@@ -114,7 +114,7 @@

 #'                                 ggplot2::aes(label = AA,

 #'                                              y = Charge + 0.1))

 #'   plot(gg)

-#' #alternativly, you can pass the data frame to sequenceMap()

+#' #alternatively, you can pass the data frame to sequenceMap()

 #' sequenceMap(sequence = exampleDF$AA,

 #'             property = exampleDF$Charge)

@@ -25,16 +25,33 @@ packages.

 **Please Refer to idpr-vignette.Rmd file for a detailed introduction to the**

 **idpr package.**

+Links to the vignettes found at the 

+[Bioconductor landing page](https://doi.org/doi:10.18129/B9.bioc.idpr) 

+

 ## Installation

-You can install the development version from [GitHub](https://github.com/) with:

+You can install the development version from 

+[Bioconductor](https://doi.org/doi:10.18129/B9.bioc.idpr) with:

+``` r

+if (!requireNamespace("BiocManager", quietly = TRUE))

+    install.packages("BiocManager")

+

+# The following initializes usage of Bioc devel

+BiocManager::install(version='devel')

+

+BiocManager::install("idpr")

+```

+

+Or you can install the development version from 

+[GitHub](https://github.com/wmm27/idpr) with:

 ``` r

 # install.packages("devtools") #if not already installed

 devtools::install_github("wmm27/idpr")

 ```

+

 ## Example

 This is a basic example to quickly profile your protein of interest:

@@ -51,3 +68,20 @@ idprofile(sequence = P53_HUMAN, #Generates the Profi

 ```

+

+**Please Refer to idpr-vignette.Rmd file for a detailed introduction to the**

+**idpr package.**

+

+## Appendix

+

+### Package citation

+```{r}

+citation("idpr")

+```

+

+### Additional Information

+```{r}

+Sys.time()

+Sys.Date()

+R.version

+```

\ No newline at end of file

@@ -11,12 +11,26 @@ also includes tools for IDP-based sequence analysis to be used in

 conjunction with other R packages.

 **Please Refer to idpr-vignette.Rmd file for a detailed introduction to

-the** **idpr package.**

+the** **idpr package.** Links to the vignettes found at the

+[Bioconductor landing page](https://doi.org/doi:10.18129/B9.bioc.idpr)

 ## Installation

 You can install the development version from

-[GitHub](https://github.com/) with:

+[Bioconductor](https://doi.org/doi:10.18129/B9.bioc.idpr) with:

+

+``` r

+if (!requireNamespace("BiocManager", quietly = TRUE))

+    install.packages("BiocManager")

+

+# The following initializes usage of Bioc devel

+BiocManager::install(version='devel')

+

+BiocManager::install("idpr")

+```

+

+Or you can install the development version from

+[GitHub](https://github.com/wmm27/idpr) with:

 ``` r

 # install.packages("devtools") #if not already installed

@@ -63,3 +77,54 @@ idprofile(sequence = P53_HUMAN, #Generates the Profi

     #> [[5]]

 <img src="man/figures/README-example-5.png" width="75%" />

+

+**Please Refer to idpr-vignette.Rmd file for a detailed introduction to

+the** **idpr package.**

+

+## Appendix

+

+### Package citation

+

+``` r

+citation("idpr")

+#> 

+#> To cite package 'idpr' in publications use:

+#> 

+#>   William McFadden and Judith Yanowitz (2020). idpr: Profiling and

+#>   Analyzing Intrinsically Disordered Proteins in R. R package version

+#>   0.99.25.

+#> 

+#> A BibTeX entry for LaTeX users is

+#> 

+#>   @Manual{,

+#>     title = {idpr: Profiling and Analyzing Intrinsically Disordered Proteins in R},

+#>     author = {William McFadden and Judith Yanowitz},

+#>     year = {2020},

+#>     note = {R package version 0.99.25},

+#>   }

+```

+

+### Additional Information

+

+``` r

+Sys.time()

+#> [1] "2020-10-17 14:40:21 EDT"

+Sys.Date()

+#> [1] "2020-10-17"

+R.version

+#>                _                           

+#> platform       x86_64-apple-darwin17.0     

+#> arch           x86_64                      

+#> os             darwin17.0                  

+#> system         x86_64, darwin17.0          

+#> status                                     

+#> major          4                           

+#> minor          0.2                         

+#> year           2020                        

+#> month          06                          

+#> day            22                          

+#> svn rev        78730                       

+#> language       R                           

+#> version.string R version 4.0.2 (2020-06-22)

+#> nickname       Taking Off Again

+```

@@ -328,7 +328,7 @@ netCharge(HUMAN_P53,

 There are also many pKa sets that are preloaded in **idpr**. 

 pKa datasets used within this vignette are cited. See the documentation for

 netCharge or pKaData within **idpr** for additional information and citations 

-for avaliable pKa sets. 

