dataesr/scientific-paragraphs-categorization huggingface dataset:
Five distinct clusters were analyzed in more detail (Figures  3 4 5 ). Subsequently, a second step in the dereplication process was carried out. First, molecules described in the Atlas of Natural Products as compounds 1 to 7 analogs were searched. Then, a second search was carried out on the Reaxys database on compounds structurally similar to molecules isolated from natural resources  [24]. Structures proposed through the literature review were annotated with level 3 using only exact mass and taxonomic information. Five molecules, i. e. , geumsanol A-C and G (14-17) and eupeniazaphilone C  (18) , were therefore annotated in cluster C, where hypocrellone  (7)  was first dereplicated  [25] [26] [27]. Two molecules, i. e. , isochromophilone IX  (6)  or penazaphilone F  (19)  and penazaphilone D  (20) , were also annotated in cluster B, including compounds 2 and 5  [28, 29].
In Silico Azaphilone Structure Prediction Using MS/MS Data and a t-SNE Molecular Network According to the first in silico dereplication steps, 4 distinct subclasses of azaphilone produced by the P. sclerotiorum SNB-CN111 strain were uncovered. In fact, these azaphilone subclasses presented one or several modifications of the same scaffold, including acylations on R1, a lactone ring at R1-R2, the presence of an oxygen or a functionalized nitrogen on Y, a diol or a methylethylene at R3-R4 and finally chlorine or hydrogen at position X. (Figure  2 ). Isolated or synthetic azaphilones do not display all this combinatorial diversity in the literature  [21]. To further annotate the azaphilone-related metabolome from the P. sclerotiorum SNB-CN111 strain, all available chemical information, including MS/MS data, chemical formulas, biosynthetic pathways and literature surveys, was gathered and combined.
Observation of two carbons, C-11 (δ C 75. 9 ppm) and C-12 (δ C 78. 4 ppm), displayed hydroxylated carbon chemical shifts and permitted the establishment of the side chain as 3,5-dimethylhept-1-ene-3,4-diol (Figures S97-S102, S117, Tables  S1  and S2 ). This attribution is in accordance with the reported NMR characterization of 15, an analog of 63 without chlorine at position X  [25] ; this compound was named chlorogeumasnol. Molecule 74 was isolated as a purple amorphous oil, and its molecular formula was determined to be C 23 H 26 NO 4 based on ESI-HRMS data ([M + H] + at m/z 380. 1863, calcd for C 23 H 25 NO 4 H+, 380. 1856, err. -1. 8 ppm). The 3,5-dimethyl-1,3-heptadienyl unit, the azaphilone scaffold and their connection were identified as described for 63. The lactone ring was established with the HMBC correlation of H-5" with C-4", C-3" and C-2" and by comparison with its analog described in the literature  [28, 32]  (Figures S103-S108, S117, Tables  S1  and S2 ).
Discussion P. sclerotiorum SNB-CN111 specialized metabolism was deeply examined among 109 extracts of microorganisms associated with termites from French Guiana. This strain showed both antimicrobial biological activity and a wide variety of molecules from the azaphilone class. The LC-MS/MS and in silico dereplication process provided an example of how an extended analysis of MS/MS data can lead to large-scale azaphilone annotation. First, it could be demonstrated that the fractionation of the crude extract allowed a better dereplication of the azaphilones. The number of features increased from 382 to 2953, leading to a more complete MN using t-SNE visualization. However, only a few azaphilones were dereplicated by querying the MS/MS. To enlarge our annotation, a literature survey was performed.
