dataesr/scientific-paragraphs-categorization huggingface dataset:
De novo repeat finding and repeat masking A de novo repeat identification was performed with RepeatModeler (1. 0. 8)  [82]. Unknown elements were screened with BlastX (E-value < 1e-5) against UniRef90 database  [91]  (subset Viridiplantae) and removed from the repeat library if necessary. The filtered Ulva repeat library was used by RepeatMasker (4. 0. 7)  [81]  to mask the repetitive elements in the assembly, which resulted in 34. 7 Mbp (35. 28%) of the genome masked.
Gene prediction We applied EVidenceModeler  [64]  to predict gene models. The consensus gene models were reconciled using the models from ab initio and orthology-aided predictions, transcripts reconstructed from RNA-Seq, and homologous models derived from the protein alignments of the available public resource (Figure  S5A , 5B). We used BRAKER1 v1. 9  [59]  to predict the gene models incorporating the RNA-Seq mapping results generated using HISAT2 v2. 0. 5  [67]. We further used Augustus v3. 2. 3 with the trained data from BRAKER1 and the protein profile extension to re-predict the gene models  [58]. Protein profiles were generated by processing the missing family identified after gene family assignment using OrthoFinder v2. 1. 2 [75]  with the following reference sequence from Phytozome v12.
1: Chlamydomonas reinhardtii v5. 5, Coccomyxa subellipsoidea C-169 v2. 0, Dunaliella salina v1. 0, Gonium pectorale (assembly ASM158458v1) and Volvox carteri v2. 1. In addition to the ab initio prediction, the RNA-Seq data were also used to reconstruct the transcripts, which consisted of consensus transcripts predicted by Scallop v0. 10. 2  [83]  and predicted coding regions of Trinity v2. 4. 0  [87]  assemblies (both de novo and genome-guided) using PASA  [64]. Spliced alignments of proteins from UniRef90 (with taxonomy ID 33090) and the FrameDP-corrected  [66]  and predicted proteins sequence of green algal transcriptomes in the oneKP project  [92]  were generated using Exonerate  [65]  seeded by Diamond  [63]. We combined the aforementioned gene models with the alignments of proteins, annotation of repetitive elements to produce a consensus gene set using EVidenceModeler.
Comparative genomic analyses For the comparative genomic analyses a custom version of Pico-Plaza  [78]  was built containing genomes and annotations of 32 eukaryotic species (Table  S7 ). Following an 'all-versus-all' blastP  [94]  protein sequence similarity search, both TribeMCL v10-201  [86]  and OrthoMCL v2. 0  [76]  were used to delineate gene families and subfamilies. Collinear regions (regions with conserved gene content and order) were detected using i-ADHoRe 3. 0  [68]  with the following settings: gf_type = TribeMCL gene families, alignment method = gg2, number of anchor point s=5, gap size = 30, cluster gap = 35, tandem gap = 30, q-value = 0. 85, probability cut-off = 0. 01, multiple hypothesis correction = FDR and level_2_only = false. The phylogenetic profile of TribeMCL gene families (excluding orphans) retrieved from pico-PLAZA and the inferred species tree topology were provided to reconstruct the most parsimonious gain and loss scenario for every gene family using the Dollop program from PHYLIP v3.
DATA AND SOFTWARE AVAILABILITY The assembled genome sequence, annotation and raw reads are accessible from ENA under BioProject number PRJEB25750. Further information on the Ulva mutabilis project is available via the Online Resource for Community Annotation of Eukaryotes (ORCAE) at http://bioinformatics. psb. ugent. be/orcae/. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • vca significance.
Acknowledgments Research support was provided by  NERC  grant  NBAF925  and  BBSRC  grant BB: K020552  (to C. A. M. and J. H. B. );  UGent Special Research Fund   BOF/01J04813  (to O. D. C. , K. V. P. ),  BOF/01SC2316  (to X. L. ) with infrastructure funded by  EMBRC Belgium -FWO  project  GOH3817N  (to O. D. C. );  EU Horizon2020 Marie Curie ITN ALFF-Project 642575  (to C. M. G. , O. D. C. , T. W. , G. C. , Y. V. D. P. );  EU COST Action FA1406 Phycomorph  (to B. C. , J. C. , J. H. B. , O. D. C. , S. A. R. and T. W. );  German Research Foundation CRC ChemBioSys 1127  (to T. W. ,  J. Bo , M. K. , G. C. , L. L. );  Postdoctoral Fellowship Grant  of the  Research Foundation -Flanders  (to J. B. -Project  12T3418N ); BBSRC-funded MIBTP PhD rotation project to R. C. and J. C. ;  United States Department of Energy   DE-EE0003373/001  (to D.
