MDTS provides the necessary infrastructure to take raw bam files of targeted sequencing trios to produce de novo deletion calls of high sensitivity and low false positives. Our method benefits tremendously from pooling information from across many trios to determine regions of interest, normalize read-depth, and to filter candidate deletion signals.
MDTS is broken down into the following major steps:
Preprocessed sample data is included with the package MDTS, but should you be
interested in the raw files, they can be found in the github repo
jmf47/MDTSdata. It includes:
## subj_id family_id father_id mother_id bam_path
## 1 F1_1 F1 F1_2 F1_3 125968958.bam
## 2 F1_3 F1 0 0 125970229.bam
## 3 F1_2 F1 0 0 125970675.bam
## 4 F2_1 F2 F2_2 F2_3 124770343.bam
## 5 F2_3 F2 0 0 124770335.bam
## 6 F2_2 F2 0 0 124770213.bam
## 7 F3_1 F3 F3_2 F3_3 125012972.bam
## 8 F3_3 F3 0 0 125012237.bam
## 9 F3_2 F3 0 0 125017898.bam
## 10 F4_1 F4 F4_2 F4_3 126330731.bam
## 11 F4_3 F4 0 0 126319754.bam
## 12 F4_2 F4 0 0 126320843.bam
## 13 F5_1 F5 F5_2 F5_3 126328714.bam
## 14 F5_3 F5 0 0 126310263.bam
## 15 F5_2 F5 0 0 126298916.bam
## 16 F6_1 F6 F6_2 F6_3 125659861.bam
## 17 F6_3 F6 0 0 125651281.bam
## 18 F6_2 F6 0 0 125651256.bam
## 19 F7_1 F7 F7_2 F7_3 124413356.bam
## 20 F7_3 F7 0 0 124411059.bam
## 21 F7_2 F7 0 0 124412226.bam
One innovation of our method is that the bins to determine sequencing read depth is calculated based on the empirical capture. These bins can be significantly different from the standard practice of using probe design coordinates to create bins. Furthermore, our binning process allows the bins to be smaller in regions of high capture, and vice verse in low capture. This dynamic scaling of the bin size makes efficient use of varying depths of coverage - allowing us to call deletions with finer resolution in areas of higher coverage.
The general approach is first to examine the mapped coverage of a subset of the full dataset across the entire genome. Basepairs that are covered passed a certain threshold in at least one sample is selected as a proto-region. Proto-regions are subdvided into non-overlapping bins such that the median number of reads falling into each bin meets a specified level spcified by the medianCoverage parameter. The choice of medianCoverage is a tradeoff between sensitivity and false positives. A larger medianCoverage decreases sensitivity and false positives. For our publication, we used medianCoverage=160, and for this example we used medianCoverage=150.
Information on GC content and mappability of the bins are also calculated at
this stage. As a result, MDTS requires a BSgenome object that is consistent with
the reference annotation the bam files were aligned to, as well as a mappability
bigwig that contains mappability information in windows consistent with
sequencing read length. In our example, we require BSgenome.Hsapiesn.UCSC.hg19
and 100mer mappability.
** In general you will need a bw file that contains the information for all autosomes 1-22 (unlike the bw file only for chr1 included in the example). ** This is the link to some hg19 mappability bw files: http://rohsdb.cmb.usc.edu/GBshape/cgi-bin/hgFileUi?db=hg19&g=wgEncodeMapability
This portion of the vignette example uses raw data from MDTSData. However
the resulting bins are included in the data directory of MDTS as a .RData.
