dataesr/scientific-paragraphs-categorization huggingface dataset:
A minimal set of domains in a NRPS comprises an adenylation (A) domain for selection and activation of amino acid monomers, a condensation (C) domain for catalyzing the formation of peptide bonds and a peptidyl carrier protein (PCP) domain for transferring the monomers/growing chain to various catalytic sites. A minimal set of domains in a PKS comprises an acyl carrier protein (ACP) domain which is catalyzed by an acyltransferase (AT) domain and the ketoacyl synthase (KS) domain for sequential decarboxylative condensations. Sequences corresponding to each predicted domain were aligned to build a domain model's position-specific scoring matrix (PSSM) using PSI-BLAST. Therefore, sequences were aligned via the MAFFT program (version 7. 310)  [34]. All PSSM files obtained were grouped and arranged in RPS database format using BLAST program (makeprofiledb)  [25].
NRPPUR database The literature search resulted in 172 NRPS-PKS BGC sequences with antimicrobial secretion being extracted from Genbank. Of these, eight sequences were discarded because they didn't show any specific hit with the conserved domains of NRPS-PKS. The other 164 sequences which were considered as NRPS-PKS BGC sequences   S1 ). Most of these BGCs, 113 (68%), were found in Actinobacteria, 23 in Proteobacteria, 13 in Ascomycota, nine in the Firmicutes, three in Cyanobacteria and two in Bacteroidetes (Fig. 2 ). Of these, 130 sequences showed homology within ATLAS database when using BLASTP while 34 sequences showed no homology with existing databases such as Antismash. The 164 BGCs showed great variability in size and in composition. They ranged from 3. 93 to 185. 25 Kbp and 155 BGCs sequences (94%) were larger than 10 Kbp.
PKS synthases had frequent non-modular organization whereas NRPS had modular enzyme organization (Table  1 ). All BGCs found in fungi had a mixed organization except one enzyme that had a modular organization. This result needs to be confirmed with more BGC from fungi. Overall, the majority of the 164 BGCs were hybrid clusters that tend to be larger and possess more functional domains with a non-modular organization, most probably giving rise to more complex products than stand-alone NRPS and PKS gene clusters. Concerning the 34 BGCs with antimicrobial activity in vitro and positive results with RPS-BLAST but no significant homology with existing databases, they were mostly found in Streptomyces genus (30 out of the 34). Of these, 25 were gene clusters for PKS. Interestingly, some of these BGCs support the biosynthesis of familiar antimicrobial products, including oxytetracycline, echinocandin B, chloreamphenicol, lincomycin, pristinamycin, spiramycin, streptothricin and novobiocin (Additional file 1: Table  S1 ).
These findings demonstrate that a functional enzymatic domain research strategy is more sensitive than a similarity search methods querying existing databases of putative antimicrobial BGCs. Hence, a total of 164 sequences were incremented to the Wang et al. database to form "NRPPUR DB", a local NRPS-PKS database composed of 3505 NRPS-PKS BGC non-redundant sequences including 715 PKS, 1568 NRPS and 1220 hybrids from Bacteria, Eukarya and Archaea (respectively 3127, 373 and 3 NRPS and PKS) (Additional file 1: Figure  S1 ). Altogether, RPS-BLAST method against NRPPUR, a database collecting BGCs.
In vitro antibacterial potency and spectrum of activity Antimicrobial activity was checked in vitro using the cross-streak method for species containing BGCs for NRPS-PKS. Streptomyces massiliensis showed activity against test organisms using the cross-streak method. S. massiliensis displayed antibacterial activities against Gram-positive species such as methicillin-susceptible Staphylococcus aureus (MSSA), methicillin-resistant S. aureus (MRSA), Staphylococcus epidermidis and Enterococcus faecalis, except for Bacillus cereus. S. massiliensis was inactive against Gram-negative bacteria such as Escherichia coli, Klebsiella pneumonia (Fig. 6 ) and Pseudomonas aeruginosa (data not shown). S. massiliensis had no activity against Candida albicans (Fig. 6 ).
