bluuebunny/crossref_metadata_2025_split huggingface dataset:
Workflow sharing with automated metadata validation and test execution to improve the reusability of published workflows
Authors: Suetake Hirotaka , Fukusato Tsukasa , Igarashi Takeo , Ohta Tazro 
Published: July 2022

BackgroundMany open-source workflow systems have made bioinformatics data analysis procedures portable. Sharing these workflows provides researchers easy access to high-quality analysis methods without the requirement of computational expertise. However, published workflows are not always guaranteed to be reliably reusable. Therefore, a system is needed to lower the cost of sharing workflows in a reusable form.ResultsWe introduce Yevis, a system to build a workflow registry that automatically validates and tests workflows to be published. The validation and test are based on the requirements we defined for a workflow being reusable with confidence. Yevis runs on GitHub and Zenodo and allows workflow hosting without the need of dedicated computing resources. A Yevis registry accepts workflow registration via a GitHub pull request, followed by an automatic validation and test process for the submitted workflow. As a proof of concept, we built a registry using Yevis to host workflows from a community to demonstrate how a workflow can be shared while fulfilling the defined requirements.ConclusionsYevis helps in the building of a workflow registry to share reusable workflows without requiring extensive human resources. By following Yevis’s workflow-sharing procedure, one can operate a registry while satisfying the reusable work-flow criteria. This system is particularly useful to individuals or communities that want to share workflows but lacks the specific technical expertise to build and maintain a workflow registry from scratch.
Latent Interacting Variable-Effects Modeling of Gut Microbiome Multi-Omics in Inflammatory Bowel Disease
Authors: Munoz Javier. E. , Brubaker Douglas. K. 
Published: July 2022

ABSTRACTLatent Interacting Variable Effects (LIVE) modeling is a framework to integrate different types of microbiome multi-omics data by combining latent variables from single-omic models into a structured meta-model to determine discriminative, interacting multi-omics features driving disease status. We implemented and tested LIVE modeling in publicly available metagenomics and metabolomics datasets from Crohn’s Disease and Ulcerative Colitis patients. Here, LIVE modeling reduced the number of feature correlations from the original data set for CD and UC to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes and IBD status through the application of stringent thresholds on generated inferential statistics. We determined LIVE modeling confirmed previously reported IBD biomarkers and uncovered potentially novel disease mechanisms in IBD. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the linear meta-model integrating -omic data types. The results of LIVE modeling and the biological relationships can be represented in networks that connect local correlation structure of single omic data types with global community and omic structure in the latent variable VIP scores. This model arises as novel tool that allows researchers to be more selective about omic feature interaction without disrupting the structural correlation framework provided by sPLS-DA interaction effects modeling. It will lead to form testable hypothesis by identifying potential and unique interactions between metabolome and microbiome that must be considered for future studies.AUTHOR SUMMARYLatent Interacting Variable Effects (LIVE) modeling integrates microbiome multiomics features by encoding them in a set of latent variables (LVs) from single-omic sparse Partial Lease Squares models, and then combine these LVs into structured metamodel to determine the most discriminative features driving IBD. We used publicly available metagenomic and metabolomics data from Crohn’s Disease and Ulcerative Colitis patients to develop LIVE modeling. LIVE modeling reduced data dimensionality efficiently and identified statistical interactions among microbiome multi-omics data, which can be visualized as a mineable network data structure. LIVE modeling confirmed features previously reported and revealed novel microbiome interactions in IBD. LIVE offers a flexible framework for multi-omic modeling that may aid in interpretation of complex microbiome datasets.
Accurate simultaneous sequencing of genetic and epigenetic bases in DNA
Authors: Füllgrabe Jens , Gosal Walraj S , Creed Páidí , Liu Sidong , Lumby Casper K , Morley David J , Ost Tobias W B , Vilella Albert J , Yu Shirong , Bignell Helen , Burns Philippa , Charlesworth Tom , Fu Beiyuan , Fordham Howerd , Harding Nick , Gandelman Olga , Golder Paula , Hodson Christopher , Li Mengjie , Lila Marjana , Liu Yang , Mason Joanne , Mellad Jason , Monahan Jack , Nentwich Oliver , Palmer Alexandra , Steward Michael , Taipale Minna , Vandomme Audrey , San-Bento Rita Santo , Singhal Ankita , Vivian Julia , Wójtowicz Natalia , Williams Nathan , Walker Nicolas J , Wong Nicola C H , Yalloway Gary , Holbrook Joanna D , Balasubaramanian Shankar 
Published: July 2022

