dataesr/scientific-paragraphs-categorization huggingface dataset:
0  [50]  and Blastp  [51]  (e-value <1e-5 ) against the NCBI nr databank were then used to perform Gene Ontology annotation  [52]  with Blast2GO  [53]. Secretomes and effectors were annotated as described in  [54]. The secretome was predicted by a combination of TMHMM (v. 2. 0)  [55] , SignalP (v4. 1)  [56]  and TargetP (v1. 1b)  [57]  results with the following criteria: no more than one transmembrane domain and either a signal peptide or an extracellular localization prediction. The SSP repertoire was predicted by applying a size cut-off of 300 amino acids to the predicted secretome and keeping only proteins predicted as effectors by EffectorP (v2. 0).
Analysis of Iso-Seq transcript isoforms The annotation of transcript isoforms was performed with sqanti3  [58]  using Iso-Seq transcripts, previously established to infer UTRs, filtered for UTR length isoforms and low expression levels (less than 10% of total RNA-Seq reads), using the ingenannot isoform_ranking tool. RNA-Seq reads were mapped to Iso-Seq transcripts with RSEM v1. 3. 3  [59]  and Differential Isoform Usage (DIU) performed with tappAS  [60]  with annotations obtained from sqanti3. Detection of antisense and lncRNA Iso-Seq transcripts Iso-Seq transcripts annotated as antisense and intergenic with sqanti3 were selected as Putative long non-coding (lnc) RNAs. Then transcripts shorter than 1 Kb in length  [61] , overlapping with TEs and containing an open reading frame (ORF) longer than 100 amino acids predicted with getorf by EMBOSS  [62]  were discarded.
The resulting "non-coding" transcripts were annotated with CPC2  [63] , and only transcripts without an ORF with a PFAM domain were kept as lncRNAs. featureCounts (v1. 5. 1)  [64]  was used to count reads per transcript, followed by differential expression analysis by edgeR  [65]  with the SARTools package (v1. 6)  [66]. Detection of polycistronic Iso-Seq transcripts For detecting polycistronic mRNAs,, read-through Iso-Seq transcripts that were previously filtered out were merged to obtain the global counts of genes that were potentially co-transcribed. To establish a robust list of co-transcribed multi-gene loci, readthrough transcripts were filtered with the gene reannotation dataset and their Iso-Seq transcripts used as evidence. Only polycistronic mRNAs supported by independent long-read single transcripts for each gene were conserved and considered as reliable.
Identification and annotation of mycoviruses Iso-Seq transcripts not mapping to the Z. tritici IPO323 reference genome were clustered with blastclust. Similarities with known sequences were analysed by blastn search against the NCBI nr database. Reconstruction of the full-length sequences of viruses was performed by de-novo assembly with SPAdes (v3. 15. 4)  [67]. RNA-dependent RNA polymerase sequences from narnaviruses related to Zt-NV1 were retrieved from NCBI and analyzed using Phylogeny. fr  [68]. Alignment of protein sequences was performed with Muscle 3. 8. 31 and curated by G-blocks. The phylogenetic analysis was performed using PhyML 3. 1 and the phylogenetic tree was drawn with TreeDyn 198. 3.
This strategy required the construction of an expression dataset using both publicly available single-stranded RNA-Seq datasets, including wheat leaf infection kinetics, and newly generated datasets using both long-read sequencing (PacBio Iso-Seq: Iso-Seq) and short-read sequencing (single-stranded Illumina RNA-Seq: RNA-Seq) (Table  S3 ). Iso-Seq based annotation of the IPO323 genome sequence and gene model selection Z. tritici mRNAs used for this study corresponded to a wide array of in vitro mycelial growth conditions (Table  S3 ). These mRNAs were used for the construction of either single-stranded Iso-Seq cDNA libraries or single-stranded Illumina cDNA libraries. The Iso-Seq sequences from each library were processed individually (cleaning, assembly) and pooled into a single dataset. Non-redundant Iso-Seq transcripts were selected at each locus using the CupCake chaining tool, giving 22,659 Iso-Seq transcripts.
