dataesr/scientific-paragraphs-categorization huggingface dataset:
WHAT WOULD SEPPE LOOK LIKE IN PRACTICE? The five global domains of activities that constitute the full cycle of development and assessment following molecule discovery would be retained, but SEPPE would change how each of these is approached, to minimize defects (Figure  1 ): 1. understand stakeholders' product needs; 2. understand stakeholders' evidence needs; 3. plan the evidence generation; 4. produce the evidence; 5. evaluate the therapeutic product, at predefined time points, based on the evidence and contexts. Existing best practices elements  12, 13, 69, 93  would be upheld and incorporated into the larger SEPPE framework and specific new approaches and concrete activities would be introduced. 50, 51  Modern, practical, and user-friendly tools like fit-for-purpose software programs incorporating all these best practices in a highly granular way would guide developers and other participants in building optimal development plans.
Examination of emerging developments in collaborative/open innovation platforms, as well as of deliberate vs. emergent strategies for implementation would also be valuable. • Additionally, examining exceptional evidence development success stories, such as the I-SPY platforms for patient-focused breast cancer therapeutic product research,  122, 123  which exemplify best thinking, strategic choices, and practices, would contribute to articulating explicitly the principles, evidence quality attributes, and practices necessary for SEPPE. On the other hand, much could, and indeed, has already been learned from instances of widely-recognized evidence planning and development miscarriages and compilations of the numerous specific types of hazards from these would also contribute to the initial reference datasets for SEPPE.
124 ther existing real-world success stories, and hypothetical failure cases compiled to illustrate problematic elements from real-world filings, would also provide rich material for study. 12  examine comprehensive lessons learned from flexible/expediting regulatory and reimbursement pathways and innovative evidence development initiatives, for strategies and rules, for example, to pick relevant drug candidates, determine core outcome measures, generate the evidence, manage entries/exits, and/or engagement. 12, 13, 52, 87, 93, 125  This foundational research would support evidence-based policy and scientific/technical, as well as software development for an SEPPE framework. This would entail: • mapping and establishing stakeholders' roles and responsibilities; NB: It would be critical for practical feasibility of implementation of SEPPE to (a) delineate context-adapted rules for governance, leadership, stewardship, facilitation, and engagement (e.
g. , who would facilitate SEPPE for each drug product?; would there be a designated third party agent?), and (b) identify practical solutions to coordinate and track deliberations, decisions, and activities over time. Adapting and coordinating these roles and responsibilities in the context of additional emerging innovation, such as disease-focused (rather than product-focused) master protocols would be another consideration; • creating the SEPPE reference datasets of attributes/hazards, which could be amassed in an open-source SEPPE database. As noted previously, food manufacturing-related hazards reference databases, in conjunction with recent food safety software developments, could serve as practical starting points to integrate these into SEPPE software; • plotting critical control (monitoring, feedback, and adjust) points for product and evidence attributes and hazards, across product life-cycle: o chart systematic/common leverage points for incorporation of recent social, scientific, and technical innovations to maximize opportunities for R&D to be patient-focused and make best use of real-world data and evidence; o explore application of existing structured and dynamic engagement, decision, and documentation tools.
Although treebased methods tended to fall in the "old" group and graphbased methods in the "young" group, factors such as systematic error was found to play a large role. Orthology inference can be used to elucidate where/when (relative to known speciation events) gene duplication and loss events occurred in specific gene families. Several tools are now available to do this for different platforms. For example, pyHam is a python library for handling orthoXML files containing Hierarchical Orthologous Groups (HOGs)  (Train et al. 2019). Haiming Tang presented a software tool for parsing gene trees to infer changes in ancestral genome content along specific branches of the species tree, and discussed applications in ancestral genome reconstruction  (Huang et al. 2019). HieranoiDB  (Kaduk et al.
2017). Methods: the New, the Updated, and the Meta The applications of orthology depend crucially on the quality of the inferences. There are a plethora of orthology inference methods, and ongoing development includes new methods, updates of existing methods, as well as some meta-methods. While most ortholog prediction methods operate on the basis of genes as the unit of evolution, domains as units might be more precise. This is due to the independent functionality of the domains, and their ability to create new genes by exon shuffling or domain promiscuity, creating complex homology relationships (Gabald on and Koonin 2013). Thus, domains could be a more correct functional unit of orthology. This idea has been implemented by Kaduk and Sonnhammer, who have introduced a new tool called Domainoid (https://bitbucket.
