dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of a role of the TGA codon in a metagenomic contig A role of TGA codon (code 11 or code 4/25) was identified for all the 4930 representative rGenomes. Just 18 of them had TGA coding for an amino acid. The predictions were confirmed by the MegaBLAST alignments of the eighteen relatively short (less 1 Mb nt in length) representative rGenomes to prokaryotic genomes in GenBank (31) (https://blast. ncbi. nlm. nih. gov/Blast. cgi). All the 18 representative rGenomes (with predicted codes 4 or 25) had best hits to genomes known to have genetic codes 4 or 25. Coversely, all the genomes with genetic code 4/25 from Gen-Bank were aligned by MegaBLAST against 4912 rGenomes (4930 -18). The alignments did not indicate a presence of any additional genomes with genetic code 4/25. Thus, all 4912 rGenomes were identified as ones with TGA coding for the end of translation (code 11).
In GeneMark-HM, a role of TGA in a contig was determined immediately upon the assignment of a contig to a pan-genome. If no such assignment to a pan-genome was made, the role of TGA was defined by the analysis of Meta-GeneMark predictions as described in Methods. The accuracy of the TGA role prediction by MetaGeneMark was tested on contigs from the 18 representative rGenomes with genetic code 4/25 (a total of 1040 contigs) as well as on the contigs from the representative rGenomes with genetic code 11. A prediction was called false positive when a contig with TGA being a stop codon was identified as s contig with TGA coding for an amino acid. The same set of contigs was used to assess the accuracy of identification of the role of TGA by MetaProdigal  (7). If a contig does not carry genes containing TGA (either as a stop or a sense codon) then the result of gene prediction will not change regardless of what role of TGA has a model of a protein-coding region used for gene prediction in the contig Therefore, in addition to counting contigs with correctly and incorrectly predicted genetic code we counted the numbers of genes predicted with errors due to the TGA misinterpretation (Table  2 ).
However, the results showed that GeneMark-HM predicted fewer overall number of genes than other gene finders, except MetaGeneAnnotator; a tool that had a higher threshold on minimal length of predicted genes. This observation suggests that the lower error rate in gene prediction (higher sensitivity) of GeneMark-HM was not a result of gene over-prediction. Notably, GeneMark-HM was the only one tool in this comparison that was using a pan-genome database of reference genes. The average accuracy of gene predictions made by GeneMark-HM in each metagenome would, therefore, depend on the proportion of sequences originated from species distant from the ones carried in the reference database. To assess effects of this variation, we used the following approach. While we used one and the same simulated metagenome made from sequences of 625 genomes, we changed the size of the reference database of genes, D, which largest instance was the set of genes in all 5082 pangenomes (Table  3 ).
Four instances of database D were as follows: (a) D4--genes of randomly selected 40% of the 625 genomes were excluded from the set of genes of 5082 pan-genomes; (b) D3--genes of randomly selected 30% of the 625 genomes were excluded; (c) D2--genes of randomly selected 20% of the 625 genomes were excluded; (d) D1--all 5082 pan-genomes were used without exclusions. As a result, we saw the percentage of genes predicted via the pangenome path increasing as the database was getting larger (from a to d); at the same time the percentage of genes predicted by MetaGeneMark was decreasing. In the set of 625 complete genomes, 410 were classified by GeneMarkS-2 (5) as genomes with the canonical Shine-Dalgarno RBS, 99 were classified as genomes with noncanonical RBS and 110 were classified as ones with frequent leaderless transcription.
At the same time, the size of the pan-genome gene sequence database grew >4-fold (due to the asymmetry of the comparison rule between query and target (see Methods) we made more frequent selection of genes longer than queries). Speaking of computational efficiency, a running time of processing a single query increased just 2-fold between using a single representative rGenome and a full pan-genome. This effect was partly due to running the fast mode of BLASTn with the k-mer size of 28 nt chosen under assumption of high similarity of query and targets  (24). This fast mode showed a sub-linear dependence of the run-time on a size of the sequence database. Use of pan-genomes has significantly--by factor 15--reduced the database volume in comparison with a number of all the genes in a non-redundant set of SGBs and tClusters (Table  5 ).
Construction of the pan-genomes significantly reduced (by factor 15) the memory footprint necessary for the GeneMark-HM operation (Table  5 ). Although a few erroneously predicted genes could be included in the pangenome database upon its construction, these 'targets' could rarely appear as hits upon similarity searches. Thus, such errors were not likely to influence accuracy of a contig assignment to a species level clade. Similarity searches with the query genes against the pangenome database was the most time-consuming step in the GeneMark-HM operation. To reduce the run-time, given that we searched for a hit with >95% ANI identity of target to a query, we used a fast MegaBLAST algorithm  (24)  with long k-mers 28). Even in this case, the similarity search step took on average ∼80% of the GeneMark-HM run-time (using a single CPU).
