From c33d324214c9d48db95d13e8f06c0ea7e998c01c Mon Sep 17 00:00:00 2001 From: richardjacton Date: Tue, 10 May 2022 12:51:46 +0000 Subject: [PATCH 1/4] Added Vignette --- DESCRIPTION | 7 +- vignettes/Generating-input.Rmd | 173 +++++++++++++++ ...S_Mutagenesis_Backcross_Mutation_Caller.ga | 1 + vignettes/assets/bib.bib | 206 ++++++++++++++++++ vignettes/assets/genomebiology.csl | 155 +++++++++++++ vignettes/graphics/Doitsidou2016_fig2.png | Bin 0 -> 528658 bytes vignettes/graphics/Doitsidou2016_fig3.png | Bin 0 -> 566339 bytes .../graphics/Download_MiModD_deletions.png | Bin 0 -> 26071 bytes vignettes/graphics/Download_filtered_vcf.png | Bin 0 -> 20195 bytes vignettes/graphics/SnpSift_filter.png | Bin 0 -> 21387 bytes vignettes/graphics/build-list-paired-data.png | Bin 0 -> 32200 bytes vignettes/graphics/import_workflow.png | Bin 0 -> 26721 bytes vignettes/graphics/pairing-dialog.png | Bin 0 -> 80590 bytes vignettes/graphics/run_workflow.png | Bin 0 -> 26242 bytes vignettes/graphics/run_workflow_2.png | Bin 0 -> 11930 bytes vignettes/graphics/select-all.png | Bin 0 -> 27287 bytes vignettes/graphics/upload.png | Bin 0 -> 19551 bytes vignettes/graphics/view_multiqc.png | Bin 0 -> 7369 bytes 18 files changed, 540 insertions(+), 2 deletions(-) create mode 100644 vignettes/Generating-input.Rmd create mode 100644 vignettes/assets/Galaxy-Workflow-EMS_Mutagenesis_Backcross_Mutation_Caller.ga create mode 100644 vignettes/assets/bib.bib create mode 100644 vignettes/assets/genomebiology.csl create mode 100644 vignettes/graphics/Doitsidou2016_fig2.png create mode 100644 vignettes/graphics/Doitsidou2016_fig3.png create mode 100644 vignettes/graphics/Download_MiModD_deletions.png create mode 100644 vignettes/graphics/Download_filtered_vcf.png create mode 100644 vignettes/graphics/SnpSift_filter.png create mode 100644 vignettes/graphics/build-list-paired-data.png create mode 100644 vignettes/graphics/import_workflow.png create mode 100644 vignettes/graphics/pairing-dialog.png create mode 100644 vignettes/graphics/run_workflow.png create mode 100644 vignettes/graphics/run_workflow_2.png create mode 100644 vignettes/graphics/select-all.png create mode 100644 vignettes/graphics/upload.png create mode 100644 vignettes/graphics/view_multiqc.png diff --git a/DESCRIPTION b/DESCRIPTION index f8b13b6..c7c09d3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -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 diff --git a/vignettes/Generating-input.Rmd b/vignettes/Generating-input.Rmd new file mode 100644 index 0000000..cb21d8d --- /dev/null +++ b/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" +) +``` + diff --git a/vignettes/assets/Galaxy-Workflow-EMS_Mutagenesis_Backcross_Mutation_Caller.ga b/vignettes/assets/Galaxy-Workflow-EMS_Mutagenesis_Backcross_Mutation_Caller.ga new file mode 100644 index 0000000..57b96d7 --- /dev/null +++ b/vignettes/assets/Galaxy-Workflow-EMS_Mutagenesis_Backcross_Mutation_Caller.ga @@ -0,0 +1 @@ +{"uuid": "26956f4f-711b-4ccd-8416-51c89f122bd0", "tags": [], "format-version": "0.1", "name": "EMS Mutagenesis Backcross 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collaborative biomedical analyses: 2020 update}, + Volume = {48}, + Year = {2020}, + Abstract = {Galaxy (https://galaxyproject.org) is a web-based computational workbench used by tens of thousands of scientists across the world to analyze large biomedical datasets. Since 2005, the Galaxy project has fostered a global community focused on achieving accessible, reproducible, and collaborative research. Together, this community develops the Galaxy software framework, integrates analysis tools and visualizations into the framework, runs public servers that make Galaxy available via a web browser, performs and publishes analyses using Galaxy, leads bioinformatics workshops that introduce and use Galaxy, and develops interactive training materials for Galaxy. Over the last two years, all aspects of the Galaxy project have grown: code contributions, tools integrated, users, and training materials. Key advances in Galaxy's user interface include enhancements for analyzing large dataset collections as well as interactive tools for exploratory data analysis. Extensions to Galaxy's framework include support for federated identity and access management and increased ability to distribute analysis jobs to remote resources. New community resources include large public servers in Europe and Australia, an increasing number of regional and local Galaxy communities, and substantial growth in the Galaxy Training Network.