dataesr/scientific-paragraphs-categorization huggingface dataset:
It would be great if the authors populate COPO with as much publicly available data as possible. E. g. , NCBI's SRA. While I'm sure not all entries have enough metadata to make this possible, even a significant number of SRA entries available for search and discovery for COPO would be immensely helpful for the research community (as well as be a test for how well COPO works on the discoverability side of FAIR. ) However, I could not find any search function in COPO for published datasets. ○ COPO has been in production for several years. Please provide some statistics on usage and adoption of COPO.
Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? PartlyAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Plant Genome Evolution, Comparative Genomics, Cyberinfrastructure for Life Science Research.
Original Draft Preparation, Writing -Review & Grant information: The initial  COPO  project was funded by a  BBSRC Biological and Bioinformatics Resources (BBR)  grant ( BB/L024055/1 ,  BB/L024101/1 ,  BB/L024071/1 ) and is now funded by the  BBSRC Core Strategic Programme  grant awarded to  Earlham Institute  ( BBS/E/T/000PR9817 ). COPO is hosted within the CyVerse UK academic cloud, funded by  BBSRC  ( BB/M018431/1 ,  BB/R000662/1 ), and run within  EI's National Capability in e-Infrastructure  ( BBS/E/T/000PR9814 ). In addition to the  BBSRC COPO  grant, SAS, PRS, ABG and DJ were funded by  BBSRC  ( BB/L005069/1 ),  Wellcome Trust  ( 212930/Z/18/Z ,  208381/A/17/Z ),  European Union  ( H2020-EU. 3. 1, 634107 ,  H2020-EU. 1. 4. 1. 3 , 654241 ,  H2020-EU. 1. 4. 1. 1 , 676559 ),  IMI  ( 116060 ) and  NIH  ( U54 AI117925 ,  1U24AI117966-01 ,  1OT3OD025459-01 ,  1OT3OD025467-01 ,  1OT3OD025462-01 ).
SAS is also funded by the  Oxford e-Research Centre, Department of Engineering Science of the University of Oxford. The computational hardware that underpins this capability is supported by a  BBSRC   Core Capability Grant  to EI, and managed by the  Norwich Biosciences Institutes Partnership (NBIP) Computing Infrastructure for Science (CiS)  group at the  Norwich Research Park. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
○○ Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? PartlyAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Plant Genome Evolution, Comparative Genomics, Cyberinfrastructure for Life Science Research.
PubMed Abstract | Publisher Full Text | Free Full Text 26. JSON. Reference Source 27. A Closer Look at the Dell EMC Isilon NAS Storage Platform. 35. DCMI: Dublin Core. Reference Source 36. The Dataverse Project -Dataverse. org. Reference Source 37. DSpace -A Turnkey Institutional Repository Application. Duraspace. org. Reference Source 38. ckan: ckan. Reference Source 39. figshare -credit for all your research. Reference Source 40. Reed RB: figshare for Institutions. J Med Libr Assoc. 2016; 104(4): 376-378. Publisher Full Text | Free Full Text 41. Harvard Dataverse. Reference Source 42. CyVerse UK -CyberInfrastructure for life science. Reference Source 43. National Capability in e-Infrastructure. Earlham Institute. 2018. Reference Source 44. What is a Container? | Docker. Docker. Virtualization Review.
AgriculturalSemantics: AgriculturalSemantics/cg-core. GitHub. Reference Source Reference Source 45. Federhen S: The NCBI Taxonomy database. Nucleic Acids Res. 2012; 40(Database issue): D136-D143. PubMed Abstract | Publisher Full Text | Free Full Text 46. European Nucleotide Archive: webin-cli. GitHub. Reference Source 47. Zenodo -Research. Shared. Reference Source 48. GARDIAN. Reference Source 49. Hannay T: Stop the deluge of science research. The Guardian. 2014. Reference Source 50. Mazzocchi F: Could Big Data Be the End of Theory in Science? A Few Remarks on the Epistemology of Data-Driven Science. EMBO Rep. 2015; 16(10): 1250-5. PubMed Abstract | Publisher Full Text | Free Full Text 51. Researchfish: Research Impact Assessment Platform. researchfish. Reference Source 52. Six.
