dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of pigment pathways We used AuCoMe to predict the capacity of five brown algae, Cladosiphon okamuranus, Ectocarpus siliculosus, Ectocarpus subulatus, Nemacystus decipiens, and Saccharina japonica to synthesize heme groups and then to catalyze three alternative pigment biosynthesis pathways in the MetaCyc database: phycoerythrobilin (PWY-5915), phycocyanobilin (PWY-5917), and phytochromobilin (PWY-7170) (Fig. 5 ). These pathways consist of four, three, and three reactions, respectively, and all have protoheme, the end-product of the heme synthesis pathway, as a starting point. They also share the same starting reaction (RXN-17523) that transforms heme into biliverdin in the presence of a reduced ferredoxin (Fig. 5 ). Regarding the heme biosynthesis pathway, the added value of AuCoMe was to predict GPR associations for N.
RXN-13968  (Karp et al , 2019)  was not considered spontaneous but had no associated enzymes in the MetaCyc database and hence was not found in any of the analyzed algal networks. We chose to manually add this reaction to the GSMNs of all five brown algae. Pathways PWY-5915 and PWY-5917 generate phycobilins. As shown in Fig. 5 , the pebA gene is associated with 1. 3. 7. 2-RXN, pebB with 1. 3. 7. 3-RXN, pcyA with 1. 3. 7. 5-RXN, and HY2 gene is with 1. 3. 7. 4-RXN. In red algae, cryptophytes, and cyanobacteria, phycobilins bind to light-harvesting proteins called phycobiliproteins. Brown algae are thought to have lost phycobiliproteins during their evolution  (Bhattacharya et al , 2004). However, studies suggested that brown algae have, in many cases, retained pebA, pebB, and HY2 genes acquired via their secondary endosymbiosis event with red algae  (Rockwell et al , 2014; Rockwell and Lagarias, 2020) , that may be associated with the synthesis of phycobilins.
AuCoMe GSMNs are consistent with species phylogeny To further assess the predictions of AuCoMe and to explore biological features, we clustered the GSMNs of all organisms of the algal dataset after the draft reconstruction as well as at the end of the pipeline by using the presence or absence of reactions in the GSMNs (see Fig. 6 A ). The initial GSMNs produced from the annotations exhibited low consistency with the phylogenetic relationships among the algae  (Strassert et al , 2021). Even well-established phylogenetic groups like red algae or brown algae were not recovered. At this step, the principal factor leading to the repartition of points in the MDS was the heterogeneity of genome annotations. An ANOSIM test supports this as it was not able to differentiate the main phylogenetic groups (R=0, P-value=0.
45). However, in the MDS made from the GSMNs after the final step of AuCoMe, we observed a clear separation between the known phylogenetic groups, supported by an ANOSIM test (R=0. 811, P-value=1e-04). This is also visible in the dendrograms clustering the GSMNs generated by the complete AuCoMe pipeline. This clustering was broadly consistent with the reference species phylogeny (Fig. 6B ): there were only three higher order inconsistencies concerning Cyanidiophora paradoxa, for which the genome version deposited in Genbank was fully lacking expert annotations  (Price et al , 2012) , Guillardia theta, the phylogenetic position of which is controversial  (Strassert et al , 2021) , and Nannochloropsis gaditana, which was the only representative of eustigmatophycean stramenopiles. The two other stramenopile groups, diatoms and brown algae, were represented by multiple species which likely minimizes errors linked with peculiarities of a single genome.
There were also some minor inconsistencies in intra-group relationships, in green algae, diatoms, brown algae, and opisthokonts. An illustration of the efficiency of AuCoMe was the de novo reconstruction of the GSMN of the glaucophyte Cyanophora paradoxa. For the reconstruction of this GSMN, we used the initially published genome sequence, which contained only two functionally-annotated genes  (Price et al , 2012). The draft reconstruction by AuCoMe enabled us to retrieve 1,675 GPRs, a number within the same range as the other species from the dataset. Accordingly, C. paradoxa branched at the basis of the dendrogram after the draft reconstruction step, whereas it moved to the archeplastids after the orthology propagation step. Even if the grouping of C. paradoxa within archeplastids with the streptophytes Chara brauni and Klebsormidium nitens does not reflect the phylogenetic relationships, this shows that AuCoMe is a reasonable proxy for handling nearly unannotated genome sequences.
