comparison report.Rmd @ 1:3a7f73d638ba draft default tip

planemo upload for repository https://github.com/Helmholtz-UFZ/ufz-galaxy-tools/blob/main/tools/phi-toolkit commit 368e8a7322c9763c648637263d4695abc146be13

0:315c2ed31af1

------------------------------------------------------------------------

```{r setup_env, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)

cat("params$outdir:", params$outdir, "\n")
```

```{r setup_libraries, message=FALSE, warning=FALSE, echo=FALSE, results='asis'}
# Define required packages
required_packages <- c("tidyverse", "janitor", "here",
                       "kableExtra", "gmoviz", "circlize",
                       "GenomicRanges", "patchwork", "fs",
                       "tools", "scales", "formattable",
                       "pdftools", "base64")

# Load required packages
invisible(lapply(required_packages, library, character.only = TRUE))
```

```{r helper_functions, echo=FALSE}
log_file <- "debug.log"

log_debug <- function(message) {
  if (!exists("log_initialized") || !log_initialized) {
    cat(paste0(Sys.time(), " - DEBUG: ", message, "\n"), file = log_file, append = FALSE)
    assign("log_initialized", TRUE, envir = .GlobalEnv)
  } else {
    cat(paste0(Sys.time(), " - DEBUG: ", message, "\n"), file = log_file, append = TRUE)
  }
}

load_file <- function(path) {
  log_debug(paste("Attempting to load:", path))
  if (file.exists(path)) {
    ext <- tools::file_ext(path)
    if (ext %in% c("tsv", "csv")) {
      data <- read_delim(path, delim = ifelse(ext == "csv", ",", "\t"), show_col_types = FALSE) %>% clean_names
      log_debug(paste("Loaded", nrow(data), "rows from", path))
      data
    } else if (ext == "fna") {
      data <- Biostrings::readDNAStringSet(path)
      log_debug(paste("Loaded", length(data), "sequences from", path))
      data
    } else {
      log_debug(paste("Skipping", path, "- unsupported file type"))
      NULL
    }
  } else {
    log_debug(paste("File does not exist:", path))
    NULL
  }
}

get_file_info <- function(path, loaded_data) {
  log_debug(paste("Processing file info for:", path))
  if (file.exists(path)) {
    ext <- tools::file_ext(path)
    if (ext %in% c("tsv", "csv", "fna")) {
      data <- loaded_data[[basename(path)]]
      rows <- if(ext == "fna") length(data) else nrow(data)
      tibble(exists = TRUE, rows = rows, size = file.size(path), path = path)
    } else {
      tibble(exists = TRUE, rows = NA_integer_, size = file.size(path), path = path)
    }
  } else {
    tibble(exists = FALSE, rows = NA_integer_, size = NA_real_, path = NA_character_)
  }
}

process_genome_folder <- function(folder, host_analyses_dir, virus_analyses_dir) {
  log_debug(paste("Processing folder:", folder))
  genome_name <- basename(folder)

  paths <- list(
    genomad = file.path(host_analyses_dir, "genomad", genome_name, paste0(genome_name, "_summary"), paste0(genome_name, "_virus_summary.tsv")),
    genomad_phages = file.path(host_analyses_dir, "genomad", genome_name, paste0(genome_name, "_summary"), paste0(genome_name, "_virus.fna")),
    genomad_annotations = file.path(host_analyses_dir, "genomad", genome_name, paste0(genome_name, "_summary"), paste0(genome_name, "_virus_genes.tsv")),
    defense_finder = file.path(host_analyses_dir, "defense-finder", genome_name, paste0(genome_name, "_defense_finder_systems.tsv")),
    checkv = file.path(virus_analyses_dir, "checkv", genome_name, "quality_summary.tsv"),
    iphop = file.path(virus_analyses_dir, "iphop", genome_name, "Host_prediction_to_genome_m90.csv"),
    drep = file.path(virus_analyses_dir, "drep_compare", genome_name, "data_tables", "Cdb.csv"),
    phatyp = file.path(virus_analyses_dir, "phatyp", genome_name, "phatyp.csv"),
    abricate = file.path(virus_analyses_dir, "abricate", genome_name, paste0(genome_name, "_virus_vfdb.tsv")),
    vibrant = file.path(virus_analyses_dir, "vibrant", genome_name, 
                        paste0("VIBRANT_", genome_name, "_virus"),
                        paste0("VIBRANT_results_", genome_name, "_virus"),
                        paste0("VIBRANT_AMG_individuals_", genome_name, "_virus.tsv"))
  )

  loaded_data <- map(paths, load_file)
  file_info <- map_dfr(paths, ~get_file_info(.x, loaded_data), .id = "file_type")

  virus_count <- if(!is.null(loaded_data$genomad)) {
    count <- sum(loaded_data$genomad$virus_score > 0.5, na.rm = TRUE)
    log_debug(paste("Virus count:", count))
    count
  } else {
    log_debug("No genomad summary found, virus count set to 0")
    0
  }

  log_debug("Returning results from process_genome_folder")
  list(file_info = file_info, virus_count = virus_count, loaded_data = loaded_data)
}
```


```{r compile_results, message=FALSE, warning=FALSE, echo=FALSE}
compile_results <- function() {
  base_dir <- params$outdir
  log_debug(paste("Base directory:", base_dir))

  host_analyses_dir <- file.path(base_dir, "host_analyses")
  virus_analyses_dir <- file.path(base_dir, "virus_analyses")

  # List all sample-level directories from all tools under virus_analyses
  tool_dirs <- list.dirs(virus_analyses_dir, full.names = TRUE, recursive = FALSE)
  
  genome_folders <- list.dirs(file.path(base_dir, "host_analyses", "genomad"), 
                              full.names = TRUE, recursive = FALSE)
  
  # cat(length(genome_folders), "sample(s) processed\n")

  log_debug("Processing genome folders")
  genome_data <- map(genome_folders, process_genome_folder, 
                     host_analyses_dir = host_analyses_dir, 
                     virus_analyses_dir = virus_analyses_dir) %>%
    set_names(basename(genome_folders)) %>%
    compact()

  log_debug("Creating summary dataframe")
  summary_df <- map_dfr(genome_data, ~{
    file_info <- .x$file_info
    tibble(
      Sample = basename(file_info$path[1]),
      Virus_Count = .x$virus_count,
      geNomad = file_info$exists[file_info$file_type == "genomad"],
      CheckV = file_info$exists[file_info$file_type == "checkv"],
      VIBRANT = file_info$exists[file_info$file_type == "vibrant"],
      dRep = file_info$exists[file_info$file_type == "drep"],
      iPHOP = file_info$exists[file_info$file_type == "iphop"],
      PhaTYP = file_info$exists[file_info$file_type == "phatyp"],
      Defense_Finder = file_info$exists[file_info$file_type == "defense_finder"],
      geNomad_Path = file_info$path[file_info$file_type == "genomad"],
      CheckV_Path = file_info$path[file_info$file_type == "checkv"],
      VIBRANT_Path = file_info$path[file_info$file_type == "vibrant"],
      dRep_Path = file_info$path[file_info$file_type == "drep"],
      PhaTYP_Path = file_info$path[file_info$file_type == "phatyp"],
      Defense_Finder_Path = file_info$path[file_info$file_type == "defense_finder"],
      Virus_Contigs = ifelse(file_info$exists[file_info$file_type == "genomad_phages"],
                            file_info$rows[file_info$file_type == "genomad_phages"],
                            0)
    )
  }) %>%
    mutate(across(ends_with("_Path"), ~ifelse(is.na(.), "Not available", as.character(.))))

  host_genomes_fasta <- list.files(
    path = file.path(params$outdir, "genomes"),
    pattern = "\\.fna$",
    full.names = TRUE
  )
  
  host_genomes_paths <- tibble(
    name = tools::file_path_sans_ext(basename(host_genomes_fasta)),
    path = host_genomes_fasta
  )

  data_gtdbtk_host <- read_tsv(
    file.path(params$outdir, "host_analyses/gtdbtk/gtdbtk.bac120.summary.tsv"),
    show_col_types = FALSE
  ) %>% clean_names()

  data_checkm_host <- read_tsv(
    file.path(params$outdir, "host_analyses/checkm2/quality_report.tsv"),
    show_col_types = FALSE
  ) %>% clean_names()

  log_debug("Returning summary dataframe, genome data, and host data")

  log_debug(paste("summary_df dimensions:", nrow(summary_df), "rows,", ncol(summary_df), "columns"))
  log_debug(paste("summary_df column names:", paste(colnames(summary_df), collapse = ", ")))
  log_debug(paste("genome_data length:", length(genome_data)))
  log_debug(paste("genome_data names:", paste(names(genome_data), collapse = ", ")))
  log_debug(paste("host_genomes_paths dimensions:", nrow(host_genomes_paths), "rows,", ncol(host_genomes_paths), "columns"))
  log_debug(paste("host_genomes_paths column names:", paste(colnames(host_genomes_paths), collapse = ", ")))
  log_debug(paste("data_gtdbtk_host dimensions:", nrow(data_gtdbtk_host), "rows,", ncol(data_gtdbtk_host), "columns"))
  log_debug(paste("data_gtdbtk_host column names:", paste(colnames(data_gtdbtk_host), collapse = ", ")))
  log_debug(paste("data_checkm_host dimensions:", nrow(data_checkm_host), "rows,", ncol(data_checkm_host), "columns"))
  log_debug(paste("data_checkm_host column names:", paste(colnames(data_checkm_host), collapse = ", ")))
  
  list(
    summary = summary_df, 
    genome_data = genome_data,
    host_genomes_paths = host_genomes_paths,
    data_gtdbtk_host = data_gtdbtk_host,
    data_checkm_host = data_checkm_host
  )
}
```

```{r run_main_function, echo=FALSE, message=FALSE, warning=FALSE}
log_debug("Starting execution of main function")
result <- compile_results()

if (is.null(result)) {
  log_debug("Main function execution failed")
  stop("Main function execution failed")
}

summary_df <- result$summary
genome_data <- result$genome_data
host_genomes_paths <- result$host_genomes_paths
data_gtdbtk_host <- result$data_gtdbtk_host
data_checkm_host <- result$data_checkm_host
log_debug("Data extracted successfully")

# Remove any extensions from names in data gtdbtk and checm2
data_gtdbtk_host <- data_gtdbtk_host %>%
  mutate(user_genome = str_remove(user_genome, "\\.[^.]+$"))

data_checkm_host <- data_checkm_host %>%
  mutate(name = str_remove(name, "\\.[^.]+$"))

result$summary <- result$summary %>%
  mutate(Sample = str_remove(Sample, "_virus_summary.tsv"))
```

