dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , Cytochromes P450s: IPR017972, IPR002401, and IPR001128, which predominately occur in the same protein) and a variety of other metabolic processes (e. g. , "fatty acid beta oxidation," GO: 0006635; "deoxyribonucleotide catabolic process," GO: 0009264; "allantoin catabolic process," GO: 0000256) as well as "cell wall biogenesis" (GO: 0042546).
6A  and B ). The GO term "oxidation-reduction process" (GO: 0055114) was strongly enriched in all three species. This dynamic is also reflected in the significant enrichments for various InterPro domains of oxidative enzymes, but especially the enrichment of Cytochrome P450s. Unlike in the ancestral EM genome, in extant EM species we found strong enrichment for transport-related genes (e. g. , "transmembrane transport," GO: 0055085) among expanded orthogroups (a stark contrast to the contraction of these in the MRCA of EM Amanita). All three species show strong enrichments for the InterPro domain "Major facilitator superfamily," a transporter (IPR016169), while both A. muscaria, but especially A. brunnescens show signatures of expansion of amino acid transporters ("amino acid transmembrane transport," GO: 0003333; IPR002293 "Amino acid/polyamine transporter I" and IPR004840 "Amino acid permease").
Thus, our data suggest a pattern of turnover: the transporter repertoire seems to have been reduced in the MRCA of the EM lineages, and then independently expanded in each derived lineage. Besides genes involved in oxidative metabolism and transport, we also detected GO terms pertaining to sugar metabolism ("glycolytic process," GO: 0006096 and "malate metabolic process," GO: 0006108) enriched in A. muscaria and A. brunnescens, respectively. Other gene families amplified within extant EM fungi but not in the MRCA of EM Amanita emphasize the independent trajectories taken by different species (fig. 6A ). These include NOD-like receptor gene families, involved in selfnonself recognition and the detection of other organisms (including the InterPro domains IPR003593 "AAAþ ATPase," IPR007111 "NACHT nucleoside triphosphatase," and IPR027417 "Ploop NTPase";  Dyrka et al.
Secretome Size Distributions Show No Conclusive Patterns That Distinguish EM from AS Fungi Effector-like small secreted proteins (SSPs) can mediate interactions between EM fungi and plants by modulating the immune system  (Plett et al. 2011 (Plett et al. , 2014)). In fact, ectomycorrhizal fungi often show expanded repertoires of SSPs, compared with asymbiotic relatives  (Pellegrin et al. 2015). While the two EM Amanita with large genomes, A. brunnescens and A. muscaria, do appear to possess expanded repertoires of SSPs, once again A. polypyramis provides conflicting evidence. Predictions of the numbers of secreted proteins range from 260 in the genome of A. polypyramis to 588 in the genome of V. volvacea; between 33% (in A. polypyramis) and 54% (in A. brunnescens) of these are shorter than 300 amino acids, and are defined as SSPs (Dryad Annotation file, supplementary fig.
Expression data were collected from EM root tips of a different individual of A. muscaria (var. muscaria) grown in association with Populus tremula x tremuloides  (Kohler et al. 2015). All symbiosisupregulated SSPs in A. muscaria were classified as either singletons or recently amplified in A. muscaria (with the exception of one gene whose age we dated to the MRCA of the Amanita, but that we determined to be a gene prediction artefact). None of the 23% of A. muscaria SSPs conserved across the genus Amanita were induced during symbiosis, although intriguingly, 4 of the 19 symbiosis-induced A. muscaria amplified SSPs have homologs in non-EM species.
2010; Daniel et al. 2017). In tomato and tobacco, the pathogen Septoria lycopersici appears to modify a plant defense compound using a BBE-type enzyme, the product of which then acts as a signaling molecule and dials down the plant defense response  (Bouarab et al. 2002). At least two of the gene families encoding BBEs expanded in the MRCA of EM Amanita are dominated by secreted proteins (Dryad Annotation file) and one is significantly upregulated in mycorrhizal root tips (table  1 ), suggesting a potential role for Amanita BBEs in the interaction with its host.
