Java程序辅导
C C++ Java Python Processing编程在线培训 程序编写 软件开发 视频讲解
C C++ Java Python Processing编程在线培训 程序编写 软件开发 视频讲解
Overview and Implementation of the GBS Pipeline Qi Sun Computational Biology Service Unit Cornell University Overview of the Data Analysis Strategy Genotyping by Sequencing (GBS) < 450 bp ApeKI site (GCWGC) ( ) 64‐base sequence tag B73 • Reduced genome representation; • Reads can be aligned without reference genome; Identification of markers with/without the reference genome SNP and small INDELs B73 Mo17 Loss of cut site Identification of Presence/Absence Variations (PAV) B73 Mo17 Reads ‐> Tags ‐> Call SNPs from Tags CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC …………. Tag 1 Tag 2 Tag 3 Reads ‐> Tags ‐> Call SNPs from Tags CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC …………. Tag 1 Tag 2 Tag 3 Reads ‐> Tags ‐> Call SNPs from Tags Tag 2 Tag 3 Tassel Stacks Tag catalog is created from pooled all individuals. Tag catalog is created from each individual separately. CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCATGTAGACGGGC …………. Tag 1 Tag 2 Tag 3 Reads ‐> Tags ‐> Call SNPs from Tags Maize N M population (5000 lines) 2.6 billion reads 6 million tags CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC ………………………………… Two ways of alignments: a. Anchored to reference genome (regular pipeline) b. Pair-wise alignment between tags (UNEAK) Reads ‐> Tags ‐> Call SNPs from Tags ApeK I site CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGCGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTGTAGACGGGC CAGCAAAAAAAAAAAAGAGGGATGGGGCGGCTTGCGTGCATGGGACACAAGCGTATAGACGGGC ………………………………… Reads ‐> Tags ‐> Call SNPs from Tags tag1 tag2 tag3 Many tags with low read depth have sequencing errors. Reads from a population p Unique tag list from ooled reads Summary of the GBS pipeline g g Call SNPs from ali ned ta s p Unique tag list from ooled reads Summary of the GBS pipeline g g Call SNPs from ali ned ta s Genotypes Match reads from each individual to the tags (tags‐to‐taxa matrix) Reads from a population Basic Filtering Strategy 02000000 4000000 6000000 8000000 10000000 12000000 1 4 7 10131619222528313437404346495255 #Depth 0 500 1000 1500 2000 2500 3000 3500 4000 1 8 1 5 2 2 2 9 3 6 4 3 5 0 5 7 6 4 7 1 7 8 8 5 9 2 9 9 Depth of Coverage Whole Genome Shotgun GBS Depths Sites Depths Tags 02000000 4000000 6000000 8000000 10000000 12000000 1 4 7 10131619222528313437404346495255 0 500 1000 1500 2000 2500 3000 3500 4000 1 8 1 5 2 2 2 9 3 6 4 3 5 0 5 7 6 4 7 1 7 8 8 5 9 2 9 9 Depths Sites Depths Tags The GBS pipeline filtered out tags with very low read depth across the population WGS GBS • Reads with sequencing errors; • Extremely rare alleles; • Reads from highly repetitive regions; 02000000 4000000 6000000 8000000 10000000 12000000 1 4 7 10131619222528313437404346495255 0 500 1000 1500 2000 2500 3000 3500 4000 1 8 1 5 2 2 2 9 3 6 4 3 5 0 5 7 6 4 7 1 7 8 8 5 9 2 9 9 Depths Sites Depths Tags WGS GBS The GBS pipeline filtered out tags with very low read depth across the population • Reads with sequencing errors; • Extremely rare alleles; Generally we do not remove tags with very high depth. As they could come from a mix of (1) good loci with efficient PCR amplification; (2) bad loci from repetitive regions. • Reads from highly repetitive regions; 0500 1000 1500 2000 2500 3000 3500 4000 1 6 1 1 1 6 2 1 2 6 3 1 3 6 4 1 4 6 5 1 5 6 6 1 6 6 7 1 7 6 8 1 8 6 9 1 9 6 Uneven coverage enrich sequencing depth in some sites, but cause missing data in others Strategies 1. Filter out sites with too much missing data; 2. Filter out genotypes with low depth to minimize heterozygous genotyping errors. 3. Imputation based on haplotypes; Filter genotyping errors Reference genome Paralogous regions Individual B Individual A Most of the genotyping errors are caused by reads from paralogous regions Pipeline Implementation 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 . http://www.maizegenetics.net 3. Get the source code from sourceforge .net http://sourceforge.net (Project name: TASSEL) GBS Pipeline on Cornell BioHPC Lab (for both Cornell and external users only) Step 1: Reserve a machine http://cbsu.tc.cornell.edu GBS Pipeline on Cornell BioHPC Lab Step 2: Upload files Fetch (mac), FileZilla (win) or WinSCP (win) GBS Pipeline on Cornell BioHPC Lab Step 3: Type the command to run pipeline tassel/run_pipeline.pl -fork1 -QseqToTagCountPlugin -i . -k rice.key -e apeki -endPlugin -runfork1 Mac: terminal window; PC: Putty Two ways to upload files to iPlant data store 1. Web interface 2. Command line tool: icommand Using iPlant GBS on iPlant Discovery Environment http://www.iplantcollaborative.org/ (Beta version now) Download the zip file: TASSEL_x.x _Standalone 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) http://www.maizegenetics.net/tassel/docs/TasselPipelineCLI.pdf Document for installation: Set up TASSEL source code in Netbeans http://www.maizegenetics.net/tassel-in-netbeans (make user use 64-bit Java and Netbeans) The intermediate files are compressed binary files • 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). BinaryToTextPlugin can be used to convert to text file