Discussion Enabled by the fast development of genome sequencing technologies, genome mining techniques looking for "natural" antimicrobial compounds are currently an important part of drug discovery efforts and many computational tools have been developed to guide wet lab experiments  [20] [21] [22] [23] [24]. This has resulted in an increase in putative NRPS-PKS proteins predicted by gene identifications tools but for which there is no experimental evidence. Moreover, these available algorithms often propose known biosynthetic clusters similar to their own. Sequence analysis of experimentally characterized BCGs clusters seems to be a very promising strategy for identification of NRPS/PKS gene clusters  [40]. Thus, NRPPUR database assembling BCGs clusters that have already demonstrated an antimicrobial activity in vitro, seems to be more biologically reliable than databases constructed only on the basis of bioinformatics methods.
Given the difficulty of predicting antimicrobial activity in silico due to the large diversity of the protein sequences and the variable organization of NRPS/PKS clusters, our search method through the identification of functional domains seems very efficient in the detection of NRPS/PKS-producing microorganisms with antimicrobial potency as shown by our tests in vitro. Concerning the in vitro analysis, the cross-streak is an easy and relatively rapid method to investigate the antagonism between microorganisms. The high sensitivity of this method compared to other diffusion methods makes it very suitable as preliminary screening for antimicrobial activity  [41]. Indeed, our searchable comprehensive database using RPS-BLAST enabled an initial in silico selection of species with potential antimicrobial activity.
Conclusions This work is a pioneering study to search for new NRPS-PKS naturally produced by the human digestive microbiota and showing potent antibiotic activity in vitro. The NRPPUR database integrates the latest experimentally verified information and provides standardized domain descriptions related to the gene clusters. Our database serves as a useful reference to facilitate research and development related to secondary metabolite types NRPS and PKS with potential antibiotic activity. A web interface (http:// www. mediterranee-infection. com/article. php?laref=955&ti-tre=nrppur-database-) has been developed allowing rpsBlast analyses to be performed to search for NRPS-PKS.
Introduction As part of the sixth coupled model intercomparison project (CMIP6,  Eyring et al. , 2016) , geoscience institutions across the world have been constructing and running coupled climate models to be submitted to a vast online and free-to-access database. In order for institutes to participate in this project, they are required to run a set of baseline simulations, denoted the Diagnostic, Evaluation and Characterization of Klima. These consist of four experiments: a coupled control simulation with preindustrial forcings (piControl), an atmosphere-only simulation with prescribed sea ice and sea surface temperatures (AMIP), a coupled control simulation with an abrupt increase in carbon dioxide forcing (CO 2 ) to four times preindustrial (abrupt4xCO2), and a coupled simulation in which preindustrial forcing increases by 1% per year (1pctCO2).
We also make comparisons with the ocean analysis product EN4  (Good et al. , 2013) , which provides gridded (1 ∘ × 1 ∘ ), optimally interpolated observations of temperature and salinity. The interpolation uses fixed decorrelation length scales (two vertical and two horizontal, with larger values near the equator) and relaxes to climatology with an e-folding timescale of 9. 5 months. In this section, we begin by comparing the model simulations with recent instrumental observations at 26. 5 ∘ N, measured at the RAPID-MOCHA array  (Cunningham et al. , 2007) , used to meaure the AMOC. We use the open source RapidMoc code  (Roberts, 2017)  and follow the method of  Roberts et al. (2013)  to make a fair comparison between models (for which we have full knowledge of the ocean state) and observations (for which we have to make various approximations).
These keywords mainly include "uncertainty" or "uncertain," "monetary policy" or "interest rate", "policy rate" or "refinancing tender". Further, we consider a financial uncertainty index (FUCI) constructed by  Ludvigson et al. (2015)  based on the methodology of  Jurado et al. (2015) , by summarizing monthly 147 financial time series including bonds, stocks and commodity markets. Ultimately, a political uncertainty indicator (PUCI) developed by  Azzimonti (2017)  is accounted for. This index is developed by referring to the frequency of newspaper articles displaying disagreements among US politicians. Due to the availability of uncertainty proxies under study, the data cover the sample period from January 1999 to September 2015 (monthly frequency). 4 Although MAUCI, MIUCI, EPUCI, FUCI and PUCI are available for longer periods, MPUCI focuses on a restricted period from January 1999 to July 2017.
66 In general, we define the lag operator by : 1   t t x Lx and k t t k x x L  . The lag polynomials is considered as polynomials in the lag operator. Let 1 1. ) (     p p L L    be the lag polynomial. As an operator, it is defined by p t p t t t x x x x L           . ) ( 2 2 1 1. 7 We build a set of Matlab routines to run the dynamic factor model (DFM). The source code is available on this link: http://www. barigozzi. eu/Codes. html.
