Biology subjects

Reinders, M.

Publications and source records attributed to Reinders, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Predicting cell population-specific gene expression from genomic sequence

Most regulatory elements, especially enhancer sequences, are cell population-specific. One could even argue that a distinct set of regulatory elements is what defines a cell population. However, discovering which non-coding regions of the DNA are essential in which context, and as a result, which genes are expressed, is a difficult task. Some computational models tackle this problem by predicting gene expression directly from the genomic sequence. These models are currently limited to predicting bulk measurements and mainly make tissue-specific predictions. Here, we present a model that leverages single-cell RNA-sequencing data to predict gene expression. We show that cell population-specific models outperform tissue-specific models, especially when the expression profile of a cell population and the corresponding tissue are dissimilar. Further, we show that our model can prioritize GWAS variants and learn motifs of transcription factor binding sites. We envision that our model can be useful for delineating cell population-specific regulatory elements.

bioinformatics↗

bioRxiv2022

Single-cell reference mapping to construct and extend cell type hierarchies

Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups often using distinct terminology. Understanding how annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge. Whereas recent computational techniques, referred to as "reference mapping" methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e., queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present "treeArches", a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

bioinformatics↗

bioRxiv2022

TopoGAN: Unsupervised manifold alignment of single-cell data

MotivationSingle-cell technologies allow deep characterization of different molecular aspects of cells. Integrating these modalities provides a comprehensive view of cellular identity. Current integration methods rely on overlapping features or cells to link datasets measuring different modalities, limiting their application to experiments where different molecular layers are profiled in different subsets of cells. ResultsWe present scTopoGAN, a method for unsupervised manifold alignment of single-cell datasets with non-overlapping cells or features. We use topological autoencoders to obtain latent representations of each modality separately. A topology-guided Generative Adversarial Network then aligns these latent representations into a common space. We show that scTopoGAN outperforms state-of-the-art manifold alignment methods in complete unsupervised settings. Interestingly, the topological autoencoder for individual modalities also showed better performance in preserving the original structure of the data in the low-dimensional representations when compared to other manifold projection methods. Taken together, we show that the concept of topology preservation might be a powerful tool to align multiple single modality datasets, unleashing the potential of multi-omic interpretations of cells. Availability and implementationImplementation available on GitHub (https://github.com/AkashCiel/scTopoGAN). All datasets used in this study are publicly available. Contactt.r.m.abdelaal@lumc.nl

bioinformatics↗

bioRxiv2021

Correcting differential gene expression analysis for cyto-architectural alterations in substantia nigra of Parkinson's disease patients reveals known and potential novel disease-associated genes and pathways

Several studies have analyzed gene expression profiles in the substantia nigra to better understand the pathological mechanisms causing Parkinsons disease (PD). However, the concordance between the identified gene signatures in these individual studies was generally low. This might be caused by a change in cell type composition as loss of dopaminergic neurons in the substantia nigra pars compacta is a hallmark of PD. Through an extensive meta-analysis of nine previously published microarray studies, we demonstrated that a big proportion of the detected differentially expressed genes was indeed caused by cyto-architectural alterations due to the heterogeneity in the neurodegenerative stage and/or technical artifacts. After correcting for cell composition, we identified a common signature that deregulated the previously unreported ammonium transport, as well as known biological processes including bioenergetic pathways, response to proteotoxic stress, and immune response. By integrating with protein-interaction data, we shortlisted a set of key genes, such as LRRK2, PINK1, and PRKN known to be related to PD; others with compelling evidence for their role in neurodegeneration, such as GSK3{beta}, WWOX, and VPC; as well as novel potential players in the PD pathogenesis, including NTRK1, TRIM25, ELAVL1. Together, these data showed the importance of accounting for cyto-architecture in these analyses and highlight the contribution of multiple cell types and novel processes to PD pathology providing potential new targets for drug development. Significance StatementThe exploration of the transcriptomic landscape in PD is pivotal for the understanding of the pathological mechanisms of this disease. Nonetheless, little attention has been paid to the influence of cell composition on the transcriptome even though it is known that cyto-architecture undergoes major alterations in neurodegenerative diseases such as PD. Our study signifies that changes in cellular architecture of human substantia nigra in PD have a strong effect on the set of detected differentially expressed genes. By reanalyzing the data and accounting for cell composition, we provide an updated description of deregulated biological processes in PD and nominate a shortlist of PD-associated genes for further investigations.

