RNAseq analysis: SNP calling. BTI bioinformatics course, spring 2013

Transcription

1 RNAseq analysis: SNP calling BTI bioinformatics course, spring 2013

2 RNAseq overview

3 RNAseq overview Choose technology 454 Illumina SOLiD 3 rd generation (Ion Torrent, PacBio) Library types Single reads Paired ends Mate-pairs Multiplexing

4 RNAseq overview

5 RNAseq workflow

6 RNAseq assembly

7 RNAseq assembly Index the reference with Bowtie2 Map the reads to the reference with tophat2 Output: BAM files for hits and unmapped reads BED files for junctions, insertions, deletions View output: Tablet ( )

8 RNAseq analysis Stats for assembly quality Check for contaminations Total length Number of contigs How many reads map?

9 RNAseq analysis Stats for assembly quality Check for contaminations Total length Number of contigs How many reads map?

10 Expression analysis Cufflinks Run cuffdiff for detection of differential expression

11 Expression analysis Cufflinks Run cuffdiff for detection of differential expression Which genes are differentially expressed $ awk 'BEGIN {OFS = \t } $14 = yes {print $0}' gene_exp.diff > significant_genes.txt cuffdiff output will be used in the R class next week

12 SNP calling SAMtools mpileup GATK File format: vcf #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT

13 SNP calling SAMtools mpileup 1. Call SNPs from bam file and convert to vcf format $ samtools mpileup -C 50 -uf reference.fa alignment.bam bcftools view -bvcg - > raw_var.bcf Can you find out what each of the above commands does?

14 SNP calling 1. Call SNPs from bam file and convert to vcf format $ samtools mpileup -C 50 -uf reference.fa alignment.bam bcftools view -bvcg - > raw_var.bcf mpileup computes the likelihood of data given each possible genotype and stores the likelihoods in the BCF format. It does not call variants.

15 SNP calling 1. Call SNPs from bam file and convert to vcf format $ samtools mpileup -C 50 -uf reference.fa alignment.bam bcftools view -bvcg - > raw_var.bcf mpileup computes the likelihood of data given each possible genotype and stores the likelihoods in the BCF format. It does not call variants. $ bcftools view raw_var.bcf vcfutils.pl varfilter -D 100 > filtered_var.vcf Can you find out what each of the above commands does?

16 SNP calling 1. Call SNPs from bam file and convert to vcf format $ samtools mpileup -C 50 -uf reference.fa alignment.bam bcftools view -bvcg - > raw_var.bcf mpileup computes the likelihood of data given each possible genotype and stores the likelihoods in the BCF format. It does not call variants. $ bcftools view raw_var.bcf vcfutils.pl varfilter -D 100 > filtered_var.vcf bcftools does the actual SNP calling, and converts the BCF to VCF

17 SNP calling 1. Call SNPs from bam file and convert to vcf format $ bcftools view raw_var.bcf vcfutils.pl varfilter -D 100 > filtered_var.vcf 2. Output is in VCF format

18 SNP calling 2. Output is in VCF format Look at your vcf output file It has a header, followed by the data How many SNPs were called? How many Indels? Read more:

19 SNP calling: effect prediction SnpEff Read the manual! SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of genetic variants (such as amino acid changes). 1. Build a snpeff database for the reference genome 2. Use snpeff to determine if SNPs occur in genes

20 SNP calling: effect prediction 1. Build a snpeff database for the reference genome $ cd ~/Software/snpEff $ mkdir data $ cd data $ mkdir genomes $ mkdir SL2.40ch04 $ cd SL2.40ch4 $ cp ~/Data/ch4_demo_dataset/annotation/ITAG2.3_gene_models _ch4.gtf genes.gtf $ cd../genomes/ $ cp ~/Data/ch4_demo_dataset/bwt2_index/SL2.40ch04.fa./ #add the new genome to the config file $ emacs snpeffect.congif

21 SNP calling: effect prediction 1. Build a snpeff database for the reference genome #add the new genome to the config file $ emacs snpeff.congif

22 SNP calling: effect prediction 1. Build a snpeff database for the reference genome #add the new genome to the config file $ emacs snpeff.congif #Build the database $ java -jar snpeff.jar build -gtf22 -v SL2.40ch04

23 SNP calling: effect prediction 1. Build a snpeff database for the reference genome 2. Use snpeff to determine if SNPs occur in genes Run this in the directory of the.vcf file! $ java -jar snpeff.jar eff SL2.40ch04 snps.vcf -c snpeff.config -v > snpeff.out What output did you get?

24 SNP calling: effect prediction 2. Use snpeff to determine if SNPs occur in genes Run this in the directory of the.vcf file! $ java -jar snpeff.jar eff SL2.40 snps.vcf -c snpeff.config -v > snpeff.out.out file has the snpeff stats snpeff_genes.txt : SNPs in genes (remember the genes.gtf file? ) snpeff_summary.html

25 SNP calling: effect prediction 2. Use snpeff to determine if SNPs occur in genes. $ java -jar snpeff.jar eff SL2.40 snps.vcf -c snpeff.config -v > snpeff.out.out file has the snpeff stats snpeff_genes.txt : SNPs in genes (remember the genes.gtf file? ) snpeff_summary.html Look at the output and Count the number of genes with SNPs How many synonymous SNPs? How many are non-synonymous?

26 SNP calling: effect prediction Read more about SnpEff output and results Other SNP calling tools GATK Freebayes Next week Introduction to R statistics