@@ -32,7 +32,7 @@

 #' @param plotResults logical value, TRUE by default.

 #'   This determines what is returned. If \code{plotResults = FALSE}, a

 #'   data frame is returned with the Sequence(s), Average Scaled Hydropathy,

-#'   and Average Net Charge. 

+#'   and Average Net Charge.

 #'   If  \code{plotResults = TRUE}, a graphical output is returned (ggplot)

 #'   showing the Charge Hydropathy Plot (recommended).

 #' @param ... additional arguments to be passed to

@@ -138,7 +138,7 @@ chargeHydropathyPlot <- function(

     dataCollected$sequence <- do.call(rbind, sequenceList)

     dataCollected$avg_scaled_hydropathy <- do.call(rbind, hydropathyList)

     dataCollected$avg_net_charge <- do.call(rbind, chargeList)

-    

+

     if (!plotResults) {

         return(dataCollected)

     }

@@ -3,27 +3,25 @@

 #' This is used to calculate the prediction of intrinsic disorder based on

 #'   the scaled hydropathy and absolute net charge of an amino acid

 #'   sequence using a sliding window. FoldIndex described this relationship and

-#'   implemented it graphically in 2005 by Prilusky, Felder, et al, 

+#'   implemented it graphically in 2005 by Prilusky, Felder, et al,

 #'   and this tool has been implemented

-#'   into multiple disorder prediction programs. When windows have a negative 

-#'   score (<0) sequences are predicted as disordered. 

-#'   When windows have a positive score (>0) sequences are predicted as 

-#'   disordered. Graphically, this cutoff is displayed by the dashed 

+#'   into multiple disorder prediction programs. When windows have a negative

+#'   score (<0) sequences are predicted as disordered.

+#'   When windows have a positive score (>0) sequences are predicted as

+#'   disordered. Graphically, this cutoff is displayed by the dashed

 #'   line at y = 0. Calculations are at pH 7.0 based on the described method and

-#'   the default is a sliding window of size 51. 

-#'   

+#'   the default is a sliding window of size 51.

+#'

 #'   The output is either a data frame or graph

 #'   showing the calculated scores for each window along the sequence.

 #'   The equation used was originally described in Uversky et al. (2000)\cr

 #'   \url{https://doi.org/10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7}

 #'   . \cr

-#'   

-#'   The FoldIndex method of using a sliding window and utilizing the uversky 

+#'   The FoldIndex method of using a sliding window and utilizing the Uversky

 #'   equation is described in Prilusky, J., Felder, C. E., et al. (2005). \cr

-#'   FoldIndex: a simple tool to predict whether a given protein sequence \cr 

+#'   FoldIndex: a simple tool to predict whether a given protein sequence \cr

 #'   is intrinsically unfolded. Bioinformatics, 21(16), 3435-3438. \cr

-#'   

-#'   

+#'

 #' @inheritParams sequenceCheck

 #' @inheritParams chargeCalculationLocal

 #' @param window a positive, odd integer. 51 by default.

@@ -42,9 +40,7 @@

 #' @seealso \code{\link{KDNorm}} for residue hydropathy values.

 #'   See \code{\link{pKaData}} for residue pKa values and citations. See

 #'   \code{\link{hendersonHasselbalch}} for charge calculations.

-#' @references Kyte, J., & Doolittle, R. F. (1982). A simple method for

-#'   displaying the hydropathic character of a protein.

-#'   Journal of molecular biology, 157(1), 105-132.

+

 #' @section Plot Colors:

 #'   For users who wish to keep a common aesthetic, the following colors are

 #'   used when plotResults = TRUE. \cr

@@ -53,15 +49,18 @@

 #'   \item Close to -1 = "#9672E6"

 #'   \item Close to 1 = "#D1A63F"

 #'   \item Close to midpoint = "grey65" or "#A6A6A6"}}

-#'    

-#'   @references

+#'

+#' @references

+#'   Kyte, J., & Doolittle, R. F. (1982). A simple method for

+#'   displaying the hydropathic character of a protein.