For example, the Microbial Genome Database for Comparative Analysis'  (Uchiyama et al. 2019 ) new pan-genome based analysis procedure starts by selecting representative genes in each species for each orthologous group. Then, it identifies a representative gene within each genus. The representative gene is selected based on several criteria, including not being a partial or split gene, the length being close to median gene length, and that the gene is not an outlier in terms of phylogenetic distance. This method allows for running fewer comparisons, saving computational power. In eggNOG  (Huerta-Cepas et al. 2016) , the sequence search tools DIAMOND  (Buchfink et al. 2015)  and HMMER  (Eddy 2011)  were both implemented to take into account close and distant homologs (or alternatively, well-or poorly sampled clades).
The benchmarks are phylogeny-based or function-based, and a user can compare their performance to other methods such as PhylomeDB, InParanoid, OMA, Ensembl Compara, Hieranoid, PANTHER, MetaPhOrs, OrthoInspector, eggNOG, SonicParanoid. Since the publication, 530 jobs have been submitted. Adrian Altenhoff presented improvements to the service that allow ortholog providers to perform benchmarking on yearly updated data sets. It utilizes a selection of the most up-to-date protein sets available from the UniProt Reference Proteomes (The UniProt Consortium 2017), recently expanded from 66 to 78 proteomes as presented by Alan Sousa da Silva. This was done to broaden the species coverage, by using the Proteome Priority Score  (Chen et al. 2011 ) to identify the best species candidate for a given phylum.
1990 ) and InterProScan  (Finn et al. 2017 ) GO predictions was done by determining the number of true positives and true negatives with regards to the best-curated Gene Ontology subsets. Those predictions which were experimentally validated were considered true positives; whereas those with a GO term with taxonomy limitations were considered as true negatives, for example, fin development assigned to a plant gene. The ongoing dialog between functional description ontology developers and orthology inference developers will further improve such approaches in the future. Advances were also reported on the Orthology Ontology, which enables the use of semantic web tools on orthology inferences. Hirokazu Chiba presented a tour of the Orthology Ontology repository on GitHub (https://github. com/qfo/ OrthologyOntology; Last accessed April 2019), including software tools for converting the standard OrthoXML format to RDF, and SPARQL queries to retrieve pairwise orthologs from an RDF triple store (from Jesualdo Tomas Fernandez-Breis).
Homoeologs can thus be thought of as orthologs between subgenomes in an allopolyploid  (Glover et al. 2016). Another type of challenge in orthology inference is the high computational demand of comparing hundreds or thousands of proteomes with each other. This Big Data problem was discussed and various solutions are proposed. Perhaps the most straightforward solution is to replace BLAST, which is used by many graph-based methods, by a much faster homology search tools such as MMseqs2  (Steinegger and So ¨ding 2017)  or DIAMOND  (Buchfink et al. 2015). Another approach under development is the SIBLINGS resource which shares precomputed all-against-all similarity scores between reference proteomes. Finally, some inference algorithms such as Hieranoid are designed to achieve near linear scaling.
Acknowledgments We thank the  Society for Molecular Biology and Evolution  for supporting the  SMBE-EGF Symposium , and the  University of Southern California  for supporting the QFO5 meeting. We wish to acknowledge the following support for attending the meetings and/or writing this manuscript:  National Institutes of Health  (grant  HG002273  for P. D. T. ; HG007822 for M. J. M. and A. S. d. S. ;  GM122480 ,  HD085901 ,  DK110520  for E. M. M. ; GM123683 for C. M. ),  Wellcome Trust  (grant  WT108749/Z/ 15/Z  for M. M. and M. P. ),  Swiss National Science Foundation  (grant  150654  for C. D. ),  European Commission  (contract  676559  for S. C. G. ),  Welch Foundation  (grant  F-1515  for E. M. M. ),  European Molecular Biology Laboratory  (for M. J. M. , M. M. , M. P. ), and  National Bioscience Database Center and Japan Science and Technology Agency (for I.
