dataesr/scientific-paragraphs-categorization huggingface dataset:
The CAT model was shown to be better fitting and less prone to LBA than site-homogeneous models for a series of classic datasets including Nematoda and Platyhelminthes  (Lartillot et al. , 2007)  as well as in phylogenomic analyses of the tree of life  (Williams et al. , 2020). The impediment of nonparametric Bayesian methods and specifically, the CAT model, is that it compounds two computationally challenging, but separately tractable problems, the non-parametric inference of the underlying distribution across sites and the exploration of tree space. The composition of these two problems is challenging and can lead to convergence problems. In all cases, however, mixture modeling approaches accounting for across-site compositional heterogeneity are complex and require considerable computational resources (e.
We employ "compositional constraint" analysis, which examines phylogenetic signal for alternative topologies as a function of per site compositional diversity measured by the effective number of amino acids. Examining simulated alignments as well as classic empirical datasets including  Microsporidia (Brinkmann et al. , 2005) , Nematoda, and Platyhelminthes  (Philippe et al. , 2005a) , we find conflicting phylogenetic signal across sites with different degrees of compositional constraints. Based on these results we apply compositional constraint analysis to two recent datasets  (Ryan et al. , 2013; Simion et al. , 2017)  aiming to resolve the early diversification of animal lineages.
3 (Table  S5 ). AU tests of the Poisson+CAT-PMSF, and LG+CAT-PMSF models show similar results (Tables  S6,  and S7 ). Finally, we note that site-heterogeneous models with LG exchangeabilities perform well, although the genuine exchangeabilities are uniformly distributed. CAT-PMSF is even more accurate when using the true topology or the true site-specific stationary distributions (Figures  S7  and S8 ). Applications to empirical data. Similar to the simulation study above, for empirical alignments the site-specific stationary distributions obtained in Step 2 of the CAT-PMSF pipeline can be used to quantify the strength of compositional constrains (i. e. , to estimate K eff values per site) and perform compositional constraint analysis. Figure  2  shows results for three datasets exhibiting classic LBA artifacts when we use site-homogeneous models for inference: The placement of Platyhelminthes and Nematoda  (Philippe et al.
, 2005a) , as well as the placement of Microsporidia  (Brinkmann et al. , 2005; Lartillot et al. , 2007). For site-homogeneous models, the site-specific log-likelihood differences between the maximum likelihood trees constrained to the two competing topologies (insets of Figure  2 ; top vs. bottom) show conflicting phylogenetic signal as a function of the strength of compositional constraint. The bias towards the topologies exhibiting LBA artifacts of more constrained sites outweighs the signal of less constrained sites in all three datasets. The site-heterogeneous LG+C60+PMSF model shows reduced, but still appar- 8 q p q p q p (a) (b) (c) Figure  1 : Highly constrained sites drive long branch attraction artifacts in the Felsenstein zone. We simulated amino acid alignments with 10 000 sites exhibiting across-site compositional heterogeneity  (Schrempf et al.
We do not expect a uniform distribution across the bins, because they are of different size. The LG, and GTR models incorrectly infer Farris-type trees if p ≥ 0. 8. 9 ent conflict compared to site-homogeneous models and the LG+C10+PMSF model (Figure  S9 ). For Platyhelminthes, the bias is strong enough to that the total likelihood across all sites is higher for the LBA topology, while for the datasets involving Nematoda and Microsporidia, the LG+C10+PMSF model recover the correct topology, albeit with reduced support. In general, the results of the LG+C10+PMSF and LG+C60+PMSF models are consistent with the observation  (Schrempf et al. , 2020)  that increasing the number of components decreases the bias introduced by more constrained sites. Pearson correlation coefficients are greater for site-homogeneous models than for models LG+C10+PMSF and LG+C60+PMSF models, but significant for each of these (Table  S4 ).
In contrast, CAT-PMSF exhibits consistent signal towards the correct topologies across all sites and datasets with no significant correlation between log-likelihood difference and site-specific K eff value (Table  S4 ). The maximum likelihood trees inferred by CAT-PMSF are consistent with the accepted phylogenetic relationships and AU tests confirm the rejection of trees with LBA topologies (Tables  S8-S10 ). The phylogenetic position of Ctenophora. Finally, we used CAT-PMSF on two metazoan datasets  (Ryan et al. , 2013; Simion et al. , 2017)  to investigate early evolutionary relationships on the animal tree of life. It is currently a matter of intense debate whether sponges (Porifera) or comb jellies (Ctenophora) are the sister group to all other animals (e. g. ,  Kapli and Telford, 2020; Li et al.
