Biology subjects

Reese, F.

Publications and source records attributed to Reese, F..

At least 19 recordsLinked to original sources

bioRxiv2025

rDNAcaller: a fast and robust pipeline to call ribosomal DNA variants

Ribosomal DNA (rDNA) genes are essential components of the ribosome, organized as tandem repeats on the mammalian genomes. Extensive genetic and copy number variation in rDNA has been reported both within and between individuals, contributing to phenotypic diversity. However, previous rDNA variant calling strategies have relied on methods designed for diploid regions and have not been systematically benchmarked. With the recent availability of a telomere-to-telomere (T2T) genome assembly, rDNA regions have been fully assembled for the first time, enabling benchmarking and optimization of rDNA variant calling strategies. We developed a customized simulator that replicates real intra- and inter-individual rDNA variation based on the T2T assembly to benchmark the performance of commonly used variant callers, including GATK, Mutect2, and Lofreq. Additionally, we optimized the preprocessing and mapping steps to remove pseudogenes and significantly improve accuracy. Based on these optimizations, we introduce rDNAcaller, a novel pipeline for accurate rDNA variant detection using short-read whole-genome sequencing. rDNAcaller integrates optimized preprocessing with the top-performing variant caller and achieves 94% precision on experimental data from the T2T cell line. Applying our pipeline to data from the 1000 Genomes Project, we identify 5,607 novel rDNA variant positions across human populations, with African individuals showing the highest number of variants. Overall, rDNAcaller is a robust and versatile tool for analyzing rDNA variation, addressing the limitations of existing methods in handling high ploidies. By enabling accurate detection of rDNA variants, it facilitates deeper exploration of rDNAs role in phenotypic diversity, supporting future genomic studies and broadening our understanding of rDNA biology in health and disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/653643v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@148419borg.highwire.dtl.DTLVardef@cb3b42org.highwire.dtl.DTLVardef@9fa802org.highwire.dtl.DTLVardef@1cf4ff0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains captures global and local expression variation

Mapping the impact of genomic variation on gene expression facilitates an understanding of the molecular basis of complex phenotypic traits and disease predisposition. Mouse models provide a controlled and reproducible framework for capturing the breadth of genomic variation observed in different genotypes across a wide variety of tissues. As part of the IGVF consortiums effort to catalog the effects of genetic variation, we uniformly characterized the transcriptomes of eight tissues from each mouse founder strain used to derive the Collaborative Cross strains, comprising five classical laboratory inbred strains and three wild-derived inbred strains. We sequenced samples from four male and four female replicates per tissue using single-nucleus RNA-seq to generate an "8-cube" dataset of 5.2 million nuclei across 106 cell types and cell states. As expected, the overall extent of transcriptome variation correlates positively with genetic divergence across the strains with the greatest differential between PWK/PhJ and CAST/EiJ. At the individual tissue level, heart and brain are relatively more similar across strains compared with gonads, adrenal, skeletal muscle, kidney, and liver. Further analyses revealed substantial strain variation, often concentrated in a few cell types as well as cell-state signatures that especially reflect strain-associated immune and metabolic trait differences. The founder 8-cube dataset provides rich transcriptome variation signatures to help explain strain-specific phenotypic traits and disease states, as illustrated by examples in tissue-resident immune cells, muscle degeneration, kidney sex differences, and the hypothalamicpituitary-adrenal axis. This data further provides a systematic foundation for the analysis of these tissues in the founder strains as well as the Collaborative Cross.

genomics↗

bioRxiv2025

Long-read transcriptomics of a diverse human cohort reveals widespread ancestry bias in gene annotation

Accurate gene annotations are fundamental for interpreting genetic variation, cellular function, and disease mechanisms. However, current human gene annotations are largely derived from transcriptomic data of individuals with European ancestry, introducing potential biases that remain uncharacterized. Here, we generate over 800 million full-length reads with long-read RNA-seq in 43 lymphoblastoid cell line samples from eight genetically-diverse human populations and build a cross-ancestry gene annotation. We show that transcripts from non-European samples are underrepresented in reference gene annotations, leading to systematic biases in allele-specific transcript usage analyses. Furthermore, we show that personal genome assemblies enhance transcript discovery compared to the generic GRCh38 reference assembly, even though genomic regions unique to each individual are heavily depleted of genes. These findings underscore the urgent need for a more inclusive gene annotation framework that accurately represents global transcriptome diversity.