Such long unassigned contigs were absent in the gut metagenomes. In the tests on real metagenomes with total sequence length of 45 Mb we observed the following run-times (using a single CPU): MetaGeneMark -10 s, MetaProdigal -3. 5 min, GeneMark-HM -12 min. The time of GeneMark-HM included the time required for the BLASTn scanning the pan-genome database (∼80% of the time). This time could be significantly improved by parallel processing.
DISCUSSION In the Results section we demonstrated that the GeneMark-HM pipeline did improve the accuracy of gene prediction in metagenomic sequences. Particularly, we observed improvements in the whole gene prediction sensitivity as well as in accuracy of the gene starts prediction (Table  3 ). The observed improvement was mainly due to selecting an accurate model for GeneMark. hmm-2 by means of assignment of a metagenomic contig to a pan-genome in the pan-genome database. The optimal model selection made GeneMark. hmm-2 capable to annotate genes in short contigs no less accurately than GeneMarkS-2 annotates complete genomes (Figure  1 , Table  3 ). The pan-genome database for the human microbiome was built from arguably the largest collection of genomes of sequenced isolates and prokaryotic genomes reconstructed in silico from metagenomics reads.
Functional diversity (FD), the diversity of organism attributes that relates to their interactions with the abiotic and biotic environment, has been increasingly used for the last two decades in ecology, biogeography and conservation. Yet, FD has many facets and their estimations are not standardized nor embedded in a single tool. mFD (multifaceted functional diversity) is an R package that uses matrices of species assemblages and species trait values as building blocks to compute most FD indices. mFD is firstly based on two functions allowing the user to summarize trait and assemblage data. Then it calculates trait-based distances between species pairs, informs the user whether species have to be clustered into functional entities and finally computes multidimensional functional space. To let the user choose the most appropriate functional space for computing multidimensional functional diversity indices, two mFD functions allow assessing and illustrating the quality of each functional space.
Next, mFD provides 6 core functions to calculate 16 existing FD indices based on trait-based distances, functional entities or species position in a functional space. The mFD package also provides graphical functions based on the ggplot library to illustrate FD values through customizable and high-resolution plots of species distribution among functional entities or in a multidimensional space. All functions include internal validation processes to check for errors in data formatting which return detailed error messages. To facilitate the use of mFD framework, we built an associated website hosting five tutorials illustrating the use of all the functions step by step.
Background Functional diversity (FD) is the diversity component  (Pollock et al. 2020 ) measuring the diversity of organism characteristics that relates to their interactions with their mFD: an R package to compute and illustrate the multiple facets of functional diversity abiotic and biotic environment, i. e. functional traits  (Petchey and Gaston 2006, Naeem et al. 2012). FD has been increasingly considered for the last two decades in all fields of ecology such as community ecology, conservation and biogeography  (McGill et al. 2006 , Violle et al. 2014). FD indeed allows assessing the response of species assemblages to natural or anthropogenic pressures  (Mouillot et al. 2013a , Trindade-Santos et al. 2020 ) and unravels the effect of species on ecosystem functioning  (Dıáz and Cabido 2001, McLean et al.
2014 ) computes seven multidimensional FD indices based on  Botta-Dukát (2005) ,  Petchey and Gaston (2006) ,  Lavorel et al. (2008) ,  Villéger et al. (2008) ,  Laliberté and Legendre (2010). However, this package computes the multidimensional space (or dendrogram) and FD indices within the same function while they correspond to independent steps  (Mouchet et al. 2008 , Maire et al. 2015) , thus potentially inducing a misunderstanding of the outputs or limiting the reproducibility of analysis  (Villéger et al. 2017). The betapart package  (Baselga and Orme 2012, Baselga et al. 2021 ) contains a set of functions to compute functional beta-diversity indices based on the overlap of species assemblages in a multidimensional space, but it does not permit the graphical representation of indices.
Therefore, we designed the mFD R package to provide a wide set of 'user-friendly' functions covering all the steps of FD-based analyses, from checking of input data (Supporting information), through the identification of functional groups, computation of trait-based distances, and building the functional space required to compute 16 functional diversity indices, including the graphical representation of key outputs (Fig. 1 ). Graphic aesthetics can be customized by the user and outputs can be displayed or saved as high-resolution files. mFD improves diagnostics, error reporting, and data visualization in a standardized way, and its website (<https:// cmlmagneville. github. io/mFD/>) provides a set of tutorials to help newcomers in the field with their analyses. mFD is currently available through Github (<https://github.
com/ CmlMagneville/mFD>) and is available on CRAN (<https:// cran. r-project. org/web/packages/mFD/index. html>). In this paper, we first described the methods used and the features of the mFD package and then explained step by step the process of computing and plotting FD indices with the mFD package. Lastly, we illustrated the most basic workflow of the mFD package using a data set gathering fruits in several baskets (Box 1).
