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diff --git a/docs/listings.json b/docs/listings.json index 6fe1c11..e0753da 100644 --- a/docs/listings.json +++ b/docs/listings.json @@ -3,7 +3,6 @@ "listing": "/index.html", "items": [ "/notebooks/2024-01-30_blast-validation.html", - "/notebooks/2024-01-23_crits-christoph.html", "/notebooks/2023-12-22_bmc-rna-sequel.html", "/notebooks/2023-12-19_project-runway-bmc-rna.html", "/notebooks/2023-11-02_project-runway-dna-deduplication.html", diff --git a/docs/notebooks/2024-01-23_crits-christoph.html b/docs/notebooks/2024-01-23_crits-christoph.html new file mode 100644 index 0000000..bdb4292 --- /dev/null +++ b/docs/notebooks/2024-01-23_crits-christoph.html @@ -0,0 +1,1092 @@ + + + + + + + + +Will’s Public NAO Notebook - Workflow analysis of Crits-Christoph et al. (2021) + + + + + + + + + + + + + + + + + + + + + + + +
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Workflow analysis of Crits-Christoph et al. (2021)

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Validating our MGS pipeline on pre-existing data, part 1.

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Author
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Will Bradshaw

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Published
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January 23, 2024

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Since my last entry, I’ve continued to work on the new Nextflow MGS pipeline. Changes since the last entry include:

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  • Successful modification of the pipeline to run using working and output directories on S3 rather than locally, and several other QOL improvements (e.g. implementation of a Nextflow config file).

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  • Various bug fixes.

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  • Modification of the human-viral identification sub-pipeline to include singly or discordantly aligned read pairs.

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  • Implementation of a “trial run” mode for the main workflow in which the raw read files are automatically subset prior to analysis to speed up testing.

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  • Creation of a separate workflow for generating large index and reference files, so that these don’t need to be stored locally or generated during every run of the main workflow.

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In order to validate pipeline performance on datasets other than our own BMC data, I’ve begun running it on pre-existing datasets from the P2RA project, starting with Crits-Christoph et al. (2021):

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  • This was first and foremost a SARS-CoV-2 sequencing project that took place in the San Francisco Bay Area in 2020. Our data consists of 18 samples, all of which were collected as 24-hour composite samples of raw sewage collected from wastewater interceptor lines in four different locations (Berkeley, Oakland, San Francisco, and Marin).

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  • Samples were concentrated using three different methods (Amicon ultrafiltration, silica columns, or milk of silica); see paper methods for more details.

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  • TODO: Describe ribodepletion and Illumina Respiratory Virus Panel enrichment.

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The raw data

+

The raw data obtained from SRA comprised 18 pairs of FASTQ files corresponding to 18 different samples. Unlike for the BMC data, there was no need to concatenate multiple FASTQ files for a given sample. What remained was roughly 298M read pairs of non-RVP-enriched data and 9M read pairs of RVP-enriched data, corresponding to roughly 45 Gb and 1 Gb of sequencing data, respectively. Reads were typically 70-80bp in length. Read qualities were high, adapter levels were relatively low, and FASTQC-measured duplication levels were moderate at 50-70%.

