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- retain original whitespacing in DESCRIPTION
- do not change vignette metadata order
- do not replace links [<text>][] with inline links [<text>](<link>)
- do not use shiny runtime in vignette
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mtmorgan committed Sep 24, 2024
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74 changes: 37 additions & 37 deletions DESCRIPTION
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Expand Up @@ -2,68 +2,68 @@ Package: AlphaMissenseR
Title: Accessing AlphaMissense Data Resources in R
Version: 1.1.5
Authors@R:
c(person(
c(person(
"Martin", "Morgan", role = c("aut", "cre"),
email = "mtmorgan.xyz@gmail.com",
comment = c(ORCID = "0000-0002-5874-8148")
), person(
), person(
"Tram", "Nguyen",
role = "aut"
), person(
), person(
"Tyrone", "Lee",
role = "ctb"
), person(
), person(
"Nitesh", "Turaga",
role = "ctb"
), person(
), person(
"Chan Zuckerberg Initiative DAF CZF2019-002443",
role = "fnd"
), person(
), person(
"NIH NCI ITCR U24CA180996",
role = "fnd"
), person(
), person(
"NIH NCI IOTN U24CA232979",
role = "fnd"
), person(
), person(
"NIH NCI ARTNet U24CA274159",
role = "fnd"
))
))
Description:
The AlphaMissense publication
<https://www.science.org/doi/epdf/10.1126/science.adg7492>
outlines how a variant of AlphaFold / DeepMind was used to predict
missense variant pathogenicity. Supporting data on Zenodo
<https://zenodo.org/record/10813168> include, for instance, 71M
variants across hg19 and hg38 genome builds. The 'AlphaMissenseR'
package allows ready access to the data, downloading individual
files to DuckDB databases for exploration and integration into *R*
and *Bioconductor* workflows.
The AlphaMissense publication
<https://www.science.org/doi/epdf/10.1126/science.adg7492>
outlines how a variant of AlphaFold / DeepMind was used to predict
missense variant pathogenicity. Supporting data on Zenodo
<https://zenodo.org/record/10813168> include, for instance, 71M
variants across hg19 and hg38 genome builds. The 'AlphaMissenseR'
package allows ready access to the data, downloading individual
files to DuckDB databases for exploration and integration into *R*
and *Bioconductor* workflows.
License: Artistic-2.0
URL: https://mtmorgan.github.io/AlphaMissenseR/
BugReports: https://github.com/mtmorgan/AlphaMissenseR/issues
Depends:
R (>= 4.3.0),
dplyr
R (>= 4.3.0),
dplyr
Imports:
rjsoncons (>= 1.0.1), DBI, duckdb (>= 0.9.1), rlang,
curl, BiocFileCache, spdl, memoise, BiocBaseUtils,
utils, stats, methods, whisker, ggplot2
rjsoncons (>= 1.0.1), DBI, duckdb (>= 0.9.1), rlang,
curl, BiocFileCache, spdl, memoise, BiocBaseUtils,
utils, stats, methods, whisker, ggplot2
Suggests:
BiocManager,
BiocGenerics,
GenomicRanges,
GenomeInfoDb,
AnnotationHub,
ensembldb,
httr,
tidyr,
r3dmol, bio3d, shiny, shiny.gosling,
colorspace,
knitr,
rmarkdown,
testthat (>= 3.0.0)
BiocManager,
BiocGenerics,
GenomicRanges,
GenomeInfoDb,
AnnotationHub,
ensembldb,
httr,
tidyr,
r3dmol, bio3d, shiny, shiny.gosling,
colorspace,
knitr,
rmarkdown,
testthat (>= 3.0.0)
biocViews: SNP, Annotation, FunctionalGenomics, StructuralPrediction,
Transcriptomics, VariantAnnotation, GenePrediction, ImmunoOncology
Transcriptomics, VariantAnnotation, GenePrediction, ImmunoOncology
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
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98 changes: 48 additions & 50 deletions vignettes/alphafold.Rmd
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@@ -1,11 +1,10 @@
---
title: "B. AlphaFold Integration"
output: rmarkdown::html_vignette
runtime: shiny
vignette: >
%\VignetteIndexEntry{B. AlphaFold Integration}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
Expand All @@ -19,15 +18,17 @@ Original version: 31 October, 2023

# Introduction

This vignette illustrates how to display
[AlphaMissense](https://www.science.org/doi/10.1126/science.adg7492)
predictions on [AlphaFold](https://alphafold.ebi.ac.uk/) predicted
protein structure.
This vignette illustrates how to display [AlphaMissense][] predictions
on [AlphaFold][] predicted protein structure.

[AlphaMissense]: https://www.science.org/doi/10.1126/science.adg7492
[AlphaFold]: https://alphafold.ebi.ac.uk/

Visualization makes use of CRAN packages
[bio3d](https://CRAN.R-project.org/package=bio3d) and
[r3dmol](https://CRAN.R-project.org/package=r3dmol). Install these (if
necessary) with
Visualization makes use of CRAN packages [bio3d][] and
[r3dmol][]. Install these (if necessary) with

[bio3d]: https://CRAN.R-project.org/package=bio3d
[r3dmol]: https://CRAN.R-project.org/package=r3dmol

```{r dependencies, eval = FALSE}
pkgs <- c("bio3d", "r3dmol")
Expand All @@ -36,8 +37,9 @@ if (length(pkgs_to_install))
BiocManager::install(pkgs_to_install)
```

Start by loading the
[AlphaMissenseR](https://mtmorgan.github.io/AlphaMissenseR) library.
Start by loading the [AlphaMissenseR][] library.