This paper describes a novel alignment-free distance-based procedure for inferring phylogenetic trees from genome contig sequences using publicly available bioinformatics tools. For each pair of genomes, a dissimilarity measure is first computed and next transformed to obtain an estimation of the number of substitution events that have occurred during their evolution. These pairwise evolutionary distances are then used to infer a phylogenetic tree and assess a confidence support for each internal branch. Analyses of both simulated and real genome datasets show that this bioinformatics procedure allows accurate phylogenetic trees to be reconstructed with fast running times, especially when launched on multiple threads. Implemented in a publicly available script, named JolyTree, this procedure is a useful approach for quickly inferring species trees without the burden and potential biases of multiple sequence alignments.
Introduction Evolutionary relationships between species are commonly represented by a phylogenetic tree inferred from multiple sequence alignments of orthologous genes. Tree reconstructions are generally performed from multiple gene datasets (e. g. core-gene set) because they enhance the overall phylogenetic signal by reducing the random error caused by a small number of characters (e. g. Hillis et al. 1994 , Huelsenbeck 1995 , Rokas et al. 2003 , Criscuolo et al. 2006). Moreover, building large multiple gene datasets is facilitated by the increasing number of available genome sequences  (Land et al. 2015). However, building a large number of orthologous sequence sets for phylogenomic purpose requires extensive genome mining and sequence processing steps that are time consuming, and often need manual interventions.
In addition, long running times are also expected when inferring a phylogenetic tree from a large multiple gene dataset on hundreds of taxa. To infer a phylogenetic tree that represents the evolutionary relationships of a set of genomes, an alternative approach is to estimate a pairwise distance between each pair of unaligned genomes, and to next build a phylogenetic tree with a fast distance-based reconstruction method. Such bioinformatics procedures are becoming popular because they allow dealing with thousands of assembled genomes, depend on few assumptions regarding their evolutionary process, and quickly lead to a phylogenetic tree with minimal manual intervention  (Chan and Ragan 2013, Zielezinski et al. 2017). More formally, a distance-based alignment-free phylogenetic inference from genome sequences could be decomposed in four main steps.
They are therefore often neglected because requiring long running times. This paper reports a new bioinformatics procedure that is based on well-argued choices for each of the four previously described steps. By analyzing simulated genome sequences, this procedure is shown to efficiently estimate the pairwise evolutionary distances between each pair of genomes, therefore allowing the reconstruction of accurate phylogenetic trees. This expected accuracy is illustrated by the analysis of 187 real genome datasets, representative of different genera within the bacterial, archaeal and eukaryotic phyla. All these analyses show that this novel bioinformatics procedure, implemented in the script JolyTree (gitlab. pasteur. fr/GIPhy/JolyTree), is an efficient approach to infer a phylogenetic tree from hundreds of genome assemblies in a few minutes.
Knowing that two genomes are often expected to diverge following complex non-homogeneous and non-stationary evolutionary processes, this last evolutionary distance estimate was chosen to correct the p-distance approximated by the formula (1), with nucleotide frequencies directly computed from the genome sequences i and j. The program FastME was chosen to perform the phylogenetic tree inference because it allows inferring accurate phylogenetic trees with very fast running times  (Desper and Gascuel 2002 , Desper and Gascuel 2003 , Huang et al. 2011 , Lefort et al. 2015). The Balanced Minimum-Evolution (BME) was selected as the criterion to optimize from the evolutionary distances d. The BME criterion considers as optimal the tree T with the smallest estimation ℓ(T) of the tree length (for more details, see  Pauplin 2000 , Desper and Gascuel 2002 , Desper and Gascuel 2003 , Pardi and Gascuel 2016).
Of note, BME is the tree optimality criterion of the greedy algorithm NJ  (Gascuel and Steel 2006) , but the tree inference implemented by FastME is based on an extensive hill-climbing method: starting from an initial tree, FastME explores the tree space using tree swapping approaches in order to find the BME phylogenetic tree. However, as the FastME program allows observing very fast running times, it has been used here to implement a more thorough phylogenetic tree search based on a data noising strategy  (Charon and Hudry 1993 , Morrison 2007 , Criscuolo 2011)  to avoid local optima. First, a hill-climbing tree search is performed by FastME to infer a BME tree T from the evolutionary distances d. Next, the following procedure is performed: (i) each evolutionary distance d is replaced by a random value d* ∈ [(1 -ϵ)d , (1 + ϵ)d ] with a fixed ϵ ∈ ]0, 1[; (ii) a tree search is performed with starting tree T to infer a BME tree T* from the noised distances d* , followed by another tree search with starting tree T* to infer a tree T' from the initial distances d ; and (iii) the tree T is replaced by T' if ℓ(T') < ℓ(T).