Fungal Strains, Sequencing, and Assembly Sources and cultures of sequenced fungi, DNA extraction and genome sequencing protocols, as well as initial genome assembly procedures are described in detail by  (Hess et al. 2014). In order to reduce redundancy in the genome sequences due to heterozygosity inherent in the first round of assemblies, we used AllpathsLG v. 46452  (Gnerre et al. 2011)  to reassemble the Amanita brunnescens, Amanita polypyramis, Amanita inopinata, and Volvariella volvacea genomes. The Amanita muscaria var. guessowii and Amanita thiersii genomes assembled at the Department of Energy, Joint Genome Institute (JGI) were used as is, since AllpathsLG was used for assembly at the JGI. Because AllpathsLG requires custom sequencing libraries, we simulated a 150-bp insert size overlapping pairedend library and a 3-kb insert size mate pair library for each genome using wgsim (https://github.
We chose a combination of gene predictors that work well for gene dense fungal genomes, namely CodingQuarry v1. 1  (Testa et al. 2015) , Augustus v3. 0. 2  (Stanke et al. 2008 ), Genemark-ES v2. 3e (Ter-Hovhannisyan et al. 2008) , and SNAP  (Korf 2004). Predicted gene models together with transcript and homologous protein alignments were passed to EvidenceModeler followed by a final round of PASA curation to tidy up putative gene models and generate a comprehensive set of predictions  (Haas et al. 2008). Completeness and redundancy of our genome assemblies and gene annotations using BUSCO v. 1. 1  (Simão et al. 2015 ) by running the software in "gene set" mode with the fungal-specific set of single-copy orthologs.
Functional Annotation Conserved domains in protein coding sequences were annotated using InterProScan v5. 4  (Jones et al. 2014)  with the options -iprlookup -goterms -pa. Assignment to conserved orthologous groups was computed using eggNOG mapper in one2one ortholog mode with the fungal database fuNOG  (Huerta-Cepas et al. 2016). Secreted proteins were predicted using a pipeline consisting of SignalP v4. 1  (Petersen et al. 2011) , TargetP 2. 0  (Emanuelsson et al. 2007 ), TMHMM 2. 0  (Krogh et al. 2001) , PS_scan  (Hulo et al. 2006), and WolfPSort v0. 2 (Horton et al. 2007 ) as implemented in  (Kohler et al. 2015). The search and family assignment of all CAZymes were performed as previously described  (Knapp et al. 2018 ) using the CAZy database (www. cazy. org) annotation pipeline  (Cantarel et al.
2009; Lombard et al. 2014). This pipeline examines all classes of CAZymes, namely Glycoside Hydrolases (GH), Polysaccharide Lyases (PL), Carbohydrate Esterases (CE), Glycosyltransferases (GT), and Carbohydrate-Binding Modules (CBM), augmented by Auxiliary Activities (AA), which break down cell wall components oxidatively  (Levasseur et al. 2013). Briefly, each protein sequence encoded by the genome was subjected to a BLAST  (Altschul et al. 1997)  search against a sequence library that contains the sequences of the domains listed in the CAZy database, in parallel to a HMMer  (Eddy 2009 ) search performed against HMM profiles corresponding to these families and subfamilies thereof. All positive hits were manually examined for final validation. Although our gene prediction pipeline includes a repeat masking step, we expect a small number of gene models to derive from transposable elements (TEs).
Gene Family Reconstruction and Analysis We chose the longest predicted protein at each locus to assemble the proteomes of all species analyzed. The predicted proteomes were combined and clustered into gene families using FastORTHO (http://enews. patricbrc. org/fastortho/; last accessed September 27, 2018) with BLASTP e-value cutoff 1e À5 , percent match cutoff 60, percent identity cutoff 30, and an inflation value of 3. Prior to phylogenomic reconstruction, we filtered the resulting clusters to exclude TE families in order to avoid introducing bias in the estimation of background birth and death rates, which we expect to be different between TEs and protein-coding genes, and between different families of TEs. We applied the following criteria for removal of a cluster: 1) the cluster contains >50% of sequences flagged as TEs, or 2) the cluster contains between 15% and 50% of flagged TEs and either contains members that encode TE-associated PFAM domains, or has representatives from less than three species indicating erratic distribution, or both.