Height azaphilones were annotated after this second dereplication step coming from the isolation process (14-21). Eight additionally reported compounds not detected by the first dereplication round were then identified thanks to our in silico strategy: 24, 34, 35, 40, 72 and 75-77  [32, 39]. In silico strategies were tested, but the results were not satisfactory enough for azaphilone analogs  [43] [44] [45] [46]. Indeed, in silico strategies display 17-25% to 87-93% annotation for "known-unknown" metabolites, depending on the database boost  [46]. However, these strategies do not perform correctly for "unknown-unknown" metabolites derived from enzymatic and chemical transformation or for original structures, as was the case for azaphilones extracted from P. sclerotiorum SNB-CN111. At present, there is a need to increase our ability to accurately annotate "unknown-unknown" characteristics to identify compounds that cannot be isolated for various reasons.
General Identification Procedure The taxonomic marker analyses were externally performed by BACTUP, France. The identification of the fungi was conducted by amplification of the ITS4 or ITS1 region of ribosomal DNA and the bacterial isolates were identified on the basis of 16S rDNA sequence analysis. The sequences were aligned with DNA sequences from GenBank, NCBI (http://www. ncbi. nlm. nih. gov, accessed on 7 June 2021), using BLASTN 2. 2. 28. The sequences were deposited in the GenBank for accession numbers.
Isolation and Identification of Penicillium Sclerotiorum SNB-CN111 The strain was isolated from a Nasutitermes similis termite aerial nest sampled in Piste de Saint-Elie (N 05 • 01,838 W 052 • 44,606 ) in French Guiana. The strain SNB-CN111 from the strain library collection at ICSN was identified as Penicillium sclerotiorum. A sample submitted for amplification and nuclear ribosomal internal transcribed spacer region ITS4 sequencing allowed for strain identification by NCBI sequence comparison. The sequence has been registered in the NCBI GenBank database (http://www. ncbi. nlm. nih. gov, accessed on 4 September 2013) under registry number KJ023726.
Data Processing and Analysis The data files were converted from the d standard data format (Agilent Technologies) to mzXML format using MSConvert software, part of the ProteoWizard package 3. 0  (21). All mzxml values were processed using MZmine2v51 as previously described  [16]. Mass detection was realized with an MS1 noise level of 1000 and an MS/MS noise level of 0. The ADAP chromatogram builder was employed with a minimum group size of scans of 3, a group intensity threshold of 1000, a minimum highest intensity of 1000 and m/z tolerance of 0. 008 (or 20 ppm). Deconvolution was performed with the ADAP wavelet algorithm according to the following settings: S/N threshold = 10, minimum feature height = 1000, coefficient/area threshold = 10, peak duration range 0. 01-1. 5 min and t R wavelet range 0.
008 (or 20 ppm), a weight for m/z at 20, a retention time tolerance of 0. 2 min and weight for t R at 50. The MGF file and the metadata were generated using the export/submit to GNPS option. Molecular networks were calculated and visualized using MetGem 1. 3 software  [14] , and MS/MS spectra were window-filtered by choosing only the top 6 peaks in the ±50 Da window throughout the spectrum. The data were filtered by removing all peaks in the ± 17 Da range around the precursor m/z. The m/z tolerance windows used to find the matching peaks were set to 0. 02 Da, and cosine scores were kept in consideration for spectra sharing at least 2 matching peaks. The number of iterations, perplexity, learning rate and early exaggeration parameters were set to 5000, 25, 200 and 12 for the t-SNE view. Figures were generated using R and related packages (ggplot2, Rcolorbrewer and gridextra), MetGem export function and ChemDraw Professional 16.
The determination of the absolute structure was confirmed by using Bayesian statistics on Bijvoet differences  [54]  based on the Olex2 results. All the molecular graphics presented here were computed with Mercury 2020. 3. 0  [55]. Crystallographic data for the two structures (1 and 5) have been deposited in the Cambridge Crystallographic Data Centre database (the deposition numbers are CCDC 2085749 and CCDC 2085750, respectively). Copies of the data can be obtained free of charge from the CCDC at www. ccdc. cam. ac. uk (accessed on 7 July 2021).