}, + Doi = {10.1093/nar/gkaa434}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Jalili2020{\_}NAR({\_}{\_}Galaxy-2020-update{\_}{\_}).pdf:pdf}, + Issn = {0305-1048}, + Pmid = {32479607}, + Url = {https://academic.oup.com/nar/article/48/W1/W395/5849904} +} + + +@Misc{Baumeister2013, + Publisher = {Baumeister Lab}, + Title = {MiModD}, + Year = {2013}, + Url = {https://sourceforge.net/projects/mimodd/} +} + + +@Article{Ewels2016, + Author = {Ewels, Philip and Magnusson, M{\aa}ns and Lundin, Sverker and K{\"{a}}ller, Max}, + Journal = {Bioinformatics (Oxford, England)}, + Number = {19}, + Pages = {3047--8}, + Title = {MultiQC: summarize analysis results for multiple tools and samples in a single report.}, + Volume = {32}, + Year = {2016}, + Abstract = {MOTIVATION Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis. RESULTS We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization. AVAILABILITY AND IMPLEMENTATION MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info CONTACT phil.ewels@scilifelab.se.}, + Doi = {10.1093/bioinformatics/btw354}, + Issn = {1367-4811}, + Pmid = {27312411}, + Url = {http://www.ncbi.nlm.nih.gov/pubmed/27312411} +} + + +@Article{Afgan2016, + Author = {Afgan, Enis and Baker, Dannon and van den Beek, Marius and Blankenberg, Daniel and Bouvier, Dave and {\v{C}}ech, Martin and Chilton, John and Clements, Dave and Coraor, Nate and Eberhard, Carl and Gr{\"{u}}ning, Bj{\"{o}}rn and Guerler, Aysam and Hillman-Jackson, Jennifer and {Von Kuster}, Greg and Rasche, Eric and Soranzo, Nicola and Turaga, Nitesh and Taylor, James and Nekrutenko, Anton and Goecks, Jeremy}, + Journal = {Nucleic Acids Research}, + Month = {jul}, + Number = {W1}, + Pages = {W3--W10}, + Title = {The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update}, + Volume = {44}, + Year = {2016}, + Abstract = {High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.}, + Doi = {10.1093/nar/gkw343}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Afgan2016{\_}NAR({\_}{\_}The-Galaxy-Platform-2016-update{\_}{\_}).pdf:pdf}, + Issn = {0305-1048}, + Pmid = {27137889}, + Url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw343} +} + + +@Article{Smith2017a, + Author = {Smith, Harold E. and Yun, Sijung}, + Editor = {Reiner, David J.}, + Journal = {PLOS ONE}, + Month = {mar}, + Number = {3}, + Pages = {e0174446}, + Title = {Evaluating alignment and variant-calling software for mutation identification in C. elegans by whole-genome sequencing}, + Volume = {12}, + Year = {2017}, + Abstract = {Whole-genome sequencing is a powerful tool for analyzing genetic variation on a global scale. One particularly useful application is the identification of mutations obtained by classical phenotypic screens in model species. Sequence data from the mutant strain is aligned to the reference genome, and then variants are called to generate a list of candidate alleles. A number of software pipelines for mutation identification have been targeted to C. elegans, with particular emphasis on ease of use, incorporation of mapping strain data, subtraction of background variants, and similar criteria. Although success is predicated upon the sensitive and accurate detection of candidate alleles, relatively little effort has been invested in evaluating the underlying software components that are required for mutation identification. Therefore, we have benchmarked a number of commonly used tools for sequence alignment and variant calling, in all pair-wise combinations, against both simulated and actual datasets. We compared the accuracy of those pipelines for mutation identification in C. elegans, and found that the combination of BBMap for alignment plus FreeBayes for variant calling offers the most robust performance.