Complete RNA-Seq analysis for single-end data: Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment with STAR using ENCODE parameters, gene quantification via multiple methods (STAR and featureCounts), and expression calculation (FPKM with Cufflinks/StringTie, normalized coverage with bedtools). Produces count tables, normalized expression values, and genomic coverage tracks. Supports stranded and unstranded libraries, generating both HTSeq-compatible counts and normalized measures for downstream analysis.
Complete RNA-Seq analysis for paired-end data: Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment with STAR using ENCODE parameters, gene quantification via multiple methods (STAR and featureCounts), and expression calculation (FPKM with Cufflinks/StringTie, normalized coverage with bedtools). Produces count tables, normalized expression values, and genomic coverage tracks. Supports stranded and unstranded libraries, generating both HTSeq-compatible counts and normalized measures for downstream analysis.
Processed results supporting a secondary analysis of public GEO intracranial aneurysm RNA-seq. Discovery in GSE122897 wall tissue (n=43; 22 ruptured vs 21 unruptured) used DESeq2 and LM22-adjusted differential expression to define a pre-specified 300-gene shortlist, an eight-gene blood-bridge panel, and a six-gene tissue rupture core validated across external cohorts. Deposit includes supplementary tables S1–S12, Extended Data Table 3, figure plotting data, TRIPOD/PROBAST contextual checklists, and the documented analysis plan. Raw GEO data are not included.
October 18, 2025 (v1)DatasetOpen Demo data and result for paper Large-scale single-cell long-read genomics enables high-resolution microbiome profiling
Di Petta, Federico · Rosati, Piermichele · Hierro Canchari, Piero · et al.
1 files · 59 MB · zipdeclared
EXTRARNAS - Data, Results and Analysis Scripts Companion material for the CIBB 2026 short paper This repository contains the datasets, execution outputs, analysis scripts, and supplementary material accompanying the paper EXTRARNAS: A Framework for Extracting RNA Structures with Multiple Tools The EXTRARNAS software itself is distributed separately. Software repository: https://github.com/bdslab/EXTRARNAS Software release (version used in the paper): https://doi.org/10.5281/zenodo.21238912 Repository structure · experiment_triple_helices/ o shared_folder_before_execution/ o shared_folder_after_execution/ o script_input_folder/ o script_output_folder/ o adb-reference/ o bpseq-reference/ o extrarnas_compare_triple_helix_or_general_with_precision_recall.py · additional-experiments/ o 5s-pseudoknot-batch/ · README.md Contents experiment_triple_helices Material used for the evaluation reported in the paper. shared_folder_before_execution Initial state of the shared Docker folder before executing EXTRARNAS. Contains: · input CSV; · local PDB structures not retrieved automatically from the PDB. shared_folder_after_execution Complete output generated by EXTRARNAS after processing the eight RNA triple-helix structures. Includes the raw outputs produced by all supported annotation tools. script_input_folder Input used by the analysis script. Contains: · BPSEQ files; · BPSEQE files; · curated BPSEQ references; · augmented dot-bracket (ADB) references. script_output_folder Output generated by the comparison script. Includes: · summary_by_molecule.csv · aggregate_by_tool.csv · pairwise_tool_overlap.csv · missing_extra_pairs.tsv · aggregate_table.tex · paper_table.tex · supplementary.tex · supplementary.pdf The supplementary PDF contains the complete per-molecule evaluation table used to generate the aggregate results reported in the paper. Reference datasets The folders · bpseq-reference · adb-reference contain the manually curated reference annotations introduced in Matarrese et al., Decoding RNA Triple Helices (2026). additional-experiments Contains additional experiments not included in the quantitative evaluation of the paper. 5s-pseudoknot-batch Batch-processing experiment on 20 RNA 5S structures. This experiment documents the execution of EXTRARNAS on an independent dataset and includes: · input dataset; · execution log; · preprocessing results; · outputs generated by each annotation tool. Some structures could not be fully processed because of current limitations of the mmCIF→PDB preprocessing pipeline (BeEM bundled conversion), rather than failures of the EXTRARNAS parsing or comparison workflow. Requirements The comparison scripts require only: · Python 3.8 or newer; · standard Python libraries. Docker is required only to reproduce the complete EXTRARNAS workflow. Running the comparison script From the experiment_triple_helices directory: python3 extrarnas_compare_triple_helix_or_general_with_precision_recall.py \ --input-dir script_input_folder \ --output-dir script_output_folder By default, the script analyzes the eight RNA molecules used in the paper. Use --all to analyze every available structure or --molecule <name> to analyze a single RNA molecule. Reproducibility The repository contains all datasets, reference annotations, execution outputs, and analysis scripts required to reproduce the evaluation reported in the accompanying paper.
