This chapter explains how to use SingleMoleculeGenomicsIO to filter single molecule footprinting data. The package contains functionality to calculate a collection of quality statistics for each read, and use these to filter out low-quality reads. This filtering can be done either on the imported SummarizedExperiment object (both for modBam and mismatchBam file types), or directly on the bam file (currently only modBam, which will generate another, filtered modBam file). SingleMoleculeGenomicsIO also provides several ways to filter out individual genomic positions based on, e.g. the genomic sequence or the read coverage.
We start by loading the required packages. In addition to the software package, we load a BSgenome object providing the mouse genome sequence.
BSgenomeName <- "BSgenome.Mmusculus.GENCODE.GRCm39.gencodeM34"
library(SingleMoleculeGenomicsIO)
library(BSgenomeName, character.only = TRUE)
library(SummarizedExperiment)
library(SparseArray)
# Load genome
gnm <- get(BSgenomeName)
genome(gnm) <- "mm39"To illustrate the position-level filtering of imported data, we first read 6mA data from a small genomic region for two samples. We add the sequence context (a single nucleotide) to be able to use this information as a basis for filtering.
se <- readModBam(
bamfiles = c(wt1 = "data/mESC_wt_6mA_rep1.bam",
wt2 = "data/mESC_wt_6mA_rep2.bam"),
modbase = "a",
regions = "chr8:39286301-39287100",
seqinfo = seqinfo(gnm),
sequenceContextWidth = 1,
sequenceReference = gnm
)
seclass: RangedSummarizedExperiment
dim: 31693 2
metadata(3): readLevelData variantPositions filteredOutReads
assays(1): mod_prob
rownames: NULL
rowData names(1): sequenceContext
colnames(2): wt1 wt2
colData names(4): sample modbase n_reads readInfoThe sequence information is stored in the rowData of se:
rowData(se)DataFrame with 31693 rows and 1 column
sequenceContext
<DNAStringSet>
1 A
2 A
3 G
4 A
5 A
... ...
31689 A
31690 A
31691 A
31692 A
31693 APosition filtering can now be performed with the filterPositions function. The filters argument define which filters to apply, as well as the order (sequence context, coverage, removal of positions without non-NA values). Here, we retain only positions where the genome sequence is an A, and the coverage (the number of overlapping reads) is at least five. The assayNameCov argument indicates which assay will be used to define the coverage. If this is a read-level assay (like here), coverage will first be calculated using flattenReadLevelAssay. For more precise control, the summary assay can also be manually calculated and added to se beforehand, and specified in assayNameCov.
sefilt <- filterPositions(
se,
filters = c("sequenceContext", "coverage", "all.na"),
sequenceContext = "A",
assayNameCov = "mod_prob",
minCov = 5
)
sefiltclass: RangedSummarizedExperiment
dim: 16729 2
metadata(3): readLevelData variantPositions filteredOutReads
assays(1): mod_prob
rownames: NULL
rowData names(1): sequenceContext
colnames(2): wt1 wt2
colData names(4): sample modbase n_reads readInfoIn this case, the position filtering reduced the number of unique positions from 31693 to 16729 by 47.2%. However, the number of non-NA values in the matrix is only reduced from 333127 to 304984 (8.4%), confirming that the filtered-out positions are generally covered only by few reads.
In addition to explicit filtering shown above, several functions in SingleMoleculeGenomicsIO and footprintR allow on-the-fly filtering for a specific sequence context (for example calcReadStats or plotRegion).
SummarizedExperiment objectIn addition to the position filtering illustrated above, SingleMoleculeGenomicsIO also provides functionality for calculating read-level quality scores and filtering out reads with low quality. The calculation of the quality scores is done using the addReadStats function, and the filtering is performed via the filterReads function.
# Calculate read statistics
sefilt <- addReadStats(
sefilt,
name = "QC"
)
# The calculated read statistics are stored in the colData
colData(sefilt)DataFrame with 2 rows and 5 columns
sample modbase n_reads readInfo
<character> <character> <integer> <List>
wt1 wt1 a 41 14.1235:22453:22149:...,15.9814:15617:15460:...,14.3505:12359:12212:...,...
wt2 wt2 a 67 15.5769:39918:39406:...,15.4317:23376:23207:...,16.8065:23487:23313:...,...
