Human spike-in experiments

workflowr

• Summary
• Checks
• Past versions

Last updated: 2023-08-23

Checks: 7 0

Knit directory: G000204_duplex/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210916)

The command set.seed(20210916) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 603ddbc

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 603ddbc. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rapp.history
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/cache/
    Ignored:    data/.DS_Store

Untracked files:
    Untracked:  ._.DS_Store
    Untracked:  ._rare-mutation-detection.Rproj
    Untracked:  DOCNAME
    Untracked:  analysis/._.DS_Store
    Untracked:  analysis/._ecoli_spikeins.Rmd
    Untracked:  analysis/calc_nanoseq_metrics.Rmd
    Untracked:  data/._.DS_Store
    Untracked:  data/._metrics.rds
    Untracked:  data/ecoli/
    Untracked:  data/ecoli_k12_metrics.rds
    Untracked:  data/human_mixture_capture_region.bed
    Untracked:  data/human_mixture_pileup
    Untracked:  data/human_mixture_refs
    Untracked:  data/human_mixture_vars
    Untracked:  data/metadata/
    Untracked:  data/metrics_efficiency_nossc.rds
    Untracked:  data/metrics_spikeins.rds
    Untracked:  data/mixtures
    Untracked:  data/natasha_anstee_metrics.rds
    Untracked:  data/ref/
    Untracked:  drop_out_rate.pdf
    Untracked:  efficiency.pdf
    Untracked:  prototype_code/
    Untracked:  scripts/
    Untracked:  stats.csv

Unstaged changes:
    Modified:   analysis/model.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/human_mixtures.Rmd) and HTML (docs/human_mixtures.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	603ddbc	mcmero	2023-08-23	SNV pileup analysis
html	3c535f7	mcmero	2023-08-21	Build site.
Rmd	cb969b1	mcmero	2023-08-21	Fixed INDEL/N filtering from NVC output
html	603da1a	mcmero	2023-08-08	Build site.
Rmd	0c24851	mcmero	2023-08-08	Added human mixture variant analysis

---

Human samples are duplicate of 1% spike-in of 8393 (son of Chinese ancestry HG-005) in 8391 (son of Eastern European Ashkenazi Jewish ancestry HG-0020). Reference.

library(ggplot2)
library(data.table)
library(dplyr)
library(here)
library(tibble)
library(stringr)
library(Rsamtools)
library(GenomicRanges)
library(seqinr)
library(parallel)
library(readxl)
library(patchwork)
library(RColorBrewer)
library(UpSetR)
library(vcfR)
library(R.utils)
library(knitr)

source(here("code/load_data.R"))
source(here("code/plot.R"))
source(here("code/efficiency_nanoseq_functions.R"))

variant_dir <- here("data/human_mixture_vars")
pileup_dir <- here("data/human_mixture_pileup")
region_bed <- here("data/human_mixture_capture_region.bed")
hg002_var_file <- here("data/human_mixture_refs/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz")
hg005_var_file <- here("data/human_mixture_refs/HG005_GRCh38_1_22_v4.2.1_benchmark.vcf.gz")

sample_names <- c("Human1pR1", "Human1pR2")

# load variant data
var_df <- load_variants(variant_dir, sample_names)
hg002_vars <- read.vcfR(hg002_var_file, verbose = FALSE)
hg005_vars <- read.vcfR(hg005_var_file, verbose = FALSE)

hg002v <- data.frame(hg002_vars@fix)
hg005v <- data.frame(hg005_vars@fix)

# get capture regions
regions <- read.delim(region_bed, sep = "\t", header = FALSE)
grx <- GRanges(seqnames = regions$V1,
               ranges = IRanges(start = regions$V2, end = regions$V3))

Variant Upset plot

Here we remove any “N” variant calls and INDELs and compare the overlaps for on- and off-target variant calls.

