Biology subjects

Krogh, A.

Publications and source records attributed to Krogh, A..

15 recordsLinked to original sources

bioRxiv2026

Extrachromosomal circular DNA formation follows a 10.5-bp periodicity reflecting the minor groove orientation around nucleosomes

Extrachromosomal circular DNA is increasingly recognized as a central feature of somatic evolution in cancer, yet the structural determinants of eccDNA formation remain poorly understood. Here, we systematically analysed eccDNA length distributions and breakpoint organization across 25 datasets spanning multiple species, tissues and experimental approaches. Applying a short-time Fourier transform to eccDNA length distributions enabled us to resolve recurrent periodicities at approximately 180 bp and 10.5 bp, consistent with nucleosome length and the helical repeat of DNA, respectively. Mapping eccDNA breakpoints relative to nucleosome dyads revealed preferential breakpoint formation in linker DNA and, within nucleosome-bound DNA, at positions where the minor groove faces towards the histone surface. Our results show that eccDNA formation is shaped not only by nucleosome positioning but also by the rotational orientation of the DNA double helix around the nucleosome. Together, these findings identify chromatin geometry as a determinant of eccDNA biogenesis and provide a structural basis for the length periodicities that characterize eccDNA formation.

genomics↗

bioRxiv2026

DeepTMHMM2 enables accurate prediction of transmembrane protein topology and subcellular location

Transmembrane -helical and {beta}-barrel proteins are a ubiquitous component of proteomes. Topology prediction infers how proteins are embedded in lipid bilayers, identifying membrane-spanning segments and their orientation. While recent methods achieve high performance for membrane-spanning segments, they cannot predict re-entrant regions and interfacial helices - membrane-associated segments that partially insert but do not cross the bilayer - nor identify which biological membrane a protein resides in. Here, we present DeepTMHMM2, the first predictor to include re-entrant regions and interfacial helices in its topologies and jointly predict localization across 17 biological membranes. Benchmark results show that DeepTMHMM2 successfully learns to predict the additional elements, while achieving strong performance on canonical -helical and {beta}-barrel topology prediction. Applying DeepTMHMM2 to Swiss-Prot reveals that non-crossing segments are a ubiquitous feature of the transmembrane proteome, with interfacial helices present in nearly a quarter of all -helical transmembrane proteins.

bioinformatics↗

bioRxiv2026

A generative model for dimensionality reduction with millions of features and few samples

MotivationIn this paper, we demonstrate that it is feasible to train a deep generative model for dimensionality reduction with millions of features using few samples, which makes this type of generative model a more versatile alternative to standard methods for dimensionality reduction. Specifically, we hypothesize that for a decoder-only model, the number of training samples required is almost independent of the feature dimensionality in most network architectures. ResultsThrough an extensive set of experiments on synthetic non-linear data, we validate this hypothesis. We also train the model on a downsampled version of the 1000 Genomes Project (1KGP) dataset to further assess its behavior under controlled reductions in sample size. Furthermore, we train a deep generative decoder (DGD) on a curated dataset from the International Cancer Genome Consortium (ICGC), which contains 4.4 million features. It is trained on approximately 4,000 samples and tested on 1,000 samples. The resulting latent representation exhibits clear clustering, and when methods are reduced to the same number of dimensions, it outperforms PCA and VAE for tumor type classification. Additionally, the DGD is computationally efficient and can be trained on a 16GB GPU. Availability and implementationCode is available at https://github.com/cpancott/ReceptiveDGD. Contactcorrado.pancotti@helmholtz-munich.de; akrogh@di.ku.dk Supplementary informationSupplementary data are available with this preprint.

bioinformatics↗

bioRxiv2026

miDGD: a multi-modal deep generative model predicts miRNA expression from bulk or single-cell mRNA expression

MicroRNAs (miRNAs) are key post-transcriptional regulators, yet standard bulk and single-cell RNA-seq do not capture them, leaving this regulatory layer invisible in most transcriptomic data. We present miDGD, a deep generative decoder that jointly models paired mRNA and miRNA profiles through a shared latent representation, enabling miRNA expression to be predicted from mRNA alone. Trained on tumors (TCGA), healthy tissues (GTEx), and human cell lines, miDGD recovers hundreds of miRNAs in held-out tumors (mean Spearman {rho} = 0.56), captures both tissue-specific and ubiquitous miRNAs, and preserves known miRNA--target repression and host-gene co-expression. Without label supervision, its latent space separates 32 cancer types (80% accuracy). Predictions remain stable at single-cell-like sparsity and transfer across datasets--from tumors to healthy tissues and from bulk to single cells--where miDGD outperforms existing supervised and activity-inference methods. miDGD thus unlocks miRNA regulation in the vast body of existing mRNA-only data, including single-cell datasets.