genomics↗

bioRxiv2024

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions1. We have expanded the Registry to include 2.35 million human and 927 thousand mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded Registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays like STARR-seq, MPRA, CRISPR perturbation, and transgenic mouse assays now cover over 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer/silencer roles in different cellular contexts. Integrating the Registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by discovering KLF1 as a novel causal gene for red blood cell traits. This expanded Registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

genomics↗

bioRxiv2024

Long-read sequencing transcriptome quantification with lr-kallisto

RNA abundance quantification has become routine and affordable thanks to high-throughput "short-read" technologies that provide accurate molecule counts at the gene level. Similarly accurate and affordable quantification of definitive fulllength, transcript isoforms has remained a stubborn challenge, despite its obvious biological significance across a wide range of problems. "Long-read" sequencing platforms now produce data-types that can, in principle, drive routine definitive isoform quantification. However some particulars of contemporary long-read datatypes, together with isoform complexity and genetic variation, present bioinformatic challenges. We show here, using ONT data, that fast and accurate quantification of long-read data is possible and that it is improved by exome capture. To perform quantifications we developed lr-kallisto, which adapts the kallisto bulk and single-cell RNA-seq quantification methods for long-read technologies.

bioinformatics↗

bioRxiv2024

The ENCODE mouse postnatal developmental time course identifies regulatory programs of cell types and cell states

Postnatal genomic regulation significantly influences tissue and organ maturation but is under-studied relative to existing genomic catalogs of adult tissues or prenatal development in mouse. The ENCODE4 consortium generated the first comprehensive single-nucleus resource of postnatal regulatory events across a diverse set of mouse tissues. The collection spans seven postnatal time points, mirroring human development from childhood to adulthood, and encompasses five core tissues. We identified 30 cell types, further subdivided into 69 subtypes and cell states across adrenal gland, left cerebral cortex, hippocampus, heart, and gastrocnemius muscle. Our annotations cover both known and novel cell differentiation dynamics ranging from early hippocampal neurogenesis to a new sex-specific adrenal gland population during puberty. We used an ensemble Latent Dirichlet Allocation strategy with a curated vocabulary of 2,701 regulatory genes to identify regulatory "topics," each of which is a gene vector, linked to cell type differentiation, subtype specialization, and transitions between cell states. We find recurrent regulatory topics in tissue-resident macrophages, neural cell types, endothelial cells across multiple tissues, and cycling cells of the adrenal gland and heart. Cell-type-specific topics are enriched in transcription factors and microRNA host genes, while chromatin regulators dominate mitosis topics. Corresponding chromatin accessibility data reveal dynamic and sex-specific regulatory elements, with enriched motifs matching transcription factors in regulatory topics. Together, these analyses identify both tissue-specific and common regulatory programs in postnatal development across multiple tissues through the lens of the factors regulating transcription.

genomics↗

bioRxiv2024

Identification of robust cellular programs using reproducible LDA that impact sex-specific disease progression in different genotypes of a mouse model of AD

The gene expression profiles of distinct cell types reflect complex genomic interactions among multiple simultaneous biological processes within each cell that can be altered by disease progression as well as genetic background. The identification of these active cellular programs is an open challenge in the analysis of single-cell RNA-seq data. Latent Dirichlet Allocation (LDA) is a generative method used to identify recurring patterns in counts data, commonly referred to as topics that can be used to interpret the state of each cell. However, LDAs interpretability is hindered by several key factors including the hyperparameter selection of the number of topics as well as the variability in topic definitions due to random initialization. We developed Topyfic, a Reproducible LDA (rLDA) package, to accurately infer the identity and activity of cellular programs in single-cell data, providing insights into the relative contributions of each program in individual cells. We apply Topyfic to brain single-cell and single-nucleus datasets of two 5xFAD mouse models of Alzheimers disease crossed with C57BL6/J or CAST/EiJ mice to identify distinct cell types and states in different cell types such as microglia. We find that 8-month 5xFAD/Cast F1 males show higher level of microglial activation than matching 5xFAD/BL6 F1 males, whereas female mice show similar levels of microglial activation. We show that regulatory genes such as TFs, microRNA host genes, and chromatin regulatory genes alone capture cell types and cell states. Our study highlights how topic modeling with a limited vocabulary of regulatory genes can identify gene expression programs in singlecell data in order to quantify similar and divergent cell states in distinct genotypes.