Next, we examined the metabolic pathways represented by the reactions that associated G. theta with the three groups, focusing on pathways that were ¿ 50% complete. The metabolic networks showed, for instance, that G. theta, (i) like haptophytes in our dataset, possess parts of the mitochondrial L-carnitine shuttle pathway, (ii) like the stramenopiles, comprises the complete pathway of glycine betaine synthesis, and, (iii) like terrestrial plants, can synthesize carnosine. We also manually examined the genes associated with these reactions, and found that in all cases, their sequences differed strongly from other sequences in the database, and could not be clearly associated with either archeplastids, stramenopiles, or haptophytes. These examples underline the fact that cryptophytes diverged from the other lineages early in the history of eukaryotes and support the hypothesis that the metabolic capacities of extant cryptophytes might reflect adaptation to their specific environment more clearly than their ancient evolutionary history.
Manual analysis by blastP searches, however, showed that potential homologs of those four proteins were present in other brown algae in the dataset. This was further confirmed by the phylogenetic analysis of the oaminophenol oxidases closely related to the S. japonica protein found by AuCoMe (SJ09941) and retrieved from the public databases (Appendix Table  S10 ). Indeed, the phylogenetic tree showed that there are five clades of brown algal sequences present in three larger groups, including some stramenopile microalgae (see Appendix Fig. S5 ) Some more distant O-aminophenol oxidases found in Symbiodinium dinoflagellates and in fungi as well as metazoans form two well-separated branches from stramenopile proteins. These results suggest that the corresponding enzymes potentially exhibit other substrate or activity specificities in stramenopiles.
Moreover, annotation efforts can greatly vary between genomes, resulting in heterogeneous annotation and metabolic prediction quality. The originality of our metabolic inference method resides in the possibility to account for, and preserve, available expert genome annotations. Not considering the genome annotations performed by specialists may lead to the omission of unique metabolic functions that are not well described in reference databases. On the other hand, comparing metabolic networks built from well-curated annotations to those built from poorly or automatically-annotated genomes will inevitably result in biases. In such cases, real metabolic differences between species cannot be distinguished from missing annotations in some genomes. AuCoMe constitutes a solution to such challenges through the propagation of expert annotations to less characterized genomes in the process of metabolic network reconstruction.
Method limitations and improvements AuCoMe incorporates several strategies to optimize the method's selectivity and sensitivity. Together these strategies collectively achieve comparable GSMN reconstruction, as demonstrated by validation experiments performed on randomly degraded E. coli genomes, although the results also highlight some of the method's limitations. A first limitation is illustrated by the comparison of AuCoMe reconstructions to the EcoCyc database considered as ground truth in our experiment. We observed that the GSMN automatically reconstructed from the reference genome substantially differs from the database. Extensive and systematic manual curation has been performed on this database since its creation in 1998 and we hypothesize that these efforts could not be all translated in the E.
coli K-12 MG1655 annotations. As a result, several reactions were systematically missing from the automatic inferences provided by AuCoMe. This example illustrates the role of manual curation and expertise in producing high quality models. The homogenization of metabolic inference proposed by AuCoMe does not aim at replacing this step but rather enable an unbiased metabolic comparison between species. Running AuCoMe on the bacterial dataset highlighted the impact of a single highly-annotated genome on metabolic inference. This dataset included the reference genome of the E. coli K-12 MG1655 strain which is better annotated than most of the other genomes considered. As a consequence, a certain number of reactions initially propagated by orthology from the E. coli K-12 MG1655 genome to others were discarded by the AuCoMe filter due to a lack of support, following the rationale that a single genome supporting an annotation propagation is not robust enough.
Reasoning on ortholog clusters, the filter implies that several congruent genome sources are mandatory to confidently achieve an annotation propagation. While the relevance of the filter was demonstrated on the algal dataset by avoiding the propagation of annotations related to photosynthesis to non-photosynthetic organisms, it may be too stringent in some applications and lead to discarding relevant reactions. Several improvements of the filtering approach could be devised. For example, the structural annotation step could be improved: the annotation of pseudogenes in Shigella species would have been avoided by considering the annotations as pseudogenes available for the identified loci. More generally, in addition to the difficulties of automatically estimating protein homology, one must keep in mind that the link between orthology and conservation of function is still a matter of active investigation and methodological debate  (Stamboulian et al , 2020; Begum et al , 2021).
