dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure analysis MMCIF files. Missing coordinates and Tag residues were identified thanks to the mmCIF files from the PDB  [1]. Naccess. The software NACCESS  [44]  was used to compute solvent accessible surface area (ASA) using default parameters. Surface residues are defined as those with a relative accessible surface area (RASA) greater than 25% as in  [9]. Interface definition. Following the definition of Levy  [9] , residues were classified into the following regions, according to the values of RASA in the monomers (RASAm) and in the complexes (RASAc): • Non-interface residues (RASAm = RASAc): Surface residues (RASAm>25%) Interior residues (RASAm <25%) • Interface residues (RASAm> RASAc): Rim (RASAc>25%) Core (RASAc <25% and RASAm>25%) Support (RASAc <25% and RASAm <25%) This cutoff of 25% allows an optimal separation between surface and interior amino-acids, in terms of composition  [9].
High-Throughput Sequencing (HTS) Technologies Next-generation sequencing (NGS) techniques using a massive parallel sequencing strategy have profoundly changed the clinical genomic landscape. HTS techniques can be classified according to their applications for investigating genomes, epigenomes, or transcriptome. NGS-based strategies that could be used in medical diagnostics vary according to the size of the interrogated genome. These strategies include capturing the few protein-coding regions of a selected panel of genes (tens to hundreds), sequencing of the entire genetic code of a person, which is called whole-genome sequencing (WGS), and sequencing parts of the genome that contain exonic regions, which is called whole-exome sequencing (WES). WGS and WES are used to discover variants associated with a cell function or a disease  [16] [17] [18].
PA involves the evaluation of patterns and relationships between individuals with related phenotypes and/or between genotype-phenotype associations. PA relies on both clinical data and high-dimensional data integration  [85] , analysis, and visualization  [81, 86, 87]. In a recent work, Kochinke et al. provided a curated database of 746 currently known genes involved in intellectual disability (ID). The genes were classified according to ID-associated clinical features. This work allowed systematic insights into the clinical and molecular landscapes of ID disorders  [88]. Kim et al. introduced the integrative phenotyping framework (iPF) for disease subtype identification. This solution allows accessible visualization of multi-omics data following effective dimension reduction. The strategy has been successfully applied to chronic obstructive lung disease (COPD)  [89].
With the tremendous amount of available biological and clinical data, the development of appropriate data mining tools is mandatory to extract the hidden information, thereby allowing its translation into actionable clinical tools  [91, 92]. As technologies keep evolving and datasets grow in volume, velocity, and variety in the big data era, a strong informatics infrastructure will be essential to embrace the PM promise of improved healthcare derived from personal data. Different computational solutions using machine learning and dimension reduction methods have been developed for omics integration  [93]. Recent studies have shown the potential of multi-omics studies to provide insightful biological inferences  [64, [94] [95] [96] [97] [98] [99]  and to help determine definitive diagnoses in the IEM field  [10, 11].
Omics Informatics Pipelines in the Clinical Environment A bioinformatics pipeline is a sequential series of computationally complex data analysis processes spanning from raw data retrieval to final results output. The series includes processing, data analysis, and interrogation of reference databases. Two main pipelines are discussed here, NGS and MS-based pipelines. Figure  2  represents a schematic overview of both pipelines. analytical process, which may affect the throughput. The MS-based omics community is aware of these limitations and actively strives to overcome them  [54, 104, 124].
Omics Informatics Pipelines in the Clinical Environment A bioinformatics pipeline is a sequential series of computationally complex data analysis processes spanning from raw data retrieval to final results output. The series includes processing, data analysis, and interrogation of reference databases. Two main pipelines are discussed here, NGS and MS-based pipelines. Figure  2  represents a schematic overview of both pipelines. The NGS pipeline comprises library construction and capture, sequencing reaction, and signal processing. Then, a base-calling step is performed to define the unaligned nucleotide sequence. The data are stored in FASTAQ file format containing quality scores. Subsequently, read alignment to a reference sequence is performed, followed by variant calling and annotation.