bioinformatics↗

bioRxiv2025

Enhancing Generative Decoders with Stochastic Training on Biomedical Data with Missingness

Biomedical datasets are often heterogeneous and affected by noise or missing values. Deep Generative Decoders (DGD) provide a promising framework for latent representation learning, but their standard training procedure relies on sample-level stochastic gradient descent (SGD), which performs poorly with incomplete data. To address this, we introduce two stochastic training strategies -- Nested SGD and Feature Dropout -- that incorporate feature-level randomness into optimization. Evaluations on biomedical tabular datasets demonstrate that Nested SGD improves robustness under missingness the best, while Feature Dropout not only improves but also accelerates convergence with lower computational cost. These results suggest that feature-level stochasticity is a practical way to strengthen biomedical AI pipelines.

bioinformatics↗

bioRxiv2023

multiDGD: A versatile deep generative model for multi-omics data

Recent technological advancements in single-cell genomics have enabled joint profiling of gene expression and alternative modalities at unprecedented scale. Consequently, the complexity of multi-omics data sets is increasing massively. Existing models for multi-modal data are typically limited in functionality or scalability, making data integration and downstream analysis cumbersome. We present multiDGD, a scalable deep generative model providing a probabilistic framework to learn shared representations of transcriptome and chromatin accessibility. It shows outstanding performance on data reconstruction without feature selection. We demonstrate on several data sets from human and mouse that multiDGD learns well-clustered joint representations. We further find that probabilistic modelling of sample covatiates enables post-hoc data integration without the need for fine-tuning. Additionally, we show that multiDGD can detect statistical associations between genes and regulatory regions conditioned on the learned representations. multiDGD is available as an scverse-compatible package (https://github.com/Center-for-Health-Data-Science/multiDGD).

bioinformatics↗

bioRxiv2023

N-of-one differential gene expression without control samples using a deep generative model

Differential gene expression analysis of bulk RNA sequencing data plays a major role in the diagnosis, prognosis, and understanding of disease. Such analyses are often challenging due to a lack of good controls and the heterogeneous nature of the samples. Here, we present a deep generative model that can replace control samples. The model is trained on RNA-seq data from healthy tissues and learns a low-dimensional representation that clusters tissues very well without supervision. When applied to cancer samples, the model accurately identifies representations close to the tissue of origin. We interpret these inferred representations as the closest normal to the disease samples and use the resulting count distributions to perform differential expression analysis of single cancer samples without control samples. In a detailed analysis of breast cancer, we demonstrate how our approach finds subtype-specific cancer driver and marker genes with high specificity and greatly outperforms the state-of-the-art method in detecting differentially expressed genes, DESeq2. We further show that the significant genes found using the model are highly enriched within cancer-specific driver genes across different cancer types. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

bioinformatics↗

bioRxiv2022

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

Transmembrane proteins span the lipid bilayer and are divided into two major structural classes, namely alpha helical and beta barrels. We introduce DeepTMHMM, a deep learning protein language model-based algorithm that can detect and predict the topology of both alpha helical and beta barrels proteins with unprecedented accuracy. DeepTMHMM (https://dtu.biolib.com/DeepTMHMM) scales to proteomes and covers all domains of life, which makes it ideal for metagenomics analyses.

bioinformatics↗

bioRxiv2021

Prediction of DNA from context using neural networks

One way to better understand the structure in DNA is by learning to predict the sequence. Here, we train a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model is a neural network that obtains an accuracy close to 54% on the human genome, which is 2% points better than modelling the data using a Markov model. In likelihood-ratio tests, we show that the neural network is significantly better than any of the alternative models by a large margin. We report on where the accuracy is obtained, observing first that the performance appears to be uniform over the chromosomes. The models perform best in repetitive sequences, as expected, although they are far from random performance in the more difficult coding sections, the proportions being ~ 70:40%. Exploring further the sources of the accuracy, Fourier transforming the predictions reveals weak but clear periodic signals. In the human genome the characteristic periods hint at connections to nucleosome positioning. To understand this we find similar periodic signals in GC/AT content in the human genome, which to the best of our knowledge have not been reported before. On other large genomes similarly high accuracy is found, while lower predictive accuracy is observed on smaller genomes. Only in mouse did we see periodic signals in the same range as in human, though weaker and of different type. Interestingly, applying a model trained on the mouse genome to the human genome results in a performance far below that of the human model, except in the difficult coding regions. Despite the clear outcomes of the likelihood ratio tests, there is currently a limited superiority of the neural network methods over the Markov model. We expect, however, that there is great potential for better modelling DNA using different neural network architectures.