bioinformatics↗

bioRxiv2021

The power of universal contextualised protein embeddings in cross-species protein function prediction

Computationally annotating proteins with a molecular function is a difficult problem that is made even harder due to the limited amount of available labelled protein training data. A recently published supervised molecular function predicting model partly circumvents this limitation by making its predictions based on the universal (i.e. task-agnostic) contextualised protein embeddings from the deep pre-trained unsupervised protein language model SeqVec. SeqVec embeddings incorporate contextual information of amino acids, thereby modelling the underlying principles of protein sequences insensitive to the context of species. We applied the existing SeqVec-based molecular function prediction model in a transfer learning task by training the model on annotated protein sequences of one training species and making predictions on the proteins of several test species with varying evolutionary distance. We show that this approach successfully generalises knowledge about protein function from one eukaryotic species to various other species, proving itself an effective method for molecular function prediction in inadequately annotated species from understudied taxonomic kingdoms. Furthermore, we submitted the performance of our SeqVec-based prediction models to detailed characterisation, first to advance the understanding of protein language models and second to determine areas of improvement. Author summaryProteins are diverse molecules that regulate all processes in biology. The field of synthetic biology aims to understand these protein functions to solve problems in medicine, manufacturing, and agriculture. Unfortunately, for many proteins only their amino acid sequence is known whereas their function remains unknown. Only a few species have been well-studied such as mouse, human and yeast. Hence, we need to increase knowledge on protein functions. Doing so is, however, complicated as determining protein functions experimentally is time-consuming, expensive, and technically limited. Computationally predicting protein functions offers a faster and more scalable approach but is hampered as it requires much data to design accurate function prediction algorithms. Here, we show that it is possible to computationally generalize knowledge on protein function from one well-studied training species to another test species. Additionally, we show that the quality of these protein function predictions depends on how structurally similar the proteins are between the species. Advantageously, the predictors require only the annotations of proteins from the training species and mere amino acid sequences of test species which may particularly benefit the function prediction of species from understudied taxonomic kingdoms such as the Plantae, Protozoa and Chromista.

bioinformatics↗

bioRxiv2021

scMoC: Single-Cell Multi-omics clustering

MotivationSingle-cell multi-omics assays simultaneously measure different molecular features from the same cell. A key question is how to benefit from the complementary data available and perform cross-modal clustering of cells. ResultsWe propose Single-Cell Multi-omics Clustering (scMoC), an approach to identify cell clusters from data with co-measurements of scRNA-seq and scATAC-seq from the same cell. We overcome the high sparsity of the scATAC-seq data by using an imputation strategy that exploits the less-sparse scRNA-seq data available from the same cell. Subsequently, scMoC identifies clusters of cells by merging clusterings derived from both data domains individually. We tested scMoC on datasets generated using different protocols with variable data sparsity levels. We show that, due to its imputation scheme, scMoC 1) is able to generate informative scATAC-seq data due to its RNA guided imputation strategy, and 2) results in integrated clusters based on both RNA and ATAC information that are biologically meaningful either from the RNA or from the ATAC perspective. AvailabilityThe code is freely available at: https://github.com/meltager/scmoc. Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

pmTR database: population matched (PM) germline allelic variants of T-cell receptor (TR) loci

T-cell receptor (TR) germline alleles are arranged, organized and made available to the research community by the IMGT database. This state-of-the-art database, however, does not provide information regarding population specificity and allelic frequencies of the genes all four human TR loci (TRA, TRB, TRG and TRD). The specificity of allelic variants to different human populations can, however, be a rich source of information when studying the genetic basis of population-specific immune responses in vaccination and disease. To make TR germline alleles available for such population-specific studies, we meticulously identified true germline alleles enriched with complete TR allele sequences and their frequencies across 26 different human populations, profiled by "1,000 Genomes data". We identified 205 TRAV, 249 TRBV, 16 TRGV and 5 TRDV germline alleles supported by at least four haplotypes (= minimum of two individuals). The diversity of germline allelic variants in the TR loci is highest in Africans followed by Non-African populations. A majority of the Non-African alleles are specific to the Asian populations, suggesting a diverse profile of TR germline alleles in different human populations. Interestingly, the alleles known in the IMGT database are frequent and common across all the superpopulations. We believe that this new set of genuine germline TR sequences represents a valuable new resource which we have made available through the new population-matched TR (pmTR) database, accessible via https://pmtrig.lumc.nl/.