QC
<List>
wt1 0.194044:0.186071:0.932636:...,0.157727:0.144965:0.947031:...,0.189361:0.176007:0.940345:...,...
wt2 0.217511:0.202377:0.941197:...,0.232775:0.221732:0.940212:...,0.161989:0.146135:0.947654:...,...sefilt$QC$wt1DataFrame with 41 rows and 13 columns
MeanModProb FracMod MeanConf MeanConfUnm MeanConfMod FracLowConf IQRModProb
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
wt1-5e78b10b-3b62-4d92-ae96-18aae3e13ae3 0.194044 0.1860714 0.932636 0.953720 0.840406 0.0864286 0.2324219
wt1-d0e863ae-8127-4233-9eaa-3c17d2af3df5 0.157727 0.1449649 0.947031 0.961562 0.861322 0.0658080 0.1074219
wt1-b1e35af5-efa1-4451-8870-0ccdee17c5c0 0.189361 0.1760073 0.940345 0.955699 0.868468 0.0702817 0.1855469
wt1-a89d3c9f-fdba-4e41-a4ef-b810a166d46c 0.092727 0.0762226 0.959181 0.968973 0.840501 0.0458685 0.0000000
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 0.111052 0.0816633 0.935930 0.949115 0.787661 0.0731463 0.0996094
... ... ... ... ... ... ... ...
wt1-a39caa23-800a-4e69-a56e-4e020e83c306 0.103593 0.0862342 0.964059 0.970835 0.892256 0.0332278 0.0527344
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 0.143284 0.1231672 0.919949 0.942881 0.756696 0.1070381 0.1542969
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 0.149654 0.1382212 0.956477 0.968115 0.883916 0.0456731 0.0800781
wt1-3d7ab39b-23d7-420d-8261-f0fff0569e84 0.195087 0.1826625 0.942946 0.957497 0.877836 0.0722394 0.1816406
wt1-b754e197-532c-4782-9e91-22e8c58b6db6 0.184400 0.1745763 0.947155 0.962038 0.876782 0.0601695 0.1513672
sdModProb ACModProb
<numeric> <list>
wt1-5e78b10b-3b62-4d92-ae96-18aae3e13ae3 0.329855 0.0908535,0.0468121,0.0501233,...
wt1-d0e863ae-8127-4233-9eaa-3c17d2af3df5 0.308085 0.0842872,0.0846944,0.0641687,...
wt1-b1e35af5-efa1-4451-8870-0ccdee17c5c0 0.332203 0.04668755, 0.03179627,-0.00729562,...
wt1-a89d3c9f-fdba-4e41-a4ef-b810a166d46c 0.233053 0.04755155, 0.02123325,-0.00550823,...
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 0.228051 -0.00865141, 0.01936452, 0.01258930,...
... ... ...
wt1-a39caa23-800a-4e69-a56e-4e020e83c306 0.256209 -0.0442483,-0.0664415,-0.0791683,...
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 0.258013 0.1143637,0.0983527,0.0838372,...
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 0.308975 0.0474403,-0.0330583,-0.0253474,...
wt1-3d7ab39b-23d7-420d-8261-f0fff0569e84 0.341323 -0.0811299,-0.0642624,-0.0958239,...
wt1-b754e197-532c-4782-9e91-22e8c58b6db6 0.335256 -0.077225,-0.119359,-0.131436,...
PACModProb SNR SignalVar NoiseVar
<list> <numeric> <numeric> <numeric>
wt1-5e78b10b-3b62-4d92-ae96-18aae3e13ae3 -0.0372147, 0.0052070,-0.0149203,... 0.15120358 0.0572504 0.0515539
wt1-d0e863ae-8127-4233-9eaa-3c17d2af3df5 0.02170916,-0.00333282,-0.01792930,... -0.00904029 0.0473096 0.0476070
wt1-b1e35af5-efa1-4451-8870-0ccdee17c5c0 -0.0031751,-0.0432073,-0.0319139,... 0.03159991 0.0557838 0.0545752
wt1-a89d3c9f-fdba-4e41-a4ef-b810a166d46c -0.02789056,-0.03286089, 0.00103895,... 0.24072350 0.0294171 0.0248963
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 0.02233742,0.00779054,0.01249628,... -0.41498682 0.0222892 0.0297179
... ... ... ... ...
