dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene sequence analyses Identification of ORFs, determination of gene organization and comparative genomic analyses of plasmids and chromosomal regions were performed at the NCBI web site (http://blast. ncbi. nlm. nih. gov/Blast. cgi), MicroScope plateform (https://www. genoscope. cns. fr/agc/microscope/ home/index. php) and INRA AGMIAL  [35]  plateform (http://maiage. jouy. inra. fr/?q=fr/logiciels). In this latter case, the Artemis program was used. Sequence comparisons were performed using BLAST tools (blastn, blastp and tblastn) with default parameters. BLAST analyses were This strain is also a derivative of strain C58 achieved on both the non-redundant nucleotide sequence collection (nr/nt) and whole-genome shotgun contigs (WGS) databases. Prediction of functional domains was achieved using InterPro  [36].
The generated data were demultiplexed by the high throughput sequencing platform of the Institute for Integrative Biology of the Cell (Gif-sur-Yvette, France) using CASAVA software suite (CASAVA-1. 8. 2; Illumina, San Diego, USA). The quality of the data was checked with the software FastQC 0. 10. 1 from Babraham Bioinformatics, Cambridge, UK). The Illumina 3′-adapter was trimmed using Cutadapt-1. 2. 1 (Python community), and only reads with a minimal length of 10 nucleotides were kept. After the trimming step, the data were mapped using TopHat2 (Center for Computational Biology, John Hopkins University, Baltimore, USA) with a seed length of 8 (default value) to the appropriate reference genome of P4 (including 3 scaffolds). Only unique reads were analyzed. Bam output files were sorted with Samtools (http://www.
htslib. org/). HTSeq-count (EMBL, Heidelberg, Germany) was used to evaluate the number of reads by gene or by CDS. The DESEQ package  [40]  was used to analyze the differential expression of genes between the reference strain P4 and the cinI mutant. Standard RT reactions were performed with 1 μg total RNA obtained as indicated above, using random hexamers as primers and the Revert Aid TM HM Inis First Strand cDNA Synthesis Kit (Fermentas) according to the manufacturer's instructions. RT-qPCRs were performed in a Lightcycler® 480 II (Roche) apparatus. The data were processed using the 2 -ΔΔCT method. For all primer sets (Table  2 ), the similarities of amplification efficiencies were controlled. The expression of the following 8 pAtP4 genes was investigated: avhB11, avhB5, traA, traG, cinR, cinI, cinX, and AGROTU_05920.
Acknowledgments The authors wish to thank  Dr. Xavier Nesme  ( University of Lyon I ) for helpful discussions on Agrobacterium taxonomy and pAt occurrence in nature,  Almudena González-Mula  and  Dr. Julien Lang  ( I2BC, Gif-sur-Yvette, France ) for advice on mutagenesis, RT-qPCR and plasmid conjugation, and  Pr. Mike DuBow  and  Dr. Peter Mergaert  ( I2BC, Gif-sur-Yvette, France ) for correction of the language in the manuscript. This work has benefited from the facilities and expertise of the high throughput sequencing platform of the  Institute for Integrative Biology of the Cell (I2BC, Gif-sur-Yvette, France ) for RNA-seq analyses. The authors are also very grateful to  Odile Thoison  ( ICSN-CNRS, Gif sur Yvette, France ) for her advice on chromatography analyses.
Funding The Institut for integrative biology of the cell benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). This work was made possible by a grant from  Agence Nationale de la Recherche (ANR)  via the  ECORUM  project ( ANR  project  11-BSV7-019 ). The financial support of  NMH  through this grant is hereby acknowledged.
Availability of data and material Agrobacterium strain P4 is available from the authors upon request. The A. tumefaciens P4 genome sequence has been deposited at DDBJ/EMBL/ GenBank under the accession number APJV00000000 (http://www. ncbi. nlm. nih. gov/nuccore/APJV00000000). Transcriptome data obtained from the high throughput sequencing platform of the Institute for Integrative Biology of the Cell are given in Additional file 5.
Definition of computational pan-genomics The term 'pan-genome' was first used by Sigaux  [13]  to describe a public database containing an assessment of genome and transcriptome alterations in major types of tumors, tissues and experimental models. Later, Tettelin et al. [9]  defined a microbial pan-genome as the combination of a 'core' genome, containing genes present in all strains, and a 'dispensable' genome (also known as flexible or accessory genome) composed of genes absent from one or more of the strains. A generalization of such a representation could contain not only the genes, but also other variations present in the collection of genomes. The idea of transitioning to a human pan-genome is also gaining attention (see http://www. technologyreview. com/news/537916/rebooting-thehuman-genome, for an example of recent media coverage).