Physical clustering of genes within the same orthogroup along the genome was determined using a custom script available on Github (https://github. com/JackyHess/ Amanita_comparative_genomics; last accessed September 27, 2018). Multiple sequence alignments of each orthogroup were inferred using the phylogeny-aware aligner PRANK (Lo ¨ytynoja and Goldman 2005) with five rounds of iterative refinement of alignment and guide tree estimated with RAxML  (Stamatakis 2014) , implemented in the tool Canopy (http://wasabiapp. org/software/canopy/; last accessed September 27, 2018). We used TrimAl v1. 2 to remove poorly aligned regions from the alignments with the option -auto-mated1  (Capella-Guti errez et al. 2009). Sequences retaining <20 residues postfiltering were removed from the alignments. Focusing on nontrivial alignments, that is, those of at least four sequences and with more than two different species, we then reconstructed individual gene trees using the following pipeline: first, we determined the best-fitting model of protein evolution using ProtTest 3.
Acknowledgments This work was supported by the  National Science Foundation  award number  1021606  to A. P. and  Research Council of Norway  project  221840  to I. S. Computational work was performed on the Abel Cluster, owned by the  University of Oslo  and  Uninett/Sigma2 , and operated by the  Department for Research Computing at USIT , the  University of Oslo ITdepartment. http://www. hpc. uio. no/. We would like to thank  Benjamin Wolfe  for storage and maintenance of RNAseq samples, as well as  Laszlo Nagy  and two anonymous reviewers for their helpful comments on this manuscript.
Data Availability RNA-seq data generated for this study were deposited at the Sequence Read Archive under accession SRP157961. Updated genome assemblies for all non-JGI genomes were deposited in NCBI under BioProject accession PRJNA485427. Genome annotations, orthogroup alignments, trees inferred for the phylogenomic analysis, and a database flat file containing functional annotations, orthogroup membership, genomic context, gene ages, and secretome predictions for all predicted proteomes were deposited in Dryad under accession doi:10. 5061/dryad. g63c748.
To this end, we make use of MetaLab, a publicly available database of 12 standardized meta-analyses of language acquisition. MetaLab is a dynamic, continuously growing database. At the time of writing, the available meta-analyses cover a variety of behavioral and neuroimaging methods (11 in total) and participant ages (from newborns to 5-year-olds). Since all meta-analyses currently in MetaLab address specific phenomena within language acquisition, our empirical analyses are adjusted to the methods typically used in this subfield of developmental research. Nonetheless, our analyses and recommendations are relevant beyond the scope of language acquisition research. Crucially, we investigate key study design choices that will be relevant to developmental research at large: sample size (and the ensuing statistical power when effect size is held constant) and method (i.
Examples of the former are native and nonnative vowel discrimination  (Tsuji & Cristia, 2014)  and online recognition of known words  (Frank, Lewis, & MacDonald, 2016) ; the latter is exemplified by learning sound categories and sound sequences in the lab after short exposure to artificial mini-languages  (Cristia, 2018). The dependent variable in all these studies is based on continuous response data, such as looking time, either measured within participants in reaction to two conditions or across participant groups receiving different exposures. Children in our data are aged 0-5 years. In our analyses, we take into account participant age for both practical and theoretical reasons. On the practical side, we expect an effect of infant age based on three aspects of child development research.
g. ,  Hunter & Ames, 1988). From a theoretical standpoint, the phenomena targeted by the meta-analyses currently in MetaLab are expected to show changes with age. In general, this change is in a positive direction: Younger participants should show smaller effects than older ones because they are not yet as experienced with, and proficient in, their native language, and thus, we expect them to improve in most linguistic skills, such as native vowel discrimination, word form recognition, and word to meaning mapping. The one exception in our collection is nonnative vowel discrimination, an ability that should and does decrease as infants tune into their native language  (Tsuji & Cristia, 2014). For a number of phenomena, theoretical predictions are not straightforward (e. g. , a preference for infant-over adult-directed speech is thought to increase in the first few months as children accumulate experience with this affective register, but could have been predicted to eventually decrease due to novelty preferences;  Hunter & Ames, 1988).
Data The data presented and analyzed here are part of a standardized collection of meta-analyses (Meta-Lab) and are freely available via the companion website at http://metalab. stanford. edu. Currently, MetaLab contains 12 meta-analyses, where core parts of each meta-analysis are standardized to allow for the computation of common effect size estimates and for analyses that span across different phenomena. These standardized variables include study descriptors (such as citation and peer review status), participant characteristics (including mean age and native language), methodological information (e. g. , what dependent variable was measured), and information necessary to compute effect sizes (number of participants, if available means and standard deviations of the dependent measure, otherwise test statistics of the key hypothesis test, such as t values or F scores).
