Analysis of the RNA binding protein (RBP) motifs for RNA-Seq and miRNAs (v2)

There are several alternative R packages and tools to perform motif enrichment analysis for RNA-binding proteins (RBPs), beyond PWMEnrich::motifEnrichment(). Here are the most notable ones:

| Tool / Package           | Enrichment        | Custom Motifs   | CLI or R? | RNA-specific?  |
| ------------------------ | ----------------- | --------------- | --------- | -------------- |
| **PWMEnrich**            | ✅                 | ✅               | R         | ✅              |
| **RBPmap**               | ✅                 | ❌ (uses own db) | Web/CLI   | ✅              |  ----> try RBPmap_results + enrichments!
| **Biostrings/TFBSTools** | ❌ (only scanning) | ✅               | R         | ❌              |  #ATtRACT + Biostrings / TFBSTools
| **rmap**                 | ✅ (CLIP-based)    | ❌               | R         | ✅              |
| **Homer**                | ✅                 | ✅               | CLI       | ⚠ RNA optional |
| **MEME (AME, FIMO)**     | ✅                 | ✅               | Web/CLI   | ⚠ Generic      |

1. Get 3UTR.fasta, 5UTR.fasta, CDS.fasta and transcripts.fasta

           mRNA Transcript
   ┌────────────┬────────────┬────────────┐
   │   5′ UTR   │     CDS    │   3′ UTR   │
   └────────────┴────────────┴────────────┘
   ↑            ↑            ↑            ↑
   Start        Start        Stop         End
   of           Codon       Codon        of
   Transcript                             Transcript
   
   ✅ Option 1: Use GENCODE and python scripts (CHOSEN!)
   
   ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/MKL-1_wt.EV_vs_parental-up.txt    #20086
   ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/MKL-1_wt.EV_vs_parental-down.txt  #634
   ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/WaGa_wt.EV_vs_parental-up.txt     #23832
   ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/WaGa_wt.EV_vs_parental-down.txt   #375
   
   #Filtering the down-regulated genes to include only protein_coding genes before extracting 3' UTRs, because
   #1. Only protein_coding genes have well-annotated 3' UTRs
   #3' UTRs are defined as the region after the CDS (coding sequence) and before the poly-A tail.
   #Non-coding RNAs (e.g., lncRNA, snoRNA, miRNA precursors) do not have CDS, and therefore don't have canonical 3' UTRs.
   #2. In GENCODE, most UTR annotations are only provided for transcripts of gene_type = "protein_coding".
   
   grep ",\"protein_coding\"," ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/MKL-1_wt.EV_vs_parental-up.txt > MKL-1_wt.EV_vs_parental-up_protein_coding.txt
   grep ",\"protein_coding\"," ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/MKL-1_wt.EV_vs_parental-down.txt > MKL-1_wt.EV_vs_parental-down_protein_coding.txt
   grep ",\"protein_coding\"," ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/WaGa_wt.EV_vs_parental-up.txt > WaGa_wt.EV_vs_parental-up_protein_coding.txt
   grep ",\"protein_coding\"," ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/WaGa_wt.EV_vs_parental-down.txt > WaGa_wt.EV_vs_parental-down_protein_coding.txt
   
   #Visit and Download: GENCODE FTP site https://www.gencodegenes.org/human/
       * GTF annotation file (e.g., gencode.v48.annotation.gtf.gz)
       * Corresponding genome FASTA (e.g., GRCh38.primary_assembly.genome.fa.gz)
   wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_48/gencode.v48.annotation.gtf.gz
   wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_48/GRCh38.primary_assembly.genome.fa.gz
   gunzip gencode.v48.annotation.gtf.gz
   gunzip GRCh38.primary_assembly.genome.fa.gz
   