High-throughput quantification of locomotor defects in mutant zebrafish larvae. Interclutches differences often account for most of the variability in behavior. To detect locomotor defects associated with loss of somatostatin 1. 1, we therefore compared the locomotion kinematics in sst1. 1 icm40/icm40 mutants to the ones in WT siblings obtained from incrossing adult zebrafish heterozygous for the mutation. We systematically analyzed kinematic parameters within the same clutches prior to genotyping individuals in order to blind experiments (see "Methods"). Inter-clutches variations are very large in behavioral datasets and the detection of minor locomotor defects due to the loss of function of a single peptide such as Somatostatin 1. 1 required to compare the behavior of hundreds of larvae from the same clutches.
First, we developed a high throughput behavior assay to simultaneously record with two cameras in parallel up to 88 fish isolated in individual wells and performing thousands of swimming events acquired at 100-200 Hz and over tens of minutes (see "Methods"). Second, we improved the source code of our tracking software ZebraZoom  41, 49, 53  to reduce the tracking error rate (under 0. 5%) by adjusting the configuration parameters (Fig. 1b , "Methods"). Finally, we adapted the source code of ZebraZoom to be executed on a High-Performance Computing (HPC) system equipped with numerous processors. We automated the process of the big dataset management and storage to be accessible by the HPC system, thereby allowing to promptly process large datasets (e. g. ~ 120,000 swimming bouts) at high speed (typically processing 3,250 frames per min per well and running 120 wells in parallel) (Fig.
2% (software available as open source, check https ://zebra zoom. org/). To speed up processing by 40 folds, we ran our tracking software on a High Performance Computing (HPC) system that allows tracking hundreds of videos simultaneously. By doing so, ZebraZoom typically took 7 h instead of 258 h to process six 10 minlong videos recorded at 100 Hz. We automated the process for managing big dataset and the file storage to be immediately accessible by the HPC system. Escape behavior setup. Larvae were placed individually in a 32-well plate on a transparent plexiglass plate where 2 speakers (Monacor, 10 W, Bremen, Germany) were held on. A custom-made Arduino Due software and circuit was used to synchronize the acoustic stimulus and the recording (Arduino script available on Github (https ://githu b.
com/FengB Quan/Quan_et_al_2020. git). To induce an acousto-vestibular (AV) stimulus, we delivered a 5 ms-long sine wave at 500 Hz through a 20 W amplifier (Adafruit, MAX9744, New York, USA) over two speakers at the volume of 1,305 dB. The stimulus occurred 200 ms after the beginning of the 1 s-long trial recorded at 650 Hz. Each larva was subjected to ten stimuli, with 3 min inter-trial interval to minimize habituation (Fig. 1c , see Supplementary Video S1). Tracking system. The kinematics of zebrafish larvae were analyzed by performing tracking of the head position, head direction and 10 points along the tail using an improved version of the ZebraZoom software  49, 53, 77. First, we improved the source code of ZebraZoom to allow flexibility in the recording conditions depending on the well configuration, the frequency of recording, among other parameters.
Visual inspection of the tracking added to the raw video was performed to check that the position and movement was correctly estimated. If a tracking error occurred, if a micro-movement occurred, some of the detection points along the larval body were inaccurate, or the heading direction was wrong, tracking failure was detected and systematically corrected by changing the configuration parameters. Second, ZebraZoom was initially available only on Windows operating system (OS), we adapted the source code to be executable on High Performance Computing (HPC) systems, which use Linux OS. Many videos were larger than several hundred gigabytes. We automated the management of large dataset from acquisition to the shared storage (Lustre) on ICM network, allowing the dataset to be immediately accessible by the ICM HPC system.
The pipeline of ZebraZoom has been adapted to be used by multiple processors on a HPC system in order to simultaneously track numerous videos. The ICM HPC system is composed of 33 servers for a total of 924 cores and 5,623 gigabytes of memory. It is connected to the Lustre file system (https ://lustr e. org) of 2. 9 petabytes (Fig. 1c , see Supplementary Video S1). Analysis of kinematic parameters. From the heading and the tail position tracked by ZebraZoom (https ://zebra zoom. org/), we extracted the timing of bouts and kinematic parameters per bout derived from the tail angle: bout duration, distance travelled, number of oscillations, amplitudes of each tail bend. Custom-made script packages (Matlab R2018b, The MathWorks Inc. , Natick, MA, USA) were used for analysis: SlowSwim Analysis and Escape Analysis (https ://githu b.
