Bioinformatics at COMAV

Maize SNP calling walkthrough

We have some 454 and Illumina maize reads and we want to look for SNPs in the the maize transcriptome. We are going to write down the commands to do the complete practice to document one possible route to obtain the SNPs, but other approaches could be used.

Download the sequence files sequence files into a directory named maize.

Read quality evaluation

ngs_user@machine:~$ cd
ngs_user@machine:~$ mkdir maize
ngs_user@machine:~$ cd maize/
ngs_user@machine:~/maize$ ls
ngs_user@machine:~/maize$ tar -xvzf snp_calling_practice.tar.gz
ngs_user@machine:~/maize$ mkdir raw_reads
ngs_user@machine:~/maize$ mv snp_calling/sm_Mo17.lb_Mo17_root.pl_454.sfastq raw_reads/mo17.454.fastq
ngs_user@machine:~/maize$ mv snp_calling/sm_B73XMo17.lb_MB_14day.pl_illumina.sfastq raw_reads/b73xmo17.illumina.fastq

Evaluate the quality with using fastqc or calculate_stats from seq_crumbs.

ngs_user@machine:~/maize$ calculate_stats raw_reads/mo17.454.fastq | head -n 40
Length stats and distribution.
minimum: 45
maximum: 310
average: 211.73
num. seqs.: 4888
Quality stats and distribution.
Q20: 93.35
Q30: 79.86
average: 32.53
ngs_user@machine:~/maize$ calculate_stats raw_reads/b73xmo17.illumina.fastq | head -n 40
Length stats and distribution.
minimum: 35
maximum: 35
average: 35.00
num. seqs.: 16084
Quality stats and distribution.
Q20: 93.38
Q30: 68.61
average: 29.47

Read cleaning

ngs_user@machine:~/maize$ mkdir clean_reads
ngs_user@machine:~/maize$ trim_quality raw_reads/mo17.454.fastq | filter_by_length -n 150 > clean_reads/mo17.454.fastq
ngs_user@machine:~/maize$ trim_quality -q 20 raw_reads/b73xmo17.illumina.fastq | filter_by_length -n 30 > clean_reads/b73xmo17.illumina.fastq
ngs_user@machine:~/maize$ ls clean_reads
b73xmo17.illumina.fastq  mo17.454.fastq

Evaluate the result.

ngs_user@machine:~/maize$ calculate_stats clean_reads/mo17.454.fastq | head -n 20
Length stats and distribution.
minimum: 150
maximum: 303
average: 226.81
num. seqs.: 2004
Quality stats and distribution.
Q20: 99.81
Q30: 96.59
average: 35.95
ngs_user@machine:~/maize$ calculate_stats clean_reads/b73xmo17.illumina.fastq | head -n 40
Length stats and distribution.
minimum: 30
maximum: 35
average: 34.70
num. seqs.: 14415
Quality stats and distribution.
Q20: 95.71
Q30: 72.56
average: 30.18

Mapping to the reference

We are going to map the 454 reads with bwasw and the Illumina reads with Illumina.

bwasw mapping

We create the reference:

ngs_user@machine:~/maize$ mkdir mapping
ngs_user@machine:~/maize$ cp snp_calling/maize_unigenes.fasta mapping/reference.fasta
ngs_user@machine:~/maize$ cd mapping
ngs_user@machine:~/maize/mapping$ bwa index -p reference -a bwtsw reference.fasta
[bwa_index] Pack FASTA... 0.02 sec
[bwa_index] Construct BWT for the packed sequence...
[main] Real time: 0.892 sec; CPU: 0.548 sec

Now we can map:

ngs_user@machine:~/maize/mapping$ bwa bwasw reference ../clean_reads/mo17.454.fastq > mapped_454.sam
[bsw2_aln] read 2004 sequences/pairs (454536 bp) ...
[main] Version: 0.7.2-r351
[main] CMD: bwa bwasw reference ../clean_reads/mo17.454.fastq
[main] Real time: 3.163 sec; CPU: 2.672 sec
ngs_user@machine:~/maize/mapping$ samtools view -b -S -o mapped_454.bam mapped_454.sam
[samopen] SAM header is present: 100 sequences.
ngs_user@machine:~/maize/mapping$ rm mapped_454.sam
ngs_user@machine:~/maize/mapping$ ls mapped_454.bam

bowtie2 mapping

We create a bowtie2 index:

ngs_user@machine:~/maize/mapping$ bowtie2-build reference.fasta reference
Wrote 4312022 bytes to primary EBWT file: reference.rev.1.bt2
Wrote 76768 bytes to secondary EBWT file: reference.rev.2.bt2
Re-opening _in1 and _in2 as input streams
Returning from Ebwt constructor