For matrices containing only categorical traits or several types of traits, the funct. dist computes Gower distance with the function gawdis() from the package gawdis. funct. dist can also be applied to functional traits coded as fuzzy groups (e. g. diet described with a proportion of several resources), and it allows the user to give different weights to each trait (e. g. to ensure that biological functions described with varying numbers of traits have eventually equal contribution to the distance metric). The Gower distance is able to deal with missing trait values (coded as NA in R objects) by accounting only for traits measured on both species of each pair. However, this feature could lead to situations where two species are identical (Gower = 0) while having different Gower distances to a third species, thus influencing FD indices values.
Hence, if the species-traits data frame contains NA, funct. dist returns a warning (this check could be overridden by the user), and it is recommended to infer missing traits values  (Johnson et al. 2020)  or delete species with missing trait values. For more information about distances, the user can refer to the mFD general workflow tutorial (<https:// cmlmagneville. github. io/mFD/articles/mFD_general_workflow. html>).
The quality. fspaces function thus computes up to four quality metrics based on the combination of the two criteria (absolute or squared deviation computed on raw or scaled distances) for all spaces built using increasing numbers of PCoA axes. The maximum number of PCoA axes to consider is set a priori by the user, but it is eventually constrained by the number of PCoA axes that present positive eigenvalues. Indeed, we recommend not applying square-root transformation before computing PCoA because distances in the Euclidean space will end up reflecting transformed traitbased distances that are not linearly related to the ones measured (see the 'Compute and Interpret Quality of Functional Spaces' tutorial on mFD website (<https://cmlmagneville. github. io/mFD/articles/Compute_and_interpret_quality_ of_functional_spaces.
html>)). Besides multidimensional spaces, the quality. fspaces function allows the user to compute the quality of a dendrogram (clustering algorithm being specified by user). However, most often dendrograms are biased representations of the traitbased distance between species with some pairs of species with close trait values being distantly related in the dendrogram (see 'Compute and Interpret Quality of Functional Spaces' tutorial on mFD website (<https://cmlmagneville. github. io/mFD/articles/Compute_and_interpret_quality_of_func-tional_spaces. html>)). Thus, dendrogram-based FD indices  (Petchey and Gaston 2002, Cardoso et al. 2014) , available in other R packages such as FD  (Laliberté and Legendre 2010, Laliberté et al. 2014) , BAT  (Cardoso et al. 2020) , and adiv  (Pavoine 2020 (Pavoine , 2021)) , could lead to erroneous ecological conclusions  (Loiseau et al.
Illustrating the distribution of species and traits values in the functional space Once the 'best' functional space has been selected, and species coordinates along each axis are extracted as an output of quality. fspaces, mFD package provides two dedicated functions for further analyses. The funct. space. plot function is designed to illustrate the position of all species from the pool along functional axes (all pairs or a subset). The user selects the colors and shapes of points to represent species as well as to display additional features, such as the convex hull shaping all species and names of (some) species. In addition, the traits. faxes. cor function is designed to test relationships between values of each trait and positions along each functional axis using r 2 and p-value from linear regression and eta 2 and p-value from Kruskall-Wallis test for continuous and non-continuous traits, respectively.
Computing multidimensional functional alphadiversity The alpha. fd. multidim function computes nine multidimensional functional alpha-diversity indices (Table  1 ). Required inputs are the matrix of species coordinates in the multidimensional space built using quality. fspaces or tr. cont. fspace functions and the matrix with species weights in studied assemblages. Users can select a subset of the nine FD indices to be computed (see package functions help for details). An optional logical argument allows the user to scale a posteriori the values of all FD indices but FEve and FDiv (whose ranges are already constrained to 0-1) by dividing raw values by the maximum value possible given coordinates of species present in the pool of all assemblages. The user alpha. fd. multidim returns a table with values of FD indices (columns) for all assemblages (rows).
beta. fd. multidim uses functions from the betapart package (ver. 1. 2) which allows internal parallel computing. The beta. fd. multidim function returns a list of distance objects with values for all pairs of assemblages. The function also returns a list containing a vector with the volume of the convex hull shaping each assemblage (i. e. FRic) and a list of vectors with names of species being vertices of the convex hull per assemblage. The beta. multidim. plot function is designed to illustrate functional beta-diversity indices by plotting the overlap between the convex hulls shaping two given species assemblages projected along pairs of functional space axes. It takes as inputs the list returned by the beta. fd. multidim function, a vector containing the name of the two assemblages to represent, a vector with names of axes to plot, and a character string referring to the values of beta-diversity indices to print (i.