Given that, as demonstrated above, phylogenetic signals in metabolism are stronger than the adaptive signals we can expect, this approach would also benefit from the development or adaptation of statistical models that could help detect signals of adaptation in an overall noisy dataset. Such models exist, for instance, to detect selective signatures in the evolution of protein-coding gene  (Shapiro and Alm, 2008) , but to our knowledge, have not been developed for metabolic networks or presence/absence signatures of genes. The second related strategy consists in focusing on phylogenetically closely related species that have only recently diverged and adapted to different environments. In such cases, we anticipate that the relative importance of drift along with the noise from the phylogenetic signal will be reduced due to the short evolutionary time since the separation.
With such datasets, we may be able to reduce the level of replication required to find biologically relevant metabolic adaptations. The range of questions that could be addressed with the appropriate dataset is long and includes metabolic adaptations to different environments  (Xu et al , 2020) , food sources and domestication  (Giannakou et al , 2020) , multicellularity  (Cock et al , 2010) , or even life-history transitions to endophytism  (Bernard et al , 2019).
Genomes and models The bacterial dataset includes the 29 bacterial Escherichia coli and Shigella strains studied in  (Vieira et al , 2011) , downloaded from public databases (see Appendix Table  S1  for details). The fungal dataset includes 74 fungal genomes which were selected according to  Wang et al (2009)  as representative of the fungal diversity, together with 3 outgroup genomes: C. elegans, D. melanogaster, and M. brevicollis. All proteomes and genomes were downloaded from the NCBI Assembly Database  (Kitts et al , 2016). The algal dataset contains 36 algal genomes selected to represent a wide diversity of photosynthetic eukaryotes and downloaded from public databases. The dataset includes 16 Viridiplantae (green algae), 5 Phaeophyceae (brown algae), 5 Rhodophyceae (red algae), 4 diatoms, 3 haptophytes, 1 cryptophyte (Guillardia theta), 1 Eustigmatophyceae (Nannochloropsis gaditana), 1 Glaucophyceae (Cyanodophora paradoxa).
The genomes of C. elegans  (Witting et al , 2018) , M. circinelloides  (Vongsangnak et al , 2016) , N. crassa  (Dreyfuss et al , 2013) , and S. cerevisiae  (Lu et al , 2019)  were selected as outgroup genomes (see Appendix Table  S3  for details). Each annotated genome of the datasets was curated manually in order to make it compatible with Pathway Tools v23. 5. Curated genomes are available at https://zenodo. org/record/6632441#. YqbMjS2FDOR.
AuCoMe, a method to reconstruct genome-scale metabolic networks homogenized across related species AuCoMe is a Python package implementing a pipeline whose steps are described in Fig. 1. The method aims at producing homogenized genome scale metabolic networks (GSMNs) for a set of heterogeneouslyannotated genomes containing closely related or outlier species of a taxonomic group. AuCoMe takes as input GenBank files containing the genome sequences, the structural annotation of the genomes (gene and protein locations), the functional annotations (especially with GO terms and EC numbers) and the protein sequences (with the qualifier "translation" for the CDS feature). The output of AuCoMe is a set of GSMNs, provided in SBML and PADMET formats  (Hucka et al , 2018; Aite et al , 2018). AuCoMe also produces a global report describing the sets of reactions added at all steps of the pipeline.
The global panmetabolism, which is the complete family of metabolic reactions included in at least one GSMN of the set of genomes, is described in a tabulated file. At the initialization step the command aucome init creates a template folder in which the user stores the input GenBank files. The aucome reconstruction command runs the draft reconstruction step, which consists in reconstructing draft GSMNs according to the set of available genome annotations. During this step, the pipeline first checks the input GenBank files using Biopython  (Cock et al , 2009). Then using the mpwt package  (Belcour et al , 2020a) , AuCoMe launches parallel processes of the PathoLogic algorithm of Pathway Tools  (Karp et al , 2019). Pathway Tools creates Pathway/Genome Databases (PGDB) for all genomes. The resulting PGDBs are converted into PADMET and SBML files  (Hucka et al , 2003 (Hucka et al , , 2018) )  using the PADMet package  (Aite et al , 2018).
