This repository contains the analysis code for F3UTER. Nat Commun (2022) - https://doi.org/10.1038/s41467-022-30017-z
Copyright 2020 Astex Therapeutics Ltd.
This repository is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the LICENSE file (GNU General Public License) for more details.
Use the F3UTER app to query predictions associated with the genes of your interest: https://astx.shinyapps.io/F3UTER/
The following snakemake pipelines can be used to Generate ERs, calculate omic features and produce 3'UTR predictions using F3UTER.
- Generate ERs: Identify unannotated intergenic expressed regions (ERs) from RNA-seq data.
- Generate F3UTER features: Calculate omic features required for F3UTER.
- Generate F3UTER predictions: Predict potentially unannotated 3'UTRs.
- Generate F3UTER predictions in mouse: Predict potentially unannotated 3'UTRs in the mouse genome.
The majority of the analysis (including the plots) presented in the manuscript can be reproduced using the following snakemake pipelines below:
- F3uter_mouse_eval: Evaluates performance of F3UTER on mouse 3'UTRs.
- F3uter_drosophila_eval: Evaluates performance of F3UTER on drosophila 3'UTRs.
- F3uter_zebrafish_eval: Evaluates performance of F3UTER on zebrafish 3'UTRs.
- F3uter_compare_3-seq: Compares F3UTER predictions with paired 3'-end sequencing data.
- PAS_predictions: Predicting poly(A) sites within ERs using existing poly(A) site prediction tools.
- PAS_comapare_3-seq: Compares poly(A) site predictions with paired 3'-end sequencing data.
- Gtex_analysis: Applies F3UTER to ERs from 39 GTEx tissues and performs all downstream analysis.
- RBP_analysis: Calculates RBP density enrichment in unannotated 3'UTR predictions.
The scripts are listed in the order of performed analysis. The output of these scripts are txt files.
Processing of ER data from Zhang et al 2020
| Script | Description |
|---|---|
| er_split_by_tissue.R | Split ERs by tissue |
| er_initial_processing.pl | Add additional gene info to ERs |
| er_dataset_generate_for_analysis.R | Select intergenic ERs for analysis |
| er_3prime_vs_5prime_analysis.R | Compare 3' intergenic ERs with 5' intergenic ERs |
An example dataset of processed 3' intergenic ERs is provided within Test_dataset.
| Script | Description |
|---|---|
| training_data_regions_generate.R | Select and process regions for training |
The training data can be downloaded from the F3UTER app: https://astx.shinyapps.io/F3UTER/
Calculating omic features, can be applied to training regions and ERs
| Script | Description |
|---|---|
| polyA_signal.R | Scan for Poly(A) signal (number of features, n=1) |
| nucleotide_frequency.R | Calculate mono- and di-nucleotide frequency (n=20) |
| sequence_conservation.R | Calculate mean phastCons score (n=1) |
| transposons_overlap.R | Calculate overlap with transposons ((n=1) |
| expression_features.R | Calculate entropy efficiency and percentage difference of expression reads (n=2) |
| dna_structural_features.R | Calculate DNA structural properties (n=16) |
The training and ER data feature matrix can be downloaded from the F3UTER app: https://astx.shinyapps.io/F3UTER/
Univariate and multivariate analysis of omic features
| Script | Description |
|---|---|
| make_ml_table.R | Compile feature matrix |
| univariate_analysis.R | Perform univariate analysis on features |
| umap_analysis.R | Perform UMAP analysis on features |
| run_classification_models.pl | Wrapper for running multinomial classification models |
| summarise_multinomial_models.R | Wrapper for summarising results from multinomial models |
| plot_multinomial_models.R | Plot results from multinomial models |
| elasticNetMultinomialLR_model.R | code for elastic net multinomial logistic regression |
| randomForestMultinomial_model.R | code for multinomial random forest classification |
| RF_vs_LR_analysis.R | Compare multinomial logistic regression and random forest results |
Construction and cross-validation of F3UTER
| Script | Description |
|---|---|
| run_f3uter_cv.pl | Wrapper for running F3UTER cross validation evaluation |
| summarise_f3uter_cv.R | Wrapper for summarising cross validation results |
| f3uter_cv.R | Code for training F3UTER |
| plot_roc_pr_curves.R | Plot ROC and precision-recall curves |
| f3uter_trained_model.R | Save F3UTER trained model |
Validation of F3UTER predictions using RNA-seq and 3'-seq data in B cells
| Script | Description |
|---|---|
| generate_Bcell_predictions.R | Generate 3'UTR predictions in B cell ER dataset using F3UTER |
| merge_erData_with_predictionData.R | Merge ER prediction data with ER raw meta-data |
| permute_random_intergenic_ERs.pl | Generate randomly selected intergenic ERs for permutation test |
| regions_to_exclude_for_permutation.R | Genomic space to mask in order to produce intergenic ER space |
| compare_knownThreePrime_with_polya.R | Compare known 3'UTRs with poly(A) site clusters |
| compare_BcellErs_with_polya.R | Compare 3'UTR predictions in B cells with poly(A) site clusters |
Applying F3UTER to ERs derived from GTEx tissues to predict unannotated 3'UTRs. All the ER predictions can be downloaded from the F3UTER app: https://astx.shinyapps.io/F3UTER/
| Script | Description |
|---|---|
| generate_gtex_predictions.R | Use F3UTER on GTEx ERs to produce predictions |
| merge_gtexErData_with_predictions.R | Merge ER prediction data with ER raw meta-data |
| calculate_gtex_prediction_numbers.R | Calculate basic stats/numbers for prediction results across tissues |
| categorise_er_tissue_specificity.R | Split ER predictions based on their tissue-specificity across 39 tissues |
| generate_categorised_geneLists_and_tables.R | Generate data tables and gene lists for each category to be used for downstream analysis |
| calculate_cncr_score.R | Code to calculate CNC scores for a query region |
| perform_cncr_analysis.R | Perform CNCR analysis on all predictions and tissue-specific groups |
Calculate RBP motif enrichment within ER predictions. See README.md for details.
9. F3UTER_app
Source code for the F3UTER online resource. App URL: https://astx.shinyapps.io/F3UTER/
All the data analysis was performed in R version 3.6.2. For analysis or plotting, the following packages were used: ggplot2_2_3.3.2, ggridges_0.5.2, ggridges_0.5.2, ggsignif_0.6.0, rstatix_0.6.0, derfinder_1.20.0, rtracklayer_1.46.0, GenomicFeatures_1.38.2, GenomicRanges_1.38.0, ggpubr_0.4.0, stringr_1.4.0, dplyr_1.0.2, tidyverse_1.3.0
Please cite this article when using F3UTER: https://www.nature.com/articles/s41467-022-30017-z
Sethi, S., Zhang, D., Guelfi, S. et al. Leveraging omic features with F3UTER enables identification of unannotated 3’UTRs for synaptic genes. Nat Commun 13, 2270 (2022). https://doi.org/10.1038/s41467-022-30017-z