BOL: Related items

mutatrix: a population genome simulator which generates simulated genomes.

Jit — Tue, 28 Jan 2020 04:06:58 -0600

genome simulation across a population with zeta-distributed allele frequency, snps, insertions, deletions, and multi-nucleotide polymorphisms

More at https://github.com/ekg/mutatrix

./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta

Address of the bookmark: https://github.com/ekg/mutatrix

Roth Lab

Tue, 11 Mar 2014 17:43:45 -0500

The Roth Lab seeks insight into biological systems through genome- and proteome-scale experimentation and analysis.

Current computational interests:

Systematic analysis of genetic epistasis to identify redundant or compensatory systems and to reveal order of action in genetic pathways.
Using knockout, knockdown, or overexpression, or other perturbation experiments in combinations of genes in S. cerevisiae, C. elegans or mouse.
Using genome-scale genotyping of natural polymorphisms in S. cerevisiae and human populations.
Alternative splicing and its relationship to protein interaction networks.
Integrating large-scale studies including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns to quantitatively assign function to genes and guide experimentation.

More at http://llama.mshri.on.ca/index.html

useR!

Jitendra Narayan — Wed, 10 Jul 2013 14:41:49 -0500

The R project actively supports two conference series, organized regularly by members from the R community: useR! - providing a forum to the R user community - and DSC - a platform for developers of statistical software.

Recently useR! conference have been organized University of Castilla-La Mancha, Albacete, Spain.

http://www.edii.uclm.es/~useR-2013//

Postdoc Position in Computational Biology

Fri, 14 Mar 2014 01:38:49 -0500

The Computational Biology Group of Interdisciplinary Center for
Clinical Research (IZKF) Aachen, RWTH Aachen University Hospital,
Aachen, invites applicants for PhD candidate or postdoctoral position
in computational biology in one of the following topics:

1) Statistical machine learning methods for the analysis of medical
epigenomics data.

2) Sequence analysis algorithms for detection of RNA-DNA interactions.

Applicants should hold a M.Sc . or PhD in Computer Science or related
areas. Experience in the analysis of biological sequences, gene
expression and gene regulation is desirable. The candidate should have
solid programming skills (C, Python and/or R) and acquaintance with
Linux. Experience with high performance computing is a plus. The
working language of the group is English.

The position is based on the German TV-L 13 salary scale, including
all German social benefits (health insurance and pension scheme). The
expected starting date is September 2014. Interested candidates should
send a CV, statement of research interests and the names of three
references to jobs@costalab.org.

More at http://costalab.org/wp/phd-and-postdoc-position-in-computational-biology/

RNA-Seq Data Pathway and Gene-set Analysis Workflows

Jit — Fri, 25 Oct 2013 08:00:48 -0500

It describe the GAGE (Luo et al., 2009) /Pahview (Luo and Brouwer, 2013) workflows on RNA-Seq data pathway analysis and gene-set analysis. The gage package (2.12.0) now includes a new tutorial, “RNA-Seq Data Pathway and Gene-set Analysis Workflows“.

First cover a full workflow from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes. The same workflow can be used for GO analysis or other types of gene set analysis too. We also describe joint workflows, i.e. to do gene-level analysis using one of the major RNA-Seq analysis tools, DEseq/DEseq2, edgeR, limma and Cufflinks, and feed the results into GAGE/Pahview for pathway analysis or visualization. All these workflows are implemented in R/Bioconductor.

The work ows cover the most common situations and issues for RNA-Seq data pathway analysis. Issues like data quality assessment are relevant for data analysis in general yet out the scope of this tutorial. Although we focus on RNA-Seq data here, but pathway analysis work ow remains similar for microarray, particularly step 3-4 would be the same. Please check gage and pathview vigenttes for details.

Note: You need to update to current release versions of R(3.0.2)/ Bioconductor(2.13) to use all the features.

Reference:

Please check it out:
http://bioconductor.org/packages/release/bioc/html/gage.html
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

BioGeek Fun

Jit — Sun, 16 Mar 2014 06:33:31 -0500

1. A futuristic computational biology student was told to write "It is in my gene!!!" on the board 100 times as a punishment. here's his response -

use warnings;
for ($count=1; $count <=100; $count++) { print "It is in my gene!!!";}

I guess, he is gonna to be a real biogeek. Nice try though. Smart kid.

2. In some perl script I found this
. . . . . .
. . . . . .
# It works for me, only God understood how it is working
while (/(<\/[^>]+>)|(<[^>]+>)|(<[^>]+>)$|([^><]+)/go) {
            $startGene=$1;
            $beginChromosome=$2;

. . . . . .
.. . . . . .
}

3. One more interesting message in Perl found …. It will must tickle you bone :)
open(my $fh, "<", "gene.txt") or kill " Me if you think this is a mistake :$!";

4. From the Perl

while () { # "The Mothership Connection is here!"
print “$_\n”; # Printing the offspring :)

5. Perl message
if ($1) { print “Just found a the error in chromosome !!!, yahoo…”; else { “That is not error, but mutation you moron!”;

6. One genome database curator walk in wine bar asked the bartender:
CREATE TABLE gene IF NOT EXISTS SexOnTheBeach;

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 - *

Jarvis’ laboratory

Tue, 18 Mar 2014 18:53:47 -0500

Dr. Jarvis’ laboratory studies the neurobiology of vocal communication. We want to know how the brain generates, perceives, and learns behavior. We use vocal communication as a model behavior. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. We use an integrative approach that combines behavioral, anatomical, electrophysiological, and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The overall goal of the research is to advance knowledge of the neural mechanisms for vocal learning and basic mechanisms of brain function.

Lab page: http://jarvislab.net/

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")

Professor and Associate Professors (PB-IV) Assistant Professors (PB-III) Job at IIIT, Allahabad

Mon, 31 Mar 2014 08:09:07 -0500

Indian Institute of Information Technology, Allahabad
Devghat, Jhalwa, Allahabad – 211012, Uttar Pradesh, India
E-mail: contact@iiita.ac.in, faculty.applications@iiita.ac.in
Web: www.iiita.ac.in Phone : 0532-2922031/27/67

Applications are invited on prescribed format along with self attested copies of the certificates for Faculty Positions in the following areas:
Sciences – Systems Biology, Computer Aided Drug Designing, Statistics, Applied Mathematics, Applied Physics. BioMedical Engineering – BioMechanics, BioMedical Instrumentation.

Last Date : May 10, 2014

Details are available on our website : http://www.iiita.ac.in

http://www.iiita.ac.in/downloads/announcements/uploads/FACULTY_Advertisement_NO-FS-01_2014130.pdf