The final output is a list of variants in VCF format for visualization and interpretation; Right: MS pipeline starts with sample preparation, depending on the MS instruments and the combined separation method. Data acquisition is performed according to the chosen mode (full scan or tandem MS). Subsequently, a pre-processing step is needed for feature extraction and data cleaning. The result is a list of features that will undergo data analysis, molecular annotation, and identification before biological interpretation. Signal processing is platform-dependent in NGS; however, open source solutions are available for pre-processing MS data.
NGS Informatics Pipeline Millions of reads are generated by most common NGS platforms using short reads that overlap either the whole genome (WGS) or a specific region (targeted sequencing). An NGS pipeline includes platform-specific software to generate the sequence derived from the primary instrument The NGS pipeline comprises library construction and capture, sequencing reaction, and signal processing. Then, a base-calling step is performed to define the unaligned nucleotide sequence. The data are stored in FASTAQ file format containing quality scores. Subsequently, read alignment to a reference sequence is performed, followed by variant calling and annotation. The final output is a list of variants in VCF format for visualization and interpretation; Right: MS pipeline starts with sample preparation, depending on the MS instruments and the combined separation method.
NGS Informatics Pipeline Millions of reads are generated by most common NGS platforms using short reads that overlap either the whole genome (WGS) or a specific region (targeted sequencing). An NGS pipeline includes platform-specific software to generate the sequence derived from the primary instrument signal; this step is called base calling. Subsequently, alignment is performed against a reference human genome sequence of the overlapping reads. Several alignment algorithms have been developed with different performance results regarding sequence variation detection  [125, 126]. The aligned reads are used as input files for single-nucleotide variant (SNV) detection, copy number variation (CNV), indels, and large rearrangements using open source or commercial tools. This step is called variant calling.
Several variant callers are available, such as Atlas  [127] , MuTect  [128] , VarScan2  [129] , and Genome Analysis Toolkit (GATK)  [130]. It should be noted that these variant callers exhibit different performances depending on the platform and variant types used  [131]. Thus, using different variant callers for a wider coverage of variants capture is recommended. Further annotation of the detected variant is performed using clinical data, Human Genome Variation Society annotations, genome-phenotype correlation, pathway analysis, and predicted effect (on transcription and translation). The quality and consistency of the interrogated online databases are crucial for this step. To avoid misdiagnoses, interpreting variants should be approached as a dynamic big data problem because these online databases are constantly evolving as disease knowledge evolves  [132].
Mass Spectrometry-Based Omics Informatics Pipeline MS-based processing methods involve four main steps: (i) data acquisition; (ii) data pre-processing; (iii) data analysis using chemometrics; and (iv) identification, network, and pathway analysis  [76]. Data files are acquired with proprietary software depending on each platform. Various proprietary data formats have been developed by MS manufacturers to handle MS data, but this raised sharing and processing limits between platforms. To address this problem, open formats have been developed such as netCDF, mzDATA, mzXML, and mzML  [134]. Data pre-processing include peak detection, peak alignment, which is a drift time correction step in separated methods (gas chromatography-MS, liquid chromatography-MS, capillary electrophoresis-MS, and ion mobility-MS).
MS-based bioinformatics pipeline challenges are the same ones described for big data scaling and interoperability issues with Laboratory Information System (LIS) in a clinical environment. However, some limitations are specific to these platforms in particular, such as sample extraction and/or derivatization, which are offline processes that should be consistently tracked. The metabolite or protein identification steps still lack smooth and streamlined informatics solutions for direct database interrogation. From an informatics perspective, NGS seems to be much more advanced to be included in clinical practice. Therefore, many endeavors are needed to enhance MS informatics infrastructures to a clinical grade  [136] , and some initiatives have already begun  [104, 137, 138].
Criteria for Omics-Based Biomarkers in Clinical Context Three main aspects entail omics-based test development: analytical development, computational modeling of the predictor, and its clinical utility validation. Given the multi-dimensional and rich information generated by omics data, mathematical modeling is the key to building classifiers for effective medical decision-making. Because omics data are high-dimensional, machine learning and chemometric methods are needed to obtain insights from the data  [91]. These methods may be divided into two main classes: unsupervised and supervised methods  [76]. Unsupervised methods are exploratory and track patterns in the data; they include principal component analysis  [153] , independent component analysis  [154] , k-means clustering  [155] , hierarchical cluster analysis  [156] , and self-organizing maps  [157].