# Summary {.tabset .tabset-fade}

## Overview Table

This table provides sample-by-sample information on detected viruses and key host genome statistics. It includes taxonomy, virus count, genome quality classification, CheckM2 metrics (completeness and contamination), and genome assembly statistics such as size and N50.

```{r render_table, message=FALSE, warning=FALSE, echo=FALSE, results='asis'}
data <- result$summary

log_debug("Assigning checkm2 host data")
checkm_host_data <- data_checkm_host %>% clean_names() %>%
  select(name, completeness, contamination, 
         contig_n50, genome_size)

log_debug("Assigning GTDB-Tk host data")
gtdbtk_data <- data_gtdbtk_host %>% 
  select(user_genome, classification)

log_debug("Defining color-blind friendly palette")
cb_friendly_colors <- list(
  green = "#009E73",
  blue = "#0072B2",
  orange = "#E69F00",
  red = "#D55E00",
  grey = "#999999"
)

log_debug("Defining function to color cells")
color_cell <- function(values, color_true = cb_friendly_colors$green, 
                       color_false = cb_friendly_colors$red) {
  ifelse(values,
         cell_spec("Yes", color = "white", bold = TRUE, background = color_true),
         cell_spec("No", color = "white", bold = TRUE, background = color_false))
}

log_debug("Defining function to create bar plot")
create_bar_plot <- function(values, max_value, color = cb_friendly_colors$grey) {
  sapply(values, function(value) {
    if(is.na(value) || !is.numeric(value)) {
      return("N/A")
    }
    bar_width <- min(max(value, 0), max_value) / max_value * 100
    sprintf('<div style="background-color: %s; width: %f%%; height: 10px;"></div>%.1f%%', 
            color, bar_width, value)
  })
}

log_debug("Defining function to format large numbers")
format_large_number <- function(x) {
  sapply(x, function(value) {
    if (is.na(value) || !is.numeric(value)) {
      return("N/A")
    } else if (value < 1000) {
      return(as.character(value))
    } else if (value < 1e6) {
      return(paste0(round(value / 1e3, 1), "K"))
    } else if (value < 1e9) {
      return(paste0(round(value / 1e6, 1), "M"))
    } else {
      return(paste0(round(value / 1e9, 1), "G"))
    }
  })
}

log_debug("Defining function to extract last known taxonomy level")
extract_last_known_taxonomy <- function(classification) {
  if (is.na(classification) || classification == "") {
    return(list(level = "Unknown", name = "Unknown"))
  }
  
  parts <- strsplit(classification, ";")[[1]]
  for (i in length(parts):1) {
    level <- sub("^[a-z]__", "", parts[i])
    if (level != "") {
      prefix <- sub("__.*$", "", parts[i])
      return(list(level = prefix, name = level))
    }
  }
  return(list(level = "Unknown", name = "Unknown"))
}

log_debug("Defining function to format taxonomy")
format_taxonomy <- function(classification) {
  result <- extract_last_known_taxonomy(classification)
  if (result$level == "Unknown") {
    return("Unknown")
  } else if (result$level == "s") {
    return(paste0("<i>", result$name, "</i>"))
  } else {
    genus <- str_replace_all(result$name, "_", " ")
    return(paste0("<i>", genus, "</i> sp."))
  }
}

log_debug("Defining function to calculate quality score and determine genome quality class")
calculate_quality_score_and_class <- function(completeness, contamination) {
  if (is.na(completeness) || is.na(contamination)) {
    return(list(
      score = cell_spec("N/A", color = "white", bold = TRUE, background = cb_friendly_colors$grey),
      class = cell_spec("Unknown", color = "white", bold = TRUE, background = cb_friendly_colors$grey),
      numeric_score = NA
    ))
  }
  
  quality_score <- completeness - (5 * contamination)
  formatted_score <- sprintf("%.1f", quality_score)
  
  if (completeness > 90 && contamination < 5) {
    class <- "High-quality draft"
    color <- cb_friendly_colors$green
  } else if (completeness >= 50 && contamination < 10) {
    class <- "Medium-quality draft"
    color <- cb_friendly_colors$blue
  } else {
    class <- "Low-quality draft"
    color <- cb_friendly_colors$red
  }
  
  list(
    score = cell_spec(formatted_score, color = "white", bold = TRUE, background = color),
    class = cell_spec(class, color = "white", bold = TRUE, background = color),
    numeric_score = quality_score
  )
}

log_debug("Preparing the data")

table_data <- data %>%
  #mutate(Sample = basename(Sample) %>% trim_sample_name()) %>%
  mutate(Sample = basename(Sample)) %>%
  left_join(checkm_host_data, by = c("Sample" = "name")) %>%
  left_join(gtdbtk_data, by = c("Sample" = "user_genome")) %>%
  mutate(
    quality_data = pmap(list(as.numeric(completeness),
                             as.numeric(contamination)),
                        calculate_quality_score_and_class),
    Quality_Score = map_chr(quality_data, ~.$score),
    Genome_Quality = map_chr(quality_data, ~.$class),
    Quality_Score_Numeric = map_dbl(quality_data, ~.$numeric_score),
    Virus_Count_Numeric = as.numeric(Virus_Count),
    Virus_Count = cell_spec(
      Virus_Count,
      color = "white",
      bold = TRUE,
      background = case_when(
        Virus_Count == 0 ~ cb_friendly_colors$red,
        Virus_Count == 1 ~ cb_friendly_colors$blue,
        Virus_Count > 1 ~ cb_friendly_colors$green
      )
    ),
    Completeness_Numeric = as.numeric(completeness),
    Completeness = create_bar_plot(as.numeric(completeness), 100),
    Contamination = create_bar_plot(as.numeric(contamination), 100),
    `N50 (contigs)` = format_large_number(as.numeric(contig_n50)),
    `Genome size (bp)` = format_large_number(as.numeric(genome_size)),
    `GTDB Taxonomy` = sapply(classification, format_taxonomy)
  ) %>%
  mutate(`#` = row_number()) %>%
  select(`#`, Sample, `GTDB Taxonomy`, Virus_Count,
         Quality_Score, Genome_Quality, Completeness, Contamination,
         `Genome size (bp)`, `N50 (contigs)`)

log_debug("Creating the table")
kbl(table_data, escape = FALSE,
    align = c("c", "l", "l", "c", rep("c", 2), rep("r", 2), rep("r", 2))) %>%
  kable_paper(full_width = TRUE) %>%
  column_spec(1, bold = TRUE, width = "2em") %>%
  column_spec(2:3, bold = TRUE) %>%
  column_spec(4:5, width = "5em") %>%
  column_spec(6:7, width = "60px") %>%
  column_spec(8:9, width = "4em") %>%
  add_header_above(c(" " = 4, "Host Genome Quality" = 2, "CheckM Metrics" = 2,
                     "Statistics" = 2)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                font_size = 9,
                html_font = "Arial",
                position = "left") %>%
  row_spec(0, bold = TRUE, color = "white", background = "#333333") %>%
  row_spec(0, extra_css = "border-bottom: 2px solid #000000;") %>%
  column_spec(9, extra_css = "border-right: 2px solid #000000;") %>%
  scroll_box(width = "100%", height = "100%",
             extra_css = "overflow-x: auto; border: 1px solid #ccc; border-radius: 4px;")
```

## Tools Documentation

The following tools are utilized in this workflow. Each tool name below is a link to its respective documentation.

**Host-analyses**

- [**CheckM2 v1.1.0**](https://github.com/chklovski/CheckM2): Assesses the quality of the host. Most useful when working with assembled genomes.

- [**GTDB-Tk v2.3.2**](https://ecogenomics.github.io/GTDBTk/index.html): Assigns a taxonomy to the host genome.

- [**Defense-Finder v2.0.0, models 2.0.2**](https://ecogenomics.github.io/GTDBTk/index.html): Detects known anti-phage systems in the host.

- [**geNomad v1.7.1**](https://portal.nersc.gov/genomad/): Predicts and annotates proviruses.

**Virus-analyses**

- [**CheckV v1.0.1**](https://pypi.org/project/checkv/): Evaluates the quality of viral genomes.

- [**dRep v3.4.5**](https://drep.readthedocs.io/en/latest/): Compares viral genomes within the same host.

- [**Abricate v1.0.1**](https://github.com/tseemann/abricate): Identifies virulence genes in the prophage genomes with the [VFDB database](https://www.mgc.ac.cn/VFs/).

- [**iPHOP v1.3.3**](https://bitbucket.org/srouxjgi/iphop/src/main/): Predicts other potential hosts of viral genomes.

- [**VIBRANT v1.2.1**](https://github.com/AnantharamanLab/VIBRANT): Used to identify Auxiliary Metabolic Genes in the prophages.

## Workflow

The workflow begins with the input of bacterial genomes by the user. These are processed by the **host-analyses** tools. Prophage prediction is  
performed by **geNomad** only. Afterward, prophages identified by **geNomad** are processed by the **virus-analyses** tools.

If more than one prophage is recovered in the same sample, **dRep** is used to compare and determine if the viruses are identical or different within the same host.

*PLACEHOLDER FOR PIPELINE*

## R Session Info

Information about the R session used to render this markdown document.

```{r}
sessionInfo()
```