Critical to this success have been catalogs of human genome variation, their linkage disequilibrium (LD) properties as well as the commercial development of cheap microarrays. One of the primary applications of the resources provided by the HapMap  [56]  and 1000 Genomes  [8]  (1KG) projects was LD-based imputation  [57] , allowing GWAS to test many more variants than typically present on a single microarray chip. Although the 1KG database is predicted to include >99% of all variants with a frequency of at least 1%  [8] , many other independent sequencing efforts revealed that many variants below 1% remain to be discovered as we continue to sequence more and more samples. This is probably best illustrated by the Exome Aggregation Consortium database  [54] , which provides detailed information on 7.
Coordinate system A pan-genome defines the space in which (pan-)genomic analyses take place. It should provide a 'coordinate system' to unambiguously identify genetic loci and (potentially nested) genetic variants. Desirable properties of such a 'coordinate system' include that nearby positions should have similar coordinates, paths representing genomes should correspond to Figure  1. A sequence graph assembled from the Homo sapiens reference genome and all transcripts of the EEF1A1 gene in Ensembl v80  [93]. Each node represents a sequence found in the reference genome or transcripts, edges (black solid lines) indicate adjacencies that have been observed in transcripts and colored horizontal bars correspond to paths of known transcripts (annotated with their Ensembl transcript ID), where each dotted line indicates that a node in the graph is part of this transcript.
[115]  show how such compressed DBGs can be constructed for a pan-genome by first identifying maximal exact matches using a suffix tree, bypassing uncompressed DBGs. Beller and Ohlebusch  [116]  and Baier et al. [117]  show how the same can be achieved more efficiently, using an FM index resp. compressed suffix trees and the Burrows-Wheeler transform. Trading efficiency for comprehensibility, compressed DBGs can also form the foundation for annotated pan-genomes stored in graph databases  [118]. Useful data structures for pan-genomes may combine some of the basic approaches discussed so far. For example, PanCake  [119]    Further examples of implementations of sequence graphs include the Global Alliance for Genomics and Health (GA4GH) 'side graph' data model and the FASTG format (http://fastg.
sour ceforge. net). Side graphs represent a pan-genome as a set of sequences and an additional set of joins, each of which defines an extra adjacency between the sides of two bases within the sequences. The GA4GH graph tools (https://github. com/ga4gh/ser ver and https://github. com/ga4gh/schemas) allow side graphs and embeddings of individual sampled genomes in that graph to be made available over the Internet, for data distribution and remote analysis.
Computational challenges Pan-genomic data have all of the standard properties of 'Big Data'-in particular, volume, variety, velocity and veracity. Especially owing to the sheer size of generated sequencing data, extreme heterogeneity of data and complex interaction on different levels, pan-genomics comes with big challenges for algorithm and software development  [122]. The International Cancer Genome Consortium has amassed a data set in excess of two petabytes in just 5 years with the conclusion to store data generally in clouds, providing an elastic, dynamic and parallel way of processing data in a cheap, flexible, reliable and secure manner  [123]. Currently large computing infrastructure providers and large public repositories (e. g. National Center for Biotechnology Information (NCBI)/European Bioinformatics Institute (EBI)/DNA Data Bank of Japan (DDBJ)) are completely separated.
We need hybrids that offer both large public repositories as well as the computing power to analyze these in the context of individual samples/data. We consider it desirable to bring the computation as close as possible to the data by uploading queries or indatabase computing. 'Distributed and parallel computing' will be necessary to successfully handle pan-genome data in practice. To this end, leveraging the capabilities of existing Big-Data frameworks is desirable and should be combined with bringing the computation as close as possible to the data. On the practical side, tackling these challenges will also involve establishing widely accepted standards for 'file formats' for sequence graphs and related data like read alignments to such graphs. On the theoretical side, studying changes in algorithmic complexity when working with sequence graphs instead of sequences will be an interesting and challenging aspect of computational pan-genomics.