Supervised methods are mainly predictive and explanatory. They model the dataset so that the class label of separate validation set samples can be predicted based on a series of mathematical models derived from the original data, namely the training set. Different supervised methods such as PLS discriminant analysis (PLS-DA)  [158]  and orthogonal PLS-DA (OPLS-DA)  [159] , as well as support vector machines  [160] , could be applied. For more details, the reader may refer to a recent review  [76]. Figure  4  presents a schematic view of the two main computational modeling strategies using machine learning techniques for omics-based biomarker implementation. The high-dimensionality characteristic of omics data requires new approaches for omics-based biomarkers development. McShane et al. described the main issues to take into account during omics-based biomarker development, including samples, analytical development of assays, computational model development, clinical utility assessment, and ethical and regulatory issues.
To handle these issues, topological data analysis (TDA) methods may help. TDA methods have emerged recently, but the concept goes back to Leonhard Euler and his work with algebraic topology in the 16th century. TDA methods acquire insight from data by analyzing their shapes (patterns) with geometric dimensional conversions  [163] [164] [165]. These methods have shown good performance in finding hidden patterns when other standard methods fail  [95, 163, 166]. Parsimony phylogenetic analysis is another promising method to handle the omics data deluge  [167]. Disease subtype classification for patient stratification is both data-dependent and method-dependent. Thus, it is urgent to have a representative and consistent reference dataset that can be used for the comparison and evaluation of methods.
Informatics and New Pathways to Clinical Actionability Informatics research and innovation are key drivers of the science underlying PM  [181]. Actionable biomarkers that aid in clinical decision-making will be envisioned by new frameworks to navigate multi-level evidence regarding whether and how a detected molecular abnormality might be a clinically relevant biomarker  [11]. Thanks to databases, accurate annotations with contextual and actionable clinical information will enable the emergence of decision support systems to provide intuitive and patient-specific actionable reports  [87, 182, 183]. Urgent areas to be addressed by clinical bioinformatics research may include biomarker discovery, computational phenotyping, and frameworks for evaluating clinical actionability and utility  [181, 184, 185].
Conclusions Current medical practice is being undermined and PM is profoundly reshaping the future of medicine through recent technological advances. Omics technologies are enabling the simultaneous measurement of a huge number of biochemical entities, including genes, genes expressions, proteins, and metabolites. After decades of reductionism, holistic approaches have begun to address inborn errors of metabolism in a systemic fashion  [9, 64, 91]. Despite some existing drawbacks, genomics and metabolomics seem to be taking the lead in the race to get into clinical practice. However, challenges such as data quality/integrity, reproducibility, and study sample sizes have to be addressed. The small number of multi-omics datasets in the field of IEM and the.
Conclusions Current medical practice is being undermined and PM is profoundly reshaping the future of medicine through recent technological advances. Omics technologies are enabling the simultaneous measurement of a huge number of biochemical entities, including genes, genes expressions, proteins, and metabolites. After decades of reductionism, holistic approaches have begun to address inborn errors of metabolism in a systemic fashion  [9, 64, 91]. Despite some existing drawbacks, genomics and metabolomics seem to be taking the lead in the race to get into clinical practice. However, challenges such as data quality/integrity, reproducibility, and study sample sizes have to be addressed. The small number of multi-omics datasets in the field of IEM and the lack of standardized and harmonized protocols affect the wide dissemination of these approaches.
