suppressMessages(library(synaptome.db))
suppressMessages(library(dplyr))
library(pander)
58 published synaptic proteomic datasets (2000-2020 years) that describe over 8,000 proteins were integrated and combined with direct protein-protein interactions and functional metadata to build a network resource.
The set includes 29 post synaptic proteome (PSP) studies (2000 to 2019) contributing a total of 5,560 mouse and human unique gene identifiers; 18 presynaptic studies (2004 to 2020) describe 2,772 unique human and mouse gene IDs, and 11 studies that span the whole synaptosome and report 7,198 unique genes.
To reconstruct protein-protein interaction (PPI) networks for the pre- and post-synaptic proteomes we used human PPI data filtered for the highest confidence direct and physical interactions from BioGRID, Intact and DIP. The resulting postsynaptic proteome (PSP) network contains 4,817 nodes and 27,788 edges in the Largest Connected Component (LCC). The presynaptic network is significantly smaller and comprises 2,221 nodes and 8,678 edges in the LCC.
The database includes: proteomic and interactomic data with supporting information on compartment, specie and brain region, GO function information for three species: mouse, rat and human, disease annotation for human (based on Human Disease Ontology (HDO)) and GeneToModel table, which links certain synaptic proteins to existing computational models of synaptic plasticity and synaptic signal transduction
The original files are maintained at Eidnburgh Datashare https://doi.org/10.7488/ds/3017
The dataset was described in the Sorokina et al. (2021).
The dataset can be used to answer frequent questions such as “What is known about my favourite gene? Is it pre- or postsynaptic? Which brain region was it identified in?” “Which publication it was reported in?” Information could be obtained by submitting gene EntrezID or Gene name
<- getGeneInfoByEntrez(1742)
t head(t)
#> # A tibble: 6 × 12
#> GeneID Localisation MGI HumanEntrez MouseEntrez HumanName MouseName
#> <int> <chr> <chr> <int> <int> <chr> <chr>
#> 1 1 Postsynaptic MGI:1277959 1742 13385 DLG4 Dlg4
#> 2 1 Postsynaptic MGI:1277959 1742 13385 DLG4 Dlg4
#> 3 1 Postsynaptic MGI:1277959 1742 13385 DLG4 Dlg4
#> 4 1 Postsynaptic MGI:1277959 1742 13385 DLG4 Dlg4
#> 5 1 Postsynaptic MGI:1277959 1742 13385 DLG4 Dlg4
#> 6 1 Postsynaptic MGI:1277959 1742 13385 DLG4 Dlg4
#> # … with 5 more variables: PaperPMID <int>, Paper <chr>, Year <int>,
#> # SpeciesTaxID <int>, BrainRegion <chr>
<- getGeneInfoByName("CASK")
t head(t)
#> # A tibble: 6 × 12
#> GeneID Localisation MGI HumanEntrez MouseEntrez HumanName MouseName
#> <int> <chr> <chr> <int> <int> <chr> <chr>
#> 1 409 Postsynaptic MGI:1309489 8573 12361 CASK Cask
#> 2 409 Postsynaptic MGI:1309489 8573 12361 CASK Cask
#> 3 409 Postsynaptic MGI:1309489 8573 12361 CASK Cask
#> 4 409 Postsynaptic MGI:1309489 8573 12361 CASK Cask
#> 5 409 Postsynaptic MGI:1309489 8573 12361 CASK Cask
#> 6 409 Postsynaptic MGI:1309489 8573 12361 CASK Cask
#> # … with 5 more variables: PaperPMID <int>, Paper <chr>, Year <int>,
#> # SpeciesTaxID <int>, BrainRegion <chr>
<- getGeneInfoByName(c("CASK", "DLG2"))
t head(t)
#> # A tibble: 6 × 12
#> GeneID Localisation MGI HumanEntrez MouseEntrez HumanName MouseName
#> <int> <chr> <chr> <int> <int> <chr> <chr>
#> 1 6 Postsynaptic MGI:1344351 1740 23859 DLG2 Dlg2
#> 2 6 Postsynaptic MGI:1344351 1740 23859 DLG2 Dlg2
#> 3 6 Postsynaptic MGI:1344351 1740 23859 DLG2 Dlg2
#> 4 6 Postsynaptic MGI:1344351 1740 23859 DLG2 Dlg2
#> 5 6 Postsynaptic MGI:1344351 1740 23859 DLG2 Dlg2
#> 6 6 Postsynaptic MGI:1344351 1740 23859 DLG2 Dlg2
#> # … with 5 more variables: PaperPMID <int>, Paper <chr>, Year <int>,
#> # SpeciesTaxID <int>, BrainRegion <chr>
Obtaining Internal database GeneIDs is a usefil intermediate step for more complex queries including those for building protein-protein interaction (PPI) networks for compartments and brain regions. Internal GeneID is specie-neutral and unique, which allows exact indentification of the object of interest in case of redundancy (e.g. one Human genes matchs on a few mouse ones, etc.)