EXPERIMENTAL MODEL AND SUBJECT DETAILS The U. mutabilis strain sequenced, a wildtype gametophyte of mating type minus (wt-G(mt _ ) (gametophyte); ([mt _ ]; G/PS-swi + ; mut-; RS140 + ; RS180 + )) was initially isolated from Ria Formosa, southern Portugal, by B. Føyn  [13, 19]. An additional haploid strain, slender, a spontaneous mutant derived from the original collection, was selected to complement the available transcriptomes because of its fast-growing nature and the ease with which thalli can be induced for gamete formation. Strains of U. mutabilis are primarily maintained at the Friedrich-Schiller-Universität Jena. Gametophytes were raised parthenogenetically from unmated gametes in Ulva Culture Medium (UCM without antibiotics) in the presence of bacterial symbionts Roseovarius sp. MS2 (Genbank: EU359909) and Maribacter sp.
MS6 (Genbank: EU359911) to secure normal thallus morphogenesis. Bacterial strains are cultivated in marine broth medium (Roth, Germany). Algae were cultured at 20C, under long day light conditions (L/D 17:7) consisting of 60-120 mol•m -2 •s -1 (50% GroLux, 50% day-light fluorescent tubes; OSRAM, München, Germany), without aeration.
Horizontal gene transfer To search for HGTs, a blast search U. mutabilis proteome (blastp, E-value < 10 -5 ) was carried out against a reduced RefSeq database complemented with data from several algal genomes  [95]. The top 1,000 Blastp hits (sorted by bit score) from each query were parsed via custom scripts to extract ≤12 representatives from each phylum to create a taxonomically diverse sample. The blastp hits were re-ordered according to query-hit identity followed by the sampling of another set of representative sequences. The query sequence was then combined with the sets of sampled representative sequences following Qiu et al. [95]. A custom script was used to sort trees consisting of U. mutabilis that was nested among prokaryotes with at least 80% bootstrap supports. Candidate HGT sequences were then reanalyzed using IQtree v1.
Experiment was performed as described in (B), and Flag-tagged NtAGO1 was further purified with @Flag antibodies. Total (input) or AGO1-bound RNA (@Flag-IP) was analyzed by native acrylamide gel electrophoresis. The ago1-57 mutation induces retention of duplexed sRNA inside AGO1 to varying degree, thereby decreasing the generation of single-stranded sRNAs loaded into RISC. clearly more abundant in ago1-57, and the single-stranded ones less abundant. From these combined results, we conclude that ago1-57 uniquely stacks passenger strands into AGO1, a feature not shared with other ago1 missense alleles. The ago1-57 Mutation Affects PhasiRNA Production and Fully Suppresses S-PTGS One striking result from the differential analysis of sRNA accumulation in the ago1-57 mutant compared with Col-0, was the significant reduction in secondary sRNAs spawned from 132 protein-coding gene loci including 27 pentatricopeptide repeat genes (PPRs), three nucleotide binding site-leucine-rich repeat genes (NBS-LRRs), four auxin-related genes (auxin response factors and auxin signaling genes), AGO1, and bHLH-encoding genes (Supplemental Data Set 1A), most of which were previously described as phasiRNA-producing loci  (Howell et al.
For each condition, two biological replicates were processed and to reduce technical variability, each sample was split and sequenced in two independent lanes. FASTQ file generation, demultiplexing, and adapter removal were done by Fasteris. These deep sequencing files have been deposited to the NCBI Gene Expression Omnibus (GSE104015). sRNA Sequencing Data Processing sRNA Mapping Reads (18 to 35 nucleotides long) with identical sequences were grouped using the processReads function from the ncPRO-seq pipeline  (Chen et al. , 2012)  and aligned against the Arabidopsis genome (TAIR10) using bowtie2  (Langmead and Salzberg, 2012). Mapping statistics are provided in Supplemental Figure  4A.
