dataesr/scientific-paragraphs-categorization huggingface dataset:
The ORI code (Oxford Nanopore Reads Identification) is available at https:// github. com/ gsiekaniec/ ORI. All sequencing data used in our experiments (raw Nanopore fastq reads) can be downloaded on the Genouest server (https:// data-access. cesgo. org/ index. php/ s/ lApWiOf1BFYUpQV). The interactive ITOL tree of S. thermophilus species is available at: https:// itol. embl. de/ tree/ 1312 5413 4671 3115 97925585, and the complete list of associated 'maximal biclusters' (subset of S. thermophilus strains and subset of associated specific genes) is available on https:// github. com/ gsiekaniec/ GeneTree.
For instance, Illumina platforms that produce short reads (up to 300 bp) are now widely used for bacterial community taxonomic profiling  [4] [5] [6]  and de novo bacterial genome assembly  [7, 8]. The overlapping reads produced are assembled into larger sequences that are mapped on sequence databases. However, read length makes these processes challenging, due to repetitive elements larger than reads, and this leads to fragmented assemblies. Third-generation sequencing technologies such as PacBio or Oxford Nanopore Technologies (ONT), producing read lengths reaching tens of kilobases or even more, can overcome this issue  [9]  but are error-prone. The best assemblers to date are hybrid, using both short and long reads  [10]. Here we consider the issue of identification from long reads only, without prior assembly of these reads.
Currently, identification from long reads hardly goes down to a level finer than genus or species  [11]. Problems include the proximity of strains within a species, the prevalence of certain species in complex samples that can blur the recognition process, and databases thatare far from cover all microbial diversity. Therefore, there is still a gap between the promises of whole genome sequencing (WGS) for microbial identification, notably at the finer-grained taxonomic levels, and its large application in demanding operational contexts  [12]. Among the possible obstacles to its diffusion is the very rapid advance in technology, which makes it difficult to produce stable identification software. While programs are becoming mature for work on short reads, the landscape is still very dynamic and uncertain for long reads.
Commonly used identification software can be broadly divided into two categories, the sequence alignment and the k-mers-based approaches. Sequence alignment, largely popularized by the program blast and its numerous variants  [13] , consists in optimally matching a read sequence with a fragment of a genomic sequence (representative software are MEGAN  [14] , MG-RAST  [15] , PanPhlAn  [16] , Centrifuge  [17]  and Kaiju  [18] ). To reduce their practical complexity, these algorithms are often limited to high-scoring alignments and loose sensitivity. Over the last few years, research effort has focused on simplified representations of genomes and reads, which has led to a significant improvement in comparison performance. The principle is to split genomic sequences into overlapping k-mers, short strings of fixed size (typically 30 nt).
Relatively few identification software programs accept long reads. The most popular is Kraken and its more recent version Kraken 2  [20] , since it is easy to instal, robust and fast. This software is based on the decomposition of genomes into k-mers and uses a very efficient storage structure. Some k-mers are specific to a unique species and some are linked to the lowest common ancestor (LCA) in terms of genus or family of all species where they occur. Thus, reads are classified at various levels of this hierarchy depending on their k-mer content. Kraken 2 rarely allows assigning a read at a level below the species because it uses a compression process (minimizers) that reduces the differences between genomes. Minimizers represent a sequence by a smaller included one, and serve to save space and time by retaining only part of the information, although they introduce a certain number of false positives during the identification process  [27].
Our tool can therefore be useful to all microbiologists wishing to control the presence or absence of certain strains possibly mixed within a sample. We chose to focus on data from the Oxford Nanopore MinION device since its low cost and real-time sequencing capacities are well suited for testing in any laboratory or even on site. As all programs can be freely downloaded from our sites, and the databases can be configured for the species of interest of each laboratory, we hope this tool will have considerable impact. not know of any application using it in this context. It was designed primarily for the identification of isolates but can handle mixtures of a few strains. Another tool, Centrifuge  [17] , follows a read alignment approach and is at the core of the species identification workflow in the platform EPI2ME (ONT).