S5 ). Furthermore, in contrast to data from genepoor sex chromosome systems, we found that nearly all the genes in the female-and male-specific coexpression modules, including the hubs, were sex-linked (fig. S6  and table  S12 ). The sex-specific gene expression networks are enriched for proteins with known reproductive functions across green plant lineages. For example, the male Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms show enrichment for microtubule-based processes, which play a role in sperm production in other systems (fig. S6  and tables S13 and S14)  (27, 28). We also found that both female and male coexpression modules are enriched for genes involved in circadian rhythm, like phytochrome, which are involved in flower development in Arabidopsis thaliana  (29).
Misjoins were characterized as a discontinuity in the GG1 or R40 linkage group. A total of 73 misjoins were identified and resolved in GG1 and 64 in R40. The resulting broken contigs were then oriented, ordered, and joined together into 13 chromosomes (12 autosomal and 1 sex chromosome designated as "U" in the GG1 release and 12 autosomal and 1 sex chromosome designated as "V" in the R40 release) using both the map and the Hi-C data. A total of 579 joins were made in GG1 and 625 in R40 during this process. Each chromosome join is padded with 10,000 Ns. Significant telomeric sequence was identified using the (TTTAGGG) n repeat, and care was taken to make sure that it was properly oriented in the production assembly. The remaining scaffolds were screened against bacterial proteins, organelle sequences, and GenBank non-redundant database and removed if found to be a contaminant.
Genome annotations Transcript assemblies were made from ~1. 5 billion pairs of 2 × 150 stranded paired-end Illumina RNA-seq reads from C. purpureus GG1 and ~1. 6 billion pairs from C. purpureus R40 using PERTRAN, which conducts genome-guided transcriptome short-read assembly via GSNAP  (47)  and builds splice alignment graphs after alignment validation, realignment, and correction  (48)   (50) , soybean  (51) , Setaria viridis  (52) , grape  (53) , Sphagnum magellanicum, and P. patens  (13) , Selaginella moellendorffii  (54) , and Chlamydomonas reinhardtii  (55) , filtered Trinity assembly ORFs described above, high-confidence gene models from the first round of C. purpureus R40 gene call, UniProt Bryopsida, and Swiss-Prot proteomes to repeat-soft-masked C. purpureus GG1 genome using RepeatMasker  (56)  with up to 2000-bp extension on both ends unless extending into another locus on the same strand.
Cscore is a protein BLASTP score ratio to MBH (mutual best hit) BLASTP score, and protein coverage is the highest percentage of protein aligned to the best of homologs. PASA-improved transcripts were selected on the basis of Cscore, protein coverage, EST coverage, and its coding sequence (CDS) overlapping with repeats. The transcripts were selected if its Cscore is larger than or equal to 0. 5 and protein coverage is larger than or equal to 0. 5, or it has EST coverage, but its CDS overlapping with repeats is less than 20%. For gene models whose CDS overlaps with repeats for more than 20%, its Cscore must be at least 0. 9 and homology coverage at least 70% to be selected. The selected gene models were subject to Pfam analysis, and gene models whose protein is more than 30% in Pfam TE domains were removed and considered weak gene models.
Peaks in Ks plots can be identified visually, but we also applied mixture models that were selected by the difference in BIC scores, such that a difference less than 3. 2 is used as a stopping criterion. Mixture models were implemented with the bic. test. wgd function available on GitHub (https://github. com/gtiley/Ks_plots). Mixture models can be problematic in their interpretation because of overfitting; therefore, we looked for peaks that were consistently detected across models and the maximum Ks value allowed  (69). When analyzing all paralogs, a single prominent peak was observable in C. purpureus with a mean between a Ks of 0. 65 and 0. 97 in GG1 and a Ks between 0. 68 and 0. 74 in R40 (table  S6 ). The more consistent results in R40 imply that more paralogs from this WGD event have survived on the V chromosome compared to the U chromosome.