The postnatal maturation of the prefrontal cortex (PFC) represents a period of increased vulnerability to risk factors and emergence of neuropsychiatric disorders. To disambiguate the pathophysiological mechanisms contributing to these disorders, we revisited the endophenotype approach from a developmental viewpoint. The extracellular matrix protein reelin which contributes to cellular and network plasticity, is a risk factor for several psychiatric diseases. We mapped the aggregate effect of the RELN risk allele on postnatal development of PFC functions by cross-sectional synaptic and behavioral analysis of reelin-haploinsufficient mice. Multivariate analysis of bootstrapped datasets revealed subgroups of phenotypic traits specific to each maturational epoch. The preeminence of synaptic AMPA/NMDA receptor content to pre-weaning and juvenile endophenotypes shifts to longterm potentiation and memory renewal during adolescence followed by NMDA-GluN2B synaptic content in adulthood.
To evaluate the interplay between structural, functional and behavioral phenotypes, we applied multivariate analysis to virtual mice created by data bootstrapping. Here, we show that multivariate analysis of bootstrapped morphometric, electrophysiological and behavioral datasets delineated a developmental sequence of prefrontal endophenotypes linked to the RELN risk allele and therefore identified neuronal targets for pharmacotherapy of reelin haploinsufficient prefrontal phenotypes. Ultimately, this strategy advances the disambiguation of complex traits markers underlying mechanisms of pharmacological rehabilitation.
Computation of virtual mice. The classical univariate analysis thus far employed precludes the consideration of the multidimensional nature of the data and evaluation of the interrelationships between structural, functional and behavioral dysfunctions. To address these issues, we next proceeded to a global analysis and used multivariate analysis of variance (MANOVA) which allows a robust comparison of wild-type mice and HRM across maturation. However, MANOVA requires all parameters to be measured in the same animal. Since this is not feasible due to experimental constraints, we created virtual mice (Supplementary Figures  2  and 3 ). For this purpose, we computed bootstrapped datasets (e. g. virtual samples) by using a parametric (normal) bootstrap method to create random samples based on experimental data distributions obtained for each parameter (Supplementary Figure  2 ).
We applied MANOVA to bootstrapped datasets and statistically analyzed the distribution pattern of all parameters measured in both genotypes during maturation as well as their global contribution to the difference between the two genotypes (Fig. 3 ). We found that the relative weight of each parameter varied across the different developmental periods (Fig. 3A top ). Analysis of the relative weight revealed a predominant contribution of NMDA-sEPSCs, AMPA/NMDA ratio and LTP during the pre-weaning and juvenile periods (P10-20 and P22-28; Fig. 3A top ). At the adolescent stage, LTP and magnitude of renewal exhibited the most elevated weight, which shifted to GluN2B-containing NMDARs at the adult stage. The weighted contribution of spine density was overall weak across maturation. We next calculated a global parameter that provides an optimized combination of measured factors: spine density, AMPA-and NMDA-sEPSCs amplitudes, AMPA/NMDA ratio, Ro25-6981-sensitivty of NMDA-EPSCs (GluN2B), LTP and magnitude of renewal, thereby permitting an optimal estimation of the statistical difference between wild-type and HRM across multiple postnatal maturation points.
This lack of consensus challenged the validity of a role of reelin dysfunctions in the etiology of psychiatric disorders. Here, we showed that HRM exhibit a reduction in the long-term retention of associative fear-conditioned memory from the juvenile period to adolescence, a behavior which became similar to wild-type during different in HRM treated with ketamine compared to wild-type mice. Note that measures presented for HRM-K are taken from datasets in ref 18. (B) The relative weight of each parameter is showed for wild-type versus HRM untreated (HRM) or versus ketamine-treated HRM (HRM-K). The increase in the variance and the median P values of the optimized parameter were: 86% and 6. 5 × 10 -4 for wild-type/HRM, 198% and 1. 4 × 10 -5 for wildtype/HRM-K. (C) Radar chart showing the divergence of relative weights (%) between wild-type versus HRM and wild-type versus ketamine-treated HRM.
To gain a more sensitive and powerful insight in our cross-sectional study and in the analysis of multiple traits of PFC postnatal maturation, we created virtual mice based on the distribution of experimental data and performed a multivariate analysis of bootstrapped datasets. For the first time, our results show that wild-type mice and reelin haploinsufficient mice are significantly different during the pre-weaning period through adulthood and that the differences between both genotypes resulted from parameter interactions specific to each maturational period and not from a simple developmental delay. By dissecting the predominant contributors to this difference, we found that synaptic glutamate receptor content prevailed at pre-weaning and juvenile stages, while synaptic plasticity and memory retention are increasing contributors at adolescence, which are superseded by synaptic NMDAR subtype in adulthood.
