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Makefile
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# Notes:
# $(@D) means the directory of the target file
#? Usage: 'make all' updates all files as needed. To re-run the entire analysis, use 'make clean all'.
#? To change configuration options, edit the 'options.config' file in this directory
include options.config
$(info Running on $(N_CORES) cores with random seed $(RANDOM_SEED). Edit options.config to change.)
$(info )
.PHONY: all
all: download \
get_sequences \
get_transcripts \
merge_zoonotic_status \
merge_and_clean_data \
calculate_genomic \
select_holdout \
feature_selection_runs \
train \
bag_predictions \
train_tax \
predict_novel \
predict_sarbeco \
make_plots
#?
#? To explicitly set the path to dependencies, use make update_path
#? This can be used multiple times
# This is needed for blast
.PHONY: update_path
update_path:
@read -p "enter path:" path; \
echo "PATH=$$path:$$PATH" > .Renviron; \
export PATH="$$path::\$$PATH"
#?
#? To run individual steps in the pipeline, combine 'make' with the command given in brackets below:
# ----------------------------------------------------------------------------------------
#? 1. Download external data (download)
# ----------------------------------------------------------------------------------------
EXTERNALDATAFILES = ExternalData/ICTV_MasterSpeciesList_2016v1.3.xlsx \
ExternalData/ICTV_MasterSpeciesList_2018b.xlsx \
ExternalData/WoolhouseBrierley_2018.xlsx \
ExternalData/Olival2017viruses.csv \
ExternalData/Olival2017associations.csv \
ExternalData/HousekeepingGenes.txt
.PHONY: download
download: $(EXTERNALDATAFILES)
ExternalData/ICTV_MasterSpeciesList_2016v1.3.xlsx:
mkdir -p ExternalData
curl -L -o $@ 'https://ictv.global/filebrowser/download/484'
ExternalData/ICTV_MasterSpeciesList_2018b.xlsx:
mkdir -p ExternalData
curl -L -o $@ 'https://ictv.global/filebrowser/download/489'
ExternalData/WoolhouseBrierley_2018.xlsx:
mkdir -p ExternalData
curl -k -L -o $(@D)/WB2018.zip 'https://datashare.is.ed.ac.uk/download/DS_10283_2970.zip'
unzip -u -d $(@D) $(@D)/WB2018.zip 'Woolhouse and Brierley RNA virus database.xlsx'
mv $(@D)/'Woolhouse and Brierley RNA virus database.xlsx' $(@D)/WoolhouseBrierley_2018.xlsx
touch $(@D)/WoolhouseBrierley_2018.xlsx # Simply updates 'last modified' date, since unzip doesn't do this
rm $(@D)/WB2018.zip
ExternalData/Olival2017.zip:
mkdir -p ExternalData
curl -L -o $@ 'https://zenodo.org/record/807517/files/ecohealthalliance/HP3-v1.0.9.zip'
ExternalData/Olival2017viruses.csv: ExternalData/Olival2017.zip
unzip -uj -d $(@D) $(@D)/Olival2017.zip 'ecohealthalliance-HP3-928327a/data/viruses.csv'
mv $(@D)/viruses.csv $(@D)/Olival2017viruses.csv
touch $(@D)/Olival2017viruses.csv
ExternalData/Olival2017associations.csv: ExternalData/Olival2017.zip
unzip -uj -d $(@D) $(@D)/Olival2017.zip 'ecohealthalliance-HP3-928327a/data/associations.csv'
mv $(@D)/associations.csv $(@D)/Olival2017associations.csv
touch $(@D)/Olival2017associations.csv
# Gene sets:
ExternalData/HousekeepingGenes.txt:
mkdir -p ExternalData
curl -L -o $@ 'https://www.tau.ac.il/~elieis/HKG/HK_genes.txt'
# ----------------------------------------------------------------------------------------
#? 2. Download virus sequences from GenBank (get_sequences)
