bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Evaluation of Computational Models for RNA modification detection using Nanopore sequencing with RNA004 Chemistry
Authors: Zou Yongji , Ahsan Mian Umair , Chan Joe , Meng Wen , Gao Shou-Jiang , Huang Yufei , Wang Kai 
Published: February 2025

AbstractDirect RNA sequencing from Oxford Nanopore Technologies (ONT) has become a valuable method for studying RNA modifications such as N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C). Recent advancements in the RNA004 chemistry substantially reduce sequencing errors compared to previous chemistries (e.g., RNA002), thereby promising enhanced accuracy for epitranscriptomic analysis. In this study, we benchmark the performance of two state-of-the-art RNA modification detection models capable of handling RNA004 data - ONT’s Dorado and m6Anet - using two wild-type (WT) cell lines, HEK293T and HeLa, with respective ground truths from GLORI and eTAM-seq, and their paired in vitro transcribed (IVT) RNA as negative controls. We found that under default settings and considering sites with ≥10% modification ratio and ≥10X coverage, Dorado has higher recall (∼0.92) than m6Anet (∼0.51) for m6A detection. Among the overlapping methylated sites between ground truth and computational predictions, there are high correlations of site-specific m6A modification stoichiometry, with correlation coefficient of ∼0.89 for Dorado-truth comparison and ∼0.72 for m6Anet-truth comparison. However, combined assessment of WT and IVT datasets show that while the per-site false positive rate (FPR) can be lower (∼8% for Dorado and ∼33% for m6Anet), both computational tools can have high per-site false discovery rate (FDR) of m6A (∼40% for Dorado and ∼80% for m6Anet) due to the low prevalence of m6A in transcriptome, with a similar trend observed for pseudouridine (∼95% FDR for Dorado). Additional motif analysis reveals that both Dorado and m6Anet exhibit high heterogeneity of false positive calls across sequence contexts, suggesting that sequence contexts help determine accuracy of specific modification calls. There is also a substantial overlap of false positive calls between the two IVT samples, suggesting a post-filtering strategy to improve modification calling by compiling a set of low-confidence sites with a probabilistic model from several IVT samples across diverse cells/tissues. Our analysis highlights key strengths and limitations of the current generation of m6A detection algorithms and offers insights into optimizing thresholds and interpretability. The IVT datasets generated by the RNA004 chemistry provides a publicly available benchmark resource for further development and refinement of computational methods.
Identification ofAmblyomma americanumantigens after vaccination with tick extracellular vesicles in white-tailed deer
Authors: Oliva Chávez Adela , Gonzalez Julia , Harvey Cristina , Ribeiro-Silva Cárita de Souza , Leal-Galvan Brenda , Persinger Kelly A. , Durski Sarah , Olafson Pia U. , Johnson Tammi L. 
Published: February 2025

AbstractAnti-tick vaccines represent a promising alternative to chemical acaricides for the management of ticks on wildlife; however little progress has been made to produce a vaccine effective in wild hosts that are critical for tick reproduction, such as the white-tailed deer (Odocoileus virginianus; WTD). To date, most tick antigens have been tested using laboratory models (i.e. rabbits); however, their expression in wild hosts has not been confirmed. We recently testedAmblyomma americanumsalivary (SG) and midgut (MG) extracellular vesicles (EVs) as vaccine candidates in WTD, which resulted in on-host female tick mortality. Using a proteomic approach, we show that these SG- and MG-EVs contain a “core-cargo” enriched in chaperones, small GTPases, actin and actin-related proteins, and other proteins previously reported in small EVs. Label-free quantitative proteomics showed significant differences in protein cargo between MG and SG-EVs (333 proteins out of 516). Pre-vaccinated and day 57 post-injection serum samples from three vaccinated and one control WTD were used to immuno-precipitate antigenic proteins from SG- and MG-EV preparations. Proteomic analysis of immunoprecipitated proteins identified thirty antigens with potential for use in anti-tick vaccines, seven of which we have categorized as high priority. These proteins represent promising candidates for anti-tick vaccine design in WTD and other wildlife hosts.
NanoBEP – A Machine Learning Based Tool for Nanobody Binding Energy Prediction
Authors: Sahoo Soma Prasad , Manickavasagam Pratibha , Priya M. Hamsa 
Published: February 2025

