Table of Contents
- Upon upload to HPC…
- Upon upload to HPC…
- Digital fingerprint md5sums (HPC script; md5_checksum.sh)
- 1. MultiQC: Initial QC (HPC script; mutliqc.sh)
- 2. Trimming and QC of ‘clean’ reads
- fastp - about/commands
- fastp and MulitiQC: Trim and QC (HPC script; fastp_mutliqc.sh)
- 3. Alignment of cleaned reads to reference
- Upload reference genome
- HISAT2 - about/commands
- samtools - about/commands
- HISAT2: Index reference and alignment (HPC script; HISAT2.sh)
Initial diagnostics upon sequence upload to HPC
Assemble cumulative md5 reference
- Genewiz provides a one txt line .md5 file for each of the .gz sequence files
You can concatenate using ‘cat *.md5 > new_file.md5’
- example: cat *.md5 > ~/Airradians_lcWGS/F0_F2_April2023/output/md5checksum/April2023_reference.md5
- first objective to assemble a cumulative file with these text lines in one .txt
run checksum
shell script: md5checksum.sh
#!/bin/bash
#SBATCH --job-name="md5checksum"
#SBATCH -t 002:00:00
#SBATCH --mem=32GB
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samuel.gurr@noaa.gov
#SBATCH --output=./Airradians_lcWGS/F1/output/md5checksum/"%x_out.%j"
#SBATCH --error=./Airradians_lcWGS/F1/output/md5checksum/"%x_err.%j"
# before running..
# make directory named output/md5checksum/
cd # nav to home dir from whereever the script is run
echo "start time stamp" $(date)
# generate md5
md5sum ../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/*.gz > Airradians_lcWGS/F1/output/md5checksum/Oct2022_check.md5
echo "md5checksum complete!" $(date)
# count the number of sequences per file - for fastq file the accession ID of each read starts with @, grep does this well + fast!
grep -c '@' ../../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/*.fastq.gz > Airradians_lcWGS/F1/output/md5checksum/Oct2022$
echo " grep '@' raw fastq read counts complete!" $(date)
run the script
sbatch md5checksum.sh
- print the digital fingerprint of the md5sum script output Oct2022_check.md5
cat Oct2022_check.md5 | head -2
9b222e2ad82096e20efdae837af55591 ../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/101_R1_001.fastq.gz
21accf228ec279d62b1417e810402110 ../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/101_R2_001.fastq.gz
- use awk to cut a mutliple character delimiter and echo to print
cat Oct2022_check.md5 | awk -F[../] '{print $1 $11}' > checksummary.txt
cat checksummary.txt | head -5
9b222e2ad82096e20efdae837af55591 101_R1_001
21accf228ec279d62b1417e810402110 101_R2_001
8165b1cbf30c6e0d473dd92d476f02b1 103_R1_001
42691c8282ef8cdda6c1d0142ecb3488 103_R2_001
f2a1da51d2b5040ffac65e18e01c876c 104_R1_001
How do these md5 checks compare to the reference? (Genewiz output) check Oct2022_reference.md5?
cat April2023_check.md5| head -2
d00b958fe8ff3f0a7308a1cd455790c7 ../../../share/nefsc/mcfarland_sequecenes/lcWGS_scallops_April2023/F0-B10_R1_001.fastq.gz
58f4d2c3af4ba150cb024181bcaaf1ec ../../../share/nefsc/mcfarland_sequecenes/lcWGS_scallops_April2023/F0-B10_R2_001.fastq.gz
| cat April2023_check.md5 | awk -F “../” ‘{print $1 $8}’ > April2023_check_summary.txt # use “” in -F to have mutliple delimiters |
| cat April2023_check_summary.txt | head -2 |
d00b958fe8ff3f0a7308a1cd455790c7 F0-B10_R1_001.fastq.gz
58f4d2c3af4ba150cb024181bcaaf1ec F0-B10_R2_001.fastq.