<- findGenesByEntrez(c(1742, 1741, 1739, 1740))
t head(t)
#> # A tibble: 4 × 8
#> GeneID MGI HumanEntrez MouseEntrez RatEntrez HumanName MouseName RatName
#> <int> <chr> <int> <int> <int> <chr> <chr> <chr>
#> 1 1 MGI:1277959 1742 13385 29495 DLG4 Dlg4 Dlg4
#> 2 6 MGI:1344351 1740 23859 64053 DLG2 Dlg2 Dlg2
#> 3 15 MGI:1888986 1741 53310 58948 DLG3 Dlg3 Dlg3
#> 4 46 MGI:107231 1739 13383 25252 DLG1 Dlg1 Dlg1
<- findGenesByName(c("SRC", "SRCIN1", "FYN"))
t head(t)
#> # A tibble: 3 × 8
#> GeneID MGI HumanEntrez MouseEntrez RatEntrez HumanName MouseName RatName
#> <int> <chr> <int> <int> <int> <chr> <chr> <chr>
#> 1 48 MGI:1933179 80725 56013 56029 SRCIN1 Srcin1 Srcin1
#> 2 585 MGI:98397 6714 20779 83805 SRC Src Src
#> 3 710 MGI:95602 2534 14360 25150 FYN Fyn Fyn
Synaptic genes are annotated with disease information from Human Disease Ontology, where available. To get disease information one can submit the list of Human Entrez Is or Human genes names, it could be also the list of Internal GeneIDs if using getGeneDiseaseByIDs
function
<- getGeneDiseaseByName (c("CASK", "DLG2", "DLG1"))
t head(t)
#> # A tibble: 6 × 4
#> HumanEntrez HumanName HDOID Description
#> <int> <chr> <chr> <chr>
#> 1 1740 DLG2 DOID:936 brain_disease
#> 2 8573 CASK DOID:936 brain_disease
#> 3 1739 DLG1 DOID:331 central_nervous_system_disease
#> 4 1740 DLG2 DOID:331 central_nervous_system_disease
#> 5 8573 CASK DOID:331 central_nervous_system_disease
#> 6 1739 DLG1 DOID:863 nervous_system_disease
<- getGeneDiseaseByEntres (c(8573, 1742, 1739))
t head(t)
#> # A tibble: 6 × 4
#> HumanEntrez HumanName HDOID Description
#> <int> <chr> <chr> <chr>
#> 1 8573 CASK DOID:936 brain_disease
#> 2 1739 DLG1 DOID:331 central_nervous_system_disease
#> 3 1742 DLG4 DOID:331 central_nervous_system_disease
#> 4 8573 CASK DOID:331 central_nervous_system_disease
#> 5 1739 DLG1 DOID:863 nervous_system_disease
#> 6 1742 DLG4 DOID:863 nervous_system_disease
Custom Protein-protein interactions based on bespoke subsets of molecules could be extracted in two general ways: “induced” and “limited.” In the first case, the command will return all possible interactors for the genes within the whole interactome. In the second case it will return only interactions between the genes of interest. PPIs could be obtained by submitting list of EntrezIDs or gene names, or Internal IDs - in all cases the interactions will be returned as a list of interacting pairs of Intenal GeneIDs.