AbstractNanobody is a special class of antibodies comprising only one variable heavy chain. Its small size and high stability over a wide range of temperature and pH, makes it an ideal candidate for biomedical applications. Designing a nanobody that can bind to a specific target protein, either for therapeutic or diagnostic purposes, requires a quick estimation of binding affinity of nanobody-protein complex. Many predictive models for protein-protein interactions have been developed leveraging the capability of machine learning techniques. The popular protein-protein interaction models, however, could not accurately predict the binding affinity of available nanobody-protein complexes. We, therefore, have developed a random forest based model that can predict the value of dissociation constant (log10Kd) at high accuracy with a Pearson’s correlation coefficient value of 0.95 and a mean absolute error of 0.44. Our cherry-picked model identifies the best protein features for the prediction through two stages of selection strategy that includes elimination of highly correlated features through graph network analysis, followed by the recursive feature elimination through random forest. Despite being a class of antibodies, a model trained only on antigen-antibody complexes couldn’t accurately predict the binding affinity of the nanobody-protein complexes. The predictability improved only when we included the data on monomeric protein complexes and some nanobody-protein complexes during training.
dandelionR: Single-cell immune repertoire trajectory analysis in R
Authors: Yu Jiawei , Xu Xiaohan , Borcherding Nicholas , Tuong Zewen Kelvin 
Published: February 2025

AbstractIntegration of single-cell RNA-sequencing (scRNA-seq) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) is extremely powerful in studying lymphocyte development. A python-based package,Dandelion, introduced VDJ-feature space method, which addresses the challenge in integration of single-cell AIR data with gene expression data and enhanced the trajectory analysis results. However, no R-based equivalent or similar methods currently exist. To fill this gap, we presentdandelionR, an R implementation ofDandelion’s trajectory analysis workflow, bringing the VDJ feature space construction and trajectory analysis using diffusion map and absorbing Markov chain to R, offering a new option for scRNA-seq and scVDJ-seq analysis to R language users.
Enhancing Protein Graph Representations through Static-Dynamic Fusion
Authors: Guo Pengkang , Correia Bruno , Vandergheynst Pierre , Probst Daniel 
Published: February 2025

AbstractMachine learning for protein modeling faces significant challenges due to proteins’ inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynamics trajectories provides new opportunities for understanding the dynamic behavior of proteins; however, computational methods for utilizing this dynamic information remain limited. We propose a novel graph representation that integrates both static structural information and dynamic correlations from molecular dynamics trajectories, enabling more comprehensive modeling of proteins. By applying relational graph neural networks (RGNNs) to process this heterogeneous representation, we demonstrate significant improvements over structure-based approaches across three distinct tasks: atomic adaptability prediction, binding site detection, and binding affinity prediction. Our results validate that combining static and dynamic information provides complementary signals for understanding proteinligand interactions, offering new possibilities for drug design and structural biology applications.
Arbor function of TRANSPARENT TESTA GLABRA 1 and LIGHT REGULATED WD scaffold proteins in the Arabidopsis circadian oscillators includes transcriptional repression through PSEUDO RESPONSE REGULATORS
Authors: Herrero Eva , Cano-Ramirez Dora , Glover Beverley J. , Webb Alex A. R. 
Published: February 2025

AbstractArabidopsis circadian oscillators contain DNA-binding proteins that function at specific times of the day. In contrast, we have discovered a unique continuous function of WD40-repeat scaffold proteins from the TRANSPARENT TESTA GLABRA-1 (TTG1), LIGHT REGULATED WD1 and LIGHT REGULATED LWD2 subfamily (TLWD) which is essential to maintain circadian rhythms. Gene expression analyses indicate multifunctional activity of TLWD in both transcriptional activation and repression. TLWD proteins interact with an array of circadian oscillator activators and repressors that act sequentially throughout the diel cycle. While TLWD proteins were known to participate in activator complexes, our data indicate a novel role of TLWD in transcriptional repression through complex formation with repressors from the PSEUDO RESPONSE REGULATOR (PRR) family. In an analogy to mechanical clocks, TLWD scaffold proteins constitute an arbor, or spindle, to which transcription factors, which represent the cogs, bind to sustain circadian rhythms.
IRF7 controls spontaneous autoimmune germinal center and plasma cell checkpoints
Authors: Fike Adam J. , Bricker Kristen N. , Gonzalez Michael V. , Maharjan Anju , Bui Tien , Nuon Keomonyroth , Emrich Scott M. , Weber Julia L. , Luckenbill Sara A. , Choi Nicholas M. , Sauteraud Renan , Liu Dajiang J. , Olsen Nancy J. , Caricchio Roberto , Trebak Mohamed , Chodisetti Sathi Babu , Rahman Ziaur S.M. 
Published: February 2025