This dataset contains processed RNA-seq data supporting the transcriptomic analyses of gastrocnemius muscle from control, diabetic model, and static exercise-treated type 2 diabetic mice. The dataset includes sample metadata, FPKM expression matrices, gene annotation information, candidate gene expression tables, enrichment analysis outputs, original company-provided processed files, and README documentation. The RNA-seq analysis was used as an exploratory, hypothesis-generating component of the study and was followed by targeted molecular validation.
ModCon is a unified framework for quantifying epitranscriptomic conservation at single-base resolution. Unlike conventional conservation methods that measure nucleotide sequence conservation, ModCon evaluates whether an RNA modification event itself is evolutionarily retained across species. Built from Oxford Nanopore direct RNA sequencing (ONT) datasets across human, mouse, and pig, ModCon integrates four complementary evidence components to generate a unified conservation score for every modified residue. These complementary signals include orthologous modification concordance, within-species recurrence, local sequence-context conservation, and a fine-tuned DNABERT-2 language-model-derived conservation score. The resulting ModCon score (0-1) provides a quantitative measure of evolutionary conservation at RNA modification sites, enabling researchers to prioritize conserved and potentially functional RNA modifications for downstream biological analyses. Dataset for ModCon 1. Reference genome and Chains: hg38ToHg19.over.chain -- Human genome liftover chain file (GRCh38 to GRCh37). hg38ToMm39.over.chain -- Cross-species liftover chain file (Human GRCh38 to Mouse GRCm39). hg38ToSusScr11.over.chain -- Cross-species liftover chain file (Human GRCh38 to Pig Sscrofa11.1). hg38_chr1.fa -- Human reference genome sequence (UCSC version, GRCh38). mm39_chr1.fa -- Mouse reference genome sequence (UCSC version, GRCm39). susScr11._chr1.fa -- Pig reference genome sequence (UCSC version, SGSC Sscrofa11.1). Note: Due to reference genome is too large, here I use chr1.fa as example, please replace it to whole reference genome. 2. Species Data: Includes 1-based coordinate modification sites derived from the Oxford Nanopore Technologies (ONT) pipeline. ontdata -- All human ONT-derived RNA modification sites. mouse_raw -- All mouse ONT-derived RNA modification sites. pig_raw --All pig ONT-derived RNA modification sites. 3. NGS data: Publicly available high-throughput Next-Generation Sequencing (NGS) modification datastes. Human(m6A,m1A,m5C,ac4C,m7G,m6Am, Am,Cm,Gm,Um,m5U,psi) Mouse (m6A,m1A,m5C) Rat (m6A) Zebrafish (m6A) SomaticSNP -- Human cancer-associated somatic mutations (e.g., from TCGA/gnomAD). GermlineSNP -- Human cancer-associated germline mutations (e.g., from TCGA/gnomAD). 4. Training Data: Data extracted from negative and positive data pools used for model training. Model_A_data -- Training data for ModCon-A. Model_C_data -- Training data for ModCon-C. Model_G_data -- Training data for ModCon-G. Model_U_data -- Training data for ModCon-U. 5. Sub-base Models: Pretrained base-specific models of ModCon to predict modification conservation levels. ModCon-A -- Pretrained model for the A base. ModCon-C -- Pretrained model for the C base. ModCon-G -- Pretrained model for the G base. ModCon-U -- Pretrained model for the U base. 6.Functional validation data: hg38_transcript -- 20024 human primary transcripts. RBP_site_hg38 -- RNA-Binding Protein (RBP) binding peaks. SplingSite_hg38_500bp -- Splice sites (5' and 3' SS) expanded with a ±500bp flanking genomic window. Code: 1. Oxford Nanopore data processing: The raw data processing pipline includes base-calling, alignment, and moficiation detection. Raw data processing pipline.sh --The raw ONT data processing pipline. 2.Model training and SHAP anlysis: train.py -- Integrated DNABERT2 big language model+MLP fine-tuning code. SHAP_analysis.py -- The SHAP analysis code to illustrate feature importance. 