# ----------------------------------------------------------------------------------------
# Some rules below actually need the individual gb files in ExternalData/Sequences/,
# but CombinedSequences.fasta is always the last file to be created during download,
# so if it is up to date, all sequences must be present:
ExternalData/Sequences/CombinedSequences.fasta: InternalData/Final_Accessions_Unique_Spp.csv
python3 Misc/DownloadSequences.py
.PHONY: get_sequences
get_sequences: ExternalData/Sequences/CombinedSequences.fasta
# ----------------------------------------------------------------------------------------
#? 3. Download human transcript sequences from Ensembl (get_transcripts)
# ----------------------------------------------------------------------------------------
CalculatedData/HumanGeneSets/TranscriptSequences.fasta: InternalData/Shaw2017_raw/ISG_CountsPerMillion_Human.csv \
ExternalData/HousekeepingGenes.txt
python3 Misc/DownloadGeneSets.py
.PHONY: get_transcripts
get_transcripts: CalculatedData/HumanGeneSets/TranscriptSequences.fasta
# ----------------------------------------------------------------------------------------
#? 4. Merge zoonotic status data (merge_zoonotic_status)
# ----------------------------------------------------------------------------------------
CalculatedData/ZoonoticStatus_Merged.rds: $(EXTERNALDATAFILES) \
InternalData/Taxonomy_UnclassifiedViruses.csv \
InternalData/SourcesOfZoonoses_BabayanZoonotic.csv \
InternalData/NameMatches_All.csv
mkdir -p CalculatedData
Rscript Scripts/MergeZoonoticStatusData.R
.PHONY: merge_zoonotic_status
merge_zoonotic_status: CalculatedData/ZoonoticStatus_Merged.rds
# ----------------------------------------------------------------------------------------
#? 5. Merge and clean final dataset (merge_and_clean_data)
# ----------------------------------------------------------------------------------------
# This has multiple outputs: using a pattern rule ensures the command is
# run just once (see https://www.cmcrossroads.com/article/rules-multiple-outputs-gnu-make)
CalculatedData/FinalData_%.rds CalculatedData/FinalData_%.csv: InternalData/AllInternalData_Checked.csv \
InternalData/NameMatches_All.csv \
InternalData/Final_Accessions_Unique_Spp.csv \
CalculatedData/ZoonoticStatus_Merged.rds
Rscript Scripts/MergeAndCleanData.R
.PHONY: merge_and_clean_data
merge_and_clean_data: CalculatedData/FinalData_Cleaned.rds
# ----------------------------------------------------------------------------------------
#? 6. Calculate genomic features (calculate_genomic)
# ----------------------------------------------------------------------------------------
# This actually creates multiple output files, but as they are always required
# together, simply ensuring the first of them gets updated
CalculatedData/GenomicFeatures-Virus.rds: CalculatedData/FinalData_Cleaned.rds \
ExternalData/Sequences/CombinedSequences.fasta \
InternalData/Shaw2017_raw/ISG_PublishedData_Web.csv \
InternalData/Shaw2017_raw/ISG_CountsPerMillion_Human.csv \
CalculatedData/HumanGeneSets/TranscriptSequences.fasta
Rscript Scripts/CalculateGenomicFeatures.R
.PHONY: calculate_genomic
calculate_genomic: CalculatedData/GenomicFeatures-Virus.rds
# ----------------------------------------------------------------------------------------
#? 7. Remove viruses with only partial genomes available (select_holdout)