wt1-a39caa23-800a-4e69-a56e-4e020e83c306 -0.0391629,-0.0370244,-0.0125482,... 0.44897184 0.0378878 0.0277553
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 0.01864350, 0.00968442,-0.04217918,... -0.42904071 0.0283721 0.0381984
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 -0.0804097,-0.0110440,-0.0389122,... 0.28448715 0.0524237 0.0430416
wt1-3d7ab39b-23d7-420d-8261-f0fff0569e84 -0.0328237,-0.0444207,-0.0324546,... 0.00281814 0.0583075 0.0581937
wt1-b754e197-532c-4782-9e91-22e8c58b6db6 -0.0731008,-0.0485207,-0.0638214,... 0.37076865 0.0633803 0.0490164# In addition, we can filter based on the read info columns added by readModBam
sefilt$readInfo$wt1DataFrame with 41 rows and 6 columns
qscore read_length aligned_length variant_label ref_strand
<numeric> <integer> <integer> <character> <character>
wt1-5e78b10b-3b62-4d92-ae96-18aae3e13ae3 14.1235 22453 22149 NA +
wt1-d0e863ae-8127-4233-9eaa-3c17d2af3df5 15.9814 15617 15460 NA -
wt1-b1e35af5-efa1-4451-8870-0ccdee17c5c0 14.3505 12359 12212 NA -
wt1-a89d3c9f-fdba-4e41-a4ef-b810a166d46c 16.6668 8845 8769 NA +
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 12.0795 6779 6601 NA +
... ... ... ... ... ...
wt1-a39caa23-800a-4e69-a56e-4e020e83c306 17.9399 9022 6511 NA -
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 13.5355 6586 3588 NA +
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 16.6164 8536 4455 NA -
wt1-3d7ab39b-23d7-420d-8261-f0fff0569e84 14.3454 7815 5147 NA -
wt1-b754e197-532c-4782-9e91-22e8c58b6db6 15.2163 15363 6143 NA -
aligned_fraction
<numeric>
wt1-5e78b10b-3b62-4d92-ae96-18aae3e13ae3 0.986461
wt1-d0e863ae-8127-4233-9eaa-3c17d2af3df5 0.989947
wt1-b1e35af5-efa1-4451-8870-0ccdee17c5c0 0.988106
wt1-a89d3c9f-fdba-4e41-a4ef-b810a166d46c 0.991408
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 0.973742
... ...
wt1-a39caa23-800a-4e69-a56e-4e020e83c306 0.721680
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 0.544792
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 0.521907
wt1-3d7ab39b-23d7-420d-8261-f0fff0569e84 0.658605
wt1-b754e197-532c-4782-9e91-22e8c58b6db6 0.399857# Visualize the read statistics to set appropriate filter thresholds
plotReadStats(
sefilt
)
# Perform filtering
sefilt <- filterReads(
sefilt,
minQscore = 13,
maxFracLowConf = 0.1,
minAlignedLength = 5000,
removeAllNApos = TRUE
)
Filtering statistics are stored in the metadata of the filtered SummarizedExperiment object.
metadata(sefilt)$filteredOutReads$wt1
<12 x 9 SparseMatrix> of type "logical" [nzcount=16 (15%)]:
Qscore Entropy FracLowConf ... CoveredFraction
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 TRUE FALSE FALSE . FALSE
wt1-e145db43-4658-4dcc-8f58-78fb09544770 TRUE FALSE TRUE . FALSE
wt1-eac97b86-e3fc-4d19-8a02-08bd861077b5 TRUE FALSE FALSE . FALSE
wt1-e06432c5-189d-4c1c-8a5b-025c2d68fee5 TRUE FALSE TRUE . FALSE
wt1-5a2f30be-48c9-4b0a-8ab1-83d54c71ef56 FALSE FALSE FALSE . FALSE
... . . . . .
wt1-fac12814-a86d-4d07-9596-d9ea4943580e FALSE FALSE FALSE . FALSE
wt1-f77f5ae9-dd3a-4e08-a390-7e80868433a7 TRUE FALSE TRUE . FALSE
wt1-fda332ae-02de-4391-9cff-620c1ae8799f FALSE FALSE FALSE . FALSE
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 FALSE FALSE TRUE . FALSE
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 FALSE FALSE FALSE . FALSE
AllNA
wt1-b58992e1-d311-49d7-bf22-81fd390687b5 FALSE
wt1-e145db43-4658-4dcc-8f58-78fb09544770 FALSE
wt1-eac97b86-e3fc-4d19-8a02-08bd861077b5 FALSE
wt1-e06432c5-189d-4c1c-8a5b-025c2d68fee5 FALSE
wt1-5a2f30be-48c9-4b0a-8ab1-83d54c71ef56 FALSE
... .