[AlphaMissenseR]: https://mtmorgan.github.io/AlphaMissenseR

```{r setup, message = FALSE}
library(AlphaMissenseR)
Expand All @@ -59,15 +61,15 @@ am_data("hg38")

# AlphaFold protein structure

AlphaMissense predictions on pathogenicity of amino acid changes can be
combined with AlphaFold (or other) predictions of protein structure.
AlphaMissense predictions on pathogenicity of amino acid changes can
be combined with AlphaFold (or other) predictions of protein
structure.

## Fast path

Figure 3F of the
[AlphaMissense](https://www.science.org/doi/10.1126/science.adg7492)
publication visualizes mean pathogenicity for UniProt id P35557. Filter
amino acid data for that identifier
Figure 3F of the [AlphaMissense][] publication visualizes mean
pathogenicity for UniProt id P35557. Filter amino acid data for that
identifier

```{r P35557}
P35557_aa <-
Expand All @@ -86,12 +88,11 @@ sections explore this visualization in more detail.

## UniProt identifiers

Both AlphaMissense and AlphaFold use UniProt identifiers. Find all
AlphaMissense amino acid substitutions with UniProt identifiers starting
with `P3555`; the choice of this identifier is so that results can be
compared with Figure 3F of the
[AlphaMissense](https://www.science.org/doi/10.1126/science.adg7492)
publication.
Both AlphaMissense and AlphaFold use UniProt
identifiers. Find all AlphaMissense amino acid substitutions with
UniProt identifiers starting with `P3555`; the choice of this
identifier is so that results can be compared with Figure 3F of the
[AlphaMissense][] publication.

```{r uniprot_ids}
uniprot_ids <-
Expand All @@ -102,11 +103,10 @@ uniprot_ids <-
uniprot_ids
```

The [AlphaMissenseR](https://mtmorgan.github.io/AlphaMissenseR) package
includes several functions that facilitate interaction with
[AlphaFold](https://alphafold.ebi.ac.uk/); these functions start with
`af_*()`. Use `af_predictions()` to discover AlphaFold predictions (via
the AlphaFold API) associated with UniProt identifiers.
The [AlphaMissenseR][] package includes several functions that
facilitate interaction with [AlphaFold][]; these functions start with
`af_*()`. Use `af_predictions()` to discover AlphaFold predictions
(via the AlphaFold API) associated with UniProt identifiers.

```{r af_predictions}
prediction <- af_predictions(uniprot_ids)
Expand All @@ -116,8 +116,9 @@ glimpse(prediction)
Note the message indicating that some UniProt identifiers (accessions)
are not found in the AlphaFold database. The query returns a tibble
containing columns with information on organism and UniProt
characteristics (including protein sequence) , as well as URLs for files
representing three-dimensional protein structure. We will use `pdbUrl`.
characteristics (including protein sequence) , as well as URLs for
files representing three-dimensional protein structure. We will use
`pdbUrl`.

## Protein structure

Expand All @@ -131,17 +132,15 @@ pdb_url <-
```

Cache the PDB file using BiocFileCache, and read the PDB file using
[bio3d](https://CRAN.R-project.org/package=bio3d).
[bio3d][].

```{r pdb}
pdb_file <- BiocFileCache::bfcrpath(rnames = basename(pdb_url), fpath = pdb_url)
pdb <- bio3d::read.pdb(pdb_file)
pdb
```

Visualize the protein using
[r3dmol](https://CRAN.R-project.org/package=r3dmol), using the 'cartoon'
style.
Visualize the protein using [r3dmol][], using the 'cartoon' style.

```{r pdb_r3dmol}
r3dmol::r3dmol() |>
Expand All @@ -155,10 +154,10 @@ r3dmol::r3dmol() |>

## Average pathogenicity

Our goal is to visualize some measure of 'average' pathogenicity on the
three-dimensional protein structure provided by AlphaFold. Start with a
specific genome sequence (e.g., `hg38`). Filter to the amino acids in
our UniProt region of interest.
Our goal is to visualize some measure of 'average' pathogenicity on
the three-dimensional protein structure provided by AlphaFold. Start
with a specific genome sequence (e.g., `hg38`). Filter to the amino
acids in our UniProt region of interest.

```{r am_data-hg38}
P35557 <-
Expand All @@ -167,9 +166,9 @@ P35557 <-
```

At each chromosome position, the AlphaMissense predictions contain
several alternative alleles and hence protein variants. The (arithmetic)
average pathogenicity (this is an extremely naive computation) at each
amino acid position is
several alternative alleles and hence protein variants. The
(arithmetic) average pathogenicity (this is an extremely naive
computation) at each amino acid position is

```{r am_aa_pathogenicity}
pathogenicity <- am_aa_pathogenicity(P35557)
Expand All @@ -178,12 +177,11 @@ pathogenicity

## Coloring amino acids by position

Individual amino acids can be colored using the `colorfunc=` argument to
`r3dmol::m_style_cartoon()`. This is a Javascript function that takes
each atom position and returns the corresponding color. The approach
taken in [AlphaMissenseR](https://mtmorgan.github.io/AlphaMissenseR) is
to use a template, ultimately replacing `...` with a vector of residue
colors.
Individual amino acids can be colored using the `colorfunc=` argument
to `r3dmol::m_style_cartoon()`. This is a Javascript function that
takes each atom position and returns the corresponding color. The
approach taken in [AlphaMissenseR][] is to use a template, ultimately
replacing `...` with a vector of residue colors.

```{r js_template}
cat(
Expand All @@ -194,8 +192,8 @@ cat(

The function `af_colorfunc_by_position()` provides a mechanism for
translating a vector of scores between zero and one into a vector of
colors. This is illustrated for a 12-amino acid sequence where the first
and last residues are uncolored.
colors. This is illustrated for a 12-amino acid sequence where the
first and last residues are uncolored.

```{r af_colorfunc_by_position}
df <- tibble(
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