Similar k-mer indexing on sequence graphs has since been used and extended in several read mapping tools such as MuGI  [129] , BGREAT  [130]  and VG (https://github. com/ekg/vg). Instead of k-mer indexing, one can also use Burrows-Wheeler-based approaches, based on appending extracted contexts around variations to the reference genome  [131]. Context extraction approaches work only on limited pattern length, as with long patterns they suffer from a combinatorial explosion in regions with many variants; the same can happen with a full sequence graph when all nearby k-mer hit combinations are checked using dynamic programming. There is also a special Burrows-Wheeler transform and an index based on that for a sequence graph  [132, 133]. This approach works on any pattern length, but the index itself can be of exponential size in the worst case; best case and average case bounds are similar to the run-length compressed indexes for sets of references like  [126].
The approach is also likely to work without exponential growth on a core sequence graph of  [128] , but as far as we know, this combination has not been explored in practice. A recent implementation (https://github. com/jltsiren/gcsa2) avoids the worst case exponential behavior by stopping the construction early; if this happens, the approach also limits the maximum read length. This implementation has been integrated into VG as an alternative indexing approach. HISAT2 (https://ccb. jhu. edu/software/hisat2/index. shtml)  [94]  implements an index structure that is also based on  [132] , but builds many small index structures that together cover the whole genome. In summary, a number of approaches to perform read mapping against a pan-genome reference under various representation models exist, and efficient implementations for daily usage are under active development.
Storing variants Storing and exchanging variant calls genotyped in a large cohort of samples increasingly becomes a bottleneck with growing cohort sizes. Some improvement is achieved by adopting binary instead of text-based data formats for variant calls, i. e. using BCF instead of VCF (http://samtools. github. io/hts-specs/), but more efficient approaches are urgently needed. Organizing data by individual rather than by variant while sorting variants by allele frequency has proven beneficial for compression and some retrieval tasks  [136]. We expect the question of storing, querying and exchanging variant data to remain an active and relevant field of research in the coming years.
The latter case, translation, where a pan-genome alignment is used to show information on the most closely related genome possible, is likely to become more popular as the number of available personal genomes grows, see  [25]  for an early example of such an approach. More adventurously than linear layouts, pan-genome displays can attempt to visualize graphs of variation. This has the flexibility of allowing arbitrary genome variation within a clean semantic model, but can prove visually complex for even small, nontrivial examples. For example, a graph of a few dozen bacterial strains contains tens to hundreds of thousands of nodes and edges. So far graph visualizations have proved popular for assemblies, and the visualization of heterozygosity, for example, DISCOVAR  [152] , contains a module that allows you to visualize subsets of an assembly graph in a figure.
One of the currently most evident processes in computational pan-genome research is the move away from linear reference genomes toward reference systems that are rooted in graph theory in some form. The effort of the Data Working Group of the GA4GH is a prominent example for this. We have also discussed how the transition in terms of data structures will affect operations such as read mapping, variant discovery, genotyping and phasing, all of which are at the core of modern genomics research. Last but not least, we have analyzed the issues that arise in visualizing pan-genomes, and we have briefly discussed future issues in uncertain data handling, recently an ever recurring theme in genome data analysis, often arising from the repetitive structure of many genomes. We have concentrated on computational challenges of pangenomics in this survey.
The Netherlands Organization for Scientific Research (NWO) Vidi (639. 072. 309 to A. S. , 864. 14. 004 to B. E. D. ); CAPES/BRASIL (to B. E. D. ); the Academy of Finland (284598 [CoECGR] to V. M. and D. V. ); the Russian Scientific Foundation (14-11-00826 to L. D. ); Institut de Biologie Computationnelle (ANR-11-BINF-0002 to E. R. ); and the French Colib'read project (ANR-12-BS02-0008 to E. R. ). NSFC 31671372 (to K. Y. ); the Dutch Graduate School for Experimental Plant Sciences (054EPS15 to S. S. ); the EMGO Institute for Health and Care Research (EMGO+) to K. O. ; the National Human Genome Research Institute (1U54HG007990 [BD2K] to B. P. and A. M. N. , 5U41HG007234 [GENCODE] to B. P. ); the W. M. Keck Foundation (DT06172015 to B. P. and A. M. N. ); the Simons Foundation (SFLIFE# 351901 to B.
