NGS Sequence data

Jason Stajich

UC Riverside

jason.stajich[at]ucr.edu

Presenter Notes

NGS sequence data

  • Quality control
  • Alignment
  • Variant calling
    • SNPs
    • Indels

Presenter Notes

Sequence data sources

  • Sanger
    • Long reads, high quality, expensive
  • Illumina
    • Short reads 50-150bp (HiSeq) and up to 250bp (MiSeq)
    • Cheap and Dense read total (HiSeq 200-300M paired-reads for ~$2k)
  • 454
    • Longish reads 300-500 bp, some homopolymer seq problems,
    • Expensive ($10k for 1M reads), recent chemistry problems
  • PacBio
    • Long reads, but small amount (10k)
    • Low seq quality and not cheap
    • Can help augement assemblies, but not good enough on its own

Presenter Notes

Sequence data source (cont)

  • SOLiD
    • Short reads, 30-50bp. Reasonably price-point for the density
    • 1/5 as many reads as Illumina HiSeq
  • Ion Torrent
    • Cheaper machine, fast, 100bp reads and reported 100M
    • Quality okay for some applications

Presenter Notes

Sequencer comparisons

Glenn TC, "Field guide to next-generation DNA sequencers" DOI:10.1111/j.1755-0998.2011.03024.x

Seqcompare

Presenter Notes

File formats

FASTQ

@SRR527545.1 1 length=76
GTCGATGATGCCTGCTAAACTGCAGCTTGACGTACTGCGGACCCTGCAGTCCAGCGCTCGTCATGGAACGCAAACG
+
HHHHHHHHHHHHFGHHHHHHFHHGHHHGHGHEEHHHHHEFFHHHFHHHHBHHHEHFHAH?CEDCBFEFFFFAFDF9

FASTA format

>SRR527545.1 1 length=76
GTCGATGATGCCTGCTAAACTGCAGCTTGACGTACTGCGGACCCTGCAGTCCAGCGCTCGTCATGGAACGCAAACG

SFF - Standard Flowgram Format - binary format for 454 reads

Colorspace (SOLiD) - CSFASTQ

@0711.1 2_34_121_F3
T11332321002210131011131332200002000120000200001000
+
64;;9:;>+0*&:*.*1-.5($2$3&$570*$575&$9966$5835'665

Presenter Notes

Quality Scores in FASTQ files

 SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.....................................................
 ..........................XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX......................
 ...............................IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII......................
 .................................JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ......................
 LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL....................................................
 !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
 |                         |    |        |                              |                     |
33                        59   64       73                            104                   126

S - Sanger        Phred+33,  raw reads typically (0, 40)
X - Solexa        Solexa+64, raw reads typically (-5, 40)
I - Illumina 1.3+ Phred+64,  raw reads typically (0, 40)
J - Illumina 1.5+ Phred+64,  raw reads typically (3, 40)
    with 0=unused, 1=unused, 2=Read Segment Quality Control Indicator (bold) 
    (Note: See discussion above).

Presenter Notes

Read naming

ID is usually the machine ID followed by flowcell number column, row, cell of the read.

Paired-End naming can exist because data are in two file, first read in file 1 is paired with first read in file 2, etc. This is how data come from the sequence base calling pipeline. The trailing /1 and /2 indicate they are the read-pair 1 or 2.

In this case #CTTGTA indicates the barcode sequence since this was part of a multiplexed run.

File: Project1_lane6_1_sequence.txt

@HWI-ST397_0000:2:1:2248:2126#CTTGTA/1
TTGGATCTGAAAGATGAATGTGAGAGACACAATCCAAGTCATCTCTCATG
+HWI-ST397_0000:2:1:2248:2126#CTTGTA/1
eeee\dZddaddddddeeeeeeedaed_ec_ab_\NSRNRcdddc[_c^d

File: Project1_lane6_2_sequence.txt

@HWI-ST397_0000:2:1:2248:2126#CTTGTA/2
CTGGCATTTTCACCCAAATTGCTTTTAACCCTTGGGATCGTGATTCACAA
+HWI-ST397_0000:2:1:2248:2126#CTTGTA/2
]YYY_\[[][da_da_aa_a_a_b_Y]Z]ZS[]L[\ddccbdYc\ecacX

Presenter Notes

Paired-end reads

These files can be interleaved, several simple tools exist, see velvet package for shuffleSequences scripts which can interleave them for you.