+
+
Code
# Data input paths
+data_dir <- "../data/2024-01-23_crits-christoph/"
+libraries_path <- file.path(data_dir, "cc-libraries.txt")
+basic_stats_path <- file.path(data_dir, "qc_basic_stats.tsv")
+adapter_stats_path <- file.path(data_dir, "qc_adapter_stats.tsv")
+quality_base_stats_path <- file.path(data_dir, "qc_quality_base_stats.tsv")
+quality_seq_stats_path <- file.path(data_dir, "qc_quality_sequence_stats.tsv")
+
+# Import data
+stages <- c("raw_concat", "cleaned", "dedup", "ribo_initial", "remove_human", "remove_other", "ribo_secondary")
+libraries <- read_tsv(libraries_path, show_col_types = FALSE) %>%
+  mutate(enrichment = str_to_title(enrichment))
+basic_stats <- read_tsv(basic_stats_path, show_col_types = FALSE) %>% 
+  inner_join(libraries, by="sample") %>% 
+  arrange(enrichment, location, collection_date) %>%
+  mutate(stage = factor(stage, levels = stages),
+         sample = fct_inorder(sample))
+adapter_stats <- read_tsv(adapter_stats_path, show_col_types = FALSE) %>% 
+  inner_join(libraries, by="sample") %>% 
+  mutate(stage = factor(stage, levels = stages), 
+         read_pair = fct_inorder(as.character(read_pair)))
+quality_base_stats <- read_tsv(quality_base_stats_path, show_col_types = FALSE) %>% 
+  inner_join(libraries, by="sample") %>% 
+  mutate(stage = factor(stage, levels = stages), 
+         read_pair = fct_inorder(as.character(read_pair)))
+quality_seq_stats <- read_tsv(quality_seq_stats_path, show_col_types = FALSE) %>% 
+  inner_join(libraries, by="sample") %>% 
+  mutate(stage = factor(stage, levels = stages), 
+         read_pair = fct_inorder(as.character(read_pair)))
+
+# Filter to raw data
+basic_stats_raw <- basic_stats %>% filter(stage == "raw_concat")
+adapter_stats_raw <- adapter_stats %>% filter(stage == "raw_concat")
+quality_base_stats_raw <- quality_base_stats %>% filter(stage == "raw_concat")
+quality_seq_stats_raw <- quality_seq_stats %>% filter(stage == "raw_concat")
+
+# Visualize basic stats
+g_nreads_raw <- ggplot(basic_stats_raw, aes(x=enrichment, y=n_read_pairs, fill=location, group=sample)) + geom_col(position="dodge") + scale_y_continuous(name="# Read pairs", expand=c(0,0)) + scale_fill_brewer(palette="Dark2", name="Location") + theme_kit
+legend_location <- get_legend(g_nreads_raw)
+g_nreads_raw_2 <- g_nreads_raw + theme(legend.position = "none")
+g_nbases_raw <- ggplot(basic_stats_raw, aes(x=enrichment, y=n_bases_approx, fill=location, group=sample)) + geom_col(position="dodge") + scale_y_continuous(name="Total base pairs (approx)", expand=c(0,0)) + scale_fill_brewer(palette="Dark2", name="Location") + theme_kit + theme(legend.position = "none")
+g_ndup_raw <- ggplot(basic_stats_raw, aes(x=enrichment, y=percent_duplicates, fill=location, group=sample)) + geom_col(position="dodge") + scale_y_continuous(name="% Duplicates (FASTQC)", expand=c(0,0), limits = c(0,100), breaks = seq(0,100,20)) + scale_fill_brewer(palette="Dark2", name="Location") + theme_kit + theme(legend.position = "none")
+g_basic_raw <- plot_grid(g_nreads_raw_2 + g_nbases_raw + g_ndup_raw, legend_location, ncol = 1, rel_heights = c(1,0.1))
+g_basic_raw
+
+

+
+
Code
# Visualize adapters
+g_adapters_raw <- ggplot(adapter_stats_raw, aes(x=position, y=pc_adapters, color=location, linetype = read_pair, group=interaction(sample, read_pair))) + geom_line() +
+  scale_color_brewer(palette = "Dark2", name = "Location") +
+  scale_linetype_discrete(name = "Read Pair") +
+  scale_y_continuous(name="% Adapters", limits=c(0,50),
+                     breaks = seq(0,50,10), expand=c(0,0)) +
+  scale_x_continuous(name="Position", limits=c(0,80),
+                     breaks=seq(0,80,20), expand=c(0,0)) +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  facet_wrap(~adapter) + theme_base
+g_adapters_raw
+
+

