dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum overall accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity.
Variant selection and validation As previously described in  Wai et al. 2020 (Wai et al. 2020) , a total of 64 variants were ascertained through Wessex Regional Genetics Laboratory in Salisbury (52 variants) or the Splicing and Disease research study (12 variants) at the University of Southampton, ethically approved by the Health Research Authority (IRAS Project ID 49685, REC 11/SC/0269) and by the University of Southampton (ERGO ID 23056). Informed consent was provided for all patients for splicing studies to be conducted. All variants had been, or were undergoing RT-PCR analysis to determine their impact on splicing using RNA from whole blood collected in PAXgene tubes, again as previously described  (Wai et al. 2020). Eight variants were excluded from the final analysis (unable to establish splicing impact before analysis period (n=3), incorrect gene/variant annotations given in the dataset distributed (n=3), variant found to impact gene expression rather than splicing (n=2)), giving a total of 56 variants in the final assessment set (Supplementary Table  1 ), which span a wide range of rare disease and cancer predisposition associations, none of which had had their impact on splicing published previously.
Acknowledgements We thank the CAGI organisers for their commitment to improving variant interpretation and for making this challenge happen. The CAGI experiment is supported by  NIH   U24 HG007346. We acknowledge the  NIHR Clinical Research Network (CRN)  in recruiting the participants and the  Musketeers Memorandum , as well as support from the  NIHR UK Rare Genetic Disease Consortium. The authors acknowledge the use of the  IRIDIS High Performance Computing Facility , and associated support services at the  University of Southampton , in the completion of this work. For the purpose of open access, the author has applied a CC BY public copyright licence to any.
Funding The  Baralle Lab  is supported by the  NIHR   Research Professorship awarded  to D. B. ( RP-2016-07-011 ). JL is supported by an  Anniversary Fellowship  from the  University of Southampton. Some of the functional validations of variants were funded by a  Wessex Medical Research Innovation Grant  awarded to JL. RDB is supported by a  New South Wales   Health Cardiovascular Disease Senior Scientist Grant.
2019) adjusted with minor allele frequency(Karczewski et al. 2020), with scores >0. 25 classified as OL splice affecting 5 YC, RDB Combined information from ClinVar(Landrum et al. 2018), gnomAD(Karczewski et al. 2020), established splicing tools (SpliceAI(Jaganathan et al. 2019) (>0. 5), MaxEntScan(Yeo and Burge 2004) (>4)), branchpoint/enhancer locations, distance to exon, splice site database. Model 1 -Base model for prediction Model 2 -Same as Model 1 but using different in-silico prediction score thresholds (SpliceAI(Jaganathan et al. 2019) (>0. 5), MaxEntScan(Yeo and Burge 2004) (>6), MMsplice(Cheng et al. 2019) (>2)) Model 3 -Required well-scoring compatible site (e. g. for donor loss, a well-scored donor within 300bp of the existing acceptor), adding branchpoint/enhancer locations as extra features 6 SMM, SpliceAI(Jaganathan et al.
Machine learning and inference The MESS inference procedure uses a Random Forest  (Breiman 2001 ) approach for model classification and parameter estimation, using python and the scikit-learn module (v0. 20. 3, Pedregosa et al. (2011) ). For complete details of implementation, see  Overcast et al. (2021). Here we first trained a machine learning classifier in a supervised fashion on the 50,000 simulated datasets (10,000 for each assembly model). We then use the trained classifier to predict model class probabilities for each of the empirical datasets (as detailed below). A confidence percentage is associated to each model. We quantified classifier accuracy using 5-fold cross-validation, and evaluated model missclassification by compiling these results into a confusion matrix. Classifier accuracy was evaluated using two different suites of simulated data axes, one composed of only SAD and genetic data, and the other composed of trait values and genetic data, to reflect the data configurations of our empirical datasets.