Differential Analysis Reads (20 to 24 nucleotides long) whose genomic position were nested TAIR10 and miRBaseV21 mature miRNA annotations were counted for each sRNA-seq libraries using in house python script (mature miRNA annotations were enlarged by 2 nucleotides up-and downstream). Raw count corresponding to loci annotated as genes, pseudogenes, tasiRNA, other RNA, and mature miRNA were used for differential analysis between Col-0 wild-type and ago1-57 mutant libraries using DEseq2 v1. 12. 4  (Love et al. , 2014). Loci with an adjusted P value lower than 0. 05 were considered as having differential sRNA accumulation as represented in the MAplot in Figures  6A  and 6B  (Supplemental Data Set 1) and were used for further analysis (Figure  7B ; Supplemental Figures  4B to 4D ).
Protein Sequence Alignments AGO sequences were retrieved from GenBank by BLASTp, using the full AGO1 protein sequence as query. Retrieved sequences were aligned using the multiple sequence comparison by log expectation (MUSCLE) packaged in Unipro UGENE v1. 26. 2. The resulting alignment was exported as an image file. Species used for the alignments are indicated in Supplemental Figure  2A : At, Arabidopsis thaliana; Pt, Populus trichocarpa; Sl, Solanum lycopersicum; Nt, Nicotiana tabacum; Os, Oryza sativa; Bd, Brachypodium distachyon; Cr, Chlamydomonas reinhardtii; Hs, Homo sapiens; Mm, Mus musculus; Rn, Rattus norvegicus; Dm, Drosophila melanogaster; Am, Apis mellifera; Ce, Caenorhabditis elegans; Sp, Schizosaccharomyces pombe.
Accession Numbers Sequence data from this article can be found in the Arabidopsis Genome Initiative or GenBank/EMBL databases under the following accession numbers: AGO1, At1g48410; AGO2, At1g31280; AGO4, At2g27040; CUL1, At4g02570; ASK1, At1G75950; and RBX1, At5g20570. P0 sequence used in this study is from the Turnip yellow virus (BWYV-FL1) genomic RNA (GenBank ID X13063. 1).
ACKNOWLEDGMENTS We thank  K. Hleibieh  for technical support for the P0 suppressor screen,  Nathalie Bouteiller  for GUS measurements,  Julia De Cillia  for gift of the XVE:P0-myc LP1 line,  Diana Navarro-Martínez , and  Raquel Sarmiento-Mañús  for expert technical help with the mapping-by-sequencing of the sup149. 1 mutation. P. G. acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP7/2007-2013)/ European Research Council  advanced Grant  338904 , and from the  Agence Nationale de la Recherche LABEX ,  ANR-10-LABX-0036_NETRNA. J. L. M. and M. R. P. were supported by grants from the  Ministerio de Economía y Competitividad of Spain  ( BIO2014-53063-P  to J. L. M. and  BIO2104-56889-R  to M. R. P. ) and the  Generalitat Valenciana  ( PROMETEOII/2014/006  to J.
Synthetic biology applies the principles of engineering to biology in order to create biological functionalities not seen before in nature. One of the most exciting applications of synthetic biology is the design of new organisms with the ability to produce valuable chemicals including pharmaceuticals and biomaterials in a greener; sustainable fashion. Selecting the right enzymes to catalyze each reaction step in order to produce a desired target compound is, however, not trivial. Here, we present Selenzyme, a free online enzyme selection tool for metabolic pathway design. The user is guided through several decision steps in order to shortlist the best candidates for a given pathway step. The tool graphically presents key information about enzymes based on existing databases and tools such as: similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions and relevant properties such as predicted solubility and transmembrane regions.
Introduction In recent years, powerful bioinformatics tools are being increasingly integrated into systems and synthetic biology pipelines  (Carbonell et al. , 2016). Synthetic biology employs the engineering principle of an iterative Design-Build-Test-Learn cycle. In the case of developing engineered organisms for the production of high-value compounds, the Design stage involves the identification of the most suitable combinations of starting substrates, enzymes, regulatory components and chassis organism for the desired biosynthetic pathway. Hence, bioinformatics tools used at this stage usually carry out database mining in order to search for the best candidate parts and devices. Some of these tools are capable of establishing possible pathways leading to a target compound by using sets of encoded reaction rules, like RDM patterns in PathPred  (Moriya et al.