+for available pKa sets.

 Additionally, see Kozlowski (2016) for further details on pKa data sets. 

 * "EMBOSS" -  (Rice, Longden, & Bleasby, 2000)

@@ -359,7 +359,7 @@ netCharge(HUMAN_P53,

 ```

-Alternativly, the user may supply a custom pKa dataset.

+Alternatively, the user may supply a custom pKa dataset.

 The format must be a data frame where: Column 1 must be a 

 character vector of residues AND Column 2 must be a numeric vector of pKa 

 values. This can be helpful if there is a data set the user prefers or if

@@ -389,7 +389,7 @@ netCharge(HUMAN_P53,

 ### Global Charge Distibution

 chargeCalculationGlobal is a function used to calculate the charge of

-each residue, indepenent of other amino acids, within a sequence. 

+each residue, independent of other amino acids, within a sequence. 

 The results are returned as a data frame (default) or a plot.

 chargeCalculationGlobal accepts the same pKa and pH arguments as netCharge. 

@@ -406,14 +406,14 @@ P53_ccg <- chargeCalculationGlobal(HUMAN_P53)

 head(P53_ccg)

 ```

-The results can return a ggplot visalizing the charge distribution.

+The results can return a ggplot visualizing the charge distribution.

 ```{r}

 chargeCalculationGlobal(HUMAN_P53,

                         plotResults = TRUE)

 ```

 (This is not the most aesthetically pleasing plot, so a sequenceMap from 

-**idpr** is reccomended in this case for visualizations.)

+**idpr** is recommended in this case for visualizations.)

 ```{r}

 P53_ccg <- chargeCalculationGlobal(HUMAN_P53) #repeating from above

@@ -425,7 +425,7 @@ sequenceMap(sequence = P53_ccg$AA,

 The C-terminus here has a charge of ~ -2 since the function aggregates the

 termini values with residue charges by default. If you wish to calculate

-the termini as seperate values, use sumTermini = FALSE. This will add 2 residues

+the termini as separate values, use sumTermini = FALSE. This will add 2 residues

 to the data frame as "NH3" and "COO"

 ```{r}

@@ -435,7 +435,7 @@ head(P53_ccg)

 ```

-If you wish to completly ignore the termini for calculation, set includeTermini

+If you wish to completely ignore the termini for calculation, set includeTermini

 = FALSE. 

 ```{r}

@@ -472,7 +472,7 @@ P53_cgl <- chargeCalculationLocal(HUMAN_P53)

 head(P53_cgl)

 ```

-Alternativly, results can be returned as a plot of each window's charge.

+Alternatively, results can be returned as a plot of each window's charge.

 ```{r}

 chargeCalculationLocal(HUMAN_P53,

                        plotResults = TRUE)

@@ -554,7 +554,7 @@ R Version

 R.version.string

 ```

-System Infomation

+System Information

 ```{r}

 as.data.frame(Sys.info())

 ```

@@ -103,7 +103,7 @@ containing a sequence of interest. All forms are handled automatically without

 user specification, and fasta files will be loaded using the ‘Bioconductor’ 

 package. Additionally, all visualizations generated by 

 ‘idpr’ are made using the ‘ggplot2’ package (30). This is to allow further 

-customizations on returned graphics.  

+customization on returned graphics.  

 Overall, ‘idpr’ aims to integrate tools for the computational analysis of 

 intrinsically disordered proteins within R. This package is used to identify

@@ -184,10 +184,14 @@ are plotted, extended IDPs occupy a unique area on the plot. Therefore, this

 graphic can be used to distinguish proteins that are extended or compact under

 native conditions. However, it is important to note that IDPs can have the 

 characteristics of a collapsed protein or an extended protein. Therefore a 

-protein within the “collapsed protein” field does not necessarly mean that it 

+protein within the “collapsed protein” field does not necessary mean that it 

 lacks intrinsic disorder under native conditions (15, 31). 

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

+**"Charge and Hydropathy Vignette" file.**

+

+

 ### Structural Tendency Plot

 The composition of amino acids and the overall chemistry of IDPs are distinctly

@@ -204,6 +208,9 @@ Disorder-promoting residues are P, E, S, Q, K, A, and G;

 order-promoting residues are M, N, V, H, L, F, Y, I, W, and C; 

 disorder‐neutral residues are D, T, and R (32). 

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

+**"Structural Tendency Vignette" file.**

+

 ### Local Charge Calculations

@@ -211,11 +218,15 @@ As stated, IDPs are enriched in charged residues. Residues of similar charge

 tend to repel one another which can prevent protein packing and promote an 

 unstructured protein configuration under native conditions (15). There are many

 pKa data sets, we utilize the IPC pKa data set for calculations (33). Beyond the

-use of IDP predictions, local charge is an impotant biochemical measurement with

-many applications. Charges are calculated using a sliding window to help 

+use of IDP predictions, local charge is an important biochemical measurement

+with many applications. Charges are calculated using a sliding window to help 

 identify regions of extreme charge. The resulting figure is similar to ProtScale

 from ExPASy (34).

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

+**"Charge and Hydropathy Vignette" file.**

+

+

 ### Local Hydropathy

 As stated, hydrophobic residues are disfavored in IDPs (15). The hydrophobic 

@@ -229,6 +240,10 @@ The resulting figure is similar to ProtScale from ExPASy (34).

 Scaled hydropathy is averaged locally along the protein using a 

 sliding window to identify regions devoid of hydropathic characteristics.

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

+**"Charge and Hydropathy Vignette" file.**

+

+

 ### IUPred

 IUPred2 analyzes an amino acid sequence and returns a score of intrinsic 

@@ -255,9 +270,12 @@ iupredAnchor(P53_ID) #IUPred2 long + ANCHOR2 prediction of scaffolding

 Redox-sensitive regions are shaded with a green background.

