Biology subjects

Rentzsch, P.

Publications and source records attributed to Rentzsch, P..

4 recordsLinked to original sources

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

Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes

Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each genes differential expression fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing datasets from rare disease and in-vitro stimulus response experiments. Compared to the standard approach, our method adjusts the bias in discovery towards highly variable genes, and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. With that, our method provides a novel view on DE and contributes towards bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease causing genes and enhance the discovery of therapeutic targets.

genomics↗

bioRxiv2024

Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs

Understanding the role of transcription and transcription factors in cellular identity and disease, such as cancer and autoimmunity, is essential. However, comprehensive data resources for cell line-specific transcription factor-to-target gene annotations are currently limited. To address this, we developed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and transcription factor binding data, we generated regulons for four common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving transcription factor knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable tool and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.

bioinformatics↗

bioRxiv2017

Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers

Metazoans are crucially dependent on multiple layers of gene regulatory mechanisms which allow them to control gene expression across developmental stages, tissues and cell types. Multiple recent research consortia have aimed to generate comprehensive datasets to profile the activity of these cell type- and condition-specific regulatory landscapes across many different cell lines and primary cells. However, extraction of genes or regulatory elements specific to certain entities from these datasets remains challenging. We here propose a novel method based on non-negative matrix factorization for disentangling and associating huge multi-assay datasets including chromatin accessibility and gene expression data. Taking advantage of implementations of NMF algorithms in the GPU CUDA environment full datasets composed of tens of thousands of genes as well as hundreds of samples can be processed without the need for prior feature selection to reduce the input size. Applying this framework to multiple layers of genomic data derived from human blood cells we unravel mechanisms of regulation of cell type-specific expression in T-cells and monocytes.

genomics↗