merge devel

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: MutantSets
 Title: What the Package Does (One Line, Title Case)
-Version: 0.0.0.9000
+Version: 0.0.0.9001
 Authors@R: 
     person(given = "Richard J.",
            family = "Acton",
@@ -12,7 +12,7 @@ License: CC BY 4.0
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2
 Depends: 
     shiny,
     shinydashboard,
@@ -34,4 +34,7 @@ Imports:
     htmlwidgets
 Suggests: 
     testthat,
-    covr
+    covr,
+    knitr,
+    rmarkdown
+VignetteBuilder: knitr

R/functions.R

@@ -347,6 +347,23 @@ loci_plot <- function(df) { ## var_type_colours !! global
 		)
 }
 
+## | SNP_freq_plot ------------------------------------------------------------
+
+SNP_freq_plot <- function(df) {
+	ggplot2::ggplot(df, ggplot2::aes(POS)) +
+		ggplot2::geom_density() +
+		ggplot2::facet_wrap(~CHROM, nrow = 1, scales = "free_x") + 
+		ggplot2::scale_x_continuous(labels = scales::comma) +
+		ggplot2::theme_light() +
+		ggplot2::theme(
+			axis.text.x = ggplot2::element_text(angle = 30, hjust = 1)
+		) + 
+		ggplot2::labs(
+			x = "Position (bp)",
+			y = "Variant Density"#,colour = "", alpha = ""
+		)
+}
+
 #' layout_ggplotly
 #' 
 #' Tweaks the layout of the x and y axis labels so they don't overlap

R/server.R

@@ -112,7 +112,13 @@ server <- function(input, output) {
 		# }
 
 		#dplyr::pull(pos)
-	})
+	}) %>%
+	bindCache(
+		input$DP_filter, input$QUAL_filter, input$QR_filter,
+		input$QA_filter, input$AF_filter, 
+		input$picked_chr
+	) %>%
+		bindEvent(input$go)
 
 	# Genotype filtering ------------------------------------------------------
 	## | Set sample names -----------------------------------------------------
@@ -186,6 +192,24 @@ server <- function(input, output) {
 		#girafe(code = print(plot))
 	})
 
+	## | Variant Denisty Plot -------------------------------------------------
+	output$vdplot <- plotly::renderPlotly({
+		loci() %>% 
+			SNP_freq_plot() %>%
+			plotly::ggplotly(dynamicTicks = TRUE, .) %>%
+			layout_ggplotly() %>%
+			plotly::layout(
+				legend = list(
+					title = list(text = ""),
+					valign = "bottom"
+				)
+			) %>%
+			plotly::config(
+				displaylogo = FALSE,
+				modeBarButtonsToRemove = list("hoverCompareCartesian")
+			) 
+
+	})
 
 	# output$chrplot_sel <- renderPrint({
 	# 	event_data("plotly_selected")

R/ui.R

@@ -6,6 +6,7 @@ sidebar <- shinydashboard::dashboardSidebar(
 	shinydashboard::sidebarMenu(
 		fileInput("vcf", "Select a VCF file", accept = ".vcf"),
 		fileInput("gff", "Select a gff file", accept = ".gff"),
+		actionButton("go","Start / Apply Filters"),
 		#menuItem("Options", tabName = "options", icon = icon("th")),
 		shinydashboard::menuItem(
 			"Filtering", tabName = "table", icon = icon("table")
@@ -81,6 +82,10 @@ body <- shinydashboard::dashboardBody(
 						#girafeOutput("chrplot")
 						#verbatimTextOutput("testpoints")
 					),
+					tabPanel(
+						"Variant Density",
+						plotly::plotlyOutput("vdplot")
+					),
 					tabPanel(
 						"Effect",
 						DT::DTOutput("effect")

README.md

@@ -6,7 +6,7 @@
 [![Travis build status](https://travis-ci.com/CECADBioinformaticsCoreFacility/MutantSets.svg?branch=master)](https://travis-ci.com/CECADBioinformaticsCoreFacility/MutantSets)
 <!-- badges: end -->
 
-The goal of MutantSets is to ...
+The goal of MutantSets is to permit the exploration of the results of whole genome sequencing and mutation calling in *C. elegans*, with the goal of identify candidate mutations responsible for phenotypes in genetic screens though mapping by sequencing.
 