3. Assessment of each evidence component: To test whether each evidence components carry independently informative conservation signals. orthologous_cocordance.Rmd sequence-context_conservation.Rmd Within_species_recurrence.Rmd 4.Integration of ModCon evidence: We integrated these signals into a quantitative ModCon score and tested whether their integration improves site-level prioritization. pairwise.Rmd -- The spearman correlation of each component. Performance of each evidence.py -- The AUROC,AUPRC and top10 enrichment value of each component. Bootstrap comparison.py -- The bootstrap with replacement of each component. 5.NGS validation code: To test whether ModCon generalizes beyond the ONT data on which it was built, we collected independent base-resolution modification datasets generated by NGS-based methods. To test whether ModCon could identify cross-species conserved modification sites (CSCM -NGS ) and show genetic constraint within human populations. m1A_CSCM.Rmd -- The m1A CSCM test. m5C_CSCM.Rmd -- The m5C CSCM test. m6A_CSCM.Rmd -- The m6A CSCM test. SNP_variant_density_and_deleterious_level.Rmd -- Including the Somatic,Germline variants ratio and related deleterious level. 6.Functional analysis: Downstream Biological Insights RNA_Binding_protein.Rmd -- The effect of the level of modification conservation on RNA binding proteins. Splicing_sites.Rmd -- Evaluates potential regulatory roles of different conserved modification levels near splice junctions. Clustering.Rmd -- clustering effects across different conservation levels Gene_ontology.Rmd -- Performs Gene Ontology (GO) and KEGG pathway enrichment analysis on different conservation level groups to uncover their potential biological roles. Note:For comprehensive usage, instructions please visit our GitHub repository: https://github.com/Gang1998c/Modcon
This repository contains code used to analyze single-cell RNA-seq data from mouse and human periprosthetic joint infection (PJI) tissue. Analyses use publicly available datasets (GEO: GSE269658, GSE241739) and include quality control, integration, annotation, and figure generation. See the README for details.
This repository contains cis-xQTL mapping results, colocalization analysis results, and transcriptome-wide association study (xTWAS) weights and association test statistics for six transcriptomic modalities generated from bulk RNA-seq data of dorsolateral prefrontal cortex (DLPFC) tissue from the ROS/MAP cohorts (n = 1,035). The RNA trait tables (BED format) used for cis-xQTL mapping and xTWAS model training were generated using the Pantry pipeline but are not included in this repository. Colocalization and xTWAS analyses were performed using the publicly available Alzheimer's disease (AD) dementia GWAS summary statistics from Bellenguez et al. ( Nature Genetics , 2022).
Ickes, Christian · Cigdem Hazal, Timucin · Harms, Bendix Christian · et al.
3 files · 13 GB · gzip, zipdeclared
TFCP_model.zip Contains the official fine-tuned DNABERT model for DNA-binding domain class prediction, as described in TFClassPredict. TFCP_precompiled.zip Contains precompiled genome-wide binding potential tracks in UCSC hg38 coordinates, provided as one genomic track per DNA-binding domain class. UNIBIND_TFBS_dataset_filtered.csv.gz This dataset comprises more than 5 million high-confidence TFBS sequences derived from the UniBind database, covering 265 TFs with TFClass annotations across 23 DBD classes. All binding sites are annotated by the DBD class of the corresponding bound TF and standardized to a uniform length of 21 bp, with underlying sequences extracted from the GRCh38/hg38 reference genome. Overlapping binding sites and those located on chromosomes X and Y were excluded to reduce class assignment ambiguity and minimize sex-chromosome-specific confounding effects. The dataset is partitioned into training, validation, and test sets using a chromosome-stratified 80:20 split, ensuring consistent chromosomal representation across all partitions and a strict separation between model development and final performance evaluation.