Overview and Implementation of the GBS Pipeline. Qi Sun Computational Biology Service Unit Cornell University

Transcription

1 Overview and Implementation of the GBS Pipeline Qi Sun Computational Biology Service Unit Cornell University

2 Overview of the Data Analysis Strategy

3 Genotyping by Sequencing (GBS) ApeKI site (GCWGC) ( ) 64-base sequence tag B73 < 450 bp Reduced genome representation; Reads can be aligned without reference genome;

4 Identification of markers with/without the reference genome B73 SNP and small INDELs Loss of cut site Mo17

5 Identification of Presence/Absence Variations (PAV) B73 Mo17

6 Reads -> Tags -> Aligned Tags -> SNPs/INDELs CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC

7 Reads -> Tags -> Aligned Tags -> SNPs/INDELs Tag 1 Tag 2 Tag 3 CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC.

8 Reads -> Tags -> Aligned Tags -> SNPs/INDELs Tag 1 Tag 2 Maize NAM population (5000 lines) 2.6 billion reads 6 million tags Tag 3 CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC.

9 Reads -> Tags -> Aligned Tags -> SNPs/INDELs Two ways of alignments: a. Anchored to reference genome (regular pipeline) b. Pair-wise alignment between tags (UNEAK)

10 Reads -> Tags -> Aligned Tags -> ApeK I site SNPs/INDELs CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC Reproducible sequencing errors

11 Summary of the GBS pipeline Tags Reads Aligned Tags Tags by Taxa SNP/INDEL HapMap Filtering

12 Summary of the GBS pipeline Reads Tags Aligned Tags Tags by Taxa All reads in the SNP/INDEL sequenced populations are merged to create HapMap Tag list. Filtering

13 Experimental Design 1. Depth of coverage 2. Choice of enzyme 3. Type of population

14 Depth of Coverage Whole Genome Shotgun GBS Sites Depths Tags Depths #Depth

15 Depth of coverage controled by multiplexing level and choice of enzyme Multiplexing level 48-plex, 96-plex, 384-plex Enzyme selection ApeKI GCWGC Expected size: 0.5 kb PstI CTGCAT Expected size: 4 kb

16 Population type determines method of filtering and imputation RIL population (used in exercise for this workshop) F1 hybrids of highly heterozygous parents Families with pedigree information

17 Pipeline Implementation

18 Three ways to access the software 1. Computers with GBS software pre-installed Cornell BioHPC Lab iplant Discovery Environment 2. Using pre-compiled Java code from Get the source code from sourceforge.net (Project name: TASSEL)

19 GBS Pipeline on Cornell BioHPC Lab (for both Cornell and external users only) Step 1: Reserve a machine

20 GBS Pipeline on Cornell BioHPC Lab Step 2: Upload files Fetch (mac), FileZilla (win) or WinSCP (win)

21 GBS Pipeline on Cornell BioHPC Lab Step 3: Type the command to run pipeline Mac: terminal window; PC: Putty tassel/run_pipeline.pl -fork1 -QseqToTagCountPlugin -i. -k rice.key -e apeki -endplugin -runfork1

22 Using iplant Two ways to upload files to iplant data store 1. Web interface 2. Command line tool: icommand

23 GBS on iplant Discovery Environment (Beta version now)

24 Set up the pre-compiled pipeline on your own computer A computer with at least 8GB or more RAM (Linux or Mac) Download TASSEL Standalone from maizegenetics.net Set up Java (64bit) BWA (for alignment to reference genome) Document for installation: Download the zip file: TASSEL_x.x _Standalone

25 Set up TASSEL source code in Netbeans (make user use 64-bit Java and Netbeans)

26 The intermediate files are compressed binary files BinaryToTextPlugin can be used to convert to text file Tag-Counts (TC): *.cnt.txt *.cnt Tag-by-taxa (TBT): *.tbt.txt *.tbt.bin Tags-on-physical-map (TOPM): *.topm.txt *.topm.bin Hapmap *.hmp.txt GDPDM blobs * 64 bp tags were represented as 2 long integers (8 bytes for long in Java).

27 1. Documentation of the tools Training project data is provided by Chih-Wei Tung & Susan McCouch.