Unfortunately, its index size can become very large if it has to include strain-level genomes. Metamaps  [28] , specifically developed for the analysis of metagenomic datasets, also uses alignments to identify species/strains, is designed for long reads and, according to its authors  [28] , seems more precise than Centrifuge. However, the current version is not simple to instal and lacks robustness (users may suffer from 'core dump' errors). This paper describes the use of long read sequencing technologies for the identification of bacteria at the strain level. We chose Streptococcus thermophilus as a model species given its importance in the food industry and the health sectors as a dairy starter and a probiotic  [29]  and because it encompasses closely related strains (see Text S1, available in the online version of this paper) with a low genomic diversity  [30, 31].
We have developed a new strain identification method called ORI (Oxford Nanopore Reads Identification) and compared it to Kraken 2 and StrainSeeker. This approach dedicated to long reads has a technical background similar to Kraken, but differs in its goal to recognize at the strain level. The software for ORI can be found and downloaded from the following site: https:// github. com/ gsiekaniec/ ORI. This work demonstrates that despite their significant error rate, the length of reads from MinION (ONT) allows fine-scale taxonomic assignment, even with mixtures and with closely related strains such as those from S. thermophilus. ORI can be easily adapted to other genera or species.
Filtering the raw reads The fastq files generated by MinION sequencing were demultiplexed and adapter-trimmed using qcat (https:// github. com/ nanoporetech/ qcat; version 1. 1. 0). MinION reads have a high error rate, in particular in homopolymers. In our sequencing data, basecalled with version 4. 4. 1 of Guppy in high-accuracy mode, the error rate was around 5 %, including around 3 % insertions/deletions. A selection step of best reads was performed in order to limit their global sequence error rate: each read must have an average quality of at least 9 and a length greater than 2000 bp (see details in Table  1 ). All data (raw Nanopore fastq reads and associated assembled genomes) can be downloaded on the Genouest server (see 'Data summary' section).
This pattern was selected for optimal classification of the reads of JIM8332, using the iedera software  [36, 37]  on all the spaced seed patterns of size in the range [9 : 21] (see Fig. S1 ). The matching positions form a word called qgram. The index containing all these qgrams was built as follows: for each strain, its reference genome was cut into k-mers on which the spaced seed pattern was applied, which yielded qgrams. Each qgram was then inserted into a compact probabilistic data structure of fixed size (about 5×10 8 bits) based on Bloom filters  [38, 39].
Data and index used for other methods In our experiments, we adjusted the index choices for Kraken 2 and StrainSeeker in order to compare the results fairly. The genome databases were exactly the same for the three methods. StrainSeeker requires another input, a classification tree on the whole set of genomes guiding the identification process. This tree was generated on the MicroScope platform (see next section) in our experiments. For Kraken 2 (version 2. 0. 9-beta), we used the default k-mer length of 35 and minimizer length 32. For the StrainSeeker (version 1. 5) index, we used a k-mer length 16.
genoscope. cns. fr; access date May 2020, v3. 14. 0) was used to annotate the genome of the 77 S. thermophilus strains, to generate a strain classification tree and to compute the pangenome. Briefly, MicroScope uses the AMIGene software to predict proteincoding genes  [42]  and combines the results of several tools to assign molecular functions  [41]. A strain classification tree is computed using the neighbour-joining algorithm with pairwise genome distances obtained from Mash software  [43]. Three sets of genes corresponding to the core, variable and strain-specific genes were downloaded from the pangenome computed by MicroScope. It is based on MICFAM gene families, which are computed using a single linkage clustering algorithm of homologous genes sharing an amino acid alignment coverage and identity above 80 %.
oRI, a new method for the identification of bacterial strains from long reads We propose a new method and software, ORI, based on indexing fragments of a set of known bacterial genomes in a compact data structure. ORI is therefore in line with the principles of a method such as Kraken, although it differs in many ways. The first step (index creation in Fig. 2 ) takes as input the sequences of known genomes and builds a data index based on a hierarchical structure of Bloom filters  [38, 39] , each one being of fixed size (5×10 8 bits). They code for the presence in each genome of small fragments of fixed size, called qgrams, which are similar to k-mers but allow for mismatched characters at some positions. There is an optional but recommended operation during this step, the merging of very closely related strains (distance below a given threshold) in a single pangenome (see sections below).