}, + Doi = {10.1371/journal.pone.0174446}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Smith2017{\_}PLoSone({\_}{\_}Evaluating-alignment-variant-calling-software-mutation-c-elegans-WGS{\_}{\_}).pdf:pdf}, + Issn = {1932-6203}, + Url = {https://dx.plos.org/10.1371/journal.pone.0174446}, + Isbn = {1111111111} +} + + +@Article{Minevich2012, + Author = {Minevich, Gregory and Park, Danny S. and Blankenberg, Daniel and Poole, Richard J. and Hobert, Oliver}, + Journal = {Genetics}, + Number = {4}, + Pages = {1249--1269}, + Title = {CloudMap: A cloud-based pipeline for analysis of mutant genome sequences}, + Volume = {192}, + Year = {2012}, + Abstract = {Whole genome sequencing (WGS) allows researchers to pinpoint genetic differences between individuals and significantly shortcuts the costly and time-consuming part of forward genetic analysis in model organism systems. Currently, the most effortintensive part of WGS is the bioinformatic analysis of the relatively short reads generated by second generation sequencing platforms. We describe here a novel, easily accessible and cloud-based pipeline, called CloudMap, which greatly simplifies the analysis of mutant genome sequences. Available on the Galaxy web platform, CloudMap requires no software installation when run on the cloud, but it can also be run locally or via Amazon's Elastic Compute Cloud (EC2) service. CloudMap uses a series of predefined workflows to pinpoint sequence variations in animal genomes, such as those of premutagenized and mutagenized Caenorhabditis elegans strains. In combination with a variant-based mapping procedure, CloudMap allows users to sharply define genetic map intervals graphically and to retrieve very short lists of candidate variants with a few simple clicks. Automated workflows and extensive video user guides are available to detail the individual analysis steps performed (http://usegalaxy.org/cloudmap). We demonstrate the utility of CloudMap for WGS analysis of C. elegans and Arabidopsis genomes and describe how other organisms (e.g., Zebrafish and Drosophila) can easily be accommodated by this software platform. To accommodate rapid analysis of many mutants from large-scale genetic screens, Cloud- Map contains an in silico complementation testing tool that allows users to rapidly identify instances where multiple alleles of the same gene are present in the mutant collection. Lastly, we describe the application of a novel mapping/WGS method ("Variant Discovery Mapping") that does not rely on a defined polymorphic mapping strain, and we integrate the application of this method into CloudMap. CloudMap tools and documentation are continually updated at http://usegalaxy.org/cloudmap. {\textcopyright} 2012 by the Genetics Society of America.}, + Doi = {10.1534/genetics.112.144204}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Minevich2012{\_}Genetics({\_}{\_}CloudMap-analysis-platform-mutant-genome-sequences{\_}{\_}).pdf:pdf}, + Issn = {00166731} +} + + +@Article{Cingolani2012, + Author = {Cingolani, Pablo and Platts, Adrian and Wang, Le Lily and Coon, Melissa and Nguyen, Tung and Wang, Luan and Land, Susan J. and Lu, Xiangyi and Ruden, Douglas M.}, + Journal = {Fly}, + Month = {apr}, + Number = {2}, + Pages = {80--92}, + Title = {A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff}, + Volume = {6}, + Year = {2012}, + Abstract = {Copy number variation (CNV) in the genome is a complex phenomenon, and not completely understood. We have developed a method, CNVnator, for CNV discovery and genotyping from read-depth (RD) analysis of personal genome sequencing. Our method is based on combining the established mean-shift approach with additional refinements (multiple-bandwidth partitioning and GC correction) to broaden the range of discovered CNVs. We calibrated CNVnator using the extensive validation performed by the 1000 Genomes Project. Because of this, we could use CNVnator for CNV discovery and genotyping in a population and characterization of atypical CNVs, such as de novo and multi-allelic events. Overall, for CNVs accessible by RD, CNVnator has high sensitivity (86{\%}-96{\%}), low false-discovery rate (3{\%}-20{\%}), high genotyping accuracy (93{\%}-95{\%}), and high resolution in breakpoint discovery ({\textless}200 