AbstractHow IRF7 promotes autoimmune B cell responses and systemic autoimmunity is unclear. Analysis of spontaneous SLE-prone mice deficient in IRF7 uncovered the IRF7 role in regulating autoimmune germinal center (GC), plasma cell (PC) and autoantibody responses and disease. IRF7, however, was dispensable for foreign antigen driven GC, PC and antibody responses. Competitive bone marrow (BM) chimeras highlighted the importance of IRF7 in hematopoietic cells in spontaneous GC and PC differentiation. Single-cell-RNAseq of SLE-prone B cells indicated IRF7 mediated B cell differentiation through GC and PC fates. Mechanistic studies revealed that IRF7 promoted B cell differentiation through GC and PC fates by regulating the transcriptome, translation, and metabolism of SLE-prone B cells. Mixed BM chimeras demonstrated a requirement for B cell-intrinsic IRF7 in IgG autoantibody production but not sufficient for promoting spontaneous GC and PC responses. Altogether, we delineate previously unknown B cell-intrinsic and -extrinsic mechanisms of IRF7-promoted spontaneous GC and PC responses, loss of tolerance, autoantibody production and SLE development.SummaryFike et al. describe previously unknown mechanisms by which IRF7 controls autoimmune B cell and autoantibody responses. Mechanistic studies guided by single-cell-RNAseq and ChIPseq reveal that IRF7 promotes B cell differentiation through germinal center and plasma cell fates by regulating the transcriptome, translation, and metabolism of lupus-prone B cells.
Multivariate Neural Markers of Individual Differences in Thought Control Difficulties
Authors: DeRosa Jacob , Smolker Harry R. , Kim Hyojeong , Groff Boman , Peacock Jarrod Lewis- , Banich Marie T. 
Published: February 2025

ABSTRACTDifficulties in controlling thought, including pathological rumination, worry, and intrusive thoughts, occur in a range of mental health disorders. Here we identify specific patterns of brain activity distributed within and across canonical brain networks that are associated with self-reported difficulties in controlling one’s thoughts. These activity patterns were derived using multivariate pattern analysis on fMRI data recorded while participants engaged in one of four operations on an item in working memory: maintaining it, replacing it with another, specifically suppressing it, or clearing the mind of all thought. Individuals who reported greater difficulties exhibited brain activation patterns that were more variable and less differentiated across the four operations in frontoparietal and default mode networks, and showed less distinct patterns of connectivity within the default mode network. These activity profiles were absent during rest but serve as promising task-based neural markers, explaining over 30% of the variance in thought control difficulties.
Seq-Scope-eXpanded: Spatial Omics Beyond Optical Resolution
Authors: Anacleto Angelo , Cheng Weiqiu , Feng Qianlu , Cho Chun-Seok , Hwang Yongha , Kim Yongsung , Si Yichen , Park Anna , Hsu Jer-En , Schrank Mitchell , Teles Rosane , Modlin Robert L. , Plazyo Olesya , Gudjonsson Johann E. , Kim Myungjin , Kim Chang H. , Han Hee-Sun , Kang Hyun Min , Lee Jun Hee 
Published: February 2025