The gene level We have computed the complete list of maximal biclusters (see section Comparing and clustering strains in the Methods) comprising a subset of S. thermophilus strains and a subset of associated specific genes (available on https:// github. com/ gsiekaniec/ GeneTree). Genes specific to the set of strains under each node of the strain classification tree produced by MicroScope are available in an interactive version of this tree via clickable links for each node (see https:// itol. embl. de/ tree/ 1312 5413 4671 3115 97925585). All the maximal biclusters cannot be represented by a node of the tree because they follow a different topology. It is already possible to perceive in this tree that some strains are very close if not identical and differ only by few unknown genes.
The genomic fragment level The proximity between genomes is usually measured with the ANI distance. Our genome index offers other ways to evaluate this proximity. We have produced a slightly more precise Jaccard distance matrix using spaced seeds. Fig. 3  shows an extract of this matrix for 28 strains in the form of a heatmap (eight strains from public genome database and 20 strains from the CIRM-BIA collection). The complete heatmap, containing the 77 S. thermophilus strains, can be found in Fig. S2. Two genomes, one from S. macedonicus and the other from L. delbrueckii subsp. bulgaricus, have been added as an external group. As expected, strains of different species or order are easily distinguished. The group of six CIRM-BIA strains at the bottom left of the heatmap that is distant from other strains clearly forms a new subgroup comprising strains that were mostly isolated from traditional Italian dairy products.
Identification results We present in this section the results of ORI in various contexts and compare them on the same database with the leading method Kraken 2  [20]  and the program dedicated to strain identification, StrainSeeker  [22]. As an indication, we have provided tables showing the precision of identification of S. thermophilus strains in all 180 experiments (see Table  S6 ) and the precision of identification of the subdominant S. thermophilus strains (see Table  S7 ).
Acknowledgements All data were processed on the Genouest bioinformatics platform (https://www. genouest. org/). LABGeM ( CEA/Genoscope  and  CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures are acknowledged for support within the MicroScope annotation platform [41]. We thank in particular  David Vallenet , scientific manager of the MicroScope platform, who provided us with text to describe its functioning. We also thank  Genoscope  ( Corinne Cruaud ,  Stefan Engelen  and  Jean-Marc Aury ) and  CNRS I2BC  ( Delphine Naquin ,  Erwin Van Dijk  and  Yan Jaszczyszyn ) for their generous support for training in the use of the MinION and associated bioinformatics tools. Finally, the reviewers greatly helped to improve the first manuscript.
thermophilus strains have been provided by the  International Centre for Microbial Resources -Food-Associated Bacteria (CIRM-BIA) ,  STLO UMR 1253   INRAE-Agrocampus, France  (https:// www6. inrae. fr/ cirm_ eng/ Food-Associated-Bacteria). All sequence data were annotated by the MicroScope Platform (1). New genome accession numbers are listed in Table S2.
Funding information This research was funded by a PhD grant from an  INRAE-INRIA  consortium agreement ( G. Siekaniec ) and an  INRIA delegation position (E. Roux). The France Génomique and  French Bioinformatics Institute  national infrastructures are funded as part of the  Investissement d'Avenir programme  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013.
Results The bowfin genome. The bowfin genome was sequenced from a single adult phenotypic male. A de novo genome assembly was constructed with Meraculous  26  and further scaffolded with Chicago  27  and Hi-C approaches  28  using the HiRise software pipeline  27  (Supplementary Figs. 1  and 2 ). The final assembly (N 50 = 41. 2 Mb) consists of 1,958 scaffolds including 23 pseudochromosomes that contain 99% of the assembly (Extended Data Fig. 1  and Supplementary Table  1 ) and match bowfin's chromosome number  21, 29  , consistent with a chromosome-level genome assembly. The final assembly (AmiCal1, National Center for Biotechnology Information (NCBI) accession PESF00000000; Supplementary Tables  1  and 2 ) is largely complete as reported by multiple core eukaryotic genes mapping approach (CEGMA) and benchmarking universal single-copy orthologs (BUSCO) scores (Supplementary Table  3 ).