We map the Illumina reads:

ngs_user@machine:~/maize/mapping$ bowtie2 --very-sensitive-local -x reference -U ../clean_reads/b73xmo17.illumina.fastq -S illumina.sam

14415 reads; of these:
14415 (100.00%) were unpaired; of these:
  210 (1.46%) aligned 0 times
  13192 (91.52%) aligned exactly 1 time
  1013 (7.03%) aligned >1 times
98.54% overall alignment rate
ngs_user@machine:~/maize/mapping$ samtools view -b -S -o mapped_illumina.bam illumina.sam
[samopen] SAM header is present: 100 sequences.
ngs_user@machine:~/maize/mapping$ rm illumina.sam

Merge, sort and index

We add the read groups to the 454 and Illumina BAMs to be able to differentiate the reads once we merge the BAM files

ngs_user@machine:~/maize/mapping$ picard-tools AddOrReplaceReadGroups ID=illumina LB=illumina PL=illumina PU=none SM=b73xmo17 SORT_ORDER=coordinate INPUT=mapped_illumina.bam OUTPUT=illumina.bam
ngs_user@machine:~/maize/mapping$ picard-tools AddOrReplaceReadGroups ID=454 LB=454 PL=454 PU=none SM=mo17 SORT_ORDER=coordinate INPUT=mapped_454.bam OUTPUT=454.bam
ngs_user@machine:~/maize/mapping$ rm  mapped_*

Now that we have the read groups set we can merge both BAM files:

ngs_user@machine:~/maize/mapping$ picard-tools MergeSamFiles SORT_ORDER=coordinate ASSUME_SORTED=true INPUT=454.bam INPUT=illumina.bam OUTPUT=maize.bam

Finally we can index the BAM file:

ngs_user@machine:~/maize/mapping$ samtools index maize.bam

BAM statistics

ngs_user@machine:~/maize/mapping$ samtools flagstat maize.bam
16526 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 duplicates
16211 + 0 mapped (98.09%:-nan%)
0 + 0 paired in sequencing
0 + 0 read1
0 + 0 read2
0 + 0 properly paired (-nan%:-nan%)
0 + 0 with itself and mate mapped
0 + 0 singletons (-nan%:-nan%)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5)
ngs_user@machine:~/maize/mapping$ samtools idxstats maize.bam
TC458157      8763    34      0
TC458158      7672    321     0
TC458159      6012    404     0
TC458160      4906    364     0
TC458161      5087    476     0
TC458162      4895    274     0
TC458163      3960    19      0

It would be also advisable to check the coverage and MAPQ distributions obtained.

Take a look at the BAM files obtained with IGV.

SNPs calling

Use Varscan or other software to identify the SNPs.

First change BAM format to mpileup format.

ngs_user@machine:~/maize/mapping$ cd ..
ngs_user@machine:~/maize$ cd snp_calling
ngs_user@machine:~/maize/snp_calling$ samtools mpileup -f maize_unigenes.fasta ../mapping/maize.bam > maize.mpileup

Now we can look for SNPs with Varscan:

ngs_user@machine:~/maize/snp_calling$ java -jar /home/ngs_user/software/VarScan.v2.3.7.jar mpileup2snp maize.mpileup --strand-filter 0  -output-vcf 1 >maize_snp.vcf

Only SNPs will be reported
Warning: No p-value threshold provided, so p-values will not be calculated
Min coverage: 8
Min reads2:   2
Min var freq: 0.2
Min avg qual: 15
P-value thresh:       0.01
Reading input from maize.mpileup
161738 bases in pileup file
120 variant positions (120 SNP, 0 indel)
0 were failed by the strand-filter
120 variant positions reported (120 SNP, 0 indel)