diff --git a/benchmark.sh b/manuscript_demo/benchmark.sh
similarity index 100%
rename from benchmark.sh
rename to manuscript_demo/benchmark.sh
diff --git a/scripts/R2_annotate.R b/scripts/deprecated/R2_annotate.R
similarity index 100%
rename from scripts/R2_annotate.R
rename to scripts/deprecated/R2_annotate.R
diff --git a/scripts/R2_annotate_retainTranscriptVersion.R b/scripts/deprecated/R2_annotate_retainTranscriptVersion.R
similarity index 100%
rename from scripts/R2_annotate_retainTranscriptVersion.R
rename to scripts/deprecated/R2_annotate_retainTranscriptVersion.R
diff --git a/scripts/R2_lift.R b/scripts/deprecated/R2_lift.R
similarity index 100%
rename from scripts/R2_lift.R
rename to scripts/deprecated/R2_lift.R
diff --git a/scripts/deprecated/R2_lift_keepTranscriptVersion.R b/scripts/deprecated/R2_lift_keepTranscriptVersion.R
new file mode 100644
index 0000000..3738968
--- /dev/null
+++ b/scripts/deprecated/R2_lift_keepTranscriptVersion.R
@@ -0,0 +1,138 @@
+#!/usr/bin/env Rscript
+args = commandArgs(trailingOnly=TRUE)
+
+# test if there is at least one argument: if not, return an error
+if (length(args)!=3) {
+ stop("\nUsage: Rscript lift.R /path/to/foo.bed /path/to/bar.gtf /path/to/output.bed", call.=FALSE)
+}
+
+suppressMessages(suppressWarnings(library(GenomicFeatures, warn.conflicts = F, quietly = T)))
+suppressMessages(suppressWarnings(library(rtracklayer, warn.conflicts = F, quietly = T)))
+suppressMessages(suppressWarnings(library(tidyverse, warn.conflicts = F, quietly = T)))
+
+
+################################################################################
+################################################################################
+################################################################################
+
+# import bed file of transcriptome alignments
+mappedLocus <- read_tsv(file = args[1], col_names = T, guess_max = 999999999999, col_types = "fddfff") %>%
+ dplyr::rename(transcript_id = 1) %>%
+ # mutate(transcript_id = gsub("\\..*","",transcript_id)) %>%
+ dplyr::rename(tx_coord_start = 2) %>%
+ dplyr::rename(tx_coord_end = 3) %>%
+ dplyr::rename(name = 4, score = 5, strand = 6)
+
+
+# collect the column names of columns 7+
+targetNames <- colnames(mappedLocus)[c(4,7:length(colnames(mappedLocus)))]
+
+# merge columns c(4,7+)
+mappedLocus <- unite(mappedLocus, metaname, c(4,7:length(colnames(mappedLocus))), sep = ">_>", remove = TRUE, na.rm = FALSE)
+
+# deselect strand
+mappedLocus <- mappedLocus %>% dplyr::select(-6)
+
+##################################################
+
+# fetch transcript structures from transcriptome annotation
+
+# read in reference transcripts
+gtf <- makeTxDbFromGFF(file=args[2], format = "gtf")
+
+# make an exon database from the reference transcripts
+exons <- exonsBy(gtf, "tx", use.names=TRUE)
+
+# remove transcript versions from the transcript names
+#fixedNames <- names(exons) %>% as_tibble() %>% mutate(value = gsub("\\..*","",value)) %>% pull(value)
+fixedNames <- names(exons) %>% as_tibble() %>% pull(value)
+names(exons) <- fixedNames