immunology↗

bioRxiv2020

Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequenc-ing data analysis

The ever-increasing number of analyzed cells in Single-cell RNA sequencing (scRNA-seq) experiments imposes several challenges on the data analysis. Current analysis methods lack scalability to large datasets hampering interactive visual exploration of the data. We present Cytosplore-Transcriptomics, a framework to analyze scRNA-seq data, including data preprocessing, visualization and downstream analysis. At its core, it uses a hierarchical, manifold preserving representation of the data that allows the inspection and annotation of scRNA-seq data at different levels of detail. Consequently, Cytosplore-Transcriptomics provides interactive analysis of the data using low-dimensional visualizations that scales to millions of cells. AvailabilityCytosplore-Transcriptomics can be freely downloaded from transcriptomics.cytosplore.org Contactb.p.f.lelieveldt@lumc.nl

bioinformatics↗

bioRxiv2020

snpXplorer: a web application to explore SNP-associations and annotate SNP-sets

Genetic association studies are frequently used to study the genetic basis of numerous human phenotypes. However, the rapid interrogation of how well a certain genomic region associates across traits as well as the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation, which involves advanced bioinformatic skills. We developed snpXplorer, an easy-to-use web-server application for exploring Single Nucleotide Polymorphisms (SNP) association statistics and to functionally annotate sets of SNPs. snpXplorer can superimpose association statistics from multiple studies, and displays regional information including SNP associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. By overlaying multiple GWAS studies, snpXplorer can be used to compare levels of association across different traits, which may help the interpretation of variant consequences. Given a list of SNPs, snpXplorer can also be used to perform variant-to-gene mapping and gene-set enrichment analysis to identify molecular pathways that are overrepresented in the list of input SNPs. snpXplorer is freely available at https://snpxplorer.net. Source code, documentation, example files and tutorial videos are available within the Help section of snpXplorer and at https://github.com/TesiNicco/snpXplorer. Key points: O_LIsnpXplorer shows GWAS summary statistics, regional information and helps deciphering GWAS outcomes C_LIO_LIsnpXplorer interactively compares association levels of a genomic region across phenotypes C_LIO_LIsnpXplorer performs variant-to-gene mapping and gene-set enrichment analysis C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/377879v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@13a9ba5org.highwire.dtl.DTLVardef@c07b9aorg.highwire.dtl.DTLVardef@f2ce4forg.highwire.dtl.DTLVardef@c6d228_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Transcriptomic signatures associated with regional cortical thickness changes in Parkinson's disease

Cortical atrophy is a common manifestation in Parkinsons disease, particularly in later disease stages. Here, we investigated patterns of cortical thickness using T1-weighted anatomical MRI data of 149 Parkinsons disease patients and 369 controls. To elucidate the molecular underpinnings of cortical thickness changes in Parkinsons disease, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and neuroimaging features. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of Parkinsons disease. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in Parkinsons disease are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in Parkinsons disease and possibly influence motor and cognitive functions.

bioinformatics↗

bioRxiv2020

SpaGE: Spatial Gene Enhancement using scRNA-seq

Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently.

bioinformatics↗

bioRxiv2020

Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

MotivationUntargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). In order to judge if metabolite levels are abnormal, analysis of a large number of reference samples is crucial to correct for variations in metabolite concentrations resulting from factors such as diet, age and gender. However, a large number of controls requires the use of out-of-batch controls, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e. technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Methods & resultsBased on six metrics, we compared existing normalization methods on their ability to reduce batch effects from eight independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method which uses 17 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age- and sex as covariates fitted on control samples obtained from all eight batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal as well as in the detection of 178 known biomarkers across 45 IEM patient samples and performed at least similar to an approach using 15 within-batch controls. Furthermore, our regression model indicates that 10-24% of the considered features showed significant age-dependent variations. ConclusionsOur comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch controls to establish clinically-relevant reference values for metabolite concentrations. These findings opens possibilities to use large scale out-of-batch control samples in a clinical setting, increasing throughput and detection accuracy. AvailabilityMetchalizer is available at https://github.com/mbongaerts/Metchalizer/

bioinformatics↗

bioRxiv2020

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

MotivationProtein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. ResultsWe applied an existing deep sequence model that had been pre-trained in an unsupervised setting on the supervised task of protein function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for deep prediction models, as a two-layer perceptron was enough to achieve state-of-the-art performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that three-dimensional structure is also potentially learned during the unsupervised pre-training. AvailabilityImplementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Contactameliavm@ugr.es Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