During this conversion, reactions from pathways with no enzyme identified in the genome (pathway hole reactions) predicted by Pathway Tools are removed as they are not associated with a gene and are not spontaneous reactions. For example, in Fig. 1  A, the draft reconstruction step generates 6 GPRs in total for the 3 considered genomes. The aucome orthology command runs the orthology propagation step, which complements the previous GSMNs with GPRs associations whose genes are predicted to be orthologues to genes from GPR relations of other GSMNs of the dataset (Fig. 1 B ). To that purpose, the pipeline relies on OrthoFinder  (Emms and Kelly, 2015, 2019)  for the inference of orthologs defined as clusters of homologous proteins shared across species. For each pair of orthologous genes shared between two species, the pipeline checks whether one of the genes is associated with an existing GPR association.
This pipeline step complements GSMNs with GPR associations from other GSMNs according to protein-against-genome alignment criteria. This enables the identification of reactions which are associated with gene sequences absent from the initial structural annotations of the input genomes. A pairwise comparison of the reactions in the GSMNs produced during the previous step is performed (Fig. 1 C ). In this comparison, if a reaction is missing in an organism, a structural verification will be performed. For each protein sequence associated with a GPR relation in a GSMN, a tblastn  (Altschul et al , 1990; Camacho et al , 2009)  with Biopython  (Cock et al , 2009)  is performed against the other genome with the missing reaction. If a match (evalue¡1e-20) is found, the gene prediction tool Exonerate  (Slater and Birney, 2005)  is run on the region linked to the best match (region +-10 KB).
If Exonerate finds a match, then the reaction associated with the protein sequence is assigned to the species where it was found to be missing. In Fig. 1  C, one reaction is added to the GSMN 2. The command aucome spontaneous runs the spontaneous completion step to fill metabolic pathways with spontaneous reactions, in order to complement each GSMN obtained after the structural-completion step with spontaneous reactions, i. e. reactions that do not need enzymes to occur. For each pathway of the MetaCyc database  (Caspi et al , 2020)  which was incomplete in a GSMN, AuCoMe checks whether adding spontaneous reactions could complete the pathway. When this is the case, the spontaneous reaction is added to the GSMN. In Fig. 1 D , two spontaneous reactions are added to the GSMN 1 and GSMN 3. Then the final PADMET and SBML files are created for each studied organism.
Validation of the filtering step One hundred random GPR associations were randomly selected and examined across the metabolic networks generated by AuCoMe for the algal dataset. Among them, 50 reactions that were predicted to be present and 50 reactions that were predicted to be absent in the metabolic networks. Regarding the former, their first associated gene was manually annotated based on reciprocal blast searches against Uniprot (Bateman et al , 2021) and the presence of conserved domains and the result of this manual annotation was compared to the predicted metabolic reaction. For absent reactions, we searched for characterized proteins known to catalyze the reaction in question, and then performed reciprocal BLASTp searches with the corresponding algal proteome. In addition, DeepEC (version 0.
4. 0) (Ryu et al , 2019)  was applied both to fungal and algal protein sequences. This tool predicts Enzyme Commission for protein sequences using 3 Convolutional Neural Networks trained on a dataset containing protein sequences associated with Enzyme Commission from SwissProt and TrEMBL. We extracted the EC numbers of reactions for which at least one GPR association was predicted according to orthology propagation for all reactions of the fungal and the algal datasets. For each EC number, we extracted the protein sequences associated with the considered reaction in the GSMNs, and we used DeepEC to infer an EC number for these proteins. Then we compared the EC number found by DeepEC (if found) to the EC number linked to the reaction by the pipeline.