Sequence assemblies and genome annotation Chromosome scale assemblies were produced for 16 laboratory mouse strains using a mixture of Illumina paired-end (40-70x), mate-pairs (3, 6, 10Kbp), fosmid and BAC-end sequences (Supplementary Table  1 ) and Dovetail Genomics Chicago™ libraries19. Pseudochromosomes were produced in parallel utilising cross-species synteny alignments resulting in genome assemblies of between 2. 254 (WSB/EiJ) to 2. 328 Gbps (AKR/J) excluding unknown gap bases. Approximately 0. 5-2% of total genome length per strain was unplaced and is composed of unknown gap bases (18-49%) and repeat sequences (61-79%) (Supplementary Table  2 ), and contain between 89-410 predicted genes per strain (Supplementary Table  3 ). Mitochondrial genome (mtDNA) assemblies for 14 strains supported previously published sequences20, although a small number of high quality novel sequence variants in AKR/J, BALB/cJ, C3H/HeJ, and LP/J conflicted with GenBank entries (Supplementary Table  4 ).
g. Itch receptors26 and γ-protocadherins27) (Figure  1b , Supplementary Figure  5 ). All of the wild-derived strain hSNP regions contained gene and CDS base pair counts larger than any classical inbred strain (≥503 and ≥0. 36Mbp, respectively; Supplementary Table  9 ). The regions identified in C57BL/6J and C57BL/6NJ (117 and 141, respectively; 145 combined) intersect known GRCm38 assembly issues including gaps, unplaced scaffolds or centromeric regions (107/145, 73. 8%). The remaining candidate regions include large protein families (15/145, 10. 3%) and repeat elements (17/145, 11. 7%) (Supplementary Data 2). We examined protein classes present in the hSNP regions by identifying 1,109 PantherDB matches, assigned to 26 protein classes, from a combined set of all genes in hSNP dense regions (Supplementary Data 3).
De novo assembly of complex gene families Our data elucidated copy number variation previously unknown in mouse strain genomes, and uncovered gene expansions, contractions and novel alleles (<80% sequence identity). For example, 23 distinct clusters of olfactory receptors (ORs) were identified indicating substantial variation among inbred strains. In mouse, phenotypic differences, particularly diet and behaviour, have been linked to distinct OR repertoires41,42. To this end, we have characterised the CAST/EiJ OR repertoire using our de novo assembly and identified 1,249 candidate OR genes (Supplementary Data 5). Relative to the reference strain (C57BL/6J), CAST/EiJ has lost 20 ORs and gained 37 gene family members; 12 novel and 25 supported by published predictions based on mRNA derived from CAST/EiJ whole olfactory mucosa (Figure  2a , Supplementary Table  13 )43.
These divergent profiles suggest that Nlrp1c is not the sole mediator of anthrax lethal toxin resistance in the mouse, and instead that several other members may also be involved. Newly annotated members Nlrp1b2 and Nlrp1d, appear functionally intact in CAST/EiJ, but were both predicted as pseudogenes in PWK/PhJ due to the presence of stop codons or frameshift mutations. In C57BL/6J, three splicing isoforms of Nlrp1b (SV1, SV2, and SV3) were reported55. A dot-plot between PWK/PhJ and the C57BL/6J reference illustrates the disruption of co-linearity at the PWK/PhJ Nlrp1b2 and Nlrp1d alleles (Figure  2d ). All of the wild-derived strains we sequenced contain full-length Nlrp1d and exhibit a similar disruption of co-linearity at these alleles relative to C57BL/6J (Supplementary Data 6), such that the SV1 isoform in C57BL/6J is derived from truncated ancestral paralogs of Nlrp1b and Nlrp1d, indicating that Nlrp1d was lost in the C57BL/6J lineage.
The genome structure of the Nlrp1 locus in PWK/PhJ, CAST/EiJ, WSB/EiJ and NOD/ShiLtJ was confirmed using Fiber-FISH (Supplementary Figure  9 ). The assemblies also revealed extensive diversity at each of the other loci examined; Immunity-related GTPases (IRGs) and Schlafen family (Slfn). IRG proteins belong to a subfamily of interferon-inducible GTPases present in most vertebrates56. In mouse, IRG protein family members contribute to the adaptive immune system by conferring resistance against intracellular pathogens such as Chlamydia trachomatis, Trypanosoma cruzi and Toxoplasma gondii57. Our de novo assembly is concordant with previously published data for CAST/EiJ49, and for the first time reveal the order, orientation, and structure of three highly divergent haplotypes present in WSB/EiJ, PWK/PhJ, and SPRET/EiJ including novel annotation of rearranged promoters, inserted processed pseudogenes, and a high frequency of LINE repeats (Supplementary Data 6).