Interleaved was requried for some assemblers, but now many support keeping them separate. However the order of the reads must be the same for the pairing to work since many tools ignore the IDs (since this requires additional memory to track these) and instead assume in same order in both files.

Orientation of the reads depends on the library type. Whether they are

---->   <----   Paired End (Forward Reverse)
<----   ---->   Mate Pair  (Reverse Forward)

Presenter Notes

Data QC

  • Trimming
    • FASTX_toolkit, sickle
    • Adapative or hard cutoff
  • Additional considerations for Paired-end data
  • Evaluating quality info with reports

Presenter Notes

FASTX toolkit

  • Useful for trimming, converting and filtering FASTQ and FASTA data
  • One gotcha - Illumina quality score changes from 64 to 33 offset
  • Default offset is 64, so to read with offset 33 data you need to use -Q 33 option
  • fastx_quality_trimmer
  • fastx_splitter - to split out barcodes
  • fastq_quality_formatter - reformat quality scores (from 33 to 64 or)
  • fastq_to_fasta - to strip off quality and return a fasta file
  • fastx_collapser - to collapse identical reads. Header includes count of number in the bin

Presenter Notes

FASTX - fastx_quality_trimmer

  • Filter so that X% of the reads have quality of at least quality of N
  • Trim reads by quality from the end so that low quality bases are removed (since that is where errors tend to be)
  • Typically we use Phred of 20 as a cutoff and 70% of the read, but you may want other settings
  • This is adaptive trimming as it starts from end and removes bases
  • Can also require a minimum length read after the trimming is complete

Presenter Notes

FASTX toolkit - fastx_trimmer

  • Hard cutoff in length is sometimes better
  • Sometimes genome assembly behaves better if last 10-15% of reads are trimmed off
  • Adaptive quality trimming doesn't always pick up the low quality bases
  • With MiSeq 250 bp reads, but last 25-30 often low quality and HiSeq with 150 bp often last 20-30 not good quality
  • Removing this potential noise can help the assembler perform better

Presenter Notes

Trimming paired data

  • When trimming and filtering data that is paired, we want the data to remain paired.
  • This means when removing one sequence from a paired-file, store the other in a separate file
  • When finished will have new File_1 and File_2 (filtered & trimmed) and a separate file File_unpaired.
  • Usually so much data, not a bad thing to have agressive filtering

Presenter Notes

Trimming adaptors

  • A little more tricky, for smallRNA data will have an adaptor on 3' end (usually)
  • To trim needs to be a matched against the adaptor library - some nuances to make this work for all cases.
    • What if adaptor has low quality base? Indel? Must be able to tolerate mismatch
  • Important to get right as the length of the smallRNAs will be calculated from these data
  • Similar approach to matching for vector sequence so a library of adaptors and vector could be used to match against
  • Sometimes will have adaptors in genomic NGS sequence if the library prep did not have a tight size distribution.

Presenter Notes

Trimming adaptors - tools

  • cutadapt - Too to matching with alignment. Can search with multiple adaptors but is pipelining each one so will take 5X as long if you match for 5 adaptors.

  • SeqPrep - Preserves paired-end data and also quality filtering along with adaptor matching

Presenter Notes

FASTQC for quality control

  • Looking at distribution of quality scores across all sequences helpful to judge quality of run
  • Overrepresented Kmers also helpful to examine for bias in sequence
  • Overrepresented sequences can often identify untrimmed primers/adaptors

Presenter Notes

FASTQC - per base quality

PerBase

Presenter Notes

FASTQC - per seq quality

PerSeq

Presenter Notes

FASTQC - per seq GC content

PerGC

Presenter Notes

FASTQC - Sequence Length

PerLength

Presenter Notes

FASTQC - kmer distribution

kmer

Presenter Notes

FASTQC - kmer table

kmer

Presenter Notes

Getting ready to align sequence

Presenter Notes

Presenter Notes

Short read aligners

Strategy requires faster searching than BLAST or FASTA approach. Some approaches have been developed to make this fast enough for Millions of sequences. Burrows-Wheeler Transform is a speed up that is accomplished through a transformation of the data. Requires and indexing of the search database (typically the genome). BWA, Bowtie ? LASTZ * ? BFAST

