dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, Ensembl  [198]  stores different information including syntenic blocks generated by Pecan  [169]  as a multi-alignment algorithm and Enredo to detect syntenic blocks. Synteny portal  [199]  and Genomicus  [200]  provide also syntenic blocks generated by inferCars  [201]  for different species but also multiple visualizations. Finally, other databases exist including ECRbase  [202]  with syntenic blocks generated from the DNA level. OrthoClusterDb  [203]  is a good example of what can be found inside these databases. Two main possibilities are available. First, it allows online access to the OrthoCluster tool  [177]  and to carry out identification of syntenic blocks on a remote server using a graphical interface facilitating the configuration and the retrieval of the results. Another possibility is to access different pre-computed syntenic blocks by OrthoCluster for different species.
Homology Relationships Databases Dataset of duplicated genes without specification of the underlying mechanism of duplication can be retrieved from public databases. These databases can be associated or not with a specific methodology with available tools for a local use. The INPARANOID 8 database for example, provides the InParanoid tool and proposes orthology analysis between 273 proteomes, mostly eukaryotic. The dataset of orthologous and paralogous relationships between genes can be downloaded by pairs of species  [204]. In HOGENOM, gene families are built from complete genomes from all three domains of life  [147]. Its clustering pipeline is based on the SiLiX clustering method  [205]. Even if this database is regularly updated, users can only retrieve families one by one according to keywords.
The total amount of paralogous genes in a species is only available upon request directly to the authors. Ensembl Compara is a specific section of the Ensembl database providing cross-species resources and analyses, at both the sequence and the gene levels. The main Ensembl database is dedicated to chordate genomes and displays now counterparts for several groups of organisms (Ensembl Genomes, Ensembl Bacteria, Ensembl Protists, Ensembl Fungi, Ensembl Plants, and Ensembl Metazoa). All these databases are associated with the Ensembl Compara system. This system provides access to protein gene families via a Perl API  [198]. These families are built using all proteins from Ensembl through a classical process using BLASTP for similarity searches and a MCL clustering with scores as weight for edges in the initial graph.
A final step aligns all sequences from a family using MAFFT  [206]. It is to note that the Ensembl Compara protein families correspond to the most similar proteins compared to its gene tree section, where paralogous relationships are also available but in a tree format. Many other repositories are available but our goal is not to be exhaustive. Among the most generalist, we can cite PhylomeDB  [148] , OMA  [207] , OrthoDB  [139] , OrthoInspector  [141] , eggNOG  [138] , or the database Homologene from the NCBI portal. For plant comparative genomic, we can cite the databases PLAZA  [208] , GreenPhyl  [209] , and Phytozome  [210]. PLAZA 4. 0 contains gene family data, phylogenetic trees, and gene colinearity information. It comprises two instances, one for monocots (Monocots PLAZA 4. 5) that includes data from 39 species and one for dicots (Dicots PLAZA 4.
0) that includes data from 55 species. The latest PLAZA instance offers one or more REST-full APIs, depending on the Platform software version. GreenPhyl 4 contains gene families and phylogenetic trees from 37 species. It has not been updated since 2015 but contains a section of manually annotated families comprising 2956 clusters. Other interesting sections are transcription factors and families specific to species or phylum (family of homologous genes found only in one species or excluding/including one phylum). Finally, the plant database Phytozome13 (last update in May 2019) contains 184 assembled and annotated genomes. Inparanoid pairwise orthology and paralogy groups have been calculated across all Phytozome proteomes and families of related genes representing the modern descendants of putative ancestral genes have been constructed at key phylogenetic nodes.
Results Generation of a library of transcription factor null mutants. A systematic review of the genes previously annotated as transcription factors (TFs) in the databases at ENSEMBL fungi, ASPGD and DBD  20  resulted in the identification of 495 putative TFs (Supplementary Data 1). To further characterise this cohort, we classified each TF according to its Pfam domains, identified using Hidden Markov Model profiling (hmmscan). The majority of TFs were shown to have either one (n = 245) or two (n = 158) functional domains associated with transcriptional regulation (Supplementary Fig. 1 ). The most common domains identified were binuclear zinc cluster (n = 192 (PFAM00172) and fungalspecific transcription domains (n = 195 (PFAM04052 and 11951); Supplementary Fig. 1 ). This is consistent with previous reports that describe domains of transcription factors in ascomycetes  21.
fumigatus are complicated by relatively low levels of homologous recombination. This problem can be mitigated by the use of strains lacking components of the non-homologous end joining machinery  25  , such as Ku70  26  , Ku80  27  and Lig4  28  , however, even in strains lacking these factors, gene replacement cassettes require around 1 kb of homologous sequence flanking each side of a target gene  27. To facilitate the disruption of all of the TFs identified, we chose to employ a fusion PCR approach similar to that described by Szewczyk et al. 29  (see schematic Supplementary Fig. 2 ) and used custom developed scripts to design the primers for amplification of the gene replacement cassettes (see the Methods section). Cassettes were successfully amplified for all 495 of the TFs, and were used to transform MFIG001, a Δku80, pyrG+ strain derived from FGSC strain A1160  30.