# Results {.tabset .tabset-fade}

```{r}
# Creating combined_unique object

combined_unique <- bind_rows(
  checkm_host_data %>%
    # select(bin_id) %>%
    # dplyr::rename(Sample = bin_id),
    select(name) %>%
    dplyr::rename(Sample = name),
  
  data %>%
    #mutate(Sample = str_remove(Sample, "_virus_summary.tsv")) %>%
    select(Sample)
) %>%
  distinct(Sample) %>%
  arrange(Sample)

log_debug(paste("combined_unique samples:", paste(combined_unique$Sample, collapse = ", ")))
```



```{r main_workflow, fig.width=6, fig.height=6, out.height="100%", out.width='100%', dpi=300, fig.align='center', warning=FALSE, message=FALSE, results='asis'}
# Process proviruses data
process_proviruses <- function(data_genomad) {
  proviruses <- data_genomad %>%
    dplyr::filter(topology == "Provirus") %>%
    dplyr::mutate(contig = sub("\\|provirus_.*", "", seq_name)) %>%  # take everything before "|provirus"
    dplyr::mutate(contig = paste0("c", as.numeric(factor(contig)))) %>% # map them to c_1, c_2, ...
    dplyr::select(seq_name, coordinates, length, contig, virus_score, n_hallmarks)
  
  proviruses <- proviruses %>%
    tidyr::separate(coordinates, into = c("start", "end"), sep = "-")
  
  proviruses$start <- as.integer(proviruses$start)
  proviruses$end <- as.integer(proviruses$end)
  
  proviruses_gr_features <- GRanges(seqnames = proviruses$contig, 
                                    ranges = IRanges(start = proviruses$start, 
                                                     end = proviruses$end))
  proviruses_gr_features$length <- proviruses_gr_features %>% ranges %>% width
  proviruses_gr_features$score <- as.numeric(proviruses$virus_score)
  proviruses_gr_features$n_hallmarks <- as.numeric(proviruses$n_hallmarks)
  
  proviruses_gr_features$n_hallmarks_pos <- 
    abs(start(proviruses_gr_features) - end(proviruses_gr_features)) / 2
  
  return(proviruses_gr_features)
}

plot_genome_ideogram <- function(genome_current, proviruses_gr_features) {
  fasta_file_path <- file.path(params$outdir, "genomes", paste0(genome_current, ".fna"))
  #cat(fasta_file_path, "\n\n")
  genome_ideogram <- getIdeogramData(fasta_file = fasta_file_path)
  
  # Replace any seqlevel to c_1, c_2, c_3, ...
  new_seqlevels <- paste0("c", seq_along(seqlevels(genome_ideogram)))
  names(new_seqlevels) <- seqlevels(genome_ideogram)
  genome_ideogram <- GenomeInfoDb::renameSeqlevels(genome_ideogram, new_seqlevels)
  colours <- rep("#a58bc5", length(seqlevels(genome_ideogram)))
  
  par(mar = c(2, 2, 2, 2))  # minimal margins around the plot
  
  gmovizInitialise(genome_ideogram,
                   sector_colours = colours,
                   sector_border_colours = colours,
                   sector_labels = FALSE
  )
  
  for (i in 1:length(proviruses_gr_features)) {
    name <- as.character(seqnames(proviruses_gr_features[i]))
    start <- as.numeric(start(proviruses_gr_features[i]))
    end <- as.numeric(end(proviruses_gr_features[i]))
    region <- data.frame(start = start, end = end)
    circos.genomicRect(seqnames = name,
                       region,
                       ytop = .5,
                       ybottom = 0,
                       track.index = 1,
                       sector.index = name,
                       border = "#e9d27d",
                       col = "#e9d27d")
  }
  
  length <- as.numeric(proviruses_gr_features$length)
  length <- ifelse(length > 1000000,
                   paste0(round(length/1000000, 2), "mb"),
                   paste0(round(length/1000, 2), "kb"))
  labels <- paste0(as.character(seqnames(proviruses_gr_features)), " (", length, ")")
  circos.labels(sectors = as.character(seqnames(proviruses_gr_features)),
                x = as.numeric(start(proviruses_gr_features)),
                labels,
                facing = "clockwise")
}

process_sample <- function(sample, combined_unique, host_genomes_paths, genome_data) {
  genome_current <- sample  # Add this line
  tryCatch({
    log_debug(paste("Starting to process sample:", sample))
    
    # Check if sample exists in genome_data
    if (!(sample %in% names(genome_data))) {
      log_debug(paste("Sample", sample, "not found in genome_data"))
      cat(paste("Error: Sample", sample, "not found in genome_data\n\n"))
      return()
    }
    
    cat(paste("## ", sample, "{.tabset .tabset-fade} \n\n"))

    host_genome_path <- host_genomes_paths$path[host_genomes_paths$name == sample]
    if (length(host_genome_path) == 0) {
      log_debug(paste("Host genome path not found for sample:", sample))
      cat(paste("Error: Host genome path not found for sample", sample, "\n\n"))
      return()
    }
    
    host_genome_ideogram <- tryCatch({
      getIdeogramData(fasta_file = host_genome_path)
    }, error = function(e) {
      log_debug(paste("Error loading host genome ideogram for sample", sample, ":", conditionMessage(e)))
      NULL
    })
    
    if (is.null(host_genome_ideogram)) {
      cat(paste("Error: Unable to load host genome ideogram for sample", sample, "\n\n"))
      return()
    }
    
    sample_data <- genome_data[[sample]]$loaded_data
    genomad_summary <- sample_data$genomad
    genomad_annotation <- sample_data$genomad_annotations
    checkv_data <- sample_data$checkv
    defense_finder_data <- sample_data$defense_finder
    abricate_data <- sample_data$abricate
    iphop_data <- sample_data$iphop
    vibrant_data <- sample_data$vibrant
    
    cat("### Host Genome\n\n")

    cat("**GTDB-Tk taxonomy**: \n\n")
    data_gtdbtk_host %>% filter(user_genome == sample) %>%
      select(classification) %>%
      kbl() %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
      kable_paper("striped", full_width = TRUE) %>%
      scroll_box(width = "100%", height = "100%") %>%
      cat()

    # Cat checkm summary for this genome
    cat("**CheckM2 Summary**:\n\n")
    #checkm_summary <- data_checkm_host %>% filter(`bin_id` == sample)
    checkm_summary <- data_checkm_host %>% filter(`name` == sample)
    checkm_summary %>% clean_names %>%
      #select(number_contigs, n50_contigs, completeness, contamination, strain_heterogeneity) %>%
      select(total_contigs, contig_n50, completeness, contamination) %>%
      kbl() %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
      kable_paper("striped", full_width = TRUE) %>%
      scroll_box(width = "100%", height = "100%") %>%
      cat()
    
    # Display defense-finder as a table
    if (!is.null(defense_finder_data) && nrow(defense_finder_data) > 0) {
      cat("**Defense-Finder Systems**:\n\n")

      defense_finder_data %>%
        select(sys_id, type, subtype, sys_beg, sys_end, protein_in_syst, genes_count, name_of_profiles_in_sys) %>%
        kbl() %>%
        kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
        kable_paper("striped", full_width = TRUE) %>%
        scroll_box(width = "100%", height = "100%") %>%
        cat()
    } else {
      cat("No Defense-Finder systems detected.\n\n")
    }
    
    if (is.null(genomad_summary) || nrow(genomad_summary) == 0) {
      log_debug(paste("No geNomad summary data found for sample:", sample))
      return()
    }
    
    if (length(seqlevels(host_genome_ideogram)) == 1) {
      host_genome_size <- sum(width(host_genome_ideogram))
    } else {
      virus_containing_contigs <- unique(sub("\\|.*", "", genomad_summary$seq_name))
      virus_containing_contigs <- paste0("c_", as.numeric(factor(virus_containing_contigs)))
      filtered_host_genome <- subset_and_update_ideogram(host_genome_ideogram, virus_containing_contigs)
      host_genome_size <- sum(width(filtered_host_genome))
    }
    
    # Process proviruses
    proviruses_gr_features <- process_proviruses(genomad_summary)
    
    cat("**Genomad and CheckV Summary**:\n\n")
    genomad_summary %>%
      select(seq_name, taxonomy, topology, coordinates, length) %>%
      left_join(
        checkv_data %>% select(contig_id, gene_count, viral_genes, checkv_quality, miuvig_quality),
        by = c("seq_name" = "contig_id")) %>%
      select(seq_name, length, gene_count, viral_genes, checkv_quality, miuvig_quality, taxonomy, topology, coordinates) %>%
      kbl() %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
      kable_paper("striped", full_width = TRUE) %>%
      scroll_box(width = "100%", height = "100%") %>%
      cat()
    
    cat("**Host Genome Ideogram with Phages**:\n\n")
    plot_genome_ideogram(sample, proviruses_gr_features)
    cat('In this circular plot, **"c"** indicates the contig, and the number that follows (e.g., **c1**) represents the contig number.  
If multiple contigs are present in the genome, each will be shown with a distinct label (e.g., **c1**, **c2**, etc.).\n\n')
    cat("\n\n")

    # Process phage genomes
    cat("### Prophages {.tabset .tabset-fade} \n\n")
    cat("**Select prophage to show: ** \n\n")
    for (i in seq_len(nrow(genomad_summary))) {
      log_debug(paste("Processing phage", i, "of", nrow(genomad_summary), "for sample", sample))
      process_phage(genomad_summary[i, ], genomad_summary, genomad_annotation, checkv_data, host_genome_size)
    }
    
    # Plot dREP if applicable
    if (nrow(genomad_summary) > 1) {
      cat("### vOTUs\n\n")
      plot_drep(sample, genomad_summary)
    }
    
    # Creating table with Abricate data
    if (nrow(abricate_data) > 0) {
      cat("### Virulence Genes {.tabset .tabset-fade} \n\n")
      cat("Screening of virulence genes present in the prophage contigs. \n\n")
      abricate_data %>% select(-number_file) %>%
      kbl() %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
      kable_paper("striped", full_width = TRUE) %>%
      scroll_box(width = "100%", height = "100%") %>% cat()
      cat("\n\n")
    }
    
    # Creating table with iPHOP
    if (nrow(iphop_data) > 0) {
      cat("### Prophage-Host Prediction {.tabset .tabset-fade} \n\n")
      cat("Prediction of potential hosts for the prophage contigs. \n\n")
      iphop_data %>%
      kbl() %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
      kable_paper("striped", full_width = TRUE) %>%
      scroll_box(width = "100%", height = "100%") %>% cat()
      cat("\n\n")
    }
    
    # Creating table with VIBRANT AMGs
    if (nrow(vibrant_data) > 0) {
      cat("### AMG Predictions {.tabset .tabset-fade} \n\n")
      cat("Prediction of auxiliary metabolic genes in the prophage contigs. \n\n")
      vibrant_data %>%
      kbl() %>%
      kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
      kable_paper("striped", full_width = TRUE) %>%
      scroll_box(width = "100%", height = "100%") %>% cat()
      cat("\n\n")
    }

  log_debug(paste("Finished processing sample:", sample))
  }, error = function(e) {
    log_debug(paste("Error in process_sample for", sample, ":", conditionMessage(e)))
    cat(paste("Error processing sample", sample, ":", conditionMessage(e), "\n\n"))
  })
}

process_phage <- function(virus, genomad_summary, genomad_annotation, checkv_data, host_genome_size) {
  cat(paste("#### Phage ID:", virus$seq_name, " {.tabset .tabset-fade} \n\n"))

  current_contig <- sub("\\|.*", "", virus$seq_name)
  
  provirus_start <- as.numeric(sub(".*provirus_(\\d+)_\\d+", "\\1", virus$seq_name))
  provirus_end <- as.numeric(sub(".*provirus_\\d+_(\\d+)", "\\1", virus$seq_name))
  virus_length <- provirus_end - provirus_start + 1
  
  current_contig_base <- sub("\\|provirus_.*", "", virus$seq_name)
  current_provirus_range <- sub(".*\\|provirus_", "", virus$seq_name)
  current_annotations <- genomad_annotation[grepl(paste0(current_contig_base, "\\|provirus_", current_provirus_range, "_"), 
                                                  genomad_annotation$gene, fixed = FALSE), ] %>%
  mutate(arrow_pos = ifelse(strand == -1, "start", "end"))
  
  
  cat("\n\n**Phage–Host Genome Ideogram:**\n\n")
  
  plot_phage_circos(virus, genomad_summary, current_annotations, virus_length, host_genome_size, provirus_start, provirus_end, checkv_data)
  
  cat("\n\n")
  cat("\n\n**Genes Annotation (geNomad):**\n\n")
  
  current_annotations %>%
    select(gene, length, marker, annotation_accessions, annotation_description) %>%
    kbl() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    kable_paper() %>%