Detection of such inconsistencies is a strong indicator for bias: Knowing the stationary distribution of a site alone should not provide us with information about the favored evolutionary history. In mathematical terms, the log-likelihood difference of a site between two hypotheses should be conditionally independent given the stationary distribution of that site. In contrast, we expect the signal obtained from more and less constrained sites be consistent up to random statistical error. In our analyses of empirical data we observed strong inconsistencies between more and less constrained sites for site-homogeneous models and hardly any inconsistencies when using CAT-PMSF (Figure  2 ). Pearson correlation coefficients and p-values confirm this observation across a wide range of simulated and empirical datasets (Tables  S3,  and S4 ).
The results are more nuanced for the alignments involving Ctenophora. In the case of site-homogeneous models, we observe the value of K eff correlates strongly with the log-likelihood difference between the two competing topologies (Figure  3 ). Moreover, for the dataset provided by Simion et al. , CAT-PMSF supports Poriferasister -similar to the results reported by the original authors, who applied the CAT model to sub-sampled alignments comprising 100 000 sites. The support of CAT-PMSF for Porifera-sister is significant, when we use the closest outgroups exclusively (Table  S11 ). If we add more distant outgroups, the results are less conclusive (Table  S11 ). Long branch attraction provides an explanation for this observation: more distant outgroups attract the outgroups closer related to the species of interest.
In turn, the elongated basal branch of animals increases bias 14 due to LBA for branches leading to the metazoan root. We interpret these findings as a confirmation for sponges being the sister group to all other animals (dataset of  Simion et al. , 2017) , and believe that the inconclusive results obtained from the dataset of  Ryan et al. (2013)  reflect a lack of phylogenetic resolution. Irrespective of the final evolutionary history of Metazoa, our results add important evidence that ignoring across-site compositional heterogeneity leads to LBA  (Phillips et al. , 2004). Further notes The results of CAT-PMSF are conservative because the CAT model estimates the site-specific stationary distributions using guide topologies prone to LBA artifacts. That is, the guide topologies are obtained with sitehomogeneous models.
Even so, CAT-PMSF correctly infers the genuine trees in the simulation study (Table  S1 ), and trees that we are convinced to be free from LBA artifacts in the analyses comprising empirical datasets (Figures S3, S2, S1, S6, S5 and S4). This observation justifies the usage of site-homogeneous models in Step 1 of the CAT-PMSF pipeline. In the simulation study, we observe severe bias when using site-homogeneous models, and no bias or reduced bias when using CAT-PMSF. Further, the absolute values of the log-likelihood differences are greater for site-homogeneous models than for site-heterogeneous models. That is, site-heterogeneous models have reduced power in discriminating between competing hypotheses (bias-variance tradeoff). In general, site-homogeneous models show conflicting signal between more and less constrained sites, but we observe hardly any such inconsistencies when using CAT-PMSF.
78. The distribution mixture model has site-specific stationary distributions. For each site, we sampled a random distribution from a universal set of distributions  (Schrempf et al. , 2020)  obtained from the HOGENOM  (Dufayard et al. , 2005)  and HSSP  (Schneider et al. , 1997)  databases. We used Felsenstein-type topologies with four leaves. The quartet trees had different branch length proportions between short (q) and long (p) branches (insets of Figure  1 ). We fixed q to 0. 1, and changed p between 0. 1 and 2. 0. We stored the randomly sampled site-specific stationary distributions used for the simulation. For the simulations we used the ELynx suite  (Schrempf, 2021). The scripts and the simulated data are available at https://github. com/drenal/cat-pmsf-paper. CAT-PMSF. Figure  4  shows an overview of the CAT-PMSF pipeline.
(3) A positive value of ∆ log L i indicates that site i supports topology B. A negative value indicates support for topology A. We ordered and binned the sites according to their K eff values. For the simulation study, we used the genuine K eff values. For the analyses of empirical datasets, we used the K eff values calculated from the site-specific stationary distribution obtained in Step 2 of the CAT-PMSF pipeline. We performed binning with windows sizes of 1. 0 K eff and summed the site-specific log-likelihood differences within each bin.
org/), the silhouette for Microsporidia is based on  Tosoni et al. (2002, Figure 7 ). Dataset involving Platyhelminthes and Nematoda. Philippe et al. (2005a)  address a well-known LBA artifact concerning the placement of Platyhelminthes and Nematoda on the tree of Bilateria. Lartillot et al. (2007)  revisit the same dataset and provide two reduced, and overlapping alignments which contain 37 species for Nematoda and 32 species for Platyhelminthes, respectively. Both alignments have a length of 35371 amino acids. Figure  2   Microsporidia evolve fast, and site-homogeneous methods fail to correctly classify them. Application of site-heterogeneous methods confirms that Microsporidia are the closest sister species of Fungi  (Brinkmann et al. , 2005). For these reasons, the dataset containing Microsporidia is ideal as a proof of concept for CAT-PMSF.
