1 / 22

Cataloging polymorphisms in genomes with NGS

Cataloging polymorphisms in genomes with NGS. Graham Etherington Graham.Etherington@tsl.ac.uk. Outline. Single-nucleotide polymorphisms (SNPs) – what, why and where. Insertions and deletions (Indels) Mapping reads to a genome SNP analysis Indel analysis Filtering – making rules

mills
Download Presentation

Cataloging polymorphisms in genomes with NGS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Cataloging polymorphisms in genomes with NGS Graham Etherington Graham.Etherington@tsl.ac.uk

  2. Outline • Single-nucleotide polymorphisms (SNPs) – what, why and where. • Insertions and deletions (Indels) • Mapping reads to a genome • SNP analysis • Indel analysis • Filtering – making rules • Tutorial

  3. What is a SNP?

  4. Why look for SNPs? • SNPs may lead to a change in function or expression of a gene. • premature stop codon • different fold in a protein • higher or lower expression of gene • Genetic markers • SNP may be linked to a gene for a given trait • response to a pathogen (susceptible or resistant)

  5. Types of SNPs • Effect of SNPs vary depending on location. • Intergenic regions • may alter the sequence of regulatory RNAs • Non-coding regions • alteration of promoter and enhancer sequences may change expression of gene • Coding regions • Substitutions • synonymous: no change in the amino acid • non-synonymous: change in amino acid

  6. Indels • Insertion/deletion • Differ from SNPs by having at least one nucleotide extra or missing when compared to a reference sequence. • Can cause frame-shifts – codons shift by one creating a different protein sequence after indel. • Indels of a length divisible by 3 cause whole amino acid insertions/deletions, but not frame-shifts.

  7. Indels • Insertions • an ‘extra’ nucleotide appears (inserted) between 2 neighbouring positions • Deletions • a nucleotide is missing (deleted) at a given positions I A F A M A ATCGCGTTTGCCATGGCC ATCGCGTTTCGCCATGGCC I A F R H G Reference Reference ATCGCGTTTGCCATGGCC ATCGCGTTGCCATGGCC I A L P W P • Sometimes a matter of perspective – does the reference have an insertion, or does the query (e.g. a read sequence) have a deletion?

  8. Analysis - the basic steps • Polymorphism analysis in Galaxy involves a number of steps: • Getting data • Quality Control and filtering • Aligning reads to reference • SNP analysis • Indel analysis

  9. Paired-end reads • Sequences can be paired-end • sequences occur as ‘pairs’ with one left-hand (forward) read and one right-hand (reverse) read. • a given distance (insert-size) between the start and end of pairs. Paired -ends Left (forward) read 76 nucleotides Right (reverse) read 76 nucleotides 500 nt DNA fragment ~350 nt gap ~500 nt ‘insert size’

  10. FASTQ Format • Illumina sequences are stored as FASTQ format. FASTA format: >AY0038453 CCTCGGAGTGGAAGGGTGAAGCTAGATTCGTGGACGAATCTATGTTAGTGGGGGAG FASTQ format: @HWI-EAS396_0001:6:1:11659:1311#0/1 CCTCGGAGTGGAAGGGTGAAGCTAGATTCGTGGACGAATCTATGTTAGTGGGGGAG +HWI-EAS396_0001:6:1:11659:1311#0/1 `_Z_`WU\R\ddadafcfafcdWdca[facdW[[^W^ca^W^ac^fcdcfab]_X^

  11. Quality Control • Illumina sequencing • Not perfect, contains errors • wrongly called bases • N’s • reads < or > expected read length • low quality • Quality control reads to select highest quality

  12. Quality Control • Quality scores • look at the per-base quality scores Quality Scores Position

  13. Mapping • Align sequenced reads to a reference genome using a next-generation sequence aligner. • Output of alignment program in Sequence Alignment/Map (SAM) format. • The SAM format describes the alignment of sequenced reads to a reference sequence.

  14. Map analysis - SNPs • SNP finding follows a number of steps: • calculating the individual read bases that cover each base in a reference • counting how many read bases are the same as the reference and how many are different • calculating the depth of coverage and quality of the read bases

  15. Map analysis - SNPs • The mpileup format • A multi-column representation of the one or more alignments with per-line base-by-base information on chromosome, position, reference base, consensus base, coverage, quality, etc. 1 2 3 4 5 6 chr1 412 A 2 ., II chr1 413 G 4 ..t, IIIH chr1 414 C 4 ...a III2 chr1 415 C 4 TTTt III7 First 3 columns are information about the reference sequence: Reference name Position Reference base Then 3 columns per sample: 4. Coverage 5. Read bases at that position 6. Read quality at that position Col 5 CharMeaning . Identical base on the same strand , Identical base on the opposite strand ^ Read starts at this position $ Read ends at this position Uppercase letter Different base on the same strand Lowercase letter Different base on the opposite strand

  16. Identifying SNPs • Use a statistical/heuristic approach to identify homozygosity/heterozygosity in mpileups • VarScan • calls SNPs, indels, and consensus genotypes. • genotypes • homozygous to reference base • homozygous to alternative base • heterozygous (combination of reference and alternative bases)

