dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome Blast Distance Phylogeny (GBDP  (Meier-Kolthoff et al. 2013) ) and Average nucleotide identity (ANI) are currently the most frequently used OGRI for assessing the relatedness between genomic sequences. Distinct ANI algorithms such as ANI based on BLAST (ANIb  (Goris et al. 2007 )), ANI based on MUMmer (ANIm  (Richter, Rosselló-Móra 2009) ) or ANI based on orthologous genes (OrthoANIb  (Lee et al. 2016) ; OrthoANIu  (Yoon et al. 2017) ; gANI,AF  (Varghese et al. 2015) ), which differ in their precision but more importantly in their calculation times  (Yoon et al. 2017) , have been developed. Indeed, improvement of calculation time for whole genomic comparison of large datasets is an essential parameter. As of November 2018, the total number of prokaryotic genome sequences publicly available in the NCBI database is 170,728.
2016; Ounit et al. 2015). While these k-mer based classifiers differ in terms of sensitivity and specificity  (Sczyrba et al. 2017) , they rely on accurate genome databases for affiliating a read to a taxonomic rank. The objective of the current work was to propose a workflow to quickly compute OGRI and visualize the outputs in an efficient manner. An alternative method based on k-mer counts was first evaluated to study species delimitation on extensive genome datasets. We employed k-mer counting to assess the similarity among genome sequences belonging to the Pseudomonas genus. Indeed, this genus contains an important diversity of species (n = 207), whose taxonomic affiliation is under constant evolution  (Peix et al. 2009; Berge et al. 2014; Gomila et al. 2017 Gomila et al. , 2015;; Peix et al.
2018; Garrido-Sanz et al. 2016; Hesse et al. 2018) , and numerous genome sequences are available in public databases. We also proposed an original visualization tool based on D3 Zoomable Circle Packing (https://gist. github. com/mbostock/7607535) for assessing the relatedness of thousands of genome sequences. Finally, the benefit of taxonomic curation of reference database on the taxonomic affiliation of metagenomics read sets was assessed. The developed workflow was integrated in the user-friendly KI-S tool which is available in the galaxy toolbox of CIRM-CFBP (https://iris. angers. inra. fr/galaxypub-cfbp).
Development of KI-S workflow An integrative workflow named KI-S (Kinship relationships Identification with Shared k-mers) was developed. At the first step, the percentage of shared k-mers (k can be selected by the user) between genome sequences is calculated with Simka  (Benoit et al. 2016)  or estimated with Kmer-db  (Deorowicz et al. 2019). A custom R script is then employed to cluster the genome sequences according to their connected components at different selected thresholds (e. g. 50% of shared 15-mers). The clustering result is visualized with Zoomable Circle Packing representation with the D3. js JavaScript library (https://gist. github. com/mbostock/7607535). The source code of the KI-S workflow is available at the following address: https://sourcesup. renater. fr/wiki/ki-s. A wrapper for accessing KI-S in a user-friendly Galaxy tool is also available at the following address: https://iris.
Taxonomic inference of metagenomic read sets Classification of nine metagenomic read sets derived from seed, germinating seeds and seedlings of common bean (Phaseolus vulgaris var. Flavert) were estimated with Clark version 1. 2. 4  (Ounit et al. 2015)  at the species level. Clark is a method based on a supervised sequence classification using discriminative k-mers  (Ounit et al. 2015). These metagenomic datasets were selected because of the high relative abundance of reads affiliated to Pseudomonas  (Torres-Cortés et al. 2018). The following Clark parameters -k 31 -t <minFreqTarget> 0 and -o <minFreqtObject> 0 were used for the taxonomic profiling. Indeed, reducing k increase the number of read assignments but also increase the probability of misclassification  (Ounit et al. 2015). Three distinct Clark databases were employed: (i) the original Clark database from NCBI/RefSeq (ii) the original Clark database supplemented with the 3,623 Pseudomonas genome sequences and their original NCBI taxonomic affiliation (iii) the original Clark database supplemented with the 3,623 Pseudomonas genome sequences whose taxonomic affiliation was corrected according to the reclassification based on the number of shared k-mers.