+
+# prepare the exons
+exons_tib <- as_tibble(as(exons, "data.frame"))
+
+# make lookup table for strand
+print("preparing strand lookup table")
+strand_lookup <- exons_tib %>%
+ dplyr::rename(transcript_id = group_name) %>%
+ dplyr::select(transcript_id, strand) %>% dplyr::distinct() %>%
+ # mutate(transcript_id = gsub("\\..*","", transcript_id)) %>%
+ dplyr::distinct()
+
+##################################################
+
+# attach the correct strand to the bed sites
+print("Repairing strand")
+mappedLocus_fixedStrand <- inner_join(mappedLocus, strand_lookup, by = "transcript_id") %>%
+ mutate(score = ".", .after = metaname)
+
+# diagnostic print statements
+# print("mappedLocus_fixedStrand")
+# print(head(mappedLocus_fixedStrand))
+
+# write out the strand-repaired file as a temporary file
+print("writing strand bedfile")
+
+# Filter for rows where columns 1-3 or 6 are NA
+na_rows <- rowSums(is.na(mappedLocus_fixedStrand[c(1, 2, 3, 6)])) > 0
+na_count <- sum(na_rows)
+
+# Filter for rows where col3 - col2 is not greater than 1
+diff_not_greater <- (mappedLocus_fixedStrand$tx_coord_end - mappedLocus_fixedStrand$tx_coord_start) <= 0
+diff_count <- sum(diff_not_greater)
+
+# Combine both filters
+filters <- na_rows | diff_not_greater
+
+# Filter the data
+filtered_data <- mappedLocus_fixedStrand[!filters, ]
+
+# Write the filtered data to a TSV file
+write_tsv(filtered_data, args[3], col_names = FALSE)
+
+# Print the count of omitted rows
+cat("Omitted", na_count, "rows due to NAs in columns 1-3 or 6.\n")
+cat("Omitted", diff_count, "rows due to zero or negative width. \n")
+
+
+
+##################################################
+
+# read in the bed sites with corrected strand
+print("importing strand bedfile")
+mappedLocus <- import.bed(args[3])
+
+# map transcript coordinates to genome
+print("mapping transcript coordinates to genome")
+genomeLocus <- mapFromTranscripts(x=mappedLocus, transcripts=exons)
+
+# bind score to output
+# the score column contains cheui-specific output (e.g. stoich, prob, coverage, which we aggregate into the score column and delimit using semicolons)
+print("binding output")
+mcols(genomeLocus)<-cbind(mcols(genomeLocus),DataFrame(mappedLocus[genomeLocus$xHits]))
+
+# convert output to tibble
+genome_coordinates = as_tibble(as(genomeLocus, "data.frame"))
+
+# prepare the output by selecting bed-like coordinates from
+print("filtering output")
+output <- genome_coordinates %>% dplyr::select(seqnames, start, end, X.name, X.seqnames, strand) %>%
+ unique() %>%
+ dplyr::rename(chr = seqnames, data = X.name, transcript = X.seqnames) %>%
+ mutate(score = ".") %>%
+ dplyr::select(chr, start, end, transcript, score, strand, data) %>%
+ mutate(
+ start = ifelse(strand == "-", start - 2, start),
+ end = ifelse(strand == "+", end + 1, end - 1)
+ )
+
+# separate the output
+output <- output %>% separate(data, sep = ">_>", into = targetNames) %>%
+ # dplyr::select(chr, start, end, name, score, strand) %>%
+ dplyr::rename("#chr" = chr)
+