AbstractDNA comprises molecular information stored via genetic bases (G, C, T, A) and also epigenetic bases, principally 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Both genetic and epigenetic information are vital to our understanding of biology and disease states. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases typically fail to capture common C-to-T mutations or distinguish 5mC from 5hmC. Here, we present a single-base-resolution sequencing methodology that will simultaneously sequence complete genetics and complete epigenetics in a single workflow. The approach is non-destructive to DNA and provides a digital readout of bases, which we exemplify by simultaneous sequencing of G, C, T, A, 5mC and 5hmC; 6-Letter sequencing. We demonstrate sequencing of human genomic DNA and also cell-free DNA taken from a blood sample of a cancer patient. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. We envisage it will be versatile across many applications in life sciences.
Collagen content and cross-links scale with passive stiffness in dystrophic mouse muscles, but are not altered with administration of collagen cross-linking inhibitor beta-aminopropionitrile
Authors: Brashear Sarah E. , Wohlgemuth Ross P. , Hu Lin-Ya , Jbeily Elias H. , Christiansen Blaine A. , Smith Lucas R. 
Published: July 2022

ABSTRACTIn Duchenne muscular dystrophy (DMD), a lack of functional dystrophin leads to myofiber instability and progressive muscle damage that results in fibrosis. While fibrosis is primarily characterized by an accumulation of extracellular matrix (ECM) components, there are changes in ECM architecture during fibrosis that relate more closely to functional muscle stiffness. One of these architectural changes in dystrophic muscle is collagen cross-linking, which has been shown to increase the passive muscle stiffness in models of fibrosis including themdxmouse, a model of DMD. We tested whether the intraperitoneal injections of beta-aminopropionitrile (BAPN), an inhibitor of the cross-linking enzyme lysyl oxidase, would reduce collagen cross- linking and passive stiffness in young and adultmdxmice compared to saline-injected controls. We found no significant differences between BAPN treated and saline treated mice in collagen cross-linking and stiffness parameters. However, we observed that while collagen cross-linking and passive stiffness scaled positively in dystrophic muscles, collagen fiber alignment scaled with passive stiffness distinctly between muscles. We also observed that the dystrophic diaphragm showed the most dramatic fibrosis in terms of collagen content, cross-linking, and stiffness. Overall, we show that while BAPN was not effective at reducing collagen cross- linking, the positive association between collagen cross-linking and stiffness in dystrophic muscles still show cross-linking as a viable target for reducing passive muscle stiffness in DMD or other fibrotic muscle conditions.Key PointsBAPN did not reduce collagen cross-linking or passive stiffness in any muscleCollagen cross-links scaled with passive stiffness in dystrophic musclesThemdxdiaphragm showed the most dramatic fibrosis related to collagen content, cross- linking, and passive stiffnessCollagen fiber alignment scales with passive stiffness differently between muscles
Pathway-specific inputs to the superior colliculus support flexible triggering of innate behaviors
Authors: Li Chen , Kühn Norma K. , Alkislar Ilayda , Dublanc Arnau Sans , Zemmouri Firdaouss , Paesmans Soraya , Reinhard Katja , Farrow Karl 
Published: July 2022

AbstractBehavioral flexibility requires the ability to modify the feedforward transmission of sensory information. The superior colliculus mediates visually guided innate defensive behaviors via cell-type specific projections. However, how brain-wide inputs to the superior colliculus are organized to modulate visual processing and enable flexible behavior remains unknown. To determine these rules, we focused on inhibitory projection (Gad2) neurons. Trans-synaptic tracing and neuronal recordings revealed that Gad2 neurons projecting to the dorsal lateral geniculate nucleus (LGd) and the parabigeminal nucleus (PBG) form two separate anatomical and functional populations, where each circuit received a different set of non-retinal inputs. Inhibiting LGd or PBG projecting Gad2 neurons resulted in opposing effects on behavior; increasing freezing or escape probability to visual looming stimuli, respectively. These data suggest that projection-specific sampling of brain-wide inputs provide a circuit design principle that enables feed-forward transfer of visual threat to be independently adjusted to produce context specific behavior.
Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods
Authors: Saarimäki Laura Aliisa , Morikka Jack , Pavel Alisa , Korpilähde Seela , del Giudice Giusy , Federico Antonio , Fratello Michele , Serra Angela , Greco Dario 
Published: July 2022