+#'   Journal of molecular biology, 157(1), 105-132.

 #'   Kozlowski, L. P. (2016). IPC – Isoelectric Point Calculator. Biology

 #'   Direct, 11(1), 55. \url{https://doi.org/10.1186/s13062-016-0159-9} \cr

 #'   Kyte, J., & Doolittle, R. F. (1982). A simple method for

 #'   displaying the hydropathic character of a protein.

 #'   Journal of molecular biology, 157(1), 105-132. \cr

 #'   Prilusky, J., Felder, C. E., et al. (2005). \cr

-#'   FoldIndex: a simple tool to predict whether a given protein sequence \cr 

+#'   FoldIndex: a simple tool to predict whether a given protein sequence \cr

 #'   is intrinsically unfolded. Bioinformatics, 21(16), 3435-3438. \cr

 #'   Uversky, V. N., Gillespie, J. R., & Fink, A. L. (2000).

 #'   Why are “natively unfolded” proteins unstructured under physiologic

@@ -71,34 +70,30 @@

 #' @export

 foldIndexR <- function(sequence,

-                       window = 51, 

+                       window = 51,

                        proteinName = NA,

                        pKaSet = "IPC_protein",

                        plotResults = TRUE,

                        ...) {

-    

     chargeDF <-

         chargeCalculationLocal(sequence = sequence, window = window,

-                               pH = 7.0, pKaSet = pKaSet, 

+                               pH = 7.0, pKaSet = pKaSet,

                                plotResults = FALSE)

     chargeDF$scaledWindowCharge <- chargeDF$windowCharge / window

-    hydropDF <-  scaledHydropathyLocal(sequence = sequence, 

+    hydropDF <-  scaledHydropathyLocal(sequence = sequence,

                                        window = window,

                                        plotResults = FALSE)

     mergeDF <- merge(hydropDF, chargeDF)

-    

-    mergeDF$foldIndex <- 

-        mergeDF$WindowHydropathy * 2.785 - 

+    mergeDF$foldIndex <-

+        mergeDF$WindowHydropathy * 2.785 -

         abs(mergeDF$scaledWindowCharge) - 1.151

-    

     if (plotResults) {

         plotTitle <- "FoldIndex Prediction of Intrinsic Disorder"

         if (!is.na(proteinName)) {

-            plotTitle <- 

-                paste0("FoldIndex Prediction of Intrinsic Disorder in ", 

+            plotTitle <-

+                paste0("FoldIndex Prediction of Intrinsic Disorder in ",

                        proteinName, sep = "")

         }

-        

         gg <-  sequencePlot(position = mergeDF$Position,

                             property = mergeDF$foldIndex,

                             hline = 0, dynamicColor = mergeDF$foldIndex,

@@ -109,5 +104,4 @@ foldIndexR <- function(sequence,

     } else {

         return(mergeDF)

     }

-    

 }

@@ -1,15 +1,15 @@

 #' idpr: profiling and analyzing Intrinsically Disordered Proteins in R

 #'

-#' idpr aims to integrate tools for the computational analysis of 

-#' intrinsically disordered proteins (IDPs) within R. This package is used to 

-#' identify known characteristics of IDPs for a sequence of interest with 

-#' easily reported and dynamic results. Additionally, this package includes 

-#' tools for IDP-based sequence analysis to be used in conjunction with other R 

-#' packages. 

+#' idpr aims to integrate tools for the computational analysis of

+#' intrinsically disordered proteins (IDPs) within R. This package is used to

+#' identify known characteristics of IDPs for a sequence of interest with

+#' easily reported and dynamic results. Additionally, this package includes

+#' tools for IDP-based sequence analysis to be used in conjunction with other R

+#' packages.

 #' \cr

 #' Please see the idpr vignettes for details on idpr functions and theory.

