JohnsonKinaseData 1.2.0
The JohnsonKinaseData package provides substrate affinities in the form of position-specific weight matrices (PWMs) for 396 human kinases originally published in Johnson et al. (Johnson et al. 2023) and Yaron-Barir et al. (Yaron-Barir et al. 2024). It includes basic functionality to pre-process user-provided phosphopetides and match them against all kinase PWMs. The aim is to give the user a simple way of predicting kinase-substrate relationships based on PWM-phosphosite matching. These predictions can serve to infer kinase activity from differential phospho-proteomic data.
The JohnsonKinaseData package can be installed using the following code:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ExperimentHub")
BiocManager::install("JohnsonKinaseData")Annotation data for all provides kinase PWMs can be accessed with:
library(JohnsonKinaseData)
anno <- getKinaseAnnotation()
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
head(anno)
#> MatrixName GeneName UniprotID EntrezID Description
#> 1 AAK1 AAK1 Q2M2I8 22848 AP2 associated kinase 1
#> 2 ALK2 ACVR1 Q04771 90 activin A receptor type 1
#> 3 ALK4 ACVR1B P36896 91 activin A receptor type 1B
#> 4 ACVR2A ACVR2A P27037 92 activin A receptor type 2A
#> 5 ACVR2B ACVR2B Q13705 93 activin A receptor type 2B
#> 6 AKT1 AKT1 P31749 207 AKT serine/threonine kinase 1
#> AcceptorSpecificity KinaseSubType KinaseFamily
#> 1 Ser/Thr <NA> Other
#> 2 Ser/Thr <NA> TKL
#> 3 Ser/Thr <NA> TKL
#> 4 Ser/Thr <NA> TKL
#> 5 Ser/Thr <NA> TKL
#> 6 Ser/Thr <NA> AGCIts includes PWM names and associated gene information, such as gene symbol, description, Entrez and Uniprot IDs. PWMs are classified by their specificity:
xtabs(~AcceptorSpecificity, anno)
#> AcceptorSpecificity
#> Ser/Thr Tyr
#> 303 93Tyrosine specific kinase PWMs are additionally classified by sub-type: receptor (RTK), non-receptor (nRTK) and non-canonical tyrosine kinases (ncTK).
xtabs(~AcceptorSpecificity + KinaseSubType, anno)
#> KinaseSubType
#> AcceptorSpecificity RTK nRTK ncTK
#> Ser/Thr 0 0 0
#> Tyr 46 32 15PWMs for non-canonical tyrosine kinases, i.e. kinases which also phosphorylate serine/threonine residues, are indicated by the _TYR suffix in the matrix name.
All PWMs are grouped into kinase families:
xtabs(~AcceptorSpecificity + KinaseFamily, anno)
#> KinaseFamily
#> AcceptorSpecificity ABL ACK AGC Alpha CAMK CK1 CMGC CSK DDR EPHR ErbB FAK FAM20
#> Ser/Thr 0 0 52 4 56 11 54 0 0 0 0 0 1
#> Tyr 2 2 0 0 0 0 0 2 2 12 3 2 0
#> KinaseFamily
#> AcceptorSpecificity FES FGFR FRK HGFR INSR JAK LTKR MUSK NGFR OTHER Other PDGFR
#> Ser/Thr 0 0 0 0 0 0 0 0 0 0 52 0
#> Tyr 2 4 3 2 3 4 2 1 3 2 0 5
#> KinaseFamily
#> AcceptorSpecificity PDHK PIKK RETR ROSR SRC STE SYK TAMR TEC TIER TKL VEGFR WEE
#> Ser/Thr 3 5 0 0 0 41 0 0 0 0 24 0 0
#> Tyr 3 0 1 1 8 3 2 3 5 1 5 3 2Kinase PWMs can be loaded with the getKinasePWM() function which returns the full list of 396 kinase PWMs.
library(JohnsonKinaseData)
pwms <- getKinasePWM()
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
head(names(pwms))
#> [1] "AAK1" "ACVR2A" "ACVR2B" "AKT1" "AKT2" "AKT3"Each PWM is a numeric matrix with amino acids as rows and positions as columns. Matrix elements are log2-odd scores measuring differential affinity relative to a random frequency of amino acids (Johnson et al. 2023).