    cat()
  cat("\n\n")
}

plot_phage_circos <- function(virus, genomad_summary, current_annotations, virus_length, host_genome_size, provirus_start, provirus_end, checkv_data) {
  tryCatch({
    log_debug("Starting plot_phage_circos function")
    log_debug(paste("Current virus:", virus$seq_name))
    log_debug(paste("Virus length:", virus_length))
    log_debug(paste("Host genome size:", host_genome_size))
    log_debug(paste("Provirus start:", provirus_start))
    log_debug(paste("Provirus end:", provirus_end))
    
    # Check for NA or invalid values in input parameters
    if (is.na(virus_length) || virus_length <= 0) {
      log_debug("Error: Invalid virus length")
      return(NULL)
    }
    if (is.na(host_genome_size) || host_genome_size <= 0) {
      log_debug("Error: Invalid host genome size")
      return(NULL)
    }
    if (is.na(provirus_start) || provirus_start < 0) {
      log_debug("Error: Invalid provirus start position")
      return(NULL)
    }
    if (is.na(provirus_end) || provirus_end <= provirus_start) {
      log_debug("Error: Invalid provirus end position")
      return(NULL)
    }
    
    # Extract contig information
    current_contig <- sub("\\|.*", "", virus$seq_name)
    log_debug(paste("Current contig:", current_contig))
    
    contig_viruses <- genomad_summary[grepl(paste0("^", current_contig), genomad_summary$seq_name), ]
    if (nrow(contig_viruses) == 0) {
      log_debug("Error: No viruses found for the current contig")
      return(NULL)
    }
    
    contig_length <- max(as.numeric(sub(".*_(\\d+)$", "\\1", contig_viruses$seq_name)))
    if (is.na(contig_length) || contig_length <= 0) {
      contig_length <- virus_length  # Use virus length as fallback if contig length is invalid
      log_debug(paste("Using virus length as contig length:", contig_length))
    } else {
      log_debug(paste("Contig length:", contig_length))
    }
    
    if (provirus_end > contig_length) {
      log_debug("Error: Provirus end position exceeds contig length")
      return(NULL)
    }
    
    log_debug("Clearing circos")
    circos.clear()
    
    log_debug("Setting circos parameters")
    circos.par(start.degree = 180, gap.degree = 10, track.margin = c(0.01, 0.01))
    
    main_color <- "#a58bc5"
    zoom_color <- "#e9d27d"
    
    zoom_start <- (provirus_start / contig_length) * 100
    zoom_end <- (provirus_end / contig_length) * 100
    
    log_debug(paste("Zoom start:", zoom_start))
    log_debug(paste("Zoom end:", zoom_end))
    
    log_debug("Initializing circos")
    circos.initialize(factors = c("Zoom", "Main"), xlim = c(0, 100))
    
    format_genome_labels <- function(x) {
      ifelse(x >= 1e6, paste0(round(x / 1e6, 2), " Mb"),
             ifelse(x >= 1e3, paste0(round(x / 1e3, 2), " Kb"),
                    paste0(x, " bp")))
    }
    
    log_debug("Adding link")
    tryCatch({
      circos.link("Main", c(zoom_start, zoom_end), "Zoom", c(0, 100),
                  rou1 = 0.8,
                  rou2 = 0.97,
                  h.ratio = 0.55, # width?
                  lty = 2,
                  lwd = 0.5,
                  h2 = 1,
                  col = "grey99", border = "grey80")
    }, error = function(e) {
      log_debug(paste("Error in circos.link:", e$message))
    })
    
    log_debug("Adding zoom track")
    circos.track(factors = "Zoom", ylim = c(0, 1), track.height = 0.15,
                 panel.fun = function(x, y) {
                   circos.rect(0, 0, 100, 1, col = zoom_color, border = NA)
                   axis_labels <- seq(0, virus_length, length.out = 6)
                   axis_positions <- seq(0, 100, length.out = 6)
                   circos.axis(h = "top", major.at = axis_positions,
                               labels = format_genome_labels(axis_labels),
                               labels.cex = 0.7, direction = "outside")

                   for (i in 1:nrow(current_annotations)) {
                     gene_start <- current_annotations$start[i]
                     gene_end <- current_annotations$end[i]
                     arrow_start <- (gene_start - provirus_start) / virus_length * 100
                     arrow_end <- (gene_end - provirus_start) / virus_length * 100

                     circos.arrow(arrow_start, arrow_end, y1 = 0, y2 = 1,
                                  arrow.head.width = 0.75, arrow.head.length = cm_x(0.1),
                                  arrow.position = current_annotations$arrow_pos[i],
                                  col = ifelse(is.na(current_annotations$annotation_description[i]), "grey", "#7fbfff"),
                                  border = ifelse(is.na(current_annotations$annotation_description[i]), "grey20", "darkblue"))
                   }
                 }, bg.border = NA)

    log_debug("Adding main track")
    circos.track(factors = "Main", ylim = c(0, 1), track.height = 0.1,
                 panel.fun = function(x, y) {
                   circos.rect(xleft = 0, ybottom = 0, xright = 100, ytop = 1, col = main_color, border = NA)

                   for (i in 1:nrow(contig_viruses)) {
                     virus_start <- as.numeric(sub(".*provirus_(\\d+)_\\d+", "\\1", contig_viruses$seq_name[i]))
                     virus_end <- as.numeric(sub(".*provirus_\\d+_(\\d+)", "\\1", contig_viruses$seq_name[i]))

                     virus_start_percent <- (virus_start / contig_length) * 100
                     virus_end_percent <- (virus_end / contig_length) * 100

                     rect_color <- if (contig_viruses$seq_name[i] == virus$seq_name) zoom_color else adjustcolor(zoom_color, alpha.f = 0.7)

                     circos.rect(xleft = virus_start_percent, ybottom = 0,
                                 xright = virus_end_percent, ytop = 1,
                                 col = rect_color, border = NA)
                   }

                   axis_labels <- seq(0, contig_length, length.out = 6)
                   axis_positions <- seq(0, 100, length.out = 6)
                   circos.axis(h = "top", major.at = axis_positions,
                               labels = format_genome_labels(axis_labels),
                               labels.cex = 0.7, direction = "outside")
                 }, bg.border = NA)
    
    log_debug("Locating phage positions")
    phage_positions <- sapply(1:nrow(contig_viruses), function(i) {
      virus_start <- as.numeric(sub(".*provirus_(\\d+)_\\d+", "\\1", contig_viruses$seq_name[i]))
      virus_end <- as.numeric(sub(".*provirus_\\d+_(\\d+)", "\\1", contig_viruses$seq_name[i]))
      ((virus_start + virus_end) / 2 / contig_length) * 100
    })
    
    log_debug("Annotating names on phage positions")
    
    # Extract start and end positions from sequence names
    start_positions <- as.numeric(sub(".*provirus_([0-9]+)_.*", "\\1", contig_viruses$seq_name))
    end_positions <- as.numeric(sub(".*provirus_[0-9]+_([0-9]+)", "\\1", contig_viruses$seq_name))
    
    # Create phage labels with the desired format
    phage_labels <- paste0(round(contig_viruses$length / 1e3, 2), " Kb")
    
    # Apply labels to circos plot
    circos.labels(
      sectors = "Main",
      x = phage_positions,
      labels = phage_labels,
      facing = "reverse.clockwise",
      niceFacing = TRUE,
      col = "black",
      cex = 0.6,
      side = "inside",
      connection_height = 0.02,
      line_col = "gray"
    )
    
    center_x <- 50
    virus_name <- virus$seq_name
    taxonomy <- virus$taxonomy
    
    log_debug("Adding taxonomy and virus name to the plot")
    circos.text(x = center_x, y = -0.2, labels = taxonomy,
                sector.index = "Zoom", track.index = 1,
                facing = "bending.inside", niceFacing = TRUE,
                adj = c(0.5, 0.7), cex = 0.8)
  
    checkv_info <- checkv_data[checkv_data$contig_id == virus$seq_name, ]
    if (nrow(checkv_info) > 0) {
      checkv_quality <- checkv_info$checkv_quality
      gene_count <- checkv_info$gene_count
      viral_genes <- checkv_info$viral_genes
      host_genes <- checkv_info$host_genes
      miuvig_quality <- checkv_info$miuvig_quality
      completeness <- checkv_info$completeness
      completeness_method <- checkv_info$completeness_method
      contamination <- checkv_info$contamination
      
      circos.text(
        x = center_x, y = -0.5, 
        labels = paste("CheckV Quality:", checkv_quality, " - miuvig Quality:", miuvig_quality),
        sector.index = "Zoom", track.index = 2,
        facing = "bending.inside", niceFacing = TRUE,
        adj = c(0.5, 0), cex = 0.7
      )
      
      circos.text(
        x = center_x, y = -1.5, 
        labels = paste("Gene Count:", gene_count, " - Viral Genes:", viral_genes, " - Host Genes:", host_genes),
        sector.index = "Zoom", track.index = 2,
        facing = "bending.inside", niceFacing = TRUE,
        adj = c(0.5, 0), cex = 0.7
      )
      
      circos.text(
        x = center_x, y = -2.5, 
        labels = paste("Completeness:", completeness, " - Contamination:", contamination),
        sector.index = "Zoom", track.index = 2,
        facing = "bending.inside", niceFacing = TRUE,
        adj = c(0.5, 0), cex = 0.7
      )
    }
    
    log_debug("Adding legend")
    # Add legend
    legend("topright", 
           legend = c("Annotated gene", "Unknown gene"),
           fill = c("#7fbfff", "grey"),
           border = c("darkblue", "grey20"),
           cex = 0.8,
           bty = "n")
    
    log_debug("Clearing circos")
    circos.clear()
    log_debug("Finished plot_phage_circos function successfully")
  }, error = function(e) {
    log_debug(paste("Error in plot_phage_circos:", e$message))
    circos.clear()
  })
}

plot_drep <- function(sample, genomad_summary) {
  drep_file_path <- file.path(params$outdir, "virus_analyses", "drep_compare", sample, "data_tables", "Cdb.csv")
  drep_data <- read_csv(drep_file_path) %>% clean_names()
  drep_data <- cbind(genomad_summary$seq_name, drep_data)
  
  cat("When more than 1 phage is detected in the host genome, we perform a clustering step using the tool dRep.\n\n")
  cat("A threshold of 0.95 was applied to the ANI similarity index to define clusters of virus operational taxonomic units (vOTUs).")
  
  cat("\n\n**Final cluster designations**\n\n")
  drep_data %>%
    kbl() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    kable_paper("striped", full_width = TRUE) %>%
    cat()
  
  # Insert the PDF plot
  plot_path <- file.path(params$outdir, "virus_analyses", "drep_compare", sample, "figures", "Primary_clustering_dendrogram.pdf")
  png_path <- file.path(params$outdir, "virus_analyses", "drep_compare", sample, "figures", "Primary_clustering_dendrogram.png")
  
  if (file.exists(plot_path)) {
    pdftools::pdf_convert(plot_path, format = "png", filenames = png_path, verbose = FALSE, dpi=150)
    base64_str <- base64enc::dataURI(file = png_path, mime = "image/png")
    cat("**Primary clustering plot**\n\n")
    cat(sprintf(
      '<div style="text-align: center;"><img src="%s" style="max-width: 100%%; height: auto; border:1px solid #ddd; border-radius:4px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); margin: 1em 0;"></div>',base64_str
    ))
  } else {
    cat("**No dRep clustering plot found.**\n\n")
  }
  
}

subset_and_update_ideogram <- function(ideogram, contigs) {
  filtered <- ideogram[seqnames(ideogram) %in% contigs]
  seqlevels(filtered) <- contigs
  seqinfo(filtered) <- seqinfo(filtered)[contigs]
  filtered
}

render_all_samples <- function(test_mode = FALSE) {
  if (test_mode) {
    if (nrow(combined_unique) > 0) {
      cat("**Select sample to show:** \n\n\n")
      current_sample <- combined_unique$Sample[6]
      process_sample(current_sample, combined_unique, host_genomes_paths, genome_data)
    } else {
      print("No samples can be further analysed.")
    }
  } else {
    cat("**Select sample to show:** \n\n\n")
    for (i in seq_len(nrow(combined_unique))) {
      current_sample <- combined_unique$Sample[i]
      process_sample(current_sample, combined_unique, host_genomes_paths, genome_data)
    }
  }
}

# Execute the main function
# Test mode processes one sample only
render_all_samples(test_mode = F)
```