Some Iso-Seq transcripts corresponded to alternative transcripts differing in their intron splicing or TSS/TTS (TSS: transcriptional starting site, TTS: transcriptional termination site). The alternative Iso-Seq transcripts that were either not supported by RNA-Seq or with a relative abundance lower than 10% according to RNA-Seq in all conditions, were filtered out. This filtering kept isoforms differentially expressed in a least one condition with a relative abundance over 10%, providing 21,052 transcripts corresponding to 8,927 loci. Most loci displayed only one isoform (50%), while other loci had either 2 to 5 isoforms (42%), or at least 6 isoforms (8%). Each single-stranded RNA-Seq library generated in the framework of this study and publicly available datasets (Table  S3 ) were assembled separately and transcripts with weak expression levels (TPM<1) were removed.
Between 8,600 and 13,000 filtered transcripts were obtained depending on the library and kept as a separate dataset providing 498,010 single-stranded assembled RNA-Seq transcripts as evidence. Most existing ab initio gene prediction tools use RNA-Seq assembled transcripts as evidence to infer the structure of gene models. However, currently only a few gene prediction tools (Eugene  [10] , LoReAn  [47] ) can use Iso-Seq transcripts as evidence. These two softwares were used to annotate the IPO323 genome sequence with Iso-Seq transcripts, RNA-Seq transcripts and reference fungal protein sequences as evidence. Eugene identified 15,810 gene models in the Z. tritici genome in a two-pass mode and strand-specific prediction allowing overlapping gene models on opposite strands. This number was reduced to 15,245 gene models after filtering out genes corresponding to TEs.
These rescued genes models were mostly not conserved across fungi (upper right red bar in Figure  2 ) and frequently had low transcriptional support (upper green bar in Figure  2 ). For gene models overlapping on opposite strands, only the gene model with the best AED score was selected. Finally, 97 additional effector-encoding genes were predicted with the rescue_effector tool of InGenAnnot. Overall, we obtained a final set of 13,414 re-annotated Gene Models (RGMs; File S1, Table  S4 ). In addition, UTRs were inferred from Iso-Seq transcripts for 7,713 genes, and for 9,856 genes (73%) when combined with RNA-Seq assembled transcripts. The average and median sizes of 5'UTRs were 315 bp and 156 bp, while they were 389 bp and 220 bp for 3'UTRs (Table  S4 ), close to the values (mean 5'UTR 275 bp and mean 3' UTR 303 bp) reported recently for the Pezizomycotina P.
The eight missing BUSCOs in RGMs were reduced to six after manual inspection. These six RGMs that were missing in BUSCO encoded a Leucyl-tRNA synthetase, a WD40repeat-containing domain protein, a Zinc finger protein, a Heavy metal-associated domain protein, a protein with an HMA domain, a PHD-type protein and a GTP binding domain protein. Their conservation across fungi is questionable, since a blastp search showed that they are missing from numerous genomes. The comparison between annotations was then performed using AED scores (Figure  2 , S1 and S2). Of the 13,414 RGMs, 11,568 (86%) passed the AED threshold of 0. 3 and 0. 1 for transcript and protein evidence, respectively (Figure  2 ). In comparison, these numbers decrease to 7,730, 8,936, 9,518 and 10,716 for the JGI, MPI, RRES and CURTIN annotations, respectively (Figure  S1 ).
Functional annotation of the reannotated IPO323 gene models Functional annotation of predicted proteins deduced from RGMs was performed using both Blast2Go and InterProScan. 5,593 RGMs exhibited a GO term or an IPR and 2,838 were annotated with at least one Enzyme Code (EC). As in previous annotations of IPO323 genome sequence  [28] ,  [73] , several tools were launched to identify genes encoding putative secreted proteins, including effectors (File S1). We identified 1,895 genes corresponding to secreted proteins with less stringent criteria than those used in a previous study that identified 970 secreted proteins using the JGI annotation  [43]. All these 970 genes were identified as RGMs. However, they increased to 1,046 mainly due to the splitting of fused gene models from the JGI annotation.
The RGM secretome included 234 small, secreted proteins (SSP) according to EffectorP and additional criteria defined in the Materials and Methods section. Among the 100 SSPs studied previously by Gohari et al. using the JGI annotation  [74] , 93 were identified as encoded by RGMs. Still, many structural differences between these RGMs and the JGI gene models were observed. The effector rescue software of InGeAnnot identified 53 SSPs among which 43 were not found in any previous annotations. Four of these 53 novel SSPs displayed a significant upregulation during infection compared to in vitro culture conditions (ZtIPO323_001210, ZtIPO323_072700, ZtIPO323_105940 and ZtIPO323_123970), suggesting a possible role in infection. In addition, genes encoding effectors missing in previous annotations, such as Avr-Stb6, were now predicted correctly.
sclerotiorum, Plasmopara viticola, and Fusarium asiaticum (Figure  S8 ). IPO323 ZtNV1 sequence was used to screen publicly available Z. tritici RNA-seq datasets. ZtNV1 was identified in all these datasets, but only with very few reads, validating the ubiquitous presence of the virus in Z. tritici. ZtFV1 was also detected in these RNA-seq data in higher amounts compared to ZtNV1 (70,000 fold).