Using whole-genome alignments (WGAs) generated with Progressive Cactus  55  , we showed that more than 50% of bowfin ncOCRs were conserved in gar, and 3,844 core bowfin ncOCRs were conserved in gar, zebrafish, mouse   9. and human (Supplementary Fig. 16  and Supplementary Table  20 ). Adding RNA-seq transcriptomes from the same stages, our dataset provides a rich resource for exploring the correlation between chromatin accessibility of putative gene-regulatory elements and gene expression. This is exemplified by bowfin ncOCRs overlapping key enhancers active during the development of a variety of vertebrate systems such as the brain  56  , heart  57  , anterior and posterior fin or limb  [58] [59] [60]  and lungs  60, 61  (Fig. 4 ). Using Hi-C contact maps from bowfin blood cells, we further identify a bowfin ncOCR at the location of an evolutionary conserved hemoglobin gene enhancer  62  , embedded within a topologically associating domain (TAD) that shows conserved synteny (Extended Data Fig.
To further investigate the comparative efficacy of our ATAC-seq dataset, we used our WGAs to identify bowfin orthologs of experimentally confirmed mammalian VISTA enhancers  56  (Supplementary Tables  22  and 23  and Supplementary Note 8). Orthologs of 60. 2% of human VISTA enhancers (600 of 996) were found in bowfin, and over half (56. 3%, 338 of 600) overlapped with bowfin ncOCRs. By contrast, orthology for only 44. 0% (449 of 996) of human enhancers could be established in the zebrafish genome (Supplementary Table  22 ), illustrating the usefulness of the slowly evolving and 'unduplicated' bowfin genome to connect accessible, non-coding, regulatory regions from human to fish. Overlap with 2,261 ncOCRs from a mouse developmental single-nucleus ATAC-seq atlas  65  (22. 5% of 10,035 mouse ncOCRs with bowfin orthology) established bowfin ncOCRs that likely function in a cell and/or tissue type-specific manner, for example, in the central nervous system, mesoderm, neural crest and many other cell types and tissues (Supplementary Table  24 ).
Here, we have identified putative enhancers for this non-model species and illustrate that our ATAC-seq dataset can be easily connected to orthologous enhancers in fish and more distant vertebrate lineages. The bowfin not only informs genome evolution in holostean fishes but also offers valuable insights into the genomic basis of its archetypical anatomy and the many developmental and physiological phenotypes in ray-finned fishes. While this species represents a once-large taxonomic group that is now mostly extinct and thus lost for studying genomic diversity, the bowfin genome adds a major sequenced branch for genetic and developmental exploration to the fish tree of life. Bowfin and gars have persisted for millions of years. Fortunately, the negative perception of holosteans as 'trash fishes' is changing to include greater appreciation for their evolutionary, ecological and cultural importance and, as shown here, their relevance for understanding vertebrate genome biology.
Acknowledgements We are thankful for grant support from  NIH   R01OD011116  (I. B. ),  NSF   DDIG DEB-1600920  (M. B. H. ),  NSF   IOS-1755242  (A. D. ) and  NSF   IOS-1755330  (J. A. Y. ). Parts of this work have been supported by the  Agence Nationale de la Recherche ,  France  ( ANR   GenoFish  project,  2016-2021 ,  ANR-16-CE12-0035 ) to H. R. C. , C. B. , E. P. , A. L. and Y. G. E. P. , C. B. and H. R. C. received support under the  program 'Investissements d' Avenir ' launched by the  French government  and implemented by  ANR  with references  ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02   PSL* Université Paris. We thank F. Feng for access to Oneida Lake bowfin spawning habitats, the  Bauer Core at Harvard University  for sequencing fin transcriptomes and  M. Gundappa  ( FISH & LINES ) for species illustrations.
Data availability The bowfin reference genome assembly (AmiCal1) is available at GenBank under the accession number PESF00000000; raw reads are available at the Sequence Read Archive under accession numbers SRR14766073-SRR14766075. Transcriptomic and ATAC-seq reads are available under accession numbers SRP281665 and SRP252716; assembled transcripts are available under accession number GIOP00000000. The MAKER gene annotation is available at https:// github. com/AndrewWT/AmiaGenomics. Data for synteny analyses and the NATURE GENETICS | www. nature. com/naturegenetics.
