Using proteasy to Retrieve and Analyze Protease Data
25 April 2023
Package
proteasy 1.2.0
1 Introduction
1.1 Motivation
Protease cleavages affect many vital physiological processes, and dysregulation of proteolytic activity is associated with a variety of pathological conditions. The field of degradomics is committed to provide insights about proteolytic events by incorporating techniques for identification and functional characterization of proteases and their substrates and inhibitors.
proteasy allows for batch identification of possible proteases for a set of substrates (protein IDs and peptide sequences), and may be an important tool in peptide-centric analyses of endogenously cleaved peptides.
This package utilizes data derived from the MEROPS database. The database is a manually curated knowledgebase with information about proteolytic enzymes, their inhibitors and substrates.
This document illustrates the functionality of proteasy through some use cases.
1.2 Package scope and limitations
Similarly to existing tools such as TopFind, and Proteasix, proteasy exists for the purpose of retrieving data about proteases by mapping peptide termini positions to known sites where a protease cleaves. Unlike the cleaver package, which allows for in-silico cleavage of amino acid sequences by specifying an enzyme, the functions in proteasy relies only on experimentally derived data to find proteases annotated to cleavage sites.
The proteasy package is limited to entries annotated in MEROPS. Moreover, proteasy currently does not allow for retrieval of proteolytic details for multiple organisms at once, or inter-organism events. The package does however provide annotation data for all organisms available in MEROPS.
The function findProtease will automatically map a peptide’s start- and end- positions in its host-protein’s sequence. When a protein sequence has repeated instances of a peptide sequence, proteasy will only match to the first instance in the protein sequence.
2 Installation
The package should be installed using as described below:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("proteasy")The package can then be loaded with:
library("proteasy")3 Usage
3.1 Find proteases by substrate search
A fast way to find which possible proteases, if any, are annotated as cleaving actors for a substrate is by using the function searchSubstrate. Setting the parameter summarize = TRUE will return only a vector of reviewed proteases, and summarize = FALSE will return a table with details about each cleaving event. We will explore the two options for kininogen-1 (P01042).
# Returns vector of reviewed proteases that cleave P01042
searchSubstrate(protein = "P01042", summarize = TRUE)## [1] "P07339" "P09668" "P07384" "P17655" "Q07075" "Q9UIQ6" "Q6P179" "Q6Q4G3"
## [9] "Q9BYF1" "P52888" "Q99797" "P22894" "Q16819" "P08473" "P42892" "Q495T6"
## [17] "P15169" "P14384" "Q96IY4" "P14735" "Q5JRX3" "O43895" "Q9NQW7" "Q9Y337"
## [25] "Q9P0G3" "Q14520" "P08246" "P23946" "Q15661" "P20231" "P06870" "P20151"
## [33] "P03952" "P48147" "P42785" "Q9UHL4"# Returns data.table with details on cleaving events
searchSubstrate(protein = "P01042", summarize = FALSE) |>
head()## Protease (Uniprot) Protease status Protease organism Protease (MEROPS)