Selection of optimal k-mer size, k-mers indexing softwares and percentage of shared k-mers Using the percentage of shared k-mers as an OGRI for species delineation first required the determination of the optimal k-mer size. This was performed by comparing the percentage of shared kmers with SIMKA  (Benoit et al. 2016 ) to a widely employed OGRI, ANIb  (Goris et al. 2007) , among 934 Pseudomonas genome sequences. Since the species delineation threshold was initially proposed following the observation of a gap in the distribution of pairwise comparison values  (Grimont 1988) , the distribution profiles obtained with k-mer lengths 11, 13, 15, 17 and 19 were compared to ANIb values. Short k-mers (k =11) were evenly shared by most strains and therefore non-discriminatory (Fig. 1 ). As the length of the k-mer increased (k ≥ 13), a multimodal distribution based on several peaks was observed (Fig.
g. FastANI, Simka, Kmer-db, Mash) were available at the time of analysis. Mash outperformed all others tools in term of computation time (Table  1 ). Indeed, computation time of 15-mers for 934 genome sequences was 7 min with Mash on a DELL Power Edge R510 server, while it took approximately 3 months for obtaining all ANIb pairwise comparisons. The outputs of these different softwares were next compared to ANIb (Fig. 2 ). FastANI was the best estimator of OGRI as indicated by the strong linear relationship of average pairwise similarities with ANIb values (Fig. 2 ). However, one small caveat is that sequence similarity values were ignored by FastANI when below 0. 76 value, which artificially improved the linear relationship. According to these linear relationships, Simka and Kmer-db performed reasonably well for ANIb values above 0.
Classification of Pseudomonas genome sequences The percentage of shared 15-mers was then used to investigate the relatedness between 3,623 Pseudomonas publicly available genome sequences. At a threshold of 50% of 15-mers, we identified 350 groups. The group containing the most abundant number of genome sequences was related to P. aeruginosa (n = 2,341), followed by the phylogroups PG1 (n = 111), PG3 (n = 92) and PG2 (n = 74) of P. syringae species complex (  (Berge et al. 2014) ; Table  S1 ). At the clustering threshold employed, 185 groups were composed of a single genome sequence, therefore highlighting the high Pseudomonas strain diversity. Graphical representation of clustering by dendrogram for a large dataset is generally not optimal. Here we employed Zoomable circle packing as an alternative to dendrogram for representing similarity between genome sequences (Fig.
Improvement of taxonomic affiliation of metagenomic read sets. The taxonomic composition of metagenome read sets is frequently estimated with k-mer based classifiers. While these k-mer based classifiers differ in terms of sensitivity and specificity, they all rely on accurate genome databases for affiliating a read to taxonomic rank. Here, we investigated the impact of database content and curation on taxonomic affiliation. Using Clark  (Ounit et al. 2015)  as a taxonomic profiler with the original Clark database, we classified metagenome read sets derived from bean seeds, germinating seeds and seedlings  (Torres-Cortés et al. 2018). Adding the 3,623 Pseudomonas genome sequences with their original taxonomic affiliation from NCBI to the original Clark database did not increase the percentage of classified reads (Fig.
4 ). However, adding the same genome sequences reclassified in groups according to their percentage of shared k-mers (k=15; threshold= 50%) increased 1. 4-fold on average the number of classified reads (Fig. 4 ). Figure  4. Percentage of classified reads. Classification of metagenome read sets derived from bean seeds, germinating seeds and seedlings with Clark  (Ounit et al. 2015). Three distinct databases were employed for read classification: the original Clark database (red), Clark database supplemented with 3,623 Pseudomonas genome sequences (green) and the Clark database supplemented with 3,623 Pseudomonas genome sequences that were classified according to their percentage of shared k-mers (blue).