| cat Oct2022_reference.md5 | awk -F[./] ‘{print $1 $3}’ > refsummary.txt |
9b222e2ad82096e20efdae837af55591 101_R1_001
21accf228ec279d62b1417e810402110 101_R2_001
8165b1cbf30c6e0d473dd92d476f02b1 103_R1_001
42691c8282ef8cdda6c1d0142ecb3488 103_R2_001
f2a1da51d2b5040ffac65e18e01c876c 104_R1_001
a90aaf9a879451b576025a4df64c18fe 104_R2_001
cbf7085e3e08f82e90f6a2049c312dd9 153_R1_001
1082f16ab44f20cf136ca478377ced27 153_R2_001
f4ef92166b83346fed65e6f418df1f7c 154_R1_001
63758272cbed1b1aaff5115f23666aa4 154_R2_001
4aaf29326e8ee3827c60a37a325186aa 155_R1_001
b04f866ca6600a08d116940dc18f9fb3 155_R2_001
89550daafe9e4c0856b0ff895a13ef7b 201_R1_001
2d5fb0ddf5a67394bcdb6f1dc48e55d5 201_R2_001
a7c1d45273b1b059effe85934655fbd3 203_R1_001
62df2ec4f6806d4758102f3426f8b371 203_R2_001
9bdc0fa2524c15bdea2978cadac0031e 204_R1_001
aefebf16610c1889886c7299cdb3b1ad 204_R2_001
7b3cd7b104f0cb63b1be5e933d471047 251_R1_001
105a4c1ec9e7d9c98cecb40ea90ed45b 251_R2_001
e8336d0070269ce885eed7be428961d6 253_R1_001
864bb789dbc214d1130539bbe68ece40 253_R2_001
7c2dc7ac72002138f2870e16d35b4f01 254_R1_001
81dfd6447ea06d6edefa5d264e4f5af5 254_R2_001
42e1b7cd5e21e508968cdbd2bc59bff0 301_R1_001
e97f0aae79c05709628757e8b75ca5e2 301_R2_001
a356e5f0b7dc458adfca95f5587c7d8c 303_R1_001
e3b7a9509d820ec3e092c4d3830ad022 303_R2_001
547fce4deb5c811955bf4ff49ea9620f 304_R1_001
dcb9614ec86df811ab647069c42a984c 304_R2_001
4f9886022ad393cb0724058a39b58cc5 351_R1_001
bb2c71fe8def1af27ed1f8deef98dc3f 351_R2_001
386084822e4b259917306fef1d41d397 352_R1_001
35ffc9f615522d1327b2adca13391881 352_R2_001
d732a8ca7f7c4ad55c2c5dc77f6306ef 353dup_R1_001
bbdaae2dc1e2ef43238877dd3eb03462 353dup_R2_001
c2a6c77ef79ee4c2d9632c109fb2d179 353_R1_001
5b9a7df7543f7019f08fcaaf60629b20 353_R2_001
a886be2b8ecce4a313798513b3c0f9a1 3_R1_001
d94352887177a3da9149a2506f0bacb7 3_R2_001
3628bc6e8ee42dc6602f29b3e879810e 4_R1_001
7a89f26826eebd2bf6b3b0f04162e381 4_R2_001
6033884aea9abc4eeb28b87197bd18b8 53_R1_001
da703a8752bca0e5965308e5d53057af 53_R2_001
c43bfd95cdd7501e2218d12d9cb100d9 54_R1_001
2fd418c554c3df68155bb0f8401ab7aa 54_R2_001
729119cc0a8c716d118cf4a21f80de83 55_R1_001
40cb63a52f6fd382a86280a49d9abbe0 55_R2_001
9d2bef38a95a59c4b17eb9d504731143 5_R1_001
d3ab7acfb6c23955471ab47ece590d56 5_R2_001
- check the read counts
| cat Oct2022_rawread_count.txt | head -2 |
../../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/101_R1_001.fastq.gz:5194197
../../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/101_R2_001.fastq.gz:6077857
use awk to cut a mutliple character delimiter and echo to print
| cat Oct2022_rawread_count.txt | awk -F’Oct2022/’ ‘{print $2}’ |
101_R1_001.fastq.gz:5194197
101_R2_001.fastq.gz:6077857
103_R1_001.fastq.gz:4774413
103_R2_001.fastq.gz:5528382
104_R1_001.fastq.gz:4722932
104_R2_001.fastq.gz:5315996
153_R1_001.fastq.gz:4486204
153_R2_001.fastq.gz:4854951
154_R1_001.fastq.gz:4348240
154_R2_001.fastq.gz:4844429
155_R1_001.fastq.gz:5368641
155_R2_001.fastq.gz:6056000
201_R1_001.fastq.gz:6455581
201_R2_001.fastq.gz:7467221
203_R1_001.fastq.gz:4561695
203_R2_001.fastq.gz:4940046
204_R1_001.fastq.gz:6838830
204_R2_001.fastq.gz:7578940
251_R1_001.fastq.gz:4134299
251_R2_001.fastq.gz:4820032
253_R1_001.fastq.gz:3520110
253_R2_001.fastq.gz:3953096
254_R1_001.fastq.gz:2457466
254_R2_001.fastq.gz:2680292
301_R1_001.fastq.gz:2531469
301_R2_001.fastq.gz:2931756
303_R1_001.fastq.gz:2159676
303_R2_001.fastq.gz:2338376