By repeating this procedure with different values ϵ ∈ ]0, 1[, this simple distance noising strategy increases the probability of reaching the BME global optimum within the tree space. Finally, the program REQ (gitlab. pasteur. fr/GIPhy/REQ) was chosen for assessing the branch confidence values of the inferred tree. This tool estimates the rate of elementary quartets (REQ) for each branch of a given phylogenetic tree from the associated distances d , as described by  Guénoche and Garreta (2001). This method simply computes the proportion of four-leaf subtrees (i. e. quartets) induced by every internal branch that are supported by the four-point condition applied to the six corresponding pairwise evolutionary distances  (Zaretskii 1965 , Buneman 1971). Therefore, this measure is not based on a random sampling (such as bootstrap-based confidence supports).
The closer this measure is to 1, the more the corresponding branch is fully supported by the pairwise evolutionary distances d. Of note, REQ running time is quite fast (e. g. ~5 seconds with n = 500 on a standard computer). The procedure described above was implemented in Bash (www. gnu. org/software/bash), therefore running on UNIX, Linux and most OS X operating systems. This implementation, named JolyTree, is freely available at gitlab. pasteur. fr/GIPhy/JolyTree. It directly reads a set of n assembled genomes in FASTA format from a specified directory. JolyTree allows ij ij ij ij ij ij ij ij ij ij setting the value of q (= 0. 00001 by default) to estimate the k-mer size with formula (2) from the size g of the largest genome. A unique sketch size s is defined as 25% of the average of the n genome lengths.
As the computation of each pairwise Mash dissimilarity could be performed independently of the other ones, JolyTree allows this costly O(n ) step to be executed on multiple threads. By default, all Mash dissimilarity values are automatically corrected by formula (3) when at least one of them is larger than 0. 1 (see below); however, this cutoff can be modified with dedicated option. The distance noising procedure is repeated 100 times by default with ϵ varying from 0. 1 (moderate noising) to 0. 7 (important noising). All arithmetic operations are performed by gawk v. 4. 1. 4 (ftp. gnu. or g/gnu/gawk). The results presented below were obtained using Mash v. 2. 1 (github. com/ marbl/Mash), FastME v. 2. 1. 5. 1 (gite. lirmm. fr/atgc/FastME), and REQ v. 1. 2 (gitlab. pasteur. fr/GIPhy/REQ).
Simulation results In order to observe the ability of JolyTree to estimate the evolutionary distance between a pair of genomes, a large number of sequence pairs was simulated. Given an evolutionary distance d varying from 0. 05 to 0. 60 (step = 0. 05), the program SeqGen v. 1. 3. 4 (Rambaut and Grassly 1997)  was used to simulate the evolution of 500 sequence pairs with d substitution events per character. For each simulated sequence pair, their length was randomly drawn from 1 million of bases (Mbs) to 10 Mbs, and their GC content from 25% to 75%. Each sequence pair evolution was simulated under the general time reversible model of nucleotide evolution (GTR; e. g. Yang 1994 ) with each of the six different rate parameters randomly drawn from ]0, 5[, and the Γ-distributed character-specific rate heterogeneity  (Yang 1993)  shaped by a parameter α randomly drawn from 1 to 5.
2a, b ) and slope values of the linear regression through the origin always very close to 1 for every value of d (Fig. 2f ). Using Mash followed by a F81 correction therefore represents an accurate approach for phylogenetic inference. However, the linear residual standard deviation (RSD) plots show that the variance of the estimates rapidly grows with d (Fig. 2c, d ), especially when using the F81 correction (Fig. 2d ). Therefore, datasets inducing large values of d (e. g. > 0. 4) could increase the possibility of inferring incorrect distance-based phylogenetic trees, or trees with inconvenient negative external branches caused by the violation of the triangle inequality  (Desper and Gascuel 2003). Of note, k-mer sizes returned by formula (2) with q < 0. 00001 have led to smaller SEE and RSD but at the cost of slightly non-linear relationships between the F81-corrected distance estimates and d, whereas q > 0.