Statistical analysis. All statistical analyses for the escape and the slow swim data were conducted by using the R software 80 (version 3. 6. 1, https ://cran. rproj ect. org/). Values were presented as mean ± standard error of the mean (SEM). The comparisons between the two genotype groups were performed by using linear mixed models (LMMs) with a fixed effect for genotype and a random effect for the clutch. All LMMs were fitted to the kinematic parameters using the function lmer in the lme4 R package. When necessary, the data were either log or square root transformed prior to the modeling to improve the model assumptions of linearity, normality and constant variance of residuals. Therefore, a significant difference between the two genotype groups was evaluated with the Anova function in the car package using a Type II Wald chi-square test.
Acknowledgements We thank  Sophie Nunes-Figueiredo ,  Monica Dicu  and  Antoine Arneau  from  Animalliance  for fish care. We thank  Dr. Adrien Jouary  from  Champalimaud Center  for the Unknown and  Pierre Tissier  from the  ICM Fab Lab  for help building the acoustic escape setup. We thank the  ICM IT  support for help with implementing behavior data storage and automating analysis process. We thank  Yannick Marie  from the  ICM genotype facility  iGenSeq for help with high-throughput genotyping. This work was supported by an  European Research Council (ERC) Starting Grants "Optoloco " # 311673 ,  ERC  Proof of Concept in 2018 (POC) # 825273 ,  New York Stem Cell Foundation (NYSCF)   Robertson Award 2016 Grant  # NYSCF-R-NI39. This work received support from the  Agence Nationale pour la Recherche (ANR)  " Investissements d' Avenir "  ANR-10-IAIHU-06  and  ANR-11-INSB-0011.
In the past, there have been only a handful of reports addressing such issues  (7) (8) (9) (10) (11). In what follows we describe the most recent version of the TopMatch program  (7) , which handles these requirements efficiently. To achieve these goals we implemented a parser to read and process mmCIF files and a generator to build molecular assemblies from the respective operation expressions. We rebuilt the TopMatch source code to enable the simultaneous processing of protein and nucleic acid chains and we added capabilities for running parallel tasks and threads on multiple processor cores. We endowed TopMatch with an interface to NGL  (12) , a molecular viewer enabling the rapid visualization of very large molecular assemblies, like complete ribosomes, and their structural similarities in 3D.
STRUCTURE MATCHING BY EXAMPLE In what follows, we compare two structures of ribosomes found in eukaryotic mitochondria (3): the mitochondrial W32 Nucleic Acids Research, 2020, Vol. 48, Web Server issue ribosome from yeast, as resolved by electron microscopy (EM) to a resolution of 3. 25 Å (PDB code: 5mrc (5)), and class 1 human mitochondrial ribosome, as resolved by EM to a resolution of 3. 5 Å (PDB code: 3j9m (4)). Both structures present spectacular macromolecular complexes assembled from around 80 components each, forming a hetero-78-mer in the case of yeast (75 proteins + 21S rRNA + 15S rRNA + tRNA) and a hetero-85-mer (81 proteins + 16S rRNA + 12S rRNA + tRNAs) in the case of the human mitoribosome. The structural features shared by these assemblies are as interesting as are the differences between them and, clearly, a thorough investigation of them requires comparative analyses on several levels of structural complexity.
Input TopMatch-web requires the atomic coordinates of the structures to be compared. Users can supply coordinates by uploading files in mmCIF format  (1, 13). However, since all structures in the Protein Data Bank (PDB) are accessible through the server by their PDB codes, it is generally not necessary to upload any files. The use of the mmCIF format is necessary to handle large molecular complexes. All structures deposited in PDB are available as mmCIF files  (1). Approximately 1100 entries are exclusively stored as mmCIF files since they cannot be represented by legacy PDB format. Among these are the largest structures known, including ribosomes and capsids of viruses. In addition there are ∼10 000 biological assemblies that cannot be represented using PDB file format. All calculations are carried out with the C ␣ atoms of the protein and the P atoms of the nucleic acid backbones.
Hence, TopMatch also works on low resolution structures as long as backbone traces specified by C ␣ or P atoms are available. TopMatch-web accepts both author-assigned and PDBassigned chain identifiers  (13) , where a comma is used to denote author-assigned chains (e. g. 5mrc,aa) and an underscore character to denote PDB-assigned chains (e. g. 5mrc WB). Biological assemblies are specified by an @ sign with the respective assembly identifiers appended (e. g. 3j9m@1). A straight PDB code, like 5mrc, specifies the complete asymmetric unit. The TopMatch-web help page provides a detailed account of the rules used to specify chains, models, and molecular assemblies.