pwms[["PLK2"]]
#> -5 -4 -3 -2 -1 0
#> A -0.036821844 -0.277009455 -0.83856373 -0.4463446 -0.186229068 NA
#> C 0.009633819 -0.034899138 -0.24690897 0.4799548 -0.467333943 NA
#> D 0.549718451 0.795766948 0.82130204 1.6459783 1.329410671 NA
#> E 0.614756952 1.127897364 2.86862751 1.2354207 0.689388627 NA
#> F 0.449006639 0.078199920 -0.41273103 -0.9773836 -0.602963759 NA
#> G 0.326652391 -0.151522275 -0.77793738 -0.6106535 -0.767584829 NA
#> H 0.148478616 -0.172018427 -0.67807191 -0.3219281 0.214995135 NA
#> I -0.311864412 -0.172018427 -1.65154094 -0.8406292 -0.519941731 NA
#> K -0.469329925 -0.647467443 -1.77349147 -1.7345631 -0.656307931 NA
#> L -0.245197993 0.144568518 -0.71785677 0.3032255 -0.511690664 NA
#> M -0.248793390 -0.206894852 -0.38948891 0.3123167 -0.194955239 NA
#> N -0.065823218 0.002018361 -0.54077824 0.9076598 0.307545102 NA
#> P -0.066578437 -0.108114249 -1.05139915 -0.4418303 0.542703792 NA
#> Q -0.530739153 -0.241782116 -0.48096139 -0.1800049 -0.264477823 NA
#> R -0.528032212 -0.715485867 -1.58640592 -1.1059389 -0.339345148 NA
#> S -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 0.00000000
#> T -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 -0.09585422
#> V -0.401253684 -0.367545642 -1.89324968 -1.3562361 -0.152804813 NA
#> W -0.034160317 -0.140189435 -1.05799229 -1.1256358 -1.093879047 NA
#> Y 0.083383588 -0.242293983 -1.12217724 -0.5640514 -0.004045212 NA
#> s 0.059632160 0.750692249 0.06873959 0.1075540 0.101650076 NA
#> t 0.059632160 0.750692249 0.06873959 0.1075540 0.101650076 NA
#> y 0.707878133 0.679784089 0.26351522 -0.1321035 2.184534212 NA
#> 1 2 3 4
#> A -0.812485602 -0.109981413 -0.53574997 -0.33515312
#> C -0.310253562 0.145612247 0.00000000 0.04362448
#> D -0.942307133 1.124791311 1.17957474 0.98389654
#> E -0.201410261 1.154194325 1.37389873 1.13638828
#> F 1.906390375 -0.122334266 -0.21541226 -0.12610808
#> G -0.918660373 -0.888701547 -0.30329392 -0.24827921
#> H -0.671163536 -0.002165667 -0.13020754 -0.01785518
#> I 0.374065718 -0.042308229 -0.25963366 -0.03785821
#> K -1.145924538 -2.141143704 -1.48196851 -1.17755536
#> L 0.032665112 -0.500013836 -0.19379970 -0.02664588
#> M 0.833902077 0.008200014 -0.23463499 -0.20273795
#> N -0.818579360 -0.015082595 0.07710624 -0.20706138
#> P -2.650181828 -0.911044318 -0.71667083 0.10218779
#> Q 0.266756562 -0.411003598 -0.01873185 -0.18852897
#> R -0.532824877 -1.190338611 -1.33715648 -1.18082233
#> S -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> T -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> V -0.008682243 -0.249993850 -0.38571419 -0.85152138
#> W -0.550465037 0.385154897 0.11769504 0.30836088
#> Y 0.360757558 0.526569660 0.07546417 -0.04751733
#> s 0.412402175 1.196984664 1.25574242 1.70655265
#> t 0.412402175 1.196984664 1.25574242 1.70655265
#> y 0.490467444 3.461305904 1.53012070 1.85199884Beside the 20 standard amino acids, also phosphorylated serine, threonine and tyrosine residues are included. These phosphorylated residues are distinct from the central phospho-acceptor (serine, threonine or tyrosine at position 0) and can have a strong impact on the affinity of a given kinase-substrate pair (phospho-priming).