+##################################################
+
+# write the output
+print("writing final output")
+write_tsv(output, args[3], col_names = T, append = FALSE)
diff --git a/scripts/cheui_diff_to_bed.sh b/scripts/deprecated/cheui_diff_to_bed.sh
similarity index 100%
rename from scripts/cheui_diff_to_bed.sh
rename to scripts/deprecated/cheui_diff_to_bed.sh
diff --git a/scripts/deprecated/compare.ipynb b/scripts/deprecated/compare.ipynb
new file mode 100644
index 0000000..2e3e5ca
--- /dev/null
+++ b/scripts/deprecated/compare.ipynb
@@ -0,0 +1,699 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0ef0f028-b645-48c1-ad19-313f8cf50333",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# Define the working directory and file paths\n",
+ "wd = \"/g/data/lf10/as7425/R2DTool_demo/\"\n",
+ "file1 = f\"{wd}/methylation_calls_annotated.bed\"\n",
+ "file2 = f\"{wd}/methylation_calls_annotated_R.bed\"\n",
+ "\n",
+ "# Read the files into Pandas dataframes\n",
+ "df1 = pd.read_csv(file1, sep='\\t', low_memory=False)\n",
+ "df2 = pd.read_csv(file2, sep='\\t', low_memory=False)\n",
+ "\n",
+ "# Assuming you want to match 'transcript' in df1 with 'transcript_id' in df2\n",
+ "df2.rename(columns={'transcript_id': 'transcript'}, inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "cea4bd5d-c5aa-4a90-b5e5-199d8eda77d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " transcript start end name score strand motif coverage \\\n",
+ "0 ENST00000000233 1007 1008 . . + CTTGAGTAA 648 \n",
+ "1 ENST00000000233 1011 1012 . . + AGTAATAAA 628 \n",
+ "2 ENST00000000233 137 138 . . + AAGCAGATG 467 \n",
+ "3 ENST00000000233 151 152 . . + TCTCATGGT 608 \n",
+ "4 ENST00000000233 164 165 . . + TTGGATGCG 544 \n",
+ "\n",
+ " stoichiometry probability ... transcript_biotype tx_len cds_start \\\n",
+ "0 0.1013215859030837 0.118574 ... protein_coding 1032 88.0 \n",
+ "1 0.3223684210526316 0.547572 ... protein_coding 1032 88.0 \n",
+ "2 0.2560240963855422 0.363126 ... protein_coding 1032 88.0 \n",
+ "3 0.5113636363636364 0.421296 ... protein_coding 1032 88.0 \n",
+ "4 0.17433414043583534 0.367664 ... protein_coding 1032 88.0 \n",
+ "\n",
+ " cds_end tx_end transcript_metacoordinate abs_cds_start abs_cds_end \\\n",
+ "0 628.0 1032.0 2.93812 919.0 379.0 \n",
+ "1 628.0 1032.0 2.94802 923.0 383.0 \n",
+ "2 628.0 1032.0 1.09074 49.0 -491.0 \n",
+ "3 628.0 1032.0 1.11667 63.0 -477.0 \n",
+ "4 628.0 1032.0 1.14074 76.0 -464.0 \n",
+ "\n",
+ " up_junc_dist down_junc_dist \n",
+ "0 463.0 NaN \n",
+ "1 467.0 NaN \n",
+ "2 NaN 18.0 \n",
+ "3 NaN 4.0 \n",
+ "4 9.0 72.0 \n",
+ "\n",
+ "[5 rows x 22 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "df1['transcript'] = df1['transcript'].str.split('.').str[0]\n",
+ "print(df1.head())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8757c384-a36b-4985-9eb0-296d04c7e9c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " transcript | \n",
+ " start | \n",
+ " end | \n",
+ " name | \n",
+ " score | \n",
+ " strand | \n",
+ " motif | \n",
+ " coverage | \n",
+ " stoichiometry | \n",
+ " probability | \n",