304_R1_001.fastq.gz:2146083
304_R2_001.fastq.gz:2373869
351_R1_001.fastq.gz:4487960
351_R2_001.fastq.gz:4953422
352_R1_001.fastq.gz:3644657
352_R2_001.fastq.gz:4160578
353dup_R1_001.fastq.gz:3992322
353dup_R2_001.fastq.gz:4546429
353_R1_001.fastq.gz:4001316
353_R2_001.fastq.gz:4572255
3_R1_001.fastq.gz:3433517
3_R2_001.fastq.gz:3705182
4_R1_001.fastq.gz:3740345
4_R2_001.fastq.gz:4222517
53_R1_001.fastq.gz:4015646
53_R2_001.fastq.gz:4385423
54_R1_001.fastq.gz:4575551
54_R2_001.fastq.gz:5036292
55_R1_001.fastq.gz:3781524
55_R2_001.fastq.gz:4210142
5_R1_001.fastq.gz:4643576
5_R2_001.fastq.gz:5090338
- now use ‘comm’ -23 to check or any discrepancies, prints unique lines between files
comm -23 <(sort checksummary.txt | uniq) <(sort refsummary.txt | uniq) # returns uniqe lines in the file checksummary.txt that do not exits in refsummary.txt
- no output == GOOD!
- any output here, the file partially uploaded or is corrupted - reupload the .gez file
Quality check of raw reads
shell script: multiQC_raw.sh
Run the sbatch
sbatch multiQC_raw.sh
#!/bin/bash
#SBATCH --job-name="multiQC_raw_reads"
#SBATCH -t 002:00:00
#SBATCH --mem=32GB
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samuel.gurr@noaa.gov
#SBATCH --output=./Airradians_lcWGS/F1/output/fastp_multiQC/raw/"%x_out.%j"
#SBATCH --error=./Airradians_lcWGS/F1/output/fastp_multiQC/raw/"%x_err.%j"
# load modules needed
module load bio/fastp/0.23.2
module load bio/fastqc/0.11.9
# NOTE! before running this job...
# mkdir a directory/directories output/fastp_multiQC/raw in the lcWGS/F1 dir
cd # can run from anywhere - initially navigaes to home dir
cd ./Airradians_lcWGS/F1/output/fastp_multiQC/ # then navs to the output dir to simplify output files
# symbolically link clean reads to fastp_multiQC dir
ln -s ../../../../../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/*.gz ./ # call backward from the directory to the share folder, input symbolic links to all .gz to folder
# hashed out if the symbolic links are already present
echo "symbolic links in output/fastp_multiQC folder SUCCESS!"
# Make an array of sequences to trim
array1=($(ls *.fastq.gz)) # call all symbolically linked .fastq.gz files now linkined symbolically in the output/fastp_multiQC folder
# fastqc loop of raw reads - output fastqc files to raw folder
for i in ${array1[@]}; do
fastqc ${i} --outdir ./raw # output in the raw dir
done
echo "QC of raw reads complete." $(date)
# Quality Assessment of Raw reads
source ../../../../python_venv/bin/activate # from the current directory, activates the bin of installed python packages, including multiqc
multiqc ./raw #Compile MultiQC report from FastQC files - output .html in raw directory ( multiQC html report)
echo "Raw MultiQC report generated." $(date)
Export multiqc report
exit sedna and run from terminal
-
save to gitrepo as multiqc_raw.html
-
here is our run from Oct 2022
scp sgurr@sedna.nwfsc2.noaa.gov:/Airradians_lcWGS/F1/output/fastp_multiQC/raw/multiqc_report.html C:/Users/samuel.gurr/Documents/Github_repositories/Airradians_multigen_OA/HPC_analysis/output/Oct2022_lcWGS_QCreport/multiqc_raw.html
- here is our run for April 2023
scp sgurr@sedna.nwfsc2.noaa.gov:/Airradians_lcWGS/F0_F2_April2023/output/fastp_multiQC/multiqc_report.html C:/Users/samuel.gurr/Documents/Github_repositories/Airradians_multigen_OA/HPC_analysis/output/April2023_lcWGS_QCreport/multiqc_raw.html
IMPORTANT! Quality check multiqc.html before you proceed!
