TSSr is a comprehensive R/Bioconductor package for analyzing transcription start site (TSS) sequencing data. It supports multiple input formats including BAM, BED, BigWig, and TSS tables, and provides a complete workflow from TSS identification through downstream analyses such as core promoter shape analysis, gene annotation, differential expression, and promoter shifting.
If you use TSSr, please cite the following article:
citation("TSSr")
#> To cite package 'TSSr' in publications use:
#>
#> Lu Z, Berry K, Hu Z, Zhan Y, Ahn T, Lin Z (2021). "TSSr: an R package
#> for comprehensive analyses of TSS sequencing data." _NAR Genomics and
#> Bioinformatics_, *3*(4), lqab108. doi:10.1093/nargab/lqab108
#> <https://doi.org/10.1093/nargab/lqab108>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {TSSr: an R package for comprehensive analyses of TSS sequencing data},
#> author = {Zhaolian Lu and Keenan Berry and Zhenbin Hu and Yu Zhan and Tae-Hyuk Ahn and Zhenguo Lin},
#> journal = {NAR Genomics and Bioinformatics},
#> year = {2021},
#> volume = {3},
#> number = {4},
#> pages = {lqab108},
#> doi = {10.1093/nargab/lqab108},
#> }For general questions about the usage of TSSr, use the official Bioconductor support forum and tag your question “TSSr”. We strive to answer questions as quickly as possible.
For technical questions, bug reports and suggestions for new features, we refer to the TSSr github page.
Or create a new TSSr object using the constructor function. Note that the input files do not need to exist at object creation time:
# Create a TSSr object using the constructor (files do not need to exist)
newObj <- TSSr(
genomeName = "BSgenome.Scerevisiae.UCSC.sacCer3",
inputFiles = c("S01.sorted.bam", "S02.sorted.bam",
"S03.sorted.bam", "S04.sorted.bam"),
inputFilesType = "bam",
sampleLabels = c("SL01", "SL02", "SL03", "SL04"),
sampleLabelsMerged = c("control", "treat"),
mergeIndex = c(1, 1, 2, 2),
refSource = "saccharomyces_cerevisiae.SGD.gff",
organismName = "saccharomyces cerevisiae"
)
newObj
#> An object of class "TSSr"
#> Slot "genomeName":
#> [1] "BSgenome.Scerevisiae.UCSC.sacCer3"
#>
#> Slot "inputFiles":
#> [1] "S01.sorted.bam" "S02.sorted.bam" "S03.sorted.bam" "S04.sorted.bam"
#>
#> Slot "inputFilesType":
#> [1] "bam"
#>
#> Slot "sampleLabels":
#> [1] "SL01" "SL02" "SL03" "SL04"
#>
#> Slot "sampleLabelsMerged":
#> [1] "control" "treat"
#>
#> Slot "librarySizes":
#> numeric(0)
#>
#> Slot "TSSrawMatrix":
#> data frame with 0 columns and 0 rows
#>
#> Slot "mergeIndex":
#> [1] 1 1 2 2
#>
#> Slot "TSSprocessedMatrix":
#> data frame with 0 columns and 0 rows
#>
#> Slot "tagClusters":
#> list()
#>
#> Slot "consensusClusters":
#> list()
#>
#> Slot "clusterShape":
#> list()
#>
#> Slot "refSource":
#> [1] "saccharomyces_cerevisiae.SGD.gff"
#>
#> Slot "refTable":
#> data frame with 0 columns and 0 rows
#>
#> Slot "organismName":
#> [1] "saccharomyces cerevisiae"
#>
#> Slot "assignedClusters":
#> list()
#>
#> Slot "unassignedClusters":
#> list()
#>
#> Slot "filteredClusters":
#> list()
#>
#> Slot "enhancers":
#> list()
#>
#> Slot "DEtables":
#> list()
#>
#> Slot "TAGtables":
#> list()
#>
#> Slot "PromoterShift":
#> list()Then read in TSS data from the input files (requires actual files):
# TSS clustering
clusterTSS(myTSSr,
method = "peakclu", peakDistance = 100, extensionDistance = 30,
localThreshold = 0.02, clusterThreshold = 1,
useMultiCore = FALSE, numCores = NULL
)
#>
#> Clustering TSS data with peakclu method...