Results The PCA (see Fig. Results of the misclassification rates when using trait values and genetic diversity confirm that the community assembly model can be correctly recovered from the simulation results in around 50% of the cases, while a random classifier would only be correct in 20% of the cases (see Fig. 4 ). The greatest confusion is between pairwise competition and β-competition, and the neutral model was the best recovered. Filtering and mean competition models were also easily distinguished by the inference procedure. A Confusion matrix with SAD and genetic diversity data shows similar results (See Appendix Fig. 6 ). Results of the inferences on the empirical datasets are summarised in Fig. 5. Considering first the three datasets with SAD and genetic data. For the Reunion spider dataset, competition is detected at around 40% while it was not inferred in  Overcast et al.
(2021). For the two Mascarene weevil datasets, the confidence percentage predicted for the neutral model remains the same as in the previous MESS analysis by  Overcast et al. (2021) , but the circa 40% predicted for both mean competition and filtering in the original analysis is overtaken by the combination of pairwise competition and β-competition. Pairwise competition dominates over β-competition model, and over mean competition, which now receives no support. Remarkably, with inclusion of more nuanced competition models, the inference of environmental filtering totally disappears for these datasets compared to  Overcast et al. (2021). Environmental filtering is only significantly detected in the subtropical rain forest tree and Galapagos snail communities, which are also the datasets that contain trait measurements.
The fact that, with inclusion of more nuanced competition models, the inference of environmental filtering totally disappears for these datasets compared to  Overcast et al. (2021)  might be explained by the improved fit of the new non-neutral models to the non-neutral signal in the data. In the previous MESS inference, all non-neutral signal was only allocated to either filtering or mean competition. In our inferences, selection is revealed by data most often when traits are available. Indeed, neutrality is without question the best fitting model for the spider and weevil datasets that lack trait data. However, a comparison of the confusion matrices (Fig. 4  and Appendix A. 4) show that the presence of trait data is not essential to the detection of filtering. This suggests that the difference we observe in our predictions might be due to a real difference in the community assembly models in action, not an artifact due to the type of data used.
The testing of our method would be greatly enhanced by an empirical dataset with all three types of data (genetic diversity, SAD and trait values) rather than having to rely on only two of these three as we did here. Such a comprehensive data set could, in the future, also be used to investigate the underlying technical reasons why no environmental filtering is inferred from data without availability of traits. For example, we could investigate whether it is the presence of trait values or the absence of species abundances distribution that influences most the percentage of inferred filtering. The β factors used in the simulations may also be refined to allow for differences among each pair of species in future work, so as to reflect species-specific interactions, which may generalise to include positive interactions as well as direct competition.
used the datasets from Overcast et al. (2021)  to compare the original MESS results to those obtained when considering the updated competition models. The data sets are: 1) a spider community from Réunion island with a standardized sampling for abundance and genetic diversity of ten 50 m x 50 m plots and 1282 individuals sequenced for one 500bp mtDNA region (COI) (Emerson et al. 2017) ; 2) two weevil communities from two Mascarene islands (one from Réunion and one from Mauritius) which have been densely sampled for abundance and sequenced for one mtDNA region ( 600bp COI) at the community-scale (Kitson et al. 2018) ; 3) three subtropical rain forest tree communities scored for multiple continuous traits and shotgun sequenced for whole cpDNA (Rossetto et al. 2015) ; 4) Galapagos snail communities collected from all major islands (three in total), sampled for one mtDNA region ( 500bp COI;(Kraemer et al.
To further investigate the role of RING1B in the repression of genes within neuronal dots, we analyzed a publicly available dataset on RING1A/B knockout in mouse motor neurons  61. We found that RING1A/B knockout leads to the upregulation of 53% (125) of dot genes and downregulation of only 4% (10) of dot genes (Figure  S18A ). This observation suggests that RING1A or RING1B proteins likely contribute to the repression of dot genes in human neurons. Since the H3K27me3 mark is deposited by the PRC2 complex, we hypothesized that PRC2 might play an important role in neuronal dot formation. Accordingly, PRC2 knockout in mouse striatal neurons leads to upregulation of TFs, many of which can be positively auto-regulated  62. Furthermore, we find that a large fraction of TFs upregulated in PRC2 knockout overlaps with the neuronal dot TFs (Figure  S18B ), suggesting that PRC2 is indeed involved in the dot formation.