## 1: P07339 reviewed Homo sapiens A01.009
## 2: P07339 reviewed Homo sapiens A01.009
## 3: P07339 reviewed Homo sapiens A01.009
## 4: P09668 reviewed Homo sapiens C01.040
## 5: P09668 reviewed Homo sapiens C01.040
## 6: P07384 reviewed Homo sapiens C02.001
## Cleaved residue Substrate name Substrate (Uniprot) Residue number
## 1: L kininogen-1 P01042 289
## 2: K kininogen-1 P01042 316
## 3: F kininogen-1 P01042 321
## 4: F Bradykinin P01042 385
## 5: F Lysyl-bradykinin P01042 385
## 6: R kininogen-1 P01042 389
## Substrate organism Protease name Cleavage type
## 1: Homo sapiens cathepsin D physiological
## 2: Homo sapiens cathepsin D physiological
## 3: Homo sapiens cathepsin D physiological
## 4: Homo sapiens cathepsin H non-physiological
## 5: Homo sapiens cathepsin H non-physiological
## 6: Homo sapiens calpain-1 physiological# The function also accepts multiple proteins as input. Let's inspect
# the last rows in the returned data.table.
searchSubstrate(protein = c("P01042", "P02461"), summarize = FALSE) |>
tail()## Protease (Uniprot) Protease status Protease organism Protease (MEROPS)
## 1: Q59EM4 unreviewed Homo sapiens S28.002
## 2: B4DPD6 unreviewed Homo sapiens S28.002
## 3: R4GNE8 unreviewed Homo sapiens S28.002
## 4: R4GMR2 unreviewed Homo sapiens S28.002
## 5: Q59EM4 unreviewed Homo sapiens S28.002
## 6: B4DPD6 unreviewed Homo sapiens S28.002
## Cleaved residue Substrate name Substrate (Uniprot) Residue number
## 1: P bradykinin (1-3) P01042 382
## 2: P bradykinin (1-3) P01042 382
## 3: P bradykinin (1-5) P01042 382
## 4: P bradykinin (1-5) P01042 382
## 5: P bradykinin (1-5) P01042 382
## 6: P bradykinin (1-5) P01042 382
## Substrate organism Protease name Cleavage type
## 1: Homo sapiens dipeptidyl-peptidase II non-physiological
## 2: Homo sapiens dipeptidyl-peptidase II non-physiological
## 3: Homo sapiens dipeptidyl-peptidase II non-physiological
## 4: Homo sapiens dipeptidyl-peptidase II non-physiological
## 5: Homo sapiens dipeptidyl-peptidase II non-physiological
## 6: Homo sapiens dipeptidyl-peptidase II non-physiological# With summarize = FALSE we get both reviewed and unreviewed proteases.3.2 Find substrates by protease search
A corresponding function, searchProtease, exists to find which (if any) substrates a protease cleaves. The function is demonstrated with MMP-12 (P39900) as an example.
# Returns vector of substrates that MMP-12 cleave
searchProtease(protein = "P39900", summarize = TRUE)## [1] "P00748" "P15502" "P02686" "P15502-2" "P10646" "P02751"
## [7] "P08519" "Q06828" "P51888" "P10909" "P02647" "P01009"
## [13] "P02671" "P02778" "P02458" "P02461" "P35556" "Q07325"
## [19] "P21810" "P07585" "P15502-1" "O14625" "P01857" "P35555"
## [25] "Q03405"# Returns data.table with details on cleaving events that involve MMP-12
searchProtease(protein = "P39900", summarize = FALSE) |> head()## Cleaved residue Protease (MEROPS) Substrate name Substrate (Uniprot)
## 1: A M10.009 coagulation factor XII P00748
## 2: A M10.009 elastin P15502
## 3: A M10.009 elastin P15502
## 4: A M10.009 elastin P15502
## 5: A M10.009 elastin P15502
## 6: A M10.009 elastin P15502
## Residue number Substrate organism Protease name Cleavage type
## 1: 379 Homo sapiens matrix metallopeptidase-12 physiological
## 2: 670 Homo sapiens matrix metallopeptidase-12 physiological
## 3: 102 Homo sapiens matrix metallopeptidase-12 physiological
## 4: 687 Homo sapiens matrix metallopeptidase-12 physiological
## 5: 90 Homo sapiens matrix metallopeptidase-12 physiological
## 6: 688 Homo sapiens matrix metallopeptidase-12 physiological
## Protease (Uniprot) Protease status Protease organism
## 1: P39900 reviewed Homo sapiens
## 2: P39900 reviewed Homo sapiens
## 3: P39900 reviewed Homo sapiens
## 4: P39900 reviewed Homo sapiens
## 5: P39900 reviewed Homo sapiens
## 6: P39900 reviewed Homo sapiens3.3 Find possible proteases for cleaved peptides
The function findProtease automatically maps the peptide sequences to the full-length protein sequence and obtains the start- and end-positions for the peptide. Then, the positions are searched against the MEROPs database and matches are returned.
