research Scientist at IIT Madras

Mon, 07 Mar 2016 04:06:13 -0600

Research Scientist/Project Associate/Project Assistant Jobs opportunity in Indian Institute of Technology Madras (IIT Madras)

Research Scientist

Qualification : Ph.D in any branch of life science or bioinformatics or computational biology Experience : Previous experience in Molecular Biology, Cell Biology, Biochemistry and Genome/big data analysis is desirable but not mandatory

No. of Vacancy : 02

Project Associate

Qualification : MSc in any branch of life science Experience Previous experience in Molecular Biology, Cell Biology and Biochemistry is desirable but not mandatory

No. of Vacancy : 02

Project Assistant

Qualification : BSc in any branch of life science or chemical science Experience Previous experience any branch in Life science, Molecular Biology, Cell Biology and Biochemistry is desirable but not mandatory

No. of Vacancy : 03
How to apply

Interested candidates can forward their profiles to email id: nctb@iitm.ac.in latest by 18th March, 2016

More at https://www.iitm.ac.in/content/national-cancer-tissue-bio-bank-department-biotechnology-iitm-chennai-vacancy-various-post
https://www.iitm.ac.in/sites/default/files/notices/vacancy.pdf