# Aggregating consensus clusters
consensusCluster(myTSSr, dis = 50, useMultiCore = FALSE)
#>
#> Creating consensus clusters...# Gene-level differential expression using DESeq2
deGene(myTSSr, comparePairs = list(c("control", "treat")),
pval = 0.01, useMultiCore = FALSE, numCores = NULL)
#>
#> Calculating gene differential expression...
#> converting counts to integer mode
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing# Calculate core promoter shifts
shiftPromoter(myTSSr, comparePairs = list(c("control", "treat")),
pval = 0.01)
#>
#> Calculating core promoter shifts...
#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
#> may be incorrect
#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> may be incorrect
#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
#> may be incorrect
#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
#> may be incorrect
#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
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#> Warning in chisq.test(data[, c("tags.x", "tags.y")]): Chi-squared approximation
#> may be incorrectTSSr provides several export functions for saving results:
# Export TSS tables
exportTSStable(myTSSr, data = "processed")
# Export cluster tables
exportClustersTable(myTSSr, data = "assigned")
# Export to BED format for genome browsers
exportClustersToBed(myTSSr, data = "consensusClusters")
# Export TSS to bedGraph
exportTSStoBedgraph(myTSSr, data = "processed")sessionInfo()
#> R version 4.6.0 RC (2026-04-17 r89917)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
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#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] TSSr_0.99.9
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3
#> [2] rstudioapi_0.18.0
#> [3] jsonlite_2.0.0
#> [4] magrittr_2.0.5
#> [5] GenomicFeatures_1.64.0
#> [6] farver_2.1.2
#> [7] rmarkdown_2.31
#> [8] BiocIO_1.22.0
#> [9] vctrs_0.7.3
#> [10] memoise_2.0.1
#> [11] Rsamtools_2.28.0
#> [12] RCurl_1.98-1.18
#> [13] base64enc_0.1-6
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#> [23] Gviz_1.56.0
#> [24] httr2_1.2.2
#> [25] cachem_1.1.0
#> [26] GenomicAlignments_1.48.0
#> [27] lifecycle_1.0.5
#> [28] pkgconfig_2.0.3
#> [29] Matrix_1.7-5
#> [30] R6_2.6.1
#> [31] fastmap_1.2.0
#> [32] MatrixGenerics_1.24.0
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#> [34] colorspace_2.1-2
#> [35] AnnotationDbi_1.74.0
#> [36] S4Vectors_0.50.0
#> [37] DESeq2_1.52.0
#> [38] Hmisc_5.2-5
#> [39] GenomicRanges_1.64.0
#> [40] RSQLite_2.4.6
#> [41] filelock_1.0.3
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#> [50] DBI_1.3.0
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#> [54] DelayedArray_0.38.1
#> [55] rjson_0.2.23
#> [56] tools_4.6.0
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#> [86] knitr_1.51
#> [87] BSgenome.Scerevisiae.UCSC.sacCer3_1.4.0
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#> [89] IRanges_2.46.0
#> [90] Seqinfo_1.2.0
#> [91] ProtGenerics_1.44.0
#> [92] SummarizedExperiment_1.42.0
#> [93] stats4_4.6.0
#> [94] xfun_0.57
#> [95] Biobase_2.72.0
#> [96] matrixStats_1.5.0
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#> [108] jquerylib_0.1.4
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#> [110] Rcpp_1.1.1-1.1
#> [111] GenomeInfoDb_1.48.0
#> [112] dbplyr_2.5.2
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#> [124] bitops_1.0-9
#> [125] txdbmaker_1.8.0
#> [126] VariantAnnotation_1.58.0
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#> [128] purrr_1.2.2
#> [129] crayon_1.5.3
#> [130] rlang_1.2.0
#> [131] KEGGREST_1.52.0