TE annotation We combined the R40 assembly (autosomes and V) with the U sex chromosome assembled from GG1 to run de novo repeat detection using the TEdenovo pipeline from the REPET package (v2. 4)  (71). Parameters were set to consider repeats with at least five copies. We obtained a library of 4699 consensus sequences that was filtered to keep only those that are found at least once as a full-length copy in the combined assembly, and we retained 2523 of them. This library of consensus sequences was then used as digital probe for wholegenome annotation by the TEannot (72) pipeline from the REPET package v2. 4. Threshold annotation scores were determined for each consensus as the 99th percentile of the scores obtained against a randomized sequence [reversed input, not complemented and masked using Tandem Repeats Finder with parameters 2 7 7 80  10 70 10 (73) ].
The library of consensus sequences was classified using PASTEC followed by manual curation  (74). To improve classification, remote homology detection was performed using HH-suite3  (75). For the density plot of genes and TEs (Fig. 1 ), we calculated the proportion of coverage of each feature in a 100-kb window with a 90-kb jump using Bedtools (v2. 27. )  (76). These results were plotted in R (v3. 5. 3) (77) using the package karyoploteR (v1. 8. 8) (78) (ceratodon_genome_plots. R, https://doi. org/10. 5061/dryad. v41ns1rsm). To examine differences in enrichment between the autosomes, U, and V, we ran a pairwise MWU for multiple tests (79, 80) using the sliding window densities (n Auto = 2736, n U = 1247, n V = 1229).
pl, https://doi. org/10. 5061/dryad. v41ns1rsm). Reads were further filtered for quality using Trimmomatic (v0. 36) (83) using leading and trailing values of 3, a window size of 10, a quality score of 30, and a minimum length of 40. We assessed the quality of the remaining reads using fastqc (v0. 11. 4) Andrews (2010), https://www. bioinformatics. babraham. ac. uk/projects/fastqc/. Filtered reads were mapped using HISAT2 (v2. 1. 0) (84) to the C. purpureus R40 genome (autosomes and V sex chromosome) concatenated with the GG1 U sex chromosome. We hard masked the U chromosome for males, and the V for females, using Bedtools (v2. 27. 1) (76) maskfasta  (85). Genes greater than 300 bp were assembled using StringTie (v1. 3. 3) (86), gene counts were extracted using StringTie's prepDE. py script (http://ccb.
jhu. edu/software/stringtie/index. shtml?t=manual#deseq), and gene IDs renamed (using mstrg_prep. pl, https://gist. github. com/gpertea/b83f1b32435e166afa92a2d388527f4b). Only genes matching the original genome annotation file were used for coexpression analyses below. To identify differentially expressed genes, we used DESeq2 (v1. 22. 2) (87), where we contrasted males and females at both the protonemal and gametophore stages. For autosomal genes, we removed those with baseMean < 1, a log 2 fold change < 2, and an adjusted P > 0. 05. For sex-linked genes, we calculated the mean normalized count across only females or males for protonema and gametophore stages separately. To identify which sex-linked genes were sex specific versus homologous, we used the output from Orthofinder below. Heatmaps of gene expression were made using variance stabilized counts using DESeq2  (88).
We converted the transformed counts to long format using reshape2 (v1. 4. 3) (89), clustered the samples using hierarchical clustering, and generated a dendrogram using ggdendro (v0. 1. 22)   (90). The plots were modified using gtable (v0. 3. 0) (91). The final heatmaps were generated with ggplot2 (v3. 3. 1) (92)  and gridExtra (v2. 3) (93) using the color palate viridis (v0. 5. 1) (ceratodon_genome_plots. R, https://doi. org/10. 5061/dryad. v41ns1rsm)  (94).
Coexpression network construction and module detection Weighted gene coexpression networks were constructed using the WGCNA R package (v1. 69) (95)  with gene expression data normalized using variance stabilizing transformation from the DESeq2 R package (v1. 26. 0)  (88). The data retained after filtering genes showing low expression levels (minimum read count = 6 and minimum total read count = 10) were used to construct coexpression network modules using the blockwise network construction procedures. Briefly, pairwise Pearson correlations between each gene pair were weighted by raising them to power (). To select a proper softthresholding power, the network topology for a range of powers was evaluated and appropriate power was chosen that ensured an approximate scale-free topology of the resulting network.