Field potentials and whole-cell patch-clamp recordings were made in layer 5, collected using an Axopatch-1D amplifier (Axon Instruments) and acquired with Clampex 10. 2 acquisition Software via a Digidata 1440A (Axon Instruments). For extracellular fields, a stimulating glass electrode filled with ACSF was placed in layer 2/3 as previously described  18. Field excitatory postsynaptic potentials (fEPSP) were evoked at 0. 1 Hz and the glutamatergic nature of the fEPSP was confirmed at the end of each experiment by perfusing the non-NMDA ionotropic glutamate receptor antagonist DNQX (20 μ M; Tocris) which specifically blocked the synaptic component without altering the non-synaptic component. LTP was induced using a theta-burst stimulation protocol consisting of five trains of burst with four pulses at 100 Hz, at 200 ms interval, repeated four times at intervals of 10 s.
Acknowledgements Work in  Dr. P. Chavis  laboratory was supported by  INSERM  and  Fondation Jérôme Lejeune  ( R13913AA ). JI was supported by the  French Ministère de la Recherche (MENRT) ,  CGC  was supported by  INSERM  and  Fondation pour la Recherche Médicale , JMO by  Fondation pour la Recherche Médicale  ( DRM20101220441 ) and LB was supported by  MENRT. The authors thank  Drs OJJ Manzoni  for helpful discussions and  Dr AF Scheyer  for critical reading of the manuscript and the microscopy core facility headed by  Dr. Michel  (InMAGIC).
(top) A schematic representation illustrating two replisomes approaching the permissive and non-permissive faces of a Tus-ter complex. The purple square represents the Tus protein. The replisome approaching from the non-permissive side is believed to be blocked by the Tus-ter complex indicated here with a flat arrowhead. (left, right) The dominant mechanism of polar fork arrest by a Tus-ter complex. (left) The DNA sequences of the different ter-sites. Here the conserved G is highlighted by a light blue rectangle. (right) The crystal structures of a non-forked Tus-bound ter-site and a forked Tus-bound ter-site approached from the non-permissive direction. Here the Tus protein is depicted in light gray. Images where generated using the Protein Data Bank (PDB) files, 1ECR and 2EWJ  (23, 66). DNA molecules are represented in purple and brown, while the C(6) residue is highlighted in red.
This enables us to investigate the effect of the Tus roadblock on both replication and segregation. Our data indicate that while the Tus-ter roadblock impedes replisome progression, the replisome is sufficiently resilient (as assessed from the DNA-bound DnaQ and DnaN molecules) to avoid complete disassembly at the barrier, proceeding to overcome it and finish the replication process in an overall time that only exceeds that of cells with the native origin of replication (oriC-dnaN) by 15%. The replication time in the absence of Tus (oriZ-dnaN:ΔTus cells) is in excellent agreement with that of oriC-dnaN cells, confirming that the Tus-ter complex is indeed the barrier responsible for the increased replication time in oriZ-dnaN cells. We found that the generation time of all Tus mutants with an ectopic or native origin of replication exceeded that of oriC-dnaN cells.
Microscopy and image analysis All microscopy experiments where conducted on a commercial Nikon Ti microscope with custom laser excitation. YPet was imaged using a 515 nm (Cobolt Fandango) laser, mCherry was imaged using a 561 nm (Cobolt Jive) laser and mCerulean was imaged using a 457 nm (Cobolt Twist) laser. The type of extended time-lapse microscopy that we conduct using microfluidics allows us to quantitatively compare intensities since the rate of photobleaching is constant, and hence a steady-state between bleaching and protein products is reached rapidly into the measurement  (43). Image acquisition was performed with an EMCCD (Andor) using commercial Nikon NIS elements software. The whole microscope body, including sample, was kept at ∼37 • C using a temperature controller (Okolabs).