wt1-fac12814-a86d-4d07-9596-d9ea4943580e FALSE
wt1-f77f5ae9-dd3a-4e08-a390-7e80868433a7 FALSE
wt1-fda332ae-02de-4391-9cff-620c1ae8799f FALSE
wt1-b6efe1fe-f2b9-4f69-bb61-f4a741969a52 FALSE
wt1-d0b3f6f8-c5d3-477e-b4c4-02dcd4602767 FALSE
$wt2
<4 x 9 SparseMatrix> of type "logical" [nzcount=4 (11%)]:
Qscore Entropy FracLowConf ... CoveredFraction
wt2-d428b49f-a652-42f3-a614-f4b431b8d024 TRUE FALSE FALSE . FALSE
wt2-16d5f3d1-6ca4-448a-8d99-3b5c85f9b518 FALSE FALSE FALSE . FALSE
wt2-8cf9d87a-3c82-4e3e-baf3-352b645b8b71 FALSE FALSE FALSE . FALSE
wt2-1ddb16c7-dc0a-4ca8-a0c4-602e021561e2 FALSE FALSE FALSE . FALSE
AllNA
wt2-d428b49f-a652-42f3-a614-f4b431b8d024 FALSE
wt2-16d5f3d1-6ca4-448a-8d99-3b5c85f9b518 FALSE
wt2-8cf9d87a-3c82-4e3e-baf3-352b645b8b71 FALSE
wt2-1ddb16c7-dc0a-4ca8-a0c4-602e021561e2 FALSEmodBam fileAs mentioned above, SingleMoleculeGenomicsIO can also be used to directly filter alignments in a modBam file. The filterReadsModBam function reads the alignments in the input file, parses them, and writes them to the output file if they pass all the designated filters. These filters are treated hierarchically - in other words, if a read does not pass a given filter, the remaining filters will not be examined and the processing continues with the next read. Here we illustrate the modBam filtering using two small example modBam files, each with 10 reads.
# input files
modbamfiles <- c("data/6mA_1_10reads.bam", "data/6mA_2_10reads.bam")
# output files
filtbamfiles <- sub("\\.bam", "_filtered.bam", modbamfiles)
res <- filterReadsModBam(
infiles = modbamfiles,
outfiles = filtbamfiles,
modbase = "a",
indexOutfiles = FALSE,
minReadLength = 6746,
minAlignedLength = 6896,
minAlignedFraction = 0.56,
minQscore = 9.7,
maxFracLowConf = 0.11,
maxEntropy = 0.29,
verbose = TRUE)ℹ start filtering of 'data/6mA_1_10reads.bam' using 4 threadsℹ merging 62 filtered chunksℹ done filtering: retained 7 of 10 records (70%)ℹ start filtering of 'data/6mA_2_10reads.bam' using 4 threadsℹ merging 62 filtered chunksℹ done filtering: retained 7 of 10 records (70%)The res object provides details about the number of reads that were filtered out at each stage.
res sample infile outfile total retained filtered_unmapped filtered_secondary
1 s1 data/6mA_1_10reads.bam data/6mA_1_10reads_filtered.bam 10 7 0 0
2 s2 data/6mA_2_10reads.bam data/6mA_2_10reads_filtered.bam 10 7 0 0
filtered_supplementary filtered_minReadLength filtered_minAlignedLength filtered_minAlignedFraction
1 0 0 1 1
2 0 1 0 0
filtered_minQscore filtered_minSNR filtered_maxFracLowConf filtered_maxEntropy
1 0 0 0 1
2 1 0 1 0sessioninfo::session_info(info = "packages")═ Session info ═══════════════════════════════════════════════════════════════════════════════════════════════════════
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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XVector * 0.50.0 2025-10-29 [1] Bioconductor 3.22 (R 4.5.1)
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[2] /tachyon/groups/gbioinfo/Appz/easybuild/software/R/4.5.2-foss-2024.05/lib64/R/library
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