Biology subjects

Kircher, M.

Publications and source records attributed to Kircher, M..

18 recordsLinked to original sources

bioRxiv2026

Massively parallel characterization and predictive modelling of neuronal regulatory variation

Disease-associated variants reside frequently in noncoding cis-regulatory elements (CREs), yet their functional consequences remain poorly understood. We performed a large-scale lentiMPRA in human excitatory neurons, quantifying the impact of >46,000 naturally occurring variants across >27,000 candidate CREs near 524 disease-associated genes. These data improved regulatory variant effect predictions beyond state-of-the-art models. Significant allelic effects occurred at comparable rates across common, rare, and singleton variants, demonstrating that, within MPRA-measurable effects, population frequency carries limited information about per-variant regulatory impact. Variant effect detectability and magnitude were governed primarily by baseline activity of the enclosing regulatory element and local sequence context. Regulatory effects were distributed across numerous transcription factors rather than concentrated in master regulators, consistent with a combinatorial enhancer architecture. We establish a large-scale functional variant catalog and provide a complementary benchmark and resource for developing and evaluating models of noncoding regulatory variation.

genetics↗

bioRxiv2026

Coordinate- and Sequence-Based Features for a new Combined Annotation-Dependent Depletion Framework of Structural Variants (CADD-SV v2.0)

Structural variants are a major source of genomic variation and contribute to human disease and evolution through diverse mechanisms, yet their functional interpretation remains challenging. We present CADD-SV v2.0, an improved machine learning framework for scoring SV deleteriousness that expands on the original CADD-SV implementation. This version introduces a unified Random Forest model trained on an expanded set of proxy-neutral and proxy-deleterious variants drawn from human and non-human primate genomes. The model integrates updated genomic annotations, including constraint metrics, regulatory elements, and chromatin architecture features. It scores Deletions, Insertions, Duplications and Inversions based on a single scoring framework that uses both the variant and its flanking regions. To complement this framework, we also explore sequence-based annotations derived from SegmentNT, a deep learning model that provides functional predictions from DNA sequence at nucleotide resolution. Our analysis evaluated whether sequence-derived functional signals can provide additional information for SV prioritization and whether additional models with these features alone or in combination with previous coordinate-based annotations can be used.\ CADD-SV v2.0 outperforms its previous version and other tools in prioritizing deleterious variants across major SV types, including some previously unsupported, and substantially improves the computational workflow, increasing predictive power for genome-wide SV interpretation.

bioinformatics↗

bioRxiv2025

Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow

As researchers and clinicians seek to identify human genomic alterations relevant to traits and disorders, identifying and aggregating evidence providing mechanistic support for associations between alterations and phenotypes remains challenging. In particular, the study of non-coding genomic variation remains a major challenge due to the lack of accurate functional annotation for activity in a given context and across alleles. Experimental evidence is critical for prioritizing and interpreting functional effects of genetic alterations. Massively Parallel Reporter Assays (MPRAs) have emerged as a powerful high-throughput approach, enabling quantification of regulatory element activity and allelic effects, and systematic dissection of gene regulatory logic and variant effects across different contexts. However, the diversity of MPRA designs, lack of standardized formats, and many potential processing parameters hamper data integration, reproducibility, and meta-analyses across studies. To address these challenges, the Impact of Genomic Variation on Function (IGVF) Consortium established an MPRA focus group to develop community standards, including harmonized file formats, and robust analysis pipelines for a wide range of library types and experimental designs. Here, we present these formats and comprehensive computational tools, MPRAlib and MPRAsnakeflow, for uniform processing from raw sequencing reads to counts, processing and visualization. Using diverse MPRA datasets, we characterize technical variability sources including barcode sequence bias, outlier barcodes, and delivery method (episomal vs. lentiviral). Our results establish best practices for MPRA data generation and analysis, facilitating robust, reproducible research and large-scale integration. The presented tools and standards are publicly available, providing a foundation for future collaborative efforts in regulatory genomics.