# remove any N calls and INDELs
var_df <- filter(var_df, ALT != "N") %>%
            filter(., (REF %>% str_split("") %>% lapply(., length) %>% unlist) == 1) %>%
            mutate(ALT = lapply(ALT, filter_out_indels) %>% as.character()) %>%
            filter(., (ALT %>% str_split("") %>% lapply(., length) %>% unlist) == 1)

# filter out off-target reads
vrx <- GRanges(seqnames = var_df$CHROM,
               ranges = IRanges(start = as.numeric(var_df$POS),
                                end = as.numeric(var_df$POS) + 1))
var_df$on_target <- overlapsAny(vrx, grx)

# calculate vafs
alt_dep <- apply(var_df, 1, get_alt_dep_nvc) %>% t() %>% data.frame()
var_df$AC <- alt_dep$X1
var_df$DP <- alt_dep$X2
var_df$VAF <- var_df$AC / var_df$DP

# make upsetplot
ulist <- NULL
for(sample in sample_names) {
    ont_ids <- var_df[var_df$sample %in% sample & var_df$on_target,]$id
    oft_ids <- var_df[var_df$sample %in% sample & !var_df$on_target,]$id
    ulist[[paste(sample, "on_target")]] <- ont_ids
    ulist[[paste(sample, "off_target")]] <- oft_ids
    
}

upset(fromList(ulist), order.by="freq", nsets=4)

Past versions of variant_filtering-1.png

Version	Author	Date
3c535f7	mcmero	2023-08-21
603da1a	mcmero	2023-08-08

Checking on-target rate from the bam files (based on reads that fall within the region using samtools view -c -L <region_bed> <consensus_bam>) yields a higher on-target rate than the variant analysis would suggest:

                Human1pR1   Human1pR2
capture_region  188409      122466
total_reads     330368      233768
on_target       0.5703      0.5239

Variant allele frequencies

Here we plot the allelic frequencies per-replicate in three plots:

• all filtered variant calls: no frequency or target filtering
• on-target variant calls: only on-target variant calls (in capture region)
• VAF-filtered on-target variant calls: all on-target variants under <0.3% VAF

ggplot(var_df, aes(VAF)) +
    geom_histogram(binwidth = 0.05) +
    facet_grid(~sample) +
    theme_minimal() +
    ggtitle("All filtered variant calls")

Past versions of vaf_plots-1.png

Version	Author	Date
3c535f7	mcmero	2023-08-21
603da1a	mcmero	2023-08-08

ggplot(var_df[var_df$on_target,], aes(VAF)) +
    geom_histogram(binwidth = 0.05) +
    facet_grid(~sample) +
    theme_minimal() +
    ggtitle("On-target variant calls")

Past versions of vaf_plots-2.png

Version	Author	Date
3c535f7	mcmero	2023-08-21
603da1a	mcmero	2023-08-08

ggplot(var_df[var_df$on_target & var_df$VAF < 0.3,], aes(VAF)) +
    geom_histogram(binwidth = 0.01) +
    facet_grid(~sample) +
    theme_minimal() +
    ggtitle("On-target variant calls < 0.3 VAF")

Past versions of vaf_plots-3.png

Version	Author	Date
3c535f7	mcmero	2023-08-21
603da1a	mcmero	2023-08-08

Variant comparison

Given the referene information, we check how many SNPs are present in the capture area. We also filter out any variants that are common between the two samples (if the same variant appears in both samples, we can”t differentiate the calls without some kind of phasing).

# filter out any INDELs
hg005v <- filter(hg005v, (ALT %>% str_split("") %>% lapply(., length) %>% unlist) == 1) %>%
          filter(., (REF %>% str_split("") %>% lapply(., length) %>% unlist) == 1)

# construct Granges for hg005 SNPs and keep only SNPs in capture area
hg5x <- GRanges(seqnames = hg005v$CHROM,
                ranges = IRanges(start = as.numeric(hg005v$POS),
                                 end = as.numeric(hg005v$POS) + 1),
                variant = hg005v$ALT)
hg5x <- hg5x[overlapsAny(hg5x, grx) %>% suppressWarnings()]

# construct Granges for hg002
hg2x <- GRanges(seqnames = hg002v$CHROM,
                ranges = IRanges(start = as.numeric(hg002v$POS),
                                 end = as.numeric(hg002v$POS) + 1),
                variant = hg002v$ALT)