+
+
Code
# Visualize quality
+g_quality_base_raw <- ggplot(quality_base_stats_raw, aes(x=position, y=mean_phred_score, color=location, linetype = read_pair, group=interaction(sample,read_pair))) +
+  geom_hline(yintercept=25, linetype="dashed", color="red") +
+  geom_hline(yintercept=30, linetype="dashed", color="red") +
+  geom_line() + 
+  scale_color_brewer(palette = "Dark2", name = "Location") +
+  scale_linetype_discrete(name = "Read Pair") +
+  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(20,45)) +
+  scale_x_continuous(name="Position", limits=c(0,80),
+                     breaks=seq(0,80,20), expand=c(0,0)) +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  theme_base
+g_quality_seq_raw <- ggplot(quality_seq_stats_raw, aes(x=mean_phred_score, y=n_sequences, color=location, linetype = read_pair, group=interaction(sample,read_pair))) +
+  geom_vline(xintercept=25, linetype="dashed", color="red") +
+  geom_vline(xintercept=30, linetype="dashed", color="red") +
+  geom_line() + 
+  scale_color_brewer(palette = "Dark2", name = "Location") +
+  scale_linetype_discrete(name = "Read Pair") +
+  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
+  scale_y_continuous(name="# Sequences", expand=c(0,0)) +
+  guides(color=guide_legend(nrow=2,byrow=TRUE),
+         linetype = guide_legend(nrow=2,byrow=TRUE)) +
+  theme_base
+g_quality_base_raw
+
+

+
+
Code
g_quality_seq_raw
+
+

+
+
+

Preprocessing

+

Both RVP-enriched and unenriched samples showed significant, but not overwhelming, reductions in read counts during preprocessing. In the case of unenriched samples, most reads were lost during the deduplication stage, with low losses observed at other stages. In enriched samples, a much larger fraction of reads were lost during the ribodepletion stages; this is consistent with all of the unenriched samples having undergone ribodepletion, while the unenriched samples did not.

+

On average, cleaning, deduplication and conservative ribodepletion together removed about 44% and 66% of total reads in unenriched and enriched samples, respectively. Host depletion, livestock depletion and secondary ribodepletion removed a further 2% and 5%, respectively.

+
+
Code
# Plot reads over preprocessing
+g_reads_stages <- ggplot(basic_stats, aes(x=stage, y=n_read_pairs,fill=location,group=sample)) +
+  geom_col(position="dodge") + scale_fill_brewer(palette="Dark2", name="Location") +
+  scale_y_continuous("# Read pairs", expand=c(0,0)) +
+  facet_wrap(.~enrichment, scales = "free_y") +
+  theme_kit + theme(aspect.ratio = 1/2)
+g_reads_stages
+
+

+
+
Code
# Plot relative read losses during preprocessing
+n_reads_rel <- basic_stats %>% select(sample, location, stage, enrichment, n_read_pairs) %>%
+  group_by(sample, location, enrichment) %>% arrange(sample, location, enrichment, stage) %>%
+  mutate(p_reads_retained = n_read_pairs / lag(n_read_pairs),
+         p_reads_lost = 1 - p_reads_retained,
+         p_reads_retained_abs = n_read_pairs / n_read_pairs[1],
+         p_reads_lost_abs = 1-p_reads_retained_abs)
+g_reads_rel <- ggplot(n_reads_rel, aes(x=stage, y=p_reads_lost,fill=location,group=sample)) +
+  geom_col(position="dodge") + scale_fill_brewer(palette="Dark2", name="Location") +
+  scale_y_continuous("% Reads Lost", expand=c(0,0), labels = function(x) x*100) +
+  facet_wrap(.~enrichment, scales = "free_y") +
+  theme_kit + theme(aspect.ratio = 1/2)
+g_reads_rel
+
+
Warning: Removed 18 rows containing missing values (`geom_col()`).
+
+
+

