Short Read Sequencing Analysis Workshop

Transcription

1 Short Read Sequencing Analysis Workshop Day 1 Introduc.on to the Workshop

2 Schedule for Week 1 Day 1: Introduc.on Workshop syllabus and schedule Basic considera.ons for sequencing depth, read length, format, etc. Test Logins Day 2: Introduc.on to Linux Understand how to use local machine to connect to a server/compute cluster Basic Linux commands, how to set up a command line VIM text editor Day 3: Cluster Usage and Data Transfer Head node vs. child nodes Job queues, modules Troubleshoo.ng failed jobs Transferring/downloading files to the server Day 4: Sequencing QC Evalua.ng quality of sequencing data with FastQC Trimming reads, why and how Skills Assessment

3 Schedule for Week 2 Day 5: Intro to Read Mapping and Visualiza.on Understand how & why we map reads SAM/BAM file formats, Samtools Introduc.on to IGV for visualiza.on Day 6: Resequencing and Variant Calling GATK best prac.ces for variant detec.on Running GATK Post-processing vcf files Day 7: ChIP-Seq Analysis Different types of ChIP-Seq experiments and analysis ENCODE best prac.ces Peak calling and mo.f calling tools Day 8: RNA-Seq I RNA-Seq basic considera.ons Splice-aware aligners Counts versus FPKM for transcrip.on levels Day 9: RNA-Seq II Differen.al expression analysis Cufflinks & DESeq packages

4 Reverse Classroom Before class: Watch Videos hgp://bficores.colorado.edu/biofron.ers-corefacility-workshops/workshops-in-the-series/shortread-2016-course-materials) During class (1-5 pm) Examples with example files (fastq, sam, bam, bed) Aker class: Homework

5 You will be learning command line This is not a GUI Spelling and Capitaliza.on mager! Google can help you learn command line unix The names for commands are not always intui.ve (you will learn many this week)

6 Programs we will be using Text editor VIM Sequencing quality FastQC Read filtering/trimming Trimmoma.c Read Mapping Bow.e2 Visualiza.on IGV Variant Calling GATK toolkit, PicardTools, VCFTools Peak Calling (ChIP-Seq) MACS Mo.f calling MEME Suite, TomTom RNA-Seq mapping (splice-aware) Tophat2 Differen.al Expression HTseq, DESeq, Cufflinks Other programs: Samtools, BEDTools

7 What this workshop won t cover De novo assembly (genome or transcriptome) 16s metagenomics Shotgun metagenomics Long-read data analysis Specialized analysis beyond the basics Coding Biosta.s.cs How to analyze YOUR data

8 If you have a problem on Vieques bf-rscrh@colorado.edu to submit a.cket Put SR2016 in the subject line Include your Iden.Key UserID somewhere in the If you are referencing a specific job (running or past), include the job number in the.cket If you are referencing a specific job, include the pbs script used to submit it Be as specific as possible descrip.on of the problem (job ran for 10 minutes vs. job started and exited immediately they point to very different possible issues. Can you even log in?!) Include the exit and error files or the path on vieques to the e and o files. Please be courteous. BIT graciously gives their.me for this workshop, we couldn t do it without them

9 Important ConsideraGons for BioinformaGcs What programs and version are being used What op.ons or variables are being used What order programs are being called in the pipeline What compute environment If you don t know these answers and report them in publica.on, your work will not be repeatable!

10 BioinformaGcs is not free Compute Resources Storage (and Backup) Analysis.me Exper.se Extrac.ng meaningful informa.on requires more than basic bioinforma.cs And we haven t even discussed biosta.s.cs... hgp://massgenomics.org/2015/10/ngs-analysis-not-free.html

11 There is no One Right Answer Every algorithm/pipeline is different Different assump.ons Different order of processing can have significant effect Different op.ons can have a significant effect Most projects will require some customiza.on or further processing to get useful data Zhang ZH, et al. (2014) PLoS ONE 9(8)

12 When to think about your analysis Don t wait un.l you have the data to think about bioinforma.cs Important experimental considera.ons Controls, replicates, sequencing depth, library prep, etc. all effect what type of analysis and how powerful it is to tell you something But if you did... All is not necessarily lost Many bad data sets have useful informa.on, can be harder to find and may limit what you can find

13 Acknowledgements Slides and NarraGon: Jessica Vera Materials: David Knox Workshop Coordinators: Jamie Prior Kershner and Jessica Vera Funding: BioFron.ers Ins.tute and Colorado Office of Economic Development and Interna.onal Trade AddiGonal Acknowledgments Compute Resources: BioFron.ers IT Staff Robin Dowell and Dowell Lab 20 16

14 Login Mac or unix Open Terminal by clicking on the magnifying glass in the corner and typing terminal and hivng enter ssh X iden.key@vieques.colorado.edu Type password When/if it says enter passphrase just hit Enter 2x!!! PC open Pugy Type vieques.colorado.edu under the host name Hit open Type iden.key Type password When/if it says enter passphrase just hit Enter 2x!!!