 #' \code{browseVignettes("idpr")}

 #' @docType package

 #' @name idpr

 NULL

-#> NULL

\ No newline at end of file

+#> NULL

@@ -86,10 +86,10 @@ sequenceCheck <- function(

     if (!all(is.character(outputType), is.character(method))) {

         stop("Error: method and outputType must be character vectors,")

     }

-    if (!any(is.character(sequence), 

-             (is(sequence)[1]  %in% c("AAString", "BString", 

+    if (!any(is.character(sequence),

+             (is(sequence)[1] %in% c("AAString", "BString",

                                       "AAStringSet", "BStringSet"))

-             )){

+             )) {

         stop("Error: sequence must be a character vector or an AAString Object")

     }

     if (!(method %in% c("stop", "warn"))) {

@@ -98,14 +98,14 @@ sequenceCheck <- function(

     }

     #-----

     #This section will confirm what to do with the amino acid sequence

-    if(is(sequence)[1] %in% c("AAString", "BString", 

-                              "AAStringSet", "BStringSet")){

+    if (is(sequence)[1] %in% c("AAString", "BString",

+                              "AAStringSet", "BStringSet")) {

         sequence <- as.character(sequence)

     }

     if (length(sequence) == 1) {

         #this is to see if the string is a .fasta / .fa file

         if (grepl("\\.fa", sequence, ignore.case = TRUE)) {

-        sequence <- Biostrings::readAAStringSet(sequence, format="fasta")

+        sequence <- Biostrings::readAAStringSet(sequence, format = "fasta")

         sequence <- as.character(sequence)

         }

         separatedSequence <- strsplit(sequence, "")

@@ -342,7 +342,7 @@ sequenceMap <- function(

         if (plyr::is.discrete(seqDF$Property)) {

             gg <- gg + ggplot2::scale_fill_manual(values = customColors)

         } else {

-            gg <- gg + 

+            gg <- gg +

                 ggplot2::scale_fill_gradient2(

                     high = customColors[1],

                     low = customColors[2],

@@ -44,7 +44,7 @@ The environmental pH is used to calculate residue charge.}

 \item{plotResults}{logical value, TRUE by default.

 This determines what is returned. If \code{plotResults = FALSE}, a

 data frame is returned with the Sequence(s), Average Scaled Hydropathy,

-and Average Net Charge. 

+and Average Net Charge.

 If  \code{plotResults = TRUE}, a graphical output is returned (ggplot)

 showing the Charge Hydropathy Plot (recommended).}

@@ -51,24 +51,24 @@ see plotResults argument

 This is used to calculate the prediction of intrinsic disorder based on

   the scaled hydropathy and absolute net charge of an amino acid

   sequence using a sliding window. FoldIndex described this relationship and

-  implemented it graphically in 2005 by Prilusky, Felder, et al, 

+  implemented it graphically in 2005 by Prilusky, Felder, et al,

   and this tool has been implemented

-  into multiple disorder prediction programs. When windows have a negative 

-  score (<0) sequences are predicted as disordered. 

-  When windows have a positive score (>0) sequences are predicted as 

-  disordered. Graphically, this cutoff is displayed by the dashed 

+  into multiple disorder prediction programs. When windows have a negative

+  score (<0) sequences are predicted as disordered.

+  When windows have a positive score (>0) sequences are predicted as

+  disordered. Graphically, this cutoff is displayed by the dashed

   line at y = 0. Calculations are at pH 7.0 based on the described method and

-  the default is a sliding window of size 51. 

-  

-  The output is either a data frame or graph

+  the default is a sliding window of size 51.

+}

+\details{

+The output is either a data frame or graph

   showing the calculated scores for each window along the sequence.