To achieve this, we analyzed gene expression data for five organs (heart, lung, liver, kidney, brain) using linear regression, LightGBM-SHAP, and Bayesian networks. Overall, data from 41 mammalian species were used for these analyses. To draw conclusions, we (i) combined the results from linear organ-specific with nonlinear machine learning models based on decision trees, (ii) used SHAP explanatory models for gene signature prioritization, and (iii) applied a Bayesian networks-based algorithm to select genes which may have potential causality relationships with MLS.
Data Collection and Processing of Gene Expression across Mammalian Species Using publicly available RNA-Seq data, we build a cross-species dataset of gene expression levels. The dataset consists of 408 samples from 41 mammalian species and covers five organs: liver, kidney, lung, brain, or heart (the full list of species and sequencing run IDs is available in Supplementary Table  S1 ). The dataset was normalized, processed, and further augmented with species data for studying the associations among gene expression levels and systemic species variables: MLS, body mass, temperature, metabolic rate, gestation period and GC content of mitochondrial DNA, all of which have been suggested to be determinants of MLS  [15] [16] [17]. Linear, LightGBM-SHAP, and Bayesian network models were employed to identify and describe the associations among gene expression levels and MLS (as described in detail in Figure  1 ).
SHAP Explanations for Universal Gene Expression Patterns To also investigate the potentially non-linear patterns of association existing between gene expression and lifespan, we further used an interpretable machine learning approach. Briefly, we applied a gradient boosting decision tree algorithm, using LightGBM  [27] , to select genes associated with MLS. We then applied SHAP (Shapley additive explanations), a game-theory approach that can be used to explain the output of machine learning models  [28] , to the LightGBM model, both as part of the selection process and as the main interpretation method. Briefly, the key difference between LightGBM and its predecessors is mainly the higher accuracy and computational efficiency. LightGBM has proven (i) to be highly effective for tabular data; (ii) to be a highly explainable model, especially when combined with the SHAP framework; (iii) that it is not sensitive to correlations in the features of the dataset, which is expected for gene expression data; and (iv) that, in comparison to other models (such as neural networks and SVR), it is less prone to overfitting on wide (many features) and small (few samples) datasets.
When predicting MLS, the stage II LightGBM-SHAP model used the genes selected by the stage I models as input (155 genes), improving the metrics for MLS prediction from Huber loss = 3. 92, MAE = 4. 733, MSE = 64. 87 and R 2 = 0. 90 in the stage I model to Huber loss = 2. 4, MAE = 3. 04, MSE = 36. 8 and R 2 = 0. 95 in the stage II model (outputting 57 genes). Interestingly, several genes included in the stage II model (17 out of 155, slightly enriched, non-significant, p = 0. 17) are also orthologous to known LAGs recorded in the GenAge database  [3] : GNAS, FXN, TERT, MSRA, XRCC6, UQCRB, MEMO1, NEIL1, RPS8, COX7C, RXRB, EIF4EBP1, RCL1, PCBP2, EIF3K, PKN3, CLHC1. To prioritize genes from the stage II model, we relied on SHAP feature importance (mean absolute contribution) as a way to evaluate the global importance of an individual gene in predicting MLS.
By definition, the variability in the genes from the Markov blanket of MLS will contain all the useful information about MLS. Subsequently, "potentially causal" gene signatures were defined as gene subsets of the Market blanket of MLS. Implementation-wise, gene signatures were found with SES, a constrained-based variable selection algorithm  [43]. While the above-mentioned approach does not fully guarantee causality, it allows a data-driven exploratory analysis to identify valid causal inferences under strict assumptions. In order to give a causal interpretation in an absolute sense, three assumptions would have to hold: the causal Markov assumption, faithfulness, and causal sufficiency  [44]. Unfortunately, with only transcriptomics data, which do not include all possible confounders, completely proving causality is not practically achievable, and the employed algorithm deals with these assumptions as best as possible (by construction, the causal Markov condition holds, and using several stratified partitions, the dataset's underlying conditional independence structure could be approximated, thus attaining faithfulness).