[Bioc-devel] MRD measurements in Leukemic patients using NGS data in r
something like this perhaps?
library(Rsamtools)
BamFiles <- BamFileList(lapply(list.files(pattern=".bam$"), BamFile))
names(BamFiles) <- sapply(strsplit(list.files(pattern=".bam$"), "\\."),
`[`, 1)
show(BamFiles)
#BamFileList of length 14
#names(14): Normal_0813_S19_R1_001 Normal_2013_S18_R1_001 ... REMC_CD19
REMC_CD3
library(Homo.sapiens)
gxs <- genes(Homo.sapiens)
names(gxs) <- mapIds(Homo.sapiens, names(gxs), "SYMBOL", "ENTREZID")
SomeGenes <- gxs[ c("HRAS", "WT1", "IGF2") ]
sbparam <- ScanBamParam(which=SomeGenes)
puparam <- PileupParam(max_depth=1e3, min_nucleotide_depth=5,
include_insertions=TRUE, min_minor_allele_depth=1)
piles <- lapply(BamFiles, pileup, ScanBamParam=sbparam, PileupParam=puparam)
filterInvariant <- function(x) {
positions <- subset(x, duplicated(pos))$pos
subset(x, pos %in% positions)
}
show(do.call(rbind, lapply(lapply(piles, filterInvariant), head, 2)))
# seqnames pos strand nucleotide count
# Normal_0813_S19_R1_001.43 chr11 1979973 - C 1
# Normal_0813_S19_R1_001.44 chr11 1979973 + T 1
# Normal_2013_S18_R1_001.16 chr11 1979889 + T 3
# Normal_2013_S18_R1_001.17 chr11 1979889 - T 1
# Normal_2714_S20_R1_001.4 chr11 1979933 + A 1
# Normal_2714_S20_R1_001.5 chr11 1979933 - A 1
# P01-00020-T06-P-cfDNA.21 chr11 1980045 + G 1
# P01-00020-T06-P-cfDNA.22 chr11 1980045 - G 1
# P01-00020-T06-P-cfDNA2.11 chr11 1979884 + T 1
# P01-00020-T06-P-cfDNA2.12 chr11 1979884 - T 1
# P01-00021-T06-P-cfDNA.5 chr11 1979879 + C 1
# P01-00021-T06-P-cfDNA.6 chr11 1979879 - C 1
# P01-00024-T06-P-cfDNA.9 chr11 1979990 + C 1
# P01-00024-T06-P-cfDNA.10 chr11 1979990 + T 2
# P01-00028-T06-N-P01.3 chr11 1979851 + A 1
# P01-00028-T06-N-P01.4 chr11 1979851 + G 1
# P01-00028-T06-P-cfDNA.10 chr11 1979948 + A 1
# P01-00028-T06-P-cfDNA.11 chr11 1979948 + G 1
# P01-00028-T06-T-P01.2 chr11 1979850 + C 3
# P01-00028-T06-T-P01.3 chr11 1979850 - C 2
# P01-00028-T06-T-P02.3 chr11 1979851 + A 1
# P01-00028-T06-T-P02.4 chr11 1979851 + C 1
Or whatever you like (GAlignments would get you the reads, GAlignmentPairs
the read pairs, and so on)
--t
On Sat, Mar 7, 2020 at 3:51 PM Mulder, R <r.mulder01 at umcg.nl> wrote:
Dear Tim,
I installed Rsamtools and ran it on a bamfile using the following command
to get nucleotide count for 2 hotspot regions.
library(Rsamtools)
bamfile <- "test.bam"
bf <- BamFile(bamfile)
param <- ScanBamParam(which=GRanges(c("4", "9"), IRanges(start=c(55599321,
5073770), end=c(55599321, 5073770))))
Now, I want to perform this on more than 1 bam file at once and on more
region.
Do you perhaps have ideas on how I could do this?