genomics↗

bioRxiv2025

Capture-C MPRA: A high-throughput method to simultaneously characterize promoter interactions and regulatory activity

Cis regulatory elements (CREs) interact with their target promoters over long genomic distances and can be identified using chromatin conformation capture (3C) assays. Their regulatory activity can be functionally characterized in a high-throughput manner using massively parallel reporter assays (MPRAs) that generally test an enhancer alongside a minimal promoter. Here, we developed a novel technology called Capture-C MPRA (ccMPRA) that combines both technologies and can simultaneously obtain chromatin interactions and measure CRE activity alongside their target promoters. We utilized ccMPRA to analyze the regulatory activity of 650 promoters interacting with 42,719 sequences. As C-based techniques also capture isolated promoters, we were able to obtain promoter baseline activity, enabling the identification of both enhancers and silencers. Analysis of CREs interacting with more than one promoter showed significant activity differences depending on the promoter. In summary, ccMPRA can simultaneously characterize chromatin interactions and regulatory activity, allowing to further dissect regulatory grammar.

genomics↗

bioRxiv2024

varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction

Machine learning and artificial intelligence are increasingly being applied to identify phenotypically causal genetic variation. These data-driven methods require comprehensive training sets to deliver reliable results. However, large unbiased datasets for variant prioritization and effect predictions are rare as most of the available databases do not represent a broad ensemble of variant effects and are often biased towards protein-coding genome, or even towards few well-studied genes. To overcome these issues, we propose several alternative training sets derived from subsets of human standing variation. Specifically, we use variants identified from whole-genome sequences of 71,156 individuals contained in gnomAD v3.0 and approximate the benign set with frequent and the deleterious set with rare standing variation. We apply the Combined Annotation Dependent Depletion framework (CADD) and train several alternative models using CADD v1.6. Using the NCBI ClinVar validation set, we demonstrate that the alternative models have state-of-the art accuracy, outperforming the widely used pathogenicity score CADD v1.6 in certain genomic regions. Being larger than conventional databases, including the evolutionary-derived training dataset of about 30 million variants in CADD, standing variation datasets cover a broader range of genomic regions and rare instances of the applied annotations. For example, they cover more recent evolutionary changes common in gene regulatory regions, which are more challenging to assess with conventional tools. Finally, datasets derived from standing variation better represent allelic changes in the human genome and do not require extensive simulations and adaptations to annotations of the evolutionary-derived sequence alterations used for CADD training. We provide datasets as well as trained models to the community for further development and application. Suggestion for a Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/614666v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@191bf80org.highwire.dtl.DTLVardef@1b3975dorg.highwire.dtl.DTLVardef@1965d97org.highwire.dtl.DTLVardef@da8e59_HPS_FORMAT_FIGEXP M_FIG C_FIG Author s SummaryHere, we are presenting the varCADD approach for predicting variant deleteriousness. Throughout time, pathogenic allelic changes are selected against by purifying selection, while neutral or beneficial changes can be passed along to next generations. Consequently, the frequencies of pathogenic variants are decreasing, beneficial alleles are increasing and frequencies of neutral variants are subject to drift. For that, allele frequencies in standing variation can be used as a proxy for their deleteriousness. To train a machine learning model for variant prioritization, frequent variants from gnomAD 3.0 were used as proxy-benign set and rare variants as proxy-deleterious set. The resulting training set exceeds excisting data sets in their size and allows for genome-wide coverage of molecular effects. The training set was annotated with sequence conservation, epigenetic, sequence-based and other features using the CADD v1.6 framework, after which a logistic regression model was trained. The output of the model can be interpreted as a probability for a variant to have a deleterious effect on genetic function.