Presenter Notes

Workflow for variant detection

  • Trim
  • Check quality
  • Re-trim if needed
  • Align
  • Possible realign around variants
  • Call variants - SNPs or Indels
  • Possibly calibrate or optimize with gold standard (possible in some species like Human)

Presenter Notes

NGS Alignment for DNA

  • Short reads (30-200bp)
    • Bowtie and BWA - implemented with the BWT algorithm, very easy to setup and run
    • SSAHA also useful, uses fair amount of memory
    • BFAST - also good for DNA, supports Bisulfide seq,color-space but more complicated to run
  • Longer reads (e.g. PacBio, 454, Sanger reads)
    • BWA has a BWA-SW mode using does a Smith-Waterman to place reads. Can tolerate large indels much better than standard BWA algorithm but slower.
    • LAST for long reads

Presenter Notes

BWA alignment choices

From BWA manual

On 350‐1000bp reads, BWA‐SW is several to tens of times faster than the existing programs. Its accuracy is comparable to SSAHA2, more accurate than BLAT. Like BLAT, BWA‐SW also finds chimera which may pose a challenge to SSAHA2. On 10‐100kbp queries where chimera detection is important, BWA‐SW is over 10X faster than BLAT while being more sensitive.

BWA‐SW can also be used to align ~100bp reads, but it is slower than the short‐read algorithm. Its sensitivity and accuracy is lower than SSAHA2 especially when the sequencing error rate is above 2%. This is the trade‐off of the 30X speed up in comparison to SSAHA2’s ‐454 mode.

When running BWA you will also need to choose an appropriate indexing method - read the manual. This applies when your genome is very large with long chromosomes.

Presenter Notes

Colorspace alignment

  • For SOLiD data, need to either convert sequences into FASTQ or run with colorspace aware aligner
    • BWA, SHRiMP, BFAST can do color-space alignment

Presenter Notes

Realignment for variant identification

  • Typical aligners are optimized for speed, find best place for the read.
  • For calling SNP and Indel positions, important to have optimal alignment
  • Realignment around variable positions to insure best placement of read alignment
    • Stampy applies this with fast BWA alignment followed by full Smith-Waterman alignment around the variable position
    • Picard + GATK employs a realignment approach which is only run for reads which span a variable position. Increases accuracy reducing False positive SNPs.

Presenter Notes

Alignment data format

  • SAM format and its Binary Brother, BAM
  • Good to keep it sorted by chromosome position or by read name
  • BAM format can be indexed allowing for fast random access
    • e.g. give me the number of reads that overlap bases 3311 to 8006 on chr2

Presenter Notes

Manipulating SAM/BAM

  • SAMtools

    • One of the first tools written. C code with Perl bindings Bio::DB::Sam (Lincoln Stein FTW!) with simple Perl and OO-BioPerl interface
    • Convert SAM <-> BAM
    • Generate Variant information, statistics about number of reads mapping
    • Index BAM files and retrieve alignment slices of chromosome regions
  • Picard - java library for manipulation of SAM/BAM files

  • BEDTools - C tools for interval query in BED,GFF and many other format fiels
    • Can generate per-base or per-window coverage from BAM files with GenomeGraph
  • BAMTools C++ tools for BAM manipulation and statistics

Presenter Notes

Using BWA,SAMtools

$ module load bwa
$ module load samtools
# index genome before we can align (only need to do this once)
$ bwa index Saccharomyces
# -t # of threads
# -q quality trimming
# -f output file
# for each set of FASTQ files you want to process these are steps
$ bwa aln -q 20 -t 16 -f SRR567756_1.sai Saccharomyces SRR567756_1.fastq
$ bwa aln -q 20 -t 16 -f SRR567756_2.sai Saccharomyces SRR567756_2.fastq
# do Paired-End alignment and create SAM file
$ bwa sampe -f SRR567756.sam Saccharomyces SRR567756_1.sai SRR567756_2.sai \
  SRR567756_1.fastq SRR567756_2.fastq

# generate BAM file with samtools
$ samtools view -b -S SRR567756.sam > SRR567756.unsrt.bam
# will create SRR567756.bam which is sorted (by chrom position)
$ samtools sort SRR567756.unsrt.bam SRR567756
# build BAM index (bai file)
$ samtools index SRR567756.bam

