From fd72cfd70f0f99a065c5c03a7ec42f136fc037f2 Mon Sep 17 00:00:00 2001
From: Ryan Wick <rrwick@gmail.com>
Date: Thu, 1 Feb 2018 08:40:57 +1100
Subject: [PATCH] Combining different polishers

---
 README.md                                     |  45 +-
 analysis.sh                                   |  53 ++
 images/polishing_methods.png                  | Bin 0 -> 55168 bytes
 nanopolish_slurm_wrapper.py                   |   5 +-
 plot_results.R                                |  81 +++
 ...lbacore_v2.1.10_medaka_nanopolish_meth.tsv | 552 ++++++++++++++++++
 ...lbacore_v2.1.10_nanopolish_meth_medaka.tsv | 552 ++++++++++++++++++
 7 files changed, 1268 insertions(+), 20 deletions(-)
 create mode 100644 images/polishing_methods.png
 create mode 100644 results/albacore_v2.1.10_medaka_nanopolish_meth.tsv
 create mode 100644 results/albacore_v2.1.10_nanopolish_meth_medaka.tsv

diff --git a/README.md b/README.md
index 3043e6d..2f806f1 100644
--- a/README.md
+++ b/README.md
@@ -22,17 +22,18 @@ This repository uses a bacterial genome to assess the read accuracy and consensu
 * [Basecallers tested](#basecallers-tested)
 * [Method](#method)
 * [Results/discussion](#resultsdiscussion)
-  * [Speed](#speed)
-  * [Total yield](#total-yield)
-  * [Read identity](#read-identity)
-  * [Relative read length](#relative-read-length)
-  * [Assembly identity](#assembly-identity)
-  * [Read vs assembly identity](#read-vs-assembly-identity)
-  * [Nanopolish assembly identity](#nanopolish-assembly-identity)
-  * [Methylation](#methylation)
-  * [Medaka](#medaka)
-  * [Training sets](#training-sets)
-  * [Combining different basecallers](#combining-different-basecallers)
+    * [Speed](#speed)
+    * [Total yield](#total-yield)
+    * [Read identity](#read-identity)
+    * [Relative read length](#relative-read-length)
+    * [Assembly identity](#assembly-identity)
+    * [Read vs assembly identity](#read-vs-assembly-identity)
+    * [Nanopolish assembly identity](#nanopolish-assembly-identity)
+    * [Methylation](#methylation)
+    * [Medaka](#medaka)
+    * [Combining different basecallers](#combining-different-basecallers)
+    * [Combining different polishers](#combining-different-polishers)
+    * [Training sets](#training-sets)
 * [Conclusions](#conclusions)
 * [References](#references)
 
@@ -349,7 +350,7 @@ While Nanopolish can correct many of these errors, it would be better if the bas
 
 
 
-# Medaka
+### Medaka
 
 Medaka is trying to solve a similar problem to Nanopolish: improving the consensus sequence accuracy using the alignment of multiple reads. It differs from Nanopolish in two significant ways. First, Medaka uses neural networks where Nanopolish uses HMMs. Second, it uses basecalled reads, not the raw signal ([though this is likely to change in the future](https://nanoporetech.github.io/medaka/future.html)). Here I test Medaka v0.2.0:
 
@@ -359,21 +360,27 @@ While Medaka could improve most assemblies, it was overall less effective than N
 
 
 
-### Training sets
+### Combining different basecallers
 
-All supervised learning depends on a good training set, and basecalling is no exception. A nice example comes from the rgrgr_r94 model in Scrappie v1.1.0 and v1.1.1. The primary difference between these two versions is that in v1.1.0, only human DNA was used to train the basecaller, whereas v1.1.1 was trained with a mixed set of genomes ([described here](https://github.com/rrwick/Basecalling-comparison/issues/1) by Scrappie author Tim Massingham). I didn't include v1.1.0 in the above plots because it's a superseded version – it's here only to show the difference a training set makes. The difference in read identity is huge, but assembly identity had a subtler improvement:
+This section previously looked at how well a combination of Albacore and Chiron reads assemble. The idea was that perhaps two different basecallers can somewhat 'cancel out' each other's systematic error, leading to a better assembly. This was the case with Albacore and Chiron v0.2, but Chiron v0.3 reads assemble so well that combining them with Albacore reads gives no improvement (it actually makes the assembly a bit worse).
 