AbstractSequencing-based spatial transcriptomics (sST) enables transcriptome-wide gene expression mapping but falls short of reaching the optical resolution (200–300 nm) of imaging-based methods. Here, we present Seq-Scope-X (Seq-Scope-eXpanded), which empowers submicrometer-resolution Seq-Scope with tissue expansion to surpass this limitation. By physically enlarging tissues, Seq-Scope-X minimizes transcript diffusion effects and increases spatial feature density by an additional order of magnitude. In liver tissue, this approach resolves nuclear and cytoplasmic compartments in nearly every single cell, uncovering widespread differences between nuclear and cytoplasmic transcriptome patterns. Independently confirmed by imaging-based methods, these results suggest that individual hepatocytes can dynamically switch their metabolic roles. Seq-Scope-X is also applicable to non-hepatic tissues such as brain and colon, and can be modified to perform spatial proteomic analysis, simultaneously profiling hundreds of barcode-tagged antibody stains at microscopic resolutions in mouse spleens and human tonsils. These findings establish Seq-Scope-X as a transformative tool for ultra-high-resolution whole-transcriptome and proteome profiling, offering unparalleled spatial precision and advancing our understanding of cellular architecture, function, and disease mechanisms.
rplec: An R package of placental epigenetic clock to estimate aging by DNA-methylation-based gestational age
Authors: Sufriyana Herdiantri , Su Emily Chia-Yu 
Published: February 2025

AbstractBackgroundLatest placental epigenetic clocks (PlECs) were claimed to be robust when applied to cases with either maternal or fetal adverse conditions. However, the accuracies in estimating gestational age (GA) were lower in earlier trimesters. We aimed to develop an R package of PlEC to estimate aging by DNA-methylation-based GA (DNAm-GA).MethodsWe utilized 1742 samples of placental DNA methylation, provided by the 2024 Placental Clock DREAM Challenge. Our PlEC was trained using only used beta values at the common CpG sites in either the Infinium HumanMethylation-450 (n=930)/-850 BeadChip arrays (n=912) from Illumina, in which 100 samples were used for the validation set. External validation was independently evaluated by the challenge organizer using a publicly-unavailable test set (n=384). Elastic regression was applied to develop a three-stage prediction model to estimate: (1) DNAm-GA among normal samples; (2) first residual DNAm-GA among samples with known phenotypes that were leading to earlier termination; and (3) second residual DNAm-GA depending on the estimated GA from the previous stages. An R package was developed to simplify our scikit-learn models into a single function and to utilize DNAm-GA for placental aging study.ResultsOur PlEC required beta values at 10,433 CpG sites and achieved the top performance in the validation set. Based on the test set, the root mean squared-error (RMSE) was 1.245 weeks. The RMSE for preterm samples were lower (0.558, 95% confidence interval [CI] 0.545, 0.570) compared to the two previous PlECs using the common dataset: (1) Lee et al (1.696, 95% CI 1.667, 1.724); and (2) Mayne et al (4.018, 95% CI 3.927, 4.108). We developedrplecR package with only two functions for preprocessing input and estimating DNAm-GA and two functions for conducting quality control and utilizing DNAm-GA for placental aging study. The simplified version of PlEC achieved similar performance with the original scikit-learn model with RMSE 0.102 (95% CI 0.101, 0.104), which was reasonably imperfect since Python and R handle floating/decimal numbers, differently.ConclusionsOur R package precisely estimated DNAm-GA and our analytical framework could utilize DNAm-GA for placental aging study. Our PlEC also allows individual assessment of placental aging in clinical settings via the residual DNAm-GA. Future studies are needed to refine the first residual GA estimation and reduce the number of predictors while maintaining the accuracy.
Statistics in biology: a survey of the three major multidisciplinary journals
Authors: Mazoit Jean Xavier 
Published: February 2025

AbstractThis article presents the results of a survey of the three major multidisciplinary journals (Nature, Proceedings of the National Academy of Science, Science). Fifty articles involving experiments necessitating the agreement of an ethics committee were searched in each of the three journals to June 30th 2023. Because PNAs announced that a Statistical Review Committee was created and working since September 2023, a further set of 50 articles was retrieved and analyzed from 2024 January 1st. The following items were checked in articles: an explicit statement of approval from an animal ethics committee, the calculation of the appropriate samples size needed and how randomization and blinding were performed, the minimum sample size reported, the presence of repeats, and the methods used to limit type I and type II errors.No clear experimental design was fully reported in any article. The major problems were 1) extremely small sample sizes (<4/group) in nearly half of the articles, 2) confusion between biological repeats and technical replications, and 3) lack of correction for multiple comparisons. These errors led to major inflation of type I and type II errors.In conclusion, only 10 percent of the articles analyzed presented correct statistical methodology.
Near-cognate tRNAs dominate codon decoding times in simulated ribosomes
Authors: Hedayioglu Fabio , Mead Emma J. , Kurusamy Sathishkumar , Thaventhiran James E.D. , Willis Anne E. , Smales C. Mark , von der Haar Tobias 
Published: February 2025