To identify the promoters of genes that are directly bound by the NCT complex, we performed chromatinimmunoprecipitation sequencing (ChIP-seq) using an anti-S-tag polyclonal antibody to isolate DNA bound to the NctA-S-tag fusion protein in vivo. We identified 4811 and 4290 NctA-binding peak regions in the absence and the presence (0. 5 mg/L) of itraconazole, respectively (q-value < 0. 01, fold enrichment >1. 5; Supplementary Data 3). A comparison of the ChIP-seq data sets showed that >70% of the peak regions are common between the conditions (Fig. 4a ). Analysis of genome-wide occupancy of NctA revealed that the large majority (~70%) of NctA-binding peaks are located within 1. 5 kb upstream of the translational start site (TSS) of an annotated gene (Fig. 4b ). In total, 33. 1% (3316 genes in no-drug conditions) and 29% (2945 genes in 0.
5 mg/L itraconazole conditions) of the total predicted ORFs were assigned to have at least one NctA-binding event within their upstream region. Frequency distribution of NctA-binding peak summits showed that NctA is predominantly positioned around 300 bp upstream from the TSS (Fig. 4b ). Applying the Multiple Em for Motif Elicitation (MEME) de novo motif discovery program  43  , we identified several conserved nucleotide motifs within the ChIP peak regions (Fig. 4c ). TATA-box like AT-rich motives were  identified as the common nucleotide motifs with the highest evalue. This is consistent with the result from previous studies with NCT orthologues, which have been shown to form a stable complex with the TATA-box-binding protein (TBP)  [44] [45] [46] [47]. The results of the motif discovery suggest that the NCT complex in A.
fumigatus also interacts with TBP to regulate gene expression as also suggested by the physical interaction studies. We investigated the correlation between NctA occupancy and mRNA levels by comparing the ChIP-seq and the RNA-seq data sets. We expected that regulation of genes in close proximity to the NctA-binding sites would be altered upon loss of nctA. However, for the total 3361 genes having at least one NctAbinding peak within 1. 5 kbp of upstream region, only 755 genes (22%) showed differential mRNA levels, and the remaining 2606 genes exhibited no significant change (Supplemental Fig. 7a, b ). Among the 755 differentially expressed genes, 352 genes were upregulated and 403 genes were downregulated in the nctA null mutant in no-drug conditions. Similar results were obtained for the itraconazole-treated (0.
Source data are provided as a Source Data file. biosynthesis, sterol levels were quantified using GC-MS. Consistent with our hypothesis, we observed a 60% increase in ergosterol content (w/w) in both mutant strains when compared with the isogenic control (P < 0. 04; Fig. 5 ). Ergosterol levels in the reconstituted strain were indistinguishable from the wild-type (P > 0. 85), and lower than observed in the knockout (P = 0. 0138). Taken together, our data suggest that NctA and NctB are negative regulators of ergosterol biosynthesis via their interaction at the promoters of cyp51A and other genes that encode components of the ergosterol biosynthetic pathway. Levels of the azole transporter CDR1B are elevated in NCT mutants. The relatively modest increase in expression of cyp51A (ca. 2. 5-fold) and cellular ergosterol content (ca.
Methods In silico identification of transcriptional factors. Putative transcription factors were identified by through a combinatorial approach. Semantic searches using the terms transcription factor, C6, C2H2, HLH, transcriptional regulator, zinc finger, bZIP, CCAAT, CBF, MYB, DNA binding and TATA were performed to identify genes annotated as transcription factors within databases at ENSEMBL fungi and ASPGD. These were combined with genes annotated as transcription factors at the DBD transcription factor database. Duplicates were removed and each gene was manually curated to remove those lacking functional domains associated with transcription factor function (as deemed by searches for PROSITE, SUPER-FAMILY, SMART or CDD domains) or those that lack functional data indicating a direct role in transactional regulation.