AbstractMechanistic toxicology has emerged as a powerful framework to inform on the safety of chemicals and guide the development of new safe-by-design compounds. Although toxicogenomics provides support towards mechanistic evaluation of chemical exposures, the implementation of toxicogenomics-based evidence in the regulatory setting is still hindered by uncertainties related to the analysis and interpretation of such data. Adverse Outcome Pathways (AOPs) are multi-scale models that link chemical exposures to adverse outcomes through causal cascades of key events (KEs). The use of mechanistic evidence through the AOP framework is actively promoted for the development of new approach methods (NAMs) and to reduce animal experimentation. However, in order to unleash the full potential of AOPs and build confidence into toxicogenomics, robust and unified associations between KEs and patterns of molecular alteration need to be established.Here, we hypothesised that systematic curation of molecular events associated with KEs would enable the modelling of AOPs through gene-level data, creating the much-needed link between toxicogenomics and the systemic mechanisms depicted by the AOPs. This, in turn, introduces novel ways of benefitting from the AOP concept, including predictive models, read-across, and targeted assays, while also reducing the need for multiple testing strategies. Hence, we developed a multi-step strategy to annotate the AOPs relevant to human health risk assessment. We show that our framework successfully highlights relevant adverse outcomes for chemical exposures with strongin vitroandin vivoconvergence, supporting chemical grouping and other data-driven approaches. Finally, we defined and experimentally validated a panel of robust AOP-derivedin vitrobiomarkers for pulmonary fibrosis.
Extensive hybridisation throughout clownfishes evolutionary history
Authors: Schmid Sarah , Micheli Baptiste , Cortesi Fabio , Donati Giulia , Salamin Nicolas 
Published: July 2022

ABSTRACTThe contribution of hybridisation in the generation of global species diversity has long been controversial among evolutionary biologists. However, it is now increasingly accepted that hybridisation has many impacts on the process of speciation. Notably, it is an important mechanism fostering adaptive radiation since it can generate new phenotypic combinations enabling the occupancy of new niches. Here, we focused on clownfish (Pomacentridae), a clade of 28 coral reef fishes displaying a mutualistic interaction with sea anemones. This behaviour is the key innovation that triggered adaptive radiation of clownfishes, as each species is able to occupy a different combination of host anemone species and habitat. Previous work suggested that hybridisation might be responsible for the extant diversity of clownfish species. To test this hypothesis, we analysed whole-genome datasets for each clownfish species. First, we reconstructed the phylogeny of the clade based on topology weighting methods, which enables the visualisation of the relationships between taxa across the genome. Then, we highlighted possible ancient hybridisation events based on a comparative genomic framework for detecting introgression in genomes. The resulting phylogeny is consistent with previous works based on a few mitochondrial and nuclear genes, and shallow nodes are now well supported in contrast to past studies. Furthermore, we detected multiple past hybridisation events throughout the evolutionary history of clownfishes, corroborating the potential role of hybridisation in the clownfish adaptive radiation. This study adds to the growing number of studies investigating the genomic mechanisms behind species diversification, drawing us closer to understanding how Earth biodiversity is generated.
GePI: Retrieval of fully automated recognition and extraction of gene and protein interaction mentions from unstructured literature
Authors: Faessler Erik , Hahn Udo , Schäuble Sascha 
Published: July 2022

AbstractMotivationKnowledge about interactions between genes and proteins is vital for bio-molecular research. A large part of this knowledge is published in written text and not accessible in a structured way. To remedy this situation, several repositories of automatically extracted interaction facts were proposed over the years. However, existing solutions lack key features such as permanently updated data resources, easy accessibility and structured result generation ready to be used for downstream analyses.ResultsWe propose GePI, a database portal for fully automated extraction and presentation of molecular interaction facts from scientific literature. GePI offers batch queries, immediate inspection of textual evidence and full text filters. To this end, GePI leverages two gene recognition and normalization approaches as well as optimized runtime for molecular event extraction. The resulting natural language processing pipeline is applied to the full set of publicly available documents from PubMed and the PubMed Central open access subset accounting for more than 33M abstracts and 4.2M complete articles as of 2022. To accommodate the rapid growth of the scientific literature, the fact database is automatically updated several times per week. In summary, our web application GePI allows for the first time a free and easy-to-use investigation of gene and protein interaction information as soon as they are published with unique query possibilities.Availability and ImplementationThe GePI web interface is available at http://gepi.coling.uni-jena.de.Contacterik.faessler@uni-jena.de
Fotomics: Fourier transform-based omics imagification for deep learning-based cell-identity mapping using single-cell omics profiles
Authors: Zandavi Seid Miad , Liu Derong , Chung Vera , Anaissi Ali , Vafaee Fatemeh 
Published: July 2022