# Citation

Cite this work: XXXXX

1:3a7f73d638ba

------------------------------------------------------------------------

```{r setup_env, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(dev = "svglite") # set output device to svg

cat("params$outdir:", params$outdir, "\n")
```

```{r setup_libraries, message=FALSE, warning=FALSE, echo=FALSE, results='asis'}
# Define required packages
# Note: update version_command if changed here!
required_packages <- c(
    "tidyverse", "janitor", "here",
    "kableExtra", "gmoviz", "circlize",
    "GenomicRanges", "patchwork", "fs",
    "tools", "scales", "formattable",
    "pdftools", "base64"
)

# Load required packages
invisible(lapply(required_packages, library, character.only = TRUE))
```

```{r helper_functions, echo=FALSE}
log_file <- "debug.log"

log_debug <- function(message) {
    if (!exists("log_initialized") || !log_initialized) {
        cat(paste0(Sys.time(), " - DEBUG: ", message, "\n"), file = log_file, append = FALSE)
        assign("log_initialized", TRUE, envir = .GlobalEnv)
    } else {
        cat(paste0(Sys.time(), " - DEBUG: ", message, "\n"), file = log_file, append = TRUE)
    }
}

load_file <- function(path) {
    log_debug(paste("Attempting to load:", path))
    if (file.exists(path)) {
        ext <- tools::file_ext(path)
        if (ext %in% c("tsv", "csv")) {
            data <- read_delim(path, delim = ifelse(ext == "csv", ",", "\t"), show_col_types = FALSE) %>% janitor::clean_names()
            log_debug(paste("Loaded", nrow(data), "rows from", path))
            data
        } else if (ext == "fna") {
            data <- Biostrings::readDNAStringSet(path)
            log_debug(paste("Loaded", length(data), "sequences from", path))
            data
        } else {
            log_debug(paste("Skipping", path, "- unsupported file type"))
            NULL
        }
    } else {
        log_debug(paste("File does not exist:", path))
        NULL
    }
}

get_file_info <- function(path, loaded_data) {
    log_debug(paste("Processing file info for:", path))
    if (file.exists(path)) {
        ext <- tools::file_ext(path)
        if (ext %in% c("tsv", "csv", "fna")) {
            data <- loaded_data[[basename(path)]]
            rows <- if (ext == "fna") length(data) else nrow(data)
            tibble::tibble(exists = TRUE, rows = rows, size = file.size(path), path = path)
        } else {
            tibble::tibble(exists = TRUE, rows = NA_integer_, size = file.size(path), path = path)
        }
    } else {
        tibble::tibble(exists = FALSE, rows = NA_integer_, size = NA_real_, path = NA_character_)
    }
}

process_genome_folder <- function(folder, host_analyses_dir, virus_analyses_dir) {
    log_debug(paste("Processing folder:", folder))
    genome_name <- basename(folder)

    paths <- list(
        genomad = file.path(host_analyses_dir, "genomad", genome_name, paste0(genome_name, "_summary"), paste0(genome_name, "_virus_summary.tsv")),
        genomad_phages = file.path(host_analyses_dir, "genomad", genome_name, paste0(genome_name, "_summary"), paste0(genome_name, "_virus.fna")),
        genomad_annotations = file.path(host_analyses_dir, "genomad", genome_name, paste0(genome_name, "_summary"), paste0(genome_name, "_virus_genes.tsv")),
        defense_finder = file.path(host_analyses_dir, "defense-finder", genome_name, paste0(genome_name, "_defense_finder_systems.tsv")),
        checkv = file.path(virus_analyses_dir, "checkv", genome_name, "quality_summary.tsv"),
        iphop = file.path(virus_analyses_dir, "iphop", genome_name, "Host_prediction_to_genome_m90.csv"),
        drep = file.path(virus_analyses_dir, "drep_compare", genome_name, "data_tables", "Cdb.csv"),
        phatyp = file.path(virus_analyses_dir, "phatyp", genome_name, "phatyp.csv"),
        abricate = file.path(virus_analyses_dir, "abricate", genome_name, paste0(genome_name, "_virus_vfdb.tsv")),
        vibrant = file.path(
            virus_analyses_dir, "vibrant", genome_name,
            paste0("VIBRANT_", genome_name, "_virus"),
            paste0("VIBRANT_results_", genome_name, "_virus"),
            paste0("VIBRANT_AMG_individuals_", genome_name, "_virus.tsv")
        )
    )

    loaded_data <- map(paths, load_file)
    file_info <- purrr::map_dfr(paths, ~ get_file_info(.x, loaded_data), .id = "file_type")

    virus_count <- if (!is.null(loaded_data$genomad)) {
        count <- sum(loaded_data$genomad$virus_score > 0.5, na.rm = TRUE)
        log_debug(paste("Virus count:", count))
        count
    } else {
        log_debug("No genomad summary found, virus count set to 0")
        0
    }

    log_debug("Returning results from process_genome_folder")
    list(file_info = file_info, virus_count = virus_count, loaded_data = loaded_data)
}
```


```{r compile_results, message=FALSE, warning=FALSE, echo=FALSE}
compile_results <- function() {
    base_dir <- params$outdir
    log_debug(paste("Base directory:", base_dir))

    host_analyses_dir <- file.path(base_dir, "host_analyses")
    virus_analyses_dir <- file.path(base_dir, "virus_analyses")

    # List all sample-level directories from all tools under virus_analyses
    tool_dirs <- list.dirs(virus_analyses_dir, full.names = TRUE, recursive = FALSE)

    genome_folders <- list.dirs(file.path(base_dir, "host_analyses", "genomad"),
        full.names = TRUE, recursive = FALSE
    )

    # cat(length(genome_folders), "sample(s) processed\n")

    log_debug("Processing genome folders")
    genome_data <- map(genome_folders, process_genome_folder,
        host_analyses_dir = host_analyses_dir,
        virus_analyses_dir = virus_analyses_dir
    ) %>%
        purrr::set_names(basename(genome_folders)) %>%
        compact()

    log_debug("Creating summary dataframe")
    summary_df <- purrr::map_dfr(genome_data, ~ {
        file_info <- .x$file_info
        tibble::tibble(
            Sample = basename(file_info$path[1]),
            Virus_Count = .x$virus_count,
            geNomad = file_info$exists[file_info$file_type == "genomad"],
            CheckV = file_info$exists[file_info$file_type == "checkv"],
            VIBRANT = file_info$exists[file_info$file_type == "vibrant"],
            dRep = file_info$exists[file_info$file_type == "drep"],
            iPHOP = file_info$exists[file_info$file_type == "iphop"],
            PhaTYP = file_info$exists[file_info$file_type == "phatyp"],
            Defense_Finder = file_info$exists[file_info$file_type == "defense_finder"],
            geNomad_Path = file_info$path[file_info$file_type == "genomad"],
            CheckV_Path = file_info$path[file_info$file_type == "checkv"],
            VIBRANT_Path = file_info$path[file_info$file_type == "vibrant"],
            dRep_Path = file_info$path[file_info$file_type == "drep"],
            PhaTYP_Path = file_info$path[file_info$file_type == "phatyp"],
            Defense_Finder_Path = file_info$path[file_info$file_type == "defense_finder"],
            Virus_Contigs = ifelse(file_info$exists[file_info$file_type == "genomad_phages"],
                file_info$rows[file_info$file_type == "genomad_phages"],
                0
            )
        )
    }) %>%
        dplyr::mutate(dplyr::across(ends_with("_Path"), ~ ifelse(is.na(.), "Not available", as.character(.))))

    host_genomes_fasta <- list.files(
        path = file.path(params$outdir, "genomes"),
        pattern = "\\.fna$",
        full.names = TRUE
    )

    host_genomes_paths <- tibble::tibble(
        name = tools::file_path_sans_ext(basename(host_genomes_fasta)),
        path = host_genomes_fasta
    )

    data_gtdbtk_host <- read_tsv(
        file.path(params$outdir, "host_analyses/gtdbtk/gtdbtk.bac120.summary.tsv"),
        show_col_types = FALSE
    ) %>% janitor::clean_names()

    data_checkm_host <- read_tsv(
        file.path(params$outdir, "host_analyses/checkm2/quality_report.tsv"),
        show_col_types = FALSE
    ) %>% janitor::clean_names()

    log_debug("Returning summary dataframe, genome data, and host data")

    log_debug(paste("summary_df dimensions:", nrow(summary_df), "rows,", ncol(summary_df), "columns"))
    log_debug(paste("summary_df column names:", paste(colnames(summary_df), collapse = ", ")))
    log_debug(paste("genome_data length:", length(genome_data)))
    log_debug(paste("genome_data names:", paste(names(genome_data), collapse = ", ")))
    log_debug(paste("host_genomes_paths dimensions:", nrow(host_genomes_paths), "rows,", ncol(host_genomes_paths), "columns"))
    log_debug(paste("host_genomes_paths column names:", paste(colnames(host_genomes_paths), collapse = ", ")))
    log_debug(paste("data_gtdbtk_host dimensions:", nrow(data_gtdbtk_host), "rows,", ncol(data_gtdbtk_host), "columns"))
    log_debug(paste("data_gtdbtk_host column names:", paste(colnames(data_gtdbtk_host), collapse = ", ")))
    log_debug(paste("data_checkm_host dimensions:", nrow(data_checkm_host), "rows,", ncol(data_checkm_host), "columns"))
    log_debug(paste("data_checkm_host column names:", paste(colnames(data_checkm_host), collapse = ", ")))

    list(
        summary = summary_df,
        genome_data = genome_data,
        host_genomes_paths = host_genomes_paths,
        data_gtdbtk_host = data_gtdbtk_host,
        data_checkm_host = data_checkm_host
    )
}
```

```{r run_main_function, echo=FALSE, message=FALSE, warning=FALSE}
log_debug("Starting execution of main function")
result <- compile_results()

if (is.null(result)) {
    log_debug("Main function execution failed")
    stop("Main function execution failed")
}

summary_df <- result$summary
genome_data <- result$genome_data
host_genomes_paths <- result$host_genomes_paths
data_gtdbtk_host <- result$data_gtdbtk_host
data_checkm_host <- result$data_checkm_host
log_debug("Data extracted successfully")

result$summary <- result$summary %>%
    dplyr::mutate(Sample = stringr::str_remove(Sample, "_virus_summary.tsv"))
```