-<p align="center"><img src="images/scrappie_comparison.png" width="60%"></p>
+I don't think this is relevant anymore, so I've removed it. You can see my earlier results in [a past version of this repository](https://github.com/rrwick/Basecalling-comparison/tree/d5ce4455c5c57d15abec1e625cafa56a7eef1a6e) if you're still interested.
 
 
 
-### Combining different basecallers
+### Combining different polishers
+
+I tried assembly polishing with both Medaka and Nanopolish (methylation-aware) to see if a joint approach could yield better accuracies. I tried both Medaka followed by Nanopolish and vice versa, but neither combination could improve upon Nanopolish alone:
+
+<p align="center"><img src="images/polishing_methods.png" width="35%"></p>
 
-This section previously looked at how well a combination of Albacore and Chiron reads assemble. The idea was that perhaps two different basecallers can somewhat 'cancel out' each other's systematic error, leading to a better assembly. This was the case with Albacore and Chiron v0.2, but Chiron v0.3 reads assemble so well that combining them with Albacore reads gives no improvement (it actually makes the assembly a bit worse).
 
-I don't think this is relevant anymore, so I've removed it. You can see my earlier results in [a past version of this repository](https://github.com/rrwick/Basecalling-comparison/tree/d5ce4455c5c57d15abec1e625cafa56a7eef1a6e) if you're still interested.
 
+### Training sets
+
+All supervised learning depends on a good training set, and basecalling is no exception. A nice example comes from the rgrgr_r94 model in Scrappie v1.1.0 and v1.1.1. The primary difference between these two versions is that in v1.1.0, only human DNA was used to train the basecaller, whereas v1.1.1 was trained with a mixed set of genomes ([described here](https://github.com/rrwick/Basecalling-comparison/issues/1) by Scrappie author Tim Massingham). I didn't include v1.1.0 in the above plots because it's a superseded version – it's here only to show the difference a training set makes. The difference in read identity is huge, but assembly identity had a subtler improvement:
 
+<p align="center"><img src="images/scrappie_comparison.png" width="50%"></p>
 
 
 
@@ -395,7 +402,7 @@ Scrappie raw v1.3.0 (rgr_r94, rgrgr_r94 and rnnrf_r94 models) also did quite wel
 
 Anyone interested in maximising assembly accuracy should be using Nanopolish. It improved all assemblies and took most up to about 99.9% (with the methylation-aware option). If you only care about assembly identity, Nanopolish makes your basecaller choice relatively unimportant.
 
-While Medaka does not improve assemblies as well as Nanopolish, it operates on basecalled reads and requires only a fasta/fastq file, not the raw fast5 files. It may therefore be the best choice for assembly polishing when raw reads are not available. However, [the 'Future directions' section of Medaka's documentation](https://nanoporetech.github.io/medaka/future.html) indicates that signal-level processing may be in its future. Furthermore, Medaka uses neural networks, unlike Nanopolish's HMMs. The authors suggest that just as neural networks have outperformed HMMs in basecallers, they will also prove superior in consensus algorithms. Watch this space!
+While Medaka does not improve assemblies as well as Nanopolish, it requires only a fasta/fastq file, not the raw fast5 files. It may therefore be the best choice for assembly polishing when raw reads are not available. However, the ['Future directions' section of Medaka's documentation](https://nanoporetech.github.io/medaka/future.html) indicates that signal-level processing may be in its future. Furthermore, Medaka uses neural networks, unlike Nanopolish's HMMs. The authors suggest that just as neural networks have outperformed HMMs in basecallers, they will also prove superior in consensus algorithms. Watch this space!
 