Acknowledgments We are deeply grateful to the  Lorentz Center  for hosting the workshop ' Future Perspectives in Computational Pan-Genomics ' ( 8-12 June 2015 ), which gave rise to this article. In particular, we like to thank the  Lorentz Center  staff, who turned organizing and attending the workshop into a great pleasure. The workshop received additional financial support by  KNAW ,  Bina Technologies , ERIBA, PacBio and Genalice. E. E. E. is an investigator of the  Howard Hughes Medical Institute.
Funding The Netherlands Organization for Scientific Research (NWO) Vidi (639. 072. 309 to A. S. , 864. 14. 004 to B. E. D. ); CAPES/BRASIL (to B. E. D. ); the Academy of Finland (284598 [CoECGR] to V. M. and D. V. ); the Russian Scientific Foundation (14-11-00826 to L. D. ); Institut de Biologie Computationnelle (ANR-11-BINF-0002 to E. R. ); and the French Colib'read project (ANR-12-BS02-0008 to E. R. ). NSFC 31671372 (to K. Y. ); the Dutch Graduate School for Experimental Plant Sciences (054EPS15 to S. S. ); the EMGO Institute for Health and Care Research (EMGO+) to K. O. ; the National Human Genome Research Institute (1U54HG007990 [BD2K] to B. P. and A. M. N. , 5U41HG007234 [GENCODE] to B. P. ); the W. M. Keck Foundation (DT06172015 to B. P. and A. M. N. ); the Simons Foundation (SFLIFE# 351901 to B.
, USA). HPLC experiments were developed on an Ultimate 3000 Chromatograph (Dionex) with Chromeleon v. 6. 8 software. The measurements were performed using an RP 18 column (Lichrospher 100 RP 18 E. C. 5 mm; 10 Â 0. 46 cm; Teknokroma) as the Scheme 1. Synthesis of arylamines not commercially available. Scheme 2. Synthesis of arylamino alcohols 14e26. stationary phase at a flow rate of 1 mL/min and with MeOH/water (80:20) as the mobile phase. The retention times (t R ) are expressed in minutes and the reference wavelength is set at 254 nm. Compounds II and III were synthesized as previously described  (Perez-Silanes et al. , 2009; Mendoza et al. , 2011). 2. 2. 2. General synthetic methods 2. 2. 2. 1. General method 1 for the synthesis of ketone derivatives (1e13). The corresponding substituted aryl methyl ketone (V) (1.
Antiplasmodial activity and genotoxicity study for chemical intermediates The commercially available intermediate 4-(4-fluorophenyl)-1,2,3,6-tetrahydropyridine (AM01) and the synthesized intermediate 4-(4-nitro-2-trifluoromethylphenyl)aminopiperidine (AM02) were tested against P. falciparum FCR-3 multidrug resistant strain using the methodology followed to evaluate final compounds (14e26). Genotoxicity test SOS/umu was performed on AM01 and AM02 using the protocol followed to evaluate final compounds  (22, 23, 24, and 25). MetaSite software 3. 0. 4 (Molecular Discovery Ltd. ) was used to predict human liver metabolism (site of metabolism) and potential metabolites using the P450 liver model that involves the three major liver isoforms (CYP3A4, CYP2C9, and CYP2D6). Although MetaSite does not have mouse CPY models, it is well known that these human isoforms have mouse homologs  (Cui et al.
Three-dimensional pharmacophore model for new APD The LigandScout software  (Wolber and Langer, 2005)  4. 09. 2 (InteLigand GmbH Ltd. ) was used to build and describe the pharmacophore models. Models were obtained using the ligand-based pharmacophore design that is implemented in the espresso module. For the APD model, the most active compounds were used as a training set (22, 23, 24, and 25; FCR-3 IC 50 0. 5 mM), which included both possible enantiomers (R and S configurations) for each compound. For the classical arylamino alcohols (CAA), a consensus model was generated using the four molecules (quinine, mefloquine, lumefantrine, and halofantrine) and respective stereoisomers. All molecules used in the pharmacophore study were drawn using ChemDraw Ultra software 7. 0 (CambridgeSoft Ltd. ) with stereoisomerism manually checked.
Pharmacophore representation including hydrophobic features (HPF), hydrogen bond donor features (HBDF), hydrogen bond acceptor features (HBAF), positive ionizable features (PIF), and aromatic ring features (ARF) were projected on molecules. For comparison of the final pharmacophore models (APD and CAA), a consensus model was constructed for APD. The R and S models were aligned using the alignment module in LigandScout. Both pharmacophores were merged and the overlapping features were interpolated. Finally, the new pharmacophore model for APD was aligned with the consensus pharmacophore model for CAA where only common features in both models were calculated and extracted.