+
+
Code
# Plot bases over preprocessing
+g_bases_stages <- ggplot(basic_stats, aes(x=stage, y=n_bases_approx,fill=location,group=sample)) +
+  geom_col(position="dodge") + scale_fill_brewer(palette="Dark2", name="Enrichment") +
+  scale_y_continuous("# Bases (approx)", expand=c(0,0)) +
+  facet_wrap(.~enrichment, scales = "free_y") +
+  theme_kit + theme(aspect.ratio = 1/2)
+g_bases_stages
+
+

+
+
+

Data cleaning with FASTP was very successful at removing adapters, with no adapter sequences found by FASTQC at any stage after the raw data. Preprocessing had little effect on sequence quality, but this is unsurprising since raw sequence quality was already high.

+

Deduplication and ribodepletion were collectively quite effective at reducing measured duplicate levels, with the former and latter respectively having the greatest effect in unenriched vs enriched samples. In both cases, the average detected duplication level after both processes was roughly 15%. Note that the pipeline still doesn’t have a reverse-complement-sensitive deduplication pipeline, so only same-orientation duplicates have been removed.

+
+
Code
g_dup_stages <- ggplot(basic_stats, aes(x=stage, y=percent_duplicates, fill=location, group=sample)) +
+  geom_col(position="dodge") + scale_fill_brewer(palette = "Dark2", name="Location") +
+  scale_y_continuous("% Duplicates", limits=c(0,100), breaks=seq(0,100,20), expand=c(0,0)) +
+  facet_wrap(.~enrichment, scales = "free_y") +
+  theme_kit + theme(aspect.ratio = 1/2)
+g_readlen_stages <- ggplot(basic_stats, aes(x=stage, y=mean_seq_len, fill=location, group=sample)) +
+  geom_col(position="dodge") + scale_fill_brewer(palette = "Dark2", name="Location") +
+  scale_y_continuous("Mean read length (nt)", expand=c(0,0)) +
+  facet_wrap(.~enrichment, scales = "free_y") +
+  theme_kit + theme(aspect.ratio = 1/2)
+legend_loc <- get_legend(g_dup_stages)
+g_dup_stages
+
+

+
+
Code
g_readlen_stages
+
+

+
+
+

High-level composition

+

As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:

+
+
Code
# Import Bracken data
+bracken_path <- file.path(data_dir, "bracken_counts.tsv")
+bracken <- read_tsv(bracken_path, show_col_types = FALSE)
+total_assigned <- bracken %>% group_by(sample) %>% summarize(
+  name = "Total",
+  kraken_assigned_reads = sum(kraken_assigned_reads),
+  added_reads = sum(added_reads),
+  new_est_reads = sum(new_est_reads),
+  fraction_total_reads = sum(fraction_total_reads)
+)
+bracken_spread <- bracken %>% select(name, sample, new_est_reads) %>%
+  mutate(name = tolower(name)) %>%
+  pivot_wider(id_cols = "sample", names_from = "name", values_from = "new_est_reads")
+# Count reads
+read_counts_preproc <- basic_stats %>% select(sample, location, method, enrichment, stage, n_read_pairs) %>%
+  pivot_wider(id_cols = c("sample", "location", "method", "enrichment"), names_from="stage", values_from="n_read_pairs")
+read_counts <- read_counts_preproc %>%
+  inner_join(total_assigned %>% select(sample, new_est_reads), by = "sample") %>%
+  rename(assigned = new_est_reads) %>%
+  inner_join(bracken_spread, by="sample")
+# Assess composition
+read_comp <- transmute(read_counts, sample=sample, location=location, method=method,
+                       enrichment=enrichment,
+                       n_filtered = raw_concat-cleaned,
+                       n_duplicate = cleaned-dedup,
+                       n_ribosomal = (dedup-ribo_initial) + (remove_other-ribo_secondary),
+                       n_unassigned = ribo_secondary-assigned,
+                       n_bacterial = bacteria,
+                       n_archaeal = archaea,
+                       n_viral = viruses,
+                       n_human = (ribo_initial-remove_human) + eukaryota,
+                       n_other = remove_human-remove_other)
+read_comp_long <- pivot_longer(read_comp, -(sample:enrichment), names_to = "classification",
+                               names_prefix = "n_", values_to = "n_reads") %>%
+  mutate(classification = fct_inorder(str_to_sentence(classification))) %>%
+  group_by(sample) %>% mutate(p_reads = n_reads/sum(n_reads)) %>%
+  mutate(loc_met = paste(location,method,sep="_"))
+read_comp_summ <- read_comp_long %>% 
+  group_by(loc_met, enrichment, classification) %>%
+  summarize(n_reads = sum(n_reads), .groups = "drop_last") %>%
+  mutate(n_reads = replace_na(n_reads,0),
+    p_reads = n_reads/sum(n_reads),
+    pc_reads = p_reads*100)
+  
+# Plot overall composition
+g_comp <- ggplot(read_comp_summ, aes(x=loc_met, y=p_reads, fill=classification)) +
+  geom_col(position = "stack") +
+  scale_y_continuous(name = "% Reads", limits = c(0,1.01), breaks = seq(0,1,0.2),
+                     expand = c(0,0), labels = function(x) x*100) +
+  scale_fill_brewer(palette = "Set1", name = "Classification") +
+  facet_wrap(.~enrichment, scales="free") +
+  theme_kit + theme(aspect.ratio = 1/3)
+g_comp
+
+