Design and implementation Our goal is to mine for candidate enzyme sequences for any desired target reaction or set of reactions in a pathway. A unique feature of Selenzyme is that target reactions do not necessarily need to exist in databases, enabling the tool to search for alternative routes for biosynthesis, degradation and transport either for natural or non-natural products. Selenzyme uses SYNBIOCHEM's biochem4j graph database  (Swainston et al. , 2017a ) (June 2017) as its main data source. The database contains relevant information on relationships between reactions (36765), chemicals (19735), enzymes (245704) and organisms (8431). A Selenzyme query consists of a target reaction, represented in. rxn format (MDL), a character string using either the SMARTS format or the derivative SMIRKS representation for reaction rules (http://www.
daylight. com/), an external database id or an EC number. The query is then screened against the reaction database in order to look for similar chemical transformations. Individual Tanimoto similarities between chemicals are obtained using pre-selected fingerprints (http://www. rdkit. org). The efficiency of such similarity screening method was previously validated for the set of reaction rules used by the RetroPath 2. 0 workflow (Delepine et al. , 2018). The user can choose to rank similarities in both reaction directions or to use only the direction of the reaction based on a consensus list that has been generated according to reaction information based on curated information from MetaCyc (Supplementary Material). The algorithm proceeds through the list of reactions ranked by decreasing similarity and collects annotated sequences associated with the reactions.
For each sequence, several useful properties such as phylogenetic distance to the host chassis and predicted physicochemical properties computed via the EMBOSS suite are collected. Optionally, a multiple sequence alignment (MSA) can be generated using T-Coffee  (Taly et al. , 2011)  and visualized using MSAviewer  (Yachdav et al. , 2016). The output generates a. csv file containing the list of top sequence candidates. On top of this core tool, there is a web server built using Flask in Python. Once the reaction query is submitted, the ranked list of sequence candidates is presented as an interactive table, which can be sorted on user-defined summary scores based on a weighted average of selected columns or properties. Additional sequences can be added or removed from the table. Moreover, a RESTful service has been implemented, so that Selenzyme can accept multiple queries from any other web-based application.
Richard Leschen started the morning session with a presentation on the morphology and classification of the series Cucujiformia, with a focus on the superfamily Cucujoidea and the Phytophaga (comprised of the sister superfamilies Chrysomeloidea and Curculionoidea)  [1, 2]. Among other things, he commented on the characteristic double tegmen in the superfamily Cleroidea (and a few other clear morphological apomorphies of this superfamily), the difficulties of resolving deep relationships in the superfamily Tenebrionoidea and the non-monophyly of Cucujoidea, which is now thought to comprise two groups: Coccinelloidea (the cerylonid group) and the core Cucujoidea (see  [3] [4] [5] ). He went on to discuss general features of the adult and larval morphology of Phytophaga. Dave Clarke provided an overview of the 1K Weevils morphology project and discussed theoretical and practical aspects of large-scale morphological dataset construction.
He began by outlining the three main goals of the project: (1) Compile a comprehensive dataset sampling ~750 genera of Curculionoidea and representing all major lineages of Curculionidae; (2) provide an independent morphological test of weevil relationships by integrating >700 adult and immature characters into one framework; and (3) improve the higher classification of Curculionidae (subfamilies/tribes) using subsets of this information for diagnosing higher taxa and by comprehensively illustrating and integrating this information into a Lucid (or similar) platform. He introduced his literature-based synthetic compilation of character data from throughout Curculionoidea by describing the relationship between the numbers of new characters introduced to the 'global' character database as new studies of weevils appear over time.
From this work, he has developed a consensus character list and database comprising both internal and external characters sampled across Curculionoidea, which tracks the usage of individual characters by various studies and thus widely across weevil phylogeny. He advocated for the use of a structured language for character construction (distinct from anatomical/morphological terminology as discussed by Chris Lyal below), such as that described by  [6]  and for organizing characters in an anatomically logical way, e. g. , by body region. The character database is dominated by adult external characters, with internal and larval characters forming a smaller proportion. Adult characters are comparatively evenly distributed among the body regions/primary anatomical divisions. He noted that many characters are probably not independent and discussed the needed balance between quality and quantity of characters.