bioinformatics↗

bioRxiv2024

LBFextract: unveiling transcription factor dynamics from liquid biopsy data

Structured abstractO_ST_ABSMotivationC_ST_ABSThe analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns. ResultsWe introduce LBFextract, a comprehensive package designed to streamline feature extraction from cfDNA data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data. Data and Code Availability and ImplementationLBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406. ContactFor further information, contact isaac.lazzeri@medunigraz.at, samantha.hasenleithner@medunigraz.at, martin.kircher@bih-charite.de. Supplementary InformationFor additional details see Supplementary Information. For usage of the package, refer to https://lbf.readthedocs.io/.

bioinformatics↗

bioRxiv2024

Massively parallel jumping assay decodes Alu retrotransposition activity

The human genome contains millions of retrotransposons, several of which could become active due to somatic mutations having phenotypic consequences, including disease. However, it is not thoroughly understood how nucleotide changes in retrotransposons affect their jumping activity. Here, we developed a novel massively parallel jumping assay (MPJA) that can test the jumping potential of thousands of transposons en masse. We generated nucleotide variant library of selected four Alu retrotransposons containing 165,087 different haplotypes and tested them for their jumping ability using MPJA. We found 66,821 unique jumping haplotypes, allowing us to pinpoint domains and variants vital for transposition. Mapping these variants to the Alu-RNA secondary structure revealed stem-loop features that contribute to jumping potential. Combined, our work provides a novel high-throughput assay that assesses the ability of retrotransposons to jump and identifies nucleotide changes that have the potential to reactivate them in the human genome.

genomics↗

bioRxiv2023

Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types

The human genome contains millions of candidate cis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific on switches providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.

genomics↗

bioRxiv2022

The Regulatory Mendelian Mutation score for GRCh38

MotivationVarious genome sequencing efforts for individuals with rare Mendelian disease have increased the research focus on the non-coding genome and the clinical need for methods that prioritize potentially disease causal non-coding variants. Some methods and annotations are not available for the current human genome build (GRCh38), for which the adoption in databases, software and pipelines was slow. ResultsHere, we present an updated version of the Regulatory Mendelian Mutation (ReMM) score, re-trained on features and variants derived from the GRCh38 genome build. Like its GRCh37 version, it achieves good performance on its highly imbalanced data. To improve accessibility and provide users with a toolbox to score their variant files and lookup scores in the genome, we developed a website and API for easy score lookup. Availability and ImplementationPre-scored whole genome files of GRCh37 and GRCh38 genome builds are available on Zenodo https://doi.org/10.5281/zenodo.6576087. The website and API are available at https://remm.bihealth.org.

bioinformatics↗

bioRxiv2022

Predicting the pathogenicity of missense variants using features derived from AlphaFold2

Each individual genome harbors multiple missense variants, which can be systematically identified via genome or exome sequencing. This class of genetic variation can alter the functional properties of the respective protein, and thereby lead to clinically relevant phenotypes, such as cancer or Mendelian diseases. Despite advances in computational prediction scores, the classification of missense variants as clinically significant or benign remains a major challenge. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2. However, the question of whether AlphaFold2 structures can improve the accuracy of computational pathogenicity prediction for missense variants remains unclear. To address this, we first engineered a set of features for each amino acid from these structures. We then trained a random forest to distinguish between proxy-benign and proxy-pathogenic missense variants derived from gnomAD. This yielded a novel AlphaFold2-based pathogenicity prediction score, termed AlphScore. Important feature classes used by AlphScore are solvent accessibility, amino acid network related features, features describing the physicochemical environment, and AlphaFold2s quality parameter (pLDDT). AlphScore alone showed lower performance than existing scores, such as CADD or REVEL. However, when AlphScore was added to those scores, the performance always increased, as measured by the approximation of deep mutational scan data, as well as the prediction of expert-curated missense variants from the ClinVar database. Overall, our data indicate that the integration of AlphaFold2 predicted structures can improve pathogenicity prediction of missense variants.

genetics↗

bioRxiv2022

DNA-functionalized Gold Nanorods for Targeted Triple-modal Optical Imaging and Photothermal Therapy of Triple-negative Breast Cancer