Meta-analyses were contributed to MetaLab directly (n = 10), or they were extracted from previously published meta-analyses related to language development (n = 2;  Colonnesi et al. , 2010; Dunst, Gorman, & Hamby, 2012). In the former case, the meta-analysis authors attempted to document as much detail as possible for each entered experiment (note that an article can contain many experiments, as shown in Table  1 ), as recommended for reproducible and dynamic meta-analyses  (Tsuji, Bergmann, & Cristia, 2014). Detailed descriptions of all phenomena covered by MetaLab, including which articles and other sources have been considered, can be found at http://metalab. stanford. edu.
Questionable Research Practices In the final set of analyses, we assess the relation between absolute observed effect sizes in single studies and the associated sample size. The rationale behind this analysis is simple: The smaller the effect size in a particular study (bear in mind that we assume that experiments sample from a distribution around the population effect), the larger the sample needed for a significant p value. If sample size decisions are made before data collection and all results are published, we expect no relation between observed effect size and sample size. If, on the contrary, authors continue to add infants to achieve significance  (Begg & Mazumdar, 1994) , there should be a negative correlation between sample size and effect size. We illustrate the link between effect size and sample size, separated by meta-analysis, in Figure  3.
The authors of this article thank all contributors and supporters of MetaLab and the anonymous reviewers whose thoughtful comments helped improve the article. Funding for this research was provided by the  Berkeley Initiative for Transparency in the Social Sciences, a program of the Center for Effective Global Action (CEGA) , with support from the  Laura and John Arnold Foundation. The authors were further supported by the  H2020 European Research Council  [ Marie Skłodowska-Curie  grant Nos 660911 and 659553], the  Agence Nationale de la Recherche  [ ANR-14-CE30-0003   MechELex ,  ANR-10-IDEX-0001-02   PSL *,  ANR-10-LABX-0087 IEC] , and the  Fondation de France.
Advances in proteomics and sequencing have highlighted many non-annotated open reading frames (ORFs) in eukaryotic genomes. Genome annotations, cornerstones of today's research, mostly rely on protein prior knowledge and on ab initio prediction algorithms. Such algorithms notably enforce an arbitrary criterion of one coding sequence (CDS) per transcript, leading to a substantial underestimation of the coding potential of eukaryotes. Here, we present OpenProt, the first database fully endorsing a polycistronic model of eukaryotic genomes to date. OpenProt contains all possible ORFs longer than 30 codons across 10 species, and cumulates supporting evidence such as protein conservation, translation and expression. OpenProt annotates all known proteins (RefProts), novel predicted isoforms (Isoforms) and novel predicted proteins from alternative ORFs (AltProts).
It incorporates cutting-edge algorithms to evaluate protein orthology and reinterrogate publicly available ribosome profiling and mass spectrometry datasets, supporting the annotation of thousands of predicted ORFs. The constantly growing database currently cumulates evidence from 87 ribosome profiling and 114 mass spectrometry studies from several species, tissues and cell lines. All data is freely available and download-able from a web platform (www. openprot. org) supporting a genome browser and advanced queries for each species. Thus, OpenProt enables a more comprehensive landscape of eukaryotic genomes' coding potential.
Recent efforts for a more comprehensive view of eukaryotic genomes' coding potential have focused on annotation of small ORFs, defined as any ORF between 10 and 100 codons, alongside associated evidence from conservation, ribosome profiling and/or mass spectrometry  (18) (19) (20). Yet, these databases suffer limitations, notably a maximum D404 Nucleic Acids Research, 2019, Vol. 47, Database issue length threshold that forbids detection of ORFs longer than 100 codons, and they do not account for the polycistronic nature of eukaryotic genomes. In parallel, proteogenomics strategies are emerging to offer an unbiased approach to the study of eukaryotic proteomes, yet they remain the expertise of a few and still depend on sample preparation adapted to the identification of small proteins  (21) (22) (23) (24).