(a) and (b), S2 and S1 show simplified and complete species trees, respectively. Dataset involving Microsporidia. The dataset provided by Brinkmann et al. (2005) comprises 40 species with 24294 amino acids. It contains an archaean outgroup and eukaryotic taxa. Of particular interest are the Microsporidia, a group of unicellular parasites which lack mitochondria and instead possess mitosomes.
TableS4). Fungi Archaea Chordata Plantae Arthropoda Amoebozoa Fungi Chordata Arthropoda Platyhelminthes Nematoda Animalia Choanofl. Fungi Microsporidia TSAR Archaea Plantae TSAR Amoebozoa Fungi Fungi Animalia Chordata Chordata Choanofl. Arthropoda Arthropoda Fungi Platyhelminthes Nematoda Microsporidia Under site-homogeneous models, sites with K eff values up to approximately 10 -12 exhibit strong preference for Ctenophora-sister (Figures 3 and S12). Sites with higher K eff values, however, switch their preference toward Porifera-sister. In contrast, under the CAT-PMSF models the Simion et al. (2017) dataset exhibits 10.
, 2017). The SOC is hypervariable between strains and has been shown to be associated with viral resistance mechanisms  (Yau et al. , 2016; Blanc-Mathieu et al. , 2017). While O. tauri offers valuable insight into the origin and evolution of biological processes in the green lineage, its low abundance in marine environments, as inferred from metagenomic dataset, hinder its broader use in investigating questions relevant to ecology and adaptation  (Demir-Hilton et al. , 2011). In addition to Ostreococcus, the Bathycoccaceae family also includes the genus Bathycoccus, a picoalga characterized by its scales-covered cell  (Eikrem & Throndsen, 1990). Mostly studied through metagenomic approaches, the two currently described Bathycoccus species, Bathycoccus prasinos  (Moreau et al. , 2012)  and Bathycoccus calidus  (Bachy et al, 2021) , show high abundance and distinct distribution patterns in marine environments.
Genome assemblies were produced using FLYE 2. 9  (Kolmogorov et al. , 2019)  (options: -nano-hq --genome-size 15m). Assembly polishing with ONT reads was performed using MEDAKA 1. 5 (https://github. com/nanoporetech/medaka) (options: -m r941_min_high_g360). Assembly correction with Illumina reads was performed using BWA 0. 7. 17  (Li, 2013)  for read mapping, Samtools 1. 9  (Danecek et al. , 2021)  and  PILON 1. 24 (Walker et al. , 2014) , with standard options. Scaffolding and direction of contigs on the reference genome (Strain RCC1105) was performed using RagTag 2. 1. 0  (Alonge et al. , 2021). Unmapped contigs were designated as contaminations and excluded (Figure  S1 , Appendix S1). Gap filling was performed using TGS-GapCloser 1. 0. 1  (Xu et al. , 2020)  with Samtools 1. 9 and PILON 1.
24. One round of Pilon correction was applied to corrected gaps for consistency with the global assembly correction (options: --p_round 1 --r_round 0 --min_nread 3). Abnormal chromosomal fusions due to assembly error were manually checked using D-GENIES 1. 5. 0  (Cabanettes and Klopp, 2018)  and corrected using samtools (samtools faidx). Assembly statistics were calculated with Assembly_stats 0. 1. 4 (https://github. com/MikeTrizna/assembly_stats) and QUAST 5. 2. 0  (Gurevich et al. , 2013). Completion was assessed through BUSCO 5. 4. 4  (Manni et al. , 2021)  using the chlorophyta_odb10 library, and quality was estimated with MERQURY 1. 1  (Rhie et al. , 2020)  with the corresponding Illumina reads as input.