bioinformatics↗

bioRxiv2021

Context dependency of nucleotide probabilities and variants in human DNA

BackgroundGenomic DNA has been shaped by mutational processes through evolution. The cellular machinery for error correction and repair has left its marks in the nucleotide composition along with structural and functional constraints. Therefore, the probability of observing a base in a certain position in the human genome is highly context-dependent. ResultsHere we develop context-dependent nucleotide models. We first investigate models of nucleotides conditioned on sequence context. We develop a bidirectional Markov model that use an average of the probability from a Markov model applied to both strands of the sequence and thus depends on up to 14 bases to each side of the nucleotide. We show how the genome predictability varies across different types of genomic regions. Surprisingly, this model can predict a base from its context with an average of more than 50% accuracy. For somatic variants we show a tendency towards higher probability for the variant base than for the reference base. Inspired by DNA substitution models, we develop a model of mutability that estimates a mutation matrix (called the alpha matrix) on top of the nucleotide distribution. The alpha matrix can be estimated from a much smaller context than the nucleotide model, but the final model will still depend on the full context of the nucleotide model. With the bidirectional Markov model of order 14 and an alpha matrix dependent on just one base to each side, we obtain a model that compares well with a model of mutability that estimates mutation probabilities directly conditioned on three nucleotides to each side. For somatic variants in particular, our model fits better than the simpler model. Interestingly, the model is not very sensitive to the size of the context for the alpha matrix. ConclusionsOur study found strong context dependencies of nucleotides in the human genome. The best model uses a context of 14 nucleotides to each side. Based on these models, a substitution model was constructed that separates into the context model and a matrix dependent on a small context. The model fit somatic variants particularly well.

bioinformatics↗

bioRxiv2020

Taxonomic classification method for metagenomics based on core protein families with Core-Kaiju

Characterizing species diversity and composition of bacteria hosted by biota is revolutionizing our understanding of the role of symbiotic interactions in ecosystems. However, determining microbiomes diversity implies the classification of taxa composition within the sampled community, which is often done via the assignment of individual reads to taxa by comparison to reference databases. Although computational methods aimed at identifying the microbe(s) taxa are available, it is well known that inferences using different methods can vary widely depending on various biases. In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and whole genome shotgun sequencing for taxonomic classification to three small mock communities of bacteria, of which the compositions are known. We show that none of these methods can infer both the true number of taxa and their abundances. We thus propose a novel approach, named Core-Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method similar to 16S, but based on emergent statistics of core protein domain families. We thus test the proposed method on the three small mock communities and also on medium- and highly complex mock community datasets taken from the Critical Assessment of Metagenome Interpretation challenge. We show that Core-Kaiju reliably predicts both number of taxa and abundance of the analysed mock bacterial communities. Finally we apply our method on human gut samples, showing how Core-Kaiju may give more accurate ecological characterization and fresh view on real microbiomes.

bioinformatics↗

bioRxiv2019

Sensitive detection of circular DNA at single-nucleotide resolution using guided realignment of partially aligned reads

Circular DNA has recently been identified across different species including human normal and cancerous tissue, but short-read mappers are unable to align many of the reads crossing circle junctions and hence limits their detection from short-read sequencing data. Here, we propose a new method, Circle-Map, that guides the realignment of partially aligned reads using information from discordantly mapped reads. We demonstrate how this approach dramatically increases sensitivity for detection of circular DNA on both simulated and real data while retaining high precision.

bioinformatics↗

bioRxiv2019

CAncer bioMarker Prediction Pipeline (CAMPP) - A standardised and user-friendly framework for the analysis of quantitative biological data.

