A systematic delineation of 3′ UTR regulatory elements and their contextual associations.
SEERS (Selective Enrichment of Episomes with Random Sequences) is a high-throughput framework designed to dissect the regulatory landscape of 3′ UTRs. By leveraging random sequence libraries and episomal enrichment, this pipeline allows for the systematic identification of regulatory motifs and the quantification of their functional impact on gene expression.
- Massively Parallel Analysis: Quantifies millions of random or specific sequences simultaneously.
- Multi-Compartment Profiling: Supports data integration from DNA, Cytoplasm, and Nucleus.
- Motif Discovery: Built-in tools for k-mer profiling and SNP effect prediction.
- Deep Learning Ready: Includes pre-trained TALE models for regulatory activity prediction.
SEERS_data_process_LiangN/
Nn_pp.R: Extract and count N45 sequences from merged FASTQ files.Nn_pp_pool.R: Aggregate counts across multiple biological and technical replicates.combine_dna_cyt_nuc.R: Compute enrichment scores (SEERS data) by normalizing Cyt/Nuc counts against DNA input.kmer_profiling.R: Statistical testing of k-mer correlations with regulatory activity.
TALE_models_LiJY_260128/
- Contains the architecture and weights for the TALE models.
- Also includes scripts and utilities for model training, evaluation/testing, and other related works.
- Bioinformatics Tools: NGmerge
- Language Environment: R (>= 4.0), Python (>= 3.9, TensorFlow 2.16+)
Merge the paired-end sequencing data:
./NGmerge -d -1 read1.fq.gz -2 read2.fq.gz -o merged.fq.gz
Download the full training dataset from Zenodo:
🔗 https://doi.org/10.5281/zenodo.18400022
| Date | Version/Update | Description |
|---|---|---|
| 2026-01-28 | v2.0 | Updated scripts for revised manuscript and new datasets. |
| 2025-04-21 | v1.2 | Added kmer_motif.ipynb and N45_dissect.ipynb. |
| 2024-12-08 | v1.1 | Added TALE_SNP_effect.ipynb. |
| 2024-08-14 | v1.0 | TF 2.16 compatibility fix & Refactored Notebooks. |