<- getPPIbyName(
t c("CASK", "DLG4", "GRIN2A", "GRIN2B","GRIN1"),
type = "limited")
head(t)
#> # A tibble: 6 × 2
#> A B
#> <int> <int>
#> 1 38 1
#> 2 7 1
#> 3 1 7
#> 4 1 38
#> 5 1 9
#> 6 9 1
<- getPPIbyEntrez(c(1739, 1740, 1742, 1741), type='induced')
t head(t)
#> # A tibble: 6 × 2
#> A B
#> <int> <int>
#> 1 1 2871
#> 2 6 2871
#> 3 15 2871
#> 4 1 617
#> 5 1 30
#> 6 156 1
Three main synaptic compartments considered in the database are “presynaptic,” “postsynaptic” and “synaptosome.” Genes are classified to compartments based on respective publications, so that each gene can belong to one or two, or even three compartments. The full list of genes for specific compartment could be obtained wuth command getAllGenes4Compartment
, which returns the table with main gene identifiers, like internal GeneIDs, MGI ID, Human Entrez ID, Human Gene Name, Mouse Entrez ID, Mouse Gene Name, Rat Entrez ID, Rat Gene Name.
If you need to check which genes of your list belong to specific compartment, you can use getGenes4Compartment
command, which will select from your list only genes associated with specific compartment. To obtain the PPI network for compartment one has to submit the lis of Internal GeneIDs obtained with previous commands.
#getting the list of compartment
<- getCompartments()
comp pander(comp)
ID | Name | Description |
---|---|---|
1 | Postsynaptic | Postsynaptic |
2 | Presynaptic | Presynaptic |
3 | Synaptosome | Synaptosome |
#getting all genes for postsynaptic compartment
<- getAllGenes4Compartment(compartmentID = 1)
gns head(gns)
#> # A tibble: 6 × 8
#> GeneID MGI HumanEntrez MouseEntrez RatEntrez HumanName MouseName RatName
#> <int> <chr> <int> <int> <int> <chr> <chr> <chr>
#> 1 1 MGI:1277959 1742 13385 29495 DLG4 Dlg4 Dlg4
#> 2 2 MGI:88256 815 12322 25400 CAMK2A Camk2a Camk2a
#> 3 3 MGI:96568 9118 226180 24503 INA Ina Ina
#> 4 4 MGI:98388 6711 20742 305614 SPTBN1 Sptbn1 Sptbn1
#> 5 5 MGI:88257 816 12323 24245 CAMK2B Camk2b Camk2b
#> 6 6 MGI:1344351 1740 23859 64053 DLG2 Dlg2 Dlg2
#getting full PPI network for postsynaptic compartment
<- getPPIbyIDs4Compartment(gns$GeneID,compartmentID =1, type = "induced")
ppi head(ppi)
#> # A tibble: 6 × 2
#> A B
#> <int> <int>
#> 1 365 148
#> 2 1048 148
#> 3 52 365
#> 4 52 1048
#> 5 321 1048
#> 6 321 365
Three are 12 brain regions considered in the database based on respective publications, so that each gene can belong to the single or to the several brain regions. Brain regions differ between species, and specie brain region information is not 100% covered in the database(e.g. we don’t have yet studies for Human Striatum, but do have for Mouse and Rat), that’s why when querying the database for brain region information you will need to specify the specie. The full list of genes for speciifiic region could be obtained wuth command getAllGenes4BrainRegion
, which returns the table with main gene identifiers, like internal Gene IDs, MGI ID, Human Entrez ID, Human Gene Name, Mouse Entrez ID, Mouse Gene Name, Rat Entrez ID, Rat Gene Name.
If you need to check which genes of your list were identified in specific region, you can use getGenes4BrainRegion
command, which will select only genes associated with specific region from your list.
To obtain the PPI network for brain region you need to submit the list of Internal GGeneIDs obtained with previous commands.