Hierarchical progressive learning of cell identities in single-cell data

Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resolutions, and do not preserve annotations when retrained on new datasets. The latter point is especially important as researchers cannot rely on downstream analysis performed using earlier versions of the dataset. Here, we present scHPL, a hierarchical progressive learning method which allows continuous learning from single-cell data by leveraging the different resolutions of annotations across multiple datasets to learn and continuously update a classification tree. We evaluate the classification and tree learning performance using simulated as well as real datasets and show that scHPL can successfully learn known cellular hierarchies from multiple datasets while preserving the original annotations. scHPL is available at https://github.com/lcmmichielsen/hierarchicalprogressivelearning.

bioinformatics↗

bioRxiv2020

Evolutionarily conserved non-protein-coding regions in the chicken genome harbor functionally important variation

The availability of genomes for many species has advanced our understanding of the non-protein-coding fraction of the genome. Comparative genomics has proven to be an invaluable approach for the systematic, genome-wide identification of conserved non-protein-coding elements (CNEs). However, for many non-mammalian model species, including chicken, our capability to interpret the functional importance of variants overlapping CNEs has been limited by current genomic annotations, which rely on a single information type (e.g. conservation). We here studied CNEs in chicken using a combination of population genomics and comparative genomics. To investigate the functional importance of variants found in CNEs we develop a ch(icken) Combined Annotation-Dependent Depletion (chCADD), a variant effect prediction tool first introduced for humans and later on for mouse and pig. We show that 73 Mb of the chicken genome has been conserved across more than 280 million years of vertebrate evolution. The vast majority of the conserved elements are in non-protein-coding regions, which display SNP densities and allele frequency distributions characteristic of genomic regions constrained by purifying selection. By annotating SNPs with the chCADD score we are able to pinpoint specific subregions of the CNEs to be of higher functional importance, as supported by SNPs found in these subregions are associated with known disease genes in humans, mice, and rats. Taken together, our findings indicate that CNEs harbor variants of functional significance that should be object of further investigation along with protein-coding mutations. We therefore anticipate chCADD to be of great use to the scientific community and breeding companies in future functional studies in chicken.

genomics↗

bioRxiv2020

Untangling biological factors influencing trajectory inference from single cell data

Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity towards the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unravelling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajetory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation. Significance StatementPseudotime inference is a bioinformatics tool used to characterize and understand the role and activity of genes involved in cell differentiation. To achieve this, the level of expression of thousands of genes are simultaneously used to order cells along a developmental axis. However, this may result in distorted trajectories as many genes are not necessary involved in cell differentiation, and might even provide the pseudotime inference tool with conflicting (confounding) information. Here we present a methodology for improving inference of the differentiation trajectories by restricting it to a small set of genes assumed to regulate cell differentiation.

bioinformatics↗

bioRxiv2019

Sparsity of Protein-Protein Interaction Networks Hinders Function Prediction in Non-Model Species

Physical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extened these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.

bioinformatics↗

bioRxiv2019

Metric Learning on Expression Data for Gene Function Prediction

MotivationCo-expression of two genes across different conditions is indicative of their involvement in the same biological process. However, using RNA-Seq datasets with many experimental conditions from diverse sources introduces batch effects and other artefacts that might obscure the real co-expression signal. Moreover, only a subset of experimental conditions is expected to be relevant for finding genes related to a particular Gene Ontology (GO) term. Therefore, we hypothesize that when the purpose is to find similar functioning genes that the co-expression of genes should not be determined on all samples but only on those samples informative for the GO term of interest.\n\nResultsTo address both types of effects, we developed MLC (Metric Learning for Co-expression), a fast algorithm that assigns a GO-term-specific weight to each expression sample. The goal is to obtain a weighted co-expression measure that is more suitable than the unweighted Pearson correlation for applying Guilt-By-Association-based function predictions. More specifically, if two genes are annotated with a given GO term, MLC tries to maximize their weighted co-expression, and, in addition, if one of them is not annotated with that term, the weighted co-expression is minimized. Our experiments on publicly available Arabidopsis thaliana RNA-Seq data demonstrate that MLC outperforms standard Pearson correlation in term-centric performance.\n\nAvailabilityMLC is available as a Python package at www.github.com/stamakro/MLC\n\nContacts.makrodimitris@tudelft.nl\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