Despite these significant studies, we still lack a systematic approach to fathom the deepest parts of eukaryotic proteomes. Here, we present OpenProt (www. openprot. org), the first database upholding a polycistronic model of eukaryotic genes to date. OpenProt distinguishes three ORF categories: already annotated ones (RefProts), novel RefORF isoforms (Isoforms, II accessions) and novel alternative ORFs (Alt-Prots, IP accessions). We define as AltProt the product of any unannotated ORF, anywhere on transcripts (ncRNAs and mRNAs), that do not display protein sequence similarity with a RefProt from the same gene (otherwise categorized as novel isoform: product from an unannotated ORF with a significant sequence similarity to a RefProt from the same gene). OpenProt currently offers deep annotation for 10 species, cumulating supporting evidence of protein orthology, translation and expression.
Mass spectrometry data analysis pipeline In order to gather protein expression evidence, OpenProt retrieves publicly available mass spectrometry (MS) based proteomics studies from ProteomeXchange  (31) , PRIDE archive  (32)  and collaborators. Such studies are re-analyzed using the OpenProt protein FASTA containing all RefProts, AltProts and Isoforms. The OpenProt MS pipeline was developed using PeptideShaker software (version 1. 13. 4)  (33)  configured to systematically run 4 search engines on raw MS files (X!Tandem, MS-GF+, Comet and OMSSA) via SearchGUI (version 3. 1. 0) (34). SearchGUI general parameters were set as previously described and then individually inferred based on studies specifics  (7). Classical MS analyses use a false discovery rate (FDR) of 1%. However, adding all AltProts and Isoforms leads to a substantial increase of the search space (about six times bigger for human).
To only account for highly confident identifications, we set the FDR at 0. 001% (Figure  1 ). Initial validations included: (a) 80% minimum overlap of RefProts identifications with the original MS study, and (b) manual validation of randomly selected spectra (supporting materials S1). Admittedly, false positives may still be and we strongly encourage seeking ORFs with evidence across multiple datasets as false-positive identifications would differ across datasets. Moreover, a novel predicted protein (AltProt or Isoform) will be identified only if it is recognized by a unique peptide. In the case where a peptide matches a novel predicted protein and a RefProt, it will always be assigned to the Ref-Prot only. The identification results are then implemented to the OpenProt database and can be downloaded or queried from the Genome Browser or the Search page.
Ribosome profiling data analysis pipeline In order to gather ORF translation evidence, OpenProt retrieves publicly available ribosome profiling (RIBO-seq) data. Ribosomal footprints raw data are re-analyzed using the PRICE workflow (version 1. 0. 2)  (35). PRICE is an entropy-based model for identification of translated ORFs from RIBO-seq data. PRICE is run with default parameters (except for the FDR) using the rescue mode (Figure  1 ), and fed with both NCBI RefSeq and Ensembl annotations (run separately). Briefly, reads mapping to ribosomal RNAs are filtered out and remaining footprints are mapped onto the genome and transcriptome with up to three mismatches  (35). Multi-mapped reads are fractionated across all possible sites unless uniquely mapped reads to nearby loci allow confident identification of the footprint coordinates  (35).
PRICE reconstitutes the set of codons most likely to give the observed reads, creating a list of ORF candidates. These are filtered according to a stringent 1% FDR (usually set at 10%) to focus on highly confident translation events  (35). The identification results are then implemented to the OpenProt database and can be downloaded or queried from the Genome Browser or the Search page. More information can be found on the web platform Help page (www. openprot. org/p/help) and in supporting materials S2.
Protein functional domain prediction All predicted proteins are also scanned to identify known functional domains. All protein sequences are run through the InterProScan algorithm (version 5. 14-53. 0) using the default parameters  (38). Domain predictions as well as gene ontology (GO) and pathway annotations are reported if significant (e-value < 10 -3 ). The results are inserted into the OpenProt database and can be downloaded or queried from the Search page.
Database and website development All data are generated using in-house Perl (version 5. 18. 2) and Python (version 2. 7. 6) scripts and stored in a Post-greSQL database (version 9. 6). All re-analyzed mass spectrometry and ribosome profiling studies are accessible from the Help page (supporting materials S1 and S2). The Open-Prot web platform was built using the Flask framework (version 1. 0. 2) and developed using HTML, SQL and JavaScript.