library(MDTS); library(BSgenome.Hsapiens.UCSC.hg19)
# Using the raw data from MDTSData
devtools::install_github("jmf47/MDTSData")
setwd(system.file("data", package="MDTSData"))
# Importing the pedigree file that includes information on where to locate the
# raw bam files
pD <- getMetaData("pD.ped")
# Information on the GC content and mappability to estimate GC and mappability
# for the MDTS bins
genome <- BSgenome.Hsapiens.UCSC.hg19; map_file = "chr1.map.bw"
# This command now subsets 5 samples to determine MDTS bins
# pD is the metaData matrix from getMetaData()
# n is the number of samples to examine to calculate the bins
# readLength is the sequencing read length
# minimumCoverage is the minimum read depth for a location to be included
# in a proto region
# medianCoverage is the median number of reads across the n samples in a bin
bins <- calcBins(metaData=pD, n=5, readLength=100, minimumCoverage=5,
medianCoverage=150, genome=genome, mappabilityFile=map_file)
Given a set of dynamic MDTS bins, we can proceed to calculate the number of
reads that fall into these bins for the entirety of our sample. We organize the
number of reads as a matrix, where each column is a sample, and each row
corresponds to a bin. This portion of the vignette is a continuation of the
usage of the raw data from MDTSdata above. However the resulting counts
matrix is also shipped as a rda in MDTS.
# pD is the phenotype matrix
# bins is the previously calculated MDTS bins
# rl is the sequencing read length
counts = calcCounts(pD, bins, rl=100)
The MDTS bins, raw counts, and pedigree files are included with MDTS and can
be loaded as follows:
load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
load(system.file("extdata", 'pD.RData', package = "MDTS"))
The MDTS bins are
bins
## GRanges object with 78 ranges and 3 metadata columns:
## seqnames ranges strand | median_count GC mappability
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric>
## [1] 1 209943004-209943263 * | 150.65 0.523077 1.000000
## [2] 1 209943264-209943466 * | 150.43 0.527094 0.938272
## [3] 1 209943467-209943569 * | 150.18 0.456311 1.000000
## [4] 1 209943570-209943629 * | 152.35 0.366667 1.000000
## [5] 1 209943630-209943672 * | 152.50 0.418605 1.000000
## ... ... ... ... . ... ... ...
## [74] 1 209948780-209948982 * | 150.23 0.581281 1.000000
## [75] 1 209948983-209949159 * | 150.55 0.581921 1.000000
## [76] 1 209949160-209949278 * | 150.72 0.436975 1.000000
## [77] 1 209949279-209949388 * | 150.38 0.436364 0.752723
## [78] 1 209949524-209950091 * | 238.77 0.369718 1.000000
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
The count matrix where each column is a sample and each row is a bin:
head(counts)
## F1_1 F1_3 F1_2 F2_1 F2_3 F2_2 F3_1 F3_3 F3_2 F4_1
## [1,] 150.65 129.31 123.43 155.43 175.73 172.14 132.65 125.79 149.64 180.47
## [2,] 149.78 159.53 150.24 214.15 228.59 209.85 191.18 179.33 205.24 225.10
## [3,] 149.75 129.33 135.42 163.86 173.12 145.15 123.50 153.89 151.25 160.07
## [4,] 152.17 134.68 137.09 172.25 173.66 128.36 117.48 130.53 123.95 142.79
## [5,] 150.15 133.17 125.89 166.29 159.87 108.33 115.97 109.10 109.76 116.64
## [6,] 150.79 139.37 125.16 164.06 147.14 103.33 112.28 103.94 109.27 105.01
## F4_3 F4_2 F5_1 F5_3 F5_2 F6_1 F6_3 F6_2 F7_1 F7_3
## [1,] 202.95 193.24 175.25 145.44 93.71 194.01 143.39 217.66 130.45 97.91
## [2,] 319.39 261.59 216.44 235.70 130.92 244.54 197.36 277.79 219.76 137.03
## [3,] 201.72 193.93 182.86 131.51 96.68 146.45 112.56 171.25 151.83 106.12
## [4,] 176.34 157.19 149.99 116.01 76.67 124.17 82.20 122.93 162.17 92.42
## [5,] 163.30 125.39 128.20 98.95 62.08 106.35 55.15 96.66 180.02 96.59
## [6,] 165.16 117.32 121.60 96.84 62.22 94.17 51.05 82.90 199.33 102.37
## F7_2
## [1,] 150.64
## [2,] 199.95
## [3,] 180.00
## [4,] 190.07
## [5,] 170.62
## [6,] 155.58
After obtaining the raw read-depth matrix, MDTS calculates a vector M score matrix of the same organization - each row corresponds to a row and each column a sample. The M score is based on a log2 transformation, followed by median polish, and GC and mappability adjust via a loess smoother. The resulting M scores have critical values where (0, -1, <-4) are generally consistent with 2, 1, and 0 copy numbers [barring Copy Number Polymorphisms].