Genome Annotation Genome assemblies were individually submitted to RepeatModeler 2. 0. 1 (https://github. com/Dfam-consortium/RepeatModeler) to build strain specific repeat libraries, which were subsequently concatenated in a non-redundant repeat library using CD-hit  (Fu et al. , 2012 ) (90% identity and coverage). This library was used with RepeatMasker 4. 1. 2 (https://github. com/rmhubley/RepeatMasker) on all samples to produce soft masked assemblies. Available Illumina RNAseq whole transcriptome of Bathycoccus prasinos strain RCC4752 (SRX554258)  (Keeling et al. , 2014)  was mapped upon the RCC4752 genome assembly using HISAT2 2. 1. 0  (Kim et al. , 2015) , and transcripts inferred using  STRINGTIE 1. 3. 4 (Shumate et al. , 2022). Plant orthologous proteins database 10 (  Kriventseva et al.
, 2019)  was used as protein evidences for annotation Training of  GENEMARK-ETP 4. 71 (Brůna et al. , 2023) and AUGUSTUS 3. 3. 3 (Stanke et al. , 2008)  ab initio prediction models were performed through BRAKER3 2. 1. 6  (Gotoh, 2008; Lomsadze et al. , 2014; Buchfink et al. , 2015; Gabriel et al. , 2023) , using transcriptome mapping data and the plant protein database as evidence. Training of SNAP  (Korf, 2004 ) ab initio prediction model was performed after a first round of MAKER 2. 31. 9  (Cantarel et al. , 2008; Campbell et al. , 2014) , using genome assembled transcripts, available de novo assembled transcripts (https://www. ncbi. nlm. nih. gov/Traces/wgs?val=HBMR01) and plant protein database as evidence. GENEMARK-ETP, AUGUSTUS and SNAP prediction models were merged through a second round of Maker to produce de novo structural annotation.
Phylogenetic analysis BUSCO  (Manni et al. , 2021)  analysis with chlorophyta_odb10 library was computed for each assembled genome and on O. tauri and M. pusilla reference genomes to be used as outgroups. This resulted in 1,201 predicted genes shared across all genomes. Amino acid sequences were aligned for each gene using MAFFT 7. 520 (options: --auto --maxiterate 1000)  (Katoh and Standley, 2013)  and converted to nucleic acid alignment using PAL2NAL 14  (Suyama et al. , 2006)  to ensure accurate coding sequence alignment. Sequence alignments were concatenated in a partitioned supermatrix using catfasta2phyml. pl script (https://github. com/nylander/catfasta2phyml). Maximum likelihood phylogenetic inferences were computed through a partitioned analysis for multi-gene alignments using IQ-TREE 2.
Mapping of metagenomic datasets Metagenomic reads from the TARA Ocean and TARA Polar Circle  (De Vargas et al. , 2015)  campaigns were mapped to genome sequences using BWA mem 0. 7. 17  (Li, 2013). Samtools 1. 13  (Danecek et al. , 2021)  was used to recover mapped reads (samtools view, options: -F 4) and to remove duplicates to avoid bias due to PCR artifacts. Using bamFilters (https://github. com/institut-de-genomique/bamFilters), mapped reads were filtered out for low-complexity bases (>75%), high-complexity bases (<30%), coverage (<80%) and identity (<95%). Remaining reads were retained for further analysis. Relative abundance was computed for each sample as the number of reads mapped normalized by the number of reads sequenced. For Bathycoccus sp. species biogeography, complete assembled genomes of strains RCC4222, RCC716 and G8 were respectively used as reference for B.
prasinos strain RCC4752 and plant orthologous protein data to train three gene prediction models through the BRAKER3 and Maker pipelines (GENEMARK-ETP, Augustus and SNAP). The output of each model was then integrated using the Maker annotation pipeline into a complete structural annotation (Figure  1B , Figure  S3 ). The structural annotation predicted 7,478 (± 217) genes, with 7,253 annotated in the strain RCC4222 while 7,900 coding genes were initially annotated in the reference  (Moreau et al. , 2012). This discrepancy may be due to a lower ratio of mono-exonic genes (73% of predicted transcripts) in our annotation, resulting from the integration of genome assembled transcripts from RNAseq data (Table  S3 ).