AbstractDifferent omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousand molecules in a biological sample or a cell. This study develops upon the concept of “omics imagification” as a process of transforming a vector representing these numerical measurements into an image with a one-to-one relationship with the corresponding sample. The proposed imagification process transforms a high-dimensional vector of molecular measurements into a two-dimensional RGB image to enable holistic molecular representation of a biological sample and to improve the classification of different biological phenotypes using automated image recognition methods in computer vision. A transformed image represents 2D coordinates of molecules in a neighbour-embedded space representing molecular abundance and gene intensity. The proposed method was applied to a single-cell RNA sequencing (scRNA-seq) data to “imagify” gene expression profiles of individual cells. Our results show that a simple convolutional neural network trained on single-cell transcriptomics images accurately classifies diverse cell types outperforming the best-performing scRNA-seq classifiers such as support vector machine and random forest.
CXCR7 promotes foetal myoblast fusion at muscle fiber tips independently of Myomaker via a ß1integrin-EGFR-dependent mechanism
Authors: Nassari Sonya , Blavet Cédrine , Duprez Delphine , Fournier-Thibault Claire 
Published: July 2022

AbstractMuscle growth must be tightly regulated during development in order to obtain the final muscle shape. Myoblast fusion is a critical step of muscle growth, driving the formation of syncytial myofibers attaching at both ends to tendons. We investigated the role of the CXCR7 chemokine receptor in foetal muscle growth during chicken limb development. We show that CXCR7 displays a regionalized expression at the tips of myofibers close to tendons in foetal limb muscles, which is exclusive to the central location of the fusion gene MYOMAKER (TMEM8C in chicken) in foetal muscles. CXCR7 promotes myoblast fusion independently of TMEM8C in chicken limb muscles and in foetal myoblast cultures and requires EGF receptor signalling. The CXCR7 ligand, CXCL12, expressed in connective tissue, increases ß1integrin activation at the myotendinous junction and CXCR7 expression at muscle tips, resulting in a fusion promoting effect independent from a direct binding of CXCL12 to CXCR7 receptor. Our results evidence a CXCR7-dependent/TMEM8C-independent fusion mechanism at the myofiber tips that regulates muscle growth at the tendon/muscle interface during foetal myogenesis.
Back to the Meadow Brown: eyespot variation and field temperature in a classic butterfly polymorphism
Authors: Mowbray Sophie , Bennie Jonathan , Rhodes Marcus W. , Smith David A.S. , ffrench-Constant Richard H. 
Published: July 2022

AbstractSince the classic work of E.B. Ford, alternate hypotheses have focused on explaining eyespot variation in the Meadow Brown butterfly strictly as a genetic polymorphism and the role of temperature in this classic example of natural selection has therefore been overlooked. Here we use large and continuous field collections from three sites in the UK to examine the effect of field temperature on total eyespot variation using the same presence/absence scoring as Ford. We show that higher developmental temperatures in the field lead to the disappearance of the spots visible while the butterfly is at rest, explaining Ford’s original observation that hindwing spotting declines across the season as temperatures increase. Analysis of wing damage supports the historical hypothesis that hindwing spots confuse aerial predators. However, as hindwing spotting declines over the season, a ‘trade-off’ is suggested between their role in deflecting predators early in the season and their later developmental cost. In contrast, the large forewing eyespot is always present, scales with forewing length and its variation is best explained by day of the year rather than developmental temperature. As this large forewing spot is thought to be involved in ‘startling’ predators, its constant presence is therefore likely required for defence. We model annual total spot variation with phenological data from the UK and derive predictions as to how spot patterns will continue to change under increasing summer temperatures, predicting that spotting will continue to decrease both across a single season and year or year as our climate warms.Summary statementWe show that a long-held example of ‘genetic’ polymorphism, eyespot variation is the Meadow Brown butterfly, is correlated with field temperature during butterfly development.
Asymmetric trichotomous data partitioning enables development of predictive machine learning models using limited siRNA efficacy datasets
Authors: Monopoli Kathryn R. , Korkin Dmitry , Khvorova Anastasia 
Published: July 2022

ABSTRACTChemically modified small interfering RNAs (siRNAs) are promising therapeutics guiding sequence-specific silencing of disease genes. However, identifying chemically modified siRNA sequences that effectively silence target genes is a challenge. Such determinations necessitate computational algorithms. Machine Learning (ML) is a powerful predictive approach for tackling biological problems, but typically requires datasets significantly larger than most available siRNA datasets. Here, we describe a framework for applying ML to a small dataset (356 modified sequences) for siRNA efficacy prediction. To overcome noise and biological limitations in siRNA datasets, we apply a trichotomous (using two thresholds) partitioning approach, producing several combinations of classification threshold pairs. We then test the effects of different thresholds on random forest (RF) ML model performance using a novel evaluation metric accounting for class imbalances. We identify thresholds yielding a model with high predictive power outperforming a simple linear classification model generated from the same data. Using a novel method to extract model features, we observe target site base preferences consistent with current understanding of the siRNA-mediated silencing mechanism, with RF providing higher resolution than the linear model. This framework applies to any classification challenge involving small biological datasets, providing an opportunity to develop high-performing design algorithms for oligonucleotide therapies.