Still, Iso-seq has potential pitfalls since this technic is not quantitative. Indeed, we identified rare Iso-seq transcripts likely corresponding to errors of the transcriptional machinery (intron retention, polycistronic transcripts). We minimized this error by filtering out low-abundance Iso-Seq transcripts based on their quantification using short-read RNA-seq. Overall, filtered Iso-seq transcripts were highly reliable in determining the genome-aligned exon structure of transcripts, while RNA-Seq offered a quantification of Iso-Seq transcript structures and isoforms. The newly established transcriptomic dataset was used to select the best gene models among those predicted by different ab initio software according to their AED transcript scores (transcript evidence), using InGenAnnot. Protein evidence also helped select the best gene model for genes not expressed under the conditions used for producing mRNAs.
The combination of six ab initio software was needed at two levels. First, a diversity of software was needed to produce a sufficient number of gene models at each locus to be selected by InGenAnnot. Indeed, none of the ab initio software was able to independently predict all the RGMs (Table  S10 ). The best ab initio software, Eugene, only predicted correctly 76% of the RGMs. Second, the use of different ab initio software allowed the rescue of gene models without evidence  (1, 846  RGMs predicted by at least 4 different ab initio software). Most rescued RGMs were not conserved across fungi and they had a low transcriptional support or they were not expressed under the available conditions (upper green bar in Figure  2 ). They typically included candidate fungal effectors that could be important for plant-fungal interactions (File 1).
Iso-Seq long reads reveals the complexity of transcripts in Z. tritici Identifying transcript isoforms is a major challenge when relying on the assembly of short RNA-seq reads, as alternative splicing sites could not be easily distinguished. Here, we took advantage of the full-length cDNAs produced by Iso-Seq long-read sequencing to identify novel exon combinations. Indeed, the assembly of RNA-Seq reads could be misleading for transcripts with more than one. However, Iso-Seq sequencing is not a quantitative method and minor transcripts were sequenced. For example, Iso-Seq transcript isoforms with long UTRs or IR without strong support from RNA-Seq data were identified in our initial dataset (Figure  4 , Figure  5 , Figure  S5 ). These low-abundance transcript isoforms could be produced by the transcriptional machinery either as by-products or to regulate gene expression.
As discussed before, the small amounts of RNA-Seq reads available in these conditions makes such comparisons difficult using the available statistical tools. In fact, manual inspection of several detected loci did not reveal clear patterns of DIU for alternative transcripts. Additionally, dense genomes, such as Z. tritici genome, are suitable for polycistronic transcription, i. e. the production of mRNA that encode several proteins. Indeed, we identified polycistronic mRNAs in Z. tritic among Iso-Seq long-read transcripts, as already observed in Agaromycotina  [78]  and F. graminearum  [77]  or C. militaris  [79]  using Iso-Seq. However, polycistronic-specific RNA-Seq reads were always detected in low abundance compared to single-gene transcripts. These RNA-seq data also showed that polycistronic transcripts mostly corresponded to genes with transcripts overlapping those from adjacent genes.
Evaluation of the reliability of an annotation is not an easy task. One of the most frequently used tools is the BUSCO software for identification of conserved proteins to evaluate the completeness and fragmentation of the predicted genes at the protein level  [49]. More recently, new datasets and methods were proposed to test the reliability of gene annotations, looking deeper into the prediction of intron and exon structures  [7]. However, this evaluation was still based on selected datasets, representing a conserved and partial view of gene content of a genome. In the case of a genome reannotation, ParsEval could give clues on overlaps of different versions of annotations with sensitivity and specificity metrics  [69]. The most descriptive tool to evaluate the reliability of an annotation with associated evidence is GAEVAL (available through AEGeAn  [97] ), which computes an integrity score weighted by such features as confirmed introns, annotation coverage and UTR identifications.