Phylogenomic of the Bathycoccus genus With this dataset we investigated the phylogenomics of the Bathycoccus genus. Maximum likelihood phylogenetic distances were inferred from nucleic acid alignments of genes shared between all strains. To avoid potential annotation biases between species caused by the predominance of B. prasinos transcriptomic data in the annotation model training, a set of 1,201 shared BUSCO genes from the chlorophyta_odb10 database were used. The inferred phylogenetic tree showed clear separations between strains of B. prasinos, B. calidus and the three Bathycoccus genome sequences from the Banyuls bay. This putative Bathycoccus sp. showed an early divergence in the phylogenetic tree that could potentially correspond to a new species (Figure  3A ). Cell ultrastructure determined by electronic microscopy revealed characteristic features of the Bathycoccus genus with a single mitochondria, a single chloroplast containing a single starch granule, and scales at the cell surface that have been described earlier for B.
prasinos  (Moreau et al. , 2012)  and B. calidus  (Bachy et al. , 2021)  (Figure  3B ). We thus named this cryptic species as Bathycoccus catiminus. The mapping of metagenomic data from TARA Ocean and TARA Polar Circle cruises were conducted on a representative genome for each species. This analysis confirms the clear latitudinal and depth separations between B. prasinos and B. calidus populations, initially described by  Vannier et al. (2016) , with B. prasinos found in temperate regions at the surface, and B. calidus in tropical water at the deep chlorophyll maximum (DCM). B. prasinos was also abundant (up to 4% of sequences) in Arctic waters. B. catiminus was also detected in metagenomic data, and shows geographical distribution patterns similar to B. prasinos, however at much lower levels of abundance than B.
Polymorphism density was computed on a 20 kb sliding window along chromosome 14, and compared to the genome-wide polymorphism to detect local divergences in sequence diversity. Lower than average polymorphism (local density/average density < 1) was detected in the outlier region of both identified haplotypes and, overall, lower polymorphism density of this region could be seen compared to the common regions located at both extremities of chromosome 14 (Figure  5B , Figure  S7 ). This clear pattern of localized reduced polymorphism could not be seen on any other non outlier chromosomes (Figure  S8 ). The geographical distribution of both BOC haplotypes in the world Ocean was assessed by mapping of TARA Ocean and TARA polar circle metagenomic dataset on their respective outlier sequences. In all stations defined by relatively abundant B.
prasinos (Figure  3 ). This new species was named Bathycoccus catiminus (from the French expression "en catimini'' meaning "hidden") in reference to its unexpected discovery and its very low abundance in metagenomic datasets compared to known Bathycoccus species. Concurrently, sequencing of ITS2 rDNA regions in the Kara sea suggests that a fourth species is yet to be isolated  (Belevich et al. , 2021 ) (Figure  S2 ), highlighting the underestimated taxonomic diversity of the Bathycoccus genus. As reviewed by  Yung et al. (2022) , the description of new species following genome sequencing is frequent in Mamiellophyceae, due to the lack of polymorphism in cellular ultrastructure and in the sequence of rRNA 18S V4 and V9 regions, commonly used as distinguishing characters  (Piganeau et al. , 2011).
prasinos (24) and of its 2 sister species. To our knowledge, this is the largest genomic resource available for a single species of green algae  (Hanschen and Starkenburg, 2020; Shi et al. , 2021). The strains and associated genomic data presented in our study, together with the large metagenomic dataset available, provide a substantial resource for the functional description and the understanding of the fundamental processes underlying the adaptation and ecological success of phytoplanktonic species.
Low polymorphism within BOC haplotypes indicates strong selective pressure We used the B. prasinos resource consisting of 24 genomes together with the existing large metagenomics dataset to conduct an analysis of outlier chromosomes. The big outlier chromosome (BOC), discovered in O. tauri, is a putative sexual chromosome that has been identified in all other Mamiellophyceae genomes  (Derelle et al. , 2006; Grimsley et al. , 2015). While two haplotypes of this BOC were identified in O. tauri, only one haplotype was previously reported for the Bathycoccus genus  (Moreau et al. , 2012; Blanc-Mathieu et al. , 2017). In this study, the 2 BOC haplotypes were identified in B. prasinos strains from both the Banyuls and the Baffin bays, suggesting that these haplotypes are not the result of populations segregation due to geographic isolation, but rather a defining feature of B.
tauri genomic data due to the representation of one of the putative mating types by just a single strain. In B. prasinos, our analysis of shared genes between BOC haplotypes revealed a phylogenetic separation, suggesting a divergent evolution of chromosome 14 shared genes between haplotypes (Figure  S5 ). Strong matingtype-specific selective pressure linked to background selection, as suggested by  Hasan et al. (2019 Hasan et al. ( , 2020)) , might be responsible for the reduced diversity and strong differentiation observed in this complex genomic context. Furthermore, this pressure may contribute to the preservation of increased gene expression and fragmented gene structure in the outlier region of the BOC. Although the fitness impact of this 400 to 500 kb haplotype-specific region still needs to be assessed, it is possible that the regional imbalances observed in metagenomic dataset in B.