Presenter Notes

BAM using Picard tools

Can convert and sort all in one go with Picard, here is a simple set of commands

qsub -l mem=2gb -I # this will span a new process on a new machine, 
module load picard # this will se the PICARD environment variable, 
# see it by typing 'env PICARD'
# in java -Xmx2g asks for 2gb of memory
java -Xmx2g -jar $PICARD/SortSam.jar I=SRR567756.sam O=SRR567756.bam \
 SORT_ORDER=coordinate VALIDATION_STRINGENCY=SILENT CREATE_INDEX=TRUE

Lots of other resources for SAM/BAM manipulation in Picard documentation on the web http://picard.sourceforge.net/command-line-overview.shtml.

Presenter Notes

View header from BAM file

$ samtools view -h SRR527547.realign.W303.bam
samtools view -h SRR527547.realign.W303.bam | more
@HD VN:1.0  GO:none SO:coordinate
@SQ SN:chrI LN:230218   UR:file:genome/Saccharomyces_cerevisiae.fa  M5:6681ac2f62509cfc220d78751b8dc524
@SQ SN:chrII    LN:813184   UR:file:genome/Saccharomyces.fa M5:97a317c689cbdd7e92a5c159acd290d2

$ samtools view -bS SRR527547.sam > SRR527547.unsrt.bam
$ samtools sort SRR527547.unsrt.bam SRR527547
# this will produce SRR527547.bam
$ samtools index SRR527547.bam
$ samtools view -h @SQ  SN:chrV LN:576874
@SQ SN:chrVI    LN:270161
@SQ SN:chrVII   LN:1090940
@SQ SN:chrVIII  LN:562643
@SQ SN:chrIX    LN:439888
@SQ SN:chrX LN:745751
@SQ SN:chrXI    LN:666816
@SQ SN:chrXII   LN:1078177
@SQ SN:chrXIII  LN:924431
@SQ SN:chrXIV   LN:784333
@SQ SN:chrXV    LN:1091291
@SQ SN:chrXVI   LN:948066
@SQ SN:chrMito  LN:85779
@PG ID:bwa  PN:bwa  VN:0.6.2-r131
SRR527547.1387762   163 chrI    1   17  3S25M1D11M1S    =   213 260 
  CACCCACACCACACCCACACACCCACACCCACACCACACC  IIIIIIIIIIIHIIIIHIIIGIIIHDDG8E?@:??DDDA@    
  XT:A:M    NM:i:1  SM:i:17 AM:i:17 XM:i:0  XO:i:1

Presenter Notes

SAM format

SAM Table

Presenter Notes

Read Groups

One component of SAM files is the idea of processing multiple files, but that these track back to specific samples or replicates.

This can be coded in the header of the SAM file

@RG ID:Strain124 PL:Illumina PU:Genomic LB:Strain124 CN:Broad

It can also be encoded on a per-read basis so that multiple SAM files can be combined together into a single SAM file and that the origin of the reads can still be preserved. This is really useful when you want to call SNPs across multiple samples.

The AddOrReplaceReadGroups.jar command set in Picard is really useful for manipulating these.

Fixing Read-Groups

I am using W303 since it is the strain name for this sequencing record.

 module load picard
 java -Xmx3g -jar $PICARD/AddOrReplaceReadGroups.jar I=SRR527545.bam \
  O=SRR527545.readgroup.bam SORT_ORDER=coordinate CREATE_INDEX=True \
  RGID=W303 RGLB=SRR527545 RGPL=Illumina RGPU=Genomic RGSM=W303 \
  VALIDATION_STRINGENCY=SILENT

Presenter Notes

samtools flagstat

 3343010 + 0 in total (QC-passed reads + QC-failed reads)
 0 + 0 duplicates
 3304818 + 0 mapped (98.86%:-nan%)
 3343010 + 0 paired in sequencing
 1671505 + 0 read1
 1671505 + 0 read2
 3259272 + 0 properly paired (97.50%:-nan%)
 3300190 + 0 with itself and mate mapped
 4628 + 0 singletons (0.14%:-nan%)
 5585 + 0 with mate mapped to a different chr
 3049 + 0 with mate mapped to a different chr (mapQ>=5)

Presenter Notes

Realigning around Indels and SNPs

To insure high quality Indelcalls, the reads need to realigned after placed by BWA or other aligner. This can be done with PicardTools and GATK.