-
view the multiqc.html report and note observations to guide trimming!
-
Per Sequence GC Content: double peak of ~0-10% GC and 30-40% GC
-
*Mean Quality Scores&: >22-2; To do: trim base calls < 30
-
Per Base Sequence Content: 15-25% C and G, 25-28% T, 35-40+% A
-
Adapter Content: high adapters present, 1-6% of sequences; To do: trim adapter sequence
-
Trimming and post-trim quality check of ‘clean’ reads
What this script will do…
--adapter_sequence=- trim adapter sequence
AGATCGGAAGAGCACACGTCTGAACTCCAGTCA - common single-end adapter in Illumina. You can run a test on a fastq.gz to count
- trim adapter sequence
multiqc ./= outputs mutliqc report of the ‘clean’ reads in the current directory
shell script: fastp_multiQC_adapters_only.sh
#!/bin/bash
#SBATCH --job-name="fastp_multiQC_adapters"
#SBATCH -t 048:00:00
#SBATCH --mem=32GB
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samuel.gurr@noaa.gov
#SBATCH --output=./Airradians_lcWGS/F1/output/fastp_multiQC/adapter_trim/"%x_out.%j"
#SBATCH --error=./Airradians_lcWGS/F1/output/fastp_multiQC/adapter_trim/"%x_err.%j"
# load modules needed
module load bio/fastp/0.23.2
module load bio/fastqc/0.11.9
# NOTE! before running this job...
# mkdir a directory/directories output/fastp_multiQC/adapter_trim in the lcWGS/F1 dir
cd # can run from anywhere - initially navigaes to home dir
cd ./Airradians_lcWGS/F1/output/fastp_multiQC/ # then navs to the output dir to simplify output files
# symbolically link clean reads to fastp_multiQC dir
# ln -s ../../../../../../share/nefsc/mcfarland_sequecenes/lcWGS_F1scallops_Oct2022/*.gz ./ # call backward from the directory to the share folder, input symbolic links to all .gz to folder
# hashed out if the symbolic links are already present (i.e. links here if you already ran the multiQC_raw.sh job)
# echo "symbolic links in output/fastp_multiQC folder SUCCESS!"
# Make an array of sequences to trim
array1=($(ls *.fastq.gz)) # call all symbolically linked .fastq.gz files now linkined symbolically in the output/fastp_multiQC folder
# fastp loop; trim the Read 1 TruSeq adapter sequence; trim poly x default 10 (to trim polyA)
for i in ${array1[@]}; do
fastp --in1 ${i} --out1 ./adapter_trim/adapter_trim.${i} --adapter_sequence=CTGTCTCTTATACACATCT --adapter_fasta adapter.fasta # --detect_adapter_for_pe # CTGTCTCTTATACACATCT # check JUST ad$
fastqc ./adapter_trim/adapter_trim.${i} --outdir ./adapter_trim # call the output files from fastp in previous line and output fastqc in the same folder with adapter_trim filename head
done
# https://support.illumina.com/bulletins/2016/12/what-sequences-do-i-use-for-adapter-trimming.html - check out the adapters to choose for trimming (Nextera XT used above)
echo "Read trimming of adapters complete." $(date)
# Quality Assessment of Trimmed Reads
source ../../../../python_venv/bin/activate # from the current directory, activates the bin of installed python packages, including multiqc
multiqc ./adapter_trim -o ./adapter_trim #Compile MultiQC report from FastQC files - output .html in adpater_trim directory ( fast_muiltiQC folder)
echo "Cleaned MultiQC report generated." $(date)
export the multiqc report
exit SEDNA and run from terminal
- save to gitrepo as multiqc_adapter_trim.html
scp sgurr@sedna.nwfsc2.noaa.gov:Airradians_lcWGS/F1/output/fastp_multiQC/adapter_trim/multiqc_report.html C:/Users/samuel.gurr/Documents/Github_repositories/Airradians_multigen_OA/HPC_analysis/output/F1_lcWGS_25samples/multiqc_adapter_trim.html
scp sgurr@sedna.nwfsc2.noaa.gov:Airradians_lcWGS/F0_F2_April2023/output/fastp_multiQC/adapter_trim/multiqc_report.html C:/Users/samuel.gurr/Documents/Github_repositories/Airradians_multigen_OA/HPC_analysis/output/April2023_lcWGS_QCreport/multiqc_adapter_trim.html
Alignment of cleaned reads to reference
Reference genome upload to HPC
exit SEDNA and run from terminal
scp **upload to HPC**
- file name: file name here
- reference paper
- reference genome file size: size here
- uploaded to location of the reference in the HPC
HISAT2 alignment
About: HISAT2 is a sensitive alignment program for mapping sequencing reads. In our case here, we will use HISAT2 to (1) index our reference genome (2) align our adapter-trimmed Bay scallop reads to the indexed reference genome.