   python extract_transcript_parts.py MKL-1_wt.EV_vs_parental-down_protein_coding.txt ~/REFs/gencode.v48.annotation.gtf ~/REFs/GRCh38.primary_assembly.genome.fa MKL-1_down
   python extract_transcript_parts.py MKL-1_wt.EV_vs_parental-up_protein_coding.txt ~/REFs/gencode.v48.annotation.gtf ~/REFs/GRCh38.primary_assembly.genome.fa MKL-1_up  #5988
   python extract_transcript_parts.py WaGa_wt.EV_vs_parental-down_protein_coding.txt ~/REFs/gencode.v48.annotation.gtf ~/REFs/GRCh38.primary_assembly.genome.fa WaGa_down  #93
   python extract_transcript_parts.py WaGa_wt.EV_vs_parental-up_protein_coding.txt ~/REFs/gencode.v48.annotation.gtf ~/REFs/GRCh38.primary_assembly.genome.fa WaGa_up  #6538
   
   ✅ Option 2-5 see at the end!
   

2. Why 3' UTR?

   🧬 miRNA, RBP, or translation/post-transcriptional regulation
   ➡️ Use 3' UTR sequences
   
   Because:
   
   Most miRNA binding and many RBP motifs are located in the 3' UTR.
   
   It’s the primary region for mRNA stability, localization, and translation regulation.
   
   🧠 Example: You're looking for binding enrichment of miRNAs or RNA-binding proteins (PUM, HuR, etc.)
   ✅ Input = 3UTR.fasta
   
   🧪 If you're testing PBRs related to:
   - Translation initiation, upstream ORFs, or 5' cap interaction:
   ➡️ Use 5' UTR
   
   - Coding mutations, protein-level motifs, or translational efficiency:
   ➡️ Use CDS
   
   - General transcriptome-wide motif search (no preference):
   ➡️ Use transcripts, or test all regions separately to localize signal
   

3. Recommended Workflow with RBPmap https://rbpmap.technion.ac.il (Too slow!)

   RBPmap itself does not compute enrichment p-values or FDR; it's a motif scanning tool.
   
   To get statistically meaningful RBP enrichments, combine RBPmap with custom permutation testing or Fisher’s exact test + multiple testing correction.
   
       1. Prepare foreground (target) and background sequences
   
           Extract 3′ UTRs of:
   
           📉 Downregulated mRNAs (foreground) — likely targeted by upregulated miRNAs
   
           ⚪ A control set of 3′ UTRs — e.g., non-differentially expressed protein-coding genes
   
               grep ",\"protein_coding\"," ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/MKL-1_wt.EV_vs_parental-all.txt > MKL-1_wt.EV_vs_parental-all_protein_coding.txt
               grep ",\"protein_coding\"," ~/DATA/Data_Ute/Data_RNA-Seq_MKL-1+WaGa/results_2025_1/degenes/WaGa_wt.EV_vs_parental-all.txt > WaGa_wt.EV_vs_parental-all_protein_coding.txt
   
               cut -d',' -f1 MKL-1_wt.EV_vs_parental-all_protein_coding.txt | sort > all_genes.txt  #19239
               cut -d',' -f1 MKL-1_wt.EV_vs_parental-up_protein_coding.txt | sort > up_genes.txt  #5988
               cut -d',' -f1 MKL-1_wt.EV_vs_parental-down_protein_coding.txt | sort > down_genes.txt  #112
               cat up_genes.txt down_genes.txt | sort | uniq > regulated_genes.txt
               comm -23 all_genes.txt regulated_genes.txt > background_genes.txt
               grep -Ff background_genes.txt MKL-1_wt.EV_vs_parental-all_protein_coding.txt > MKL-1_wt.EV_vs_parental-background_protein_coding.txt  #13139
   
               cut -d',' -f1 WaGa_wt.EV_vs_parental-all_protein_coding.txt | sort > all_genes.txt  #19239
               cut -d',' -f1 WaGa_wt.EV_vs_parental-up_protein_coding.txt | sort > up_genes.txt  #6538
               cut -d',' -f1 WaGa_wt.EV_vs_parental-down_protein_coding.txt | sort > down_genes.txt  #93
               cat up_genes.txt down_genes.txt | sort | uniq > regulated_genes.txt
               comm -23 all_genes.txt regulated_genes.txt > background_genes.txt
               grep -Ff background_genes.txt WaGa_wt.EV_vs_parental-all_protein_coding.txt > WaGa_wt.EV_vs_parental-background_protein_coding.txt  #12608
   
