Biology subjects

Haeussler, M.

Publications and source records attributed to Haeussler, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Intrinsic coordination of dynamic molecular signatures shape the human prefrontal cortex

The cerebral cortex drives human cognition through the coordinated activity of discrete cortical areas, each harboring specialized molecular, structural and functional characteristics. Central to this organization is the prefrontal cortex (PFC), a hub for executive function that displays disproportionate expansion in humans and selective vulnerability to neurodevelopmental disorders. Previous work has identified a collection of PFC-enriched marker genes with dynamic expression trajectories, and re-analysis of these datasets converge these markers into 18 distinct molecular signatures of spatiotemporal PFC identity. However, the intrinsic gene networks that coordinate these molecular signatures to shape the human PFC remains unclear. Through pooled CRISPR activation screens in human primary cortical tissues, we have evaluated the ability of PFC-enriched transcription factors to intrinsically pattern PFC molecular identity. Our screens identify novel roles for the neurogenesis regulator, YBX1, in the activation of human PFC fate. In parallel screens and knock-down experiments in human cortical organoids, we define how YBX1 acts in concert with other PFC determinants to activate molecular signatures of PFC identity. Our findings support a model in which PFC patterning is orchestrated by cohorts of intrinsic determinants that initiate, potentiate, and modulate PFC gene signatures, conferring robustness to the development of the human PFC.

neuroscience↗

bioRxiv2025

Standardizing RNA-seq Analysis of Fungal Pathogens Using BRC-Analytics and Agentic AI: A Candidozyma auris Case Study

Candidozyma auris has emerged as a critical global health threat due to multidrug resistance and healthcare-associated transmission. While RNA-seq has become the primary tool for studying C. auris pathogenesis, inconsistent use of reference genomes and bioinformatics tools complicate cross-study comparisons. Here we demonstrate how BRC-Analytics, a platform for pathogen genomics, combined with an agentic AI assistant, enables reproducible RNA-seq analysis. By re-analyzing data from two publications we achieved near-perfect correlation with published results despite annotation version differences. We addressed provenance challenges associated with using AI agents with Galaxy by forcing them to invoke Galaxys native tools rather than manipulating data directly. For custom analyses outside Galaxys toolset, we provide standalone JupyterLite notebooks that reproduce our analysis without AI involvement. This framework--combining AI-assisted automation with rigorous provenance tracking--establishes a template for standardized, reproducible fungal pathogen genomics. To the best of our knowledge, this is the first example of integration between public data repositories, reproducible analysis workflows, and agentic AI tools. Our subsequent efforts will focus on improving the seamlessness of this integration.

genomics↗

bioRxiv2025

From data to publication in a browser with BRC-Analytics: Evolutionary dynamics of coding overlaps in measles virus

The analytical landscape of pathogen research is often fragmented, hindering transparency and reproducibility due to diverse genomic data sources, numerous software tools, and suboptimal integration methods. Here we introduce BRC-analytics, a novel browser-based environment that unifies authoritative sources of genomic data with community-curated best analysis practices on a freely accessible public computational infrastructure. We demonstrate its capabilities by analyzing the evolutionary dynamics within the P/V/C locus of the measles virus, a complex system involving overlapping coding regions and RNA editing. Our analysis, conducted entirely within BRC-analytics, reveals asymmetric evolution of the locuss reading frames under distinct selective pressures. BRC-analytics streamlines the entire research process--from data collection and primary analysis (e.g., variant calling) to interpretation (e.g., using integrated JupyterLite notebooks and LLMs) and publication--into a single web browser session. This eliminates the need for local installations and manual data transfers, implicitly tracking provenance and ensuring reproducibility. The platforms goal is to provide true data-to-publication functionality, making advanced pathogen genomics accessible to a broader research community regardless of their computational expertise or infrastructure access.