Best,
Rene
------------------------------
*Van:* Tim Triche, Jr. <tim.triche at gmail.com>
*Verzonden:* vrijdag 6 maart 2020 13:57:10
*Aan:* Mulder, R
*CC:* bioc-devel at r-project.org
*Onderwerp:* Re: [Bioc-devel] MRD measurements in Leukemic patients using
NGS data in r
Oh hey, one last thing ? if all you want is to get nucleotide counts per
region of interest, just use pileup() in Rsamtools, with bamWhich(GRanges)
holding a GRanges (Genomic Ranges) of your regions added to scanBamParams
for each BAM. It sounds awkward but in practice it is super fast and will
give you all the nucleotide and read level information you could want. One
of my interns implemented this for mitochondrial variant calling in
MTseeker when we got sick of using gmapR and being flagged for errors on
not-Linux. (We gutted the entire package recently and have new, insanely
deep examples from Oxford Nanopore direct RNA sequencing and from large
single cell datasets; I need to add those and get the package back out of
purgatory).
That said, in the end you will want a LOT of validation material so this
is very much just a starting point. But still, it?s your starting point, in
R at least. And truthfully I much prefer R/Bioconductor idioms to (say)
pysam or the like. htsnim is nice but then you?ll be implementing the ML
bits from scratch, so I think your instincts to try R first are sensible.
Good luck! Even if you use this for something else besides MRD, I think it
will become a useful exercise.
--t
On Mar 5, 2020, at 4:36 PM, Tim Triche, Jr. <tim.triche at gmail.com> wrote:
?
a few thoughts:
1) look into Shearwater (
https://bioconductor.org/packages/release/bioc/html/deepSNV.html
<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbioconductor.org%2Fpackages%2Frelease%2Fbioc%2Fhtml%2FdeepSNV.html&data=02%7C01%7Cr.mulder01%40umcg.nl%7C1d7ff8bff12b40c27b7a08d7c1cde470%7C335122f9d4f44d67a2fccd6dc20dde70%7C0%7C0%7C637190962347080053&sdata=LpUeflml9u2XLENpmDAEuDBRANXZpjkFbIEiJAeP2pw%3D&reserved=0>),
then
2) talk to Todd Druley @ WashU, Elli Pappaemanuil @ MSKCC, Ruud & Bob @
Erasmus, the usual suspects
3) plan to validate w/ddPCR (at the absolute very least) and be aware that
most MRD in leukemia is done by a combination of BCR/TCR + breakpoint PCR
(lymphoid/fusion-driven) or DFN flow (myeloid + normal cyto)
not saying that ML-based methods might not help, but if you've got a
30x-100x genome (or even 1000x FM1) and are trying to compete with existing
standard approaches that can detect molecules at 1e-6, it'll be rough. An
alternative approach (that has been used repeatedly) is to throw caution to
the wind, generate primers for numerous subject-specific somatic variants,
and use the ensemble to try and model MRD (speaking of ML). On the one
hand, that could give the clinic a "customer for life"; on the other hand,
it's not conducive to large-scale automation & deployment. As far as I
know, it never got much traction, in leukemia or anywhere else. (Consider
that flow cytometry is capable of detecting 1-in-10K to 1-in-a-million
cells in most clinical flow labs.)
Best of luck! (and if you're not already working with UMI-tagged reads,
please talk to the people in #2 above; the reason most people won't go
below 5% VAF is that you get thwacked by error rates at that level, and the
reason most NGS-based MRD is based on UMIs is that existing PCR-based
methods have 6 logs sensitivity.)
--t
On Thu, Mar 5, 2020 at 4:08 PM Mulder, R <r.mulder01 at umcg.nl> wrote:
Hi, I was wondering if anyone could help me with a script and support for the above mentioned goal. For this I have several BAM files for which I want to determine de nucleotide count per region of interest. The latter could be several hotspot mutation sites. I would like to get an overall overview of all the BAM files. Next I want to use these counts to determine for any new BAM file if the count for a particular genomic position is higher than the allowable range, hence could indicate if a mutation is present. For this I would like to use the modified Thompson Tau test. I think machine learning could be used for this. So, why do I want to do all this? Well, normal NGS pipelines only deal with variants at a frequency of 5%. Mutatios below this frequency are often missed. To know if a mutation is present below this level, you showed dive into the alignment and most often manually investigate the base calls. I know that this races some questions regarding call qualities, but then again our conventional assays have actually confirmed some of these low mutations. In addition, NGS can be used to determine the presence of low frequent mutation which is of great importance for determining the measurable residual disease after treatment. I am new to r and bioconductor so I would be very thankful if someone could help me in my mission to setting up a script for this purpose. Thanks, Rene Mulder Laboratory Medicine University Medical Center Groningen The Netherlands
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