, 2010; Frichot et al. , 2013 Frichot et al. , , 2015;; Gautier, 2015). Yet, defining relevant environmental characteristics or traits as proxy for invasion success remains challenging and might even be viewed as the key aim. Therefore, we propose to simply summarize invasion success into a binary variable corresponding to the population's historical status (i. e. , invasive or native) based on previous studies. By extension, functional annotation of the associated variants identified may provide insights into candidate traits underlying invasion success  (Estoup et al. , 2016; Li et al. , 2008; Wu et al. , 2019). The Bayesian hierarchical model initially proposed by  Coop et al. (2010) , later extended in  Gautier (2015)  and implemented in the software BayPass, represents one of the most flexible and powerful frameworks to carry out EAA since it efficiently accounts for the correlation structure among allele frequencies in the sampled populations.
The estimation of the C 2 statistic was here performed using the Markov-Chain Monte Carlo (MCMC) algorithm implemented in the BayPass software  (Gautier, 2015). respectively, were then obtained as: XtX (i) = αi t αi C 2 (i) = 1 c t c αi t c 2 (5) Under the null hypothesis, XtX (i) ∼ χ 2 J and C 2 (i) ∼ χ 2 1 allowing one to rely on standard decision making procedures, e. g. based on pvalues or more preferably on q-values to control for multiple-testing issues  (Storey and Tibshirani, 2003).
6×10 -5 (Table  1 ). At The overall differentiation was estimated using the recently developed F ST estimator for Pool-Seq data  (Hivert et al. , 2018). It ranged from 8. 86% to 9. 02% (8. 95% on average) for the autosomal data sets and from 17. 6% to 17. 8% (17. 8% on average) for the X-chromosome data sets. Although a higher genetic differentiation is expected for the X-chromosome even under equal contribution of males and females to demography, the almost twice higher overall differentiation observed for the X chromosome compared to autosomes might have been accentuated by unbalanced sex-ratio (e. g. , polyandry), male-biased dispersal or a higher impact of selection on the X-chromosome  (Clemente et al. , 2018). Inferring sex-specific demography was beyond the scope of the present study, but for our purposes, this finding justified to perform separate genome scans on autosomal and X-linked SNPs.
The distribution of the p-values derived from the C 2 statistics was well-behaved, being close to uniform for higher p-values (Figure  S5A ). To account for multiple testing issues, we used the qvalue R package  (Storey and Tibshirani, 2003)  to compute the individual SNP q-values plotted in Figure  3B. A striking feature of the resulting Manhattan plot was the lack of clustering of SNPs with high q-values which might be related to a small extent of linkage disequilibrium (LD) across the D. suzukii populations, as expected from their large effective populations sizes  (Fraimout et al. , 2017). We identified 101 SNPs (including three X-linked) that were significant at the 1% q-value threshold (i. e. , 1% of these 101 SNPs are expected to be false positives). As a matter of comparison, we also estimated the BF for association of the (standardized) population allele frequencies with the native or invasive status of the population, i.
To that end, we relied on the Bayesian hierarchical model implemented in the software BayPass that has several valuable properties including (i) the accurate estimation of the scaled covariance matrix of population allele frequencies (Ω), (ii) the integration over the uncertainty of the across population allele frequencies (π parameter), and (iii) the inclusion of additional layers of complexities such as the sampling of reads from (unobserved) allele counts in Pool-Seq data  (Gautier, 2015). However, as previously mentioned, the Bayesian hierarchical modeling results in shrinking the posterior means of the (lower-level) model parameters  (Kruschke, 2014)  and also related statistics such as, here, the C 2 and XtX differentiation statistics. To ensure proper calibration of the two corresponding estimates, we hence needed to rely on the rescaled posterior means of the standardized allele frequencies.
Simulation study w(o) = I i=1 2 k=1 (1+e k (p(o))(1-g i (o))s i (k)) (6) where g i (o) is the genotype of individual o at marker i coded as the number of the reference allele (0, 1 or 2). In a second time, we evaluated the robustness of the new C 2 criterion as well as the BF statistic to the occurrence of bottleneck events, as the latter are expected (especially in the context of biological invasion) to strongly impact the genetic variation of populations within invaded areas and between invasive and native areas (e. g,  Estoup et al. , 2016). To this aim, we used the software DIYABC v2. 1. 0 (Cornuet et al. , 2014)  to simulate data sets composed of selectively neutral and independent SNP loci under two invasion scenarios depicted in Figure  2   Hudson (2002)  which is equivalent to applying a default MAF threshold on the simulated data sets.