   The equation used was originally described in Uversky et al. (2000)\cr

   \url{https://doi.org/10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7}

   . \cr

-  

-  The FoldIndex method of using a sliding window and utilizing the uversky 

+  The FoldIndex method of using a sliding window and utilizing the Uversky

   equation is described in Prilusky, J., Felder, C. E., et al. (2005). \cr

-  FoldIndex: a simple tool to predict whether a given protein sequence \cr 

+  FoldIndex: a simple tool to predict whether a given protein sequence \cr

   is intrinsically unfolded. Bioinformatics, 21(16), 3435-3438. \cr

 }

 \section{Plot Colors}{

@@ -80,15 +80,19 @@ This is used to calculate the prediction of intrinsic disorder based on

   \item Close to -1 = "#9672E6"

   \item Close to 1 = "#D1A63F"

   \item Close to midpoint = "grey65" or "#A6A6A6"}}

-   

-  @references

+}

+

+\references{

+Kyte, J., & Doolittle, R. F. (1982). A simple method for

+  displaying the hydropathic character of a protein.

+  Journal of molecular biology, 157(1), 105-132.

   Kozlowski, L. P. (2016). IPC – Isoelectric Point Calculator. Biology

   Direct, 11(1), 55. \url{https://doi.org/10.1186/s13062-016-0159-9} \cr

   Kyte, J., & Doolittle, R. F. (1982). A simple method for

   displaying the hydropathic character of a protein.

   Journal of molecular biology, 157(1), 105-132. \cr

   Prilusky, J., Felder, C. E., et al. (2005). \cr

-  FoldIndex: a simple tool to predict whether a given protein sequence \cr 

+  FoldIndex: a simple tool to predict whether a given protein sequence \cr

   is intrinsically unfolded. Bioinformatics, 21(16), 3435-3438. \cr

   Uversky, V. N., Gillespie, J. R., & Fink, A. L. (2000).

   Why are “natively unfolded” proteins unstructured under physiologic

@@ -96,12 +100,6 @@ This is used to calculate the prediction of intrinsic disorder based on

   415-427.

   \url{https://doi.org/10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7}

 }

-

-\references{

-Kyte, J., & Doolittle, R. F. (1982). A simple method for

-  displaying the hydropathic character of a protein.

-  Journal of molecular biology, 157(1), 105-132.

-}

 \seealso{

 \code{\link{KDNorm}} for residue hydropathy values.

   See \code{\link{pKaData}} for residue pKa values and citations. See

@@ -5,12 +5,12 @@

 \alias{idpr}

 \title{idpr: profiling and analyzing Intrinsically Disordered Proteins in R}

 \description{

-idpr aims to integrate tools for the computational analysis of 

-intrinsically disordered proteins (IDPs) within R. This package is used to 

-identify known characteristics of IDPs for a sequence of interest with 

-easily reported and dynamic results. Additionally, this package includes 

-tools for IDP-based sequence analysis to be used in conjunction with other R 

-packages. 

+idpr aims to integrate tools for the computational analysis of

+intrinsically disordered proteins (IDPs) within R. This package is used to

+identify known characteristics of IDPs for a sequence of interest with

+easily reported and dynamic results. Additionally, this package includes

+tools for IDP-based sequence analysis to be used in conjunction with other R

+packages.

 \cr

 Please see the idpr vignettes for details on idpr functions and theory.

 \code{browseVignettes("idpr")}

@@ -11,7 +11,7 @@ vignette: >

 knitr::opts_chunk$set(

   collapse = TRUE,

   comment = "#>",

-  fig.width = 6, 

+  fig.width = 6,

   fig.height = 4

 )

 ```

@@ -34,7 +34,6 @@ tend to be aliphatic, hydrophobic, aromatic, or form tertiary structures

 Therefore, there is a distinct difference of biochemistry

 between IDPs and ordered proteins. 

-

 It was shown in Uversky, Gillespie, & Fink (2000) that both high net charge and 

 low mean hydropathy are properties of IDPs. One explanation is that a high net 

 charge leads to increased repulsion of residues causing an extended structure 

@@ -78,7 +77,7 @@ This was described in Prilusky, J., Felder, C. E., et al. (2005).

 The idpr package can be installed from Bioconductor with the following line of 

 code. It requires the BiocManager package to be installed

 ```{r}

-#BiocManager::install("idpr") 