2. 5) (96) and ggnetwork (v0. 5. 8) (97) R packages and to focus on the relevant gene pair relationships; network depictions were limited to an adjacency threshold of 0. 2 and the top 3000 edges/interactions between nodes/gene models. GO and KEGG pathway enrichment analysis GO enrichment analysis was carried out using topGO, an R Bioconductor package (v2. 38. 1) (98) with Fisher's exact test; only GO terms with P < 0. 05 were considered significant. To identify redundant GO terms, semantic similarity among GO terms was measured using Wang's method implemented in the GOSemSim, an R package (v2. 12. 1)  (99). KEGG (100) pathway enrichment analysis was performed on the basis of hypergeometric distribution test, and pathways with P < 0. 05 were considered enriched.
To reduce protein redundancy, we next ran our ORFs through CD-HIT (4. 6. 3) (108, 109) using a 0. 99 threshold. We first found orthogroups for the in-frame genes using Orthofinder (v2. 2. 0)  (61, 62). We built trees for genes annotated on the M. polymorpha and C. purpureus sex chromosomes by first filtering clusters for at least eight species present in the tree (orthogroup_filter. pl, https://doi. org/10. 5061/dryad. v41ns1rsm). For these clusters, we wrote FASTA files for both amino acid and cds files of genes clustered within an orthogroup (fasta_from_OrthoFinder. pl, https:// doi. org/10. 5061/dryad. v41ns1rsm). We next aligned our amino acid fasta files using MAFFT (v7. 407)  (110). We back-translated our alignments to DNA using pal2nal (v14)  (111). Alignments were filtered for column occupancy of 0.
5 using trimal (v1. 2) (112) and filtered to remove any sequences less than 300 bp (alignment_ length_filter. pl, https://doi. org/10. 5061/dryad. v41ns1rsm). These final alignments were used to build bootstrapped trees using RAxML (v8. 2. 8) (  113 ) using the GTRGAMMA model and 100 bootstrap replicates. We visually analyzed trees to determine when genes became sex-linked. To accomplish this, we identified the clades that contained annotated U-and V-linked genes and determined the most distantly related species found in the same clade (e. g. , fig. S4 ). All trees and alignments can be found on Dryad under https://doi. org/10. 5061/dryad. v41ns1rsm. All tree plots were made using ggtree (v1. 14. 6)  (114, 115)  in R (v3. 5. 3) (77) and edited in Inkscape (v0. 92. 2) (https://inkscape. org/en/) (ceratodon_genome_plots.
R, https://doi. org/10. 5061/dryad. v41ns1rsm). To identify the ancestral element from which sex-linked genes descended, trees with one-to-one U-V orthologs were rooted using Azolla, Salvinia, Selaginella, Takakia, or Sphagnum as an outgroup (in this order of preference) using newick utils (v1. 6)  (116) , and only the longest isoform within a clade for the same sample was retained (edlwtre2. pl, https://doi. org/10. 5061/dryad. v41ns1rsm). To determine the closest P. patens gene, we used an in-house script (physco_outgroup. py, https://doi. org/10. 5061/dryad. v41ns1rsm), which used ETE3  (117)  to first identify the sex-linked genes and then find the closest P. patens gene based on branch length. For these genes, we also determined whether a C. purpureus chromosome 5 paralog was present and, of these reported, only those that clearly showed gene duplication, presumably from the WGD event.