 ```{r}

-iupredRedox(P53_ID) #IUPred2 long with enviornmental context

+iupredRedox(P53_ID) #IUPred2 long with environmental context

 ```

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

+**"IUPred Vignette" file.**

+

 ***

 ## Visualizing Discrete Values

@@ -326,8 +344,6 @@ et al. (2001) (25).

 ## References

-References

-

 1. Dunker AK, Lawson JD, Brown CJ, Williams RM, Romero P, Oh JS, et al. Intrinsically disordered protein. Journal of Molecular Graphics and Modelling. 2001;19(1):26-59.

 2. Tompa P. Intrinsically unstructured proteins. Trends in biochemical sciences. 2002;27(10):527-33.

 3. Uversky VN. Intrinsically disordered proteins from A to Z. The International Journal of Biochemistry & Cell Biology. 2011;43(8):1090-103.

@@ -371,3 +387,28 @@ References

 41. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Research. 2008;36(suppl_2):W5-W9.

 42. Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic acids research. 2019;47(W1):W636-W41.

 43. Pagès H, Aboyoun P, Gentleman R, DebRoy S. Biostrings: Efficient manipulation of biological strings. R package version. 2020;2(0).

+

+

+

+

+### Additional Information

+R Version

+```{r}

+R.version.string

+```

+

+System Information

+```{r}

+as.data.frame(Sys.info())

+```

+

+```{r}

+sessionInfo()

+```

+

+```{r, results="asis"}

+citation()

+```

+

+

+

@@ -146,7 +146,9 @@ head(iupredLongDF)

 ### iupredType = "short"

 iupredType =  “short” is the setting to predict small regions of intrinsic

 disorder in proteins, optimized for missing regions of protein structures saved 

-to the Protein Databank (PDB). It is important to note that this tends to favor

+to the Protein Databank (PDB). Its goal is to predict 

+regions that are not represented in crystallographic experiments.

+It is important to note that this tends to favor

 disorder at the N- and C- terminus (Dosztányi, 2018). 

 ```{r}

@@ -166,8 +168,7 @@ head(iupredShortDF)

 ### iupredType = "glob"

 iupredType =  “glob” is the setting that is to help reduce the noise of small 

 disordered regions in otherwise ordered regions and to help identify sequences 

-that are likely to have a specific and rigid fold. Its goal is to predict 

-regions that are not represented in crystallographic experiments 

+that are likely to have a specific and rigid fold. 

 (Dosztányi, 2018). 

 ```{r}

 p53_ID <- "P04637"

@@ -233,9 +234,9 @@ cystine residues to serine when simulating a reducing or

 This eliminates any structural stabilization by disulfide bonds 

 (Mészáros et al., 2018).

-Redox-plus predictions are shown in blue, Redox-minus predications are shown

-in purple. Any region identified as "Redox Senstitive" will be highlighted in

-light green (does not appear if there are no senstitive regions predicted).

+Redox-plus predictions are shown in blue, Redox-minus predictions are shown

+in purple. Any region identified as "Redox Sensitive" will be highlighted in

+light green (does not appear if there are no sensitive regions predicted).

 ```{r}

 p53_ID <- "P04637"

 iupredRedox(p53_ID,

@@ -258,10 +259,10 @@ head(iupredRedoxDF)

 While the aesthetics of the plots above are meant to represent a middleground of

-the graphics avaliable on 

+the graphics available on 

 and the other plots generated by **idpr**, a user may wish to use the data

 frames for data analysis or unique graphics. Another way to represent the data

-is using the sequenceMap() funciton.