For serine/threonine specific kinase PWMs, the central phospho-acceptor measures the favorability of serine over threonine. The user can exclude this favorability measure by setting the parameter includeSTfavorability to FALSE, in which case the central position doesn’t contribute to the PWM score.
getKinasePWM(includeSTfavorability=FALSE)[["PLK2"]]
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> -5 -4 -3 -2 -1 0 1
#> A -0.036821844 -0.277009455 -0.83856373 -0.4463446 -0.186229068 NA -0.812485602
#> C 0.009633819 -0.034899138 -0.24690897 0.4799548 -0.467333943 NA -0.310253562
#> D 0.549718451 0.795766948 0.82130204 1.6459783 1.329410671 NA -0.942307133
#> E 0.614756952 1.127897364 2.86862751 1.2354207 0.689388627 NA -0.201410261
#> F 0.449006639 0.078199920 -0.41273103 -0.9773836 -0.602963759 NA 1.906390375
#> G 0.326652391 -0.151522275 -0.77793738 -0.6106535 -0.767584829 NA -0.918660373
#> H 0.148478616 -0.172018427 -0.67807191 -0.3219281 0.214995135 NA -0.671163536
#> I -0.311864412 -0.172018427 -1.65154094 -0.8406292 -0.519941731 NA 0.374065718
#> K -0.469329925 -0.647467443 -1.77349147 -1.7345631 -0.656307931 NA -1.145924538
#> L -0.245197993 0.144568518 -0.71785677 0.3032255 -0.511690664 NA 0.032665112
#> M -0.248793390 -0.206894852 -0.38948891 0.3123167 -0.194955239 NA 0.833902077
#> N -0.065823218 0.002018361 -0.54077824 0.9076598 0.307545102 NA -0.818579360
#> P -0.066578437 -0.108114249 -1.05139915 -0.4418303 0.542703792 NA -2.650181828
#> Q -0.530739153 -0.241782116 -0.48096139 -0.1800049 -0.264477823 NA 0.266756562
#> R -0.528032212 -0.715485867 -1.58640592 -1.1059389 -0.339345148 NA -0.532824877
#> S -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 NA -0.532824877
#> T -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 NA -0.532824877
#> V -0.401253684 -0.367545642 -1.89324968 -1.3562361 -0.152804813 NA -0.008682243
#> W -0.034160317 -0.140189435 -1.05799229 -1.1256358 -1.093879047 NA -0.550465037
#> Y 0.083383588 -0.242293983 -1.12217724 -0.5640514 -0.004045212 NA 0.360757558
#> s 0.059632160 0.750692249 0.06873959 0.1075540 0.101650076 NA 0.412402175
#> t 0.059632160 0.750692249 0.06873959 0.1075540 0.101650076 NA 0.412402175
#> y 0.707878133 0.679784089 0.26351522 -0.1321035 2.184534212 NA 0.490467444
#> 2 3 4
#> A -0.109981413 -0.53574997 -0.33515312
#> C 0.145612247 0.00000000 0.04362448
#> D 1.124791311 1.17957474 0.98389654
#> E 1.154194325 1.37389873 1.13638828
#> F -0.122334266 -0.21541226 -0.12610808
#> G -0.888701547 -0.30329392 -0.24827921
#> H -0.002165667 -0.13020754 -0.01785518
#> I -0.042308229 -0.25963366 -0.03785821
#> K -2.141143704 -1.48196851 -1.17755536
#> L -0.500013836 -0.19379970 -0.02664588
#> M 0.008200014 -0.23463499 -0.20273795
#> N -0.015082595 0.07710624 -0.20706138
#> P -0.911044318 -0.71667083 0.10218779
#> Q -0.411003598 -0.01873185 -0.18852897
#> R -1.190338611 -1.33715648 -1.18082233
#> S -0.109981413 -0.21541226 -0.12610808
#> T -0.109981413 -0.21541226 -0.12610808
#> V -0.249993850 -0.38571419 -0.85152138
#> W 0.385154897 0.11769504 0.30836088
#> Y 0.526569660 0.07546417 -0.04751733
#> s 1.196984664 1.25574242 1.70655265
#> t 1.196984664 1.25574242 1.70655265
#> y 3.461305904 1.53012070 1.85199884In order to disable scoring of phosphosites that do no contain a matching phospho-acceptor, i.e. S/T in case of serine/threonine PWMs or K in case of tyrosine PWMs, parameter matchAcceptorSpecificity can be set to TRUE. In this case the log2-odd score of non matching residues is set to -Inf:
getKinasePWM(matchAcceptorSpecificity=TRUE)[["PLK2"]]
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> -5 -4 -3 -2 -1 0
#> A -0.036821844 -0.277009455 -0.83856373 -0.4463446 -0.186229068 -Inf
