GATK Best Practices Workshop: Data Pre-Processing

GATK Best Practices Workshop
Data Pre-Processing

This past Monday and Tuesday I was able to attend the GATK Best Practices Seminars being held at the Broad Institute in Cambridge, MA.

Here's the download of the Monday morning session:

And by way of note...all slides from the workshop can be found on a link via the GATK forum.

By way of introduction...

• GATK doesn't really do 'mapping' though they have suggestions for tools of preferred use for mapping.
• GATK is a post-processing tool.
• The integrated genome viewer (IGV) is a user friendly tool for visualizing whole genome data.
• Be sure to pay attention to your study design...are you 'deep' sequencing of 'shallow' sequencing? This will affect data processing and interpretation.

With deep sequencing designs:

• You will have increased sensitivity for variant detection
•  More accurate genotyping
• Caveat: No information about multiple samples as a deep sequencing design often times means you can only do 1 or very few samples.

With shallow sequencing designs:

• Your sensitivity will depend on the frequency of the variation of interest.
• Not as accurate genotyping potentially.
• You may discover more 'total' variants across more samples, however your confidence in real variants versus 'error' due to not deep enough sequencing may be reduced.

 Best Practices:

• Based on human whole genome or whole exome analysis
• Definitely hit up the documentation, it is quite extensive
• Not necessarily applicable to all datasets, can be used as a general guideline but if you are working with bacteria or viruses (like me) then some parts may not be applicable or calibrated the way you need them to be so you'll have to 'play around'
• Depends on design (see above)
• Use the forum, a great place to lob questions at the developers regarding the tools.

So here's the layout: Also available on their website

credit:  http://www.broadinstitute.org/gatk/guide/best-practices

Other suggestions before we jumped in per the Exome sequencing they have been doing:

• Add 50 bp of 'padding' on either side of your intervals (ie. exome, genes, loci, regions, etc...)
• Run at least 50 samples/run
• If you don't have 50 samples you can pull from the 1000 genomes project and do 'joint-calling', if you don't work on humans...pull samples from your preferred database make sure formatting, meta-information, is the same though so you'll have to do some more manipulation potentially.
• You can also use 'hard filters' per the best practices recommendations.

BTW....Tangent if you ever get the opportunity to attend seminar/talk with Eric Banks, do it. He's a fabulous speaker who answers any question great or small.

Let's get started...

Blog Series: WoG: Cesky Krumlov; Day 4: Assembly

Dr. Rayan Chikhi

Pennsylvania State University

Topic: De novo Assembly

A whole day of assembly!

There is no single program right now that is considered 'the assembler'. Different assemblers have advantages and disadvantages as well as things they are generally useful and not useful for. So one thing in todays assemblers is that they all take a lot of time and memory to run--especially when doing de novo assembly. One of the exceptions is the program Minia, developed by Dr. Chikhi which was designed to run efficiently using low memory requirement.

One of the important things that you need to know for assembly is what a k-mer is. A k-mer is any sequences with length k.

AGC is a k-mer with k=3

AGCT is a k-mer with k=4

AGCTT is a k-mer with k=5

You hopefully get the idea. 

There are two essential methods that assemblers use to assemble: de Bruijn graphs and overlap/string graphs. Now we sort of covered this in the Assembly prep blog...lets see if I can explain this better here now...

Blog Series: WoG, Cesky Krumlov; Day 3: Genomics Study Design, a.k.a. "To seq or not to seq, that is the question!"

So I totally slacked off today and went to lunch instead of writing the usual afternoon blog of the morning session, I hope you'll all forgive me, but to be fair those of you in the U.S. weren't even out of bed by the time lunch for me rolled around!

All of the presentations so far have been really awesome and informational so I hope you will take advantage of all the slides being posted on the website!

Today's morning session is great for PIs and students wishing to design sequencing experiments and determining to get an NGS platform.

I will be interjecting during this blog post...my interjections will be in a different color (probably green, because I like the color green).

Blog Series: Workshop on Genomics, Cesky Krumlov; Preparation--Assembly

Section 4: Assembly

This next section deals with an introduction to assembly and assemblers. All the suggested readings are freely available which is awesome:

1. Birol, I et al., 2009. De novo transcriptome assembly with ABySS. Bioinformatics. 25:2872-2877.
2. Zerbino, DR and E Birney. 2008. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Research 18:821-829.
3. Langmead, B. 2010. Aligning short sequencing reads with Bowtie. Current Protocols in Bioinformatics, Chapter 11, Unit 11.7.

There two general ways in which you can compile short reads to make your genome; reference mapping assembly and de novo assembly. There are benefits and caveats to both.