Hi-C data processing Raw reads of each Hi-C library were mapped to the human hg38 reference genome with the distiller-nf pipeline (https://github. com/open2c/distiller-nf, v. 0. 3. 3)  75  which utilizes BWA for genome mapping procedure, ensuring valid reads filtering, 'binning', and generating list of valid pair contacts which transformed into binned matrices of Hi-C interactions. Most of the Hi-C analysis was performed on Hi-C matrices merged by cell type. Merging was done at 1 kbp resolution using cooler. merge_coolers() function of a cooler library  76  , v. 0. 8. 11. The resulting merged Hi-C maps were sampled to the equal number of contact pairs using cooltools. sample() function of a cooltools library  77  , v. 0. 5. 1. To avoid possible bias due to Hi-C ligation artifacts, the values at the main diagonal were removed prior to sampling using cooler and custom python code.
LD score regression analysis in loop anchors To study the link between these loops and human heritable traits, we applied LD score regression to the anchors of loops in Groups 1 and 4 defined above using the stratified LD score regression method, which allows partitioning of heritability from GWAS summary statistics. We obtained GWAS summary statistics files for a total of 234 traits from either the United Kingdom Biobank (UKBB) or from individual GWAS studies. We followed the LD score regression tutorial available at https://github. com/bulik/ldsc/wiki, using default parameters as suggested. The results are presented as LDSC enrichment p-values, which were adjusted for multiple comparisons with the Benjamini-Hochberg method.
05. On Figure  4B , FitHiC2 filtering was not used, and all pairs of manually annotated regions were considered. Average interaction was calculated for merged Hi-C maps at 10 kbp resolution using coolpup. py software (v. 1. 0. 0)  87. Publicly available ChIP-seq data for H3K27me3-enriched regions in ENCODE broadPeak format were used  36. To find cliques (networks) of NeuN(+) and NeuN(-) interactions, networkx library (v. 2. 6. 3) was used  88. N loci form a clique of size N if each pair of these loci forms a significant contact according to FitHiC2. Diagrams from Figure  4D  were plotted using pyCircos python library (v. 0. 3. 0, https://github. com/ponnhide/pyCircos). The list of housekeeping genes was obtained from HRT Atlas v1. 0 database  89. The list of transcription factors was obtained from Lambert et al.
Data and code availability Hi-C data generated in this study were deposited to the GEO database under the accession number <. >. Reviewer token: <. >. Processed ChIP-seq H3K27me3 and chromHMM chromatin states data were obtained from syn25716684. Processed ChIP-seq H3K9me3 data were obtained from ENCODE website (https://www. encodeproject. org/). Accession numbers are: ENCFF557YDE for astrocytes and ENCFF985OQZ for the fetal brain data. Lamin B1 ChIP-seq data were obtained from syn25931622. RNA-seq data on human NeuN(+) and NeuN(-) cells were obtained from GSE96615. scRNA-seq on human fetal gene expression was obtained from descartes. brotmanbaty. org, "matrix of normalized gene expression values". snRNA-seq data on post-mortem human brain cortex was provided by Allen Institute for Brain Science (2022) in Allen Cell Types Database.
The dataset is available from https://portal. brain-map. org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq. Publicly available dorsolateral prefrontal cortex (DLPFC), lung, spleen and bladder Hi-C data were obtained from GSE87112, hESCs -from GSE52457, mESCs and mouse NPC -from 4D Nucleome Data Portal, accession numbers 4DNESDXUWBD9 and 4DNESJ9SIAV5, differentiated mouse neurons -from GSE96107, human NeuN(+) and NeuN(-) cells from BA9 cortex area -from syn21760712. Custom code that was created for Hi-C data analyses can be requested by contacting the corresponding authors.