Targeted imaging and therapy for triple-negative breast cancer (TNBC) in the perioperative period are believed to be imperative for better disease management and improved life expectancy. Still, they are not yet available in clinical settings, and only a few nanoparticle-based theranostic agents potentially offer these capabilities. Herein, we develop an innovative class of biocompatible triple-modality nanoprobes (TMNPs) that offer optical imaging using optoacoustic, fluorescence, and surface-enhanced Raman scattering (SERS), as well as photothermal therapy (PTT) with near-infrared (NIR) light. The TMNPs are fabricated by immobilizing positively charged NIR fluorophores on negatively charged DNA-coated gold nanorods (AuNR), then silica encapsulation. The DNA-based design allows the screening of commercially available positively charged NIR fluorophores for the optimum fluorescence and SERS signals. After the design optimization, we functionalize TMNPs with folate groups to target folate receptor1 (FOLR1)-overexpressing TNBC in vitro and in vivo. Our results reveal that TMNPs preferentially accumulate in the FOLR1 positive tumors in TNBC patient-derived xenograft mouse models and show excellent imaging capabilities with all three imaging modalities. Selective exposure of the tumor with NIR laser further shows efficient thermal tissue ablation without causing systemic toxicity. Collectively, TMNP holds great promise for real-time multiplexed imaging of cancer biomarkers and therapeutic capability.

bioengineering↗

bioRxiv2021

CADD-SV - a framework to score the effects of structural variants in health and disease

While technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotated pathogenic or benign SVs. We overcome this problem by using a surrogate training-objective, the Combined Annotation Dependent Depletion (CADD) of functional variants. We use human and chimpanzee derived SVs as proxy-neutral and contrast them with matched simulated variants as proxy-pathogenic, an approach that has proven powerful for SNVs. Our tool computes summary statistics over diverse variant annotations and uses random forest models to prioritize deleterious structural variants. The resulting CADD-SV scores correlate with known pathogenic and rare population variants. We further show that we can prioritize somatic cancer variants as well as non-coding variants known to affect gene expression. We provide a website and offline-scoring tool for easy application of CADD-SV (https://cadd-sv.bihealth.org/).

bioinformatics↗

bioRxiv2020

The impact of different negative training data on regulatory sequence predictions

Regulatory regions, like promoters and enhancers, cover an estimated 5-15% of the human genome. Changes to these sequences are thought to underlie much of human phenotypic variation and a substantial proportion of genetic causes of disease. However, our understanding of their functional encoding in DNA is still very limited. Applying machine or deep learning methods can shed light on this encoding and gapped k-mer support vector machines (gkm-SVMs) or convolutional neural networks (CNNs) are commonly trained on putative regulatory sequences. Here, we investigate the impact of negative sequence selection on model performance. By training gkm-SVM and CNN models on open chromatin data and corresponding negative training dataset, both learners and two approaches for negative training data are compared. Negative sets use either genomic background sequences or sequence shuffles of the positive sequences. Model performance was evaluated on three different tasks: predicting elements active in a cell-type, predicting cell-type specific elements, and predicting elements relative activity as measured from independent experimental data. Our results indicate strong effects of the negative training data, with genomic backgrounds showing overall best results. Specifically, models trained on highly shuffled sequences perform worse on the complex tasks of tissue-specific activity and quantitative activity prediction, and seem to learn features of artificial sequences rather than regulatory activity. Further, we observe that insufficient matching of genomic background sequences results in model biases. While CNNs achieved and exceeded the performance of gkm-SVMs for larger training datasets, gkm-SVMs gave robust and best results for typical training dataset sizes without the need of hyperparameter optimization.