#getting the full list of brain regions
<- getBrainRegions()
reg pander(reg)
ID | Name | Description | InterlexID | ParentID |
---|---|---|---|---|
1 | Brain | Whole brain | ILX:0101431 | 1 |
2 | Forebrain | Whole forebrain | ILX:0104355 | 1 |
3 | Midbrain | Midbrain | ILX:0106935 | 1 |
4 | Cerebellum | Cerebellum | ILX:0101963 | 1 |
5 | Telencephalon | Telencephalon | ILX:0111558 | 2 |
6 | Hypothalamus | Hypothalamus | ILX:0105177 | 2 |
7 | Hippocampus | Hippocampus | ILX:0105021 | 5 |
8 | Striatum | Striatum | ILX:0111098 | 5 |
9 | Cerebral cortex | Neocortex | ILX:0101978 | 5 |
10 | Frontal lobe | Frontal lobe/frontal cortex | ILX:0104451 | 9 |
11 | Occipital lobe | Occipital lobe | ILX:0107883 | 9 |
12 | Temporal lobe | Temporal lobe | ILX:0111590 | 9 |
13 | Parietal lobe | Parietal lobe | ILX:0108534 | 9 |
14 | Prefrontal cortex | Prefrontal cortex | ILX:0109209 | 10 |
15 | Motor cortex | Motor cortex | ILX:0107119 | 10 |
16 | Visual cortex | Visual cortex | ILX:0112513 | 11 |
17 | Medial cortex | Medial cortex | ILX:0106634 | 9 |
18 | Caudal cortex | Caudal cortex | NA | 9 |
#getting all genes for mouse Striatum
<- getAllGenes4BrainRegion(brainRegion = "Striatum",taxID = 10090)
gns head(gns)
#> # A tibble: 6 × 12
#> GeneID Localisation MGI HumanEntrez MouseEntrez HumanName MouseName PMID
#> <int> <chr> <chr> <int> <int> <chr> <chr> <int>
#> 1 1 Postsynaptic MGI:12… 1742 13385 DLG4 Dlg4 3.01e7
#> 2 2 Postsynaptic MGI:88… 815 12322 CAMK2A Camk2a 3.01e7
#> 3 3 Postsynaptic MGI:96… 9118 226180 INA Ina 3.01e7
#> 4 4 Postsynaptic MGI:98… 6711 20742 SPTBN1 Sptbn1 3.01e7
#> 5 6 Postsynaptic MGI:13… 1740 23859 DLG2 Dlg2 3.01e7
#> 6 7 Postsynaptic MGI:95… 2904 14812 GRIN2B Grin2b 3.01e7
#> # … with 4 more variables: Paper <chr>, Year <int>, SpeciesTaxID <int>,
#> # BrainRegion <chr>
#getting full PPI network for postsynaptic compartment
<- getPPIbyIDs4BrainRegion(
ppi $GeneID, brainRegion = "Striatum",
gnstaxID = 10090, type = "limited")
head(ppi)
#> # A tibble: 6 × 2
#> A B
#> <int> <int>
#> 1 365 148
#> 2 52 365
#> 3 321 365
#> 4 257 365
#> 5 846 1587
#> 6 407 348
Combine information from PPI data.frame obtained with functions like getPPIbyName
, getPPIbyEntrez
, getPPIbyIDs4Compartment
or getPPIbyIDs4BrainRegion
with information about genes obtained from getGenesByID
to make interpretable undirected PPI graph in igraph format. In this format network could be further analysed and visualized by algorithms from igraph package.
library(igraph)
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:dplyr':
#>
#> as_data_frame, groups, union
#> The following objects are masked from 'package:BiocGenerics':
#>
#> normalize, path, union
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
<-getIGraphFromPPI(
ggetPPIbyIDs(c(48, 129, 975, 4422, 5715, 5835), type='lim'))
plot(g,vertex.label=V(g)$RatName,vertex.size=25)
If Igraph is not an option, the PPI network could be exported as an interpretible table to be processed with other tools, e.g. Cytoscape,etc.