# ----------------------------------------------------------------------------------------
# NOTE: Holdout not currently used (not enough data), but this script also
# separates out viruses with partial genomes, which should not be used
# for training, so we still need it
CalculatedData/%_Holdout.rds CalculatedData/%_Training.rds: CalculatedData/FinalData_Cleaned.rds
Rscript Scripts/SelectHoldoutData.R $(RANDOM_SEED) --holdoutProportion 0
.PHONY: select_holdout
select_holdout: CalculatedData/SplitData_Holdout.rds
# ----------------------------------------------------------------------------------------
#? 8. Compare performance of differing numbers of features (feature_selection_runs)
# ----------------------------------------------------------------------------------------
# Feature sets / data required to calculate them:
TRAIN_REQUIREMENTS = CalculatedData/SplitData_Training.rds
TAXONOMY_REQUIREMENTS = InternalData/Taxonomy_UnclassifiedViruses.csv \
ExternalData/ICTV_MasterSpeciesList_2018b.xlsx \
PN_REQUIREMENTS = ExternalData/Sequences/CombinedSequences.fasta
DIRECT_GENOMIC = CalculatedData/GenomicFeatures-Virus.rds
RELATIVE_GENOMIC = CalculatedData/GenomicFeatures-Distances.rds
# Data common to all possible train calls:
TRAIN_REQUIREMENTS = CalculatedData/SplitData_Training.rds
RunData/FeatureSelection_Top%: $(TRAIN_REQUIREMENTS) $(TAXONOMY_REQUIREMENTS) $(PN_REQUIREMENTS) $(DIRECT_GENOMIC) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) \
--includeTaxonomy --includePN --includeVirusFeatures \
--includeISG --includeHousekeeping --includeRemaining \
--topFeatures $*
RunData/FeatureSelection_NoSelection: $(TRAIN_REQUIREMENTS) $(TAXONOMY_REQUIREMENTS) $(PN_REQUIREMENTS) $(DIRECT_GENOMIC) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) \
--includeTaxonomy --includePN --includeVirusFeatures \
--includeISG --includeHousekeeping --includeRemaining \
--topFeatures 5000
SELECTION_RUN_IDS = RunData/FeatureSelection_Top10 \
RunData/FeatureSelection_Top50 \
RunData/FeatureSelection_Top100 \
RunData/FeatureSelection_Top125 \
RunData/FeatureSelection_Top150 \
RunData/FeatureSelection_Top175 \
RunData/FeatureSelection_Top200 \
RunData/FeatureSelection_NoSelection
.PHONY: feature_selection_runs
feature_selection_runs: $(SELECTION_RUN_IDS)
# ----------------------------------------------------------------------------------------
#? 9. Train models (train)
# ----------------------------------------------------------------------------------------
# NOTE: $(notdir $(@)) means the last part of the target, i.e. 'RunID' in 'RunData/RunID'
N_FEATS = 125 # Best set of models from previous step included top 125 features
# - All possible features:
# (already have this from previous step - FeatureSelection_Top125)
# - Virus direct
RunData/VirusDirect: $(TRAIN_REQUIREMENTS) $(DIRECT_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includeVirusFeatures --topFeatures $(N_FEATS)
# - Taxonomy
RunData/Taxonomy: $(TRAIN_REQUIREMENTS) $(TAXONOMY_REQUIREMENTS)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includeTaxonomy --topFeatures $(N_FEATS)
RunData/Taxonomy_LongRun: $(TRAIN_REQUIREMENTS) $(TAXONOMY_REQUIREMENTS)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includeTaxonomy --topFeatures $(N_FEATS) \
--nboot 1000 --nseeds 100
# - Phylogenetic neighbourhood
# (now using a reduced ["minimal"] set of PN features only, since we know
# from other work that reservoirs, etc. are not predictive)
RunData/PN: $(TRAIN_REQUIREMENTS) $(PN_REQUIREMENTS)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includePN --topFeatures $(N_FEATS)