 
 
diff --git a/analysis.sh b/analysis.sh
index 8e158e5..cb964fe 100644
--- a/analysis.sh
+++ b/analysis.sh
@@ -54,6 +54,8 @@ mkdir -p 11_nanopolish_meth
 mkdir -p 12_nanopolish_meth_data
 mkdir -p 13_medaka
 mkdir -p 14_medaka_data
+mkdir -p 15_combined_polish
+mkdir -p 16_combined_polish_data
 
 # Create a table of basic info about each read.
 python3 "$python_script_dir"/read_table.py 01_raw_fast5 > 04_read_data/read_data.tsv
@@ -94,6 +96,18 @@ for f in $read_files; do
     medaka_assembly_alignment=14_medaka_data/"$set"_medaka.paf
     medaka_assembly_data=14_medaka_data/"$set"_medaka.tsv
 
+    medaka_nanopolish_assembly_dir=15_combined_polish
+    medaka_nanopolish_assembly=15_combined_polish/"$set"_medaka_nanopolish_meth.fasta
+    medaka_nanopolish_assembly_pieces=16_combined_polish_data/"$set"_medaka_nanopolish_meth_pieces.fasta
+    medaka_nanopolish_assembly_alignment=16_combined_polish_data/"$set"_medaka_nanopolish_meth.paf
+    medaka_nanopolish_assembly_data=16_combined_polish_data/"$set"_medaka_nanopolish_meth.tsv
+
+    nanopolish_medaka_assembly_dir=15_combined_polish
+    nanopolish_medaka_assembly=15_combined_polish/"$set"_nanopolish_meth_medaka.fasta
+    nanopolish_medaka_assembly_pieces=16_combined_polish_data/"$set"_nanopolish_meth_medaka_pieces.fasta
+    nanopolish_medaka_assembly_alignment=16_combined_polish_data/"$set"_nanopolish_meth_medaka.paf
+    nanopolish_medaka_assembly_data=16_combined_polish_data/"$set"_nanopolish_meth_medaka.tsv
+
     printf "\n\n\n\n"
     echo "NORMALISE READ HEADERS: "$set
     echo "--------------------------------------------------------------------------------"
@@ -175,4 +189,43 @@ for f in $read_files; do
     python3 "$python_script_dir"/read_length_identity.py $medaka_assembly_pieces $medaka_assembly_alignment > $medaka_assembly_data
     rm $medaka_assembly_pieces $medaka_assembly_alignment
 