# Create a vector of Uniprot IDs and corresponding peptide sequences
protein <- c("P02671", "P02671", "P68871",
"P01011", "P68133", "P02461",
"P0DJI8", "P0DJI8", "P0DJI8")
peptide <- c("FEEVSGNVSPGTR", "FVSETESR", "LLVVYPW",
"ITLLSAL", "DSYVGDEAQS", "AGGFAPYYG",
"FFSFLGEAFDGAR", "EANYIGSDKY", "GGVWAAEAISDAR")
# If we do not specify start position (start_pos) and end position
# (end_pos), the function will automatically assign these values by
# matching the provided peptide sequence against the full-length
# protein sequence.
res <- findProtease(protein = protein,
peptide = peptide,
organism = "Homo sapiens")## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'AnnotationDbi'# The resulting S4 object can be inspected in three ways;
# (to save space we show only the first six rows)
# 1. Display sequence data for the provided input:
substrates(res) |> head()## Substrate name Substrate (Uniprot)
## 1: fibrinogen alpha chain P02671
## 2: fibrinogen alpha chain P02671
## 3: hemoglobin subunit beta P68871
## 4: alpha-1-antichymotrypsin P01011
## 5: Serum amyloid A protein P0DJI8
## 6: Serum amyloid A protein P0DJI8
## Substrate sequence
## 1: MFSMRIVCLVLSVVGTAWTADSGEGDFLAEGGGVRGPRVVERHQSACKDSDWPFCSDEDWNYKCPSGCRMKGLIDEVNQDFTNRINKLKNSLFEYQKNNKDSHSLTTNIMEILRGDFSSANNRDNTYNRVSEDLRSRIEVLKRKVIEKVQHIQLLQKNVRAQLVDMKRLEVDIDIKIRSCRGSCSRALAREVDLKDYEDQQKQLEQVIAKDLLPSRDRQHLPLIKMKPVPDLVPGNFKSQLQKVPPEWKALTDMPQMRMELERPGGNEITRGGSTSYGTGSETESPRNPSSAGSWNSGSSGPGSTGNRNPGSSGTGGTATWKPGSSGPGSTGSWNSGSSGTGSTGNQNPGSPRPGSTGTWNPGSSERGSAGHWTSESSVSGSTGQWHSESGSFRPDSPGSGNARPNNPDWGTFEEVSGNVSPGTRREYHTEKLVTSKGDKELRTGKEKVTSGSTTTTRRSCSKTVTKTVIGPDGHKEVTKEVVTSEDGSDCPEAMDLGTLSGIGTLDGFRHRHPDEAAFFDTASTGKTFPGFFSPMLGEFVSETESRGSESGIFTNTKESSSHHPGIAEFPSRGKSSSYSKQFTSSTSYNRGDSTFESKSYKMADEAGSEADHEGTHSTKRGHAKSRPVRDCDDVLQTHPSGTQSGIFNIKLPGSSKIFSVYCDQETSLGGWLLIQQRMDGSLNFNRTWQDYKRGFGSLNDEGEGEFWLGNDYLHLLTQRGSVLRVELEDWAGNEAYAEYHFRVGSEAEGYALQVSSYEGTAGDALIEGSVEEGAEYTSHNNMQFSTFDRDADQWEENCAEVYGGGWWYNNCQAANLNGIYYPGGSYDPRNNSPYEIENGVVWVSFRGADYSLRAVRMKIRPLVTQ
## 2: MFSMRIVCLVLSVVGTAWTADSGEGDFLAEGGGVRGPRVVERHQSACKDSDWPFCSDEDWNYKCPSGCRMKGLIDEVNQDFTNRINKLKNSLFEYQKNNKDSHSLTTNIMEILRGDFSSANNRDNTYNRVSEDLRSRIEVLKRKVIEKVQHIQLLQKNVRAQLVDMKRLEVDIDIKIRSCRGSCSRALAREVDLKDYEDQQKQLEQVIAKDLLPSRDRQHLPLIKMKPVPDLVPGNFKSQLQKVPPEWKALTDMPQMRMELERPGGNEITRGGSTSYGTGSETESPRNPSSAGSWNSGSSGPGSTGNRNPGSSGTGGTATWKPGSSGPGSTGSWNSGSSGTGSTGNQNPGSPRPGSTGTWNPGSSERGSAGHWTSESSVSGSTGQWHSESGSFRPDSPGSGNARPNNPDWGTFEEVSGNVSPGTRREYHTEKLVTSKGDKELRTGKEKVTSGSTTTTRRSCSKTVTKTVIGPDGHKEVTKEVVTSEDGSDCPEAMDLGTLSGIGTLDGFRHRHPDEAAFFDTASTGKTFPGFFSPMLGEFVSETESRGSESGIFTNTKESSSHHPGIAEFPSRGKSSSYSKQFTSSTSYNRGDSTFESKSYKMADEAGSEADHEGTHSTKRGHAKSRPVRDCDDVLQTHPSGTQSGIFNIKLPGSSKIFSVYCDQETSLGGWLLIQQRMDGSLNFNRTWQDYKRGFGSLNDEGEGEFWLGNDYLHLLTQRGSVLRVELEDWAGNEAYAEYHFRVGSEAEGYALQVSSYEGTAGDALIEGSVEEGAEYTSHNNMQFSTFDRDADQWEENCAEVYGGGWWYNNCQAANLNGIYYPGGSYDPRNNSPYEIENGVVWVSFRGADYSLRAVRMKIRPLVTQ
## 3: MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
## 4: MERMLPLLALGLLAAGFCPAVLCHPNSPLDEENLTQENQDRGTHVDLGLASANVDFAFSLYKQLVLKAPDKNVIFSPLSISTALAFLSLGAHNTTLTEILKGLKFNLTETSEAEIHQSFQHLLRTLNQSSDELQLSMGNAMFVKEQLSLLDRFTEDAKRLYGSEAFATDFQDSAAAKKLINDYVKNGTRGKITDLIKDLDSQTMMVLVNYIFFKAKWEMPFDPQDTHQSRFYLSKKKWVMVPMMSLHHLTIPYFRDEELSCTVVELKYTGNASALFILPDQDKMEEVEAMLLPETLKRWRDSLEFREIGELYLPKFSISRDYNLNDILLQLGIEEAFTSKADLSGITGARNLAVSQVVHKAVLDVFEEGTEASAATAVKITLLSALVETRTIVRFNRPFLMIIVPTDTQNIFFMSKVTNPKQA
## 5: MKLLTGLVFCSLVLGVSSRSFFSFLGEAFDGARDMWRAYSDMREANYIGSDKYFHARGNYDAAKRGPGGAWAAEVITDARENIQRFFGHGAEDSLADQAANEWGRSGKDPNHFRPAGLPEKY
## 6: MKLLTGLVFCSLVLGVSSRSFFSFLGEAFDGARDMWRAYSDMREANYIGSDKYFHARGNYDAAKRGPGGAWAAEVITDARENIQRFFGHGAEDSLADQAANEWGRSGKDPNHFRPAGLPEKY
## Substrate length Peptide Start position End position
## 1: 866 FEEVSGNVSPGTR 413 425
## 2: 866 FVSETESR 540 547
## 3: 147 LLVVYPW 32 38
## 4: 423 ITLLSAL 380 386
## 5: 122 FFSFLGEAFDGAR 21 33
## 6: 122 EANYIGSDKY 44 53# 2. Show which known proteases may have cleaved this protein:
proteases(res) |> head()## Protease name Protease (Uniprot) Protease status
## 1: cathepsin D P07339 reviewed
## 2: cathepsin B P07858 reviewed
## 3: legumain, animal-type Q99538 reviewed
## 4: procollagen C-peptidase P13497 reviewed
## 5: vertebrate tolloid-like 1 protein O43897 reviewed
## 6: cathepsin D V9HWI3 unreviewed
## Protease (MEROPS) Protease URL
## 1: A01.009 https://www.ebi.ac.uk/merops/cgi-bin/pepsum?id=A01.009
## 2: C01.060 https://www.ebi.ac.uk/merops/cgi-bin/pepsum?id=C01.060
## 3: C13.004 https://www.ebi.ac.uk/merops/cgi-bin/pepsum?id=C13.004
## 4: M12.005 https://www.ebi.ac.uk/merops/cgi-bin/pepsum?id=M12.005
## 5: M12.016 https://www.ebi.ac.uk/merops/cgi-bin/pepsum?id=M12.016
## 6: A01.009 https://www.ebi.ac.uk/merops/cgi-bin/pepsum?id=A01.009# 3. Display details of associated proteolytic events:
cleavages(res) |> head()## Substrate (Uniprot) Peptide Protease (Uniprot) Protease status