# Summary {.tabset .tabset-fade}

This table provides sample-by-sample information on detected viruses and key host genome statistics. It includes taxonomy, virus count, genome quality classification, CheckM2 metrics (completeness and contamination), and genome assembly statistics such as size and N50.

```{r render_table, message=FALSE, warning=FALSE, echo=FALSE, results='asis'}
data <- result$summary

log_debug("Assigning checkm2 host data")
checkm_host_data <- data_checkm_host %>%
    janitor::clean_names() %>%
    dplyr::select(
        name, completeness, contamination,
        contig_n50, genome_size
    )

log_debug("Assigning GTDB-Tk host data")
gtdbtk_data <- data_gtdbtk_host %>%
    dplyr::select(user_genome, classification)

log_debug("Defining color-blind friendly palette")
cb_friendly_colors <- list(
    green = "#009E73",
    blue = "#0072B2",
    orange = "#E69F00",
    red = "#D55E00",
    grey = "#999999"
)

log_debug("Defining function to color cells")
color_cell <- function(values, color_true = cb_friendly_colors$green,
                       color_false = cb_friendly_colors$red) {
    ifelse(values,
        kableExtra::cell_spec("Yes", color = "white", bold = TRUE, background = color_true),
        kableExtra::cell_spec("No", color = "white", bold = TRUE, background = color_false)
    )
}

log_debug("Defining function to create bar plot")
create_bar_plot <- function(values, max_value, color = cb_friendly_colors$grey) {
    sapply(values, function(value) {
        if (is.na(value) || !is.numeric(value)) {
            return("N/A")
        }
        bar_width <- min(max(value, 0), max_value) / max_value * 100
        sprintf(
            '<div style="background-color: %s; width: %f%%; height: 10px;"></div>%.1f%%',
            color, bar_width, value
        )
    })
}

log_debug("Defining function to format large numbers")
format_large_number <- function(x) {
    sapply(x, function(value) {
        if (is.na(value) || !is.numeric(value)) {
            return("N/A")
        } else if (value < 1000) {
            return(as.character(value))
        } else if (value < 1e6) {
            return(paste0(round(value / 1e3, 1), "K"))
        } else if (value < 1e9) {
            return(paste0(round(value / 1e6, 1), "M"))
        } else {
            return(paste0(round(value / 1e9, 1), "G"))
        }
    })
}

log_debug("Defining function to extract last known taxonomy level")
extract_last_known_taxonomy <- function(classification) {
    if (is.na(classification) || classification == "") {
        return(list(level = "Unknown", name = "Unknown"))
    }

    parts <- strsplit(classification, ";")[[1]]
    for (i in length(parts):1) {
        level <- sub("^[a-z]__", "", parts[i])
        if (level != "") {
            prefix <- sub("__.*$", "", parts[i])
            return(list(level = prefix, name = level))
        }
    }
    return(list(level = "Unknown", name = "Unknown"))
}

log_debug("Defining function to format taxonomy")
format_taxonomy <- function(classification) {
    result <- extract_last_known_taxonomy(classification)
    if (result$level == "Unknown") {
        return("Unknown")
    } else if (result$level == "s") {
        return(paste0("<i>", result$name, "</i>"))
    } else {
        genus <- str_replace_all(result$name, "_", " ")
        return(paste0("<i>", genus, "</i> sp."))
    }
}

log_debug("Defining function to calculate quality score and determine genome quality class")
calculate_quality_score_and_class <- function(completeness, contamination) {
    if (is.na(completeness) || is.na(contamination)) {
        return(list(
            score = kableExtra::cell_spec("N/A", color = "white", bold = TRUE, background = cb_friendly_colors$grey),
            class = kableExtra::cell_spec("Unknown", color = "white", bold = TRUE, background = cb_friendly_colors$grey),
            numeric_score = NA
        ))
    }

    quality_score <- completeness - (5 * contamination)
    formatted_score <- sprintf("%.1f", quality_score)

    if (completeness > 90 && contamination < 5) {
        class <- "High-quality draft"
        color <- cb_friendly_colors$green
    } else if (completeness >= 50 && contamination < 10) {
        class <- "Medium-quality draft"
        color <- cb_friendly_colors$blue
    } else {
        class <- "Low-quality draft"
        color <- cb_friendly_colors$red
    }

    list(
        score = kableExtra::cell_spec(formatted_score, color = "white", bold = TRUE, background = color),
        class = kableExtra::cell_spec(class, color = "white", bold = TRUE, background = color),
        numeric_score = quality_score
    )
}

log_debug("Preparing the data")

table_data <- data %>%
    # dplyr::mutate(Sample = basename(Sample) %>% trim_sample_name()) %>%
    dplyr::mutate(Sample = basename(Sample)) %>%
    dplyr::left_join(checkm_host_data, by = c("Sample" = "name")) %>%
    dplyr::left_join(gtdbtk_data, by = c("Sample" = "user_genome")) %>%
    dplyr::mutate(
        quality_data = purrr::pmap(
            list(
                as.numeric(completeness),
                as.numeric(contamination)
            ),
            calculate_quality_score_and_class
        ),
        Quality_Score = purrr::map_chr(quality_data, ~ .$score),
        Genome_Quality = purrr::map_chr(quality_data, ~ .$class),
        Quality_Score_Numeric = purrr::map_dbl(quality_data, ~ .$numeric_score),
        Virus_Count_Numeric = as.numeric(Virus_Count),
        Virus_Count = kableExtra::cell_spec(
            Virus_Count,
            color = "white",
            bold = TRUE,
            background = dplyr::case_when(
                Virus_Count == 0 ~ cb_friendly_colors$red,
                Virus_Count == 1 ~ cb_friendly_colors$blue,
                Virus_Count > 1 ~ cb_friendly_colors$green
            )
        ),
        Completeness_Numeric = as.numeric(completeness),
        Completeness = create_bar_plot(as.numeric(completeness), 100),
        Contamination = create_bar_plot(as.numeric(contamination), 100),
        `N50 (contigs)` = format_large_number(as.numeric(contig_n50)),
        `Genome size (bp)` = format_large_number(as.numeric(genome_size)),
        `GTDB Taxonomy` = sapply(classification, format_taxonomy)
    ) %>%
    dplyr::mutate(`#` = row_number()) %>%
    dplyr::select(
        `#`, Sample, `GTDB Taxonomy`, Virus_Count,
        Quality_Score, Genome_Quality, Completeness, Contamination,
        `Genome size (bp)`, `N50 (contigs)`
    )

log_debug("Creating the table")
kableExtra::kbl(table_data,
    escape = FALSE,
    align = c("c", "l", "l", "c", rep("c", 2), rep("r", 2), rep("r", 2))
) %>%
    kableExtra::kable_paper(full_width = TRUE) %>%
    kableExtra::column_spec(1, bold = TRUE, width = "2em") %>%
    kableExtra::column_spec(2:3, bold = TRUE) %>%
    kableExtra::column_spec(4:5, width = "5em") %>%
    kableExtra::column_spec(6:7, width = "60px") %>%
    kableExtra::column_spec(8:9, width = "4em") %>%
    kableExtra::add_header_above(c(
        " " = 4, "Host Genome Quality" = 2, "CheckM Metrics" = 2,
        "Statistics" = 2
    )) %>%
    kableExtra::kable_styling(
        bootstrap_options = c("striped", "hover", "condensed", "responsive"),
        font_size = 9,
        html_font = "Arial",
        position = "left"
    ) %>%
    kableExtra::row_spec(0, bold = TRUE, color = "white", background = "#333333") %>%
    kableExtra::row_spec(0, extra_css = "border-bottom: 2px solid #000000;") %>%
    kableExtra::column_spec(9, extra_css = "border-right: 2px solid #000000;") %>%
    kableExtra::scroll_box(
        width = "100%", height = "100%",
        extra_css = "overflow-x: auto; border: 1px solid #ccc; border-radius: 4px;"
    )
```