               python extract_transcript_parts.py MKL-1_wt.EV_vs_parental-background_protein_coding.txt ~/REFs/gencode.v48.annotation.gtf ~/REFs/GRCh38.primary_assembly.genome.fa MKL-1_background
               python extract_transcript_parts.py WaGa_wt.EV_vs_parental-background_protein_coding.txt ~/REFs/gencode.v48.annotation.gtf ~/REFs/GRCh38.primary_assembly.genome.fa WaGa_background
   
               foreground.fasta: 你的目标（前景）序列，例如下调基因的 3′UTRs。
               background.fasta: 你的背景对照序列，例如未显著差异表达的基因的 3′UTRs。
   
       2. Run RBPmap separately on both sets (in total of 6 calculations)
   
           * Submit both sets of UTRs to RBPmap.
           * Use the same settings (e.g., “human genome”, “high stringency”, "Apply conservation filter" etc.)
           * Choose all RBPs
           * Download motif match outputs for both sets
   
       3. Count motif hits per RBP in each set
   
           You now have:
           For each RBP:
           a: number of target 3′ UTRs with a motif match
           b: number of background UTRs with a motif match
           c: total number of target UTRs
           d: total number of background UTRs
   
       4. Perform Fisher’s Exact Test per RBP
   
           For each RBP, construct a 2x2 table:
   
           Motif Present   Motif Absent
           Foreground (targets)    a   c - a
           Background  b   d - b
   
       5. Adjust p-values for multiple testing
       Use Benjamini-Hochberg (FDR) correction (e.g., in Python or R) across all RBPs tested.
   
       6.✅ Summary
   
           Step    Tool
           Prepare Database of RNA-binding motifs  ATtRACT
           3′ UTR extraction   extract_transcript_parts.py
           Motif scan  RBPmap or FIMO
           Count motif hits    Your own parser (Python or R)
           Fisher’s exact test scipy.stats or fisher.test()
           FDR correction  multipletests() or p.adjust()
   
       python rbp_enrichment.py rbpmap_downregulated.tsv rbpmap_background.tsv rbp_enrichment_results.csv
   

4. Quick Drop-In Plan (RBPmap Alternative with FIMO for motif scan)

   1. [ATtRACT + FIMO (MEME suite)]
   
       ATtRACT: Database of RNA-binding motifs.
       FIMO: Fast and scriptable motif scanning tool.
   
       #Download RBP motifs (PWM) from ATtRACT DB; Convert to MEME format (if needed); Use FIMO to scan UTR sequences
   
       grep "Homo_sapiens" ATtRACT_db.txt > attract_human.txt
   
       #cut -f12 attract_human.txt | sort | uniq > valid_ids.txt
   
       python convert_attract_pwm_to_meme.py
   
       fimo --thresh 1e-4 --oc fimo_foreground_MKL-1_down attract_human.meme ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/MKL-1_down.3UTR.fasta
       fimo --thresh 1e-4 --oc fimo_foreground_MKL-1_up attract_human.meme ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/MKL-1_up.3UTR.fasta
       fimo --thresh 1e-4 --oc fimo_background_MKL-1_background attract_human.meme ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/MKL-1_background.3UTR.fasta
       fimo --thresh 1e-4 --oc fimo_foreground_WaGa_down attract_human.meme ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/WaGa_down.3UTR.fasta
       fimo --thresh 1e-4 --oc fimo_foreground_WaGa_up attract_human.meme ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/WaGa_up.3UTR.fasta
       fimo --thresh 1e-4 --oc fimo_background_WaGa_background attract_human.meme ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/WaGa_background.3UTR.fasta
       #end
   
