BOL: Related items

Bioinformatics Protocols

Rahul Nayak — Mon, 05 May 2014 10:21:41 -0500

RNA Seq

Basic Galaxy Tutorial

RNA-Seq tutorial based on Trapnell et al. (2012) Nature Protocols

In this tutorial we cover the concepts of RNA-Seq differential gene expression (DGE) analysis using a very small synthetic dataset from a well studied organism.

Advanced Galaxy Tutorial

RNA-Seq (Advanced) Tutorial

In this tutorial we compare the performance of three statistically-based differential expression tools:

* CuffDiff

* EdgeR

* DESeq2

Advanced Command Line Tutorial

Graphical Output with CummeRbund introduces some basic commands using the cummeRbund package of the R programming language

You will need to install R, RStudio and cummeRbund on your PC (explained in the Tutorial). You will learn how to produce graphical output from RNA-Seq analysis previously done using a Cuffdiff analysis.

Variant Detection

Basic Galaxy Tutorial

Variant Detection tutorial

In this tutorial we cover the concepts of detecting small variants (SNVs and indels) in human genomic DNA using a small set of reads from chromosome 22.

Advanced Galaxy Tutorial

Variant Detection (Advanced) Tutorial

In this tutorial we compare the performance of three statistically-based variant detection tools:

* SAMtools: Mpileup

* GATK: Unified Genotyper

* FreeBayes

Each of these tools takes as its input a BAM file of aligned reads and generates a list of likely variants in VCF format

Pipelines are for those who are comfortable with using the UNIX command line; and often allow more control over branching and iteration logic.

WGS/exome GATK-based variant calling pipeline

This is a basic variant-calling and annotation pipeline developed at the Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne. It is based around BWA, GATK and ENSEMBL and was originally designed for human (or similar) data. The master branch is configured for WGS data; there is an exome branch configured for variant calling in exome data.

To run the pipeline you will need Rubra: https://github.com/bjpop/rubra. Rubra uses the python Ruffus library: http://www.ruffus.org.uk/.

Protocols

Familial Variant Calling

In this protocol we discuss and outline the process of calling familial related mutations.

Somatic Variant Calling

In this protocol we discuss and outline the process of identifying somatic variants or mutations.

Assembly

Basic Galaxy Tutorial

Genome assembly tutorial

In this tutorial we carry out de novo assembly of a microbial genome. We have also written a De novo Genome Assembly for Illumina Data Protocol for a more generic description of the method.

Protocol

De novo Genome Assembly for Illumina Data

In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes. Use our Genome assembly tutorial to learn a specific case of using Galaxy to carry out de novo assembly of a microbial genome.

Small RNAs

Basic Galaxy Tutorial

Quality control for small RNA

This tutorial covers initial steps of the workflow for analysis of short RNA expression such as a quality control of the raw reads, processing of the raw reads for the subsequent analysis and initial quality assessment of the library.

ChIP Seq

Protocol

ChIP-Seq

In this protocol we discuss ChIP-Seq: a method to analyze the interaction between proteins and DNA.

Amplicons

Protocol

Amplicon Alignment

In this protocol we discuss and outline the process of aligning custom amplicons using primers for high precision.

Learn Galaxy

Introduction to Galaxy, for those who are very new to Galaxy.

Using Histories and Workflows, for those with some Galaxy knowledge.

The Galaxy project website has many tutorials and screencasts about using Galaxy and the tools, and developing new tools.

Address of the bookmark: https://genome.edu.au/wiki/Learn

Integrative Genomics Viewer (IGV) tutorial

Neel — Sat, 12 Jul 2014 15:16:23 -0500

The Integrative Genomics Viewer (IGV) from the Broad Center allows you to view several types of data files involved in any NGS analysis that employs a reference genome, including how reads from a dataset are mapped, gene annotations, and predicted genetic variants.

http://www.broadinstitute.org/igv/

Address of the bookmark: https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial

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

OpenCPU

Rahul Nayak — Sun, 05 Jul 2015 18:34:46 -0500

OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R.

The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

Painless package development for R

Abhi — Tue, 03 May 2016 05:31:06 -0500

Devtools makes package development a breeze: it works with R’s existing conventions for code structure, adding efficient tools to support the cycle of package development. With devtools, developing a package becomes so easy that it will be your default layout whenever you’re writing a significant amount of code.

Before you get started be sure to check out:

Address of the bookmark: https://www.rstudio.com/products/rpackages/devtools/

Fools guide

Poonam Mahapatra — Sun, 02 Apr 2017 14:31:18 -0500

This website and accompaning documents are intended as a tool to help researchers dealing with non-model organisms acquire and process transcriptomic high-throughput sequencing data without having to learn extensive bioinformatics skills. It covers all steps from tissue collection, sample preparation and computer setup, through addressing biological questions with gene expression and SNP data.

http://sfg.stanford.edu/denovo.html

http://sfg.stanford.edu/sequencing.html

http://sfg.stanford.edu/BLAST.html

http://sfg.stanford.edu/denovo.html

Address of the bookmark: http://sfg.stanford.edu/guide.html

Bioinformatics Tutorial !

Neel — Mon, 22 Aug 2022 23:56:22 -0500

This site aims to be a useful resource for bioinformatics beginners. Feel free to jump right in with the section most relevant to you, and if you're not sure, then the place to start is definitely Unix

Address of the bookmark: https://astrobiomike.github.io/

BioStats class tutorial

Shruti Paniwala — Thu, 16 Mar 2017 01:50:50 -0500

Nice biostat turorial by Ingo Ruczinski

Address of the bookmark: http://www.biostat.jhsph.edu/~iruczins/teaching/

Machine learning training and courses in bioinformatics !

Rahul Nayak — Tue, 31 Dec 2019 19:33:07 -0600

Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

Omega2: metagenome assembly pipeline

Jit — Mon, 10 Jul 2017 05:56:07 -0500

Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets.

Address of the bookmark: http://omega.omicsbio.org/