High-through sequencing has become fundamental for deciphering sequence variants between two paired-samples in different conditions, which has been vastly used in detecting somatic variants between tumor and matched normal samples in current research of oncogenesis.The SICtools package is designed to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh parsing the allele frequency of genotypes (single nucleotide and short indel) at each base position across the genome region of interest. The difference is inferred by two complementary measurements, fisher exact test p.value and euclidean distance d.value. For SNV comparison, the internal input is the base count (A,T,G,C) in a given position, parsed from pileup output from the two bam files; for indel comparison, reads for different indel alleles that span no less than 2bp on both sides of extended indel region (e.g. homopolymer region) are counted as internal input.The candidate variants with relatively lower p.value and higher d.value can thus be easily identified from the output of SICtools.
Getting started with SICtools for inspection of SNV/Indel difference
between two bam files is quite easy. Two critical functions
(snpDiff and indelDiff) will be available in R
session with loading the package.
## Warning: replacing previous import 'plyr::count' by 'matrixStats::count' when
## loading 'SICtools'The essential capability provided by SICtools is its
input. The two bam files to be compared should be aligned by same
aligner of the same version (important!) and the same reference genome.
In theory, the two bam files should be in near relationship, which means
that the SNV/Indel differences are not expected too many. The input
coordinate for region of interest should be the same format as the
reference genome. The argument list of function snpDiff is
below,
snpDiff(bam1, bam2, refFsa, regChr, regStart, regEnd, minBaseQuality = 13, minMapQuality = 0, nCores = 1, pValueCutOff = 0.05, baseDistCutOff = 0.1, verbose = TRUE)
Except the three main paramters (bam1,bam2
and refFsa), the region coordinate arguments
(regChr,regStart and regEnd) are
necessary to restrict the genome region of interest, since the
comparison would be time consuming if the chromosome units of the
species are very long, for example, human. Meanwhile, these coordinate
arguments make the parallel calculation possible. For instance, the long
chromosome could be chunked into pieces and compared in parallel, and
the final output would be combined together. Even in the genome region
of interest, a parameter nCores is to set the threads for
parallel calculation, which will greatly short the time in the case of
long region input.
In order to control the input quality of reads, two quality filter
arguments (minBaseQuality and minMapQuality)
are provided. The minBaseQuality score is stranded Phred
score of Sanger format for each base. The ‘minMapQuality’ score is based
on the aligner used, which means differnt threshods would be adopted to
control the mapping quality of the whole reads for different
aligners.
Two output control parameters (pValueCutOff and
baseDistCutOff) are used to filter the output. Since most
of genomic positions to compare are the same in theory,
p.value of exact fisher test would be 1 and
d.value of Euclidean distance would be 0 in
thousands, even millons of positions, which is not expected as output,
and will be excluded from the final output. The default
pValueCutOff = 0.05, baseDistCutOff = 0.1 is proper for
comparison between germline samples. Lower baseDistCutOff
and higher pValueCutOff is probably needed for somatic
samples.
If verbose = TRUE, the progress information of genomic positions will be showed on screen
The output of SNV/Indel comparisons is a data.frame. It
will report the base count/read count for each allele, p.value (from
fisher exact test) and d.value (from euclidean distance) filtered by
pre-defined cutoff of p.value and d.value. If nothing difference, NULL
will be returned.
The example will detect SNV differences between two bam files in the region “chr04:962501-1026983”. Setting “pValueCutOff=1,baseDistCutOff=0” will detect tiny differences, while the exact same genotype positions will be excluded from output by default.
bam1 <- system.file(package='SICtools','extdata','example1.bam')
bam2 <- system.file(package='SICtools','extdata','example2.bam')
refFsa <- system.file(package='SICtools','extdata','example.ref.fasta')
snpDiffDf <- snpDiff(bam1,bam2,refFsa,'chr04',962501,1026983,pValueCutOff=1,baseDistCutOff=0)## Warning in setup_parallel(): No parallel backend registered## [1] "chr04 962501 1026983"## Warning in applyPileups(ppFiles, calcInfoRange, param = regSplitParam): 'applyPileups' is deprecated.