+ " ... | \n",
+ " utr5_len | \n",
+ " utr3_len | \n",
+ " cds_start | \n",
+ " cds_end | \n",
+ " tx_end | \n",
+ " transcript_metacoordinate | \n",
+ " abs_cds_start | \n",
+ " abs_cds_end | \n",
+ " up_junc_dist | \n",
+ " down_junc_dist | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " ENST00000000233 | \n",
+ " 1007 | \n",
+ " 1008 | \n",
+ " . | \n",
+ " . | \n",
+ " + | \n",
+ " CTTGAGTAA | \n",
+ " 648 | \n",
+ " 0.1013215859030837 | \n",
+ " 0.118574 | \n",
+ " ... | \n",
+ " 88 | \n",
+ " 401 | \n",
+ " 88 | \n",
+ " 631 | \n",
+ " 1032 | \n",
+ " 2.937656 | \n",
+ " 919 | \n",
+ " 376 | \n",
+ " 463.0 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " ENST00000000233 | \n",
+ " 1011 | \n",
+ " 1012 | \n",
+ " . | \n",
+ " . | \n",
+ " + | \n",
+ " AGTAATAAA | \n",
+ " 628 | \n",
+ " 0.3223684210526316 | \n",
+ " 0.547572 | \n",
+ " ... | \n",
+ " 88 | \n",
+ " 401 | \n",
+ " 88 | \n",
+ " 631 | \n",
+ " 1032 | \n",
+ " 2.947631 | \n",
+ " 923 | \n",
+ " 380 | \n",
+ " 467.0 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " ENST00000000233 | \n",
+ " 137 | \n",
+ " 138 | \n",
+ " . | \n",
+ " . | \n",
+ " + | \n",
+ " AAGCAGATG | \n",
+ " 467 | \n",
+ " 0.2560240963855422 | \n",
+ " 0.363126 | \n",
+ " ... | \n",
+ " 88 | \n",
+ " 401 | \n",
+ " 88 | \n",
+ " 631 | \n",
+ " 1032 | \n",
+ " 1.090239 | \n",
+ " 49 | \n",
+ " -494 | \n",
+ " NaN | \n",
+ " 18.0 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " ENST00000000233 | \n",
+ " 151 | \n",
+ " 152 | \n",
+ " . | \n",
+ " . | \n",
+ " + | \n",
+ " TCTCATGGT | \n",
+ " 608 | \n",
+ " 0.5113636363636364 | \n",
+ " 0.421296 | \n",
+ " ... | \n",
+ " 88 | \n",
+ " 401 | \n",
+ " 88 | \n",
+ " 631 | \n",
+ " 1032 | \n",
+ " 1.116022 | \n",
+ " 63 | \n",
+ " -480 | \n",
+ " NaN | \n",
+ " 4.0 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " ENST00000000233 | \n",
+ " 164 | \n",
+ " 165 | \n",
+ " . | \n",
+ " . | \n",
+ " + | \n",
+ " TTGGATGCG | \n",
+ " 544 | \n",
+ " 0.17433414043583534 | \n",
+ " 0.367664 | \n",
+ " ... | \n",
+ " 88 | \n",
+ " 401 | \n",
+ " 88 | \n",
+ " 631 | \n",
+ " 1032 | \n",
+ " 1.139963 | \n",
+ " 76 | \n",
+ " -467 | \n",
+ " 9.0 | \n",
+ " 72.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 25 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " transcript start end name score strand motif coverage \\\n",
+ "0 ENST00000000233 1007 1008 . . + CTTGAGTAA 648 \n",
+ "1 ENST00000000233 1011 1012 . . + AGTAATAAA 628 \n",
+ "2 ENST00000000233 137 138 . . + AAGCAGATG 467 \n",
+ "3 ENST00000000233 151 152 . . + TCTCATGGT 608 \n",
+ "4 ENST00000000233 164 165 . . + TTGGATGCG 544 \n",
+ "\n",
+ " stoichiometry probability ... utr5_len utr3_len cds_start cds_end \\\n",
+ "0 0.1013215859030837 0.118574 ... 88 401 88 631 \n",
+ "1 0.3223684210526316 0.547572 ... 88 401 88 631 \n",
+ "2 0.2560240963855422 0.363126 ... 88 401 88 631 \n",
+ "3 0.5113636363636364 0.421296 ... 88 401 88 631 \n",
+ "4 0.17433414043583534 0.367664 ... 88 401 88 631 \n",
+ "\n",