genomics↗

bioRxiv2024

KegAlign: Optimizing pairwise alignments with diagonal partitioning

Our ability to generate sequencing data and assemble it into high quality complete genomes has rapidly advanced in recent years. These data promise to advance our understanding of organismal biology and answer longstanding evolutionary questions. Multiple genome alignment is a key tool in this quest. It is also the area which is lagging: today we can generate genomes faster than we can construct and update multiple alignments containing them. The bottleneck is in considerable computational time required to generate accurate pairwise alignments between divergent genomes, an unavoidable precursor to multiple alignments. This step is typically performed with lastZ, a very sensitive and yet equally slow tool. Here we describe an optimized GPU-enabled pairwise aligner KegAlign. It incorporates a new parallelization strategy, diagonal partitioning, with the latest features of modern GPUs. With KegAlign a typical human/mouse alignment can be computed in under 6 hours on a machine containing a single NVidia A100 GPU and 80 CPU cores without the need for any pre-partitioning of input sequences: a [~]150x improvement over lastZ. While other pairwise aligners can complete this task in a fraction of that time, none achieves the sensitivity of KegAligns main alignment engine, lastZ, and thus may not be suitable for comparing divergent genomes. In addition to providing the source code and a Conda package for KegAlign we also provide a Galaxy workflow that can be readily used by anyone.

genomics↗

bioRxiv2024

A Panoramic View of Cell Population Dynamics in Mammalian Aging

To elucidate the aging-associated cellular population dynamics throughout the body, here we present PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissue samples, encompassing a range of organs across different life stages, sexes, and genotypes. This comprehensive dataset allowed us to identify more than 3,000 unique cellular states and catalog over 200 distinct aging-associated cell populations experiencing significant depletion or expansion. Our panoramic analysis uncovered temporally structured, organ- and lineage-specific shifts of cellular dynamics during lifespan progression. Moreover, we investigated aging-associated alterations in immune cell populations, revealing both widespread shifts and organ-specific changes. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte-dependent. The breadth and depth of our cell-omics methodology not only enhance our comprehension of cellular aging but also lay the groundwork for exploring the complex regulatory networks among varied cell types in the context of aging and aging-associated diseases. One Sentence SummaryPanSci, a single-cell transcriptome atlas of over 20 million cells throughout the mouse lifespan, unveils the temporal architecture of aging-associated cellular population dynamics, organ-specific immune cell shifts, and the lymphocytes role in organismal aging.

genomics↗

bioRxiv2023

Single cell analysis of dup15q syndrome reveals developmental and postnatal molecular changes in autism

Duplication 15q (dup15q) syndrome is the most common genetic cause of autism spectrum disorder (ASD). Due to a higher genetic and phenotypic homogeneity compared to idiopathic autism, dup15q syndrome provides a well-defined setting to investigate ASD mechanisms. Previous bulk gene expression studies identified shared molecular changes in ASD. However, how cell type specific changes compare across different autism subtypes and how they change during development is largely unknown. In this study, we used single cell and single nucleus mRNA sequencing of dup15q cortical organoids from patient iPSCs, as well as post-mortem patient brain samples. We find cell-type specific dysregulated programs that underlie dup15q pathogenesis, which we validate by spatial resolved transcriptomics using brain tissue samples. We find degraded identity and vulnerability of deep-layer neurons in fetal stage organoids and highlight increased molecular burden of postmortem upper-layer neurons implicated in synaptic signaling, a finding shared between idiopathic ASD and dup15q syndrome. Gene co-expression network analysis of organoid and postmortem excitatory neurons uncovers modules enriched with autism risk genes. Organoid developmental modules were involved in transcription regulation via chromatin remodeling, while postmortem modules were associated with synaptic transmission and plasticity. The findings reveal a shifting landscape of ASD cellular vulnerability during brain development.

neuroscience↗

bioRxiv2023

A Meta-Atlas of the Developing Human Cortex Identifies Modules Driving Cell Subtype Specification