<-getTableFromPPI(getPPIbyIDs(c(48, 585, 710), type='limited'))
tbl
tbl#> # A tibble: 2 × 16
#> A B MGI.A HumanEntrez.A MouseEntrez.A RatEntrez.A HumanName.A
#> <int> <int> <chr> <int> <int> <int> <chr>
#> 1 710 710 MGI:95602 2534 14360 25150 FYN
#> 2 585 585 MGI:98397 6714 20779 83805 SRC
#> # … with 9 more variables: MouseName.A <chr>, RatName.A <chr>, MGI.B <chr>,
#> # HumanEntrez.B <int>, MouseEntrez.B <int>, RatEntrez.B <int>,
#> # HumanName.B <chr>, MouseName.B <chr>, RatName.B <chr>
version | R version 4.1.1 Patched (2021-09-10 r80880) |
os | Ubuntu 18.04.5 LTS |
system | x86_64, linux-gnu |
ui | X11 |
language | (EN) |
collate | C |
ctype | en_US.UTF-8 |
tz | America/New_York |
date | 2021-10-08 |
package | ondiskversion | |
---|---|---|
AnnotationDbi | AnnotationDbi | 1.55.1 |
AnnotationHub | AnnotationHub | 3.1.5 |
assertthat | assertthat | 0.2.1 |
Biobase | Biobase | 2.53.0 |
BiocFileCache | BiocFileCache | 2.1.1 |
BiocGenerics | BiocGenerics | 0.39.2 |
BiocManager | BiocManager | 1.30.16 |
BiocVersion | BiocVersion | 3.14.0 |
Biostrings | Biostrings | 2.61.2 |
bit | bit | 4.0.4 |
bit64 | bit64 | 4.0.5 |
bitops | bitops | 1.0.7 |
blob | blob | 1.2.2 |
bslib | bslib | 0.3.0 |
cachem | cachem | 1.0.6 |
callr | callr | 3.7.0 |
cli | cli | 3.0.1 |
crayon | crayon | 1.4.1 |
curl | curl | 4.3.2 |
DBI | DBI | 1.1.1 |
dbplyr | dbplyr | 2.1.1 |
desc | desc | 1.3.0 |
devtools | devtools | 2.4.2 |
digest | digest | 0.6.28 |
dplyr | dplyr | 1.0.7 |
ellipsis | ellipsis | 0.3.2 |
evaluate | evaluate | 0.14 |
fansi | fansi | 0.5.0 |
fastmap | fastmap | 1.1.0 |
filelock | filelock | 1.0.2 |
fs | fs | 1.5.0 |
generics | generics | 0.1.0 |
GenomeInfoDb | GenomeInfoDb | 1.29.8 |
GenomeInfoDbData | GenomeInfoDbData | 1.2.7 |
glue | glue | 1.4.2 |
highr | highr | 0.9 |
htmltools | htmltools | 0.5.2 |
httpuv | httpuv | 1.6.3 |
httr | httr | 1.4.2 |
igraph | igraph | 1.2.6 |
interactiveDisplayBase | interactiveDisplayBase | 1.31.2 |
IRanges | IRanges | 2.27.2 |
jquerylib | jquerylib | 0.1.4 |
jsonlite | jsonlite | 1.7.2 |
KEGGREST | KEGGREST | 1.33.0 |
knitr | knitr | 1.34 |
later | later | 1.3.0 |
lifecycle | lifecycle | 1.0.0 |
magrittr | magrittr | 2.0.1 |
memoise | memoise | 2.0.0 |
mime | mime | 0.11 |
pander | pander | 0.6.4 |
pillar | pillar | 1.6.2 |
pkgbuild | pkgbuild | 1.2.0 |
pkgconfig | pkgconfig | 2.0.3 |
pkgload | pkgload | 1.2.2 |
png | png | 0.1.7 |
prettyunits | prettyunits | 1.1.1 |
processx | processx | 3.5.2 |
promises | promises | 1.2.0.1 |
ps | ps | 1.6.0 |
purrr | purrr | 0.3.4 |
R6 | R6 | 2.5.1 |
rappdirs | rappdirs | 0.3.3 |