Discussion Classification of bacterial strains on the basis on their genome sequence similarities has emerged over the last decade as an alternative to the cumbersome DNA-DNA hybridizations  (Meier-Kolthoff et al. 2013; Rosselló-Móra, Amann 2015). Although ANIb is one widely employed method for investigating genomic relatedness, its intensive computational time prohibits its use for comparing large genome datasets  (Yoon et al. 2017). In contrast, investigating the percentage of shared k-mer is scalable for comparing thousands of genome sequences. In a method based on k-mer counts, choosing the length of k is a compromise between accuracy and speed. The distribution of shared k-mer values between genome sequences is impacted by k length. For k = 15, four peaks were observed at 15%, 50%, 80% and 100% of shared k-mers.
The second peak is close to an ANIb value of 0. 95 and falls in the so called grey or fuzzy zone (Rosselló-Móra, Amann 2015) where taxonomists might decide to split or merge species. Hence, according to our working dataset, it seems that 50% of 15-mers is a good proxy for estimating Pseudomonas group. Despite the diverse range of habitats colonized by different Pseudomonas populations  (Peix et al. 2018) , it is likely that the percentage of shared k-mers has to be adapted when investigating other bacterial genera. Indeed, since population dynamics, lifestyle and location impact molecular evolution, it is somewhat illusory to define a fixed threshold for species delineation  (Bromham 2009). While 15-mers is a good starting point for investigating infra-specific to infra-generic relationships between genome sequences, the computational speed of KI-S offers the possibility to perform large scale genomic comparisons at different k sizes to select the most appropriate threshold.
Genomic relatedness using whole genome sequences has become the standard method for bacterial strain identification and bacterial taxonomy  (Meier-Kolthoff et al. 2013; Rosselló-Móra, Amann 2015; Parks et al. 2018). This is primarily motivated by fast and inexpensive sequencing of bacterial genomes together with the limited availability of cultured specimen for performing classical polyphasic approach. Whether full genome sequences should represent the basis of taxonomic classification is an ongoing debate between systematicians  (Garrity 2016). While this consideration is well beyond the objectives of this work, obtaining a classification of bacterial genome sequences into coherent groups is of general interest. Indeed, the number of misidentified genome sequences is exponentially growing in public databases.
A number of initiatives such as Digital Protologue Database (DPD (Rosselló-Móra, Sutcliffe 2019)), Microbial Genomes Atlas (MiGA (Rodriguez-R et al. 2018)), Life Identification Numbers database (LINbase  (Vinatzer et al. 2017 )) or the Genome Taxonomy Database (GTDB  (Parks et al. 2018 )) proposed services to classify and rename bacterial strains based on ANIb values or single copy marker proteins. Using the percentage of shared k-mers between unknown bacterial genome sequences and reference genome sequences associated to these databases could provide a rapid complementary approach for bacterial classification. Moreover, KI-S includes a friendly visualization interface that could help systematicians to curate whole genome databases. Indeed, zoomable circle packing could be employed for highlighting (i) misidentified strains, (ii) bacterial taxa that possess representative type strains or (iii) bacterial taxa that contain few genome sequences.
Association between a taxonomic group and its distribution across a range of habitats is useful for inferring the role of this taxa on its host or environment. For instance, community profiling approaches based on molecular marker such as hypervariable regions of 16S rRNA gene have been helpful for highlighting correlations between host fitness and microbiome composition. Higher taxonomic resolution of microbiome composition could be achieved with metagenomics through k-mer based classification of reads. Using the Pseudomonas genus as a use-case, we showed that increasing the breadth of genomic database without investigating the relatedness of genome sequences did not improved the proportion of classified reads. Worse, an unresolved classification may limit the number of species-specific k-mers identified by CLARK and therefore the number of classified reads.
Interestingly, an inverse relationship between the number of genome sequences in NCBI RefSeq database and the number of classified reads at the species level was also recently highlighted with other k-mer-based read classifiers  (Nasko et al. 2018). On the contrary, prior classification of the genomic database improve the number of classified reads at the species level. Hence, investigating the relationships between bacterial genome sequences not only benefits bacterial taxonomy but also indirectly microbial ecology.
a b s t r a c t MicroVIP is an open source software that assembles, in a unified web-application running on distributed computing resources, simulators of the main fluorescent microscopy imaging modalities (with existing codes or newly developed). MicroVIP provides realistic simulated images including several sources of noise (microfluidic blur effect, diffraction, Poisson noise, camera read out noise). MicroVIP also includes a module which simulates single cells with fluorescent markers and a module to analyze the simulated images with textural and pointillist feature spaces. MicroVIP is shown to be of value for supervised machine learning. It allow to automatically generate large sets of training images and virtual instrumentation to optimize the optical parameters before realizing real experiments.