Protein evolution To examine protein evolution of sex-linked and autosomal genes, we first pruned the trees described above at the closest P. patens homolog (prune_tree. py, https://doi. org/10. 5061/dryad. v41ns1rsm) (117). For genes that had a C. purpureus chromosome 5 homolog, the R40 and GG1 leaves were identified instead and pruned at the closest homolog in P. patens. The chromosome 5 homologs were used to assess dN/dS on autosomal genes in C. purpureus and were 9 of 12 specifically targeted given the recent fusion of the chromosome 5 homeolog to the sex chromosomes. All other copies of a C. purpureus gene were removed, and which copy of a gene to keep for all other species was chosen at random. To get dN/dS ratios, we used PAML (v4. 9a) (66) (additional scripts for this analysis in https://doi.
org/10. 5061/ dryad. v41ns1rsm). For the sex-linked gene trees, we allowed the U and V to evolve at different rates than the rest of the tree. For the chromosome 5 homologs, the GG1 and R40 branches could evolve at a different rate than the rest of the tree. dN/dS values >5 were removed from further analyses. To determine whether there is a significant difference in dN/dS on autosomal, U-, and V-linked genes, we ran a pairwise MWU for multiple tests  (79, 80)  (n Autosomes = 61, n U = 314, n V = 315).
Ka and Ks analysis FASTA files of in-frame C. purpureus and M. polymorpha sex-linked genes were aligned (see above) and converted to axt format (array_ hash_extractor_fasta_unlock_ks. pl and aln_to_axt. pl, https://doi. org/10. 5061/dryad. v41ns1rsm). Ka, Ks, and Ka/Ks were calculated using KaKs_Calculator (v2. 0) (118) using the Goldman and Yang model (  67 ) on only one-to-one UV orthologs. Ks was plotted on the U and V sex chromosomes (Fig. 3 ) in R (v3. 5. 3) (77) using karyoploteR (v1. 8. 8)  (78)  and edited in Inkscape (v0. 92. 2) (https://inkscape. org/en/). One gene with Ks > 3, but coalescence in C. purpureus was removed from the plot (ceratodon_genome_plots. R, https://doi. org/10. 5061/ dryad. v41ns1rsm).
Codon analyses To analyze codon-usage biases, we used CodonW (v1. 4. 2; J. Penden, https://sourceforge. net/projects/codonw). We first removed any gene that had no expression to remove potential pseudogenes. We also removed genes with less than 200 codons to reduce the variance around calculated codon values (alignment_length_filter. pl, https:// doi. org/10. 5061/dryad. v41ns1rsm) (119). We ran a correspondence analysis on autosomal, U-, and V-linked genes together to determine the optimal codons in C. purpureus. We next separately determined the fop usage  (120) , ENC, and GC content of the third synonymous position of a codon (GC3s) on autosomes, U-, and V-linked genes. To determine whether there is a significant difference between fop, ENC, and GC3s between autosomes, U, and V, we ran a pairwise MWU for multiple tests  (79, 80)  in R (v3.
5. 3) (77) (n Autosomes = 15,677, n U = 797, n V = 736) and plotted the results (Fig. 2 ) using ggplot2 (v3. 2. 1) (92) using default box-plot elements (ceratodon_genome_plots. R, https://doi. org/10. 5061/dryad. v41ns1rsm).
Acknowledgments: We thank  R. Quatrano ,  D. Cove , and the  Quatrano laboratory at Washington University in St. Louis  for incubating the C. purpureus genome project;  B. Hauser ,  T. Colquhoun , and  D. Garner  for assisting with the hormone and light perturbations; and  B. Goffinet ,  M. Mack ,  S. Robinson ,  T. Rosenstiel ,  B. Shaw , and the late  N. Miller  for providing field collections. S. Renner ,  S. Otto ,  M. Kirkpatrick , and  M. Hahn  provided valuable feedback on a draft of the manuscript, and we thank two anonymous reviewers for helpful feedback on the current version. The  One Thousand Plant Transcriptome Initiative  provided early access to moss and liverwort data, and the  University of Florida Interdisciplinary Center for Biotechnology Research and HiPerGator  provided vital technical support throughout the project.