Human brain development requires the generation of hundreds of diverse cell types, a process targeted by recent single-cell transcriptomic profiling efforts. Through a meta-analysis of seven of these published datasets, we have generated 225 meta-modules - gene co-expression networks that can describe mechanisms underlying cortical development. Several meta-modules have potential roles in both establishing and refining cortical cell type identities, and we validated their spatiotemporal expression in primary human cortical tissues. These include meta-module 20, associated with FEZF2+ deep layer neurons. Half of meta-module 20 genes are putative FEZF2 targets, including TSHZ3, a transcription factor associated with neurodevelopmental disorders. Human cortical organoid experiments validated that both factors are necessary for deep layer neuron specification. Importantly, subtle manipulations of these factors drive slight changes in meta-module activity that cascade into strong differences in cell fate - demonstrating how of our meta-atlas can engender further mechanistic analyses of cortical fate specification.

developmental biology↗

bioRxiv2022

Spatial cell type mapping of multiple sclerosis lesions

Multiple sclerosis (MS) is a prototypic chronic-inflammatory disease of the central nervous system. After initial lesion formation during active demyelination, inflammation is gradually compartmentalized and restricted to specific tissue areas such as the lesion rim in chronic-active lesions. However, the cell type-specific and spatially restricted drivers of chronic tissue damage and lesion expansion are not well understood. Here, we investigated the properties of subcortical white matter lesions by creating a cell type-specific spatial map of gene expression across various inflammatory lesion stages in MS. An integrated analysis of single-nucleus and spatial transcriptomics data enabled us to uncover patterns of glial, immune and stromal cell subtype diversity, as well as to identify cell-cell communication and signaling signatures across lesion and non-lesion tissue areas in MS. Our results provide insights into the conversion of the tissue microenvironment from a homeostatic to a pathogenic or dysfunctional state underlying lesion progression in MS. We expect that this study will help identify spatially resolved cell type-specific biomarkers and therapeutic targets for future interventional trials in MS.

neuroscience↗

bioRxiv2022

Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus

The human frontal cortex and hippocampus play critical roles in learning and cognition. We investigated the epigenomic and 3D chromatin conformational reorganization during the development of the frontal cortex and hippocampus, using more than 53,000 joint single-nucleus profiles of chromatin conformation and DNA methylation (sn-m3C-seq). The remodeling of DNA methylation predominantly occurs during late-gestational to early-infant development and is temporally separated from chromatin conformation dynamics. Neurons have a unique Domain-Dominant chromatin conformation that is different from the Compartment-Dominant conformation of glial cells and non-brain tissues. We reconstructed the regulatory programs of cell-type differentiation and found putatively causal common variants for schizophrenia strongly overlap with chromatin loop-connected, cell-type-specific regulatory regions. Our data demonstrate that single-cell 3D-regulome is an effective approach for dissecting neuropsychiatric risk loci.

genomics↗

bioRxiv2022

The dynseq genome browser track enables visualization of context-specific, dynamic DNA sequence features at single nucleotide resolution

We introduce the dynseq genome browser track, which displays DNA nucleotide characters scaled by user-specified, base-resolution scores provided in the BigWig file format. The dynseq track enables visualization of context-specific, informative genomic sequence features. We demonstrate its utility in three popular genome browsers for interpreting cis-regulatory sequence syntax and regulatory variant interpretation by visualizing nucleotide importance scores derived from machine learning models of regulatory DNA trained on protein-DNA binding and chromatin accessibility experiments.

genomics↗

bioRxiv2021

A new SARS-CoV-2 lineage that shares mutations with known Variants of Concern is rejected by automated sequence repository quality control

We report a SARS-CoV-2 lineage that shares N501Y, P681H, and other mutations with known variants of concern, such as B.1.1.7. This lineage, which we refer to as B.1.x (COG-UK sometimes references similar samples as B.1.324.1), is present in at least 20 states across the USA and in at least six countries. However, a large deletion causes the sequence to be automatically rejected from repositories, suggesting that the frequency of this new lineage is underestimated using public data. Recent dynamics based on 339 samples obtained in Santa Cruz County, CA, USA suggest that B.1.x may be increasing in frequency at a rate similar to that of B.1.1.7 in Southern California. At present the functional differences between this variant B.1.x and other circulating SARS-CoV-2 variants are unknown, and further studies on secondary attack rates, viral loads, immune evasion and/or disease severity are needed to determine if it poses a public health concern. Nonetheless, given what is known from well-studied circulating variants of concern, it seems unlikely that the lineage could pose larger concerns for human health than many already globally distributed lineages. Our work highlights a need for rapid turnaround time from sequence generation to submission and improved sequence quality control that removes submission bias. We identify promising paths toward this goal.