# counts is the raw read depth of [MDTS bins x samples]
# bins is the previously calculated MDTS bins
mCounts <- normalizeCounts(counts, bins)
## Log Transforming Counts
## GC Adjust
## Mappability Adjust
The second advantage of MDTS is the use of Minimum Distances for candidate de novo deletion identification. The assumption underlying the development of the Minimum Distance is that it is vastly more likely for a deletion to be inherited than de novo if the proband shares a deletion with one of the parents in the trio. Based on that assumption, the Minimum Distance is calculated per family - bin combination. For each bin, the Minimum Distance is smallest (in absolute terms) difference between the proband’s M score and both parents’.
For example, if the M scores are (-1, -1, 0) for a proband and the 2 parents respectively, a situation consistent with an inherited deletion in the proband from parent 1, the Minimum Distance of the 2 possible pairwise comparisons is 0. On the other hand, if the M scores are (-1, 0, 0) for the proband and the 2 parents, the Minimum Distance stands out at -1, and is consistent with a de novo deletion.
To calculate the Minimum Distance of the dataset, MDTS takes as input the M score matrix. The output matrix is organized where each row corresponds still to a bin, but each column now refers to a trio.
# mCounts is the normalized read depth of [MDTS bins x samples]
# bins is the previously calculated MDTS bins
# pD is the phenotype matrix
md <- calcMD(mCounts, pD)
Using the Minimum Distance matrix calculated above, MDTS uses the tried and true Circular Binary Segmentation method to infer deletion states. Inferred candidate de novo deletions are further filtered to remove likely false positive signals that arose out of regions of highly variable read-depth (generally indicative of sequence artifacts or polymorphic events).
# md is the Minimum Distance of [MDTS bins x trio]
# bins is the previously calculated MDTS bins
# mCounts is the normalized read depth of [MDTS bins x samples]
cbs <- segmentMD(md=md, bins=bins)
## Analyzing: F1_1
## Analyzing: F2_1
## Analyzing: F3_1
## Analyzing: F4_1
## Analyzing: F5_1
## Analyzing: F6_1
## Analyzing: F7_1
denovo <- denovoDeletions(cbs, mCounts, bins)
## [1] "Selecting Candidate de Novo deletions"
## [1] "Calculating problematic bins"
## [1] "Filtering candidates by problematic bins"
In our example, the output is a single detected de novo deletion in family F4:
denovo
## GRanges object with 0 ranges and 2 metadata columns:
## seqnames ranges strand | m famid
## <Rle> <IRanges> <Rle> | <numeric> <character>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
No signals were picked up from the CNP region or elsewhere.
sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.66.0
## [3] rtracklayer_1.58.0 Biostrings_2.66.0
## [5] XVector_0.38.0 GenomicRanges_1.50.0
## [7] GenomeInfoDb_1.34.0 IRanges_2.32.0
## [9] S4Vectors_0.36.0 BiocGenerics_0.44.0
## [11] MDTS_1.18.0
##
## loaded via a namespace (and not attached):
## [1] DNAcopy_1.72.0 knitr_1.40
## [3] magrittr_2.0.3 zlibbioc_1.44.0
## [5] GenomicAlignments_1.34.0 BiocParallel_1.32.0
## [7] lattice_0.20-45 rjson_0.2.21
## [9] stringr_1.4.1 tools_4.2.1
## [11] parallel_4.2.1 SummarizedExperiment_1.28.0
## [13] grid_4.2.1 Biobase_2.58.0
## [15] xfun_0.34 matrixStats_0.62.0
## [17] yaml_2.3.6 crayon_1.5.2
## [19] BiocIO_1.8.0 Matrix_1.5-1
## [21] GenomeInfoDbData_1.2.9 restfulr_0.0.15
## [23] codetools_0.2-18 bitops_1.0-7
## [25] RCurl_1.98-1.9 evaluate_0.17
## [27] DelayedArray_0.24.0 stringi_1.7.8
## [29] compiler_4.2.1 Rsamtools_2.14.0
## [31] MatrixGenerics_1.10.0 XML_3.99-0.12