genomics↗

bioRxiv2023

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

AbstractThe Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

genomics↗

bioRxiv2023

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's Disease

The pathogenesis of Alzheimers disease (AD) depends on environmental and heritable factors, with remarkable differences evident between individuals at the molecular level. Here we present a transcriptomic survey of AD using spatial transcriptomics (ST) and single-nucleus RNA-seq in cortical samples from early-stage AD, late-stage AD, and AD in Down Syndrome (AD in DS) donors. Studying AD in DS provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD. Our analysis revealed spatial and cell-type specific changes in disease, with broad similarities in these changes between sAD and AD in DS. We performed additional ST experiments in a disease timecourse of 5xFAD and wildtype mice to facilitate cross-species comparisons. Finally, amyloid plaque and fibril imaging in the same tissue samples used for ST enabled us to directly link changes in gene expression with accumulation and spread of pathology.

neuroscience↗

bioRxiv2023

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3 end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3 processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

genomics↗

bioRxiv2023

Long-read sequencing and profiling of RNA-binding proteins reveals the pathogenic mechanism of aberrant splicing of an SCN1A poison exon in epilepsy

Pathogenic loss-of-function SCN1A variants cause a spectrum of seizure disorders. We previously identified variants in individuals with SCN1A-related epilepsy that fall in or near a poison exon (PE) in SCN1A intron 20 (20N). We hypothesized these variants lead to increased PE inclusion, which introduces a premature stop codon, and, therefore, reduced abundance of the full-length SCN1A transcript and Nav1.1 protein. We used a splicing reporter assay to interrogate PE inclusion in HEK293T cells. In addition, we used patient-specific induced pluripotent stem cells (iPSCs) differentiated into neurons to quantify 20N inclusion by long and short-read sequencing and Nav1.1 abundance by western blot. We performed RNA-antisense purification with mass spectrometry to identify RNA-binding proteins (RBPs) that could account for the aberrant PE splicing. We demonstrate that variants in/near 20N lead to increased 20N inclusion by long-read sequencing or splicing reporter assay and decreased Nav1.1 abundance. We also identified 28 RBPs that differentially interact with variant constructs compared to wild-type, including SRSF1 and HNRNPL. We propose a model whereby 20N variants disrupt RBP binding to splicing enhancers (SRSF1) and suppressors (HNRNPL), to favor PE inclusion. Overall, we demonstrate that SCN1A 20N variants cause haploinsufficiency and SCN1A-related epilepsies. This work provides insights into the complex control of RBP-mediated PE alternative splicing, with broader implications for PE discovery and identification of pathogenic PE variants in other genetic conditions.

genetics↗

bioRxiv2023

The ENCODE Uniform Analysis Pipelines

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses. Database URL: https://www.encodeproject.org/

bioinformatics↗

bioRxiv2022

High dimensional co-expression networks enable discovery of transcriptomic drivers in complex biological systems

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly-regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, the most ubiquitous bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high dimensional transcriptomics data such as single-cell and spatial RNA-seq. hdWGCNA provides built-in functions for network inference, gene module identification, functional gene enrichment analysis, statistical tests for network reproducibility, and data visualization. In addition to conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using publicly available single-cell datasets from Autism spectrum disorder and Alzheimers disease brain samples, identifying disease-relevant co-expression network modules in specific cell populations. hdWGCNA is directly compatible with Seurat, a widely-used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly one million cells.

bioinformatics↗

bioRxiv2022

PyWGCNA: A Python package for weighted gene co-expression network analysis

MotivationWeighted gene co-expression network analysis (WGCNA) is frequently used to identify modules of genes that are co-expressed across many RNA-seq samples. However, the current R implementation is slow, not designed to compare modules between multiple WGCNA networks, and results are hard to interpret and visualize. We introduce the PyWGCNA Python package designed to identify and compare co-expression modules from RNA-seq data. ResultsWe apply PyWGCNA to two distinct datasets of brain bulk RNA-seq from MODEL-AD to identify modules associated with the genotypes. We compare the resulting modules to each other to find modules with significant overlap across the datasets. AvailabilityThe PyWGCNA library for Python 3 is available on PyPi at https://pypi.org/project/PyWGCNA and on GitHub at https://github.com/mortazavilab/PyWGCNA.