Module 1: Generation of biological objects Module 2 can load any type of 3D fluorescent point cloud. To offer a baseline we created Module 1 which models chromatin (mimicking the yeast genome as described in  [11] ) in a single cell. For each chromatin chain, 100 configurations have been modeled beforehand using InfMod3DGen  [11]. MicroVIP generates fluorescent markers placed along these chains, with distances between two consecutive markers drawn from a random distribution parameterized by the user (see 2. 2. 2). This module outputs the list of 3D markers coordinates (see 2. 2. 3), used as input of module 2.
Software functionalities 2. 2. 1. Interface MicroVIP's interface is reproduced in Fig. 3. It requires only five user inputs described below. Execution name. This field enables users to provide a name identifying their execution in order to monitor its progress in VIP. One can notably see the inputs and outputs used for an execution, and access execution logs as well as data and charts to visualize file transfer and computation times. Results directory. This is a logical path inside VIP File Transfer structure where execution outputs will be made accessible. Number of cells. This is the number of statistically independent instances MicroVIP has to run with the same set of parameters except for the seed (which must be different). This allows generation of simulated data for a whole population of cells modeled with the same biological parameters and imaged with the same microscopy setup.
MicroVIP instances are launched in parallel on European Grid Infrastructure (EGI), enabling a significant time gain in data generation compared to multiple successive MicroVIP executions (see 5. 1). Pipeline. We introduced this input for flexibility of MicroVIP pipeline with regards to the user's needs. It takes values 0, 1 and 2 and allows one to execute only the first module, the first two modules, or all three of them respectively. In other words, it dictates if simulation should stop after ground truth modeling (0), continue until microscopy image simulation (1) or also perform features extraction  (2). It mostly exists to avoid the time consuming extraction of multiple features when the user only needs the simulated images.
Configuration file The last user input is a. ini text file containing all necessary parameters for each of the three MicroVIP modules. It gives MicroVIP the ability to be completely adaptable and customizable while maintaining the accessibility offered by a trivial graphical user interface. Several example files are available on VIP, and one of them is given in Appendix. ini is a standard file format for configuration files. It allows definition of parameter values as well as comments, and can be structured in several sections. MicroVIP configuration file takes advantage of these features, providing a comprehensive description of each parameter, and grouping them in sections corresponding to the module they play a role in.
tif file format. This directory will be empty if module 2 is not executed (pipeline value 0). final_images. This directory contains simulated microscopy 3D image stacks in. tif file format. It will be empty if module 2 is not executed (pipeline value 0). extracted_features. This folder contains 2D, 3D and pointillist features extracted for each simulated image. These are stored in. json file format, in a JSON object with keys ''2D'', ''3D'' and ''Pointillist''. Each key refers to a JSON object mapping features extraction method names to 1D features vectors. More information can be found at https://gitlab. in2p3. fr/guillaume. vanel/microvip/-/wikis/ Features. This directory will be empty if MicroVIP module 3 is not executed (pipeline value 0 or 1). To.
Application MicroVIP can be used for a variety of microscopy applications. In particular, it can be used for research and development in bioimaging application such as single cell imaging (which is natively included in the platform). Additionally, it can be used for virtual instrumentation by realizing low cost and fast in silico tests to determine the best tools and settings before a real microscopy experiment. Moreover, MicroVIP can be used for ma-  chine learning applications such as deep learning based superresolution, also style transfer from an imaging modality to another, or data augmentation as it enables fast and inexpensive realization of a 3D microscopy images database with associated ground truth, for calibration and training of image analysis and/or cell image classification tools.