MotivationRecent improvements in -omics and next-generation sequencing (NGS) technologies, and the lowered costs associated with generating these types of data, have made the analysis of high-throughput datasets standard, both for forming and testing biomedical hypotheses. Alongside new wet-lab methodologies, our knowledge of how to normalise bio-data has grown extensively. By removing latent undesirable variances, we obtain standardised datasets, which can be more easily compared between studies. These advancements mean that non-experts in bioinformatics are now faced with the challenge of performing computational data analysis, pre-processing and visualisation. One example could be the analysis of biological data to pinpoint disease-related biomarkers for experimental validation. In this case, bio-researchers will desire an easy and standardised way of analysing high-throughput datasets.\n\nResultsHere we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper intended to aid non-experts in bioinformatics with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. CAMPP performs missing value imputation and normalisation followed by (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis and (IV) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist biomedical researchers in the analysis of quantitative biological data, whilst ensuring an appropriate biostatistical framework.\n\nAvailability and ImplementationCAMPP is available at https://github.com/ELELAB/CAMPP

bioinformatics↗

bioRxiv2018

Early Pleistocene enamel proteome sequences from Dmanisi resolve Stephanorhinus phylogeny.

Ancient DNA (aDNA) sequencing has enabled unprecedented reconstruction of speciation, migration, and admixture events for extinct taxa1. Outside the permafrost, however, irreversible aDNA post-mortem degradation2 has so far limited aDNA recovery within the {small tilde}0.5 million years (Ma) time range3. Tandem mass spectrometry (MS)-based collagen type I (COL1) sequencing provides direct access to older biomolecular information4, though with limited phylogenetic use. In the absence of molecular evidence, the speciation of several Early and Middle Pleistocene extinct species remain contentious. In this study, we address the phylogenetic relationships of the Eurasian Pleistocene Rhinocerotidae5-7 using {small tilde}1.77 million years (Ma) old dental enamel proteome sequences of a Stephanorhinus specimen from the Dmanisi archaeological site in Georgia (South Caucasus)8. Molecular phylogenetic analyses place the Dmanisi Stephanorhinus as a sister group to the woolly (Coelodonta antiquitatis) and Mercks rhinoceros (S. kirchbergensis) clade. We show that Coelodonta evolved from an early Stephanorhinus lineage and that this genus includes at least two distinct evolutionary lines. As such, the genus Stephanorhinus is currently paraphyletic and its systematic revision is therefore needed. We demonstrate that Early Pleistocene dental enamel proteome sequencing overcomes the limits of ancient collagen- and aDNA-based phylogenetic inference, and also provides additional information about the sex and taxonomic assignment of the specimens analysed. Dental enamel, the hardest tissue in vertebrates, is highly abundant in the fossil record. Our findings reveal that palaeoproteomic investigation of this material can push biomolecular investigation further back into the Early Pleistocene.

evolutionary biology↗

bioRxiv2017

Sugar metabolism of the first thermophilic Planctomycetes Thermogutta terrifontis: comparative genomic and transcriptomic approache

Xanthan gum, a complex polysaccharide comprising glucose, mannose and glucuronic acid residues, is involved in numerous biotechnological applications in cosmetics, agriculture, pharmaceuticals, food and petroleum industries. Additionally, its oligosaccharides were shown to possess antimicrobial, antioxidant and few other properties. Yet, despite its extensive usage, little is known about xanthan gum degradation pathways and mechanisms.\n\nThermogutta terrifontis R1 was described as the first thermophilic representative of the Planctomycetes phylum. As other cultivated planctomycetes, it grows well on various carbohydrates including oligo- and polysaccharides, however, its capability of anaerobic growth with or without electron acceptors was a novel finding among the representatives of this phylum.\n\nThe aim of this work is to examine T. terrifontis catabolic pathways with a special focus on the xanthan gum degradation pathway using genomic and transriptomic sequencing. Genomic analysis revealed more than a hundred glycosidases, polysaccharide lyases and other CAZymes, involved in oligo- and polysaccharide degradation by T. terrifontis, proteins of central carbohydrate metabolism and aerobic and anaerobic respiration. Furthermore, the combination of genomic and transcriptomic approaches revealed a putative novel xanthan gum degradation pathway involving unusual catalytic steps and enzymes: novel glycosidase(s) of DUF1080 family, hydrolyzing xanthan gum beta-glucosidic backbone linkages and beta-mannosidases instead of xanthan lyases for degradation of terminal beta-mannosidic linkages. Surprisingly, the genes coding DUF1080 proteins were found in high number in T. terrifontis and in many other Planctomycetes genomes, which, together with our observation that xanthan gum being a selective substrate for many planctomycetes, supports the important role of DUF1080 in xanthan gum degradation. Our findings shed light on the metabolism of the first thermophilic planctomycete, capable to degrade a number of polysaccharides, either aerobically or anaerobically, including the biotechnologically important bacterial polysaccharide xanthan gum. The results serve as good foundation for future exploration of T. terrifontis and its enzymes in biotechnological applications.

microbiology↗