More information on HISAT2 can be read here!
Main arguments used below:
(1) Index the reference genome
hisat2-build =
builds a HISAT2 index from a set of DNA sequences. Outputs 6 files that together consitute the index.
ALL output files are needed to slign reads to the reference genome and the original sequence FASTA file(s)
are no longer used for th HISAT2 alignment
-f <reads.fasta> =
the reads (i.e
Note: other options for your reads are -q for FASTQ files,
--qseq for QSEQ files, etc. - check here for more file types
(2) align reads to reference
-x <hisat2-indx> =
base name for the index reference genome, first looks in the current directory then in the directory specified in HISAT_INDEXES environment variable
--dta =
important! reports the alignments tailored for StringTie assembler.
With this option, HISAT2 requires longer anchor lengths for de novo discovery of splice sites.
This leads to fewer alignments with short-anchors, which helps transcript assemblers improve
significantly in computation and memory usage.
This is important relative to Cufflinks --dta-cufflinks
in which HISAT2 looks for novel splice sites with three signals (GT/AG, GC/AG, AT/AC)
-U <r> =
Comma-separated list of files contained unparied reads to be aligned.
In other words…, our array of post-trimmed ‘clean’ TagSeq reads
-p NTHREADS =
Runs on separate processors, increasing -p increases HISAT2’s memory footprint, increasing -p from 1 to 8
increased the footprint by a few hundred megabytes
Note: Erin Chile from Putnam Lab ran -p 8 for HISAT2 alignment of Montipora here
-S <hit> =
file to write SAM alignments to. By default, alignments are written to the “standard out” or “stdout” filehandle (i.e. the console).
Note: HISAT2 also has several criteria to trim such as the phred score and base count 5’ (left) and 3’ (right) Since we already assessed quality and trimmed, we will not use these commands
samtools
About: used to manipuate alignments to a binary BAM format - files contain the spliced reads alignemnts sorted by the reference position with a tag to indicate the genomic strand that produced the RNA from which the read was sequenced. samtools quickly extracts alignments overlapping particular genomic regions - outputs allow viewers to quickly display alignments in each genomic region note: the SAM format output by HISAT2 must be sorted and converted to BAM format using the samtools program
sort =
sorts the alignments by the leftmost coordinates
-o <out.bam =
outputs as a specified .bam file
-@ threads =
Set number of sorting and compression threads. By default, operation is single-threaded
more information on samtools commands here
HPC Job: HISAT2 Index Reference and Alignment
- create directory output\hisat2
mkdir HISAT2
- index reference and alignment
input
- Argopecten_irradians_irradians_genome.fasta = reference genome
- adapter_trim/.fastq.gz *= all trimmed lcWGS reads
ouput
- Airradians_ref = indexed reference by hisat2-build; stored in the output/hisat2 folder as 1.hy2, 2.ht2… 8.ht2
-
.sam *=hisat2 output, readable text file; removed at the end of the script* -
.bam *=converted binary file complementary to the hisat sam files*
shell script: HISAT2.sh
#!/bin/bash
#SBATCH --job-name="hisat2_align_Airradians"
#SBATCH -t 072:00:00
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samuel.gurr@noaa.gov
#SBATCH --output=./Airradians_lcWGS/F1/output/alignment/hisat2/"%x_out.%j"
#SBATCH --error=./Airradians_lcWGS/F1/output/alignment/hisat2/"%x_err.%j"