  17. Variant Calling Format (VCF) ##fileformat=VCFv4.1 ##source=VarScan2 ##INFO=<ID=ADP,Number=1,Type=Integer,Description="Average per-sample depth of bases with Phred score >= 15"> ##INFO=<ID=WT,Number=1,Type=Integer,Description="Number of samples called reference (wild-type)"> ##INFO=<ID=HET,Number=1,Type=Integer,Description="Number of samples called heterozygous-variant"> ##INFO=<ID=HOM,Number=1,Type=Integer,Description="Number of samples called homozygous-variant"> ##INFO=<ID=NC,Number=1,Type=Integer,Description="Number of samples not called"> ##FILTER=<ID=str10,Description="Less than 10% or more than 90% of variant supporting reads on one strand"> ##FILTER=<ID=indelError,Description="Likely artifact due to indel reads at this position"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality"> ##FORMAT=<ID=SDP,Number=1,Type=Integer,Description="Raw Read Depth as reported by SAMtools"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Quality Read Depth of bases with Phred score >= 15"> ##FORMAT=<ID=RD,Number=1,Type=Integer,Description="Depth of reference-supporting bases (reads1)"> ##FORMAT=<ID=AD,Number=1,Type=Integer,Description="Depth of variant-supporting bases (reads2)"> ##FORMAT=<ID=FREQ,Number=1,Type=String,Description="Variant allele frequency"> ##FORMAT=<ID=PVAL,Number=1,Type=String,Description="P-value from Fisher's Exact Test"> ##FORMAT=<ID=RBQ,Number=1,Type=Integer,Description="Average quality of reference-supporting bases (qual1)"> ##FORMAT=<ID=ABQ,Number=1,Type=Integer,Description="Average quality of variant-supporting bases (qual2)"> ##FORMAT=<ID=RDF,Number=1,Type=Integer,Description="Depth of reference-supporting bases on forward strand (reads1plus)"> ##FORMAT=<ID=RDR,Number=1,Type=Integer,Description="Depth of reference-supporting bases on reverse strand (reads1minus)"> ##FORMAT=<ID=ADF,Number=1,Type=Integer,Description="Depth of variant-supporting bases on forward strand (reads2plus)"> ##FORMAT=<ID=ADR,Number=1,Type=Integer,Description="Depth of variant-supporting bases on reverse strand (reads2minus)"> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Sample1 chr01 37 . A T . PASS ADP=27;WT=0;HET=1;HOM=0;NC=0 GT:GQ:SDP:DP:RD:AD:FREQ:PVAL:RBQ:ABQ:RDF:RDR:ADF:ADR 0/1:3:30:23:14:9:39.13%:7.4174E-4:39:37:14:0:9:0 chr01 55 . A G . PASS ADP=16;WT=0;HET=0;HOM=1;NC=0 GT:GQ:SDP:DP:RD:AD:FREQ:PVAL:RBQ:ABQ:RDF:RDR:ADF:ADR 1/1:4:36:12:0:9:75%:2.0568E-5:0:36:0:0:9:0 chr01 85 . A G . PASS ADP=34;WT=0;HET=1;HOM=0;NC=0 GT:GQ:SDP:DP:RD:AD:FREQ:PVAL:RBQ:ABQ:RDF:RDR:ADF:ADR 0/1:5:37:35:19:16:45.71%:1.637E-6:35:36:19:0:16:0 chr01 88 . A T . PASS ADP=30;WT=0;HET=1;HOM=0;NC=0 GT:GQ:SDP:DP:RD:AD:FREQ:PVAL:RBQ:ABQ:RDF:RDR:ADF:ADR 0/1:2:33:31:23:8:25.81%:2.3332E-3:36:19:23:0:8:0 INFO ADP=27;WT=0;HET=2;HOM=0;NC=0 ADP=16;WT=0;HET=0;HOM=2;NC=0 ADP=34;WT=1;HET=1;HOM=0;NC=0 ADP=30;WT=1;HET=1;HOM=0;NC=0 ADP=61;WT=1;HET=1;HOM=0;NC=0 ADP=80;WT=1;HET=1;HOM=0;NC=0 ADP=88;WT=1;HET=1;HOM=0;NC=0 ADP=101;WT=1;HET=1;HOM=0;NC=0 ##INFO=<ID=ADP,Number=1,Type=Integer,Description="Average per-sample depth of bases with Phred score >= 15"> ##INFO=<ID=WT,Number=1,Type=Integer,Description="Number of samples called reference (wild-type)"> ##INFO=<ID=HET,Number=1,Type=Integer,Description="Number of samples called heterozygous-variant"> ##INFO=<ID=HOM,Number=1,Type=Integer,Description="Number of samples called homozygous-variant"> ##INFO=<ID=NC,Number=1,Type=Integer,Description="Number of samples not called">

  18. Identifying SNPs • Filter snps based on some rules • Coverage: what minimum depth of coverage do you require • Genotype: what genotypes, or combination of genotypes are you looking for • Alternative allele frequency: 0.5? 0.33?

  19. Identifying Indels • SAM file has information about how the reads map to genome – includes insertions and deletions • Indel extraction output in Galaxy allows us to visualise indels • VarScan also has a tool to extract Indels • Like SNP-calling, provide some parameters to filter for most probable indels.

  20. Visualisation • Galaxy Trackster • view read alignments • view SNPs • view Indels

  21. Visualisation A>T SNP

  22. Tutorial • Time to put what I’ve said into practice. • Use Galaxy: galaxy.tsl.ac.uk • Go through the Tutorial. Don’t rush things. Take your time and make sure you understand what each step does and how you are creating a pipeline of analysis.

More Related