RunData/PN_LongRun: $(TRAIN_REQUIREMENTS) $(PN_REQUIREMENTS)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includePN --topFeatures $(N_FEATS) \
--nboot 1000 --nseeds 100
# - ISG
RunData/ISG: $(TRAIN_REQUIREMENTS) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includeISG --topFeatures $(N_FEATS)
# - Housekeeping
RunData/Housekeeping: $(TRAIN_REQUIREMENTS) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includeHousekeeping --topFeatures $(N_FEATS)
# - Remaining
RunData/Remaining: $(TRAIN_REQUIREMENTS) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) --includeRemaining --topFeatures $(N_FEATS)
# - All genome features
RunData/AllGenomeFeatures: $(TRAIN_REQUIREMENTS) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) \
--includeVirusFeatures --includeISG --includeHousekeeping --includeRemaining \
--topFeatures $(N_FEATS)
RunData/AllGenomeFeatures_LongRun: $(TRAIN_REQUIREMENTS) $(RELATIVE_GENOMIC)
Rscript Scripts/TrainAndValidate.R $(RANDOM_SEED) $(notdir $(@)) --nthread $(N_CORES) \
--includeVirusFeatures --includeISG --includeHousekeeping --includeRemaining \
--topFeatures $(N_FEATS) --nboot 1000 --nseeds 100
ALL_RUN_IDS = VirusDirect Taxonomy PN ISG Housekeeping Remaining \
AllGenomeFeatures AllGenomeFeatures_LongRun PN_LongRun
TRAIN_OUTPUT_FOLDERS = $(patsubst %, RunData/%, $(ALL_RUN_IDS))
.PHONY: train
train: $(TRAIN_OUTPUT_FOLDERS)
# From here on: checking all rundata directories for the files needed / created below:
VPATH = $(TRAIN_OUTPUT_FOLDERS)
# ----------------------------------------------------------------------------------------
#? 10. Bagged predictions (bag_predictions)
# ----------------------------------------------------------------------------------------
# Currently only using bagging for long runs - need each virus to occur enough test sets:
RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds: | RunData/AllGenomeFeatures_LongRun
Rscript Scripts/CalculateBaggedPredictions.R $(RANDOM_SEED) AllGenomeFeatures_LongRun --Ntop 100
RunData/PN_LongRun/PN_LongRun_Bagged_predictions.rds: | RunData/PN_LongRun
Rscript Scripts/CalculateBaggedPredictions.R $(RANDOM_SEED) PN_LongRun --Ntop 100
RunData/Taxonomy_LongRun/Taxonomy_LongRun_Bagged_predictions.rds: | RunData/Taxonomy_LongRun
Rscript Scripts/CalculateBaggedPredictions.R $(RANDOM_SEED) Taxonomy_LongRun --Ntop 100
.PHONY: bag_predictions
bag_predictions: RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds \
RunData/PN_LongRun/PN_LongRun_Bagged_predictions.rds \
RunData/Taxonomy_LongRun/Taxonomy_LongRun_Bagged_predictions.rds
# ----------------------------------------------------------------------------------------
#? 11. Fit taxonomy-based heuristic (train_tax)
# ----------------------------------------------------------------------------------------
# Get accuracy of generalising zoonotic status from family:
RunData/TaxonomyHeuristic/Test_BootstrapPredictions.rds: CalculatedData/SplitData_Training.rds
Rscript Scripts/TrainFamilyHeuristic.R $(RANDOM_SEED) TaxonomyHeuristic --nthread $(N_CORES)
.PHONY: train_tax
train_tax: RunData/TaxonomyHeuristic/Test_BootstrapPredictions.rds
# ----------------------------------------------------------------------------------------
#? 12. Find and predict novel viruses (predict_novel)