Computing the functionally unique, specialized, and endangered indices Beyond FD indices estimated at the scale of species assemblages, the field of functional ecology also has a long history of assessing species-specific indices in relation to their trait values  (Olden et al. 2006 , Violle et al. 2017). Recently,  Pimiento et al. (2020)  introduced the FUSE (functionally unique, specialized, and endangered) index that combines functional uniqueness, specialization, and global endangerment to identify threatened species of particular importance for functional diversity. The mFD package provides for the first time a function to calculate this index. The fuse function allows calculating the FUSE index as well as several metrics at the species level describing their functional specialization and uniqueness  (Mouillot et al.
Box 1. Example We illustrate the most common workflow of the mFD package using a data set gathering 10 baskets made of 8 types of fruits (from a pool of 25) with varying biomass ('fruits_baskets' data frame, Supporting information). Each fruit is characterized by 5 traits (fruits_traits data frame) whose types are described in the 'fruits_traits_cat' data frame (Table  2 ). The mFD package is based on three main objects: a matrix describing the distribution of species into assemblages, the fruits_baskets matrix in this example, a first data frame containing the traits values for each species, the fruits_traits data frame here, and a second data frame with details about each trait, the fruits_traits_cat data frame. The first step after importing these three objects in R is to check their content using the summary functions: sp.
tr. summary function summarizes the fruits_traits data frame and asb. sp. summary function summarizes the fruits_ baskets matrix, which returns, among others, the species richness per baskets. Functional distance between all species from the 10 baskets is computed using the funct. dist function. Here, we applied the Gower distance since traits were not all continuous, and we scaled center continuous traits according to their mean. Then, we assessed the quality of PCoA-based functional spaces with up to 10 dimensions and of the UPGMA dendrogram. For that, we used the quality. fspaces function and computed the mad index (mean absolute deviation): Here, according to the mad index, the best functional space is the one with four dimensions as it has the lowest index value: The quality of functional spaces built with up to six PC axes and UPGMA dendrogram are illustrated using the quality.
The green and yellow surfaces respectively show a two-dimensional projection of the multidimensional convex hull shaped by the fruits from basket 1 and basket 5. Beta-diversity indices are computed using the beta. fd. multidim function with Jaccard family indices here: Turnover and nestedness-resultant components of dissimilarity indices are shown below only for pairs involving basket 1. Beta indices are then plotted using the beta. multidim. plot function for the basket 1 and basket 5: In Fig. 5 , the convex hulls filled by species belonging to basket 1 and basket 5 are plotted respectively in green and yellow. Further illustrations of the use of the functions listed above as well as of the functions for working with continuous traits and computing distance-based indices or entities-based indices of the mFD package are available as tutorials in the package vignette (<https://cmlmagneville.
Conclusion The mFD package allows assessing all FD indices available to date with all types of traits in an integrated, standardized, and comprehensive manner. Importantly, all methodological choices to be made by the user are included as parameters of the functions to ensure reproducibility. The functions use common formatting and terminology for inputs and outputs which ease computing the successive steps within and between the three main approaches (entities-, distance-, and coordinates-based indices). In addition, all functions include internal checks of inputs (e. g. match of species names between traits and assemblages matrix, absence of missing values) that return detailed error messages if needed. All graphical functions can produce ready-to-publish figures with customizable parameters (e.
g. shape, size, colors of points, and convex hulls). mFD package was developed in R. 4. 0, works for R ≥ 3. 5, and depends directly on 16 packages (Supporting information). It can be cited using the output of citation (package = 'mFD') and by citing this article. The website associated with the package (<https://cmlmagneville. github. io/mFD/index. html>) presents five reproducible tutorials as R markdown files. The unique features of mFD functions and associated online materials will thus make it easier for students and researchers in functional ecology to compute FD in a reproducible way. The functions from mFD could even be applied for researches outside functional ecology, such as to measure phenotypic variability within populations or between species, for example trophic position based on variables such as stable isotope ratios of carbon and nitrogen  (Cucherousset and Villéger 2015)  or to map vegetation status using telemetry data (Feret and de Boissieu 2019).
Funding -N. Loiseau was supported by a grant from  University of Montpellier MUSE (BUBOT). D. Mouillot  and  N. Loiseau  were part of the  FREE group  funded by the  Centre for the Synthesis and Analysis of Biodiversity (CESAB) of the Foundation for Research on Biodiversity (FRB)  and  EDF. S. Villéger  and  D. Mouillot  were supported by  European Union 's  Horizon 2020 (MaCoBioS project , grant agreement no. 869710 ). S. Villéger  and  A. Escalas  were supported by  ANR  grant ( EXOFISHMED  project, grant agreement no. ANR-17-CE32-0003 ). C. Albouy was funded by an 'étoile montante' fellowship from the  'Pays de la Loire ' region (no. 2020_10792 ). E. Maire was funded by the  FAIRFISH  project (ERC grant number:  759457 ).
This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and concluded that each of these models provides a good match to experimental results in some domains but not in others.