Intrinsically linked lineage specificity of transposable elements, lncRNA genes, and transcriptional regulation

Overview This repository contains code and data associated with the following study: Lin J, Wu Y, Zeng J, Xiong W, He S, Pontarotti P, Zhu H. Intrinsically linked lineage specificity of transposable elements, lncRNA genes, and transcriptional regulation. The study systematically investigates the relationship between lineage-specific (LS) transposable elements (TEs), lncRNAs, and transcriptional regulation in humans and mice, with applications to spermatogenesis, Alzheimer's disease, and cross-species expression divergence. The identified LS lncRNAs can be re-examined using the Infernal program based on annotated lncRNAs and genomes. The identified lncRNA DBDs and DBSs can be re-examined using the LongTarget and Fasim-LongTarget programs based on annotated genes and transcripts. The identified TE distributions in lncRNA exons, DBDs, and DBSs can be re-examined based on the RepeatMasker database. TE lineages can be re-examined by searching TE sequences in mammalian genomes. All these genome sequences, annotations, databases, and programs are publicly available; other raw data and third-party programs are also publicly available (see papers cited in this manuscript). Data from which key results and findings (sections 2.1 and 2.2) are obtained are provided as supplementary tables. Data and code relating to section 2.3 are in GTExAnalysisCode.zip. Data and code relating to section 2.4 are in AD_analysis.zip. Data and code relating to section 2.5 are in Spermatogenesis_analysis.zip. Repository Structure |-GTExAnalysisCode |-AD_analysis |-data |-PathwayAnnotation |-Case1-Alzheimer disease |-Spermatogenesis_analysis |-eGRAMCode |-datafiles |-spermatogenesis-results |-HEGHVG |-classifiedcells Contents 1. Data and code relating to section 2.3 (GTExAnalysisCode.zip) This folder contains multiple subfolders, each with scripts for analyzing the impacts of simian/rodent TE-derived exons and HS/MS lncRNAs on gene expression and molecular signaling across tissues. Data processing includes: - Computing median TPM expression of simian-specific and rodent-specific exons from GTEx (human) and MACA (mouse) bulk RNA-seq wiggle files. - Normalizing exon TPM values to z-scores across tissues. - Computing per-tissue Spearman correlations between HS lncRNAs and their predicted target genes (human GTEx data) and between MS lncRNAs and their predicted target genes (mouse MACA data). - Quantifying cross-species transcriptional divergence (|Δz-score|) of KEGG pathway genes across 10 matched human–mouse tissue pairs, with pancreas as the background reference, and generating the ridge plot figure. 2. Data and code relating to section 2.4 (AD_analysis.zip) This folder contains the following sub-folders and related files. 2.1 eGRAM.py This is the eGRAM version used in this study for RNA-seq data. 2.2 data/ This sub-folder contains (a) the human and mouse DBS matrices (HS_lncRNA_DBS_matrix, MS_lncRNA_DBS_matrix), (b) the human and mouse input files (human_AD_TPM, mouse_AD_TPM). 2.3 PathwayAnnotation/ This sub-folder contains KEGG genes, links, and pathways in humans and mice. Note that, as a component in the eGRAM environment, this sub-folder should also be used by eGRAMv3R1-2025Apr.py for scRNA-seq data. 2.4 Case1-Alzheimer disease/ This sub-folder contains the result files of AD analysis. 3. Data and code relating to section 2.5 (Spermatogenesis_analysis.zip) This folder contains the following sub-folders and related files. 3.1 eGRAMCode/ This sub-folder contains (a) the eGRAM version for scRNA-seq data (eGRAMv3R1-2025Apr.py), (b) the human and mouse DBS matrices (Hmarker_HlncRNA_DBS, Mmarker_MlncRNA_DBS), and (c) the code for drawing supplementary figures related to Figure 5 (20250604draw-suppFig.py). 3.2 datafiles/ This sub-folder contains 10 input files, each a gene expression profile, one per cell type per species (5 cell types × 2 species: human and mouse). Each file is a CSV matrix of log-normalized scRNA-seq expression values with genes as rows (annotated by gene symbol and gene type: lncRNA, TF, or marker) and cells as columns. These files are directly used with eGRAMCode/ to reproduce the spermatogenesis regulatory module analysis. Source: Murat et al. Reconstruction of ancestral chromosomes and their evolution through mammalian spermatogenesis. Nature 617, 632–638 (2023). 3.3 spermatogenesis-results/ This sub-folder contains the result files generated by eGRAMv3R1. For each of the 10 input files (5 cell types × 2 species), eGRAMv3R1 generates a sub-folder (e.g., H-RS-zero85-dbs36, with “H” and “RS” indicating humans and RS cells, and "zero85" and "dbs36" indicating the two key parameters of zero_proportion and DBS affinity. Each sub-folder contains: - main.csv: The main module table listing regulators, regulator sets, target genes, and enriched KEGG/WikiPathways terms for each identified module. - moduleEdge: Cytoscape-compatible edge list for network visualization. - sig_lnc_lnc_corr_OR.csv / sig_lnc_lnc_corr_AND.csv: lncRNA–lncRNA significant correlation matrices (MIC-or-TIC and MIC-and-TIC). - sig_lnc_target_corr_OR.csv / sig_lnc_target_corr_AND.csv: lncRNA–target significant correlation matrices. - gProfiler-multiquery.csv: Enriched GO terms. - Additional intermediate files documenting regulator sets, target sets, and module structures before and after redundancy removal. 3.4 HEGHVG/ This sub-folder contains data and programs for identifying highly expressed genes (HEGs) and highly variable genes (HVGs). HEGs and HVGs, together with 1:1 orthologous genes conserved in mammals, are used to identify the 395 conserved spermatogenesis markers. The two Python programs are for extracting the subset of HVGs overlapping with conserved spermatogenesis markers and lineage-specific lncRNAs, and for generating multi-panel expression dynamics plots (mean expression across the five cell types, one panel per gene), respectively. 3.5 classifiedcells/ This sub-folder contains different types of spermatogenesis cells extracted from the published data (Murat et al. 2023). They cover five cell types (ST, SG, SC, RS, and ES) in humans, chimpanzees, mice, opossums, and playpusses. Cells are filtered using interquartile range (IQR) criteria. Source: Murat et al. Reconstruction of ancestral chromosomes and their evolution through mammalian spermatogenesis. Nature 617, 632–638 (2023).