BOL: Related items

Ten quick tips for deep learning in biology

Neel — Fri, 25 Mar 2022 18:35:12 -0500

By taking a comprehensive and careful approach to deep learning based on critical thinking about research questions, planning to maintain rigor, and discerning how work might have far-reaching consequences with ethical dimensions, the life science community can advance reproducible, interpretable, and high-quality science that is enriching and beneficial for both scientists and society.

Address of the bookmark: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009803

kebabs: package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods

Rahul Nayak — Fri, 04 Mar 2022 00:14:11 -0600

The kebabs package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define similarity measures between sequences. The package implements some of the most important kernels for sequence analysis in a very flexible and efficient way and extends the standard position-independent functionality of these kernels in a novel way to take the position of patterns in the sequences into account for the similarity measure.

http://www.bioinf.jku.at/software/kebabs/

http://bioconductor.org/packages/release/bioc/vignettes/kebabs/inst/doc/kebabs.pdf

Address of the bookmark: http://www.bioinf.jku.at/software/kebabs/

GRAbB: Selective Assembly of Genomic Regions, a New Niche for Genomic Research

Rahul Nayak — Sat, 26 Jan 2019 18:58:16 -0600

GRAbB is shown to be more efficient than MITObim in terms of speed, memory and disk usage. The other functionalities (handling multiple targets simultaneously and extracting homologous regions) of the new program are not matched by other programs. The program is available with explanatory documentation at https://github.com/b-brankovics/grabb. GRAbB has been tested on Ubuntu (12.04 and 14.04), Fedora (23), CentOS (7.1.1503) and Mac OS X (10.7). Furthermore, GRAbB is available as a docker repository: brankovics/grabb (https://hub.docker.com/r/brankovics/grabb/).

Address of the bookmark: https://github.com/b-brankovics/grabb

auN: a new metric to measure assembly contiguity

Jit — Tue, 02 Aug 2022 01:18:47 -0500

Given a de novo assembly, we often measure the “average” contig length by N50. N50 is neither the real average nor median. It is the length of the contig such that this and longer contigs cover at least 50% of the assembly. A longer N50 indicates better contiguity. We can similarly define Nx such that contigs no shorter than Nx covers x% of the assembly. The Nx curve plots Nx as a function of x, where x is ranged from 0 to 100.

Address of the bookmark: https://lh3.github.io/2020/04/08/a-new-metric-on-assembly-contiguity

R package for visualising GO enrichment

Jit — Mon, 22 Jul 2013 12:25:09 -0500

An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations.

Links @ http://raivokolde.github.io/GOsummaries/

Lab @ http://biit.cs.ut.ee/about/main

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

GrapheR !!!

John Parker — Thu, 14 Aug 2014 14:02:17 -0500

What a wonderful gem GrapheR is.... Oh yes it is. GrapheR is a GUI for base graphics in R by http://www.maximeherve.com/. The package provides a graphical user interface for creating base charts in R. It is ideal for beginners in R, as the user interface is very clear and the code is written along side into a text file, allowing users to recreate the charts directly in the console.

Adding and changing legends? Messing around with the plotting window settings? It is much easier/quicker with this GUI than reading the help file and trying to understand the various parameters.
Here is a little example using the iris data set.

library(GrapheR)
data(iris)
run.GrapheR()

This will bring up a window that helps me to create the chart and tweak the various parameters.

Finally, I find the underlying R code in a file created by GrapheR. For more details read also the package vignette, which is available in English, French and German!

RStudio

Jitendra Narayan — Sat, 27 Dec 2014 06:50:58 -0600

RStudio IDE is a powerful and productive user interface for R. It’s free and open source, and works great on Windows, Mac, and Linux.

The developers and expert trainers are the authors of several popular R packages, including ggplot2, plyr, lubridate, and others.

More at http://www.rstudio.com/

http://www.rstudio.com/products/RStudio/

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

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 - *