BOL: Related items

One page R survival guide !!

Rahul Nayak — Thu, 28 May 2015 21:10:12 -0500

There any many of the documents have been developed and tested by scientist around the world. I found this one really useful. The data used is available for download as data.zip.

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

Templates for the Data Scientist
1. A Template for Preparing Data: *OnePageR - *R
2. A Template for Building Models: *OnePageR - *R
Getting Started as a Data Scientist
1. Getting Started with R and Rattle: *Lecture - *Laboratory
2. Introducing and Interacting with R: *Lecture - *Laboratory
3. BasicR - OnePage(R) - Writing R scripts
Dealing With Data
1. Read Data into R: *OnePageR - *R
2. Explore and Summarise Data: *OnePageR - *R
3. Transform Data: *OnePageR - *R
4. Dealing with Dates and Time: (PDF, R) Dates and Time
5. Visualising Data with GGPlot2: *OnePageR - *R
6. Visualising Data with Maps *OnePageR - *R
7. Spatial (R) Spatial Analysis
8. Handling Big Data *OnePageR - *R
Descriptive Analytics
1. Cluster Analysis: *Lecture - *OnePageR - *R
2. Association Analysis: *Lecture
Predictive Analytics
1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
3. SVM (R)
4. KernLab (R)
5. NeuralNetworks (R)
6. NNet (R)
Model Delivery
1. Evaluating Models: *OnePageR - *R
2. Evaluation (R)
3. Scoring (R)
4. PMML (R) Exporting Models for Deployment
Advanced Topics
1. Text Mining: *OnePageR - *R
Advanced R Topics
1. Plots (PDF, R) Miscellaneous Plots
2. Functions (PDF, R) Writing Functions in R
3. Parallel (PDF, R) Parallel Execution
4. Packaging (R) Pulling it Together into a Package
5. Doing R with Style: *OnePageR - *R
6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *

RA Bioinformatics at NATIONAL BUREAU OF ANIMAL GENETIC RESOURCES

Thu, 28 May 2015 19:25:33 -0500

NATIONAL BUREAU OF ANIMAL GENETIC RESOURCES

Near Basant Vihar G.T. Road Bypass P.O. Box No.129

Karnal - 132001 (Haryana)

WALK-IN-INTERVIEW

A walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132001 at 10:30 AM on 10.06.2015 to select One Research Associate as per details given below:

1. One post of Research Associate under National Fellow project entitled “Genome data mining to unravel molecular basis of thermotolerance and adaptation to diverse environments in native cattle and buffaloes”.

The post duration is Upto 22.05.2016 or earlier & Co-terminus with the project.

Essential Qualifications: Master’s degree (M.Sc. / M.V.Sc.) in Biotechnology/ Animal Genetics and Breeding/ Life Sciences/ Bioinformatics with 2 Years research experience in relevant subject or Ph.D in any of the above subjects.

Desirable: Working Experience in molecular biology, gene expression/ microarray data analysis, SNP genotyping and sequence data analysis, mammalian cell-culture handling etc.

Emolument: Rs. 23,000/- per month + HRA as per admissibility

Research Associate: ONE

Duration of engagement: Upto 22.05.2016 or earlier Co-terminus with the project

Age Limit:  40 years for Men  45 years for women as on date of interview

Note: Relaxation in age will be admissible for SC/ST & OBC candidates as per Govt. of India /ICAR norms

1. The applicants must bring with them original documents and brief of research work done during post graduation along with a set of photocopy and latest two passport size photographs. 2. A panel of selected candidates will also be made which may be utilized for filling of positions of shorter durations in future if demand arises. 3. Experience certificate in original, if any 4. The above positions are purely on temporary basis and are coterminus with the project. No TA/DA will be paid to attend the interview. 5. Any other clarifications can be had on the date of interview. 6. The Director’s decision will be final and binding on all respects.

Advertisement: http://210.212.93.85/RAadvertisiment.pdf

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")

JRF Position – Bioinformatics Department, Aravind Medical Research Foundation (AMRF), Madurai.

Fri, 12 Jun 2015 05:42:12 -0500

Applications are invited from eligible candidates for the post of Junior Research Fellow (JRF) to work at the Department of Bioinformatics, Aravind Medical Research Foundation in the following DST-SERB funded project “Clinical exome analysis pipeline for eye disease next-generation sequencing panel”.

Post: Junior Research Fellow (1 Position)

Duration: Three years

Qualification: First class in M.Sc/M.tech in Bioinformatics/Life Sciences/Biophysics/ Biostatistics/Bioengineering. Experience in Database development, NGS data analysis, Systems Biology and Structural Bioinformatics is desired. Preference will be given to the candidates with good computer programming skills in C, C++, R, Perl, PHP, Unix Scripting etc.