AbstractThe codon sequence of messenger RNAs affects ribosome dynamics, translational control, and transcript stability. Here we describe an advanced computational modelling tool and its application to studying the effect of different tRNA species on the codon decoding process. We show that simulated codon decoding times are sensitive to the abundance of near-cognate tRNA species as well as cognate species, an aspect of the decoding system that is not fully considered in other computational modelling studies. We demonstrate that codon decoding times predicted by models that accurately define near-cognate tRNAs and that are parameterised with high-quality tRNA abundance datasets are highly similar to ribosome dwell times determined using experimental ribosome footprinting data, thereby confirming both the importance of near-cognate tRNAs for the codon decoding process and the general accuracy of our modelling tools.
ANALYSIS OF THE IMPACT OF FIVE METHANE–MITIGATING FEED ADDITIVES ON MILK PRODUCTION AND ASSOCIATED PARAMETERS ACROSS MULTIPLE COMMERCIAL DAIRY FARMS
Authors: Altshuler Yaniv , Chebach Tzruya Calvao , Cohen Shalom , Gatica Joao 
Published: February 2025

ABSTRACTThe effect of methane–mitigating feed additives on dairy cows has been widely explored; however, confusing conclusions have been reached due to factors such as the inclusion of different doses and experimental conditions in which the additives are tested, or even a small sample size. We present the first extensive study assessing the effects of methane–mitigating feed additives on milk production across several commercial dairy farms. This study used a previously developed predictive AI–driven model based on microbiome samples; the model predicts farms where a significant reduction of methane emissions is expected due to the applied feed additives. Thus, in this study, each feed additive was supplied to a large number of farms, widely distributed across different climatic areas in Israel. The data analysis followed two simulated scenarios: (1) a naive approach, where feed additives are supplied indiscriminately, and (2) an optimized approach, where feed additives are supplied only to farms with a high likelihood of being positively impacted in terms of reduced enteric methane emissions (50% of the farms). The results show that each feed additive significantly increased milk production compared to the control groups. This increase in milk production was significantly higher in the optimized scenario. Other related parameters such as somatic cells were also improved. Our results suggest that the feed additives positively affect milk production, reaching a maximum expression when the AI–driven model is applied.
Integrative Analysis of Drug-Gene Expression Signatures in Human Pluripotent Stem Cells Identifies Novel Drug Candidates for ALS and Monogenic Diseases
Authors: Roussange Florine , Gide Jacqueline , Tournois Johana , Cailleret Michel , Boland Anne , Battail Christophe , Deleuze Jean-François , Polvèche Hélène , Auboeuf Didier , Brockmann Knut , Kabashi Edor , Marian Anca , El Kassar Lina , Blondel Sophie , Salachas François , Bruneteau Gaëlle , Peschanski Marc , Martinat Cécile , Baghdoyan Sandrine 
Published: February 2025

AbstractThe classical paradigm of drug screening often faces significant limitations due to the challenges associated with identifying molecular or cellular read-outs that are relevant to specific diseases. This issue is particularly pronounced for the thousands of diseases of genetic origin, given the abundance of databases listing disease-associated changes in gene expression and various transcripts, indicating that potential read-outs may be concealed within these resources. To remedy this, an alternative approach was tested: compounds were evaluated for their effects on gene expression and alternative splicing in a healthy cell model, and the resulting data were matched to molecular signatures of diseases. A subset of 50 FDA-approved drugs was tested on mesenchymal stem cells derived from a human pluripotent stem cell line. Over half of the compounds altered gene expression, many affecting pathways linked to monogenic diseases. One hit, increased SQSTM1 expression induced by prazosin, was further validated in ALS models caused by SQSTM1 haploinsufficiency, including patient-derived fibroblasts, SQSTM1-depleted hiPSC-derived motor neurons, and a zebrafish model. Extending this paradigm could involve testing diverse cell types and larger drug libraries.One Sentence SummaryA novel drug screening approach for monogenic diseases integrating human pluripotent stem cell derivatives and RNA sequencing to profile gene expression.