       #TODO_TOMORROW: mv PBS_analysis RBP_analysis
   
       #Test
       python run_enrichment.py \
           --attract ATtRACT_db.txt \
           --fimo_fg fimo_foreground_WaGa_down/fimo.tsv \
           --fimo_bg fimo_foreground2/fimo.tsv \
           --output rbp_enrichment_test.csv
   
       python run_enrichment.py \
           --attract ATtRACT_db.txt \
           --fimo_fg fimo_foreground_MKL-1_up/fimo.tsv \
           --fimo_bg fimo_background_MKL-1_background/fimo.tsv \
           --output rbp_enrichment_MKL-1_up.csv
       python run_enrichment.py \
           --attract ATtRACT_db.txt \
           --fimo_fg fimo_foreground_MKL-1_down/fimo.tsv \
           --fimo_bg fimo_background_MKL-1_background/fimo.tsv \
           --output rbp_enrichment_MKL-1_down.csv
       python run_enrichment.py \
           --attract ATtRACT_db.txt \
           --fimo_fg fimo_foreground_WaGa_up/fimo.tsv \
           --fimo_bg fimo_background_WaGa_background/fimo.tsv \
           --output rbp_enrichment_WaGa_up.csv
       python run_enrichment.py \
           --attract ATtRACT_db.txt \
           --fimo_fg fimo_foreground_WaGa_down/fimo.tsv \
           --fimo_bg fimo_background_WaGa_background/fimo.tsv \
           --output rbp_enrichment_WaGa_down.csv
   
       #工具 功能  关注点 应用场景
       FIMO    精确查找 motif 出现位置 motif 在什么位置出现   找出具体结合位点
       AME 统计 motif 富集情况   哪些 motif 在某组序列中更富集  比较 motif 是否显著出现更多
   
       如你还在做差异表达后的RBP富集分析，可以考虑先用 FIMO 扫描，再用你自己写的代码 + Fisher’s exact test 做类似 AME 的工作，或直接用 AME 做分析
   
       # Generate the attract_human.meme inkl. Gene_name!
       #python generate_named_meme.py pwm.txt attract_human.txt
       python generate_attract_human_meme.py pwm.txt ATtRACT_db.txt
   
       #ERROR during running ame --> DEBUG!
       #--control ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/WaGa_all.3UTR.fasta \
       ame --control --shuffle-- \
       --oc ame_out \
       --scoring avg \
       --method fisher --verbose 5 ../Data_RNA-Seq_MKL-1+WaGa/motif_analysis/WaGa_down.3UTR.fasta attract_human.meme
   
   2. GraphProt2 (ALTERNATIVE_TODO)
   
       ML-based tool using sequence + structure
   
       Pre-trained models for many RBPs
   
       ✅ Advantages:
   
       Local, GPU/CPU supported
   
       More biologically realistic (includes structure)
   

5. miRNAs motif analysis using ATtRACT + FIMO

   ✅ Goal
   
       * Extract their sequences
       * Generate a background set
       * Run RBP enrichment (e.g., with RBPmap or FIMO)
       * Get p-adjusted enrichment stats (e.g., Fisher + BH)
   
       5.1 (Optional)
       Input_1. DE results (differential expression file from smallRNA-seq)
           Example file: smallRNA_upregulated.txt
           Format: 1st column = miRNA ID (e.g., hsa-miR-21-5p), optionally with other stats.
   
       Input_2. Reference FASTA (Reference sequences from miRBase or GENCODE)
           From miRBase:
           mature.fa.gz → contains mature miRNA sequences
           hairpin.fa.gz → for pre-miRNAs
   
           python extract_miRNA_fasta.py smallRNA_upregulated.txt mature.fa up_mature_miRNAs.fa
           python extract_miRNA_fasta.py smallRNA_downregulated.txt hairpin.fa down_precursor_miRNAs.fa
   
       5.2 (Advanced)
           Extract Sequences + Background Set
   
           Inputs:
               * up_miRNA.txt and down_miRNA.txt: DE results (first column = miRNA name, e.g., hsa-miR-21-5p)
               * mature.fa or hairpin.fa from miRBase
   
           Outputs:
               * mirna_up.fa
               * mirna_down.fa
               * mirna_background.fa
   
           python prepare_miRNA_sets.py up_miRNA.txt down_miRNA.txt mature.fa mirna
   
       🔬 What You Can Do Next
   
       Goal    Tool    Input
       * RBP motif enrichment in pre-miRNAs    RBPmap, FIMO, AME   up_precursor_miRNAs.fa
       * Motif comparison (up vs down miRNAs)  DREME, MEME, HOMER  Up/down mature miRNAs
       * Build background for enrichment   Random subset of other miRNAs   Filtered from hairpin.fa
   