+
+
Code
# Plot composition of minor components
+read_comp_minor <- read_comp_summ %>% filter(classification %in% c("Archaeal", "Viral", "Human", "Other"))
+palette_minor <- brewer.pal(9, "Set1")[6:9]
+g_comp_minor <- ggplot(read_comp_minor, aes(x=loc_met, y=p_reads, fill=classification)) +
+  geom_col(position = "stack") +
+  scale_y_continuous(name = "% Reads", breaks = seq(0,0.1,0.01),
+                     expand = c(0,0), labels = function(x) x*100) +
+  scale_fill_manual(values=palette_minor, name = "Classification") +
+  facet_wrap(.~enrichment, scales="free") +
+  theme_kit + theme(aspect.ratio = 1/3)
+g_comp_minor
+
+

+
+
+

In the unenriched samples, about 80% of reads are low-quality, duplicates, ribosomal, or unassigned, leaving about 20% of other assignable reads. Of these, most (16-20%) were bacterial. The fraction of human reads ranged from 0.6 to 1.5%; that of livestock reads from 0.04 to 0.1%; and that of viral reads from 0.2 to 0.5% – about 2 orders of magnitude higher than observed in our BMC data.

+

As expected, the panel-enriched samples show significantly higher levels of viral reads than the unenriched samples, between 0.5 and 6.4% of all reads. Conversely, with the exception of one sample with 33% bacterial reads, most enriched samples showed much lower bacterial read abundance (0.8 to 4.4%). Human reads are also moderately reduced (0 to 1%), though not as dramatically as bacteria.

+

Human-viral reads, part 1: methods

+
+
Code
hv_reads_all_path <- file.path(data_dir, "hv_hits_putative_all.tsv")
+hv_reads_filtered_path <- file.path(data_dir, "hv_hits_putative_filtered.tsv")
+hv_reads_all <- read_tsv(hv_reads_all_path, show_col_types = FALSE) %>% 
+  inner_join(libraries, by="sample") %>% 
+  arrange(enrichment, location, collection_date) %>%
+  mutate(sample = fct_inorder(sample))
+hv_reads_filtered_1 <- hv_reads_all %>% 
+  filter(!classified | assigned_hv)
+hv_reads_filtered <- read_tsv(hv_reads_filtered_path, show_col_types = FALSE) %>%
+  inner_join(libraries, by="sample") %>% 
+  arrange(enrichment, location, collection_date) %>%
+  mutate(sample = fct_inorder(sample))
+
+# Count
+n_hv_reads_all <- hv_reads_all %>% group_by(sample, location, method, enrichment) %>% count %>% inner_join(basic_stats %>% filter(stage == "remove_other") %>% select(sample, n_read_pairs), by="sample") %>% rename(n_putative = n, n_total = n_read_pairs) %>% mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)
+n_hv_filtered_1 <- hv_reads_filtered_1 %>% group_by(sample, location, method, enrichment) %>% count %>% rename(n_filtered_1 = n) %>% inner_join(n_hv_reads_all, by=c("sample", "location", "method", "enrichment")) %>% mutate(p_reads = n_filtered_1/n_total, pc_reads = p_reads*100)
+n_hv_filtered <- hv_reads_filtered %>% group_by(sample, location, method, enrichment) %>% count %>% inner_join(basic_stats %>% filter(stage == "remove_other") %>% select(sample, n_read_pairs), by="sample") %>% rename(n_putative = n, n_total = n_read_pairs) %>% mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)
+
+
+