# ----------------------------------------------------------------------------------------
# Novel viruses defined as spp added to the latest ICTV taxonomy release
# - Matching accession numbers taken from ICTV's virus metadata resource
# Get ICTV data
ExternalData/NovelViruses/ICTV_MasterSpeciesList_2019.v1.xlsx:
mkdir -p $(@D)
curl -L -o $@ 'https://talk.ictvonline.org/files/master-species-lists/m/msl/9601/download'
ExternalData/NovelViruses/ICTV_VMR_2019.v1.xlsx:
mkdir -p $(@D)
curl -L -o $@ 'https://talk.ictvonline.org/taxonomy/vmr/m/vmr-file-repository/9603/download'
# Download matching sequences ($^ means all prerequisites)
ExternalData/NovelViruses/NovelViruses.gb: ExternalData/NovelViruses/ICTV_MasterSpeciesList_2019.v1.xlsx \
ExternalData/NovelViruses/ICTV_VMR_2019.v1.xlsx
python3 Scripts/FindNovelViruses.py $^ ExternalData/NovelViruses/NovelViruses
# Predict
Predictions/NovelViruses.predictions.csv: ExternalData/NovelViruses/NovelViruses.gb \
RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_ModelFits.rds \
CalculatedData/GenomicFeatures-Virus.rds \
CalculatedData/SplitData_Training.rds \
CalculatedData/GenomicFeatures-HumanCombined.rds
Rscript Scripts/PredictNovel.R genbank ExternalData/NovelViruses/NovelViruses.gb \
ExternalData/NovelViruses/NovelViruses.csv \
Predictions/NovelViruses \
--random_seed $(RANDOM_SEED)
.PHONY: predict_novel
predict_novel: Predictions/NovelViruses.predictions.csv
# ----------------------------------------------------------------------------------------
#? 13. Predict Sarbecoviruses (predict_sarbeco)
# ----------------------------------------------------------------------------------------
# Get data
ExternalData/sarbecovirus/boni_et_al_NRR1_alignment.fas:
mkdir -p $(@D)
curl -L -o $@ 'https://raw.githubusercontent.com/plemey/SARSCoV2origins/master/alignments/sarbecovirus/NRR1/NRR1.fas'
ExternalData/sarbecovirus/sarbecovirus_raw.gb: ExternalData/sarbecovirus/boni_et_al_NRR1_alignment.fas
python3 Scripts/get_sarbecovirus_seqs.py
# Predict
Predictions/sarbecovirus.predictions.csv: ExternalData/sarbecovirus/sarbecovirus_raw.gb \
RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_ModelFits.rds \
CalculatedData/GenomicFeatures-Virus.rds \
CalculatedData/SplitData_Training.rds \
CalculatedData/GenomicFeatures-HumanCombined.rds
Rscript Scripts/PredictNovel.R genbank ExternalData/sarbecovirus/sarbecovirus_raw.gb \
ExternalData/sarbecovirus/sarbecovirus_metadata.csv \
Predictions/sarbecovirus \
--exclude "Severe acute respiratory syndrome-related coronavirus" \
--random_seed $(RANDOM_SEED)
# Make a matching phylogeny for plotting
# - This requires iqtree. Install using conda install -c bioconda iqtree=2.0.3
# - The phylogeny is included, so this dependency is not generally needed
# - Using a GTR+G model, as used by Boni et al. for their ML trees (https://doi.org/10.1038/s41564-020-0771-4)
ExternalData/sarbecovirus/sarbeco_ml_phylogeny.treefile: ExternalData/sarbecovirus/boni_et_al_NRR1_alignment.fas
iqtree -redo -s $< -m GTR+G -nt AUTO --threads-max $(N_CORES) --prefix ExternalData/sarbecovirus/sarbeco_ml_phylogeny -o "BtKY72|Bat-R_spp|Kenya|KY352407|2007-10"
.PHONY: predict_sarbeco
predict_sarbeco: Predictions/sarbecovirus.predictions.csv
# ----------------------------------------------------------------------------------------
#? 14. Plot (make_plots)
# ----------------------------------------------------------------------------------------
Plots/Figure1.pdf: CalculatedData/SplitData_Training.rds \