       ✅ RBP Enrichment from RBPmap Results
       🔹 Use RBPmap output (typically CSV or TSV)
       🔹 Compare hit counts in input vs background
       🔹 Perform Fisher's exact test + Benjamini-Hochberg correction
       🔹 Plot significantly enriched RBPs
   
       📁 Requirements
       You’ll need:
   
       File    Description
       rbpmap_up.tsv   RBPmap result file for upregulated set
       rbpmap_background.tsv   RBPmap result file for background set
   
       📝 These should have columns like:
   
       Motif Name or Protein
   
       Sequence Name or Sequence ID
       (If different, I’ll show you how to adjust.
   
       python analyze_rbpmap_enrichment.py rbpmap_up.tsv rbpmap_background.tsv enriched_up.csv enriched_up_plot.png
   
       ✅ Output
       enriched_up.csv
       RBP FG_hits BG_hits pval    padj    enriched
       ELAVL1  24  2   0.0001  0.003   ✅
       HNRNPA1 15  10  0.04    0.06    ❌
   
       enriched_up_plot.png
       Barplot showing top significant RBPs (lowest FDR)
   
       🧰 Customization Options
       Would you like:
   
           * Support for multiple RBPmap files at once?
   
           * To match by RBP family?
   
           * A full report (PDF/HTML) of top hits?
   
           * Let me know, and I’ll tailor the next script!
   

6. RBP enrichments via FIMO (The same to the workflow in 4)

   1. Collect the 3′ UTR sequences: Use the 3UTR.fasta file generated earlier, filtered to protein-coding and downregulated genes.
   
   2. Prepare Motif Database (MEME format)
   
       * ATtRACT: https://attract.cnic.es
       * RBPDB: http://rbpdb.ccbr.utoronto.ca
       * Ray2013 (CISBP-RNA motifs) — available via MEME Suite
       * [RBPmap motifs (if downloadable)]
       #Example format: rbp_motifs.meme
   
   2. Run FIMO to Scan for RBP Motifs (Similar to RBPmap)
   
       fimo --oc fimo_up rbp_motifs.meme mirna_up.fa
       fimo --oc fimo_down rbp_motifs.meme mirna_down.fa
       fimo --oc fimo_background rbp_motifs.meme mirna_background.fa
       #This produces fimo.tsv in each output folder.
   
   3. Run RBP motif enrichment using MEME Suite using AME (Analysis of Motif Enrichment):
   
       ame \
       --control control_3UTRs.fasta \
       --oc ame_out \
       --scoring avg \
       --method fisher \
       3UTR.fasta \
       rbp_motifs.meme
   
       Where:
   
       * 3UTR.fasta = your downregulated genes’ 3′ UTRs
       * control_3UTRs.fasta = background UTRs (e.g., random protein-coding genes not downregulated)
       * rbp_motifs.meme = motif file from RBPDB or Ray2013
   
   4. Interpret Results: Output includes RBP motifs enriched in your downregulated mRNAs' 3′ UTRs.
   
       You can then link enriched RBPs to known interactions with your upregulated miRNAs, or explore their regulatory roles.
   
   5. ✅ Bonus: Predict Which mRNAs Are Targets of Your miRNAs
   
       Use tools like: miRanda, TargetScan, miRDB
   
       Then intersect predicted targets with your downregulated genes to identify likely functional interactions.
   
   6. Summary
   
       Goal    Input   Tool / Approach
       RBP enrichment  3UTR.fasta of downregulated genes   AME with RBP motifs
       Background/control  3′ UTRs from non-differential or upregulated genes
       Link miRNA to targets   Use TargetScan / miRanda    Intersect with down genes
   
   7. Would you like:
   
       * Ready-to-use RBP motif .meme file?
       * Script to generate background sequences?
       * Visualization options for the enrichment results?
   