rbibutils | rbibutils | 2.2.3 |
Rcpp | Rcpp | 1.0.7 |
RCurl | RCurl | 1.98.1.5 |
Rdpack | Rdpack | 2.1.2 |
remotes | remotes | 2.4.0 |
rlang | rlang | 0.4.11 |
rmarkdown | rmarkdown | 2.11 |
rprojroot | rprojroot | 2.0.2 |
RSQLite | RSQLite | 2.2.8 |
S4Vectors | S4Vectors | 0.31.3 |
sass | sass | 0.4.0 |
sessioninfo | sessioninfo | 1.1.1 |
shiny | shiny | 1.7.0 |
stringi | stringi | 1.7.4 |
stringr | stringr | 1.4.0 |
synaptome.data | synaptome.data | 0.99.3 |
synaptome.db | synaptome.db | 0.99.8 |
testthat | testthat | 3.0.4 |
tibble | tibble | 3.1.4 |
tidyselect | tidyselect | 1.1.1 |
usethis | usethis | 2.0.1 |
utf8 | utf8 | 1.2.2 |
vctrs | vctrs | 0.3.8 |
withr | withr | 2.4.2 |
xfun | xfun | 0.26 |
xtable | xtable | 1.8.4 |
XVector | XVector | 0.33.0 |
yaml | yaml | 2.2.1 |
zlibbioc | zlibbioc | 1.39.0 |
loadedversion | attached | is_base | date | |
---|---|---|---|---|
AnnotationDbi | 1.55.1 | FALSE | FALSE | 2021-06-07 |
AnnotationHub | 3.1.5 | TRUE | FALSE | 2021-08-12 |
assertthat | 0.2.1 | FALSE | FALSE | 2019-03-21 |
Biobase | 2.53.0 | FALSE | FALSE | 2021-05-19 |
BiocFileCache | 2.1.1 | TRUE | FALSE | 2021-06-23 |
BiocGenerics | 0.39.2 | TRUE | FALSE | 2021-08-18 |
BiocManager | 1.30.16 | FALSE | FALSE | 2021-06-15 |
BiocVersion | 3.14.0 | FALSE | FALSE | 2021-05-19 |
Biostrings | 2.61.2 | FALSE | FALSE | 2021-08-04 |
bit | 4.0.4 | FALSE | FALSE | 2020-08-04 |
bit64 | 4.0.5 | FALSE | FALSE | 2020-08-30 |
bitops | 1.0-7 | FALSE | FALSE | 2021-04-24 |
blob | 1.2.2 | FALSE | FALSE | 2021-07-23 |
bslib | 0.3.0 | FALSE | FALSE | 2021-09-02 |
cachem | 1.0.6 | FALSE | FALSE | 2021-08-19 |
callr | 3.7.0 | FALSE | FALSE | 2021-04-20 |
cli | 3.0.1 | FALSE | FALSE | 2021-07-17 |
crayon | 1.4.1 | FALSE | FALSE | 2021-02-08 |
curl | 4.3.2 | FALSE | FALSE | 2021-06-23 |
DBI | 1.1.1 | FALSE | FALSE | 2021-01-15 |
dbplyr | 2.1.1 | TRUE | FALSE | 2021-04-06 |
desc | 1.3.0 | FALSE | FALSE | 2021-03-05 |
devtools | 2.4.2 | FALSE | FALSE | 2021-06-07 |
digest | 0.6.28 | FALSE | FALSE | 2021-09-23 |
dplyr | 1.0.7 | TRUE | FALSE | 2021-06-18 |
ellipsis | 0.3.2 | FALSE | FALSE | 2021-04-29 |
evaluate | 0.14 | FALSE | FALSE | 2019-05-28 |
fansi | 0.5.0 | FALSE | FALSE | 2021-05-25 |
fastmap | 1.1.0 | FALSE | FALSE | 2021-01-25 |
filelock | 1.0.2 | FALSE | FALSE | 2018-10-05 |
fs | 1.5.0 | FALSE | FALSE | 2020-07-31 |
generics | 0.1.0 | FALSE | FALSE | 2020-10-31 |
GenomeInfoDb | 1.29.8 | FALSE | FALSE | 2021-09-05 |
GenomeInfoDbData | 1.2.7 | FALSE | FALSE | 2021-09-24 |
glue | 1.4.2 | FALSE | FALSE | 2020-08-27 |
highr | 0.9 | FALSE | FALSE | 2021-04-16 |