The combination of different gene prediction software with distinct intrinsic characteristics, could be a good way to avoid drawbacks from each software, in particular when ab-initio gene predictors fail to find a consensus gene model. In the same way as EvidenceModeler  [36]  or TSERBA  [98] , InGenAnnot is able to select the best gene model based on AED scores with defined evidence thresholds. We used additional criteria to select the best gene model when evidence was lacking (gene model predicted by all or a minimal number of software). Since each gene model had AED metrics, it could be compared to other gene sets, allowing postfiltering or prioritization in the manual curation process.
Conclusion In the era of the massive sequencing of compact fungal genomes, inferring gene models by evidence is essential and complementary to ab-initio gene prediction methods. In this paper, we used the recent Iso-seq technology and developed a novel software, InGenAnnot, to drastically improve the gene annotation of Z. tritici, an important fungal plant pathogen. We additionally identify lncRNA and mycoviruses as being expressed during plant infection. We expect that both the improved sequencing technology and our new software will be used widely to improve the gene prediction of many species of importance, in particular in plant pathogens with dense genomes, and reveal new insights into the role of transcriptome complexity in plant-pathogen interactions.
Acknowledgments We are grateful to the  Genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12) for providing help and/or computing and/or storage resource. BIOGER benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). We also thank the BARIC workgroup (https://www. cesgo. org/catibaric/) for providing storage and computational resources. Rothamsted Research (JR, DS and RK) co-authors were supported by the  Biotechnology and Biological Scientific Research Council (BBSRC)  of the  United Kingdom  through the institute strategic grants " 20:20   Wheat" and  " Designing Future Wheat " (grant numbers  BB/J/00426X/1  and  BBS/E/C000I0250 ). The work (proposal:  10. 46936/10. 25585/60008023 ) conducted by the  U.
Availability of data and materials availability All raw sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO) under accession GSE218898 with data accessions: GSM6758342 to GSM6758379. Processed data files of assembled RNA-Seq transcripts and filtered Iso-Seq reads were associated to the submission. Sequence of the new mycovirus ZtNV1 was deposited to NCBI under accession OP903463. Previous Z. tritici IP0323 gene annotations, new annotations (RGMs, Isoforms, LncRNAs) and annotation file, denoted file S1 (z. tritici. IP0323. annotations. txt ), are available at: https://doi. org/10. 57745/CVIRIB. A genome browser with all annotations and evidence was set up at: https://bioinfo. bioger. inrae. fr/portal/genome-portal/12/ A new IPO323 genome web site at (https://mycocosm.
Comprehensive genome collections are the backbone for metagenomics and metatranscriptomics. If genome databases are available, shotgun metagenomics and metatranscriptomics have shown to provide unprecedented insights  17, 19  , e. g. , into vegetation change over glacial cycles  15  , historic population genomic processes  20, 21  , and kingdom-spanning processes of ecosystem functioning  22. Large genome sequencing initiatives like the Earth Biogenome Project  23  will provide this data ultimately, but progress so far mainly focused on large, prominent organisms, such as mammals  24  , birds  25  , insects 26 and plants  15. In addition to serving taxonomic identi cation, broad (many distinct groups) and dense (many species from a group) sequencing of genomes additionally allows identifying common patterns of gene evolution and test the taxonomic generality of hypotheses on genome evolution.
Repeats are frequently considered to increase genome size  40, 41. If the repeats themselves are biased in base composition, this should re ect in the overall GC content. Interestingly, GC content is also linked to resource availability  42  , which may be linked to habitat generalism via higher metabolic exibility  43. Even though most of these observations originate from bacterial studies, ample evidence exists that the environment may in uence base composition also in metazoans  42, [44] [45] [46]. We tested these hypotheses simultaneously with a series of structural equation models (SEMs, Fig. 3a ). To parametrize the SEM and connect the 232 new genome assemblies with ecological traits, we rst gathered trait data from original literature. Information about habitat preferences was added from the Edaphobase data warehouse for soil biodiversity (https://portal.
edaphobase. org/). We focussed on three traits: a) body length as a proxy for body size (minimum female adult body length for nematodes, and mean adult body length for all other taxa), b) reproduction mode, and c) the number of known habitat types where a species occurs, as a proxy of habitat generality (based on CORINE -Coordination of Information on the Environment 47 ). We annotated repetitive elements with species-speci c repeat libraries. We estimated effective population size (theta) directly from the genome data by making use of the genome-wide heterozygosity in the reference individual. This proxy measure of effective population size was calculated individually for each genome assembly with at least 4X coverage. Genomic and ecological traits are accessible in Supp. Table  1. The SEMs supported only two of the hypothesised causal relationships when these were modelled for all taxa together (Fig.