7. Other options to get sequences of 3UTR, 5UTR, CDS and mRNA transcripts

   ✅ Option 2: Use Ensembl BioMart (web-based, no coding) --> Lasting too long!
   
       Go to Ensembl BioMart https://www.ensembl.org/biomart/martview/7b826bcbd0cec79021977f8dc12a8f61
   
       Select:
   
       Database: Ensembl Genes
       Dataset: Homo sapiens genes (GRCh38 or latest)
   
       Click on “Filters” → expand Region or Gene to limit your selection (optional).
       Click on “Attributes”:
       Under Sequences, check:
       Sequences
       3' UTR sequences
   
       Optionally add gene IDs, transcript IDs, etc.
   
       Click “Results” to view/download the FASTA of 3' UTRs.
   
   ✅ Option 3: Use GENCODE (precompiled annotations) and gffread
   
       Use a tool like gffread (from the Cufflinks or gffread package) to extract 3' UTRs:
   
           #gffread gencode.v44.annotation.gtf -g GRCh38.primary_assembly.genome.fa -w all_utrs.fa -U
           #gffread -w three_prime_utrs.fa -g GRCh38.fa -x cds.fa -y proteins.fa -U -F gencode.gtf
   
           grep -P "\tthree_prime_utr\t" gencode.v48.annotation.gtf > three_prime_utrs.gtf
           gtf2bed < three_prime_utrs.gtf > three_prime_utrs.bed
           bedtools getfasta -fi GRCh38.primary_assembly.genome.fa -bed three_prime_utrs.bed -name -s > three_prime_utrs.fa
   
           gffread gencode.v48.annotation.gtf -g GRCh38.primary_assembly.genome.fa -U -w all_with_utrs.fa
   
       Add -U flag to extract UTRs, and filter post hoc for only 3' UTRs if needed.
   
   ✅ Option 4: Use Bioconductor in R (UCSC-ID, not suitable!)
   
       # Install if not already installed
       if (!requireNamespace("BiocManager", quietly = TRUE))
           install.packages("BiocManager")
       BiocManager::install("GenomicFeatures")
       BiocManager::install("txdbmaker")
       #sudo apt-get update
       #sudo apt-get install libmariadb-dev
       #(optional)sudo apt-get install libmysqlclient-dev
       install.packages("RMariaDB")
   
       # Load library
       library(GenomicFeatures)
   
       # Create TxDb object for human genome
       txdb <- txdbmaker::makeTxDbFromUCSC(genome="hg38", tablename="refGene")
   
       # Extract 3' UTRs by transcript
       utr3 <- threeUTRsByTranscript(txdb, use.names=TRUE)
   
   # View or export as needed
   
   ✅ Option 5: Extract 3′ UTRs Using UCSC Table Browser (GUI method)
       🔗 Website:
       UCSC Table Browser
   
       🔹 Step-by-Step Instructions
       1. Set the basic parameters:
       Clade: Mammal
   
       Genome: Human
   
       Assembly: GRCh38/hg38
   
       Group: Genes and Gene Predictions
   
       Track: GENCODE v44 (or latest)
   
       Table: knownGene or wgEncodeGencodeBasicV44
   
       Choose knownGene for RefSeq-like models or wgEncodeGencodeBasicV44 for GENCODE
   
       2. Region:
       Select: genome (default)
   
       3. Output format:
       Select: sequence
   
       4. Click "get output"
       🔹 Sequence Retrieval Options:
       On the next page (after clicking "get output"), you’ll see sequence options.
   
       Configure as follows:
       ✅ Output format: FASTA
   
       ✅ Which part of the gene: Select only
       → UTRs → 3' UTR only
   
       ✅ Header options: choose if you want gene name,
   

8. ⚡️ Bonus: Combine with miRNA-mRNA predictions

   Once you have RBPs enriched in downregulated mRNAs, you can intersect:
       * Which RBPs overlap miRNA binding regions (e.g., via CLIPdb or POSTAR)
       * Check if miRNAs and RBPs compete or co-bind
   This can lead to identifying miRNA-RBP regulatory modules.
   

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