 ## Installation
 
@@ -30,3 +30,8 @@ To start the app run:
 MutantSets::launchApp()
 ```
 
+For instructions on how to prepare data for use in this app see the vignette:
+
+```r
+vignette("Generating-input", package = "MutantSets")
+```

vignettes/Generating-input.Rmd

@@ -0,0 +1,173 @@
+---
+title: "Mutation Mapping Pipiline for *C. elegans* EMS mutagenesis and Backcross Experiments"
+author: "Richard J. Acton"
+date: "`r Sys.Date()`"
+output:
+  html_document:
+    fig_caption: yes
+    number_sections: yes
+    toc: yes
+    df_print: paged
+vignette: >
+  %\VignetteIndexEntry{Generating-input}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+bibliography: "assets/bib.bib"
+csl: "assets/genomebiology.csl"
+link-citations: yes
+linkcolor: blue
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+# Quick Start
+
+- __Inputs:__ 
+	- paired-end fastq files to a galaxy [@Afgan2016; @Jalili2020] history as `list of dataset pairs`
+	- A suitable genome fasta file (*C. elegans*, ce11.fa.gz - Compatible with WBcel235.86 used by SnpEff)
+- Run the Pipeline: https://usegalaxy.eu/u/richardjacton/w/c-elegans-ems-mutagenesis-mutation-caller
+- __Outputs:__ 
+	- [`MultiQC`](https://multiqc.info/) HTML report with QC info on the input fastqs, trimming, mapping, and deduplication steps.
+	- `.vcf` file with variants from all samples (FreeBayes mutation caller)
+	- `.vcf` file with variants from all samples (MiModD mutation caller)
+	- `.gff` file with deletions from all samples (MiModD deletion calling tool)
+- Perform Quality filtering and appropriate set subtractions with [`MutantSets`](https://github.com/RichardJActon/MutantSets) or alternatively the `MiModD VCF Filter` or `SnpSift Filter` tools to identify candidate variants.
+- (optionally) `MiModD NacreousMap` for visualisation of mutation locations and `MiModD Report Variants` for HTML mutation list
+
+NB samples are expected to be of the form 'A123_0001_S1_R1_L001.fq.gz', sample Identifiers are extracted from this with a regular expression: `\w+_(\d+)_S\d+_L\d+.*`. This would yield the sample identifier of: 0001. If your file does not conform to this pattern you may need to update this regex by editing the rules in the 'apply rule to collection' step of the workflow.
+
+# Background
+
+Doitsidou et al. reviewed Sequencing-Based Approaches for Mutation Mapping and Identification in *C. elegans* [@Doitsidou2016]. They describe three main approaches to mapping by sequencing:
+
+1. Hawaiian variant mapping
+2. EMS-density mapping
+3. Variant discovery mapping
+
+This pipeline is currently only compatible with 2 of them, EMS-density mapping & Variant discovery mapping (VDM).
+
+![](graphics/Doitsidou2016_fig2.png)
+
+![](graphics/Doitsidou2016_fig3.png)
+
+## Research Need
+
+The Schumacher lab identified a need for an analysis pipeline to map and identify mutations in Ethyl methanesulfonate (EMS) mutagenesis forward genetic screens.
+
+Previously a tool called `CloudMap` [@Minevich2012] had been used for this purpose on a Galaxy server.
+`CloundMap` is no longer under active development and has been deprecated from [Galaxy Europe](https://usegalaxy.eu/) and replaced by `MiModD` [Docs](https://mimodd.readthedocs.io/en/doc0.1.8/index.html)
+
+## Choice of Tools
+
+In a comparison of *C. elegans* mutation calling pipelines Smith et al. [@Smith2017a] indicated that they had good results with the `FreeBayes` [@Garrison2012].
+So I have initially included this tool here in addition to the`MiModD` mutation caller to evaluate their relative performance.
+They also found the the `BBMap` aligner yielded better results however this is not available in Galaxy so I have opted for `Bowtie2` for expediency.
+
+# Pipeline Summary
+
+[Pipeline File (Local)](assets/Galaxy-Workflow-EMS_Mutagenesis_Backcross_Mutation_Caller.ga)
+
+https://usegalaxy.eu/u/richardjacton/w/c-elegans-ems-mutagenesis-mutation-caller
+
+- Adapter and Quality Trimming with [`fastp`](https://github.com/OpenGene/fastp) [@Chen2018]
+- Alignment with [`bowtie2 --sensitive-local`](https://github.com/BenLangmead/bowtie2) [@Langmead2012]
+- [`samtools view`](https://github.com/samtools/samtools) requiring that reads are mapped in a proper pair [@Li2009b]