## 1: P02671 FEEVSGNVSPGTR P07339 reviewed
## 2: P02671 FVSETESR P07339 reviewed
## 3: P68871 LLVVYPW P07339 reviewed
## 4: P01011 ITLLSAL P07339 reviewed
## 5: P0DJI8 FFSFLGEAFDGAR P07858 reviewed
## 6: P0DJI8 EANYIGSDKY P07858 reviewed
## Cleaved residue Cleaved terminus Cleavage type
## 1: F N physiological
## 2: F N physiological
## 3: L N pathological
## 4: L C physiological
## 5: F N physiological
## 6: E N physiological# We can find out what proportion of matching cleaving events by reviewed
# proteases occur at N- versus C-terminus
cl <- cleavages(res)[`Protease status` == "reviewed"]
cl$`Cleaved terminus` |> table() |> prop.table() |> round(digits = 2)##
## C N
## 0.3 0.7# And inspect the distribution of cleaved amino acids
cl$`Cleaved residue` |> table() |> barplot(cex.names = 2)
# Find which protease is involved in the greatest number of cleaving events
cl[!duplicated(Peptide), .(count = .N), by = `Protease (Uniprot)`]## Protease (Uniprot) count
## 1: P07339 4
## 2: P07858 2
## 3: Q99538 1
## 4: P13497 1# If start- and end-positions are specified, the function will not attempt
# to automatically look up sequence data for the specified protein/peptide.
cl_by_pos <- findProtease(
protein = "P02671",
peptide = "FEEVSGNVSPGTR",
start_pos = 413,
end_pos = 425
)
# However, this means sequence details for substrates is not available.
substrates(cl_by_pos)## Substrate name Substrate (Uniprot) Substrate sequence
## 1: fibrinogen alpha chain P02671 NA
## Substrate length Peptide Start position End position
## 1: NA FEEVSGNVSPGTR 413 4253.4 Look up a protease on MEROPS
We may want to read up on the details of an entry directly in MEROPS. The function browseProtease takes a UniProt or MEROPS ID and opens the MEROPS summary page which corresponds to that ID in a web browser.
browseProtease("P07339", keytype = "UniprotID") # (opens web browser)4 Additional examples
4.1 Plot cleavages as a protein-protein interaction network.
Here we visualize the cleaving events of two substrates as a protein-protein interaction network between substrates (red) and associated proteases (blue).