genomics↗

bioRxiv2020

UCSC Cell Browser: Visualize Your Single-Cell Data

SummaryAs the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. Availability and implementationWe provide the UCSC Cell Browser as a free website where users can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. Contactcells@ucsc.edu

bioinformatics↗

bioRxiv2020

The UCSC SARS-CoV-2 Genome Browser

BackgroundResearchers are generating molecular data pertaining to the SARS-CoV-2 RNA genome and its proteins at an unprecedented rate during the COVID-19 pandemic. As a result, there is a critical need for rapid and continuously updated access to the latest molecular data in a format in which all data can be quickly cross-referenced and compared. We adapted our genome browser visualization tool to the viral genome for this purpose. Molecular data, curated from published studies or from database submissions, are mapped to the viral genome and grouped together into "annotation tracks" where they can be visualized along the linear map of the viral genome sequence and programmatically downloaded in standard format for analysis. ResultsThe UCSC Genome Browser for SARS-CoV-2 (https://genome.ucsc.edu/covid19.html) provides continuously updated access to the mutations in the many thousands of SARS-CoV-2 genomes deposited in GISAID and the international nucleotide sequencing databases, displayed alongside phylogenetic trees. These data are augmented with alignments of bat, pangolin, and other animal and human coronavirus genomes, including per-base evolutionary rate analysis. All available annotations are cross-referenced on the virus genome, including those from major databases (PDB, RFAM, IEDB, UniProt) as well as up-to-date individual results from preprints. Annotated data include predicted and validated immune epitopes, promising antibodies, RT-PCR and sequencing primers, CRISPR guides (from research, diagnostics, vaccines, and therapies), and points of interaction between human and viral genes. As a community resource, any user can add manual annotations which are quality checked and shared publicly on the browser the next day. ConclusionsWe invite all investigators to contribute additional data and annotations to this resource to accelerate research and development activities globally. Contact us at genome-www@soe.ucsc.edu with data suggestions or requests for support for adding data. Rapid sharing of data will accelerate SARS-CoV-2 research, especially when researchers take time to integrate their data with those from other labs on a widely-used community browser platform with standardized machine-readable data formats, such as the SARS-CoV-2 Genome Browser.

genomics↗

bioRxiv2020

User-friendly, scalable tools and workflows for single-cell analysis

Single-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

bioinformatics↗

bioRxiv2019

Single cell epigenomic atlas of the developing human brain and organoids

Dynamic changes in chromatin accessibility coincide with important aspects of neuronal differentiation, such as fate specification and arealization and confer cell type-specific associations to neurodevelopmental disorders. However, studies of the epigenomic landscape of the developing human brain have yet to be performed at single-cell resolution. Here, we profiled chromatin accessibility of >75,000 cells from eight distinct areas of developing human forebrain using single cell ATAC-seq (scATACseq). We identified thousands of loci that undergo extensive cell type-specific changes in accessibility during corticogenesis. Chromatin state profiling also reveals novel distinctions between neural progenitor cells from different cortical areas not seen in transcriptomic profiles and suggests a role for retinoic acid signaling in cortical arealization. Comparison of the cell type-specific chromatin landscape of cerebral organoids to primary developing cortex found that organoids establish broad cell type-specific enhancer accessibility patterns similar to the developing cortex, but lack many putative regulatory elements identified in homologous primary cell types. Together, our results reveal the important contribution of chromatin state to the emerging patterns of cell type diversity and cell fate specification and provide a blueprint for evaluating the fidelity and robustness of cerebral organoids as a model for cortical development.