bp in 90{\%} of cases with high sequencing coverage). Furthermore, CNVnator is complementary in a straightforward way to split-read and read-pair approaches: It misses CNVs created by retrotransposable elements, but more than half of the validated CNVs that it identifies are not detected by split-read or read-pair. By genotyping CNVs in the CEPH, Yoruba, and Chinese-Japanese populations, we estimated that at least 11{\%} of all CNV loci involve complex, multi-allelic events, a considerably higher estimate than reported earlier. Moreover, among these events, we observed cases with allele distribution strongly deviating from Hardy-Weinberg equilibrium, possibly implying selection on certain complex loci. Finally, by combining discovery and genotyping, we identified six potential de novo CNVs in two family trios.}, + Doi = {10.4161/fly.19695}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Cingolani2012{\_}LandesBioSci({\_}{\_}SnpEff{\_}{\_}).pdf:pdf}, + Issn = {1933-6934}, + Url = {http://www.tandfonline.com/doi/abs/10.4161/fly.19695}, + Keywords = {2012 landes bioscience,drosophila melanogaster,next generation dna sequencing,personal genomes,whole-genome snp analysis} +} + + +@Article{Li2009b, + Author = {Li, H. and Handsaker, B. and Wysoker, A. and Fennell, T. and Ruan, J. and Homer, N. and Marth, G. and Abecasis, G. and Durbin, R.}, + Journal = {Bioinformatics}, + Month = {aug}, + Number = {16}, + Pages = {2078--2079}, + Title = {The Sequence Alignment/Map format and SAMtools}, + Volume = {25}, + Year = {2009}, + Doi = {10.1093/bioinformatics/btp352}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Li2009{\_}Bioinf({\_}{\_}SAMtools{\_}{\_}).pdf:pdf}, + Issn = {1367-4803}, + Pmid = {19505943}, + Url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp352} +} + + +@Article{Doitsidou2016, + Author = {Doitsidou, Maria and Jarriault, Sophie and Poole, Richard J.}, + Journal = {Genetics}, + Month = {oct}, + Number = {2}, + Pages = {451--474}, + Title = {Next-Generation Sequencing-Based Approaches for Mutation Mapping and Identification in Caenorhabditis elegans}, + Volume = {204}, + Year = {2016}, + Abstract = {The use of next-generation sequencing (NGS) has revolutionized the way phenotypic traits are assigned to genes. In this review, we describe NGS-based methods for mapping a mutation and identifying its molecular identity, with an emphasis on applications in Caenorhabditis elegans. In addition to an overview of the general principles and concepts, we discuss the main methods, provide practical and conceptual pointers, and guide the reader in the types of bioinformatics analyses that are required. Owing to the speed and the plummeting costs of NGS-based methods, mapping and cloning a mutation of interest has become straightforward, quick, and relatively easy. Removing this bottleneck previously associated with forward genetic screens has significantly advanced the use of genetics to probe fundamental biological processes in an unbiased manner.}, + Doi = {10.1534/genetics.115.186197}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Doitsidou20XX{\_}Genetics{\_}wormbook{\_}wormmethods({\_}{\_}NGS-based-mutation-mapping-identification-C-elegans{\_}{\_}).pdf:pdf}, + Issn = {0016-6731}, + Url = {http://www.genetics.org/cgi/doi/10.1534/genetics.115.186197}, + Keywords = {Caenorhabditis elegans,Mapping-by-sequencing,Mutation identification,Next-generation sequencing,Positional cloning,WormBook} +} + + +@Article{Garrison2012, + Author = {Garrison, Erik and Marth, Gabor}, + Month = {jul}, + Pages = {1--9}, + Title = {Haplotype-based variant detection from short-read sequencing}, + Year = {2012}, + Abstract = {The direct detection of haplotypes from short-read DNA sequencing data requires changes to existing small-variant detection methods. Here, we develop a Bayesian statistical framework which is capable of modeling multiallelic loci in sets of individuals with non-uniform copy number. We then describe our implementation of this framework in a haplotype-based variant detector, FreeBayes.