# Results {.tabset .tabset-fade}

```{r}
# Creating combined_unique object

combined_unique <- bind_rows(
    checkm_host_data %>%
        # dplyr::select(bin_id) %>%
        # dplyr::rename(Sample = bin_id),
        dplyr::select(name) %>%
        dplyr::rename(Sample = name),
    data %>%
        # dplyr::mutate(Sample = stringr::str_remove(Sample, "_virus_summary.tsv")) %>%
        dplyr::select(Sample)
) %>%
    distinct(Sample) %>%
    arrange(Sample)

log_debug(paste("combined_unique samples:", paste(combined_unique$Sample, collapse = ", ")))
```



```{r main_workflow, fig.width=6, fig.height=6, out.height="100%", out.width='100%', dpi=300, fig.align='center', warning=FALSE, message=FALSE, results='asis'}
# Process proviruses data
process_proviruses <- function(data_genomad) {
    proviruses <- data_genomad %>%
        dplyr::filter(topology == "Provirus") %>%
        dplyr::mutate(contig = sub("\\|provirus_.*", "", seq_name)) %>% # take everything before "|provirus"
        dplyr::mutate(contig = paste0("c", as.numeric(factor(contig)))) %>% # map them to c_1, c_2, ...
        dplyr::select(seq_name, coordinates, length, contig, virus_score, n_hallmarks)

    proviruses <- proviruses %>%
        tidyr::separate(coordinates, into = c("start", "end"), sep = "-")

    proviruses$start <- as.integer(proviruses$start)
    proviruses$end <- as.integer(proviruses$end)

    proviruses_gr_features <- GRanges(
        seqnames = proviruses$contig,
        ranges = IRanges(
            start = proviruses$start,
            end = proviruses$end
        )
    )
    proviruses_gr_features$length <- proviruses_gr_features %>%
        ranges() %>%
        width()
    proviruses_gr_features$score <- as.numeric(proviruses$virus_score)
    proviruses_gr_features$n_hallmarks <- as.numeric(proviruses$n_hallmarks)

    proviruses_gr_features$n_hallmarks_pos <-
        abs(start(proviruses_gr_features) - end(proviruses_gr_features)) / 2

    return(proviruses_gr_features)
}

plot_genome_ideogram <- function(genome_current, proviruses_gr_features) {
    fasta_file_path <- file.path(params$outdir, "genomes", paste0(genome_current, ".fna"))
    # cat(fasta_file_path, "\n\n")
    genome_ideogram <- gmoviz::getIdeogramData(fasta_file = fasta_file_path)

    # Replace any seqlevel to c_1, c_2, c_3, ...
    new_seqlevels <- paste0("c", seq_along(GenomeInfoDb::seqlevels(genome_ideogram)))
    names(new_seqlevels) <- GenomeInfoDb::seqlevels(genome_ideogram)
    genome_ideogram <- GenomeInfoDb::renameSeqlevels(genome_ideogram, new_seqlevels)
    colours <- rep("#a58bc5", length(GenomeInfoDb::seqlevels(genome_ideogram)))

    par(mar = c(2, 2, 2, 2)) # minimal margins around the plot

    gmoviz::gmovizInitialise(genome_ideogram,
        sector_colours = colours,
        sector_border_colours = colours,
        sector_labels = FALSE
    )

    for (i in 1:length(proviruses_gr_features)) {
        name <- as.character(seqnames(proviruses_gr_features[i]))
        start <- as.numeric(start(proviruses_gr_features[i]))
        end <- as.numeric(end(proviruses_gr_features[i]))
        region <- data.frame(start = start, end = end)
        circlize::circos.genomicRect(
            seqnames = name,
            region,
            ytop = .5,
            ybottom = 0,
            track.index = 1,
            sector.index = name,
            border = "#e9d27d",
            col = "#e9d27d"
        )
    }

    length <- as.numeric(proviruses_gr_features$length)
    length <- ifelse(length > 1000000,
        paste0(round(length / 1000000, 2), "mb"),
        paste0(round(length / 1000, 2), "kb")
    )
    labels <- paste0(as.character(seqnames(proviruses_gr_features)), " (", length, ")")
    circlize::circos.labels(
        sectors = as.character(seqnames(proviruses_gr_features)),
        x = as.numeric(start(proviruses_gr_features)),
        labels,
        facing = "clockwise"
    )
}

process_sample <- function(sample, combined_unique, host_genomes_paths, genome_data) {
    genome_current <- sample # Add this line
    tryCatch(
        {
            log_debug(paste("Starting to process sample:", sample))

            # Check if sample exists in genome_data
            if (!(sample %in% names(genome_data))) {
                log_debug(paste("Sample", sample, "not found in genome_data"))
                cat(paste("Error: Sample", sample, "not found in genome_data\n\n"))
                return()
            }

            cat(paste("## ", sample, "{.tabset .tabset-fade} \n\n"))

            host_genome_path <- host_genomes_paths$path[host_genomes_paths$name == sample]
            if (length(host_genome_path) == 0) {
                log_debug(paste("Host genome path not found for sample:", sample))
                cat(paste("Error: Host genome path not found for sample", sample, "\n\n"))
                return()
            }

            host_genome_ideogram <- tryCatch(
                {
                    gmoviz::getIdeogramData(fasta_file = host_genome_path)
                },
                error = function(e) {
                    log_debug(paste("Error loading host genome ideogram for sample", sample, ":", conditionMessage(e)))
                    NULL
                }
            )

            if (is.null(host_genome_ideogram)) {
                cat(paste("Error: Unable to load host genome ideogram for sample", sample, "\n\n"))
                return()
            }

            sample_data <- genome_data[[sample]]$loaded_data
            genomad_summary <- sample_data$genomad
            genomad_annotation <- sample_data$genomad_annotations
            checkv_data <- sample_data$checkv
            defense_finder_data <- sample_data$defense_finder
            abricate_data <- sample_data$abricate
            iphop_data <- sample_data$iphop
            vibrant_data <- sample_data$vibrant

            cat("### Host Genome\n\n")

            cat("**GTDB-Tk taxonomy**: \n\n")
            data_gtdbtk_host %>%
                dplyr::filter(user_genome == sample) %>%
                dplyr::select(classification) %>%
                kableExtra::kbl() %>%
                kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                kableExtra::kable_paper("striped", full_width = TRUE) %>%
                kableExtra::scroll_box(width = "100%", height = "100%") %>%
                cat()

            # Cat checkm summary for this genome
            cat("**CheckM2 Summary**:\n\n")
            # checkm_summary <- data_checkm_host %>% dplyr::filter(`bin_id` == sample)
            checkm_summary <- data_checkm_host %>% dplyr::filter(`name` == sample)
            checkm_summary %>%
                janitor::clean_names() %>%
                # dplyr::select(number_contigs, n50_contigs, completeness, contamination, strain_heterogeneity) %>%
                dplyr::select(total_contigs, contig_n50, completeness, contamination) %>%
                kableExtra::kbl() %>%
                kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                kableExtra::kable_paper("striped", full_width = TRUE) %>%
                kableExtra::scroll_box(width = "100%", height = "100%") %>%
                cat()

            # Display defense-finder as a table
            if (!is.null(defense_finder_data) && nrow(defense_finder_data) > 0) {
                cat("**Defense-Finder Systems**:\n\n")

                defense_finder_data %>%
                    dplyr::select(sys_id, type, subtype, sys_beg, sys_end, protein_in_syst, genes_count, name_of_profiles_in_sys) %>%
                    kableExtra::kbl() %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                    kableExtra::kable_paper("striped", full_width = TRUE) %>%
                    kableExtra::scroll_box(width = "100%", height = "100%") %>%
                    cat()
            } else {
                cat("No Defense-Finder systems detected.\n\n")
            }

            if (is.null(genomad_summary) || nrow(genomad_summary) == 0) {
                q
                log_debug(paste("No geNomad summary data found for sample:", sample))
                return()
            }

            if (length(GenomeInfoDb::seqlevels(host_genome_ideogram)) == 1) {
                host_genome_size <- sum(width(host_genome_ideogram))
            } else {
                virus_containing_contigs <- unique(sub("\\|.*", "", genomad_summary$seq_name))
                virus_containing_contigs <- paste0("c_", as.numeric(factor(virus_containing_contigs)))
                filtered_host_genome <- subset_and_update_ideogram(host_genome_ideogram, virus_containing_contigs)
                host_genome_size <- sum(width(filtered_host_genome))
            }

            # Process proviruses
            proviruses_gr_features <- process_proviruses(genomad_summary)

            cat("**Genomad and CheckV Summary**:\n\n")
            genomad_summary %>%
                dplyr::select(seq_name, taxonomy, topology, coordinates, length) %>%
                dplyr::left_join(
                    checkv_data %>% dplyr::select(contig_id, gene_count, viral_genes, checkv_quality, miuvig_quality),
                    by = c("seq_name" = "contig_id")
                ) %>%
                dplyr::select(seq_name, length, gene_count, viral_genes, checkv_quality, miuvig_quality, taxonomy, topology, coordinates) %>%
                kableExtra::kbl() %>%
                kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                kableExtra::kable_paper("striped", full_width = TRUE) %>%
                kableExtra::scroll_box(width = "100%", height = "100%") %>%
                cat()

            cat("**Host Genome Ideogram with Phages**:\n\n")
            plot_genome_ideogram(sample, proviruses_gr_features)
            cat('In this circular plot, **"c"** indicates the contig, and the number that follows (e.g., **c1**) represents the contig number.
If multiple contigs are present in the genome, each will be shown with a distinct label (e.g., **c1**, **c2**, etc.).\n\n')
            cat("\n\n")

            # Process phage genomes
            cat("### Prophages {.tabset .tabset-fade} \n\n")
            cat("**Select prophage to show: ** \n\n")
            for (i in seq_len(nrow(genomad_summary))) {
                log_debug(paste("Processing phage", i, "of", nrow(genomad_summary), "for sample", sample))
                process_phage(genomad_summary[i, ], genomad_summary, genomad_annotation, checkv_data, host_genome_size)
            }

            # Plot dREP if applicable
            if (nrow(genomad_summary) > 1) {
                cat("### vOTUs\n\n")
                plot_drep(sample, genomad_summary)
            }

            # Creating table with Abricate data
            if (nrow(abricate_data) > 0) {
                cat("### Virulence Genes {.tabset .tabset-fade} \n\n")
                cat("Screening of virulence genes present in the prophage contigs. \n\n")
                abricate_data %>%
                    dplyr::select(-number_file) %>%
                    kableExtra::kbl() %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                    kableExtra::kable_paper("striped", full_width = TRUE) %>%
                    kableExtra::scroll_box(width = "100%", height = "100%") %>%
                    cat()
                cat("\n\n")
            }

            # Creating table with iPHOP
            if (nrow(iphop_data) > 0) {
                cat("### Prophage-Host Prediction {.tabset .tabset-fade} \n\n")
                cat("Prediction of potential hosts for the prophage contigs. \n\n")
                iphop_data %>%
                    kableExtra::kbl() %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                    kableExtra::kable_paper("striped", full_width = TRUE) %>%
                    kableExtra::scroll_box(width = "100%", height = "100%") %>%
                    cat()
                cat("\n\n")
            }

            # Creating table with VIBRANT AMGs
            if (nrow(vibrant_data) > 0) {
                cat("### AMG Predictions {.tabset .tabset-fade} \n\n")
                cat("Prediction of auxiliary metabolic genes in the prophage contigs. \n\n")
                vibrant_data %>%
                    kableExtra::kbl() %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
                    kableExtra::kable_paper("striped", full_width = TRUE) %>%
                    kableExtra::scroll_box(width = "100%", height = "100%") %>%
                    cat()
                cat("\n\n")
            }

            log_debug(paste("Finished processing sample:", sample))
        },
        error = function(e) {
            log_debug(paste("Error in process_sample for", sample, ":", conditionMessage(e)))
            cat(paste("Error processing sample", sample, ":", conditionMessage(e), "\n\n"))
        }
    )
}

process_phage <- function(virus, genomad_summary, genomad_annotation, checkv_data, host_genome_size) {
    cat(paste("#### Phage ID:", virus$seq_name, " {.tabset .tabset-fade} \n\n"))

    current_contig <- sub("\\|.*", "", virus$seq_name)

    provirus_start <- as.numeric(sub(".*provirus_(\\d+)_\\d+", "\\1", virus$seq_name))
    provirus_end <- as.numeric(sub(".*provirus_\\d+_(\\d+)", "\\1", virus$seq_name))
    virus_length <- provirus_end - provirus_start + 1

    current_contig_base <- sub("\\|provirus_.*", "", virus$seq_name)
    current_provirus_range <- sub(".*\\|provirus_", "", virus$seq_name)
    current_annotations <- genomad_annotation[grepl(paste0(current_contig_base, "\\|provirus_", current_provirus_range, "_"),
        genomad_annotation$gene,
        fixed = FALSE
    ), ] %>%
        dplyr::mutate(arrow_pos = ifelse(strand == -1, "start", "end"))


    cat("\n\n**Phage-Host Genome Ideogram:**\n\n")

    plot_phage_circos(virus, genomad_summary, current_annotations, virus_length, host_genome_size, provirus_start, provirus_end, checkv_data)

    cat("\n\n")
    cat("\n\n**Genes Annotation (geNomad):**\n\n")

    current_annotations %>%
        dplyr::select(gene, length, marker, annotation_accessions, annotation_description) %>%
        kableExtra::kbl() %>%
        kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