# before running..
# mkdir(s) as Airradians_lcWGS/F1/output/alignment/hisat2/
BASEDIR=~/ # base directory
REFDIR=~/refs # the reference folder
PYTHONENV=~/python_venv/bin # python enrnvrionment
DATDIR=~/Airradians_lcWGS/F1/output/fastp_multiQC/adapter_trim/ # directory of trimmed and filtered fastq.gz files
OUTDIR=~/Airradians_lcWGS/F1/output/alignment/hisat2/
# nav to hd
cd ~ #nav back to home directory (allows job to be run from anywhere)
# load modules, requires hisat2 and samtools
module load bio/hisat2/2.2.1
module load bio/samtools/1.11
# symbolically link clean reads to hisat2 dir
ln -s $DATDIR/*.fastq.gz $OUTDIR/ # call the .fastq.gz output from fastp trim - make symb link to output/hisat2
echo "Symbolic directories successfully linked"
# activate python for hisat2-build
source $PYTHONENV/activate # activate python virtual envriomment to call python and run hisat2-build
#echo "Python virtual env activated"
# index the reference genome for Panopea generosa output index to working directory
hisat2-build -f $REFDIR/Argopecten_irradians_irradians_genome.fasta $OUTDIR/Airradians_ref
echo "Referece genome indexed. Starting alingment" $(date)
# exit python virtual envrionment
deactivate
# This script exports alignments as bam files
# sorts the bam file because Stringtie takes a sorted file for input (--dta)
# removes the sam file because it is no longer needed
array=($(ls $OUTDIR/*.fastq.gz)) # call the symbolically linked sequences - make an array to align
for i in ${array[@]}; do
hisat2 -p 8 --dta -x $OUTDIR/Airradians_ref -U ${i} -S ${i}.sam
samtools sort -@ 8 -o ${i}.bam ${i}.sam
echo "${i} bam-ified!"
rm ${i}.sam
done
- HISAT2 complete
Look at your percent alignment!
-
- use
grep -hnr "overall"as your diagnostic delimiter where % mapping is reported by the error file from the hisat2 job
- use
grep -hnr "overall" <**add your error report here**> > percent_mapped.txt
- example of the ‘percent_mapped.txt file below
cat percent_mapped.txt -5 | head -5
84:78.45% overall alignment rate 91:75.11% overall alignment rate 98:79.48% overall alignment rate 105:76.95% overall alignment rate 112:80.56% overall alignment rate
- we see that our alignment at least for the first few files was ~75-81%!
ANGSD: Genotype Likelihood
ANGSD wikipedia-like site for all call definitions and tutorial items here: http://www.popgen.dk/angsd/index.php/Main_Page
About some core ANGSD calls…
-GL =
* 1: SAMtools model
* 2: GATK model
- quality filtering
-minQ = minimum phred quality score
-minMapQ = minimum mapping quality score
- note: studies use a minQ of 30-32 depending on how consersative you want your base calls
-minInd = the minimum representation of the variant in your data.
-
note: contemporary studies use a threshold so >80% of individuals are represented
-
thoughts… if you have 100 from population A and 100 from population B, should you:
- (a) GLs as -minInd 160 for all 200 samples
- (b) two GLs separately for each population with -minQ 80 for 100 samples
pvalues
-snp_pval = SNP p value
- angsd tutorials set this quite low to 1X10-6 (0.000001), however studies do set this to simply 0.05 also
-sb_pvalue = strand bias
-hetbias_pval = hetbias p value, based on reads of genotypes that are called to be heterozygotes - requires the -doGeno option
-anc = ancetral state, if you do not have this you can use the assembly that you have maped against but remember to add -fold 1 n readSFS and real SFS sf2theta step
-doMaf =+9+
* 1: Known major, and Known minor.
Here both the major and minor allele is assumed to be known (inferred or given by user).
The allele frequency is the obtained using based on the genotype likelihoods.
The allele frequency estimator from genotype likelihoods are from this publication but using the EM algorithm and is briefly described here.
* 2: Known major, Unknown minor.Here the major allele is assumed to be known (inferred or given by user) however the minor allele is not determined.
Instead we sum over the 3 possible minor alleles weighted by their probabilities.
* 4: frequency based on genotype posterior probabilities.If genotype probabilities are used as input to ANGSD the allele frequency is estimated directly on these by summing over the probabitlies.
* 8: frequency based on base counts.This method does not rely on genotype likelihood or probabilities but instead infers the allele frequency directly on the base counts.