+- Removal of PCR duplicates with [`Picard MarkDuplicates`](https://broadinstitute.github.io/picard/command-line-overview.html#MarkDuplicates) [@BroadInstitute2019]
+- Left alignment of indels in the BAM files using [`FreeBayes`](https://github.com/ekg/freebayes) [@Garrison2012]
+- [`MultiQC`](https://github.com/ewels/MultiQC) aggregating quality metrics from trimming, deduplication and alignment [@Ewels2016]
+- Variant calling with [`FreeBayes`](https://github.com/ekg/freebayes) [@Garrison2012], [`MiModD`](https://mimodd.readthedocs.io/) [@Baumeister2013] variant caller and deletion caller
+- SNP effect annotation with [`SnfEff eff`](http://snpeff.sourceforge.net/SnpEff.html) [@Cingolani2012]
+- SNP type annotation with [`SnpSift Variant Type`](http://snpeff.sourceforge.net/SnpSift.html) [@Cingolani2012]
+
+# Instructions (Step-by-Step)
+
+__1. Upload Data to galaxy__
+
+![](graphics/upload.png)
+
+__2. Select all fastq files and create a paired list__
+
+![](graphics/select-all.png)
+
+![](graphics/build-list-paired-data.png)
+
+__3. Pair the fastq files__
+
+![](graphics/pairing-dialog.png)
+
+__4. Import the [workflow](https://usegalaxy.eu/u/richardjacton/w/c-elegans-ems-mutagenesis-mutation-caller)__
+
+![](graphics/import_workflow.png)
+
+__5. Run the workflow__
+
+Select the paired list object and a genome sequence file as inputs
+
+![](graphics/run_workflow.png)
+
+__6. Check Quality Control Information__
+
+Inspect the `MultiQC` output for signs of technical problems with your data.
+Consult with your friendly local bioinformatician if there are QC issues you can't diagnose.
+
+![](graphics/view_multiqc.png)
+
+__7. Preliminary quality filtering `SnpSift filter`__
+
+Locate the [`SnpSift filter`](https://pcingola.github.io/SnpEff/SnpSift.html#filter) tool in the galaxy tools panel and apply some initial quality filters, simply `( QUAL > 15)` or `20` is probably sufficient. 
+Starting with a low stringency filter and applying more stringent criteria when inspecting your candidate mutations it is probably advisable to avoid throwing out possible mutations.
+Some initial filtering is advisable as the full-sized VCF files may be too large to be easily read by the candidate mutant inspection tool in the next steps.
+You can check how many lines are in your VCF files by selecting them in the Galaxy history.
+
+![](graphics/SnpSift_filter.png)
+
+__8. Download Data__
+
+The main `FreeBayes` VCF file:
+
+![](graphics/Download_filtered_vcf.png)
+
+The `MiModD` deletion calls:
+
+![](graphics/Download_MiModD_deletions.png)
+
+__9. Load the results in the `MutantSets` Shiny App to identify candidate mutations__
+
+If running the App locally, install the [`R`](https://www.r-project.org/) package from: https://github.com/RichardJActon/MutantSets
+
+R package installation and running the app locally:
+
+```
+# install.packages("remotes") # If you don't already have remotes/devtools
+# remotes::install_github("knausb/vcfR") # If vcfR fails to install from CRAN
+remotes::install_github("RichardJActon/MutantSets")
+MutantSets::launchApp() # opens the app in a web browser
+```
+
+- Load the VCF and (optionally) the gff deletion mutant files into `MutantSets`
+- (Optionally) Name your samples something easier to understand
+- Use the genotype filters to subtract the appropriate sets
+- Tweak quality and allele frequency thresholds to get a small set of high quality candidates
+- Assess the candidate mutations by clicking on them and looking at their predicted effects and genomic locations
+- Download your top results as a `.tsv` file (openable in excel)
+
+__You should now have some candidate mutants to screen - Good Luck!__
+
+# Feedback
+
+Please direct bug reports, feature requests, and questions to the maintainer of the mutant sets package via [github issues](https://github.com/RichardJActon/MutantSets/issues. 
+
+# References
+
+```{r, echo=FALSE, include=FALSE, eval=FALSE}
+getCitations::getCitations(
+	normalizePath("C-elegans_Backcross_mutation_calling_Galaxy_Workflow.Rmd"),
+	normalizePath("assets/bib.bib"),
+	"~/Documents/bibtex/library.bib"
+)
+```
+