+is using the sequenceMap() function.

 ```{r}

 iupredLongDF <- iupred(p53_ID,

@@ -280,6 +281,10 @@ sequenceMap(sequence = iupredLongDF$AA,

 ```

+**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 

@@ -290,6 +295,28 @@ If a user does not have the protein name or info to search, a BLAST search on

 UniProt may be helpful at https://www.uniprot.org/blast/ 

 (UniProt Consortium, 2019).

+## Use

+Please note that these functions are only meant to access the IUPred2A REST API. 

+The functions within **idpr** are **not** designed by the IUPred2A developers. 

+The authors of **idpr** do not control, manage, or maintain any 

+aspect of IUPred2A. Therefore, **idpr** is unable to guarantee access to 

+the API.

+

+

+The user MUST follow the IUPred2A Terms of Use in addition to the terms

+for use of **idpr**.

+

+When publishing or using any data generated with IUPred2A, the user must cite the 

+appropriate publication(s) for the IUPred2A service. This may change as the

+program updates or improves. **idpr** does not control updates to IUPred2A.

+

+

+The current website (as of 10/15/20) for IUPred2A is found here:

+[https://iupred2a.elte.hu/](https://iupred2a.elte.hu/). 

+The authors of **idpr** strongly recommend visiting this page to follow any

+updates and changes as well as confirming appropriate use per the IUPred2A

+terms of use. 

+

 ## References

@@ -344,7 +371,7 @@ R Version

 R.version.string

 ```

-System Infomation

+System Information

 ```{r}

 as.data.frame(Sys.info())

 ```

@@ -105,7 +105,7 @@ sequenceMap(

 There are multiple customization options to allow for improved graphing. 

 One is the organization of the labels. 

 You are able to represent the sequence with both amino acid residues and their 

-location in the sequence, but you can choose one or the other (or nethier).

+location in the sequence, but you can choose one or the other (or neither).

 This is specified by the 'labelType' argument

 ```{r}

@@ -138,7 +138,7 @@ sequenceMap(

 ```

 The text can also be rotated, via the 'rotationAngle' argument, for ease of 

-reading. This is espeically helpful for larger sequences with dense graphics.

+reading. This is especially helpful for larger sequences with dense graphics.

 ```{r}

 sequenceMap(

   sequence = tendencyDF$AA,

@@ -217,7 +217,7 @@ sequenceMap(

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

 object and additional features can be added and annotated. The example below 

-will annotate a metal binding residue and the region that the P53 protien binds 

+will annotate a metal binding residue and the region that the P53 protein binds 

 DNA. These annotations and locations were retrieved from UniProt 

 (UniProt Consortium 2019).

 ```{r}

@@ -299,10 +299,10 @@ of the residues on the map.

 To solve this, sequenceMapCoordinates() will return the row (y value) and the 

 column (x value) as a data frame for each residue visualized with sequenceMap(). 

-The puporse of this is to make adding annotations easier and customizable.

+The purpose of this is to make adding annotations easier and customizable.

-As shown before, nbResidues deterines how many residues will be on each

+As shown before, nbResidues determines how many residues will be on each

 row. Make sure nbResidues is equal to the value used in sequenceMap(). 

 To get the coordinates, the amino acid sequence must be supplied. The output

@@ -319,7 +319,7 @@ head(coord_DF)

 ## Sequence Plot

-The funtions for calculating charge and scaled hydropathy and the iupred

+The functions for calculating charge and scaled hydropathy and the iupred

 functions all have plotting options. The plotting for these are done with the

 sequencePlot() function to have a uniform aesthetic. If you wish to make a plot

 with custom values, sequencePlot() can still be used.

@@ -399,7 +399,7 @@ R Version

 R.version.string

 ```

-System Infomation

+System Information

 ```{r}

 as.data.frame(Sys.info())

 ```

@@ -78,7 +78,7 @@ head(tendencyDF)

 ```

-For convient plotting, use structuralTendencyPlot().

+For convenient plotting, use structuralTendencyPlot().

 Results can be as a pie chart or bar plot. 

 ```{r}

@@ -188,7 +188,7 @@ structuralTendencyPlot(P53_MOUSE,

 In addition to the compositional profile of each residue, a summary of the 

 profile focused only on the structural tendency can be given by setting

 summarize = TRUE. This shifts the focus from amino acid identity to the general 

-composition. The graphType is preseved.

+composition. The graphType is preserved.

 ```{r}

 structuralTendencyPlot(P53_MOUSE,

@@ -246,7 +246,7 @@ R Version

 R.version.string

 ```

-System Infomation

+System Information

 ```{r}

 as.data.frame(Sys.info())

 ```