As a matter of fact, each locus will be characterized by the presence of at least a single copy of a variant over all genes sampled from all studied populations (i. e. pooling all genes genotyped at the locus). We further applied a 1% MAF threshold on the simulated data sets (i. e. , similar to that used on real data) before analyzing them using BayPass (hence a total of 165,020 and 152,321 SNPs analyzed with BayPass under the scenarios Inv1 and Inv2, respectively). S2 ). Samples were collected between 2013 and 2016 using baited traps (with a vinegar-alcohol-sugar mixture) and sweep nets, and stored in ethanol. Only four of the 22 samples were composed of flies which directly emerged in the lab from fruits collected in the field (Table  S2 ). Native Asian samples consisted of a total of six sample sites including four Chinese and two Japanese localities.
A detailed description of the two invasion scenarios with values of the historical and demographical parameters used to simulate the two SNP data sets is provided in Figures 2A and 2B. We used the ''Simulate dataset + SNP option'' of DIYABC v2. 1. 0 to generate autosomal SNP genotypes (n = 50 diploid individuals sampled per population) at 250,000 loci under both the scenarios Inv1 and Inv2, following the algorithm by.
Acknowledgments We wish to thank our three anonymous reviewers for their very helpful and constructive comments. AE, MG and LO acknowledge financial support from the  National Research Fund ANR (France)  through  the  project  ANR-16-CE02-0015-01  (SWING), the  Languedoc-Roussillon region (France)  through the  European Union  program  FEFER FSE IEJ 2014-2020  (project  CPADROL ) and the  INRA scientific department SPE  ( AAP-SPE 2016  and  2018 ). MGX acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). We are grateful to the genotoul bioinformatics.
5 ng/l into 25 l of a PCR mixture containing 2. 5 l of each Nextera Index primer (Illumina), 0. 2 mM of each dNTP (Thermofisher), 0. 04 U/l of Phusion DNA polymerase (Thermofisher) and the corresponding buffer at the recommended concentration. The mixture was subjected to an initial denaturation step of 3 min at 95 • C followed by 20 cycles of: 30 s at 95 • C, 30 s at 55 • C, 30 s at 72 • C and the run was concluded by 5 min of final extension at 72 • C. PCR products were then purified on a 1% agarose gel as above. Indexed DNA libraries were then quantified as recommended by Illumina, loaded and analyzed on a MiSeq intrument using a MiSeq Reagent kit V2 300 cycles cartridge and a pair-end protocol. Sequencing data analysis. Fastq data files were analyzed using a custom Python-written pipeline.
In contrast to bacterial tRNAs for Gly, the eukaryotic tRNA Gly do not exhibit the potential for 32-38 WC pair (mainly C/C or C/A in all eukaryotes tested on the GtR-NAdb database (70)). Explanations have been suggested for the presence of eukaryotic G34-containing tRNA Gly : structural incompatibility of A34 in the anticodon loop  (71)  or frame maintenance  (72). We note also the conserved presence of GU pairs in the alignments of anticodon stem of eukaryotic tRNA Gly  (70).
ACKNOWLEDGEMENTS We are grateful to  Friedrich Preusser ,  Fatima Alghoul  and  Lauriane Gross  for technical assistance and we also warmly thank  Sandrine Koechler  and  Abdelmalek Alioua  from the  IBMP Gene Expression Analysis  facility for technical assistance with high-throughput sequencing. The facility is supported and funded as part of the  LabEx NetRNA and MitoCross. The mass spectrometry instrumentation was granted from  Université de Strasbourg  ( IdEx 2015 Equipement mi-lourd ).
FUNDING Agence Nationale pour la Recherche [Ribofluidix ,  ANR-17-CE12-0025-01/02  and  RFS ,  ANR-15-CE11-0021-01 ];  University of Strasbourg  [PEPS 2015-IDEX];  Centre National de la Recherche Scientifique ;  LabEx  [ ANR-10-LABX-0036  NETRNA];  French National Research Agency. Funding for open access charge: CNRS.