        kableExtra::kable_paper() %>%
        cat()
    cat("\n\n")
}

plot_phage_circos <- function(virus, genomad_summary, current_annotations, virus_length, host_genome_size, provirus_start, provirus_end, checkv_data) {
    tryCatch(
        {
            log_debug("Starting plot_phage_circos function")
            log_debug(paste("Current virus:", virus$seq_name))
            log_debug(paste("Virus length:", virus_length))
            log_debug(paste("Host genome size:", host_genome_size))
            log_debug(paste("Provirus start:", provirus_start))
            log_debug(paste("Provirus end:", provirus_end))

            # Check for NA or invalid values in input parameters
            if (is.na(virus_length) || virus_length <= 0) {
                log_debug("Error: Invalid virus length", virus_length)
                return(NULL)
            }
            if (is.na(host_genome_size) || host_genome_size <= 0) {
                log_debug("Error: Invalid host genome size")
                return(NULL)
            }
            if (is.na(provirus_start) || provirus_start < 0) {
                log_debug("Error: Invalid provirus start position")
                return(NULL)
            }
            if (is.na(provirus_end) || provirus_end <= provirus_start) {
                log_debug("Error: Invalid provirus end position")
                return(NULL)
            }

            # Extract contig information
            current_contig <- sub("\\|.*", "", virus$seq_name)
            log_debug(paste("Current contig:", current_contig))

            contig_viruses <- genomad_summary[grepl(paste0("^", current_contig), genomad_summary$seq_name), ]
            if (nrow(contig_viruses) == 0) {
                log_debug("Error: No viruses found for the current contig")
                return(NULL)
            }

            contig_length <- max(as.numeric(sub(".*_(\\d+)$", "\\1", contig_viruses$seq_name)))
            if (is.na(contig_length) || contig_length <= 0) {
                contig_length <- virus_length # Use virus length as fallback if contig length is invalid
                log_debug(paste("Using virus length as contig length:", contig_length))
            } else {
                log_debug(paste("Contig length:", contig_length))
            }

            if (provirus_end > contig_length) {
                log_debug("Error: Provirus end position exceeds contig length")
                return(NULL)
            }

            log_debug("Clearing circos")
            circlize::circos.clear()

            log_debug("Setting circos parameters")
            circlize::circos.par(start.degree = 180, gap.degree = 10, track.margin = c(0.01, 0.01))

            main_color <- "#a58bc5"
            zoom_color <- "#e9d27d"

            zoom_start <- (provirus_start / contig_length) * 100
            zoom_end <- (provirus_end / contig_length) * 100

            log_debug(paste("Zoom start:", zoom_start))
            log_debug(paste("Zoom end:", zoom_end))

            log_debug("Initializing circos")
            circlize::circos.initialize(factors = c("Zoom", "Main"), xlim = c(0, 100))

            format_genome_labels <- function(x) {
                ifelse(x >= 1e6, paste0(round(x / 1e6, 2), " Mb"),
                    ifelse(x >= 1e3, paste0(round(x / 1e3, 2), " Kb"),
                        paste0(x, " bp")
                    )
                )
            }

            log_debug("Adding link")
            tryCatch(
                {
                    circlize::circos.link("Main", c(zoom_start, zoom_end), "Zoom", c(0, 100),
                        rou1 = 0.8,
                        rou2 = 0.97,
                        h.ratio = 0.55, # width?
                        lty = 2,
                        lwd = 0.5,
                        h2 = 1,
                        col = "grey99", border = "grey80"
                    )
                },
                error = function(e) {
                    log_debug(paste("Error in circlize::circos.link:", e$message))
                }
            )

            log_debug("Adding zoom track")
            circlize::circos.track(
                factors = "Zoom", ylim = c(0, 1), track.height = 0.15,
                panel.fun = function(x, y) {
                    circlize::circos.rect(0, 0, 100, 1, col = zoom_color, border = NA)
                    axis_labels <- seq(0, virus_length, length.out = 6)
                    axis_positions <- seq(0, 100, length.out = 6)
                    circlize::circos.axis(
                        h = "top", major.at = axis_positions,
                        labels = format_genome_labels(axis_labels),
                        labels.cex = 0.7, direction = "outside"
                    )

                    for (i in 1:nrow(current_annotations)) {
                        gene_start <- current_annotations$start[i]
                        gene_end <- current_annotations$end[i]
                        arrow_start <- (gene_start - provirus_start) / virus_length * 100
                        arrow_end <- (gene_end - provirus_start) / virus_length * 100

                        circlize::circos.arrow(arrow_start, arrow_end,
                            arrow.head.width = 0.75, arrow.head.length = cm_x(0.1),
                            arrow.position = current_annotations$arrow_pos[i],
                            col = ifelse(is.na(current_annotations$annotation_description[i]), "grey", "#7fbfff"),
                            border = ifelse(is.na(current_annotations$annotation_description[i]), "grey20", "darkblue")
                        )
                    }
                }, bg.border = NA
            )

            log_debug("Adding main track")
            circlize::circos.track(
                factors = "Main", ylim = c(0, 1), track.height = 0.1,
                panel.fun = function(x, y) {
                    circlize::circos.rect(xleft = 0, ybottom = 0, xright = 100, ytop = 1, col = main_color, border = NA)

                    for (i in 1:nrow(contig_viruses)) {
                        virus_start <- as.numeric(sub(".*provirus_(\\d+)_\\d+", "\\1", contig_viruses$seq_name[i]))
                        virus_end <- as.numeric(sub(".*provirus_\\d+_(\\d+)", "\\1", contig_viruses$seq_name[i]))

                        virus_start_percent <- (virus_start / contig_length) * 100
                        virus_end_percent <- (virus_end / contig_length) * 100

                        rect_color <- if (contig_viruses$seq_name[i] == virus$seq_name) zoom_color else adjustcolor(zoom_color, alpha.f = 0.7)

                        circlize::circos.rect(
                            xleft = virus_start_percent, ybottom = 0,
                            xright = virus_end_percent, ytop = 1,
                            col = rect_color, border = NA
                        )
                    }

                    axis_labels <- seq(0, contig_length, length.out = 6)
                    axis_positions <- seq(0, 100, length.out = 6)
                    circlize::circos.axis(
                        h = "top", major.at = axis_positions,
                        labels = format_genome_labels(axis_labels),
                        labels.cex = 0.7, direction = "outside"
                    )
                }, bg.border = NA
            )

            log_debug("Locating phage positions")
            phage_positions <- sapply(1:nrow(contig_viruses), function(i) {
                virus_start <- as.numeric(sub(".*provirus_(\\d+)_\\d+", "\\1", contig_viruses$seq_name[i]))
                virus_end <- as.numeric(sub(".*provirus_\\d+_(\\d+)", "\\1", contig_viruses$seq_name[i]))
                ((virus_start + virus_end) / 2 / contig_length) * 100
            })

            log_debug("Annotating names on phage positions")

            # Extract start and end positions from sequence names
            start_positions <- as.numeric(sub(".*provirus_([0-9]+)_.*", "\\1", contig_viruses$seq_name))
            end_positions <- as.numeric(sub(".*provirus_[0-9]+_([0-9]+)", "\\1", contig_viruses$seq_name))

            # Create phage labels with the desired format
            phage_labels <- paste0(round(contig_viruses$length / 1e3, 2), " Kb")

            # Apply labels to circos plot
            circlize::circos.labels(
                sectors = "Main",
                x = phage_positions,
                labels = phage_labels,
                facing = "reverse.clockwise",
                niceFacing = TRUE,
                col = "black",
                cex = 0.6,
                side = "inside",
                connection_height = 0.02,
                line_col = "gray"
            )

            center_x <- 50
            virus_name <- virus$seq_name
            taxonomy <- virus$taxonomy

            log_debug("Adding taxonomy and virus name to the plot")
            circlize::circos.text(
                x = center_x, y = -0.2, labels = taxonomy,
                sector.index = "Zoom", track.index = 1,
                facing = "bending.inside", niceFacing = TRUE,
                adj = c(0.5, 0.7), cex = 0.8
            )

            checkv_info <- checkv_data[checkv_data$contig_id == virus$seq_name, ]
            if (nrow(checkv_info) > 0) {
                checkv_quality <- checkv_info$checkv_quality
                gene_count <- checkv_info$gene_count
                viral_genes <- checkv_info$viral_genes
                host_genes <- checkv_info$host_genes
                miuvig_quality <- checkv_info$miuvig_quality
                completeness <- checkv_info$completeness
                completeness_method <- checkv_info$completeness_method
                contamination <- checkv_info$contamination

                circlize::circos.text(
                    x = center_x, y = -0.5,
                    labels = paste("CheckV Quality:", checkv_quality, " - miuvig Quality:", miuvig_quality),
                    sector.index = "Zoom", track.index = 2,
                    facing = "bending.inside", niceFacing = TRUE,
                    adj = c(0.5, 0), cex = 0.7
                )

                circlize::circos.text(
                    x = center_x, y = -1.5,
                    labels = paste("Gene Count:", gene_count, " - Viral Genes:", viral_genes, " - Host Genes:", host_genes),
                    sector.index = "Zoom", track.index = 2,
                    facing = "bending.inside", niceFacing = TRUE,
                    adj = c(0.5, 0), cex = 0.7
                )

                circlize::circos.text(
                    x = center_x, y = -2.5,
                    labels = paste("Completeness:", completeness, " - Contamination:", contamination),
                    sector.index = "Zoom", track.index = 2,
                    facing = "bending.inside", niceFacing = TRUE,
                    adj = c(0.5, 0), cex = 0.7
                )
            }

            log_debug("Adding legend")
            # Add legend
            legend("topright",
                legend = c("Annotated gene", "Unknown gene"),
                fill = c("#7fbfff", "grey"),
                border = c("darkblue", "grey20"),
                cex = 0.8,
                bty = "n"
            )

            log_debug("Clearing circos")
            circlize::circos.clear()
            log_debug("Finished plot_phage_circos function successfully")
        },
        error = function(e) {
            log_debug(paste("Error in plot_phage_circos:", e$message))
            circlize::circos.clear()
        }
    )
}

plot_drep <- function(sample, genomad_summary) {
    drep_file_path <- file.path(params$outdir, "virus_analyses", "drep_compare", sample, "data_tables", "Cdb.csv")
    drep_data <- read_csv(drep_file_path) %>% janitor::clean_names()
    drep_data <- cbind(genomad_summary$seq_name, drep_data)

    cat("When more than 1 phage is detected in the host genome, we perform a clustering step using the tool dRep.\n\n")
    cat("A threshold of 0.95 was applied to the ANI similarity index to define clusters of virus operational taxonomic units (vOTUs).")

    cat("\n\n**Final cluster designations**\n\n")
    drep_data %>%
        kableExtra::kbl() %>%
        kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
        kableExtra::kable_paper("striped", full_width = TRUE) %>%
        cat()

    # Insert the PDF plot
    plot_path <- file.path(params$outdir, "virus_analyses", "drep_compare", sample, "figures", "Primary_clustering_dendrogram.pdf")
    png_path <- file.path(params$outdir, "virus_analyses", "drep_compare", sample, "figures", "Primary_clustering_dendrogram.png")

    if (file.exists(plot_path)) {
        pdftools::pdf_convert(plot_path, format = "png", filenames = png_path, verbose = FALSE, dpi = 150)
        base64_str <- base64enc::dataURI(file = png_path, mime = "image/png")
        cat("**Primary clustering plot**\n\n")
        cat(sprintf(
            '<div style="text-align: center;"><img src="%s" style="max-width: 100%%; height: auto; border:1px solid #ddd; border-radius:4px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); margin: 1em 0;"></div>', base64_str
        ))
    } else {
        cat("**No dRep clustering plot found.**\n\n")
    }
}

subset_and_update_ideogram <- function(ideogram, contigs) {
    filtered <- ideogram[seqnames(ideogram) %in% contigs]
    GenomeInfoDb::seqlevels(filtered) <- contigs
    seqinfo(filtered) <- seqinfo(filtered)[contigs]
    filtered
}

render_all_samples <- function(test_mode = FALSE) {
    if (test_mode) {
        if (nrow(combined_unique) > 0) {
            cat("**Select sample to show:** \n\n\n")
            current_sample <- combined_unique$Sample[6]
            process_sample(current_sample, combined_unique, host_genomes_paths, genome_data)
        } else {
            print("No samples can be further analysed.")
        }
    } else {
        cat("**Select sample to show:** \n\n\n")
        for (i in seq_len(nrow(combined_unique))) {
            current_sample <- combined_unique$Sample[i]
            process_sample(current_sample, combined_unique, host_genomes_paths, genome_data)
        }
    }
}

# Execute the main function
# Test mode processes one sample only
render_all_samples(test_mode = F)
```