2018). JolyTree inferred the tree represented in Fig. 3b  with sketch size s = 500,000 and k-mer size k = 19 (q = 0. 00001). As every pairwise Mash dissimilarity was < 0. 1, the F81 correction was not used. Observed running times were 59, 64, 73, 148, and 391 seconds with 16, 12, 8, 6, and 4 threads, respectively, on an Intel Xeon E5-1660 v4 (16Gb RAM) under Linux Debian 4. 9. 110-3+deb9u6. To measure the overall phylogenetic signal induced by the estimated distances, the Arboricity coefficient (arb; Guénoche and Garreta 2001) was computed, as well as the mean value ̅ related to the  plot approach  (Holland et al. 2002). Both coefficients allow the overall treelikeness of a set of pairwise evolutionary distances to be assessed by validating whether each taxon quartet verifies the four-point condition  (Zaretskii 1965 , Buneman 1971 ).
Treelike distances are assessed by arb close to 1 (e. g. arb > 0. 8) and ̅ close to 0 (e. g. ̅ < 0. 2). The dataset simulated by  Lees et al. (2018)  led to arb = 0. 9791 and ̅ = 0. 0400, therefore assessing that the estimated distances accurately recover the phylogenetic signal induced by the genome sequences. In consequence, both the model and inferred trees are broadly similar (Fig. 3 ). All internal branches of length > 0. 00005 (44% of the 93 internal branches) are correctly reconstructed (Fig. 3b ), most of them being validated by branch supports of 1. 00 (only three are not associated to the maximum branch confidence value, but they remain well-supported, i. e. 0. 90, 0. 82, 0. 81; see Fig. 3b ). The other internal branches (i. e. branch length < 0. 00005) are moderately supported (e.
Real-case analyses To observe the ability of JolyTree to quickly infer accurate species trees from real data, genome assemblies from the RefSeq collection (O'  Leary et al. 2015)  were considered. For each genus, available assemblies were gathered when their number was between 30 and 300, leading to 187 genome sequence sets (bacteria: 180; archaea: 6; fungi: 1) of varying size (e. g. n̅ = 76 and g̅ = 4. 1 Mb). Each dataset was next analyzed by JolyTree (default options) to infer a phylogenetic tree representing the evolutionary relationships within the corresponding genus. All inferred trees are available in Suppl. material 1 together with several descriptive statistics (genome numbers and sizes, GC contents, maximum dissimilarities and distances, treelikeness coefficients, mean branch supports).
g. no negative branch for 113 of the 189 datasets; Suppl. material 1), therefore showing that JolyTree is often able to catch the phylogenetic signal induced by many real genome datasets. Of note, an important number of inferred internal branches are well-supported (e. g. 140 datasets allows observing rates of elementary quartets larger than 0. 70 on average; (Suppl. material 1), therefore showing that FastME is able to infer robust phylogenetic trees from the evolutionary distance matrices built from the different genome datasets. It should be stressed that the data noising strategy had a moderate usefulness on these datasets, as no better tree (according to the BME criterion) than the one inferred by FastME alone was obtained for 125 datasets. This could be explained by the overall good treelikeness of the estimated evolutionary distances (Suppl.
material 1), which likely involves few local optima. However, this step remains important in order to assess that an inferred tree is the BME one. Let T be the tree inferred by FastME alone, and T the one inferred by JolyTree via the data noising strategy. The Fig. 4  represents the distribution of the different values (ℓ(T ) -ℓ(T))/ℓ(T ). Among the 62 datasets for which FastME was trapped in a local optimum, Fig. 4  shows a quite moderate improvement for many of them (e. g. less than 0. 1% improvement of the BME tree optimality criterion for 57 datasets). However, Fig. 4  also shows that in some cases, the data noising strategy implemented by JolyTree allows observing up to 0. 66% tree length improvement, i. e. from ℓ(T ) = 1. 7324 to ℓ(T) = 1. 7210 (Megasphaera). The BME tree search approach used by JolyTree can therefore be useful for inferring accurate phylogenetic trees, especially when the overall treelikeness is weak.