Illustrative example MicroVIP already enabled to discover non intuitive regimes where sub-resolved low-cost microscopes could perform as well as expansive super-resolved microscopes for cell sorting  [19, 20]. We provide another example where MicroVIP simulator is used to generate synthetic 3D light-sheet fluorescence microscopy images acquired in a microfluidic system with a flow rate of 50 nl/min with objective lens=60x, water immersion and NA=1. 1. Generated images and their ground truth were used to train the deep learning Content Aware Image Restoration (CARE) denoising model  [28]. The model trained on synthetic data was then used to denoise real microscopy images acquired in the same conditions. Images were simulated by adapting MicroVIP configuration file (see 2. 2. 2) to match the real acquisition parameters.
Impact MicroVIP possibly interests several scientific communities including: (i) computer scientists interested in data challenge on bioimaging since they need large amount of annotated datasets, (ii) physicists interested in optimizing optical parameters with virtual instrumentation and (iii) biologists specialized in chromatin imaging in single cell (since this use case is already fully implemented). The impact of MicroVIP will be enhanced, as de-scribed in this section, thanks to the deployment of MicroVIP on the resources of the EGI e-infrastructure and via the availability of tutorials.
Conclusions In this article, we presented MicroVIP, a microscopy image simulator that assembles the main fluorescence microscopies in a single web application. On top of this generic functionality, MicroVIP also includes: (i) a specific use case to generate single cells with markers located on chromatin (ii) a set of standard textural and pointillist features to feed machine learning algorithms. We have illustrated the interest of this software to generate at high-throughput large datasets for training machine learning algorithms or to optimize optical systems via virtual instrumentation. The software is available for public use with an easy interface via Virtual Imaging Platform (VIP). Its code is accessible online under GNU GPL license, along with a comprehensive documentation wiki. MicroVIP already enabled realistic simulation of single-cell fluorescent microscopy imaging for a wide variety of microscopes.
Appendix. Example. ini configuration file ; Default parameter file for MicroVIP applicatons. ; See https://www. creatis. insa-lyon. fr/site7/en/PROCHIP for more information. ; MicroVIP, Microscopy image simulation and analysis tool ; Copyright (C) 2021 Ali Ahmad, Guillaume Vanel, ; CREATIS, Universite Lyon 1, Insa de Lyon, Lyon, France. ; ; This file is part of MicroVIP. ; MicroVIP is free software: you can redistribute it and/or modify ; it under the terms of the GNU General Public License as published by ; the Free Software Foundation, either version 3 of the License, or ; any later version. ; ; This program is distributed in the hope that it will be useful, ; but WITHOUT ANY WARRANTY; without even the implied warranty of ; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ; GNU General Public License for more details.
; ; You should have received a copy of the GNU General Public License ; along with this program. If not, see <https://www. gnu. org/licenses/>. ; This parameter file is divided in three sections, each one used ; by one module of MicroVIP application. If you do not run all three ; modules, you may leave unrelevant sections unmodified. They will ; be ignored at runtime. You can modify other parameters at will after ; carefully their description. ; Parameters for Cell generator module.
Acknowledgments This work has been developed during the thesis of  Ali AHMAD  supported by project  EU H2020 FET Open, PROCHIP , ''Chromatin organization PROfiling with high-throughput super-resolution microscopy on a CHIP'', grant agreement no. 801336  (https:// pro-chip. eu/. The application presented in this work is available through the Virtual Imaging Platform [29], using the resources provided by the biomed virtual organisation of the EGI infrastructure. https://operations-portal. egi. eu/vo/view/voname/biomed.
In quantum crystallography, theoretical calculations and crystallographic refinements are closely intertwined. This means that the employed software must be able to perform both quantum-mechanical calculations and crystallographic least-squares refinements. So far, the program Tonto is the only one able to do that. The lamaGOET interface described herein deals with this issue since it interfaces dedicated quantum-chemical software (the widely used Gaussian package and the specialized ELMOdb program) with the refinement capabilities of Tonto. Three different flavours of quantum-crystallographic refinements of the dipetide glycyl-l-threonine dihydrate are presented to showcase the capabilities of lamaGOET: Hirshfeld atom refinement (HAR), HAR-ELMO, namely HAR coupled with extremely localized molecular orbitals, and X-ray constrained wavefunction fitting.