Comparison to the EcoCyc database The complementarity between the orthology propagation step (second step) and the structural verification step (third step) was assessed using replicate genomes of E. coli K-12 MG1655 modified by a simulated degradation of its annotations. Degraded replicates were then given as input to the AuCoMe method, and the resulting GSMNs were finally compared to the ground truth Ecocyc metabolic network in order to estimate the precision and recall of the method. A non-degraded GSMN for E. coli K-12 MG1655 was obtained by running AuCoMe on its genome associated with 28 other E coli and Shigella genomes from  Vieira et al (2011). This run allowed us to identify the genes associated with the metabolism that would be the target of the simulated degradation. Then, the E.
coli K-12 MG1655 genome was degraded to cellular organism, to focus on the impact of genome annotations on GSMN reconstructions by AuCoMe, rather than on the effect of the automatic completion by the EcoCyc source performed by Pathway Tools when analyzing E. coli K-12 MG1655. Each degraded replicate was associated with the 28 other E. coli and Shigella genomes, producing a synthetic dataset, which was analyzed by AuCoMe. This procedure therefore generated 31 synthetic bacterial datasets, plus the dataset with non-degraded E. coli K-12 MG1655 genome, which was called dataset 0. Their characteristics are detailed in Appendix Table  S4. For each E. coli K-12 MG1655 replicate in a dataset, AuCoMe produced a GSMN, which was compared to the metabolic database literature-based curated EcoCyc, considered as ground truth  (Karp et al , 2002 (Karp et al , , 2018;; Keseler et al , 2021).
We considered the reactions both present in a GSMN produced by AuCoMe and in the EcoCyc database as True Positives (TPs). False Positives (FPs) are reactions that are present in the GSMN produced by AuCoMe but not present in the EcoCyc metabolic network, and False Negatives (FN) are reactions present in the EcoCyc metabolic network but not present in the GSMN produced by AuCoMe. There were no True Negative reactions because each considered reaction either belongs to GSMNs produced by AuCoMe or the EcoCyc metabolic network. The F-measure of each AuCoMe dataset was defined as F = 2P R P +R , where P = T P T P +F P is the precision (number of reactions inferred by AuCoMe and present in EcoCyc among all the reactions predicted by AuCoMe) and R = T P T P +F N is the recall (number of reactions inferred by AuCoMe and present in EcoCyc among all reactions in EcoCyc).
Phylogenetic analysis of the brown algal o-aminophenol oxidases A dataset of 193 protein sequences was constructed using the closest homologues of the S. japonica oaminophenol oxidase (SJ09941) in brown algae and extended to more distant sequences present in other organisms. Sequences were submitted to NGPhylogeny. fr via the A la carte"  (Lemoine et al , 2019 ) pipeline as follows. The alignment was carried out by MAFFT  (Katoh and Standley, 2013)  using default parameters and automatically cleaned with trimAl  (Capella-Gutiérrez et al , 2009)  to obtain 372 informative positions. Then a maximum likelihood phylogenetic reconstruction was carried out using default parameters of the PhyML-SMS tool  (Guindon et al , 2010; Lefort et al , 2017)  allowing the best substitution model selection. Bootstrap analysis  (Lemoine et al , 2018)  with 100 replicates (¿ 70 %) was used to provide estimates for the phylogenetic tree topology.
OG: orthologs. GPR: Gene-protein-reaction relationship. Figure Legends Figure  2 : Application of the AuCoMe pipeline to the bacterial, fungal and algal datasets of genomes. The figure depicts the number of reactions identified for each species at each step of the Au-CoMe pipeline: reactions recovered by the draft reconstruction step (blue), unreliable reactions predicted by orthology propagation and removed by the filter (gray), robust reactions predicted by orthology propagation that passed the filter (orange), additional reactions predicted by the structural verification step (green), and spontaneous completion (red). The final metabolic networks encompass all these reactions except the non-reliable ones. The panmetabolism (all the reactions occurring in any of the organisms after the final step of AuCoMe) is presented in brown in B, C and D.
The colored bars depict the number of reactions added to the degraded network at the different steps of the method (the blue, orange, green, grey, and red color legends are as described in the figure  2 ). The table shown as axis indicates the dataset number and the percentage of functional or structural annotation impacted by the degradation for the corresponding column in both subfigures. (B) F-measures after comparison of the GSMNs recovered for each E. coli K-12 MG1655 genome replicate with a gold-standard network. Reactions inferred by each AuCoMe step for each replicate were compared to the gold-standard EcoCyc GSMN, allowing for the computation of F-measures. F-measures obtained after the draft reconstruction step, the orthology propagation step, or the structural verification step are shown as blue circles, orange triangles, and green crosses, respectively.