Acknowledgements We are grateful to the  Core Centrum of IDB RAS and Skoltech BioImaging and Spectroscopy Core Facility  for the excellent technical assistance. We thank the  Center for Precision Genome Editing and Genetic Technologies for Biomedicine, IGB RAS  for cell sorting. We thank  Prof. Mikhail S. Gelfand  ( Skolkovo Institute of Science and Technology ) and  Dr. Aleksandra A. Galitsyna  for helpful advice,  Skoltech Genomics Core Facility  and  Prof. Maria Logacheva  ( Skolkovo Institute of Science and Technology ) for sequencing and quality control of Hi-C libraries, Arkuda HPC cluster provided by the  Skolkovo Institute of Science and Technology  for computational resources, and  National BioService Russian Biospecimen CRO (St. Petersburg, Russia ) for providing brain samples. Lamin B1 ChIP-seq, human Hi-C of BA9 cortex area, H3K27me3 ChIP-seq and chromHMM chromatin states data for this publication were obtained from  NIMH  Repository & Genomics Resource, a centralized national biorepository for genetic studies of psychiatric disorders.
Acknowledgements The authors thank all personnel at the  Boisbonne Center for Gene Therapy (ONIRIS, INSERM, Nantes, France)  for handling and care of the rodents used in this study. We also thank the vector core of TaRGeT,  UMR 1089 (CPV, INSERM and Nantes Université , which is a bioproduction and biotherapy national integrator ( ANR-22-AIBB-0001 ), http://umr1089. univ-nantes. fr) for the production of the rAAV vectors used in this study. This research was supported by the  Fondation d'Entreprise Thérapie Génique en Pays de Loire , the  Centre Hospitalier Universitaire (CHU) of Nantes , the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Nantes Université , and by grants from the  French National Agency for Research  (" Investissements d'Avenir"   Equipex ArronaxPlus.
Acknowledgements I first thank  Vincent Calvez  for reviewing this manuscript and offering great suggestions to improve it, and  Ophélie Ronce  for initially introducing me to the biological question question underlying this work and for subsequent insightful discussions. I also thank  Sepideh Mirrahimi ,  Amandine Véber  and  Sally Otto  for valuable feedback. I thank the  Foundation for Mathematical Sciences in Paris (FSMP)  for funding my work and acknowledge having received partial funding from the  ANR  project  DEEV ANR-20-CE40-0011-01  and from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No  865711 ).
Genomic analyses, using whole exome sequencing (WES) and whole genome sequencing (WGS) techniques, are frequently performed to diagnose patients with IEMs  [46]. These techniques allow the identification of variation of coding and noncoding regions of the genome respectively, by comparing the genome with a database containing known pathogenic disease variants  [47]. However, they lead to a diagnostic yield of 25-60 %, therefore other omics analyses such as transcriptomics, metabolomics or proteomics are often used to complement the genomics analysis. Indeed, combining genomics with transcriptomics increases the diagnostic yield of 10-35 %  [46]. Transcriptomics is defined as the complete set of RNA transcripts in a cell that are produced by the genome under specific circumstances  [45]. Transcriptomics is performed with an RNA-sequencing technique to quantify and investigate the impact of coding and noncoding variants on RNA sequences  [47].
This has motivated the field of systems biology, which aims to comprehensively characterize the components of biological systems such as cells, organisms, or populations to understand their function  [66]. Two main approaches can be distinguished, top-down and bottom-up systems biology  [66]. In the top-down approach, analyses start from large-scale omics datasets and potential mechanisms are inferred through multivariate statistical methods or machine learning to derive novel hypotheses  [66]. The advantage of top-down methods is the scope and comprehensiveness of included data; however, they are limited in mechanistic detail. In contrast, in the bottom-up approach, the components of biological systems, such as enzymatic reactions, are formulated manually resulting in mechanistic models  [66].
A variety of algorithms with different assumptions and parameters have been developed that enable for the extraction of context-specific models from a generic genome-scale reconstruction based on transcriptomic or proteomic data  [81]. Taking advantage of the increasing number of transcriptomic, proteomic, metabolomic, and fluxomic datasets published, a multitude of studies have applied constraint-based modeling to the contextualization of high-throughput datasets from cohorts of patients and matched controls, enabling their stratification based on the generated of samplespecific metabolic fluxes, and the prediction of potential biomarkers  [67, 79]. Such data-driven modeling frameworks have also enabled the predictions of personalized dietary or probiotic interventions  [82, 83]. Thus far, these modeling frameworks have been largely applied to common, multifactorial diseases with a metabolic component, e.