Voucher specimens are deposited in the Senckenberg museum collection in Görlitz. For larger taxa such as Chilopoda, Diplopoda, Isopoda, Enchytraeidae and Lumbricidae, the species-level morphological identi cation was possible before DNA extraction, and only a single leg, a few body segments or musculature of mouthparts were used for DNA extraction, the rest of the body was kept as a voucher. For medium sized taxa like Acari and Collembola that normally would require clearing in lactic acid prior to species identi cation, the specimens were presorted on family or genus level, the whole specimens were used for non-destructive DNA extraction, and nally species-level identi cations were carried out with recovered vouchers. In cases where non-destructive DNA extraction did not deliver su cient amounts of DNA or the voucher was lost during extraction, identi cation was validated by aligning species markers (28S, COI) from the whole-genome sequence data with existing species markers in GenBank or generated by us.
Illumina sequencing Sequencing libraries for each specimen, or pool of specimens, were prepared in-house at Senckenberg, Frankfurt, Germany with the BEST protocol  70  or with the NEBnext ULTRA II DNA Library Prep Kit, according to the manufacturer's protocol. Short-read Illumina sequencing (300-bp paired-end) was carried out at Novogene Europe (Cambridge, UK) using the NovaSeq 6000 platform, with unique dual indexing as the library tagging strategy for multiplexing on the lanes. Our central aim with the genome database was to improve species identi cations. As this can be achieved with low sequencing coverage  71  , our initial sequencing efforts targeted 2 gigabase (Gb) per species. We increased efforts to 10 Gb per species as sequencing became more affordable.
Genome assembly pipeline We established a pipeline to assemble reads into draft genomes (Supp. Figure  3 ). First, the sequencing adapters were trimmed using Trimmomatic (v0. 39; paramaters: ILLUMINACLIP:adapters. fasta:2:30:10:8:true SLIDINGWINDOW:4:20 MINLEN:50 TOPHRED33  72  ). The trimmed reads were queried against the human genome (GRCh38 assembly on NCBI) using Kraken2 (v2. 0. 9-beta; --con dence set to 0. 2, other parameters default  73  ), and all 'human' positive reads, if any, were discarded. The remaining reads were then assembled using SPAdes (v3. 14. 1; default settings  74  ). The resulting contigs were then queried against the NCBI non-redundant nucleotide database using blastn (megablast mode, -max_target_seqs 10, -max_hsps 1, -evalue 1e-25), and against the NCBI nonredundant protein database using Diamond (blastx mode, --sensitive --max-target-seqs 1, --evalue 1e-25  75  ).
NCBI databases were downloaded on 27-Oct-2020. Blobtools2 (v2. 3. 3 76 ) was used to perform a taxonomic assignment based on the Blast and Diamond results, using the 'bestsumorder' rule. The contigs assigned to the phylum of the target organism as well as the unassigned contigs were kept (i. e. contigs assigned to other phyla were considered obvious contaminants and removed). Redundans (v0. 14a  77  ) was used to reduce the amount of duplication in the retained contigs, as well as further scaffolding and gap closing (default parameters were used). The resulting scaffolds were used as the nal assembly draft for subsequent analyses. The Burrows-Wheeler Aligner (BWA) was then used to map the reads on the assembly and samtools 78 (v1. 11-2-g26d7c73) to compute and plot the mapping statistics (e.
Quality assessment of assemblies using BUSCO Benchmarking Universal Single-Copy Orthologs (BUSCO) databases  27  are sets of genes for speci c taxon groups, where every gene in the BUSCO set is expected to be present once in each member species. We searched for BUSCO genes in our nal assemblies as a quality indicator of genome assembly completeness, we used the most speci c BUSCO database that was available for each of the invertebrate groups (nematoda_odb10 BUSCO genes for nematode assemblies, arthropoda_odb10 for arthropods, metazoa_odb10 for tardigrades, enchytraeids and earthworms). We selected the genome assembly with the highest percentage of complete BUSCO genes as the species representative if more than a single replicate per species was available. This resulted in a total of 232 genome assembly drafts used for downstream analyses.