PCR amplification was carried out using the protocols defined by Szewczyk et al. 29  Transformation was carried out as previously reported  30. The sequences of all primers used are given in Supplementary Data 1. Validation of homologous recombination and single integration of the deletion cassette was performed by PCR (Supplementary Fig. 2 ). PhusionFlash High-Fidelity Master mix (ThermoFisher Scientific) was used for all reactions. Primers P1 with hph-chk 5′-Rv and hph-chk 3′-Fw with P4 were used to amplify a region of about 1 kb from within the deletion cassette to the flanking region outside of the deletion cassette. Furthermore, PCR was performed with P1 and P4 as primers to check the purity of the gene knockout strain. Drug sensitivity screening. Conidia of 484 TFKO strains were inoculated in 25 -mL culture flasks containing ACM + 100 µM hygromycin.
0) in a 24-well glass bottom plate. The culture was incubated at 37 °C, and either optical density (600 nm) was measured on a Synergy 2 Multidetection Microplate reader (BioTek) or images were taken on a Leica SP8X confocal microspoce (Leica). Sporulation and hyphal length were measured in ImageJ. S-tag co-immunoprecipitation. C-terminal S-tagged cassettes were generated by fusion PCR using primers SP1-SP8 (Supplementary Data 1). Three separate PCR reactions were performed for initial amplification of the cassette components. Primers SP1 and SP2 were used to amplify the 5′ flank and TF coding sequence, primers SP3 and SP4 amplified the downstream region of the TF (ca. 1 kb) from MFIG001 genomic DNA. Primers SP5 and SP6 were used to amplify the S-Tag, G5A linker and pyrG gene from pHL81  76  PCR products were purified by solid phase extraction with the QIAquick® PCR purification kit (Qiagen).
11. 3, and then Illumina adapters were trimmed from them using Trimmomatic. The resulting reads were aligned to the A. fumigatus A1163 CADRE genome from Ensemble fungi, version 26 using Bowtie2. Peak calling was carried out using a Model-based Analysis for ChIP-Sequencing (MACS2 77 ) version 2. 1. 0 with a q-value cut-off of 0. 01. The results reported herein are for the combined reads from two biological replicate samples. All ChIP-seq experiments were carried out in two biological replicate samples. All ChIP-seq data sets are deposited in the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE129967 in a GEO Superseries GSE133491. Conserved motif discovery. The reproducible 100 bp of the merged ChIP peak regions with more than twofold read enrichment compared to the corresponding were analysed for conserved nucleotide motifs using the MEME suite 78 version 4.
All RNA-seq experiments were carried out in three biological replicates. Differential expression analysis was performed using DESeq  80. All RNA-seq data sets are available in the NCBI Gene Expression Omnibus under accession number GSE133464 in a GEO Superseries GSE133491. Gene set enrichment analysis. Gene ontology, functional category and KEGG pathway enrichment analysis were carried out using the FungiFun2 2. 2. 8 BETA 81 web-based server (https://elbe. hki-jena. de/fungifun/fungifun. php) with the A. fumigatus A1163 genome annotation. Differentially expressed genes showing more than twofold enrichment with FDR < 0. 05 were subjected to the enrichment analysis. Significance level of the enrichment was analysed using the Benjamini-Hochberg adjustment method with a P-value cut-off <0. 05.
Lobes for fungal burden and cytokine analysis were snap-frozen in liquid nitrogen and stored at -80 °C until use. Lobes for histological analysis were immediately fixed in 4. 0% (v/v) formaldehyde (Sigma-Aldrich), and subsequently embedded in paraffin. Four-micrometer sections were stained with haematoxylin-eosin (H&E) and Grocott's Methenamine Silver (G. M. S). Images were taken using a Pannoramic 250 Flash Slide Scanner (3D HISTECH) using brightfield illumination. Fungal burden analysis. The total genomic DNA was extracted from the infected lung samples using a standard CTAB DNA extraction method. Fungal burden was determined by quantitative real-time PCR (qPCR) as described previously  83. qPCR was performed in a 7500 Fast Real-Time PCR system (Applied Biosystems) using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) with the primers listed in the Supplementary Dataset 1.
