BOL: Related items

progressiveCactus

Abhimanyu Singh — Thu, 18 May 2017 05:29:29 -0500

Progressive Cactus is a whole-genome alignment package.

Distribution package for the Prgressive Cactus multiple genome aligner. Dependencies are linked as submodules

https://github.com/glennhickey/progressiveCactus

Address of the bookmark: https://github.com/glennhickey/progressiveCactus

OMTools: a software package for visualizing and processing optical mapping data

BioJoker — Fri, 29 Mar 2019 01:21:54 -0500

OMTools, an efficient and intuitive data processing and visualization suite to handle and explore large-scale optical mapping profiles. OMTools includes modules for visualization (OMView), data processing and simulation. These modules together form an accessible and convenient pipeline for optical mapping analyses.

https://github.com/TF-Chan-Lab/OMTools

Address of the bookmark: https://github.com/TF-Chan-Lab/OMTools

GROMACS: a versatile package to perform molecular dynamics

Neel — Thu, 06 Aug 2020 22:40:38 -0500

GROMACS is a versatile package to perform molecular dynamics, i.e simulate the Newtonian equations of motion for systems with hundreds to millions of particles. GROMACS is able to work with many biochemical molecules like proteins, lipids and nucleic acids. The WeNMR GROMACS web portal combines the versatility of this molecular dynamics package with the calculation power of the eNMR grid. This will enable you to perform many simulations from the comfort of your internet browser anywhere in the world. The server is furthermore aimed to provide a user friendly and efficient MD experience by performing many preparation and optimization steps automatically.

GROMACS conda https://bioconda.github.io/recipes/gromacs/README.html

Address of the bookmark: http://haddock.science.uu.nl/enmr/services/GROMACS/main.php

The Snakemake Wrappers repository

BioStar — Thu, 19 Aug 2021 04:39:34 -0500

The Snakemake Wrapper Repository is a collection of reusable wrappers that allow to quickly use popular tools from Snakemake rules and workflows.

More at https://github.com/snakemake/snakemake-wrappers

Address of the bookmark: https://snakemake-wrappers.readthedocs.io/en/stable/

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//

Rdatamining.com : R and Data Mining

Poonam Mahapatra — Thu, 15 Aug 2013 18:37:23 -0500

This website presents examples, documents and resources on data mining with R.
Documents on using R for data mining are available to download for non-commercial personal use, including R Reference card for Data Mining, R and Data Mining: Examples and Case Studies and Time Series Analysis and Mining with R.

Address of the bookmark: http://www.rdatamining.com/

Upgrade R 3.0.3

Rahul Nayak — Mon, 10 Mar 2014 11:23:51 -0500

R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls and surveys of data miners are showing R's popularity has increased substantially in recent years. Recently the new version of R codename “Warm Puppy" have been released.

You can download the latest version from here http://cran.rstudio.com/ . Or, if you are using Windows, you can upgrade to the latest version using the installr package http://cran.r-project.org/web/packages/installr/ . Simply run the following code:

# installing/loading the package:
if(!require(installr)) {
install.packages("installr"); require(installr)} #load / install+load installr

updateR()

I try to keep the installr package updated and useful. If you have any suggestions or remarks on the package, you’re invited to leave a comment below.

If you use the global library system http://www.r-statistics.com/2010/04/changing-your-r-upgrading-strategy-and-the-r-code-to-do-it-on-windows/ , you can run the following in the new version of R:

source("http://www.r-statistics.com/wp-content/uploads/2010/04/upgrading-R-on-windows.r.txt")
New.R.RunMe()

Reference:

http://www.r-statistics.com/2014/03/r-3-0-3-is-released/

Surrogate Variable Analysis (SVA)

Jit — Thu, 30 Oct 2014 08:01:58 -0500

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways:

(1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS),

(2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and

(3) removing batch effects with known control probes (Leek 2014 biorXiv).

Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

More at http://www.bioconductor.org/packages/release/bioc/html/sva.html

A guide for complete R beginners :- R Syntax

Archana Malhotra — Fri, 20 Feb 2015 23:41:03 -0600

R is a functional based language, the inputs to a function, including options, are in brackets. Note that all dat and options are separated by a comma

Function(data, options)

Even quit is a function

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

Vector: a list of numbers, equivalent to a column in a table
Data Frame = a collection of vectors. Equivalent to a table

Hint:

Up/Down arrow keys can be use to cycle through previous commands

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 - *