#> C 0.009633819 -0.034899138 -0.24690897 0.4799548 -0.467333943 -Inf
#> D 0.549718451 0.795766948 0.82130204 1.6459783 1.329410671 -Inf
#> E 0.614756952 1.127897364 2.86862751 1.2354207 0.689388627 -Inf
#> F 0.449006639 0.078199920 -0.41273103 -0.9773836 -0.602963759 -Inf
#> G 0.326652391 -0.151522275 -0.77793738 -0.6106535 -0.767584829 -Inf
#> H 0.148478616 -0.172018427 -0.67807191 -0.3219281 0.214995135 -Inf
#> I -0.311864412 -0.172018427 -1.65154094 -0.8406292 -0.519941731 -Inf
#> K -0.469329925 -0.647467443 -1.77349147 -1.7345631 -0.656307931 -Inf
#> L -0.245197993 0.144568518 -0.71785677 0.3032255 -0.511690664 -Inf
#> M -0.248793390 -0.206894852 -0.38948891 0.3123167 -0.194955239 -Inf
#> N -0.065823218 0.002018361 -0.54077824 0.9076598 0.307545102 -Inf
#> P -0.066578437 -0.108114249 -1.05139915 -0.4418303 0.542703792 -Inf
#> Q -0.530739153 -0.241782116 -0.48096139 -0.1800049 -0.264477823 -Inf
#> R -0.528032212 -0.715485867 -1.58640592 -1.1059389 -0.339345148 -Inf
#> S -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 0.00000000
#> T -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 -0.09585422
#> V -0.401253684 -0.367545642 -1.89324968 -1.3562361 -0.152804813 -Inf
#> W -0.034160317 -0.140189435 -1.05799229 -1.1256358 -1.093879047 -Inf
#> Y 0.083383588 -0.242293983 -1.12217724 -0.5640514 -0.004045212 -Inf
#> s 0.059632160 0.750692249 0.06873959 0.1075540 0.101650076 NA
#> t 0.059632160 0.750692249 0.06873959 0.1075540 0.101650076 NA
#> y 0.707878133 0.679784089 0.26351522 -0.1321035 2.184534212 -Inf
#> 1 2 3 4
#> A -0.812485602 -0.109981413 -0.53574997 -0.33515312
#> C -0.310253562 0.145612247 0.00000000 0.04362448
#> D -0.942307133 1.124791311 1.17957474 0.98389654
#> E -0.201410261 1.154194325 1.37389873 1.13638828
#> F 1.906390375 -0.122334266 -0.21541226 -0.12610808
#> G -0.918660373 -0.888701547 -0.30329392 -0.24827921
#> H -0.671163536 -0.002165667 -0.13020754 -0.01785518
#> I 0.374065718 -0.042308229 -0.25963366 -0.03785821
#> K -1.145924538 -2.141143704 -1.48196851 -1.17755536
#> L 0.032665112 -0.500013836 -0.19379970 -0.02664588
#> M 0.833902077 0.008200014 -0.23463499 -0.20273795
#> N -0.818579360 -0.015082595 0.07710624 -0.20706138
#> P -2.650181828 -0.911044318 -0.71667083 0.10218779
#> Q 0.266756562 -0.411003598 -0.01873185 -0.18852897
#> R -0.532824877 -1.190338611 -1.33715648 -1.18082233
#> S -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> T -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> V -0.008682243 -0.249993850 -0.38571419 -0.85152138
#> W -0.550465037 0.385154897 0.11769504 0.30836088
#> Y 0.360757558 0.526569660 0.07546417 -0.04751733
#> s 0.412402175 1.196984664 1.25574242 1.70655265
#> t 0.412402175 1.196984664 1.25574242 1.70655265
#> y 0.490467444 3.461305904 1.53012070 1.85199884Phosphorylated peptides are often represented in two different formats: (1) the phosphorylated residues are indicated by an asterix as in SAGLLS*DEDC, (2) phosphorylated residues are given by lower case letters as in SAGLLsDEDC. In order to unify the phosophosite representation for PWM matching, JohnsonKinaseData provides the function processPhosphopeptides(). It takes a character vector with phospho-peptides, aligns them to the central phospho-acceptor position and pads and/or truncates the surrounding residues. By default this means, 5 upstream residues, a central acceptor and 5 downstream residues. The central phospho-acceptor position is defined as the left closest phosphorylated residue to the midpoint of the peptide given by floor(nchar(sites)/2)+1. This midpoint definition is also the default alignment position if no phosphorylated residue was recognized.