Evolutionary Development of the Weevil Rostrum (Steve Davis) Steve Davis opened the afternoon session with a presentation on the weevil rostrum. He noted that certain Salpingidae (Tenebrionoidea) also have a rostrum (like Curculionoidea), but that the salpingid rostrum differs in having free tendons. In Curculionoidea, the tendons are supported by internal apodemes that extend from the head sulci. He used micro CT scanning to see more detail. He also obtained histological sections across the length of the rostrum from exemplar weevils from different lineages of Curculionoidea and found that apodemes sometimes fuse internally, especially posteriorly. Anteriorly, the pharyngeal plate is supported by apodemes. The weevil phylogeny reconstructed on the basis of rostrum anatomy  [9]  is similar to phylogenies reconstructed from molecular data (e.
Malar shape is distinct for Rhynchophorus Herbst and may be an important character to distinguish the larvae of the various subtribes within Rhynchophorini. Setae and the arrangement of these setae and pores on the epipharynx and labrum appear to be among the most important larval features for the subfamily Dryophthorinae; however, terminology and homology of pores, for example epipharyngeal sensory pores, cf. accessory sensory pores and pores of the anterolateral and anteromedian epipharyngeal setae  [28] , need to be addressed. Mandibles show differences as well as the shape and number of spiracles. vSysLab is used to code characters http://www. vsyslab. osu. edu/. Each image has a unique identifier to link to the specimen, including an annotation of the provenance of each character. This approach allows one to enter characters, view the character state and code each state collaboratively if necessary.
Logically Reconciling Conflicting Belid Weevil Classifications and Phylogenies (Nico Franz) Nico Franz's talk focused mainly on collaboration, data culture and specimen management. In particular, given that weevil phylogenies and classifications are expected to change and conflict with each other for decades to come, he asked how (well) are we keeping up with this change, including in our data repositories? He showed logic-reasoned examples of multi-phylogeny and -classification alignments of different succeeding or simultaneously endorsed arrangements for Belidae (cf. ,  [29, 30] ). Regarding specimen management, he further introduced the Symbiota Collections of Arthropods (SCAN) as a portal for collaborations involving specimens. As of April 2018, this portal holds >180,000 records of >12,000 therein recognized species of Curculionoidea.
By using double-digest RAD-seq  [69]  to generate genetic data for both weevils and plants, Bruno is comparing the degree to which beetle population structure is associated with the population structure in their host plants and whether this varies between weevils with different modes of interaction. He also presented his ongoing work to develop PCR-generated target enrichment probes  [70]  and RAD-seq  [71]  for reconstructing lower-level phylogenies among the species of weevils associated with palms and also briefly shared highlights from his work mining the literature for information on egg morphospace. Bruno and collaborators developed a database of egg size measurements gleaned from thousands of references and linked this data to phylogenetic information. This could provide a model on how to gather data from the literature to study weevil biology once a comprehensive phylogenetic tree is produced under the 1K Weevils project.
Sequeira); (4) hybridization as a possible mechanism for the origin of parthenogenetic reproduction in weevils through the study of intra-individual variation in ribosomal sequences  [96]. 2. 2. 9. The Contribution of Mitogenome Sequences to the Reconstruction of the Phylogeny of Weevils (Julien Haran (Speaker); Martijn Timmermans; Alfried P. Vogler) Julien Haran reported on his collaborative works that used mitochondrial genome (mitogenome) sequences in reconstructing the phylogeny of weevils. The development of high-throughput sequencing of full mitochondrial genomes in beetles  [97]  has made possible the exploration of weevil phylogenetic relationships based on this relatively large and easy-to-handle sequence dataset (12 protein coding genes, approximately 10,000 base pairs). Phylogenetic analyses were first conducted on 27 taxa, including major basal groups and subfamilies, representing the main lineages of Curculionidae  [43].
These data were later included in a dataset of 245 mitogenome sequences of Coleoptera to assess the position of weevils in the order Coleoptera  [98]. In parallel, a protocol to assemble new mitogenomes was developed and taxon sampling was increased to 122 species of Curculionoidea  [44]. All trees reconstructed from the analysis of mitogenome sequences were consistent with previous molecular phylogenetic reconstructions  [10, 14]. The basal position of families considered as primitive (Anthribidae, Nemonychidae, Attelabidae, Rhynchitidae, Brentidae incl. Nanophyinae, and Apioninae) was supported. The results also supported the existence of an intermediate clade containing the Dryophthorinae, the Brachycerinae, and the Platypodinae at the base of a large clade containing all true weevil lineages (Curculionidae and Scolytinae).