bioinformatics↗

bioRxiv2022

L-GIREMI uncovers RNA editing sites in long-read RNA-seq

Using third-generation sequencers, long-read RNA-seq is increasingly applied in transcriptomic studies given its major advantage in characterizing full-length transcripts. A number of methods have been developed to analyze this new type of data for transcript isoforms and their abundance. Another application, which is significantly under-explored, is to identify and analyze single nucleotide variants (SNVs) in the RNA. Identification of SNVs, such as genetic mutations or RNA editing sites, is fundamental to many biomedical questions. In long-read RNA-seq, SNV analysis presents significant challenges, due to the well-known relatively high error rates of the third-generation sequencers. Here, we present the first study to detect and analyze RNA editing sites in long-read RNA-seq. Our new method, L-GIREMI, effectively handles sequencing errors and biases in the reads, and uses a model-based approach to score RNA editing sites. Applied to PacBio long-read RNA-seq data, L-GIREMI affords a high accuracy in RNA editing identification. In addition, the unique advantage of long reads allowed us to uncover novel insights about RNA editing occurrences in single molecules and double-stranded RNA (dsRNA) structures. L-GIREMI provides a valuable means to study RNA nucleotide variants in long-read RNA-seq.

bioinformatics↗

bioRxiv2021

A catalog of transcription start sites across 115 human tissue and cell types

Accurate transcription start site (TSS) annotations are essential for understanding transcriptional regulation and its role in human disease. Gene collections such as GENCODE contain annotations for tens of thousands of TSSs, but not all of these annotations are experimentally validated nor do they contain information on cell type-specific usage. Therefore, we sought to generate a collection of experimentally validated TSSs by integrating RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression (RAMPAGE) data from 115 cell and tissue types, which resulted in a collection of approximately 50 thousand representative RAMPAGE peaks. These peaks were primarily proximal to GENCODE-annotated TSSs and were concordant with other transcription assays. Because RAMPAGE uses paired-end reads, we were then able to connect peaks to transcripts by analyzing the genomic positions of the 3 ends of read mates. Using this paired-end information, we classified the vast majority (37 thousand) of our RAMPAGE peaks as verified TSSs, updating TSS annotations for 20% of GENCODE genes. We also found that these updated TSS annotations were supported by epigenomic and other transcriptomic datasets. To demonstrate the utility of this RAMPAGE rPeak collection, we intersected it with the NHGRI/EBI GWAS catalog and identified new candidate GWAS genes. Overall, our work demonstrates the importance of integrating experimental data to further refine TSS annotations and provides a valuable resource for the biological community.

genomics↗

bioRxiv2021

Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

Alternative RNA isoforms are defined by promoter choice, alternative splicing, and polyA site selection. Although differential isoform expression is known to play a large regulatory role in eukaryotes, it has proved challenging to study with standard short-read RNA-seq because of the uncertainties it leaves about the full-length structure and precise termini of transcripts. The rise in throughput and quality of long-read sequencing now makes it possible, in principle, to unambiguously identify most transcript isoforms from beginning to end. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here, we develop and characterize long-read Split-seq (LR-Split-seq), which uses a combinatorial barcoding-based method for sequencing single cells and nuclei with long reads. We show that LR-Split-seq can associate isoforms with cell types with relative economy and design flexibility. We characterize LR-Split-seq for whole cells and nuclei by using the well-studied mouse C2C12 system in which mononucleated myoblast cells differentiate and fuse into multinucleated myotubes. We show that the overall results are reproducible when comparing long- and short-read data from the same cell or nucleus. We find substantial evidence of differential isoform expression during differentiation including alternative transcription start site (TSS) usage. We integrate the resulting isoform expression dynamics with snATAC-seq chromatin accessibility to validate TSS-driven isoform choices. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells that can be further quantified with companion deep short-read scRNA-seq from the same cell populations.

genomics↗

bioRxiv2020

Swan: a library for the analysis and visualization of long-read transcriptomes

MotivationLong-read RNA-sequencing technologies such as PacBio and Oxford Nanopore have discovered an explosion of new transcript isoforms that are difficult to visually analyze using currently available tools. We introduce the Swan Python library, which is designed to analyze and visualize transcript models. ResultsSwan finds 4,909 differentially expressed transcripts between cell lines HepG2 and HFFc6, including 279 that are differentially expressed even though the parent gene is not. Additionally, Swan discovers 1,021 reproducible exon skipping and 73 intron retention events not recorded in the GENCODE v29 annotation. AvailabilityThe Swan library for Python 3 is available on PyPi and on GitHub at https://pypi.org/project/swan-vis/1.0/ and https://github.com/mortazavilab/swan_paper.

bioinformatics↗