Amplification reactions were performed in triplicate in a final volume of 20 µL using 0. 5 µM forward primer, 0. 5 µM reverse primer and 100 ng of total DNA. qPCR data were analysed as described  83. The statistical significance of variances between fungal burdens was calculated by using a non-parametric Mann-Whitney t test. Cytokine expression analysis. The total RNA was extracted from the infected lung samples using the RNeasy kit (Qiagen). First-strand cDNA was synthesised using the iScript cDNA synthesis kit (Bio-Rad) with 500 ng of the total RNA as a template. Amplification reactions were performed using the iTaq Universal SYBR Green Supermix (Bio-Rad) in a final volume of 20 µL using 0. 5 µM forward primer, 0. 5 µM reverse primer and 2 µL of fivefold diluted cDNA. Primers used in this analysis are listed in Supplementary Dataset 1.
Following co-incubation with A. fumigatus spores, cell culture supernatants were collected, and the level of inflammatory markers or cell toxicity via lactate dehydrogenase assay were measured (Promega). The concentration of IL-8 and IL-6 were determined in A549 epithelial cells cocultured with A. fumigatus strains by using the Human IL-8/CXCL8 and IL-6 DuoSet ELISA according to the manufacturer's instructions (R&D systems). Experiments were performed in five biological replicates and technical triplicates. For data analysis, a four-parameter logistic (4-PL) curve was created plotting the absorbance versus Log 10 concentration of the standards and then sample concentrations determined by using a non-linear regression. Differences in IL-8 and IL-6 concentration between A549 cells challenged with ΔnctA, nctA rec and the isogenic control spores and uninfected controls were determined by one-way multiparametric ANOVA with Dunnet's correction using GraphPad Prism 7.
Acknowledgements The authors would like to thank the  Genomic Technology Core Facility , the  Bioinformatics Core Facility , the  Biological Mass Spectrometry Facility , the  Bioimaging Facility , the  Flow cytometry Facility , and the  Electron Microscope (EM) core Facility in the University of Manchester  for their technical support. The EM core facility is supported by the  Wellcome Trust   equipment grant. We would also like to thank  Dr Ian Donaldson  of the  Bioinformatis Core Facility  at the  University of Manchester  for providing support analysing the ChIP-seq data,  Dr Gareth Howell  of the  Flow cytometry Core Facility the University of Manchester  for his assistance in conducting FACS analysis,  Dr. Darren Thomson  at the for his support in microscopy experiments. This work was supported by the  Medical Research Council (MRC)  grant  MR/M02010X/1  to M.
B. , P. B. and E. B. , the  Wellcome  trust grant  208396/Z/17/Z  to P. B. and M. B. and by  NIH   R01AI143198-01  to M. B. , P. B. and W. S. M. S. G. was co-funded by the  NIHR Manchester Biomedical Research Centre  and a  NC3Rs Training Fellowship  ( NC/P002390/1 ). R. A. C. holds an Investigator in the  Pathogenesis of Infectious Diseases Award  supported by the  Burroughs Wellcome Fund (BWF)  and is also supported by a  National Institute of Allergy and Infectious Diseases (NIAID)  award  2R01AI081838.
Data availability All RNA-seq and ChIP-seq data sets are available in the NCBI Gene Expression Omnibus (GEO) under accession number GSE133491. This GEO Superseries includes two SubSeries for the RNA-seq dataset (GSE133464 and the ChIP-seq dataset [GSE129967]. A processed format of the RNA-seq and the ChIP-seq data sets is included in Supplementary Data 2 (RNA-seq) and 3 (ChIP-seq). The source data underlying Figs. 1a-c , 2b-d, 3a-b and d, 4b, 5, 6c-d, 7a-h, 8a-c and e, and Supplementary Figs 1a, b, 3c, 5c-e, 6a-d, and 9 are provided as a Source Data file.
Statistical Analysis Further data analysis was performed using R software (R development Core Team, 2013) and the phyloseq, vegan, and DESeq2 R packages  (McMurdie and Holmes, 2013; Love et al. , 2014)  hosted on the INRAE MIGALE bioinformatics platform, using the biom file generated by the FROGS affiliation tool and a phylogenetic tree that was built using the align_seqs tool followed by the make_phylogeny tool of the QIIME pipeline  (Caporaso et al. , 2010). Analysis of alpha and beta diversity was performed after rarefaction with the rarefy_even_depth function of phyloseq. Alpha-diversity was determined using the estimate_richness function of the plot_richness function of phyloseq through the estimation of several widely used diversity indices, the observed richness, Chao1, Shannon, Simpson, and inverse Simpson indices.