library(igraph)
library(data.table)
# Make a vector of substrates we want to investigate
proteins <- c('P01011','P02671')
# Look up known proteases which cleave these substrates, and associated data
# annotated to the cleaving events
res <- searchSubstrate(protein = proteins, summarize = FALSE)
# Filter to keep proteases with Uniprot status "reviewed"
res <- res[`Protease status` == "reviewed"]
# To create a network, we only need two columns of interactors
# i.e. the proteases with cleaving action on the substrates
res <- res[, c("Protease (Uniprot)", "Substrate (Uniprot)", "Cleavage type")]
# Construct the DAG
g <- igraph::graph_from_data_frame(res,
directed = TRUE,
vertices = unique(
c(res$`Protease (Uniprot)`,
res$`Substrate (Uniprot)`)))
# This will allow us to return to a layout we like
# (and where the legend fits nicely)
set.seed(104)
plot(g,
vertex.label.family = "Helvetica",
vertex.size = 14,
vertex.color = ifelse(V(g)$name %in% res$`Protease (Uniprot)`,
"#377EB8", "#E41A1C"),
vertex.label.cex = 1,
vertex.label.color = "white",
edge.arrow.size = .6,
edge.color = ifelse(E(g)$`Cleavage type` == "physiological",
"#01665E", "#8E0152"),
layout = igraph::layout.davidson.harel)
legend(title = "Nodes", cex = 2, horiz = FALSE,
title.adj = 0.0, inset = c(0.0, 0.2),
"bottomleft", bty = "n",
legend = c("Protease", "Substrate"),
fill = c("#377EB8", "#E41A1C"), border = NA)
legend(title = "Edges", cex = 2, horiz = FALSE,
title.adj = 0.0, inset = c(0.0, 0.0),
"bottomleft", bty = "n",
legend = c("Physiological", "Non-physiological"),
fill = c("#01665E", "#8E0152"), border = NA)
4.2 Annotated sequence similarity heatmaps
proteasy automatically finds protein sequences from protein IDs (thanks to ensembldb and Rcpi). We can use substrates() to access them. Here, we look study similarity matrices of some protein- and peptide-level sequences and plot them as heatmaps, which we annotate with cleavage data.
# Load additional libraries
library(Rcpi)
library(viridis)
suppressPackageStartupMessages(library(ComplexHeatmap))
# Prepare input: protein and associated peptide sequences
protein <- c('P01011','P01011','P01034','P01034',
'P01042','P02671','P02671','P68871',
'P68871','P01042')
peptide <- c('LVETRTIVRFNRPFLMIIVPTDTQNIFFMSKVTNPK','ITLLSAL',
'KAFCSFQIY','AFCSFQIY','DIPTNSPELEETLTHTITKL','FEEVSGNVSPGTR',
'FVSETESR','LLVVYPW','VDEVGGEALGR','KIYPTVNCQPLGMISLM')
# Find cleaving data associated with above substrates
res <- findProtease(protein = protein,
peptide = peptide,
organism = "Homo sapiens")
# Get substrate info
ss <- substrates(res)
# Show only unique sequences
ss_uniq <- ss[!duplicated(`Substrate sequence`)]
# Calculate protein (substrate) sequence similarity
psimmat = Rcpi::calcParProtSeqSim(ss_uniq$`Substrate sequence`,
type = 'global',
submat = 'BLOSUM62')
rownames(psimmat) <- colnames(psimmat) <- ss_uniq$`Substrate (Uniprot)`
# Plot as a heatmap
ComplexHeatmap::Heatmap(psimmat, col = viridis::mako(100))
# We can do the same thing for peptide sequences,
# and annotate each row (cleaved peptide) with
# information about cleaved residue and termini
# Get cleavage details
cl <- cleavages(res)
# Calculate peptide sequence similarity
pep_psimmat = Rcpi::calcParProtSeqSim(cl$Peptide, type = 'global',
submat = 'BLOSUM62')