}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Garrison2012{\_}ArxiV({\_}{\_}FreeBayes{\_}{\_}).pdf:pdf}, + Url = {http://arxiv.org/abs/1207.3907}, + Archiveprefix = {arXiv}, + Arxivid = {1207.3907}, + Eprint = {1207.3907} +} + + +@Article{Chen2018, + Author = {Chen, Shifu and Zhou, Yanqing and Chen, Yaru and Gu, Jia}, + Journal = {Bioinformatics}, + Month = {sep}, + Number = {17}, + Pages = {i884--i890}, + Title = {fastp: an ultra-fast all-in-one FASTQ preprocessor}, + Volume = {34}, + Year = {2018}, + Abstract = {Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2-5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.}, + Doi = {10.1093/bioinformatics/bty560}, + File = {:home/richardjamesacton/Documents/PhD{\_}Stuff/Papers/Chen2018{\_}Bioinf({\_}{\_}fastp{\_}{\_}).pdf:pdf}, + Issn = {1367-4803}, + Pmid = {30423086}, + Url = {https://academic.oup.com/bioinformatics/article/34/17/i884/5093234} +} + + +@Article{Langmead2012, + Author = {Langmead, Ben and Salzberg, Steven L}, + Journal = {Nature Methods}, + Month = {apr}, + Number = {4}, + Pages = {357--359}, + Title = {Fast gapped-read alignment with Bowtie 2}, + Volume = {9}, + Year = {2012}, + Abstract = {As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.}, + Doi = {10.1038/nmeth.1923}, + File = {:home/richardjamesacton/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Langmead, Salzberg - 2012 - Fast gapped-read alignment with Bowtie 2(2).pdf:pdf}, + Issn = {1548-7091}, + Pmid = {22388286}, + Url = {http://www.ncbi.nlm.nih.gov/pubmed/22388286}, + Keywords = {Algorithms,Computational Biology,Computational Biology/*methods,Computational Biology: methods,DNA,DNA/methods,DNA: methods,Databases,Genetic,Genome,Human,Human/genetics,Human: genetics,Humans,Sequence Alignment,Sequence Alignment/methods,Sequence Alignment: methods,Sequence Analysis} +} + + +@Misc{BroadInstitute2019, + Publisher = {Broad Institute}, + Title = {Picard Toolkit}, + Year = {2019}, + Url = {http://broadinstitute.github.io/picard/} +} diff --git 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literal 0 HcmV?d00001 From dbd54c0a68f22843f2c1ba71f963f5022f012ad4 Mon Sep 17 00:00:00 2001 From: richardjacton Date: Tue, 10 May 2022 12:53:55 +0000 Subject: [PATCH 2/4] only react to filter changes on button click --- R/server.R | 8 +++++++- R/ui.R | 1 + 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/R/server.R b/R/server.R index 9dc5a00..7fe8754 100644 --- a/R/server.R +++ b/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 ----------------------------------------------------- diff --git a/R/ui.R b/R/ui.R index 26dcabe..77eaf86 100644 --- a/R/ui.R +++ b/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") From 99d689efefa02e1797f9cd2f445aa442773d551c Mon Sep 17 00:00:00 2001 From: richardjacton Date: Tue, 10 May 2022 12:54:32 +0000 Subject: [PATCH 3/4] basic variant density plot --- R/functions.R | 17 +++++++++++++++++ R/server.R | 18 ++++++++++++++++++ R/ui.R | 4 ++++ 3 files changed, 39 insertions(+) diff --git a/R/functions.R b/R/functions.R index a032732..abed051 100644 --- a/R/functions.R +++ b/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 diff --git a/R/server.R b/R/server.R index 7fe8754..3c40912 100644 --- a/R/server.R +++ b/R/server.R @@ -192,6 +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") diff --git a/R/ui.R b/R/ui.R index 77eaf86..b5ce9cf 100644 --- a/R/ui.R +++ b/R/ui.R @@ -82,6 +82,10 @@ body <- shinydashboard::dashboardBody( #girafeOutput("chrplot") #verbatimTextOutput("testpoints") ), + tabPanel( + "Variant Density", + plotly::plotlyOutput("vdplot") + ), tabPanel( "Effect", DT::DTOutput("effect") From bac29198e752e584dd52127edf9d4c3a0f9b5405 Mon Sep 17 00:00:00 2001 From: richardjacton Date: Tue, 10 May 2022 13:36:57 +0000 Subject: [PATCH 4/4] version bump and readme update --- DESCRIPTION | 2 +- README.md | 7 ++++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index c7c09d3..d9573ae 100644 --- a/DESCRIPTION +++ b/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", diff --git a/README.md b/README.md index 7737cb6..8836ff7 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![Travis build status](https://travis-ci.com/RichardJActon/MutantSets.svg?branch=master)](https://travis-ci.com/RichardJActon/MutantSets) -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") +```