+#BiocManager::install("idpr")

 ```

 The most recent version of the package can be installed with the following line 

@@ -185,7 +184,7 @@ print(TP53_Sequences)

 ```{r}

 gg <- chargeHydropathyPlot(

   sequence = TP53_Sequences,

-  pKaSet = "IPC_protein") 

+  pKaSet = "IPC_protein")

 plot(gg)

 ```

@@ -206,12 +205,19 @@ chargeHydropathyPlot(

 ## Using FoldIndexR to predict folded and unfolded windows. 

+Predictions are made on a scale of -1 to 1, where any residues with 

+a negative score are predicted disordered (green; under 0), 

+and any residue with a positive score are predicted ordered (purple; above 0).

+

+Functionally, this uses a large sliding window, (default 51) as described in

+Prilusky, J., Felder, C. E., et al. (2005), for both scaled hydropathy and

+local charge. 

 ```{r}

 foldIndexR(sequence = HUMAN_P53,

            plotResults = TRUE)

 ```

-Prilusky, J., Felder, C. E., et al. (2005). 

+

 ## Calculating Scaled Hydropathy

@@ -61,7 +61,7 @@ The following matrices are available within **idpr**:

 The idpr package can be installed from Bioconductor with the following line of 

 code. It requires the BiocManager package to be installed

 ```{r}

-#BiocManager::install("idpr") 

+#BiocManager::install("idpr")

 ```

 The most recent version of the package can be installed with the following line 

@@ -347,7 +347,7 @@ BLOSUM_MSA <- msa(TP53_Sequences,

                  gapOpening = 10,

                  gapExtension = 0.5)

-print(BLOSUM_MSA, show="complete")

+print(BLOSUM_MSA, show = "complete")

 ```

@@ -358,7 +358,7 @@ EDSS_MSA <- msa(TP53_Sequences,

                 gapOpening = 19,

                 gapExtension = 2)

-print(EDSS_MSA, show="complete")

+print(EDSS_MSA, show = "complete")

 ```

@@ -370,10 +370,11 @@ The user guide to **msa** shows an example of converting the sequence alignment

 Therefore, the IDP-specific matrices can be used for this type of analysis.

 The conversion uses both the **ape** and **seqinr** packages. 

 ```{r fig1, fig.height = 4, fig.width = 6}

-EDSS_MSA_Tree <- msa::msaConvert(EDSS_MSA, type="seqinr::alignment")

+EDSS_MSA_Tree <- msa::msaConvert(EDSS_MSA, type = "seqinr::alignment")

 d <- seqinr::dist.alignment(EDSS_MSA_Tree, "identity")

 p53Tree <- ape::nj(d)

-plot(p53Tree, main="Phylogenetic Tree of p53 Sequences\nAligned with EDSSMat62")

+plot(p53Tree,

+     main = "Phylogenetic Tree of p53 Sequences\nAligned with EDSSMat62")

 ```

@@ -13,7 +13,7 @@ vignette: >

 knitr::opts_chunk$set(

   collapse = TRUE,

   comment = "#>",

-  fig.width = 6, 

+  fig.width = 6,

   fig.height = 4

 )

 ```

@@ -121,7 +121,7 @@ sequence-based analysis into R.

 The package can be installed from Bioconductor with the following line of code.

 This requires the BiocManager package to be installed.

 ```{r}

-#BiocManager::install("idpr") 

+#BiocManager::install("idpr")

 ```

 The most recent version of the package can be installed with the following line 

@@ -308,7 +308,7 @@ head(p53_tendency_DF) #see the first few rows of the generated data frame

 sequenceMap(sequence = P53_HUMAN,

             property = p53_tendency_DF$Tendency,

-            customColors = c("#F0B5B3", "#A2CD5A", "#BF3EFF"))  #generate the map

+            customColors = c("#F0B5B3", "#A2CD5A", "#BF3EFF")) #generate the map

 ```

 sequenceMap() does accept continuous values as well. Additionally, custom plots

@@ -13,7 +13,7 @@ vignette: >

 knitr::opts_chunk$set(

   collapse = TRUE,

   comment = "#>",

-  fig.width = 6, 

+  fig.width = 6,

   fig.height = 4

 )

 ```

@@ -21,7 +21,6 @@ knitr::opts_chunk$set(

 # Fetching IUPred Predictions of Intrinsic Disorder

-

 ## Quick Start

 The functions iupred(), iupredAnchor(), and iupredRedox() are all 

@@ -91,7 +90,7 @@ Both type of results will be shown for examples.

