Biology subjects

Magnus, M.

Publications and source records attributed to Magnus, M..

5 recordsLinked to original sources

bioRxiv2025

RNAhub - an automated pipeline to search and align RNA homologs with secondary structure assessment

The complexity in the generation of RNA multiple sequence alignments and assessment of the accuracy of such alignments contributes to the challenges in the utilization of RNA alignments in diverse integrative methods. RNAhub is a freely available user-friendly web server for a reliable generation of RNA multiple sequence alignments and the detection of the presence of structural RNA utilizing evolutionary information. This web-based tool, developed by the integration of existing computational approaches, takes an RNA sequence as input and automatically retrieves and aligns sequences homologous to the input (query) RNA sequence through an iterative and structure-agnostic approach. Based on the alignment, this tool statistically assesses whether the query RNA sequence has a conserved RNA structure using covariation analysis. The web server allows the user to efficiently search the sequence of interest against carefully curated, ready-to-use genomic databases to produce a multiple sequence alignment. Using this alignment, our tool either detects the presence of a conserved structural RNA, finds evidence against the presence of a conserved structure, or cannot make any assessment due to a lack of sequence diversity in the alignment. The web server is freely available at https://rnahub.org. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC="FIGDIR/small/642701v3_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@540f6forg.highwire.dtl.DTLVardef@c15e53org.highwire.dtl.DTLVardef@117ced2org.highwire.dtl.DTLVardef@b679c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

On inputs to deep learning for RNA 3D structure prediction

Today, there are several effective deep learning models for predicting the 3D structure of proteins. Building on their success, models have been developed for predicting the 3D structure of non-coding RNAs. Unfortunately, these models are much less accurate than their protein counterparts. In this paper, we highlight differences between protein and RNA structure, and demonstrate methods for deep learning targeted at addressing those differences, with the aim of prompting discussion on these topics. We present an RNA-specific pipeline for generating structural Multiple Sequence Alignments (MSAs). Derived from the structural alignments, we introduce engineered evolutionary features that strongly inform RNA structure. Further, from the crystal structure, we derive structural features describing RNA base pairing. These evolutionary and structural features can be used in loss functions at different stages of training. Finally, we discuss different cropping strategies informed by RNA structure.

bioinformatics↗

bioRxiv2024

Mindfulness training impacts brain network dynamics linked to stress response in young adolescents.

Mindfulness-based interventions (MBI) may lead to lower levels of psychological distress, including depression, anxiety, and stress in adolescents. Past research has advanced the discovery of neural architecture recruited by MBI. However, the brain mechanisms through which mindfulness exerts more resilient responses to social stressors in teens remain unclear. Here, we examined how MBI modulates changes in brain network dynamics following social stress with different affective valence (i.e., neutral, negative, and positive). For this aim, we carried out a longitudinal randomized controlled trial in which non-clinical adolescents underwent MBI for 8 weeks. They completed a psychosocial stress task before and following MBI. Functional magnetic resonance imaging (fMRI) and self-reported measurements of psychological distress were collected in both measurement points (i.e., "pre" and "post" MBI). We computed co-activation patterns on fMRI data to characterize dynamic functional connectivity within whole-brain networks. The results depicted how MBI modulates transient co-activation changes in dorsal medial regions of the brain default network (DN) following the experience of stress. However, these brain changes were not specific to the affective valence of stressful stimuli. The relationship between the DN dynamics and the measurements of psychological distress was mediated by MBI. Globally, our findings support a model in which MBI causally mediate brain-behavior interactions related to psychosocial stress in adolescents.

neuroscience↗

bioRxiv2024

RNA3DB: a dataset for training and benchmarking deep learning models for RNA structure prediction

With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods. In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/578025v3_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cb3aa2org.highwire.dtl.DTLVardef@7685fdorg.highwire.dtl.DTLVardef@156ca35org.highwire.dtl.DTLVardef@f58d72_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWhile there is a recent surge in applying deep learning to RNA structure prediction, domain experts have raised concerns about generalization and current trends in benchmarking. C_LIO_LIMany of the concerns primarily relate to how novel RNA families-i.e. families unseen in the training set-are benchmarked, and whether the models are effective at handling such cases. Performance on bench-marks reflective of real-world applications, such as CASP15 and RNA-Puzzles, is poor for RNA deep learning models. C_LIO_LIWe present a dataset-RNA3DB-that is designed for training and bench-marking deep learning models for RNA structure prediction. RNA3DB provides coverage of all RNA chains found in the Protein Data Bank (PDB). C_LIO_LIRNA3DB is clustered into groups that are both sequentially and structurally non-redundant, providing a robust way of creating training, validation, and testing sets for deep learning models. Along with the dataset, we also provide a transparent methodology as well as the source-code, making our tool both reproducible and customizable. C_LI

bioinformatics↗

bioRxiv2021

RNA fragment assembly with experimental restraints

We present RNA Masonry, a computer program and a web service for a fully automated assembly of RNA fragments into geometrically plausible models fulfilling user-provided secondary structure constraints and restraints on tertiary contacts and Small Angle X-ray Scattering (SAXS) data. We illustrate the method description with its recent application to structural studies of viral RNAs with SAXS restraints. The program web server is available at http://iimcb.genesilico.pl/rnamasonry. Contactgchojnowski@embl-hamburg.de

bioinformatics↗