ppeps <- c("SAGLLS*DEDC", "GDtND", "EKGDSN__", "HKRNyGsDER", "PEKS*GyNV")
sites <- processPhosphopeptides(ppeps)
sites
#> # A tibble: 5 × 3
#> sites processed acceptor
#> <chr> <chr> <chr>
#> 1 SAGLLS*DEDC SAGLLSDEDC_ S
#> 2 GDtND ___GDTND___ T
#> 3 EKGDSN__ _EKGDSN____ S
#> 4 HKRNyGsDER _HKRNYGsDER Y
#> 5 PEKS*GyNV __PEKSGyNV_ SIf a peptide contains several phosphorylated residues, option onlyCentralAcceptor controls how to select the acceptor position. Setting onlyCentralAcceptor=FALSE will return all possible aligned phosphosites for a given input peptide. Note that in this case the output is not parallel to the input.
sites <- processPhosphopeptides(ppeps, onlyCentralAcceptor=FALSE)
sites
#> # A tibble: 7 × 3
#> sites processed acceptor
#> <chr> <chr> <chr>
#> 1 SAGLLS*DEDC SAGLLSDEDC_ S
#> 2 GDtND ___GDTND___ T
#> 3 EKGDSN__ _EKGDSN____ S
#> 4 HKRNyGsDER _HKRNYGsDER Y
#> 5 HKRNyGsDER KRNyGSDER__ S
#> 6 PEKS*GyNV __PEKSGyNV_ S
#> 7 PEKS*GyNV PEKsGYNV___ YA warning is raised if the central acceptor is not serine, threonine or tyrosine.
Once peptides are processed to sites, the function scorePhosphosites() can be used to create a matrix of kinase-substrate match scores.
selected <- sites |>
dplyr::pull(processed)
scores <- scorePhosphosites(pwms, selected)
dim(scores)
#> [1] 7 396
scores[,1:5]
#> AAK1 ACVR2A ACVR2B AKT1 AKT2
#> SAGLLSDEDC_ -6.794078 -0.1666423 0.30390179 -5.8821117 -4.7783302
#> ___GDTND___ -4.803921 -1.0410203 -0.56120674 -2.8360934 -2.5125933
#> _EKGDSN____ -8.274386 -1.5402977 -0.92960511 -0.6188352 -0.8554523
#> _HKRNYGsDER 1.286478 -2.5560251 -1.74870769 1.0901866 2.7031941
#> KRNyGSDER__ -6.290564 -1.9202469 -1.38766899 -3.0601553 -1.7486155
#> __PEKSGyNV_ 1.695554 -0.1171313 0.06161951 -4.7296786 -3.6486856
#> PEKsGYNV___ -3.099221 -2.1144168 -0.86427402 -0.3383336 -0.4906393The PWM scoring can be parallelized by supplying a BiocParallelParam object to BPPARAM=.
scores <- scorePhosphosites(pwms, selected, BPPARAM=BiocParallel::SerialParam())By default, the resulting score is the log2-odds score of the PWM. Alternatively, by setting scoreType="percentile", a percentile rank of the log2-odds score is calculated, using for each PWM a background score distribution.
scores <- scorePhosphosites(pwms, selected, scoreType="percentile")
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
scores[,1:5]
#> AAK1 ACVR2A ACVR2B AKT1 AKT2
#> SAGLLSDEDC_ 22.375586 79.73910 83.79933 14.73447 14.59609
#> ___GDTND___ 53.371824 67.48779 74.89617 56.34769 53.31220
#> _EKGDSN____ 7.927565 57.36739 69.80942 79.14942 74.56646
#> _HKRNYGsDER 98.050345 32.25318 54.60051 88.90565 93.83754
#> KRNyGSDER__ 29.304770 48.35330 61.93582 53.01150 64.98986
#> __PEKSGyNV_ 98.620247 80.26811 81.54857 28.17005 32.26440
#> PEKsGYNV___ 75.754887 43.40279 70.76463 81.09288 77.47741Quantifying PWM matches by percentile rank was first described in Yaffe et al. 2001 (Yaffe et al. 2001). The background score distributions used here are derived from matching each PWM to either the 85’603 unique phosphosites published in Johnson et al. 2023 (serine/threonine PWMs) or the 6659 unique phosphosites published in Yaron-Barir et al. 2024 (tyrosine PWMs). They can be accessed with:
bg <- getBackgroundScores(phosphoAcceptor='Tyr')
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cachewhere phosphoAcceptor can be either Ser/Thr or Tyr. The corresponding mappings of log2-odd scores to percentile ranks can be accessed with function getScoreMaps() which returns a list of mapping functions, one for each kinase PWM.
Note that these percentile ranks do not account for phospho-priming, as non-central phosphorylated residues were missing in the background sites. I.e. the percentile ranks cannot reflect the impact of phospho-priming.
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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#> locale:
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#> time zone: America/New_York
#> tzcode source: system (glibc)
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] JohnsonKinaseData_1.2.0 BiocStyle_2.34.0
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#> loaded via a namespace (and not attached):
#> [1] KEGGREST_1.46.0 xfun_0.48 bslib_0.8.0
#> [4] Biobase_2.66.0 vctrs_0.6.5 tools_4.4.1
#> [7] generics_0.1.3 stats4_4.4.1 curl_5.2.3
#> [10] parallel_4.4.1 tibble_3.2.1 fansi_1.0.6
#> [13] AnnotationDbi_1.68.0 RSQLite_2.3.7 blob_1.2.4
#> [16] pkgconfig_2.0.3 checkmate_2.3.2 dbplyr_2.5.0
#> [19] S4Vectors_0.44.0 lifecycle_1.0.4 GenomeInfoDbData_1.2.13
#> [22] stringr_1.5.1 compiler_4.4.1 Biostrings_2.74.0
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#> [31] pillar_1.9.0 crayon_1.5.3 jquerylib_0.1.4
#> [34] BiocParallel_1.40.0 cachem_1.1.0 mime_0.12
#> [37] ExperimentHub_2.14.0 AnnotationHub_3.14.0 tidyselect_1.2.1
#> [40] digest_0.6.37 stringi_1.8.4 purrr_1.0.2
#> [43] dplyr_1.1.4 bookdown_0.41 BiocVersion_3.20.0
#> [46] fastmap_1.2.0 cli_3.6.3 magrittr_2.0.3
#> [49] utf8_1.2.4 withr_3.0.2 backports_1.5.0
#> [52] filelock_1.0.3 UCSC.utils_1.2.0 rappdirs_0.3.3
#> [55] bit64_4.5.2 rmarkdown_2.28 XVector_0.46.0
#> [58] httr_1.4.7 bit_4.5.0 png_0.1-8
#> [61] memoise_2.0.1 evaluate_1.0.1 knitr_1.48
#> [64] IRanges_2.40.0 BiocFileCache_2.14.0 rlang_1.1.4
#> [67] glue_1.8.0 DBI_1.2.3 BiocManager_1.30.25
#> [70] BiocGenerics_0.52.0 jsonlite_1.8.9 R6_2.5.1
#> [73] zlibbioc_1.52.0Johnson, Jared L., Tomer M. Yaron, Emily M. Huntsman, Alexander Kerelsky, Junho Song, Amit Regev, Ting-Yu Lin, et al. 2023. “An Atlas of Substrate Specificities for the Human Serine/Threonine Kinome.” Journal Article. Nature 613 (7945): 759–66. https://doi.org/10.1038/s41586-022-05575-3.
Yaffe, Michael B., German G. Leparc, Jack Lai, Toshiyuki Obata, Stefano Volinia, and Lewis C. Cantley. 2001. “A Motif-Based Profile Scanning Approach for Genome-Wide Prediction of Signaling Pathways.” Journal Article. Nature Biotechnology 19 (4): 348–53. https://doi.org/10.1038/86737.
Yaron-Barir, Tomer M., Brian A. Joughin, Emily M. Huntsman, Alexander Kerelsky, Daniel M. Cizin, Benjamin M. Cohen, Amit Regev, et al. 2024. “The Intrinsic Substrate Specificity of the Human Tyrosine Kinome.” Journal Article. Nature 629 (8014): 1174–81. https://doi.org/10.1038/s41586-024-07407-y.