The process for identifying human-viral reads was similar to that described in my last two entries:

+
    +
  1. After removing human and livestock reads with BBMap, the remaining reads were aligned to a DB of human-infecting virus genomes with Bowtie2, using permissive alignment parameters. This retained roughly 0.3% to 0.4% of surviving reads (57k to 130k reads) for unenriched samples and 1% to 67% (650 to 25k reads) for enriched samples.
  2. +
  3. Reads that aligned successfully with Bowtie2 were run through Kraken2 (using the standard 16GB database) and reads that were assigned to any non-human-virus taxon were excluded. This retained roughly 0.17% to 0.29% of surviving reads (30k to 81k reads) for unenriched samples, and made little difference to the enriched samples.
  4. +
  5. For surviving reads, the length-adjusted alignment score \(S=\frac{\text{bowtie2 alignment score}}{\ln(\text{read length})}\) was calculated, and reads were filtered out if they didn’t meet at least one of the following four criteria: +
      +
    • The read pair is assigned to a human-infecting virus by both Kraken and Bowtie2
    • +
    • The read pair contains a Kraken2 hit to the same taxid as it is assigned to by Bowtie2.
    • +
    • The read pair is unassigned by Kraken and \(S>15\) for the forward read
    • +
    • The read pair is unassigned by Kraken and \(S>15\) for the reverse read
    • +
    +
  6. +
+

Applying all of these filtering steps leaves a total of 7042 read pairs across all unenriched samples (0.004% of surviving reads) and >110,000 read pairs across all enriched samples (4% of surviving reads). Even focusing only on the unenriched samples, this is obviously too many to validate manually,

+
+
Code
mrg <- hv_reads_filtered %>%
+  mutate(kraken_label = ifelse(assigned_hv, "Kraken2 HV\nassignment",
+                               ifelse(hit_hv, "Kraken2 HV\nhit",
+                                      "No hit or\nassignment"))) %>%
+  group_by(sample) %>% arrange(desc(adj_score_fwd), desc(adj_score_rev)) %>%
+  mutate(seq_num = row_number())
+# Import Bowtie2/Kraken data and perform filtering steps
+g_mrg <- ggplot(mrg, aes(x=adj_score_fwd, y=adj_score_rev)) +
+  geom_point(alpha=0.5, shape=16) + 
+  scale_x_continuous(name="S(forward read)", limits=c(0,40), breaks=seq(0,100,10), expand = c(0,0)) +
+  scale_y_continuous(name="S(reverse read)", limits=c(0,40), breaks=seq(0,100,10), expand = c(0,0)) +
+  facet_grid(kraken_label~enrichment, labeller = labeller(kit = label_wrap_gen(20))) +
+  theme_base + theme(aspect.ratio=1)
+g_mrg
+
+

+
+
+ + + + +
+
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@@ -638,7 +639,7 @@
-
+
@@ -139,7 +139,7 @@

Automating BLAST validation of human viral read assignment

- +Experiments with BLASTN remote mode
@@ -151,29 +151,7 @@