These results were validated against known biomarkers of IEMs from multiple sources with moderate to good accuracy  [100]. The potential of the human reconstruction to predict IEMs was further demonstrated by mapping a compendium of 235 IEMs, grouped into 16 metabolic pathways onto Recon1  [101]. A topological analysis of the compendium revealed that 53 IEM pairs shared at least one reaction, and acetyl-CoA was a key metabolite in many IEMs  [101]. In its current form, the IEM compendium, accounting for 255 diseases, 288 unique genes, and 1872 unique reactions, can be browsed at the Virtual Metabolic Human (VMH) database  [102]. Expansions and manual iterative refinement of the human reconstruction have led to an improved accuracy to predict biomarkers, i. e. , changes in consumption and secretion of metabolites after simulating the appropriate enzyme defects, in IEM phenotypes.
3. Schematic overview of a constraint-based modeling framework for personalized simulations of IEM patients. a) Generation of personalized whole-body models by integrating multi-omics data, dietary information, and clinical parameters from cohorts of patients with rare diseases and controls. Transcriptomic and/or proteomic data are integrated into the organ-specific models through dedicated algorithms, and metabolomics data can be mapped onto biofluid compartments. Dietary information and physiological parameters (e. g. , weight, height, heart rate) are also integrated. Finally, metagenomics data can be mapped by combining the personalized whole-body model with a community model of the individual's gut microbiome. The personalized host-microbiome model allows for the prediction of dietary, prebiotic, or probiotic interventions, and metabolic fluxes altered in patients.
The interpretation of fluxes could be facilitated by multivariate statistics and machine learning methods  [80]. Ultimately, the workflow could lead to personalized dietary and therapeutic interventions that could be validated in future studies, and, hence, pave the way towards precision medicine  [80]. Through a similar workflow, outlined in Fig. 3a , personalized wholebody models could readily be constructed for patients with rare diseases. As discussed above, the predictive potential of Harvey for biomarkers of IEMs has already been demonstrated  [105]. Personalized models of patients with gene defects could be further parameterized with individual dietary regimes, physiological data (e. g. , weight, height, heart rate)  [105]. Personalized WBM would allow individual-specific, organresolved, mechanistic predictions of metabolic alterations and biomarkers in biofluids in IEM patients.
Field site The experiment was performed at the Australian Facility for Taphonomic Experimental Research (AFTER), privately owned by the University of Technology, Sydney (UTS). The site is located in the Hawkesbury region of western Sydney and consists of 4. 86 ha of Cumberland Dry Sclerophyll Forest  [33]. Weather was monitored by a HOBO® U30 Weather Station erected near the entrance to the facility, and readings of temperature (°C) and rainfall (mm) were taken on an hourly basis. Additional hourly temperature (°C) readings were taken from local data loggers using HOBO MX2302 (Onset Computer Corporation) placed on and near the donors. These readings were used to capture the temperature change of the remains as decomposition progressed. On Donor 1 (Control), one sensor was placed on top of the torso and one underneath the torso, with both between the donor and the T-shirt.
Data processing Data processing was performed using ChromaTOF® (version 4. 72. 0. 0, LECO) and the software's statistical compare function. A signal-to-noise ratio of 150 was used with a baseline offset of 0. 8. Peak widths of 30 s and 15 s were used for the first and second dimensions respectively, and compounds were tentatively identified using the National Institute of Standards Technology (NIST) spectral library with a minimum similarity match of 80%. Peak alignment was performed between the samples with a spectral match of 75%. Samples were then classed according to their donor or environmental plot (Donor 1: donor n = 32, environmental n = 31; Donor 2: donor n = 32, environmental n = 32; Lime plot: n = 8). An analyte was retained if it was present in at least two samples or in 30% of samples within a class.