htmltools | 0.5.2 | FALSE | FALSE | 2021-08-25 |
httpuv | 1.6.3 | FALSE | FALSE | 2021-09-09 |
httr | 1.4.2 | FALSE | FALSE | 2020-07-20 |
igraph | 1.2.6 | TRUE | FALSE | 2020-10-06 |
interactiveDisplayBase | 1.31.2 | FALSE | FALSE | 2021-07-30 |
IRanges | 2.27.2 | FALSE | FALSE | 2021-08-18 |
jquerylib | 0.1.4 | FALSE | FALSE | 2021-04-26 |
jsonlite | 1.7.2 | FALSE | FALSE | 2020-12-09 |
KEGGREST | 1.33.0 | FALSE | FALSE | 2021-05-19 |
knitr | 1.34 | TRUE | FALSE | 2021-09-09 |
later | 1.3.0 | FALSE | FALSE | 2021-08-18 |
lifecycle | 1.0.0 | FALSE | FALSE | 2021-02-15 |
magrittr | 2.0.1 | FALSE | FALSE | 2020-11-17 |
memoise | 2.0.0 | FALSE | FALSE | 2021-01-26 |
mime | 0.11 | FALSE | FALSE | 2021-06-23 |
pander | 0.6.4 | TRUE | FALSE | 2021-06-13 |
pillar | 1.6.2 | FALSE | FALSE | 2021-07-29 |
pkgbuild | 1.2.0 | FALSE | FALSE | 2020-12-15 |
pkgconfig | 2.0.3 | FALSE | FALSE | 2019-09-22 |
pkgload | 1.2.2 | FALSE | FALSE | 2021-09-11 |
png | 0.1-7 | FALSE | FALSE | 2013-12-03 |
prettyunits | 1.1.1 | FALSE | FALSE | 2020-01-24 |
processx | 3.5.2 | FALSE | FALSE | 2021-04-30 |
promises | 1.2.0.1 | FALSE | FALSE | 2021-02-11 |
ps | 1.6.0 | FALSE | FALSE | 2021-02-28 |
purrr | 0.3.4 | FALSE | FALSE | 2020-04-17 |
R6 | 2.5.1 | FALSE | FALSE | 2021-08-19 |
rappdirs | 0.3.3 | FALSE | FALSE | 2021-01-31 |
rbibutils | 2.2.3 | FALSE | FALSE | 2021-08-09 |
Rcpp | 1.0.7 | FALSE | FALSE | 2021-07-07 |
RCurl | 1.98-1.5 | FALSE | FALSE | 2021-09-17 |
Rdpack | 2.1.2 | FALSE | FALSE | 2021-06-01 |
remotes | 2.4.0 | FALSE | FALSE | 2021-06-02 |
rlang | 0.4.11 | FALSE | FALSE | 2021-04-30 |
rmarkdown | 2.11 | FALSE | FALSE | 2021-09-14 |
rprojroot | 2.0.2 | FALSE | FALSE | 2020-11-15 |
RSQLite | 2.2.8 | FALSE | FALSE | 2021-08-21 |
S4Vectors | 0.31.3 | FALSE | FALSE | 2021-08-26 |
sass | 0.4.0 | FALSE | FALSE | 2021-05-12 |
sessioninfo | 1.1.1 | FALSE | FALSE | 2018-11-05 |
shiny | 1.7.0 | FALSE | FALSE | 2021-09-22 |
stringi | 1.7.4 | FALSE | FALSE | 2021-08-25 |
stringr | 1.4.0 | FALSE | FALSE | 2019-02-10 |
synaptome.data | 0.99.3 | TRUE | FALSE | 2021-10-08 |
synaptome.db | 0.99.8 | TRUE | FALSE | 2021-10-08 |
testthat | 3.0.4 | FALSE | FALSE | 2021-07-01 |
tibble | 3.1.4 | FALSE | FALSE | 2021-08-25 |
tidyselect | 1.1.1 | FALSE | FALSE | 2021-04-30 |
usethis | 2.0.1 | FALSE | FALSE | 2021-02-10 |
utf8 | 1.2.2 | FALSE | FALSE | 2021-07-24 |
vctrs | 0.3.8 | FALSE | FALSE | 2021-04-29 |
withr | 2.4.2 | FALSE | FALSE | 2021-04-18 |
xfun | 0.26 | FALSE | FALSE | 2021-09-14 |
xtable | 1.8-4 | FALSE | FALSE | 2019-04-21 |
XVector | 0.33.0 | FALSE | FALSE | 2021-05-19 |