In contrast, the EFCAB3 locus only has low level measurable expression in testis. This is consistent with the promoter of the full length gene being present upstream of the EFCAB13 version, which is supported by H3K4Me3 analysis (Supplementary Figure  14 ). In mice, Efcab3 is specifically expressed during development throughout many tissues with high expression in the upper layers of the cortical plate (see URLs ), and is located in the immediate vicinity of the genomic 17q21. 31 syntenic region linked to brain structural changes both in mice and humans64. We used CRISPR to create Efcab3-like mutant mice (Efcab3 em1(IMPC)Wtsi ; see methods) and recorded 188 primary phenotyping measures (Supplementary Data 9). We also measured 40 brain parameters across 22 distinct brain structures (Supplementary Table  15+16 ) (see methods).
Scaffold break parameters are shown in Supplementary Table  20. For CAST/EiJ, PWK/PhJ, and SPRET/EiJ, we further scaffolded the assemblies with Dovetail Genomics long range libraries. Each input genome assembly, along with its associated Chicago library read pairs in FASTQ format, were used as input data for HiRise, a software pipeline designed specifically to scaffold genomes using Chicago library data2. Shotgun and Chicago library sequences were aligned to the draft input assembly using a modified SNAP read mapper (see URLs ). The separations of Chicago read pairs mapped within draft scaffolds were analyzed by HiRise to produce a likelihood model for genomic distance between read pairs, and the model was used to identify putative mis-joins and score prospective joins. After scaffolding, shotgun sequences were used to close gaps between contigs.
Pseudo-chromosomes The scaffolds were assembled into chromosome-scale scaffolds using Ragout v2. 0. Ragout identifies large conserved regions between genomes (hierarchical synteny blocks) by combining the whole genome alignment, with a de-Bruijn graph simplification algorithm67. Assembly scaffolds are further joined into chromosomes so as to minimize the number of structural differences (such as inversions or chromosomal translocations) between references and a target genome. We used the C57BL/6J GRCm38 sequence as a single reference and found that 95% adjacent synteny block pairs from the assemblies, were also adjacent in C57BL/6J reference. Each of the genomes were assembled into a complete set of chromosomes with less than 5% of unlocalized sequence (Supplementary Data 10). On average, 10% of synteny block adjacencies in the assembled genomes were not presented in C57BL/6J reference.
Gene prediction and annotation Three techniques were used to produce the gene annotation for each mouse strain. First, whole genome alignments produced by progressiveCactus66 were used as input to transMap, producing an initial set of orthologs. These initial orthologs, along with strain specific RNA-Seq (Supplementary Table  8 ), were input to AUGUSTUS74 one at a time to apply local strain specific refinement. A consensus finding algorithm was employed to decide between possible versions of an orthologous transcript. We also created a de novo set of strain-specific genes and isoforms from Comparative Augustus (AugustusCGP)24 using the strain specific RNA-Seq and the progressiveCactus alignment. A subsequent round of consensus finding was employed to incorporate these transcripts into the final consensus annotation set.
The progressiveCactus whole genome alignments were used to project annotations from GENCODE VM861 onto each of the strain specific assemblies using transMap75. These transMapped transcript alignments were evaluated by a series of binary classifiers that attempt to diagnose differences between the parent and target genome. These classifiers include evaluating if a transcript maps multiple times, the proportion of unknown bases, splice site validity, both frameshifting and non-frameshifting indels, and small alignment gaps. These comparative transcripts were given to the gene-finding tool AUGUSTUS13 as strong hints (external evidence) in conjunction with weaker hints derived from all available RNA-seq data for the given strain. The RNA-Seq hints were generated for each of the novel strains by aligning RNA-Seq reads to the native genome with the spliced aligner STAR16.