developmental biology↗

bioRxiv2019

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication/s supporting a single gene that best explains a patients disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts, downloads and further parses hundreds of thousands of full text articles in search of information supporting the causality and associated phenotypes of any published genetic variant. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patients given set of phenotypes. Diagnosis of singleton patients (without relatives exomes) is the most time-consuming scenario. AMELIE ranked the causative gene at the very top in 2/3 of 215 diagnosed singleton Mendelian patients. Evaluating only the top 11 AMELIE scored genes of 127 (median) candidate genes per patient results in rapid diagnosis for 90+% of cases. AMELIE-based evaluation of all cases is 3-19x more efficient than hand-curated database-based approaches. We replicate these results on a cohort of clinical cases from Stanford Childrens Health and the Manton Center for Orphan Disease Research. An analysis web portal with our most recent update, programmatic interface and code will be available at AMELIE.stanford.edu. A pilot run of the web portal has already served many thousands of job submissions from dozens of countries.

genetics↗

bioRxiv2018

AVADA Enables Automated Genetic Variant Curation Directly from the Full Text Literature

PurposeThe primary literature on human genetic diseases includes descriptions of pathogenic variants that are essential for clinical diagnosis. Variant databases such as ClinVar and HGMD collect pathogenic variants by manual curation. We aimed to automatically construct a freely accessible database of pathogenic variants directly from full-text articles about genetic disease.\n\nMethodsAVADA (Automatically curated VAriant DAtabase) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic variants and genes in full text of primary literature and converts them to genomic coordinates for rapid downstream use.\n\nResultsAVADA automatically curated almost 60% of pathogenic variants deposited in HGMD, a 4.4-fold improvement over the current state of the art in automated variant extraction. AVADA also contains more than 60,000 pathogenic variants that are in HGMD, but not in ClinVar. In a cohort of 245 diagnosed patients, AVADA correctly annotated 38 previously described diagnostic variants, compared to 43 using HGMD, 20 using ClinVar and only 13 (wholly subsumed by AVADA and ClinVars) using the best automated abstracts-only based approach.\n\nConclusionAVADA is the first machine learning tool that automatically curates a variants database directly from full text literature. AVADA is available upon publication at http://bejerano.stanford.edu/AVADA.

genetics↗

bioRxiv2018

The UCSC Repeat Browser allows discovery and visualization of evolutionary conflict across repeat families

BackgroundNearly half the human genome consists of repeat elements, most of which are retrotransposons, and many of these sequences play important biological roles. However repeat elements pose several unique challenges to current bioinformatic analyses and visualization tools, as short repeat sequences can map to multiple genomic loci resulting in their misclassification and misinterpretation. In fact, sequence data mapping to repeat elements are often discarded from analysis pipelines. Therefore, there is a continued need for standardized tools and techniques to interpret genomic data of repeats. ResultsWe present the UCSC Repeat Browser, which consists of a complete set of human repeat reference sequences derived from the gold standard repeat database RepeatMasker. The UCSC Repeat Browser contains mapped annotations from the human genome to these references, and presents all of them as a comprehensive interface to facilitate work with repetitive elements. Furthermore, it provides processed tracks of multiple publicly available datasets of biological interest to the repeat community, including ChIP-SEQ datasets for KRAB Zinc Finger Proteins (KZNFs) - a family of proteins known to bind and repress certain classes of repeats. Here we show how the UCSC Repeat Browser in combination with these datasets, as well as RepeatMasker annotations in several non-human primates, can be used to trace the independent trajectories of species-specific evolutionary conflicts. ConclusionsThe UCSC Repeat Browser allows easy and intuitive visualization of genomic data on consensus repeat elements, circumventing the problem of multi-mapping, in which sequencing reads of repeat elements map to multiple locations on the human genome. By developing a reference consensus, multiple datasets and annotation tracks can easily be overlaid to reveal complex evolutionary histories of repeats in a single interactive window. Specifically, we use this approach to retrace the history of several primate specific LINE-1 families across apes, and discover several species-specific routes of evolution that correlate with the emergence and binding of KZNFs.

genomics↗