yaml | 2.2.1 | FALSE | FALSE | 2020-02-01 |
zlibbioc | 1.39.0 | FALSE | FALSE | 2021-05-19 |
source | |
---|---|
AnnotationDbi | Bioconductor |
AnnotationHub | Bioconductor |
assertthat | CRAN (R 4.1.0) |
Biobase | Bioconductor |
BiocFileCache | Bioconductor |
BiocGenerics | Bioconductor |
BiocManager | CRAN (R 4.1.1) |
BiocVersion | Bioconductor |
Biostrings | Bioconductor |
bit | CRAN (R 4.1.0) |
bit64 | CRAN (R 4.1.0) |
bitops | CRAN (R 4.1.0) |
blob | CRAN (R 4.1.0) |
bslib | CRAN (R 4.1.1) |
cachem | CRAN (R 4.1.0) |
callr | CRAN (R 4.1.0) |
cli | CRAN (R 4.1.0) |
crayon | CRAN (R 4.1.0) |
curl | CRAN (R 4.1.0) |
DBI | CRAN (R 4.1.0) |
dbplyr | CRAN (R 4.1.0) |
desc | CRAN (R 4.1.0) |
devtools | CRAN (R 4.1.0) |
digest | CRAN (R 4.1.1) |
dplyr | CRAN (R 4.1.0) |
ellipsis | CRAN (R 4.1.0) |
evaluate | CRAN (R 4.1.0) |
fansi | CRAN (R 4.1.0) |
fastmap | CRAN (R 4.1.0) |
filelock | CRAN (R 4.1.0) |
fs | CRAN (R 4.1.0) |
generics | CRAN (R 4.1.0) |
GenomeInfoDb | Bioconductor |
GenomeInfoDbData | Bioconductor |
glue | CRAN (R 4.1.0) |
highr | CRAN (R 4.1.0) |
htmltools | CRAN (R 4.1.0) |
httpuv | CRAN (R 4.1.1) |
httr | CRAN (R 4.1.0) |
igraph | CRAN (R 4.1.0) |
interactiveDisplayBase | Bioconductor |
IRanges | Bioconductor |
jquerylib | CRAN (R 4.1.0) |
jsonlite | CRAN (R 4.1.0) |
KEGGREST | Bioconductor |
knitr | CRAN (R 4.1.1) |
later | CRAN (R 4.1.0) |
lifecycle | CRAN (R 4.1.0) |
magrittr | CRAN (R 4.1.0) |
memoise | CRAN (R 4.1.0) |
mime | CRAN (R 4.1.0) |
pander | CRAN (R 4.1.1) |
pillar | CRAN (R 4.1.0) |
pkgbuild | CRAN (R 4.1.0) |
pkgconfig | CRAN (R 4.1.0) |
pkgload | CRAN (R 4.1.1) |
png | CRAN (R 4.1.0) |
prettyunits | CRAN (R 4.1.0) |
processx | CRAN (R 4.1.0) |
promises | CRAN (R 4.1.0) |
ps | CRAN (R 4.1.0) |
purrr | CRAN (R 4.1.0) |
R6 | CRAN (R 4.1.0) |
rappdirs | CRAN (R 4.1.0) |
rbibutils | CRAN (R 4.1.1) |
Rcpp | CRAN (R 4.1.0) |
RCurl | CRAN (R 4.1.1) |
Rdpack | CRAN (R 4.1.1) |
remotes | CRAN (R 4.1.0) |
rlang | CRAN (R 4.1.0) |
rmarkdown | CRAN (R 4.1.1) |
rprojroot | CRAN (R 4.1.0) |
RSQLite | CRAN (R 4.1.0) |
S4Vectors | Bioconductor |
sass | CRAN (R 4.1.0) |
sessioninfo | CRAN (R 4.1.0) |
shiny | CRAN (R 4.1.1) |
stringi | CRAN (R 4.1.0) |
stringr | CRAN (R 4.1.0) |
synaptome.data | Bioconductor |
synaptome.db | Bioconductor |
testthat | CRAN (R 4.1.0) |
tibble | CRAN (R 4.1.0) |
tidyselect | CRAN (R 4.1.0) |
usethis | CRAN (R 4.1.0) |
utf8 | CRAN (R 4.1.0) |
vctrs | CRAN (R 4.1.0) |
withr | CRAN (R 4.1.0) |
xfun | CRAN (R 4.1.1) |
xtable | CRAN (R 4.1.0) |
XVector | Bioconductor |
yaml | CRAN (R 4.1.0) |
zlibbioc | Bioconductor |