Need to Deduplicate reads (change W303 into the name of the strain you are working on)

qsub -l mem=2gb -I # this will span a new process on a new machine, 
module load picard
java -Xmx2g -jar $PICARD/MarkDuplicates.jar I=W303.sorted.bam \
  O=W303.dedup.bam METRICS_FILE=W303.dedup.metrics \
  CREATE_INDEX=true VALIDATION_STRINGENCY=SILENT

Then identify Intervals around variants

module load GATK # this will set the $GATK variable
# which points to the jar file for java
java -Xmx2g -jar $GATK -T RealignerTargetCreator \
 -R genome/Saccharomyces.fa \
 -o W303.intervals -I W303.dedup.bam

Then realign based on these intervals

java -Xmx2g -jar $GATK -T IndelRealigner \
 -R genome/Saccharomyces.fa \
 --targetIntervals W303.intervals -I W303.dedup.bam -o W303.realign.bam

Presenter Notes

flagstats after Deduplicate reads

3343010 + 0 in total (QC-passed reads + QC-failed reads)
28822 + 0 duplicates  <-----
3304818 + 0 mapped (98.86%:-nan%)
3343010 + 0 paired in sequencing
1671505 + 0 read1
1671505 + 0 read2
3259272 + 0 properly paired (97.50%:-nan%)
3300190 + 0 with itself and mate mapped
4628 + 0 singletons (0.14%:-nan%)
5585 + 0 with mate mapped to a different chr
3049 + 0 with mate mapped to a different chr (mapQ>=5)

Presenter Notes

SAMtools and VCFtools to call SNPs

module load samtools
# this generates a BCF file which is the raw set of SNPs called
samtools mpileup -D -S -gu -f genome/Saccharomyces.fa W303.realign.bam | \
 bcftools view -bvcg - > W303.raw.bcf
# convert this BCF file into VCF but first filter by max-depth to remove repeats
bcftools view W303.raw.bcf | vcfutils.pl varFilter -D100 > W303.filter.vcf

Presenter Notes

GATK to call SNPs

# run GATK with 4 threads (-nt)
# call SNPs only (-glm, would specific INDEL for Indels or can ask for BOTH)
# here is a script you could submit with qsub, by saving the following lines
# into a file called (gatk_genotype.sh)
# then doing qsub -d `pwd` gatk_genotype.sh
# or just run the commands below by first getting a login to a cluster node
# with qsub -l mem=3gb -I

#PBS -l mem=3gb -N GATK
module load GATK
java -Xmx3g -jar $GATK -T UnifiedGenotyper \
  -glm SNP -I W303.realign.bam -R genome/Saccharomyces.fa \
  -o W303.GATK.vcf -nt 4

# could compare these SNPs look compared to the non-realigned data
java -Xmx3g -jar $GATK -T UnifiedGenotyper \
  -glm SNP -I W303.bam -R genome/Saccharomyces.fa \
  -o W303.no_realign_GATK.vcf -nt 4

Presenter Notes

GATK to call INDELs

# run GATK with 4 threads (-nt)
# call SNPs only (-glm, would specific INDEL for Indels or can ask for BOTH)
# same messages as the previous slide in terms of a qsub -I or qsub a script
module load GATK
java -jar $GATK -T UnifiedGenotyper\
  -glm INDEL -I W303.realign.bam \
  -R genome/Saccharomyces.fa -o W303.GATK_INDEL.vcf -nt 4

Presenter Notes

VCF Files

Variant Call Format - A standardized format for representing variations. Tab delimited but with specific ways to encode more information in each column.

##FORMAT=<ID=AD,Number=.,Type=Integer,Description="Allelic depths for the ref and alt alleles in the order listed">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
##INFO=<ID=AC,Number=A,Type=Integer,Description="Allele count in genotypes, for each ALT allele, in the same order as listed">
##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency, for each ALT allele, in the same order as listed">