+    # printf "\n\n\n\n"
+    # echo "NANOPOLISH (METHYLATION-AWARE) OF MEDAKA ASSEMBLY: "$set
+    # echo "--------------------------------------------------------------------------------"
+    # python3 "$python_script_dir"/nanopolish_slurm_wrapper.py $medaka_assembly $all_reads_fixed_names $raw_fast5_dir $medaka_nanopolish_assembly_dir $nanopolish_exec_dir $threads meth
+    # rm "$all_reads_fixed_names".index*
+    # rm "$medaka_assembly".fai
+
+    # printf "\n\n\n\n"
+    # echo "ASSESS MEDAKA THEN NANOPOLISH (METHYLATION-AWARE) ASSEMBLY: "$set
+    # echo "--------------------------------------------------------------------------------"
+    # python3 "$python_script_dir"/chop_up_assembly.py $medaka_nanopolish_assembly 10000 > $medaka_nanopolish_assembly_pieces
+    # minimap2 -x map10k -t $threads -c reference.fasta $medaka_nanopolish_assembly_pieces > $medaka_nanopolish_assembly_alignment
+    # python3 "$python_script_dir"/read_length_identity.py $medaka_nanopolish_assembly_pieces $medaka_nanopolish_assembly_alignment > $medaka_nanopolish_assembly_data
+    # rm $medaka_nanopolish_assembly_pieces $medaka_nanopolish_assembly_alignment
+
+    # printf "\n\n\n\n"
+    # echo "MEDAKA OF NANOPOLISH (METHYLATION-AWARE) ASSEMBLY: "$set
+    # echo "--------------------------------------------------------------------------------"
+    # if [[ $all_reads_fixed_names = *"fastq.gz" ]]; then
+    #     temp_reads="$nanopolish_medaka_assembly_dir"/"$set".fastq
+    # else
+    #     temp_reads="$nanopolish_medaka_assembly_dir"/"$set".fasta
+    # fi
+    # gunzip -c "$all_reads_fixed_names" > $temp_reads
+    # source $medaka
+    # medaka_consensus -i $temp_reads -d $assembly -o "$nanopolish_medaka_assembly_dir"/"$set"_nanopolish_medaka -p $pomoxis -t $threads
+    # deactivate
+    # cp "$nanopolish_medaka_assembly_dir"/"$set"_nanopolish_medaka/consensus.fasta "$nanopolish_medaka_assembly"
+    # rm $temp_reads
+    # rm -r "$nanopolish_medaka_assembly_dir"/"$set"_nanopolish_medaka
+
+    # printf "\n\n\n\n"
+    # echo "ASSESS MEDAKA ASSEMBLY: "$set
+    # echo "--------------------------------------------------------------------------------"
+    # python3 "$python_script_dir"/chop_up_assembly.py $nanopolish_medaka_assembly 10000 > $nanopolish_medaka_assembly_pieces
+    # minimap2 -x map10k -t $threads -c reference.fasta $nanopolish_medaka_assembly_pieces > $nanopolish_medaka_assembly_alignment
+    # python3 "$python_script_dir"/read_length_identity.py $nanopolish_medaka_assembly_pieces $nanopolish_medaka_assembly_alignment > $nanopolish_medaka_assembly_data
+    # rm $nanopolish_medaka_assembly_pieces $nanopolish_medaka_assembly_alignment
+
 done
diff --git a/images/polishing_methods.png b/images/polishing_methods.png
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HcmV?d00001

diff --git a/nanopolish_slurm_wrapper.py b/nanopolish_slurm_wrapper.py
index c817a3a..320d640 100644
--- a/nanopolish_slurm_wrapper.py
+++ b/nanopolish_slurm_wrapper.py
@@ -45,7 +45,10 @@ def main():
     set_name = assembly_filename.split('/')[-1].split('.fasta')[0]
     print('\nPreparing to run Nanopolish for ' + set_name)
 
-    final_assembly = ('../' + set_name + '.fasta').replace('_assembly.fasta', '_nanopolish.fasta')
+    if assembly_filename.endswith('medaka.fasta'):
+        final_assembly = ('../' + set_name + '.fasta').replace('medaka.fasta', 'medaka_nanopolish.fasta')
+    else:
+        final_assembly = ('../' + set_name + '.fasta').replace('_assembly.fasta', '_nanopolish.fasta')
     if methylation_aware:
         final_assembly = final_assembly.replace('_nanopolish.fasta', '_nanopolish_meth.fasta')
 
diff --git a/plot_results.R b/plot_results.R
index 9e273f2..41c9820 100644
--- a/plot_results.R
+++ b/plot_results.R
@@ -409,3 +409,84 @@ blank <- rectGrob(gp=gpar(col="white"))
 scrappie_comparison_plot <- grid.arrange(p1, blank, p2, ncol=3, widths=c(0.425, 0.15, 0.425))
 # ggsave(scrappie_comparison_plot, file='plots/scrappie_comparison.pdf', width = 6, height = 4)
 