# check variants that overlap, we will keep these if they call a different base
unique_vars <- hg5x[overlapsAny(hg5x, hg2x)]$variant != hg2x[overlapsAny(hg2x, hg5x)]$variant
hg5x <- c(hg5x[!overlapsAny(hg5x, hg2x)], hg5x[overlapsAny(hg5x, hg2x)][unique_vars])

kable(hg5x)

seqnames	start	end	width	strand	variant
chr1	114714012	114714013	2	*	G
chr3	128486108	128486109	2	*	T
chr5	177516672	177516673	2	*	T
chr9	5069837	5069838	2	*	A
chr10	87970403	87970404	2	*	T
chr11	32396399	32396400	2	*	C
chr11	64805130	64805131	2	*	A
chr11	64810148	64810149	2	*	C
chr17	7676301	7676302	2	*	T

kable(var_df[var_df$POS %in% start(hg5x) & var_df$CHROM %in% seqnames(hg5x),])

	CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	Sample1	sample	id	on_target	AC	DP	VAF
146	chr5	177516672	NA	C	T	NA	NA	AC=2;AF=0.0104712041885	GT:AC:AF:NC	0:2:0.0104712041885:C=189,T=2,	Human1pR1	chr5_177516672	TRUE	2	191	0.0104712
261	chr10	87970403	NA	C	T	NA	NA	AC=2;AF=0.00393700787402	GT:AC:AF:NC	0:2:0.00393700787402:C=506,T=2,	Human1pR1	chr10_87970403	TRUE	2	508	0.0039370
295	chr11	32396399	NA	T	C	NA	NA	AC=4;AF=0.0121951219512	GT:AC:AF:NC	0:4:0.0121951219512:C=4,T=324,	Human1pR1	chr11_32396399	TRUE	4	328	0.0121951
527	chr1	114714012	NA	A	T	NA	NA	AC=5,2;AF=0.0505050505051,0.020202020202	GT:AC:AF:NC	0:5,2:0.0505050505051,0.020202020202:A=92,T=2,N=5,	Human1pR2	chr1_114714012	TRUE	2	99	0.0202020
590	chr3	128486108	NA	C	T	NA	NA	AC=3;AF=0.0260869565217	GT:AC:AF:NC	0:3:0.0260869565217:C=112,T=3,	Human1pR2	chr3_128486108	TRUE	3	115	0.0260870
832	chr11	32396399	NA	T	C	NA	NA	AC=3;AF=0.0125	GT:AC:AF:NC	0:3:0.0125:C=3,T=237,	Human1pR2	chr11_32396399	TRUE	3	240	0.0125000

As a sanity check, bcftools v1.17 mpileup was run on the bam files no BAQ and min base Q of 0. Below are the VAFs for the consensus bam variants:

h1mp_cons <- read.vcfR(file.path(pileup_dir, "Human1pR1_pileup.vcf"),
                      verbose = FALSE)
h2mp_cons <- read.vcfR(file.path(pileup_dir, "Human1pR2_pileup.vcf"),
                      verbose = FALSE)

h1mp_cons <- data.frame(h1mp_cons@fix) %>% cbind(., data.frame(h1mp_cons@gt))
h2mp_cons <- data.frame(h2mp_cons@fix) %>% cbind(., data.frame(h2mp_cons@gt))
h1mp_cons$ID <- paste(h1mp_cons$CHROM, h1mp_cons$POS, sep = "_")
h2mp_cons$ID <- paste(h2mp_cons$CHROM, h2mp_cons$POS, sep = "_")

hg5x$ID <- paste(seqnames(hg5x), start(hg5x), sep = "_")
h1mp_cons <- h1mp_cons[h1mp_cons$ID %in% hg5x$ID,] %>% distinct()
h2mp_cons <- h2mp_cons[h2mp_cons$ID %in% hg5x$ID,] %>% distinct()

h1mp_cons$VAF <- apply(h1mp_cons, 1, get_variant_fraction, hg5x)
h2mp_cons$VAF <- apply(h2mp_cons, 1, get_variant_fraction, hg5x)

kable(h1mp_cons)

CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	Human1pR1_HJ7VFDSX7_GTGAAGTG.GAGCAATC_L002	VAF
chr1	114714012	chr1_114714012	A	T,<*>	0	NA	DP=139;I16=128,3,0,8,7860,471600,74,3628,7860,471600,480,28800,2981,71929,62,690;QS=0.992424,0.00757576,0;VDB=0.000156924;SGB=-0.651104;RPBZ=-4.70428;MQBZ=0;MQSBZ=0;BQBZ=-10.9469;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:131,1,0	0.0000000
chr10	87970403	chr10_87970403	C	T,<*>	0	NA	DP=508;I16=241,265,1,1,30360,1.8216e+06,120,7200,30360,1.8216e+06,120,7200,9855,224105,26,626;QS=0.996063,0.00393701,0;VDB=0.7;SGB=-0.453602;RPBZ=1.29364;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:506,2,0	0.0039370
chr11	32396399	chr11_32396399	T	C,<*>	0	NA	DP=327;I16=145,179,3,0,19440,1.1664e+06,180,10800,19440,1.1664e+06,180,10800,6715,156443,75,1875;QS=0.990826,0.00917431,0;VDB=0.292464;SGB=-0.511536;RPBZ=0.32827;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:324,3,0	0.0091743
chr11	64805130	chr11_64805130	G	A,<*>	0	NA	DP=255;I16=150,103,0,2,15180,910800,62,3604,15180,910800,120,7200,4794,107560,14,106;QS=0.996063,0.00393701,0;VDB=0.98;SGB=-0.453602;RPBZ=0.00481312;MQBZ=0;MQSBZ=0;BQBZ=-11.2472;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:253,1,0	0.0039370
chr11	64810148	chr11_64810148	G	<*>	0	NA	DP=74;I16=69,5,0,0,4412,263824,0,0,4440,266400,0,0,1479,33997,0,0;QS=1,0;MQ0F=0	PL:AD	0,223,255:74,0	0.0000000
chr17	7676301	chr17_7676301	G	T,<*>	0	NA	DP=233;I16=104,128,0,1,13892,832624,60,3600,13920,835200,60,3600,4693,111063,25,625;QS=0.9957,0.00430046,0;SGB=-0.379885;RPBZ=-0.892202;MQBZ=0;MQSBZ=0;BQBZ=0.0656532;SCBZ=-0.375988;MQ0F=0	PL:AD	0,255,255,255,255,255:232,1,0	0.0042918
chr3	128486108	chr3_128486108	C	<*>	0	NA	DP=171;I16=125,44,1,1,10140,608400,4,8,10140,608400,120,7200,3290,74914,26,626;QS=1,0;MQ0F=0	PL:AD	0,255,255:169,0	0.0000000
chr5	177516672	chr5_177516672	C	T,<*>	0	NA	DP=191;I16=111,78,1,1,11312,677824,120,7200,11340,680400,120,7200,3941,92315,50,1250;QS=0.989503,0.0104969,0;VDB=0.66;SGB=-0.453602;RPBZ=0.0578716;MQBZ=0;MQSBZ=0;BQBZ=0.102869;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:189,2,0	0.0104712
chr9	5069837	chr9_5069837	G	<*>	0	NA	DP=411;I16=262,149,0,0,24660,1.4796e+06,0,0,24660,1.4796e+06,0,0,8384,193948,0,0;QS=1,0;MQ0F=0	PL:AD	0,255,255:411,0	0.0000000

kable(h2mp_cons)

CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	Human1pR2_HJ7VFDSX7_CATGGCTA.CACACATC_L002	VAF
chr1	114714012	chr1_114714012	A	T,<*>	0	NA	DP=99;I16=88,4,0,7,5520,331200,130,7220,5520,331200,420,25200,1994,47390,29,169;QS=0.978723,0.0212766,0;VDB=0.000181347;SGB=-0.636426;RPBZ=-4.39045;MQBZ=0;MQSBZ=0;BQBZ=-8.27711;SCBZ=0;MQ0F=0	PL:AD	0,171,255,255,255,255:92,2,0	0.0000000
chr10	87970403	chr10_87970403	C	T,<*>	0	NA	DP=359;I16=180,178,1,0,21480,1.2888e+06,60,3600,21480,1.2888e+06,60,3600,7182,165412,25,625;QS=0.997214,0.00278552,0;SGB=-0.379885;RPBZ=0.42945;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:358,1,0	0.0027855
chr11	32396399	chr11_32396399	T	C,<*>	0	NA	DP=240;I16=101,136,1,2,14220,853200,180,10800,14220,853200,180,10800,5067,118617,75,1875;QS=0.9875,0.0125,0;VDB=0.529687;SGB=-0.511536;RPBZ=0.14647;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:237,3,0	0.0125000
chr11	64805130	chr11_64805130	G	A,<*>	0	NA	DP=134;I16=75,58,1,0,7980,478800,60,3600,7980,478800,60,3600,2571,58357,7,49;QS=0.992537,0.00746269,0;SGB=-0.379885;RPBZ=1.38334;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=0;MQ0F=0	PL:AD	0,255,255,255,255,255:133,1,0	0.0074627
chr11	64810148	chr11_64810148	G	<*>	0	NA	DP=54;I16=46,8,0,0,3240,194400,0,0,3240,194400,0,0,1044,23184,0,0;QS=1,0;MQ0F=0	PL:AD	0,163,255:54,0	0.0000000
chr17	7676301	chr17_7676301	G	T,<*>	0	NA	DP=180;I16=89,90,0,1,10740,644400,60,3600,10740,644400,60,3600,3798,89408,10,100;QS=0.994444,0.00555556,0;SGB=-0.379885;RPBZ=1.54957;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=-0.352792;MQ0F=0	PL:AD	0,255,255,255,255,255:179,1,0	0.0055556
chr3	128486108	chr3_128486108	C	T,<*>	0	NA	DP=115;I16=84,28,3,0,6720,403200,180,10800,6720,403200,180,10800,2125,48073,57,1139;QS=0.973913,0.026087,0;VDB=0.839668;SGB=-0.511536;RPBZ=-0.658166;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=0;MQ0F=0	PL:AD	0,187,255,255,255,255:112,3,0	0.0260870
chr5	177516672	chr5_177516672	C	<*>	0	NA	DP=127;I16=66,61,0,0,7620,457200,0,0,7620,457200,0,0,2635,60853,0,0;QS=1,0;MQ0F=0	PL:AD	0,255,255:127,0	0.0000000
chr9	5069837	chr9_5069837	G	<*>	0	NA	DP=314;I16=184,130,0,0,18840,1.1304e+06,0,0,18840,1.1304e+06,0,0,6627,155741,0,0;QS=1,0;MQ0F=0	PL:AD	0,255,255:314,0	0.0000000

Below are the VAFs for the raw bam variants:

h1mp_raw <- read.vcfR(file.path(pileup_dir, "raw/Human1pR1_pileup.vcf"),
                      verbose = FALSE)
h2mp_raw <- read.vcfR(file.path(pileup_dir, "raw/Human1pR2_pileup.vcf"),
                      verbose = FALSE)

h1mp_raw <- data.frame(h1mp_raw@fix) %>% cbind(., data.frame(h1mp_raw@gt))
h2mp_raw <- data.frame(h2mp_raw@fix) %>% cbind(., data.frame(h2mp_raw@gt))
h1mp_raw$ID <- paste(h1mp_raw$CHROM, h1mp_raw$POS, sep = "_")
h2mp_raw$ID <- paste(h2mp_raw$CHROM, h2mp_raw$POS, sep = "_")

h1mp_raw <- h1mp_raw[h1mp_raw$ID %in% hg5x$ID,] %>% distinct()
h2mp_raw <- h2mp_raw[h2mp_raw$ID %in% hg5x$ID,] %>% distinct()

h1mp_raw$VAF <- apply(h1mp_raw, 1, get_variant_fraction, hg5x)
h2mp_raw$VAF <- apply(h2mp_raw, 1, get_variant_fraction, hg5x)

kable(h1mp_raw)

CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	Human1pR1_HJ7VFDSX7_GTGAAGTG.GAGCAATC_L002	VAF
chr1	114714012	chr1_114714012	A	T,C,G	0	NA	DP=2378;I16=1279,814,17,268,58892,2.04342e+06,3241,53669,125062,7.4859e+06,16455,965889,44345,1.04588e+06,5570,126562;QS=0.945924,0.0325317,0.0122472,0.00929706;VDB=0.951167;SGB=-0.693147;RPBZ=-6.80848;MQBZ=-3.22071;MQSBZ=-1.95439;BQBZ=-18.5457;SCBZ=8.01938;MQ0F=0.000420521	PL:AD	0,255,255,255,255,255,255,255,255,255:2093,191,73,21	0.0088310
chr10	87970403	chr10_87970403	C	A,T,G	0	NA	DP=7819;I16=3717,3854,109,139,238893,9.18864e+06,3308,73484,453674,2.72003e+07,14591,868753,154278,3.56539e+06,4928,114654;QS=0.985996,0.00797969,0.00504496,0.000979604;VDB=0.919782;SGB=-0.693147;RPBZ=2.66365;MQBZ=-1.49661;MQSBZ=0.0801869;BQBZ=-16.6284;SCBZ=4.80631;MQ0F=0.000127894	PL:AD	0,255,255,255,255,255,255,255,255,255:7571,188,38,21	0.0048606
chr11	32396399	chr11_32396399	T	C,A,G	0	NA	DP=5553;I16=2293,3120,103,37,179654,7.04057e+06,3156,102066,324202,1.9437e+07,8197,487841,114568,2.69134e+06,3132,75962;QS=0.982653,0.012653,0.0040769,0.00061722;VDB=0.0845183;SGB=-0.693147;RPBZ=-0.233849;MQBZ=-0.913227;MQSBZ=-0.0782107;BQBZ=-7.44466;SCBZ=1.47405;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:5413,67,60,11	0.0120699
chr11	64805130	chr11_64805130	G	A,T,C	0	NA	DP=4358;I16=2409,1882,31,36,142568,5.51512e+06,1050,26306,256936,1.54053e+07,3916,233440,88208,2.05576e+06,1416,33540;QS=0.992565,0.00418323,0.00239535,0.000855975;VDB=0.320096;SGB=-0.693147;RPBZ=0.410199;MQBZ=-0.779995;MQSBZ=-0.281104;BQBZ=-8.23094;SCBZ=2.33746;MQ0F=0.000229463	PL:AD	0,255,255,255,255,255,255,255,255,255:4291,19,35,13	0.0043598
chr11	64810148	chr11_64810148	G	C,T,<*>	0	NA	DP=843;I16=589,244,9,1,28948,1.06525e+06,214,6202,49888,2.98899e+06,600,36000,15283,339683,205,5025;QS=0.992678,0.00656949,0.000752754,0;VDB=0.517259;SGB=-0.670168;RPBZ=0.047697;MQBZ=0.348338;MQSBZ=-4.26096;BQBZ=-4.40035;SCBZ=0.50099;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:833,8,2,0	0.0094899
chr17	7676301	chr17_7676301	G	T,C,A	0	NA	DP=3530;I16=1298,2167,16,49,113069,4.55887e+06,713,13305,206814,1.23788e+07,3783,222911,74360,1.76666e+06,1456,34806;QS=0.993328,0.00587403,0.000702802,9.54422e-05;VDB=0.0563998;SGB=-0.693147;RPBZ=0.0431235;MQBZ=-1.06911;MQSBZ=-0.565955;BQBZ=-9.52428;SCBZ=2.28313;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:3465,56,8,1	0.0158640
chr3	128486108	chr3_128486108	C	T,A,G	0	NA	DP=3461;I16=1927,1471,40,23,113127,4.42568e+06,1155,32769,203473,1.21928e+07,3765,225101,67788,1.55613e+06,1346,31404;QS=0.989772,0.005465,0.0037848,0.000978677;VDB=0.0583748;SGB=-0.693147;RPBZ=1.6185;MQBZ=-0.321333;MQSBZ=-0.401551;BQBZ=-6.98816;SCBZ=1.45631;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:3398,19,34,9	0.0054913
chr5	177516672	chr5_177516672	C	T,A,G	0	NA	DP=3518;I16=1997,1457,44,20,115800,4.43498e+06,1463,44079,206866,1.24004e+07,3737,221777,75292,1.7941e+06,1421,34423;QS=0.987627,0.00754921,0.00435857,0.000465478;VDB=0.0479845;SGB=-0.693147;RPBZ=2.98368;MQBZ=-0.781218;MQSBZ=0.275029;BQBZ=-5.31556;SCBZ=2.09774;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:3454,25,34,5	0.0071063
chr9	5069837	chr9_5069837	G	T,A,C	0	NA	DP=5598;I16=3814,1579,138,67,168735,6.502e+06,2284,41542,322933,1.93601e+07,12194,727162,113627,2.66584e+06,4304,100780;QS=0.985848,0.0110098,0.00220659,0.000935778;VDB=0.0333858;SGB=-0.693147;RPBZ=3.25191;MQBZ=-0.69118;MQSBZ=0.161522;BQBZ=-16.4947;SCBZ=2.76591;MQ0F=0.000535906	PL:AD	0,255,255,255,255,255,255,255,255,255:5393,176,12,16	0.0021440

kable(h2mp_raw)

CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	Human1pR2_HJ7VFDSX7_CATGGCTA.CACACATC_L002	VAF
chr1	114714012	chr1_114714012	A	T,C,G	0	NA	DP=2169;I16=1259,707,12,191,56707,1.94058e+06,2356,34750,117234,7.00777e+06,11805,695233,41293,970483,4313,99021;QS=0.959398,0.0219532,0.0133034,0.00534498;VDB=0.227244;SGB=-0.693147;RPBZ=-5.67825;MQBZ=-2.17381;MQSBZ=-2.41906;BQBZ=-17.6354;SCBZ=8.93585;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:1966,113,73,17	0.0078377
chr10	87970403	chr10_87970403	C	A,T,G	0	NA	DP=7908;I16=3827,3773,148,160,240875,8.63876e+06,4327,93611,454888,2.72553e+07,18114,1.07416e+06,155667,3.6211e+06,6449,150153;QS=0.982219,0.0120663,0.00471526,0.000999098;VDB=0.693789;SGB=-0.693147;RPBZ=2.24651;MQBZ=-1.78029;MQSBZ=-0.0471676;BQBZ=-20.5534;SCBZ=3.38189;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:7600,238,45,23	0.0056919
chr11	32396399	chr11_32396399	T	C,A,G	0	NA	DP=5660;I16=2228,3298,78,56,186452,6.92944e+06,2767,79371,330678,1.98204e+07,7983,476705,117563,2.75273e+06,2988,70368;QS=0.985352,0.0101496,0.00310757,0.00139051;VDB=0.783816;SGB=-0.693147;RPBZ=-0.144853;MQBZ=-0.614132;MQSBZ=0.221435;BQBZ=-9.83269;SCBZ=2.39853;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:5526,59,50,23	0.0104277
chr11	64805130	chr11_64805130	G	A,T,C	0	NA	DP=3916;I16=2008,1843,42,23,129841,4.87608e+06,1280,35080,230629,1.38301e+07,3840,228168,77102,1.78753e+06,1282,29582;QS=0.990301,0.00522913,0.00354427,0.000925912;VDB=0.76911;SGB=-0.693147;RPBZ=2.31656;MQBZ=-0.573209;MQSBZ=0.0288966;BQBZ=-7.56984;SCBZ=2.99803;MQ0F=0.000510725	PL:AD	0,255,255,255,255,255,255,255,255,255:3851,21,34,7	0.0053667
chr11	64810148	chr11_64810148	G	C,T,<*>	0	NA	DP=676;I16=428,237,10,1,23176,854776,225,6323,39791,2.38279e+06,660,39600,11634,253154,230,5362;QS=0.990406,0.00818659,0.00140707,0;VDB=0.861107;SGB=-0.676189;RPBZ=-0.778572;MQBZ=0.365666;MQSBZ=-3.11839;BQBZ=-4.96894;SCBZ=0.0339176;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:665,8,3,0	0.0118343
chr17	7676301	chr17_7676301	G	T,C,<*>	0	NA	DP=3166;I16=1115,1980,11,60,102575,3.81673e+06,1413,40473,184584,1.10418e+07,4119,242163,66203,1.5576e+06,1436,32344;QS=0.986287,0.0133975,0.000315799,0;VDB=0.531092;SGB=-0.693147;RPBZ=1.0634;MQBZ=-1.16002;MQSBZ=-0.543083;BQBZ=-6.83871;SCBZ=2.58645;MQ0F=0.000631712	PL:AD	0,255,255,255,255,255,255,255,255,255:3095,68,3,0	0.0214782
chr3	128486108	chr3_128486108	C	T,A,G	0	NA	DP=3162;I16=1737,1339,65,21,103870,3.84005e+06,2004,60350,183941,1.10177e+07,5098,303330,61503,1.41556e+06,1741,39241;QS=0.981135,0.0149016,0.00292761,0.00103549;VDB=0.900481;SGB=-0.693147;RPBZ=-1.60971;MQBZ=-0.580684;MQSBZ=-0.562229;BQBZ=-6.6964;SCBZ=1.5936;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:3076,49,27,10	0.0154965
chr5	177516672	chr5_177516672	C	T,A,G	0	NA	DP=3382;I16=1921,1418,33,10,112476,4.12708e+06,908,26062,199867,1.19788e+07,2547,151745,70651,1.65268e+06,1008,24242;QS=0.992032,0.00549653,0.00198755,0.000483695;VDB=0.170142;SGB=-0.693146;RPBZ=-0.726899;MQBZ=-0.431209;MQSBZ=0.285921;BQBZ=-5.5266;SCBZ=0.926467;MQ0F=0	PL:AD	0,255,255,255,255,255,255,255,255,255:3339,19,18,5	0.0056196
chr9	5069837	chr9_5069837	G	T,A,C	0	NA	DP=5837;I16=3973,1612,183,69,173527,6.14438e+06,3110,47700,334288,2.00398e+07,15008,896888,118701,2.79869e+06,5467,127981;QS=0.982244,0.0155491,0.00162546,0.000581326;VDB=0.381863;SGB=-0.693147;RPBZ=6.07266;MQBZ=-0.579316;MQSBZ=0.155051;BQBZ=-20.229;SCBZ=2.41617;MQ0F=0.000171321	PL:AD	0,255,255,255,255,255,255,255,255,255:5585,230,12,10	0.0020559