6%) as well as members of the Lachnospiraceae (2. 3%), Ruminococcaceae (4. 3%), and Comamonadaceae (2. 1%) families (see Supplementary Figure  S2 ). As a control, in order to check for biases related to the study design, we evaluated whether microbiota composition was related to the animal group (animals were split into three groups for the assay; see section "Materials and Methods"). No significant effect of this factor was revealed using permutational multivariate ANOVA (p > 0. 5), indicating that modifications of parameters such as ambient temperature, diet, bedding between the three groups of animals did not significantly affect microbiota composition. Similarly, no significant effect was observed on alpha-diversity. Likewise the DNA extraction serial number did not significantly affect the microbiota composition and diversity, indicating that, although we could not exclude the presence of contaminating sequences related to DNA extraction ("kitome") in our dataset, this contamination could not account for differences in microbiota composition.
ACKNOWLEDGMENTS We are grateful to the  INRAE GENOTOUL genomics and transcriptomics platform (GeT PlaGe ), and to the INRAE GENOTOUL (https://www. genotoul. fr/)and  INRAE MIGALE  (http://migale. jouy. inra. fr/)bioinformatics platforms for providing computational resources. We thank the entire staff of the  Le Pin beef and dairy cattle experimental facility  for its technical assistance during the sampling campaign. We are very grateful to  Hélène Falentin  for useful discussions on the manuscript. We thank  Gail Wagman  for revising the English.
2014; Moore et al. 2015 Moore et al. , 2016;; Smirnova and Golubkin 2017; Boisvert et al. 2018; Justino et al. 2019, among others). Ongoing ocean modeling activities also require highresolution forcing functions accounting for mesoscale atmospheric features. Existing datasets used for forcing model experiments such as Drakkar forcing set (DFS4/5), Coordinated Ocean-Ice Reference Experiments (CORE), and JRA-55-do are based on global reanalyses  (Large and Yeager 2004, 2009; Brodeau et al. 2010; Danabasoglu et al. 2014 Danabasoglu et al. , 2016;; Tsujino et al. 2018 ) with a spatial resolution of approximately 50-100 km. At the same time, modern eddy-resolving ocean general circulation models use resolutions finer than 1/108, equivalent to a few kilometers at subpolar latitudes  (Deshayes et al.
Facing this challenge, the P. P. Shirshov Institute of Oceanology of the Russian Academy of Sciences (IORAS) in cooperation with the Institut des Géosciences de l'Environnement (IGE) developed a high-resolution (14 km) atmospheric downscaling experiment for the North Atlantic Ocean [North Atlantic Atmospheric Downscaling (NAAD)]. In NAAD, the nonhydrostatic WRF Model was forced at the lateral boundaries by the ERA-Interim reanalysis over the  40-yr period (1979-2018). In the following, we describe the NAAD model configuration and production strategy in section 2 followed by a short description of NAAD products and data availability (section 3). Then we turn to the NAAD evaluation (section 4) and pilot applications (section 5). In the conclusive section 6 we discuss the NAAD added value and perspectives of further developments of the product.
The LoRes experiment (with resolution comparable to ERA-Interim) will be used to quantify the added value of the HiRes experiment, which cannot be directly compared with ERA-Interim (constrained by data assimilation and using a very different model configuration). All experiments were run for the 40-yr period from January 1979 to December 2018. Details of the model settings for the HiRes and LoRes experiments are presented in Table  1. Most parameterizations were used in both HiRes and LoRes NAAD experiments. We used the Kain-Fritsch (KF) convective parameterization scheme  (Kain 2004 ). The RRTMG longwave and shortwave radiation schemes  (Iacono et al. 2008)  were used for terrestrial and solar radiation processes, which additionally utilize effective cloud water, ice and snow radii from the single-moment 6-class (WSM6) scheme for microphysics  (Hong and Lim 2006)  in HiRes, and subgrid convective cloud information from KF for a more accurate estimation of atmospheric optical depth.
Horizontal eddy viscosity coefficients are obtained in the WRF dynamic core independently using the Smagorinsky first-order closure approach. Parameterizations of microphysics were nevertheless different in HiRes and LoRes. Thus, the WSM6 scheme for microphysics  (Hong and Lim 2006)  was used in the NAAD-HiRes and WSM 5-class (WSM5;  Hong et al. 2004 ). Additionally, employing entrainment information from KF was applied in the NAAD-LoRes case. For the long-term runs with WRF in both HiRes and LoRes experiments, the RRTMG scheme uses climatological ozone and aerosol data. The ozone data were adapted from the Community Atmospheric Model radiation scheme with latitudinal (2. 828 step), height, and temporal (monthly) variation. The aerosol data were based on the  Tegen et al. (1997)  dataset with relatively coarse spatial (58 in longitude and 48 in latitude) and temporal (monthly) resolution.