+
+
+
+
+
+
+
+
+
+
+# The following code is for a violin plot comparing different polishing strategies.
+basecaller <- "Albacore v2.1.10"
+no_spaces <- gsub(" ", "_", basecaller)
+
+polishing_names <- c(paste(basecaller, "(nopolishing)"),
+                     paste(basecaller, "+Medaka"),
+                     paste(basecaller, "+Nanopolish"),
+                     paste(basecaller, "+Medaka +Nanopolish"),
+                     paste(basecaller, "+Nanopolish +Medaka"))
+polishing_labels <- gsub(" ", "\n", polishing_names, fixed=TRUE)
+polishing_labels <- gsub("(nopolishing)", "(no polishing)", polishing_labels, fixed=TRUE)
+polishing_colours <- c("#CCCCCC", "#4F84CC", "#DD616C", "#846DC2", "#AB61BA")
+names(polishing_colours) <- polishing_names
+polishing_fill_scale <- scale_fill_manual(name = "Polishing", values = polishing_colours)
+
+no_polish_data_filename <- paste("results/", tolower(no_spaces), "_assembly.tsv", sep="")
+medaka_data_filename <- paste("results/", tolower(no_spaces), "_medaka.tsv", sep="")
+nanopolish_data_filename <- paste("results/", tolower(no_spaces), "_nanopolish_meth.tsv", sep="")
+medaka_nanopolish_data_filename <- paste("results/", tolower(no_spaces), "_medaka_nanopolish_meth.tsv", sep="")
+nanopolish_medaka_data_filename <- paste("results/", tolower(no_spaces), "_nanopolish_meth_medaka.tsv", sep="")
+
+length_column <- paste("Length_", no_spaces, sep="")
+rel_length_column <- paste("Rel_len_", no_spaces, sep="")
+
+basecaller_identities <- c()
+
+identity_column <- paste("Identity_", no_spaces, "_no_polish", sep="")
+no_polish_data <- load_tsv_data(no_polish_data_filename, c("Name", length_column, identity_column, rel_length_column))
+basecaller_identities <- c(basecaller_identities, identity_column)
+
+identity_column <- paste("Identity_", no_spaces, "_medaka", sep="")
+medaka_data <- load_tsv_data(medaka_data_filename, c("Name", length_column, identity_column, rel_length_column))
+basecaller_identities <- c(basecaller_identities, identity_column)
+
+identity_column <- paste("Identity_", no_spaces, "_nanopolish", sep="")
+nanopolish_data <- load_tsv_data(nanopolish_data_filename, c("Name", length_column, identity_column, rel_length_column))
+basecaller_identities <- c(basecaller_identities, identity_column)
+
+identity_column <- paste("Identity_", no_spaces, "_medaka_nanopolish", sep="")
+medaka_nanopolish_data <- load_tsv_data(medaka_nanopolish_data_filename, c("Name", length_column, identity_column, rel_length_column))
+basecaller_identities <- c(basecaller_identities, identity_column)
+
+identity_column <- paste("Identity_", no_spaces, "_nanopolish_medaka", sep="")
+nanopolish_medaka_data <- load_tsv_data(nanopolish_medaka_data_filename, c("Name", length_column, identity_column, rel_length_column))
+basecaller_identities <- c(basecaller_identities, identity_column)
+
+polish_data <- data.frame(Name = numeric())
+polish_data <- merge(polish_data, no_polish_data, by=1, all=TRUE)
+polish_data <- merge(polish_data, medaka_data, by=1, all=TRUE)
+polish_data <- merge(polish_data, nanopolish_data, by=1, all=TRUE)
+polish_data <- merge(polish_data, medaka_nanopolish_data, by=1, all=TRUE)
+polish_data <- merge(polish_data, nanopolish_medaka_data, by=1, all=TRUE)
+
+assembly_lengths <- polish_data[grepl("Length_", names(polish_data))]
+polish_data["Length"] <- round(apply(assembly_lengths, 1, median, na.rm = TRUE))
+
+polish_identities <- polish_data[,c("Name", "Length", basecaller_identities)]
+colnames(polish_identities) <- c("Name", "Length", polishing_names)
+polish_identities <- melt(polish_identities, id=c("Name", "Length"))
