Biology subjects

Seelow, D.

Publications and source records attributed to Seelow, D..

6 recordsLinked to original sources

bioRxiv2026

Quantification of the effects of single nucleotide variants in NKX2.1 transcription factor binding sites

Transcription factors recognise and bind specific DNA sequence patterns in promoters and enhancers thereby regulating gene expression. Variations in the DNA sequence of transcription factor binding sites (TFBSs) can alter gene regulation and may disrupt development. The transcription factor NKX2.1 is a crucial regulator of thyroid, lung, and neural development. Mutations in its coding gene NKX2-1 may cause choreoathetosis and congenital hypothyroidism with or without pulmonary dysfunction (CAHTP, OMIM #610978). Most genetically solved patients carry mutations in the coding regions of NKX2-1 that affect DNA binding, while the majority of patients with CAHTP-like symptoms do not carry mutations in the NKX2-1 coding sequence. We hypothesise that variations in the DNA-sequence at promoter or enhancer sites to which the transcription factor NKX2.1 binds could cause disease as well. We employed EMSA-seq to quantify the effects of genetic variation on NKX2.1 binding strength and used this data to train neural network models to forecast the influence of DNA variation on NKX2.1 binding. We validated our models using microscale thermophoresis, X-ray crystallography, and publicly available ChIP-seq data sets. The neural networks were able to detect TFBSs in ChIP-seq data and can thus be used to evaluate whole genome sequencing data of CAHTP-patients in order to prioritise potential disease-causing variants in regulatory elements. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/708450v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@167cedeorg.highwire.dtl.DTLVardef@3e5291org.highwire.dtl.DTLVardef@19eb7f9org.highwire.dtl.DTLVardef@1404057_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Alternative splicing is coupled to gene expression in a subset of variably expressed genes

Numerous factors regulate alternative splicing of human genes at a co-transcriptional level. However, how alternative splicing depends on the regulation of gene expression is poorly understood. We leveraged data from the Genotype-Tissue Expression (GTEx) project to show a significant association of gene expression and splicing for 6874 (4.9%) of 141,043 exons in 1106 (13.3%) of 8314 genes with substantially variable expression in ten GTEx tissues. About half of these exons demonstrate higher inclusion with higher gene expression, and half demonstrate higher exclusion, with the observed direction of coupling being highly consistent across different tissues and in external datasets. The exons differ with respect to sequence characteristics, enriched sequence motifs, RNA polymerase II binding, and inferred transcription rate of downstream introns. The exons were enriched for hundreds of isoform-specific Gene Ontology annotations, suggesting that the coupling of expression and alternative splicing described here may provide an important gene regulatory mechanism that might be used in a variety of biological contexts. In particular, higher inclusion exons could play an important role during cell division.

genomics↗

bioRxiv2022

Discovery of a non-canonical GRHL1 binding motif using deep convolutional and recurrent neural networks

Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation. In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach. Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.

bioinformatics↗

bioRxiv2020

VarFish - Collaborative and Comprehensive Variant Analysis for Diagnosis and Research

VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of panel and exome variant data for rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Filtered variants are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows user to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the softwares extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g., after update of genotype or phenotype databases.

bioinformatics↗

bioRxiv2018

The impact of sex on alternative splicing

Over 95% of human genes undergo alternative splicing (AS) in a developmental, tissue-specific, or signal transduction-dependent manner. Here, we present a large-scale survey of sex-biased differential alternative splicing (DAS) across 7027 samples of 39 tissues from 532 individuals (351 males and 181 females) from the Genotype-Tissue Expression project. We detected a total of 1278 statistically significant DAS events affecting 888 different genes and 4417 significant differential gene expression (DGE) events in 3221 genes. Only 267 (29.3%) of the differentially spliced genes were also differentially expressed. Genes that displayed sex-biased DGE or DAS across multiple tissues were enriched in functions related to signaling including histone demethylation. The probability of a gene showing significant differential AS varies by chromosome and is highest for the X chromosome, with differentially spliced X chromosomal genes additionally being more likely to escape X chromosomal inactivation. A small but significant association was found between sex-biased AS and transcripts that undergo physiological nonsense-mediated decay (NMD). We show a significant overlap of differential splicing and genes that display estrogen-induced alternative splicing, that are involved in estrogen response pathway. Further, we show overlap of the involved exons with estrogen-receptor bindings sites. Our results provide a comprehensive survey of sex-biased AS and its characteristics across a large collection of human tissues.

genomics↗

bioRxiv2018

Phenotero: annotate as you write

Controlled vocabularies and ontologies have become a valuable resource for knowledge representation, data integration, and downstream analyses in the biomedical domain. In precision medicine, especially in clinical genetics, the Human Phenotype Ontology (HPO) as well as disease ontologies like the Orphanet Rare Disease Ontology (ORDO) or Medical Subject Headings (MeSH) are often used for deep phenotyping of patients and coding of clinical diagnoses. However, the process of assigning ontology classes (annotating) to patient descriptions is often disconnected from the process of writing patient reports or manuscripts in word processing software such as Microsoft Word or LibreOffice. This additional workload and the requirement to install dedicated software may discourage usage of ontologies for parts of the target audience.\n\nTo improve this situation, we present Phenotero, a freely available and simple solution to annotate patient phenotypes and diseases at the time of writing clinical reports or manuscripts. We adopt Zotero, a well-established, actively developed citation management software to generate a tool which allows to reference classes from ontologies within clinical reports or manuscripts at the time of writing. We expect this approach to decrease the additional workload to a minimum while ensuring high quality associations with ontology classes. Standardised collection of phenotypic information at the time of describing the patient allows for streamlining of clinic workflow, efficient data entry, and will subsequently promote clinical and molecular diagnosis, remove ambiguousness from manuscripts, and allow sharing of anonymised patient phenotype data with ultimate goal of a better understanding of the disease. Thus, we hope that our integrated approach will further promote the usage of ontologies and controlled vocabularies in the clinical setting and in the biomedical domain.

bioinformatics↗