+ " tx_end transcript_metacoordinate abs_cds_start abs_cds_end \\\n",
+ "0 1032 2.937656 919 376 \n",
+ "1 1032 2.947631 923 380 \n",
+ "2 1032 1.090239 49 -494 \n",
+ "3 1032 1.116022 63 -480 \n",
+ "4 1032 1.139963 76 -467 \n",
+ "\n",
+ " up_junc_dist down_junc_dist \n",
+ "0 463.0 NaN \n",
+ "1 467.0 NaN \n",
+ "2 NaN 18.0 \n",
+ "3 NaN 4.0 \n",
+ "4 9.0 72.0 \n",
+ "\n",
+ "[5 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1f115e32-1825-4054-adf3-f67abf936c74",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " transcript start end_rust name_rust score_rust strand_rust \\\n",
+ "0 ENST00000000233 1007 1008 . . + \n",
+ "1 ENST00000000233 1011 1012 . . + \n",
+ "2 ENST00000000233 137 138 . . + \n",
+ "3 ENST00000000233 151 152 . . + \n",
+ "4 ENST00000000233 164 165 . . + \n",
+ "\n",
+ " motif_rust coverage_rust stoichiometry_rust probability_rust ... \\\n",
+ "0 CTTGAGTAA 648 0.1013215859030837 0.118574 ... \n",
+ "1 AGTAATAAA 628 0.3223684210526316 0.547572 ... \n",
+ "2 AAGCAGATG 467 0.2560240963855422 0.363126 ... \n",
+ "3 TCTCATGGT 608 0.5113636363636364 0.421296 ... \n",
+ "4 TTGGATGCG 544 0.17433414043583534 0.367664 ... \n",
+ "\n",
+ " utr5_len utr3_len cds_start_R cds_end_R tx_end_R \\\n",
+ "0 88 401 88 631 1032 \n",
+ "1 88 401 88 631 1032 \n",
+ "2 88 401 88 631 1032 \n",
+ "3 88 401 88 631 1032 \n",
+ "4 88 401 88 631 1032 \n",
+ "\n",
+ " transcript_metacoordinate_R abs_cds_start_R abs_cds_end_R \\\n",
+ "0 2.937656 919 376 \n",
+ "1 2.947631 923 380 \n",
+ "2 1.090239 49 -494 \n",
+ "3 1.116022 63 -480 \n",
+ "4 1.139963 76 -467 \n",
+ "\n",
+ " up_junc_dist_R down_junc_dist_R \n",
+ "0 463.0 NaN \n",
+ "1 467.0 NaN \n",
+ "2 NaN 18.0 \n",
+ "3 NaN 4.0 \n",
+ "4 9.0 72.0 \n",
+ "\n",
+ "[5 rows x 45 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Merge the dataframes on 'transcript' and 'start' columns\n",
+ "merged_df = pd.merge(df1, df2, on=['transcript', 'start'], suffixes=('_rust', '_R'))\n",
+ "\n",
+ "# Output the merged dataframe if needed (can also save to file if required)\n",
+ "print(merged_df.head())\n",
+ "\n",
+ "# Optionally, save the merged dataframe to a file\n",
+ "# merged_df.to_csv(f\"{wd}/merged_output.bed\", sep='\\t', index=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f4216e1c-0edf-4524-bbb9-a1d281f17790",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['transcript', 'start', 'end_rust', 'name_rust', 'score_rust',\n",
+ " 'strand_rust', 'motif_rust', 'coverage_rust', 'stoichiometry_rust',\n",
+ " 'probability_rust', 'gene_id_rust', 'gene_name_rust',\n",
+ " 'transcript_biotype_rust', 'tx_len_rust', 'cds_start_rust',\n",
+ " 'cds_end_rust', 'tx_end_rust', 'transcript_metacoordinate_rust',\n",
+ " 'abs_cds_start_rust', 'abs_cds_end_rust', 'up_junc_dist_rust',\n",
+ " 'down_junc_dist_rust', 'end_R', 'name_R', 'score_R', 'strand_R',\n",
+ " 'motif_R', 'coverage_R', 'stoichiometry_R', 'probability_R',\n",
+ " 'transcript_biotype_R', 'gene_name_R', 'gene_id_R', 'tx_len_R',\n",
+ " 'cds_len', 'utr5_len', 'utr3_len', 'cds_start_R', 'cds_end_R',\n",