The resulting read alignments were quality filtered by coverage (>=80%), identity (>=90%) and uniqueness, i. e. when a read mapped to multiple loci, the best alignment for that read was only kept, if the alignment score of the second best was considerably worse. For the remaining reads (approximately 70%), strain-specific exonpart and intron hints were generated. The transcripts resulting from both transMap as well as AUGUSTUS were evaluated by a consensus finding algorithm that attempts to use a combination of fidelity to the reference and a series of binary classifiers to construct a consensus gene set. See the pipeline documentation for details on this process (see URLs ). For each transMapped transcript alignment t, one way to identify its structure was a pipeline component we here refer to as AugustusTMR (TM=transMap, R=RNA-Seq).
The aim was to try to produce all splice forms from the reference (parent) genome that likely also exist in the target genome. In the genomic region around t, AUGUSTUS was set to predict a gene structure without alternative splicing using evidence from t itself as well as from all RNA-Seq alignments in that region. Thereby, the evidence from t on the location of exons, introns and start and stop codon was given a much higher weight in order to produce the original splice form, also in cases where the majority of target RNA-Seq suggests a different major splice form. However, when part of a transcript structure was unclear, e. g. an unalignable transcript part, RNA-Seq evidence could help fill in missing parts. By design, AugustusTMR restricts gene finding to regions that align to a reference gene, and thus is not able to predict genes missing in the reference annotation or genes in unaligned regions.
To find novel splice forms and genes, Augustus is run in CGP (comparative gene prediction) mode, a recent extension24 that takes a whole-genome alignment of related species or strains and simultaneously predicts coding genes in all input genomes. In AugustusCGP the same types of evidence can be incorporated for either a subset or all species/strains. With the genome alignment, evidence is transferred across genomes. This makes it possible to exploit the combined evidence for gene finding and to discover genes, that for example, are only weakly expressed and partially supported in the reference strain, but that have a high expression in other strains. In this application, two different types of evidence are used 1. ) the RNA-Seq hints for each of the novel strains from above and 2. ) annotation evidence from GENCODE VM8 for the C57BL/6J reference strain.
For the latter, coding sequence (CDS) and intron hints were generated from the GENCODE VM8 proteincoding gene set for the reference strain. The resulting AugustusCGP gene sets were quality filtered based on how well the exonintron structure of a transcript was supported by the combined RNA-Seq evidence (>=80% of the introns with splice junction support and >=80% of CDS exons with a read coverage of at least 10 reads per kilobase of mRNA). One of the challenges of gene finders is to distinguish coding genes from pseudogenes and expressed non-coding genes that contain partial open reading frames. All AugustusCGP transcripts that partially aligned to a reference transcript annotated as pseudogene or non-coding gene were also discarded. The AugustusCGP transcripts were incorporated into the consensus gene set through a subsequent round of consensus finding.
All animals were regularly monitored for health and welfare concerns and were additionally checked prior to and after procedures. The Efcab3-like gene has previously been represented by two loci MGI:3651790 and MGI:1918144, corresponding to the 5' and 3' regions respectively. Both loci have been targeted using a conditional approach as part of the IKMC resource. The Efcab3-like gene was targeted using CRISPR/Cas9 methodology as defined in77. Briefly, the constitutive coding exon 5 (chr11:104700610-104700692, GRCm38) which is wellsupported by RNAseq data in multiple tissues (ENSMUST00000212287; ENSMUSE00000376310 [ENSEMBL v90]) was deleted using the SpCas9 endonuclease to induce a frameshift mutation. Pairs of flanking gRNAs were designed using the WTSI Genome Editing (WGE) tool78 creating four gRNAs (two gRNAs 5' and two gRNAs 3' to the CE region, Supplementary Table  21 ).
Cas9 mRNA (Trilink, San Diego, CA) together with the four gRNAs was injected into the cytoplasm of 1-cell C57BL/6NTac zygotes. Injected embryos were briefly cultured and oviductal embryo transfer performed in 0. 5 days post-coital pseudopregnant female recipients (CBA/C57BL/6J). F0 mice were screened for the exon deletion by a combination of end-point PCR and loss of WT allele qPCR. Positive F0 mice were further bred with C57BL/6NTac mice. F1 mice were re-screened by PCR and breakpoints confirmed by Sanger sequencing (Supplementary data 11). A single genotypeconfirmed F1 mouse was used to establish the colony (Efcab3 em1(IMPC)Wtsi ) used to generate mice for phenotyping. The care and use of mice in the study was carried out in accordance with UK Home Office regulations, UK Animals (Scientific Procedures) Act of 1986 under a UK Home Office license that approved this work, which was reviewed regularly by the WTSI Animal Welfare and Ethical Review Body.