 The idpr package can be installed from Bioconductor with the following line of 

 code. It requires the BiocManager package to be installed.

 ```{r}

-#BiocManager::install("idpr") 

+#BiocManager::install("idpr")

 ```

 The most recent version of the package can be installed with the following line 

@@ -214,7 +213,7 @@ iupredAnchor(p53_ID,

 The data frame for iupredAnchor has a similar layout to iupred(),

 with an additional column for ANCHOR2 scores.

 ```{r}

-iupredAnchorDF <- iupredAnchor(p53_ID, 

+iupredAnchorDF <- iupredAnchor(p53_ID,

                                plotResults = FALSE)

 head(iupredAnchorDF)

 ```

@@ -250,7 +249,7 @@ sensitive region was predicted. When redoxSensitive == TRUE, the residue is

 predicted to be in a redox sensitive region, when FALSE the residue is not 

 predicted to be in a redox sensitive region. 

 ```{r}

-iupredRedoxDF <- iupredRedox(p53_ID, 

+iupredRedoxDF <- iupredRedox(p53_ID,

                              plotResults = FALSE)

 head(iupredRedoxDF)

 ```

@@ -272,19 +271,14 @@ iupredLongDF <- iupred(p53_ID,

 sequenceMap(sequence = iupredLongDF$AA,

             property = iupredLongDF$IUPred2,

-            customColors = c("darkolivegreen3", "grey65", "darkorchid1")) + 

+            customColors = c("darkolivegreen3", "grey65", "darkorchid1")) +

   ggplot2::labs(title = "Prediction of Intrinsic Disorder in HUMAN P53",

                 subtitle = "By IUPred2A long")

-

-

-                         

 ```

-

-**For further details, please refer to idpr's **

+**For further details, please refer to idpr's**

 **"Sequence Map Vignette" file.**

-

 ## Getting the UniProt Accession

 To make a connection to the IUPred2A REST API, a UniProt Accession ID is 

@@ -11,12 +11,11 @@ vignette: >

 knitr::opts_chunk$set(

   collapse = TRUE,

   comment = "#>",

-  fig.width = 6, 

+  fig.width = 6,

   fig.height = 4

 )

 ```

-

 ## Introduction

 One way to visualize results both within **idpr** and with data from other 

@@ -44,7 +43,7 @@ and stored within the **idpr** package for examples.

 The package can be installed from Bioconductor with the following line of code.

 It requires the BiocManager package to be installed.

 ```{r}

-#BiocManager::install("idpr") 

+#BiocManager::install("idpr")

 ```

 The most recent version of the package can be installed with the following line 

@@ -70,10 +69,9 @@ The values can be discrete, like the output of structuralTendency(), or

 continuous, like the output of chargeCalculationGlobal()

 ```{r}

-tendencyDF <- structuralTendency(sequence = P53_HUMAN) 

+tendencyDF <- structuralTendency(sequence = P53_HUMAN)

 head(tendencyDF)

-

 chargeDF <- chargeCalculationGlobal(sequence = P53_HUMAN,

                                     includeTermini = FALSE)

 head(chargeDF)

@@ -89,13 +87,12 @@ values in 'Charge'.