## Use 'pileup' instead.
## See help("Deprecated")## chr pos ref A1 C1 G1 T1 N1 A2 C2 G2 T2 N2 p.value d.value
## 1 chr04 962623 G 0 0 34 2 0 0 0 47 0 0 1.851308e-01 0.07856742
## 2 chr04 962801 G 0 0 47 0 0 0 0 0 45 0 2.487221e-27 1.41421356
## 3 chr04 962865 C 2 29 0 0 0 0 49 0 0 0 1.471519e-01 0.09123958
## 4 chr04 962984 G 0 0 28 0 0 0 1 30 1 0 1.000000e+00 0.07654655
## 5 chr04 962998 T 0 0 1 14 0 0 0 0 25 0 3.750000e-01 0.09428090
## 6 chr04 963005 A 16 0 0 0 0 19 1 0 0 0 1.000000e+00 0.07071068
## 7 chr04 1026413 T 1 0 0 15 0 0 0 0 21 0 4.324324e-01 0.08838835
## 8 chr04 1026421 C 0 17 1 0 0 0 22 0 0 0 4.500000e-01 0.07856742
## 9 chr04 1026533 T 0 0 0 22 0 0 0 1 18 0 4.634146e-01 0.07443229
## 10 chr04 1026599 A 16 0 0 0 0 19 0 1 0 0 1.000000e+00 0.07071068
## 11 chr04 1026603 T 0 0 0 17 0 0 0 1 18 0 1.000000e+00 0.07443229
## 12 chr04 1026608 C 0 20 0 0 0 1 19 0 0 0 1.000000e+00 0.07071068
## 13 chr04 1026683 C 0 25 0 0 0 21 0 0 0 0 1.440190e-13 1.41421356
## 14 chr04 1026799 C 2 32 0 0 0 2 32 0 0 0 1.000000e+00 0.00000000
## 15 chr04 1026833 C 1 15 0 0 0 0 29 0 0 0 3.555556e-01 0.08838835
## 16 chr04 1026916 T 0 0 1 19 0 0 0 0 20 0 1.000000e+00 0.07071068A simple scatter plot will show the most different candidates locating at top-right.
plot(-log10(snpDiffDfp.value), snpDiffDfd.value,col=‘brown’)
For more complex situation with hundreds of outputs, sorting the data frame by p.value and d.value would be very helpful to set custom cutoffs after mannually check.
## chr pos ref A1 C1 G1 T1 N1 A2 C2 G2 T2 N2 p.value d.value
## 2 chr04 962801 G 0 0 47 0 0 0 0 0 45 0 2.487221e-27 1.41421356
## 13 chr04 1026683 C 0 25 0 0 0 21 0 0 0 0 1.440190e-13 1.41421356
## 3 chr04 962865 C 2 29 0 0 0 0 49 0 0 0 1.471519e-01 0.09123958
## 1 chr04 962623 G 0 0 34 2 0 0 0 47 0 0 1.851308e-01 0.07856742
## 15 chr04 1026833 C 1 15 0 0 0 0 29 0 0 0 3.555556e-01 0.08838835
## 5 chr04 962998 T 0 0 1 14 0 0 0 0 25 0 3.750000e-01 0.09428090
## 7 chr04 1026413 T 1 0 0 15 0 0 0 0 21 0 4.324324e-01 0.08838835
## 8 chr04 1026421 C 0 17 1 0 0 0 22 0 0 0 4.500000e-01 0.07856742
## 9 chr04 1026533 T 0 0 0 22 0 0 0 1 18 0 4.634146e-01 0.07443229
## 14 chr04 1026799 C 2 32 0 0 0 2 32 0 0 0 1.000000e+00 0.00000000
## 6 chr04 963005 A 16 0 0 0 0 19 1 0 0 0 1.000000e+00 0.07071068
## 10 chr04 1026599 A 16 0 0 0 0 19 0 1 0 0 1.000000e+00 0.07071068
## 12 chr04 1026608 C 0 20 0 0 0 1 19 0 0 0 1.000000e+00 0.07071068
## 16 chr04 1026916 T 0 0 1 19 0 0 0 0 20 0 1.000000e+00 0.07071068
## 11 chr04 1026603 T 0 0 0 17 0 0 0 1 18 0 1.000000e+00 0.07443229
## 4 chr04 962984 G 0 0 28 0 0 0 1 30 1 0 1.000000e+00 0.07654655Detecting indel differences between two bam files is usually mislead
by finding two indel lists seperately and then overlap them. Instead,
indelDiff will firstly extract the read counts of each
indel genotypes in two bam files at the same genomic position, and then
calculate p.value of fisher exact test and
d.value of Euclidean distance for this position. In case
that the reads can’t span the long indel, only reads that cover more
than 2bp adjacent the given indel region are taken into
consideration.
