pub-PABPN1_APA-shift_Raz

This is the code repository of scripts and code used in the data analysis of article: "PABPN1 loss-of-function causes APA-shift in oculopharyngeal muscular dystrophy", by Milad Shademan, Hailiang Mei, Yavuz Ariyurek, Susan Kloet, Vered Raz

The directories are organized based on the actual steps taken during the data analysis.

step1_proximal_distal_annotations

This the script used to create GTF file annotating proximal and distal regions of all 3' UTR. Because motif based annotation file (see create_motif_based_gtf.py) does not give reliable annotations, we opt for a simpler and a bit naive approach by dividing 3' UTR into just one proximal and one distal region. Since this division is a bit abtrary, we allow a parameter to specify a shift of the division point between these two regions to investigate the robustness of this approach, SHIFT_RATIO_3UTR

step2_run_biowdl_RNAseq_pipeline

All mouse and human reads in FASTQ format are processed using BioWDL RNAseq pipeline version 5.0.0. It first filtered using Cutadapt (v2.10) to remove all remaining adapter sequences. Then the reads were aligned to the Ensembl transcriptome version 104 using STAR (v2.7.5a) including UMI based deduplication using UMI-Tools (v1.1.1) to generate transcriptome-based alignment in BAM format.

step3_quantification_APA

With this APA proximal and distal annotation GTF file generated in step 1 and the human and mouse transcriptome-based alignment file generated in step2 as input, we quantified the APA enrichment signal at both proximal and distal regions using featureCounts (v2.0.1) with option “-M” to include multiple mapped reads.

step4_differential_analysis_APA

APA-shift calculates the ratio of reads between proximal to distal at the 3’UTR. Only reads at the 3’UTR are considered. The code includes: filtering of transcripts with low read counts, calculation of proximal to distal ratio, statistical analysis to determine transcripts with APA-shift between control (‘CTRL’) and the condition of interest (‘COI’), and calculation of fold change ratios.

In summary, this code performs the following tasks:

1. Reads an Excel file containing gene expression data.
2. Filters and preprocesses the data by selecting relevant rows and columns.
3. Computes log-ratios and conducts t-tests for each gene.
4. Calculates p-values and adjusts them using the False Discovery Rate (FDR) method.
5. Calculates log-fold changes.
6. Writes the results to an output CSV file. Make sure to replace "C:/your/path/file.xlsx" with the actual path to your Excel file and review the code to ensure it is compatible with your specific dataset and analysis requirements.

step5_visualization

APA_shift results are visualized using the tools here: ggVolcanoR. Different cut-off criteria can be used, and different axis adjustments are available. Pointing and labeling options are useful for the output. Different output file and formats, like Images and tables are available.