 ```{r}

 sequenceMap(

   sequence = tendencyDF$AA,

-  property = tendencyDF$Tendency) 

-

+  property = tendencyDF$Tendency)

 sequenceMap(

   sequence = as.character(chargeDF$AA),

   property = chargeDF$Charge, #character vector

-  customColors = c("blue", "red", "grey30")) 

+  customColors = c("blue", "red", "grey30"))

 ```

@@ -194,25 +191,24 @@ sequenceMap(

   rotationAngle = 90) #45 residues each row

 ```

-

 You can also specify colors for discrete values using a vector of colors. This

 is done with the "customColors" argument.

 ```{r}

 sequenceMap(

   sequence = tendencyDF$AA,

   property = tendencyDF$Tendency,

-  customColors = c("#999999", "#E69F00", "#56B4E9")) 

+  customColors = c("#999999", "#E69F00", "#56B4E9"))

 ```

 Continuous variables custom colors are specified with a vector in the order of 

 "High value", "Low  Value", "Middle Value". Here the order is high = purple, 

-low = pink, and middle = light grey

+low = pink, and middle = light grey.

 ```{r}

 sequenceMap(

   sequence = as.character(chargeDF$AA),

-  property = chargeDF$Charge, 

+  property = chargeDF$Charge,

   customColors = c("purple", "pink", "grey90")

-  ) 

+  )

 ```

 Since the output is a ggplot, the visualization is able to be assigned to an

@@ -237,14 +233,14 @@ ggSequence <- ggSequence +

                         y = 8.05,

                         yend = 8.05,

                        color = "#FF3562",

-                       size = 1.5) + 

+                       size = 1.5) +

               annotate("segment",

                        x = 1,

                        xend  = 12.5,

                         y = 3.05,

                         yend = 3.05,

                        color = "#FF3562",

-                       size = 1.5) + 

+                       size = 1.5) +

               annotate("segment",

                        x = 1,

                        xend  = 40.5,

@@ -260,13 +256,11 @@ ggSequence <- ggSequence +

                         color = "#FF3562",

                        size = 1.5) +

               annotate("text",

-                       x = 36.35, 

+                       x = 36.35,

                        y = 0.65,

                        label = "= DNA Binding",

                        size = 3.5,

                        hjust = 0)

-  

-  

 # Adding a plot title

 ggSequence <- ggSequence +

               labs(title = "P53 Structural Tendency") +

@@ -280,7 +274,7 @@ ggSequence <- ggSequence +

                          show.legend = FALSE,

                          inherit.aes = FALSE) +

               annotate("text",

-                       x = 4.5, 

+                       x = 4.5,

                        y = 4.3,

                        label = "Metal Binding",

                         size = 3)

@@ -13,7 +13,7 @@ vignette: >

 knitr::opts_chunk$set(

   collapse = TRUE,

   comment = "#>",

-  fig.width = 6, 

+  fig.width = 6,

   fig.height = 4

 )

 ```

@@ -42,7 +42,7 @@ disorder‐neutral residues are D, T, and R (Uversky, 2013).

 The package can be installed from Bioconductor with the following line of code.

 It requires the BiocManager package to be installed

 ```{r}

-#BiocManager::install("idpr") 

+#BiocManager::install("idpr")

 ```

 The most recent version of the package can be installed with the following line 

@@ -127,7 +127,7 @@ Another possibility is the use of the sequenceMap() function within **idpr**.

 sequenceMap(

   sequence = tendencyDF$AA,

   property = tendencyDF$Tendency,

-  customColors = c("#999999", "#E69F00", "#56B4E9"))  

+  customColors = c("#999999", "#E69F00", "#56B4E9"))

 ```

 structuralTendency defines order- and disorder-promoting residues based on 

@@ -154,12 +154,11 @@ tendencyDF <- structuralTendency(P53_MOUSE,

                  disorderNeutral = c("H", "M", "T", "D"),

                  orderPromoting = c("W", "C", "F", "I", "Y", "V", "L", "N"))

 head(tendencyDF)

-  

-  

+

 sequenceMap(

   sequence = P53_MOUSE,

   property = tendencyDF$Tendency,

-  customColors = c("#999999", "#E69F00", "#56B4E9"))    

+  customColors = c("#999999", "#E69F00", "#56B4E9"))

 ```