- example: doMaf 1 outputs ‘knownEM’ as an allele frequency value based on geneotype liklihoods of the minor and manjor allele assumed as known
-doMajorMinor =
* 1: Infer major and minor from GL
* 2: Infer major and minor from allele counts
* 3: use major and minor from a file (requires -sites file.txt)
* 4: Use reference allele as major (requires -ref)
* 5: Use ancestral allele as major (requires -anc)
-P =
* number of threads that you are running your GL
-ref =
* if emplying -doMajorMinor 4, if forces the major allele according to the reference, you will need to define this by a fasta (___.fa) file
Before getting started..
we first need a file containg the root to all bam files from home dir - below as ‘bam_filelist.txt’
ls Airradians_lcWGS/F1/output/alignment/hisat2/*gz.bam > Airradians_lcWGS/F1/output/alignment/hisat2/bam_filelist.txt
Now we can run a test script for the angsd call for bam file input
shell script: angsd_GL.sh
#!/bin/bash
#SBATCH --job-name="angsd_GL"
#SBATCH -t 500:00:00
#SBATCH --mail-type=ALL
#SBATCH --mem=64GB
#SBATCH --mail-user=samuel.gurr@noaa.gov
#SBATCH --output=./Airradians_lcWGS/F1/output/angsd/GL/"%x_out.%j"
#SBATCH --error=./Airradians_lcWGS/F1/output/angsd/GL/"%x_err.%j"
# before running..
# mkdir(s) as Airradians_lcWGS/F1/output/alignment/angsd/GL
BASEDIR=~/ # base directory
DATDIR=~/Airradians_lcWGS/F1/output/alignment/hisat2 # directory of trimmed and filtered fastq.gz files
OUTDIR=~/Airradians_lcWGS/F1/output/angsd/GL/
# nav to hd
BASEDIR #nav back to home directory (allows job to be run from anywhere)
# load modules, requires hisat2 and samtools
module load bio/angsd/0.933
# run angsd GL
angsd -out $OUTDIR/angsd_mafs_SAMtools.gz -bam $DATDIR/bam_filelist.txt -GL 1 -doMaf 2 -doMajorMinor 1 -P 5
Let’s look at the output file while the job runs…
| zcat angsd_mafs_SAMtools.gz.mafs.gz | head -20 |
chromo position major minor unknownEM nInd
Contig0 1 C A 0.000001 38
Contig0 2 A C 0.000000 38
Contig0 3 T A 0.000000 38
Contig0 4 G A 0.000000 39
Contig0 5 A C 0.000000 39
Contig0 6 G A 0.000000 41
Contig0 7 A C 0.000000 41
Contig0 8 A G 0.076930 41
Contig0 9 T C 0.000000 42
Contig0 10 A C 0.000000 41
Contig0 11 G A 0.000000 45
Contig0 12 C A 0.000000 45
Contig0 13 T A 0.000000 45
Contig0 14 G T 0.000001 45
Contig0 15 T A 0.000000 45
Contig0 16 A G 0.000001 45
Contig0 17 T A 0.000000 45
Contig0 18 G A 0.261971 45
Contig0 19 G A 0.000000 45
How to read this output?
-
we have 6 total columns as..
-
‘chromo’ == the chromosome number # because of the scaffold stage of this genome reference (NCBI Airradians fasta), these are simply contigs and the position on contigs
-
‘position’ == bp number/position within each chromosome # as described above - not chromosome, contig for us! (wish it was chromosome!)
-
‘major’ == major allele at the noted position
-
‘minor’ == minor allele at the noted position
-
‘unknownEM’ == the minor allele frequency
-
‘nInd’ == the number of individuals for which there is coverage at each SNP
-
-
thus, at Contig0 18 we have a high frequency of th eminor allale ‘A’ exhibited by 45 individuals, but wait…. how is this possible if we only have 25 ini
| zcat angsd_mafs_SAMtools.gz.mafs.gz | tail -20 |
Contig52 675542 T A 0.000003 21
Contig52 675543 A G 0.037912 21
Contig52 675544 A C 0.000003 21
Contig52 675545 C A 0.000003 22
Contig52 675546 T A 0.000005 22
Contig52 675547 T A 0.000005 19
Contig52 675548 T A 0.000005 20
Contig52 675549 A C 0.000003 14
Contig52 675550 G A 0.000004 15
Contig52 675551 A C 0.000004 13
Contig52 675552 A G 0.000006 12
Contig52 675553 G A 0.000003 9
Contig52 675554 G A 0.000005 7
Contig52 675555 C A 0.000006 8
Contig52 675556 A C 0.000006 8
Contig52 675557 C A 0.000006 8
Contig52 675558 A C 0.000006 8
Contig52 675559 A C 0.000006 8
-
…and why do later contigs show more relevant sample numbers?