Session information

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /stornext/System/data/apps/R/R-4.3.0/lib64/R/lib/libRblas.so 
LAPACK: /stornext/System/data/apps/R/R-4.3.0/lib64/R/lib/libRlapack.so;  LAPACK version 3.11.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Melbourne
tzcode source: system (glibc)

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] knitr_1.43           R.utils_2.12.2       R.oo_1.25.0         
 [4] R.methodsS3_1.8.2    vcfR_1.14.0          UpSetR_1.4.0        
 [7] RColorBrewer_1.1-3   patchwork_1.1.2      readxl_1.4.3        
[10] seqinr_4.2-30        Rsamtools_2.16.0     Biostrings_2.68.1   
[13] XVector_0.40.0       GenomicRanges_1.52.0 GenomeInfoDb_1.36.1 
[16] IRanges_2.34.1       S4Vectors_0.38.1     BiocGenerics_0.46.0 
[19] stringr_1.5.0        tibble_3.2.1         here_1.0.1          
[22] dplyr_1.1.2          data.table_1.14.8    ggplot2_3.4.2       
[25] workflowr_1.7.0     

loaded via a namespace (and not attached):
 [1] ade4_1.7-22             tidyselect_1.2.0        viridisLite_0.4.2      
 [4] farver_2.1.1            bitops_1.0-7            fastmap_1.1.1          
 [7] RCurl_1.98-1.12         promises_1.2.0.1        digest_0.6.33          
[10] lifecycle_1.0.3         cluster_2.1.4           processx_3.8.2         
[13] magrittr_2.0.3          compiler_4.3.0          rlang_1.1.1            
[16] sass_0.4.7              tools_4.3.0             utf8_1.2.3             
[19] yaml_2.3.7              labeling_0.4.2          plyr_1.8.8             
[22] BiocParallel_1.34.2     memuse_4.2-3            withr_2.5.0            
[25] grid_4.3.0              fansi_1.0.4             git2r_0.32.0           
[28] colorspace_2.1-0        scales_1.2.1            MASS_7.3-58.4          
[31] cli_3.6.1               rmarkdown_2.23          vegan_2.6-4            
[34] crayon_1.5.2            generics_0.1.3          rstudioapi_0.15.0      
[37] httr_1.4.6              ape_5.7-1               cachem_1.0.8           
[40] zlibbioc_1.46.0         splines_4.3.0           cellranger_1.1.0       
[43] vctrs_0.6.3             Matrix_1.6-1            jsonlite_1.8.7         
[46] callr_3.7.3             jquerylib_0.1.4         glue_1.6.2             
[49] codetools_0.2-19        ps_1.7.5                stringi_1.7.12         
[52] gtable_0.3.3            later_1.3.1             munsell_0.5.0          
[55] pillar_1.9.0            htmltools_0.5.5         GenomeInfoDbData_1.2.10
[58] R6_2.5.1                pinfsc50_1.2.0          rprojroot_2.0.3        
[61] evaluate_0.21           lattice_0.21-8          highr_0.10             
[64] httpuv_1.6.11           bslib_0.5.0             Rcpp_1.0.11            
[67] gridExtra_2.3           nlme_3.1-162            permute_0.9-7          
[70] mgcv_1.8-42             whisker_0.4.1           xfun_0.39              
[73] fs_1.6.3                getPass_0.2-2           pkgconfig_2.0.3