Intermediates AM01 and AM02 (IC 50 ¼ 8. 2 mM and IC 50 ¼ 4. 3 mM, respectively) showed low antiplasmodial activity compared to their final products. To explore the probability of obtaining the corresponding intermediates as potential metabolites, an in silico approach using MetaSite software was performed. This approach allowed for a simple, fast, and inexpensive method of identifying the most probable site of metabolism (SoM) of the most active compounds and therefore, predicts P450-derived metabolites. As shown in Fig. 2 , MetaSite predicted the P450 2C9-, P450 3A4-, and P450 2D6-catalyzed N-dealkylation of 22, 23, and 25 as the most probable biotransformation pathway. Our predictions indicated that N-dealkylation would occur principally on the side chain a-carbon hydrogen(s) next to the amino aliphatic nitrogen for 22 and 25, and next to the tertiary amine for 23.
Thus, PM2 was discarded as a target for APD. Interestingly, our findings were better understood when we compared our results with other hybrid studies (computational and experimental approaches). Friedman and Caflisch (2009)  performed high-throughput docking simulations using consensus-scoring methods to screen a database of 40,000 molecules resulting in 11 arylamino alcohols, halofantrine and 10 structurally related to halofantrine and lumefantrine, that were plasmepsin inhibitors (PM1, PM2, and PM3). Important facts when analyzing and comparing our results include the following: (1) Halofantrine shares the g-amino alcohol moiety with APD (Fig. 1);  (2) According to the proposed docking experiments, halofantrine may share the same theoretical binding mode as APD  (Friedman and Caflisch, 2009; Mendoza et al.
Acknowledgments This work was supported by  PIUNA Project-University of Navarra and Foundation CAN  (grant number:  70391 ). The authors are grateful to  Universidad de Antioquia for its Sustainability Strategic Plan  and the  Institute of Tropical Health (ISTUN) of University of Navarra  for the financial support and help. Miguel Quiliano  is grateful to " Programa Nacional de Innovaci on para la competitividad y productividad " ( Inn ovate-Perú ) for his PhD scholarship (grant  065-FINCYT-BDE-2014 ). Adela Mendoza  acknowledges  ADA  ( University of Navarra ) for a PhD scholarship. The authors thank  MF Richards  for critical reading of this manuscript.
e. pressure-temperature-time path) of main tectonic units. The latter also provides information on the burial and exhumation processes, constraining the vertical tectonic evolution of these units. Using the interactive plate-tectonic reconstruction and visualization software GPlates (www. gplates. org) [Boyden et al. , 2011] , we reconstruct the kinematics of the eastern Mediterranean region backward from the present-day plate configuration to the late Cretaceous. Relative motions of deforming domains are described by finite rotations given by the geographical.
A C C E P T E D M A N U S C R I P T coordinates of the rotation pole and the rotation angle. We then obtain, for each defined domain, a succession of total reconstruction poles, corresponding to the finite rotation from the present-day position to the position at a given time in the past (positive and negative angles of rotation depicting, respectively, clockwise and counterclockwise rotations) (see Supplementary Materials, table  S1 ). All these rotations are defined with respect to the Moesian platform, which is considered as having belonged to stable Eurasia since the late Cretaceous (figure  3 )  [van Hinsbergen et al. , 2008]. Finally, in order to discuss the space and time evolution of magmatism across the study area, we compile a GIS database of the magmatic products emplaced in this region since the late Cretaceous.
Acknowledgments This work was funded by the  French Geological Survey (BRGM) ,  Région Centre , the  Institut Universitaire de France , the  European Research Council (ERC)  under the 7th  Framework Programme  of the  European Union  ( ERC Advanced Grant , grant agreement No. 290864 , RHEOLITH) and the  Labex VOLTAIRE  ( ANR-10-LABX-100-01 ). We thank  Laurent Guillou-Frottier  and  Guillaume Bertrand  for constructive remarks and  Frances Westall ,  Chris Rollins  and  Brian Cox  for helpful proofreading and corrections. Aral Okay ,  Loïc Labrousse  and  Olivier Vanderhaeghe  provided suitable comments on an earlier version of this paper that led to a greatly improved manuscript.