#CHROM  POS ID  REF ALT QUAL    FILTER  INFO    FORMAT  SRR527545
chrI    141 .   C   T   47.01   .   AC=1;AF=0.500;AN=2;BaseQRankSum=-0.203;DP=23;Dels=0.00;
FS=5.679;HaplotypeScore=3.4127;MLEAC=1;MLEAF=0.500;MQ=53.10;MQ0=0;MQRankSum=-2.474;QD=2.04;ReadPosRankSum=-0.771;
SB=-2.201e+01   GT:AD:DP:GQ:PL  0/1:19,4:23:77:77,0,565

chrI    286 .   A   T   47.01   .   AC=1;AF=0.500;AN=2;BaseQRankSum=-0.883;DP=35;Dels=0.00;
FS=5.750;HaplotypeScore=0.0000;MLEAC=1;MLEAF=0.500;MQ=46.14;MQ0=0;MQRankSum=-5.017;QD=1.34;ReadPosRankSum=-0.950;
SB=-6.519e-03   GT:AD:DP:GQ:PL  0/1:20,15:35:77:77,0,713

Presenter Notes

Filtering Variants

GATK best Practices http://www.broadinstitute.org/gatk/guide/topic?name=best-practices emphasizes need to filter variants after they have been called to removed biased regions.

These refer to many combinations of information. Mapping quality (MQ), Homopolymer run length (HRun), Quality Score of variant, strand bias (too many reads from only one strand), etc.

-T VariantFiltration -o SRR527545.GATK_filtered.vcf \
--variant SRR527545.GATK.vcf -R genome/Saccharomyces.fa \
--clusterWindowSize 10  -filter "QD<8.0" -filterName QualByDepth \
-filter "MQ>=30.0" -filterName MapQual \
-filter "HRun>=4" -filterName HomopolymerRun \
-filter "QUAL<100" -filterName QScore \
-filter "MQ0>=10 && ((MQ0 / (1.0 * DP)) > 0.1)" -filterName MapQualRatio \
-filter "FS>60.0" -filterName FisherStrandBias \
-filter "HaplotypeScore > 13.0" -filterName HaplotypeScore \
-filter "MQRankSum < -12.5" -filterName MQRankSum  \
-filter "ReadPosRankSum < -8.0" -filterName ReadPosRankSum  >& output.filter.log

Presenter Notes

VCFtools

A useful tool to JUST get SNPs back out from a VCF file is vcf-to-tab (part of vcftools).

$ vcf-to-tab < INPUT.vcf > OUTPUT.tab

#CHROM  POS REF SRR527545
chrI    141 C   C/T
chrI    286 A   A/T
chrI    305 C   C/G
chrI    384 C   C/T
chrI    396 C   C/G
chrI    476 G   G/T
chrI    485 T   T/C
chrI    509 G   G/A
chrI    537 T   T/C
chrI    610 G   G/A
chrI    627 C   C/T

Presenter Notes

VCFtools to evaluate and manipulate

$ vcftools --vcf SRR527545.GATK.vcf --diff SRR527545.filter.vcf
N_combined_individuals: 1
N_individuals_common_to_both_files: 1
N_individuals_unique_to_file1:  0
N_individuals_unique_to_file2:  0
Comparing sites in VCF files...
Non-matching REF at chrI:126880 C/CTTTTTTTTTTTTTTT. Diff results may be unreliable.
Non-matching REF at chrI:206129 A/AAC. Diff results may be unreliable.
Non-matching REF at chrIV:164943 C/CTTTTTTTTTTTT. Diff results may be unreliable.
Non-matching REF at chrIV:390546 A/ATTGTTGTTGTTGT. Diff results may be unreliable.
Non-matching REF at chrXII:196750 A/ATTTTTTTTTTTTTTT. Diff results may be unreliable.
Found 8604 SNPs common to both files.
Found 1281 SNPs only in main file.
Found 968 SNPs only in second file.

# calculate Tajima's D in binsizes of 1000 bp [if you have multiple individuals]
$ vcftools --vcf Sacch_strains.vcf --TajimaD 1000

Presenter Notes

Can compare strains in other ways

PCA plot of strains from the SNPs converted to 0,1,2 for homozygous Ref, Homozygous Alt allele, or heterozygous (done in R)

PCA_1

Presenter Notes

Zoomed PCA plot

PCA_2

Presenter Notes

Summary

  • Reads should be trimmed, quality controlled before use. Preserving Paired-End info is important
  • Alignment of reads with several tools possible, BWA outlined here
  • SAMTools and Picard to manipulate SAM/BAM files
  • Genotyping with SAMtools and GATK
  • Summarizing and manipulating VCF files with VCFtools

Presenter Notes