bioinformatics↗

bioRxiv2019

Concurrent Genome and Epigenome Editing by CRISPR-Mediated Sequence Replacement

Recent advances in genome editing have facilitated the direct manipulation of not only the genome, but also the epigenome. Genome editing is typically performed by introducing a single CRISPR/Cas9-mediated double stranded break (DSB), followed by NHEJ or HDR mediated repair. Epigenome editing, and in particular methylation of CpG dinucleotides, can be performed using catalytically inactive Cas9 (dCas) fused to a methyltransferase domain. However, for investigations of the role of methylation in gene silencing, studies based on dCas9-methyltransferase have limited resolution and are potentially confounded by the effects of binding of the fusion protein. As an alternative strategy for epigenome editing, we tested CRISPR/Cas9 dual cutting of the genome in the presence of in vitro methylated exogenous DNA, i.e. to drive replacement of the DNA sequence intervening the dual cuts via NHEJ. In a proof-of-concept at the HPRT1 promoter, successful replacement events with heavily methylated alleles of a CpG island resulted in functional silencing of the HPRT1 gene. Although still limited in efficiency, our study demonstrates concurrent epigenome and genome editing in a single event, and opens the door to investigations of the functional consequences of methylation patterns at single CpG dinucleotide resolution. Our results furthermore support the conclusion that promoter methylation is sufficient to functionally silence gene expression.

genomics↗

bioRxiv2019

A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays

Massively parallel reporter assays (MPRAs) functionally screen thousands of sequences for regulatory activity in parallel. Although MPRAs have been applied to address diverse questions in gene regulation, there has been no systematic comparison of how differences in experimental design influence findings. Here, we screen a library of 2,440 sequences, representing candidate liver enhancers and controls, in HepG2 cells for regulatory activity using nine different approaches (including conventional episomal, STARR-seq, and lentiviral MPRA designs). We identify subtle but significant differences in the resulting measurements that correlate with epigenetic and sequence-level features. We also test this library in both orientations with respect to the promoter, validating en masse that enhancer activity is robustly independent of orientation. Finally, we develop and apply a novel method to assemble and functionally test libraries of the same putative enhancers as 192-mers, 354-mers, and 678-mers, and observe surprisingly large differences in functional activity. This work provides a framework for the experimental design of high-throughput reporter assays, suggesting that the extended sequence context of tested elements, and to a lesser degree the precise assay, influence MPRA results.

genomics↗

bioRxiv2018

Saturation mutagenesis of disease-associated regulatory elements

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we performed saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitution and deletion mutations. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or various integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and also comprise a gold-standard dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations.

genetics↗

bioRxiv2018

Massively parallel dissection of human accelerated regions in human and chimpanzee neural progenitors

Using machine learning (ML), we interrogated the function of all human-chimpanzee variants in 2,645 Human Accelerated Regions (HARs), some of the fastest evolving regions of the human genome. We predicted that 43% of HARs have variants with large opposing effects on chromatin state and 14% on neurodevelopmental enhancer activity. This pattern, consistent with compensatory evolution, was confirmed using massively parallel reporter assays in human and chimpanzee neural progenitor cells. The species-specific enhancer activity of assayed HARs was accurately predicted from the presence and absence of transcription factor footprints in each species. Despite these striking cis effects, activity of a given HAR sequence was nearly identical in human and chimpanzee cells. These findings suggest that HARs did not evolve to compensate for changes in the trans environment but instead altered their ability to bind factors present in both species. Thus, ML prioritized variants with functional effects on human neurodevelopment and revealed an unexpected reason why HARs may have evolved so rapidly.

evolutionary biology↗

bioRxiv2018

Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing

Determining the pathogenicity of human genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes will likely require generalizable, scalable assays. Here we describe Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance in a single experiment. We apply VAMP-seq to quantify the abundance of 7,595 single amino acid variants of two proteins, PTEN and TPMT, in which functional variants are clinically actionable. We identify 1,079 PTEN and 805 TPMT single amino acid variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and our abundance data suggest that a PTEN variant accounting for ~10% of PTEN missense variants in melanomas functions via a dominant negative mechanism. Finally, we demonstrate that VAMP-seq can be applied to other genes, highlighting its potential as a generalizable assay for characterizing missense variants.

genetics↗