Selected candidates will be paid fellowship as per existing DST norms.

How to apply:

Candidates are requested to apply through one of the two modes given below
1. Online application – Click here to submit the online application https://docs.google.com/forms/d/16h2GLnQ-Ny-tLtlgfY3Bx3sCjeHJE30cfhJaDqW_uRs/viewform?c=0&w=1
2. Application forms can be downloaded from here.https://docs.google.com/file/d/0BwwJEudQStxFWXdNWXl4NWtDaWc/edit
Filled in application form should be sent by post to Dr. D. Bharanidharan, Department of Bioinformatics, Aravind Medical Research Foundation No 1, Anna Nagar Madurai – 625 020,

Candidates should apply by online or submit their applications by post on or before 15th June, 2015. Only Short listed candidates will be called for an interview. No TA/DA will be paid.

MinION_GC: An R script to do some QC on MinION data

Radha Agarkar — Sun, 03 Dec 2017 15:19:18 -0600

Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

Pattern Matching Problem Solution with Perl

Jit — Tue, 09 Jun 2015 23:58:45 -0500

Problem at http://rosalind.info/problems/1c/

#Find all occurrences of a pattern in a string.
#Given: Strings Pattern and Genome.
#Return: All starting positions in Genome where Pattern appears as a substring. Use 0-based indexing.

use strict;
use warnings;

my $string="GATATATGCATATACTT";
my $subStr="ATAT";
my $kmer=length($subStr);

kmerMatch ($string, $subStr, $kmer);

sub kmerMatch { #Check the exact matching kmers with sliding window
my ($string, $myStr, $kmer)=@_;
for (my $aa=0; $aa<=(length($string)-$kmer); $aa++) {
    my $myWin=substr $string, $aa,$kmer;
    if ($myWin eq $myStr) {
        #print "$myWin eq $myStr\n";
        print $aa;
    }
}
}

R 3.5.0 has been Released!

Jit — Thu, 26 Apr 2018 11:31:58 -0500

The latest version of R is a major release! It comes with a ton of new features, including performance and speed improvements
All R packages will now be byte-compiled, hence boosting packages installed from GitHub
You may need to re-install all previously installed R packages; old scripts however will continue to work normally

More at https://cran.r-project.org/doc/manuals/r-release/NEWS.html

Ensembl 27

Jit — Tue, 16 Jun 2015 16:10:36 -0500

What is new?

Expansion of Protists and Fungi with hundreds of annotated genomes
Variation data for bread wheat, rice, Aedes aegypti, and Ixodes scapularis
Whole genome alignments for O. longistaminata and T. cacao
Non-coding RNA gene models in Bacteria
New assembly of tomato (version 2.50)
Full support for UCSC Track Hub format for hosting your own data in Ensembl

More at http://www.ensembl.info/blog/2015/06/16/ensembl-genomes-release-27-is-out/

Making 2D Hilbert Curve

Jit — Mon, 17 Sep 2018 05:43:35 -0500

Hilbert curve is a type of space-filling curves that folds one dimensional axis into a two dimensional space, but still keeps the locality. It has advantages to visualize data with long axis in following two aspects:

greatly improve resolution of the visualization fron n to √nn;
easy to visualize clusters because generally data points in the axis will also be close in the 2D space.

This package aims to provide an easy and flexible way to visualize data through Hilbert curve. The implementation and example figures are based on following sources:

Address of the bookmark: https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html

RA Bioinformatics at Institution: Centre for Human Genetics, Bangalore

Wed, 17 Jun 2015 19:14:37 -0500

Institution: Centre for Human Genetics,
Bangalore
Discipline: Molecular Genetics of Human Disease Biology

Minimum qualification: MSc in any branch of life sciences

Applications are invited for the position of a Research Assistant in the Centre for Human Genetics, Bangalore.

The project involves identification of mutations in MPS (mucopolysaccharidosis) patients, and study of their predicted effects to understand how the mutations lead to disease.

Techniques used will be genomic DNA isolation, PCR, DNA sequencing and sequence analysis. Computational tools would also be used to analyse and interpret data.

Candidates may be assigned work in the ongoing project or in new ones.

The candidate who is selected and joins would acquire hands-on experience in research and the capability to conduct insightful research.

Candidates applying for the position should have an MSc in any branch of life sciences. Those with research experience in cell and molecular biology, and high NET/ GATE score would be preferred.

The successful applicant is expected to stay for at least one and a half years.

Please apply with CV to Sudha Srinivasan (sudha@ibab.ac.in), stating where you saw this ad.