ExternalData/ICTV_MasterSpeciesList_2018b.xlsx \
InternalData/Taxonomy_UnclassifiedViruses.csv \
RunData/TaxonomyHeuristic/Test_BootstrapPredictions.rds \
$(TRAIN_OUTPUT_FOLDERS) \
RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds
Rscript Scripts/Plotting/MakeFigure1.R
Plots/Figure2.pdf: Plots/Figure1.pdf \
ExternalData/ICTV_MasterSpeciesList_2018b.xlsx \
CalculatedData/SplitData_Training.rds \
CalculatedData/GenomicFeatures-Virus.rds \
CalculatedData/GenomicFeatures-Distances.rds \
RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds
Rscript Scripts/Plotting/MakeFigure2.R
Plots/Figure3.pdf: RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds \
Predictions/NovelViruses.predictions.csv \
CalculatedData/SplitData_Training.rds \
Plots/Figure1.pdf \
ExternalData/NovelViruses/ICTV_MasterSpeciesList_2019.v1.xlsx \
InternalData/NovelVirus_Hosts_Curated.csv \
ExternalData/NovelViruses/NovelViruses.gb
Rscript Scripts/Plotting/MakeFigure3.R
Plots/Figure4.pdf: Plots/Figure3.pdf
Rscript Scripts/Plotting/MakeFigure4.R
Plots/Figure5.pdf: Plots/Figure1.pdf \
Plots/Figure3.pdf \
ExternalData/NovelViruses/ICTV_MasterSpeciesList_2019.v1.xlsx \
Predictions/sarbecovirus.predictions.csv \
ExternalData/sarbecovirus/sarbeco_ml_phylogeny.treefile
Rscript Scripts/Plotting/MakeFigure5.R
## SI figures extending figure 1
# S1
Plots/Supplement_RawData.pdf: Plots/Figure1.pdf \
CalculatedData/SplitData_Training.rds
Rscript Scripts/Plotting/MakeSupplementaryFigure_RawData.R
# S2
Plots/Supplement_family_auc.pdf: Plots/Figure1.pdf \
CalculatedData/SplitData_Training.rds \
RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds
Rscript Scripts/Plotting/MakeSupplement_FamilyAUC.R
# S3:
Plots/Supplement_RelatednessModelRanks.pdf: CalculatedData/SplitData_Training.rds \
Plots/Figure1.pdf \
RunData/Taxonomy_LongRun/Taxonomy_LongRun_Bagged_predictions.rds \
RunData/PN_LongRun/PN_LongRun_Bagged_predictions.rds
Rscript Scripts/Plotting/MakeSupplement_RelatednessModelRanks.R
# S4:
Plots/Supplement_TrainingSetRanks.pdf: Plots/Figure3.pdf \
Plots/Figure1.pdf \
CalculatedData/ZoonoticStatus_Merged.rds \
CalculatedData/SplitData_Training.rds
Rscript Scripts/Plotting/MakeSupplement_TrainingSetRanks.R
# S5:
Plots/Supplement_ScreeningSuccessRate.pdf: Plots/Figure1.pdf \
RunData/Taxonomy_LongRun/Taxonomy_LongRun_Bagged_predictions.rds \
RunData/PN_LongRun/PN_LongRun_Bagged_predictions.rds
Rscript Scripts/Plotting/MakeSupplement_ScreeningSuccessRate.R
# S6:
Plots/Supplement_HighPriority_MissingZoonoses.pdf: Plots/Figure3.pdf \
Plots/Figure1.pdf \
CalculatedData/ZoonoticStatus_Merged.rds \
CalculatedData/SplitData_Training.rds
Rscript Scripts/Plotting/MakeSupplement_HighPriority_MissedZoonoses.R
## SI figures extending figure 2
# S7 & S8:
Plots/Supplement_bk_plots.pdf: Plots/Figure2.pdf \
ExternalData/ICTV_MasterSpeciesList_2018b.xlsx \
CalculatedData/SplitData_Training.rds \
CalculatedData/GenomicFeatures-Virus.rds
Rscript Scripts/Plotting/MakeSupplementaryFigure_ClustersVsTaxonomy.R
Plots/Combine_tanglegrams.pdf: Plots/Supplement_bk_plots.pdf
cd Plots && pdflatex -synctex=1 -interaction=nonstopmode Combine_tanglegrams.tex
# S9:
Plots/SupplementaryFigure_FeatureClusters.pdf: Plots/Figure2.pdf
Rscript Scripts/Plotting/MakeSupplementaryFigure_FeatureClusters.R
# S10
Plots/SupplementaryFigure_EffectDirection.pdf: Plots/Figure2.pdf