# Right side annotation: cleaved residue
rsd <- cl$`Cleaved residue`
cols <- c("#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072")
names(cols) <- unique(rsd)
ha1 <- ComplexHeatmap::columnAnnotation(`cleaved residue` = rsd,
col = list(`cleaved residue` = cols))
# Right side annotation: Terminus
tn <- cl$`Cleaved terminus`
cols <- c("#B3E2CD", "#FDCDAC")
names(cols) <- unique(tn)
ha2 <- ComplexHeatmap::columnAnnotation(terminus = tn,
col = list(terminus = cols))
rownames(pep_psimmat) <- cl$`Substrate (Uniprot)`
# Plot as a heatmap
ComplexHeatmap::Heatmap(
pep_psimmat,
name = "sequence\nsimilarity",
col = viridis::mako(100),
show_column_names = FALSE,
row_names_gp = grid::gpar(fontsize = 6.5),
top_annotation = c(ha1, ha2)
)
5 Session Information
## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ComplexHeatmap_2.16.0 viridis_0.6.2 viridisLite_0.4.1
## [4] Rcpi_1.36.0 data.table_1.14.8 igraph_1.4.2
## [7] proteasy_1.2.0 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.4
## [3] shape_1.4.6 magrittr_2.0.3
## [5] magick_2.7.4 GenomicFeatures_1.52.0
## [7] rmarkdown_2.21 GlobalOptions_0.1.2
## [9] BiocIO_1.10.0 zlibbioc_1.46.0
## [11] vctrs_0.6.2 Cairo_1.6-0
## [13] memoise_2.0.1 Rsamtools_2.16.0
## [15] RCurl_1.98-1.12 htmltools_0.5.5
## [17] progress_1.2.2 itertools_0.1-3
## [19] curl_5.0.0 sass_0.4.5
## [21] bslib_0.4.2 cachem_1.0.7
## [23] GenomicAlignments_1.36.0 lifecycle_1.0.3
## [25] iterators_1.0.14 pkgconfig_2.0.3
## [27] Matrix_1.5-4 R6_2.5.1
## [29] fastmap_1.1.1 GenomeInfoDbData_1.2.10
## [31] MatrixGenerics_1.12.0 clue_0.3-64
## [33] digest_0.6.31 colorspace_2.1-0
## [35] AnnotationDbi_1.62.0 S4Vectors_0.38.0
## [37] GenomicRanges_1.52.0 RSQLite_2.3.1
## [39] filelock_1.0.2 fansi_1.0.4
## [41] httr_1.4.5 compiler_4.3.0
## [43] bit64_4.0.5 doParallel_1.0.17
## [45] BiocParallel_1.34.0 DBI_1.1.3
## [47] fingerprint_3.5.7 highr_0.10
## [49] R.utils_2.12.2 biomaRt_2.56.0
## [51] rappdirs_0.3.3 DelayedArray_0.26.0
## [53] rjson_0.2.21 tools_4.3.0
## [55] R.oo_1.25.0 glue_1.6.2
## [57] restfulr_0.0.15 GOSemSim_2.26.0
## [59] cluster_2.1.4 generics_0.1.3
## [61] gtable_0.3.3 rcdk_3.7.0
## [63] R.methodsS3_1.8.2 ensembldb_2.24.0
## [65] hms_1.1.3 xml2_1.3.3
## [67] utf8_1.2.3 XVector_0.40.0
## [69] BiocGenerics_0.46.0 foreach_1.5.2
## [71] pillar_1.9.0 stringr_1.5.0
## [73] circlize_0.4.15 rJava_1.0-6
## [75] dplyr_1.1.2 BiocFileCache_2.8.0
## [77] lattice_0.21-8 rtracklayer_1.60.0
## [79] bit_4.0.5 tidyselect_1.2.0
## [81] GO.db_3.17.0 Biostrings_2.68.0
## [83] knitr_1.42 gridExtra_2.3
## [85] bookdown_0.33 IRanges_2.34.0
## [87] ProtGenerics_1.32.0 SummarizedExperiment_1.30.0
## [89] stats4_4.3.0 xfun_0.39
## [91] Biobase_2.60.0 matrixStats_0.63.0
## [93] stringi_1.7.12 EnsDb.Hsapiens.v86_2.99.0
## [95] lazyeval_0.2.2 yaml_2.3.7
## [97] evaluate_0.20 codetools_0.2-19
## [99] rcdklibs_2.8 tibble_3.2.1
## [101] BiocManager_1.30.20 cli_3.6.1
## [103] munsell_0.5.0 jquerylib_0.1.4
## [105] Rcpp_1.0.10 GenomeInfoDb_1.36.0
## [107] dbplyr_2.3.2 png_0.1-8
## [109] XML_3.99-0.14 parallel_4.3.0
## [111] ggplot2_3.4.2 blob_1.2.4
## [113] prettyunits_1.1.1 AnnotationFilter_1.24.0
## [115] bitops_1.0-7 scales_1.2.1
## [117] crayon_1.5.2 GetoptLong_1.0.5
## [119] rlang_1.1.0 KEGGREST_1.40.0