The input of indelDiff is the same as
snpDiff, however, the output genotype names of
indelDiff are different. For function snpDiff,
‘A’, ‘T’,‘G’ and ‘C’ are four informative base, while for the output of
indelDiff, the three genotypes are ‘ref’,‘altGt1’ and
‘altGt2’, which means two alternative indel genotypes will be considered
in the given position in both bam files.
A simple input example and its output is
## Warning in setup_parallel(): No parallel backend registered## [1] "chr07" "828636" "G"
## [1] "chr07" "828714" "C"## chr pos ref altGt1 altGt2 refBam1Count altGt1Bam1Count
## 2 chr07 828714 CAAAAAAA CAAAAAAAA <NA> 0 7
## 1 chr07 828636 GCCA GCCACCA GTACCA 34 0
## altGt2Bam1Count refBam2Count altGt1Bam2Count altGt2Bam2Count p.value
## 2 NA 17 0 NA 2.889305e-06
## 1 0 34 1 1 1.000000e+00
## d.value
## 2 1.41421356
## 1 0.06804138Detecting SNV/Indel difference between two bam files is frequently
used in many research fields of high-throughput sequencing. We thus
provide these two simple R functions to make the comparison easy and
accurate. Though the cutoff of p.value and
d.value of SICtools are usually determined by
cutsom data, a scatter plot -log10(p.value) vs. d.value is
very helpful to achieve it based on our own expericence.
The following package and versions were used in the production of this vignette.
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
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## time zone: Etc/UTC
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SICtools_1.37.0 plyr_1.8.9 matrixStats_1.4.1
## [4] stringr_1.5.1 doParallel_1.0.17 iterators_1.0.14
## [7] foreach_1.5.2 Rsamtools_2.23.1 Biostrings_2.75.3
## [10] XVector_0.47.0 GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [13] IRanges_2.41.2 S4Vectors_0.45.2 BiocGenerics_0.53.3
## [16] generics_0.1.3
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## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 compiler_4.4.2 crayon_1.5.3
## [4] Rcpp_1.0.13-1 bitops_1.0-9 jquerylib_0.1.4
## [7] BiocParallel_1.41.0 yaml_2.3.10 fastmap_1.2.0
## [10] R6_2.5.1 knitr_1.49 maketools_1.3.1
## [13] GenomeInfoDbData_1.2.13 bslib_0.8.0 rlang_1.1.4
## [16] stringi_1.8.4 cachem_1.1.0 xfun_0.49
## [19] sass_0.4.9 sys_3.4.3 cli_3.6.3
## [22] magrittr_2.0.3 zlibbioc_1.52.0 digest_0.6.37
## [25] lifecycle_1.0.4 vctrs_0.6.5 glue_1.8.0
## [28] evaluate_1.0.1 codetools_0.2-20 buildtools_1.0.0
## [31] rmarkdown_2.29 httr_1.4.7 tools_4.4.2
## [34] htmltools_0.5.8.1 UCSC.utils_1.3.0