Rscript Scripts/Plotting/MakeSupplementaryFigure_EffectDirection.R
## SI figures extending figure 3
# S11:
Plots/Supplement_NovelVirus_Hosts.pdf: Plots/Figure3.pdf
Rscript Scripts/Plotting/MakeSupplement_NovelVirusHosts.R
# S12:
Plots/Supplement_methods_derived_genome_features.pdf: CalculatedData/SplitData_Training.rds \
Plots/Figure2.pdf \
CalculatedData/GenomicFeatures-Virus.rds
Rscript Scripts/Plotting/Supplement_IllustrateDerivedGenomeFeatureCalcs.R
# S13:
Plots/Supplement_FeatureSelection.pdf: $(SELECTION_RUN_IDS)
Rscript Scripts/Plotting/MakeSupplement_FeatureSelection.R
## SI tables
# Table SI
Plots/TableS1.csv: Plots/Figure3.pdf
cp Plots/Intermediates/combined_virus_ranks.csv Plots/TableS1.csv
.PHONY: make_plots
make_plots: Plots/Figure1.pdf \
Plots/Figure2.pdf \
Plots/Figure3.pdf \
Plots/Figure4.pdf \
Plots/Figure5.pdf \
Plots/Supplement_RawData.pdf \
Plots/Supplement_family_auc.pdf \
Plots/Supplement_RelatednessModelRanks.pdf \
Plots/Supplement_TrainingSetRanks.pdf \
Plots/Supplement_ScreeningSuccessRate.pdf \
Plots/Supplement_HighPriority_MissingZoonoses.pdf \
Plots/Supplement_bk_plots.pdf \
Plots/SupplementaryFigure_FeatureClusters.pdf \
Plots/SupplementaryFigure_EffectDirection.pdf \
Plots/Supplement_NovelVirus_Hosts.pdf \
Plots/Supplement_methods_derived_genome_features.pdf \
Plots/Supplement_FeatureSelection.pdf \
Plots/TableS1.csv
# ----------------------------------------------------------------------------------------
# Cleanup
# ----------------------------------------------------------------------------------------
.PHONY: confirm as_distributed clean
confirm:
@echo -n "Removing generated files - are you sure? [y/N] " && read ans && [ $${ans:-N} = y ]
#?
#? Other commands:
#? as_distributed: Return directory to the state in which it was distributed
as_distributed: confirm
-rm -rfv ExternalData
-rm -rfv Plots
-rm -rfv Predictions
-rm -rfv cached_blast_searches
-rm -fv .Renviron
-find CalculatedData -maxdepth 1 -not -name CalculatedData -not -name GenomicFeatures-*.rds -not -name SplitData_Training.rds -exec rm -rf {} \;
-find RunData -maxdepth 1 -not -name RunData -not -name AllGenomeFeatures_LongRun -not -name PN_LongRun -exec rm -rf {} \;
-rm -fv RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagged_predictions.rds
-rm -fv RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_Bagging_AUCs.rds
-rm -fv RunData/AllGenomeFeatures_LongRun/AllGenomeFeatures_LongRun_CalculatedData.rds
-rm -fv RunData/PN_LongRun/PN_LongRun_Bagged_predictions.rds
-rm -fv RunData/PN_LongRun/PN_LongRun_Bagging_AUCs.rds
-rm -fv RunData/PN_LongRun/PN_LongRun_CalculatedData.rds
#? clean: Remove all intermediate files, including those required for predictions (which are distributed)
clean: as_distributed
-rm -rfv RunData
-rm -rfv CalculatedData
# ----------------------------------------------------------------------------------------
# Auto document this file
# - Comments above that start with a ? become help strings
# ----------------------------------------------------------------------------------------
.PHONY: help
help: Makefile
@grep "^#?" $< | cut -c4-
# ----------------------------------------------------------------------------------------
# Make options
# ----------------------------------------------------------------------------------------
.DELETE_ON_ERROR:
.SECONDARY:
.NOTPARALLEL: # Individual scripts are already parallel
#?
#?