-
NOTE: are the regions with SNP positions of high or poor mapping quality? are the regions with SNP position of excessive coverage? You may need to supplement a csv(s) with categorical justification of regions with varied quality or putatively high-repeated regions (excessive coverage), filtering these cumulative SNP hits downstream in R
Appears we need to merge our paired-end reads!!
Should we merge BEFORE or AFTER mapping?
* (1) merging after mapped
* (2) merge before mapped
Merge bams after mapping (hisat2 outputs)
IMPORTANT: merge lanes here (.bam stage)
- nav to target dir
<navigate the the hisat2 directory with you paired-end .bam outputs>
-
example file ‘adapter_trim.352_R2_001.fastq.gz.bam’
-
mkdir for merged bam files
mkdir merged_bam_files
cd merged_bam_files
- enter interactive mode
interactive
- load samtools
module load bio/samtools/1.15.1
- ID file lists the sample ID characters (i.e. adapter_trim.101) - sample .bam file adapter_trim.101_R1_001.fastq.gz.bam
ls *R1_001*.bam | awk -F '[_]' '{print $1"_"$2}' | sort | uniq > ID
- output looks like this:
../adapter_trim.101
../adapter_trim.103
../adapter_trim.104
../adapter_trim.153
../adapter_trim.154
../adapter_trim.155
../adapter_trim.201
../adapter_trim.203
../adapter_trim.251
../adapter_trim.253
../adapter_trim.254
../adapter_trim.3
../adapter_trim.301
../adapter_trim.303
../adapter_trim.304
../adapter_trim.351
../adapter_trim.352
../adapter_trim.353
../adapter_trim.353dup
../adapter_trim.4
../adapter_trim.5
../adapter_trim.53
../adapter_trim.54
../adapter_trim.55
- for loop using
samtoolsto merge bam files together by sample ID (‘ID’ created above) - example of files to merge: adapter_trim.101_R1_001.fastq.gz.bam and adapter_trim.101_R2_001.fastq.gz.bam - where ‘R1’ and ‘R2’ are paired-end reads and the ID called adapter_trim.101 as $i below… (note: all .gz file names end with ‘001.fastq.gz.bam’ no matter the lane)
for i in `cat ./ID`;
do samtools merge $i\.bam $i\_R1_001.fastq.gz.bam $i\_R2_001.fastq.gz.bam;
done
- when run in
interactivemode, this loop takes up to ~1-2 hours
Call root directory of the merged bam files
-
when running angsd, we will need to call these merged bam files as a txt list file
-
below is an example calling bam file lists for the Low pCO2 and moderately elevated pCO2 F1 cohort - an example with the first 25 samples form October 2022
- Moderately-elevated pCO2 cohort, 12 animals with the IDs in the 300s and 200s
ls -d “$PWD”/.35.bam “$PWD”/30.bam “$PWD”/.2.bam > Mod_pCO2_bamlist.txt
- Low pCO2 cohort, 12 animals with the IDs 3,4,5 and in the 100s
ls -d “$PWD”/.3.bam “$PWD”/.4.bam “$PWD”/.5.bam “$PWD”/.1.bam > Low_pCO2_bamlist.txt
Lets run ANGSD!
- Objective: obtain SNP calls. chromosome/contig positions to infer genotype liklihoods and putativelly adaptive molecular candidates (we also have differential expression work!)
Terms to know regarding assemblies:
contigs - genome assemblies are hierarchical - contigs are the shortest assembly component of a genome in which a contiguous length of genomic sequence is known on a high confidence level
scaffolds - comprised of contigs, a longer component on this assembly hierachy, created from chaining contigs together using additional information on the orientation of the contigs and their relative position. Contigs in scallods can be separated by gaps represented as ‘N’
chromosomes - assembled seqeunces from compoentns (scallods and thus originally contigs) the largest genome assembly cmoponent comprised of assembled scaffolds, requires sufficient mapping information to build from the scaffold level