+colnames(polish_identities) <- c("Read_name", "Length", "Polishing", "Identity")
+
+polishing_plot <- ggplot(polish_identities, aes(x = Polishing, y = Identity, weight = Length, fill = Polishing)) +
+  geom_violin(draw_quantiles = c(0.5), bw=0.06, width=1.1) +
+  polishing_fill_scale + my_theme + guides(fill=FALSE) +
+  scale_y_continuous(expand = c(0, 0), breaks = seq(0, 100, 0.1), minor_breaks = seq(0, 100, 0.05), labels = scales::unit_format("%")) +
+  scale_x_discrete(labels=polishing_labels) +
+  coord_cartesian(ylim=c(99.2, 100)) +
+  labs(title = "", x = "", y = "assembly identity")
+polishing_plot
+ggsave(polishing_plot, file='plots/polishing_methods.pdf', width = 4.75, height = 4)
+
diff --git a/results/albacore_v2.1.10_medaka_nanopolish_meth.tsv b/results/albacore_v2.1.10_medaka_nanopolish_meth.tsv
new file mode 100644
index 0000000..677b85b
--- /dev/null
+++ b/results/albacore_v2.1.10_medaka_nanopolish_meth.tsv
@@ -0,0 +1,552 @@
+Name	Length	Identity	Relative length
+1	10000	99.89005497251374	99.98000399920016
+10	10000	99.96001199640108	99.98000399920016
+100	10000	99.94001799460162	100.0
+101	10000	99.94002399040384	99.96001599360255
+102	10000	99.91004497751125	99.98000399920016
+103	10000	99.94001199760048	99.9900009999
+104	10000	99.94001199760048	100.02000400080016
+105	10000	99.94000599940006	100.03000900270081
+106	10000	99.88004798080767	99.9900009999
+107	10000	99.93004197481511	99.94003597841295
+108	10000	99.96000399960003	100.02000400080016
+109	10000	99.87007795322806	99.9900009999
+11	10000	99.99	100.0100010001
+110	10000	99.89003299010297	100.02000400080016
+111	10000	99.96001199640108	99.98000399920016
+112	10000	99.9000399840064	100.0
+113	10000	99.85014985014985	99.94003597841295
+114	10000	99.89008792965627	99.94003597841295
+115	10000	99.93002099370189	100.0100010001
+116	10000	99.95001999200319	99.97000899730081
+117	10000	99.91005396761943	99.96001599360255
+118	10000	99.95001499550135	99.98000399920016
+119	10000	99.91001799640073	100.02000400080016
+12	10000	99.9000399840064	99.9900009999
+120	10000	99.89003299010297	100.03000900270081
+121	10000	99.9000499750125	99.96001599360255
+122	10000	99.95000499950005	100.03000900270081
+123	10000	99.95001499550135	99.98000399920016
+124	10000	99.93002099370189	99.9900009999
+125	10000	99.89003299010297	100.0100010001
+126	10000	99.91003598560576	100.0
+127	10000	99.94000599940006	99.9900009999
+128	10000	99.91004497751125	99.98000399920016
+129	10000	99.95000499950005	100.03000900270081
+13	10000	99.93002099370189	100.0
+130	10000	99.9500099980004	100.0100010001
+131	10000	99.87010391686651	99.95002498750625
+132	10000	99.92003198720512	99.98000399920016
+133	10000	99.880059970015	99.98000399920016
+134	10000	99.96	100.04001600640257
+135	10000	99.86004198740378	100.05002501250625
+136	10000	99.92003198720512	99.9900009999
+137	10000	99.92000799920008	100.03000900270081
+138	10000	99.95000499950005	100.03000900270081
+139	10000	99.88008394124112	99.95002498750625
+14	10000	99.91003598560576	100.0
+140	10000	99.93001399720056	100.0100010001
+141	10000	99.95001499550135	99.98000399920016
+142	10000	99.95000499950005	100.03000900270081
+143	10000	99.91002699190243	99.9900009999
+144	10000	99.86998699869987	99.98999699909973
+145	10000	99.9000399840064	100.0
+146	10000	99.93001399720056	100.0100010001
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diff --git a/results/albacore_v2.1.10_nanopolish_meth_medaka.tsv b/results/albacore_v2.1.10_nanopolish_meth_medaka.tsv
new file mode 100644
index 0000000..723d9ae
--- /dev/null
+++ b/results/albacore_v2.1.10_nanopolish_meth_medaka.tsv
@@ -0,0 +1,552 @@
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