+ " 'tx_end_R', 'transcript_metacoordinate_R', 'abs_cds_start_R',\n",
+ " 'abs_cds_end_R', 'up_junc_dist_R', 'down_junc_dist_R'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(merged_df.columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "83107972-69e8-4188-ae8c-370fd504e942",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from scipy.stats import linregress\n",
+ "\n",
+ "# Function to plot scatterplot with trendline and R^2 value\n",
+ "def plot_scatter_with_trendline(df, x_col, y_col, log_scale=False):\n",
+ " \"\"\"\n",
+ " Plots a scatterplot with a trendline and R^2 value for two columns in a DataFrame.\n",
+ "\n",
+ " Parameters:\n",
+ " df (DataFrame): The DataFrame containing the data.\n",
+ " x_col (str): The name of the column to use as the x-axis.\n",
+ " y_col (str): The name of the column to use as the y-axis.\n",
+ " log_scale (bool): Whether to apply log-log scaling to both axes.\n",
+ " \"\"\"\n",
+ " print(df.columns)\n",
+ " # Check if columns exist in the DataFrame\n",
+ " if x_col not in df.columns or y_col not in df.columns:\n",
+ " raise ValueError(\"One or both columns not found in the DataFrame.\")\n",
+ " \n",
+ " # Creating a scatter plot\n",
+ " plt.figure(figsize=(10, 6))\n",
+ " sns.scatterplot(data=df, x=x_col, y=y_col, alpha=0.6)\n",
+ "\n",
+ " # Optionally apply log-log scaling\n",
+ " if log_scale:\n",
+ " plt.xscale('log')\n",
+ " plt.yscale('log')\n",
+ "\n",
+ " # Fit a linear regression model to get the trendline and R^2 value\n",
+ " slope, intercept, r_value, p_value, std_err = linregress(df[x_col].dropna(), df[y_col].dropna())\n",
+ " plt.plot(df[x_col], intercept + slope*df[x_col], color='red', label=f'Fit Line: y={slope:.2f}x+{intercept:.2f}')\n",
+ "\n",
+ " # Plot settings\n",
+ " plt.title(f'Scatter Plot with Trendline between {x_col} and {y_col}')\n",
+ " plt.xlabel(x_col)\n",
+ " plt.ylabel(y_col)\n",
+ " plt.legend(title=f'R-squared = {r_value**2:.2f}')\n",
+ "\n",
+ " # Show plot\n",
+ " plt.show()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f5cda0bd-845d-445a-854b-a9b943cbc939",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['transcript', 'start', 'end_rust', 'name_rust', 'score_rust',\n",
+ " 'strand_rust', 'motif_rust', 'coverage_rust', 'stoichiometry_rust',\n",
+ " 'probability_rust', 'gene_id_rust', 'gene_name_rust',\n",
+ " 'transcript_biotype_rust', 'tx_len_rust', 'cds_start_rust',\n",
+ " 'cds_end_rust', 'tx_end_rust', 'transcript_metacoordinate_rust',\n",
+ " 'abs_cds_start_rust', 'abs_cds_end_rust', 'up_junc_dist_rust',\n",
+ " 'down_junc_dist_rust', 'end_R', 'name_R', 'score_R', 'strand_R',\n",
+ " 'motif_R', 'coverage_R', 'stoichiometry_R', 'probability_R',\n",
+ " 'transcript_biotype_R', 'gene_name_R', 'gene_id_R', 'tx_len_R',\n",
+ " 'cds_len', 'utr5_len', 'utr3_len', 'cds_start_R', 'cds_end_R',\n",
+ " 'tx_end_R', 'transcript_metacoordinate_R', 'abs_cds_start_R',\n",
+ " 'abs_cds_end_R', 'up_junc_dist_R', 'down_junc_dist_R'],\n",
+ " dtype='object')\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "