dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). Second, they have the ability to reproduce results  (Cohen-Boulakia et al. , 2017)  obtained with the Phenomenal software which is guaranteed by the use of OpenAlea and its companion tools  (Pradal et al. , 2017). OpenAlea is indeed a SWfS able to keep track of the exact series of software used to design a scientific experiment and is provided with a provenance module, collecting parameter settings and datasets produced and processed during an execution. Moreover, to be able to process very large datasets obtained from phenotyping platforms, we have designed the infrastructure InfraPhenoGrid to distribute the computation of workflows on distributed computing facilities  (Pradal et al. , 2017; 2018; Heidsieck et al. , 2019). Finally, OpenAlea is built by an open-source community, which provides models and tools that are very valuable for plant phenotyping.
Discussion In line with some other initiatives, such as PlantCV  (Gehan et al. , 2017)   RGB multi view acquisition systems like PhenoArch are widely spread, which makes our workflows probably also valid for processing data from other platforms. Whilst this is true for the image processing workflow itself, which can be run on every environment running Python, the 3D reconstruction workflow requires some adaptation due to specific acquisition and setup and data management strategies of each platform. In particular, the link with data management strategies is a key element to reach full automation, by retrieving camera calibration data and plant images as well as storing the resulted images and intermediate data. The complete integration of Phenomenal workflows in Python eases this automation.
This limitation remains even when refining the voxel size, and may therefore be intrinsic to the space carving method. Phenomenal also allows to quantify leaf angle distribution and its dynamics, which are key factors modulating light interception  (Liu et al, 2017). These traits have been extracted by  Lacube et al. , 2017  but not in an automatic way, therefore limiting their use in large datasets. More originally, thanks to its connection to OpenAlea platform, Phenomenal allows retrieving some functional traits with models. Coupling with FSPM models could also improve the precision of analysis like Alvarez Prado et al. (  2017 ) that inverted a simple evaporation model to get stomatal conductance estimates of thousands of plants in PhenoArch. Moreover, on maize plants, Phenomenal allows to separate the growing and mature leaves, and therefore allows the quantification of the whorl development.
However, while the segmentation has been developed for maize, it can help analyzing other cereal species that have a relative similar architecture (e. g. sorghum, wheat, barley, rice). For example, we have successfully tested the ability of the Phenomenal maize analysis workflow to process existing published dataset of sorghum from  McCormick et al. , (2016). Phenomenal algorithms currently focuses on the independent analysis of images acquired through time on phenotyping platform. This allows to analyze kinetics for plant-level characteristics (e. g. volume, total length leaf counts). For organ-based traits, in which one wants to analyze for example the growth of individual leaves over time, a temporal analysis would require tracking. Tracking will also allow to get more robust analysis as some errors (e.
Conclusions Phenomenal provides a robust, end-to-end and open-source software library that allows setting up fully automated and data-intensive imaging analysis workflows for high throughput phenotyping platforms. It is able to manage large-scale data sets using distributed computing infrastructure. Based on botanical analysis of 3D reconstruction, Phenomenal provides new traits for assessment and analysis in HTP platforms. Accuracy on trait estimation is satisfactory for mature leaves but will require improvements for analysis of growing leaves. More originally, thanks to its connection to OpenAlea platform, Phenomenal allows to use models to estimate light interception and thus take into account the competition for light in analysis of platform experiments. Phenomenal algorithms currently focus on independent analysis of images acquired through time on phenotyping platforms.
Validation with manual measurements In order to compare manual and automatic measurements, 126 maize plants belonging to 26 genotypes with contrasting plant architectures were manually measured destructively at the end of experiment ZA16. For each plant, length, area, maximum and average width of the leaves and the stem were measured by a leaf area meter (LI-COR 3100 area meter, LI-COR, Lincoln, NE, USA). Before the destructive measurement, each plant was imaged with the PhenoArch acquisition system on 13 views (12 side views and 1 top view). From those images, the architectural traits were derived from the Phenomenal workflow configured with voxel diameter of 4 mm and a tolerance of photo-consistency equal to. To match the leaf data obtained from the workflow with the manual measurements, the visible leaf tips of a plant were first manually tagged on the best side view (the view with the binarized image with the largest convex hull).
Then, the 3D leaf tips detected in the workflow were projected onto the same view. Finally, the distance between the projected tip and corresponding manually tagged tip were calculated (Supplementary Material S2). To compare results from the Phenomenal workflow with manual measurements, comparisons were plotted (Fig. 6 ), which were optimized in regard for the visualization of large datasets using semi-transparent markers as proposed recently by  Dhutia et al. (2017) , with a chosen αtransparency value adjusted to the size and number of overlapping markers. To allow to access both negative and positive errors, we also calculated the mean absolute error (MAE): 𝑀𝐴𝐸 = 1 𝑛 ! !!! 𝑦 ! -𝑦 ! and the normalized root mean squared difference (nRMSD).
Validation with synthetic data The ADEL maize model  (Fournier and Andrieu, 1999)  simulates 3D geometry and topology of maize plants from a set of architectural parameters. The precise 3D geometry of each organ on the 3D mesh is therefore known, as well as the topological organization of the plant. We used the ADEL-maize model to simulate 1000 3D-maize architecture with random architectural parameters (Fig. 4  G, H, I and J). For each plant generated, model parameters were randomly and uniformly selected in specific intervals: plant height varied between 100 and 300 centimeters, the number of phytomers between 2 and 12 and base stem diameter between 1. 25 and 7. 5 cm. The other parameters are kept to ADEL default values. The geometry of these simulated plants were projected to different planes, which correspond to the ones of the PhenoArch phenotyping system, to create 2D binary images of these plants,.
The organ segmentation workflow was challenged on a public dataset of 3D sorghum plants containing 1187 plants analyzed by McCormick et al. (2016). In their experiment, McCormick et al. provided semi-automatically segmented plants into leaves and stems. The Phenomenal workflow has been applied to process all this dataset automatically (Fig. 4D, E and F). The stem and leaf detection algorithm was considered by McCormick et al. (2016) as the main limiting step to the complete automation of their workflow on sorghum plants, and the authors kept this step manual to preserve the performance of their pipeline. The number of leaves identified was exact in 65% of cases, and for 87 % of the remaining plants the difference was only 1 leaf, i. e.
below or above the manually determined insertion point. It should be noted that, unlike McCormick et al (2016) , the error in the location of stem endpoint does not impact the detection and counting of individual leaves. The comparison of stem length estimates with reference values yielded very consistent results among datasets, in terms of bias and error (Fig. 5). The mean absolute error was found within one leaf to leaf interval for actual plants (sorghum or maize). Note that McCormick et al (2016) observed a bias of 5. 75 and a root mean square error of 6. 67 between manual measurement on actual plants and manual measurement on image for stem length. Comparison with a synthetic dataset -The workflow was finally challenged by comparing image-based extracted features with corresponding known references on thousand synthetic 14 3D maize plants (corresponding to 4488 mature and 4291 growing leaves) obtained with the ADEL 3D-maize simulation model(Fournier & Andrieu, 1998)  available in OpenAlea (Fig.
Walt et al. , 2014)an derWalt et al. , 2014), Phenomenal is a step towards an open-source ecosystem around high throughput image analysis of phenotyping platforms. However, to our knowledge, this is the first end-to-end open-source library that is able to analyze and reconstruct 3D models from images on large datasets in a reproducible way. While some algorithms are already available in other software, most of them have been enhanced to process actual HTP data automatically.
Acknowledgments The authors acknowledge the support of  France Grilles  for providing computing resources on the  French National Grid Infrastructure , of  Institut Français de Bioinformatique  project (IFB,  PIA INBS 2012 ) for providing the cloud computing infrastructure, and the ' Infrastructure Biologie Santé '  PHENOME-EMPHASIS  project ( ANR-11-INBS-0012 ) funded by the  National Research Agency  and the  'Programme d'Investissements d'Avenir' (PIA). MM has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship  under Grant agreement No.
Sequence binning techniques enable the recovery of a growing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly, incurring the computational cost and drawbacks of the latter, e. g. biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets. We present here a scalable pre-assembly binning scheme (i. e. operating on unassembled short reads) enabling latent genomes recovery by leveraging sparse dictionary learning and elastic-net regularization, and its use to recover hundreds of metagenome-assembled genomes, including very low-abundance genomes, from a joint analysis of microbiomes from the LifeLines-Deep population cohort (n=1135, > 10 10 reads). We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale, and that despite lower genome reconstruction yields compared to assembly-based approaches, bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles.
They were thus not designed to scale to large multi-sample terabase-sized short read datasets. In this perspective, assembly can be viewed as a pre-processor to reduce the computational burden of binning. A pioneering pre-assembly binning scheme  [11]  was proposed a couple of years ago, with the read partitioning problem formulated by analogy to the latent semantic analysis (LSA) technique widely used in natural langage processing (NLP). The core idea to view metagenomes as linear mixtures of genomic variables can lead to read clustering formulations based on the deconvolution of latent variables ("eigengenomes") driving the k-mer (subsequences of length k) abundance covariance across samples. The raw sequence data is first summarized in a sample by k-mer occurrence matrix (analogous to term-document matrices in NLP), approximating the abundance of k-mers across samples.
Bin-first and assembly-first strategies recover distinct and complementary genome sets A second experiment aimed at directly comparing the genome recovery yield of assembly-first versus bin-first strategies on a real-life dataset. We selected the raw sequence data from 18 (randomly chosen) individuals of the LifeLinesDeep cohort  [43] , and either assembled these individually (i. e. on a sample by sample basis) with metaSPAdes (v3. 13. 0) followed by contig binning across samples with the MetaBat2 adaptive algorithm  [19] , or clustered the raw reads using our read-level binning pipeline, followed by metaSPAdes assembly of the resulting partitions/bins. Fourteen nearly (>90%) complete and uncontaminated (<5%) genomes were recovered using the assembly-first approach, versus seven using the bin-first method.
Enhanced accuracy of sparse coding based read binning versus state of the art read binning Besides the two pioneering read binning methods already mentioned (Abun-danceBin  [41]  and MetaCluster  [42] ), we could also mention CompostBin  [9] , which is a PCA-based read level binning algorithm that was designed and tested on Sanger reads. BiMeta  [38]  and MetaProb  [14]  are other tools that operate at the read-level, but describe themselves as "assembly-assisted", meaning they rely on the detection of read overlaps. BiMeta was tested on 454 reads simulating bacterial communities of a dozen of different genomes at most and on the Acid Mine Drainage dataset of ref  [37] , which is of low complexity and consists in Sanger reads. MetaProb shares some principles with BiMeta: it is also "assemblyassisted" and was tested on the same low-complexity synthetic datasets as the latter.
The authors also tested their method on a real microbiome sample consisting in 43 million reads, but only after filtering the latter down to 2 million reads. Thus, all the above methods were designed to operate on a individual samples, at a time were scalability issues were less acute. Moreover, with the exception of AbundanceBin which exploits a coverage signal extracted from unique k-mers, the other methods are better described as composition-based, using a nucleotide composition signal measured from short k-mers (typically of length 4 or 5). We developed our method with scalability in mind, as we wanted it to be able to process on the order of 10 10 short reads and to be able to process increasingly larger multi-sample datasets by simply stacking additional computing resource. In this respect, there is only one competing method left, Latent Strain Analysis  [11] , that is both scalable and designed to operate on unassembled short reads from a large number of samples.
To evaluate our method, we first compared its read clustering accuracy (measured in terms of precision, recall and F-value metrics, see Methods) with that of the original LSA method by using previously described benchmark datasets  [15]  (downloadable from http://www. genoscope. cns. fr/SCdata/vc50/), for which read to genome assignments were known (ref  [15]  and Methods). The results from these experiments are summarized in Table  1 , and show improved accuracy of the sparse-coding framework over both the original LSA and a naive k-means algorithms. K-means LSA Sparse Coding Precision 0,52 0,58 0,72 Recall 0,63 0,64 0,82 F-Value 0,57 0,61 0,77 Table  1 : Binning accuracy estimates: LSA refers to the original algorithm of ref  [11] , with a cosine similarity threshold of 0. 7 as recommanded by the authors, k-means refers to a direct clustering of the columns of the abundance matrix, with the number of clusters set to 1000 (equal to the number of components for the sparse decomposition), see main text and Methods.
84 to 99. 59). As the genomic origin of each read is known in the virtual cohort dataset, these plots show, for each strain (represented by a horizontal line), the distribution of its reads among the full set of clusters/bins generated by the pipeline (and arbitrarily ordered along the x-axis). The left panel illustrates that the 7 strains from the B. amyloliquefaciens species are mostly separated into two groups according to whether their main cluster is located near x-coordinate 220 or x-coordinate 500. The right panel on the other hand shows that the 8 strains of the S. islandicus species share a common "core" cluster (located near the origin), while variable portion of their genomes are segregated into distinct "variable" clusters. Overall, this analysis makes apparent a partial separation of closely related strains (left panel), as well as the differential segregation of the core (i.
Sensitivity and scalability on real-life data By scalability, we refer to the ability of the method to adapt to order of magnitude change in the input (raw reads), and its ability to maintain its functionality and performance under high demand (i. e increasingly higher data volumes). To assess the sensitivity and scalability of the sparse coding method, we applied it to a real world dataset of over 10 10 reads (about 10 terabytes of raw sequence data) derived from 1135 gut microbiomes of healthy Dutch individuals from the LifeLinesDeep cohort  [43]. The pre-assembly binning of the cohort's reads resulted in 983 partitions, which were then assembled individually using the Spades engine  [4]  (Methods). The distribution of assembly sizes is shown in Figure  3 , making apparent that the vast majority of partitions are bacterialgenome sized (i.
Recovery of very low-abundance genomes A key motivation for the pre-assembly processing of reads was the theoretical possibility to aggregate reads from low abundance organisms across samples. To assess whether we could indeed identify such consistently low abundance genomes in real-life datasets, we characterized the abundance of a subset of > 70% complete genomes from the LifeLinesDeep cohort analysis by directly mapping the raw reads of the original samples against them. Given the large size of the cohort, this analysis was not performed on the full set of MAGs but on a limited number of genomes, the aim being to validate the ability of the method to retrieve such low-abundance genomes by exhibiting some of them. The relative enrichment levels of these genomes was measured as the fraction of raw reads contributed by each sample to them (Methods), and is illustrated in Figure  4  for two genomes, with the left panel showing an example of a consistently low-abundance genome (i.
Assessing novelty against reference genome compendia To investigate the extent to which the recovered genomes could correspond to novel organisms, we screened a subset of 164 of them (more than 50% complete with less than 5% contamination, accessible at http://www. genoscope. cns. fr/SCdata/MAGs/) against several reference genome libraries. We first compared the genomes against the Kraken 2 [39] database built from NCBI's Refseq bacteria, archaea and viral libraries (on October 2018). Only 21 out of the 164 genomes compared had at least one fragment classified against this reference database (Methods). We also compared the genomes against the "Global Human Gastrointestinal Bacteria Genome Collection" (HGG, ref  [13] ), that represents one of the most comprehensive resources of gastrointestinal bacterial reference sequences currently available.
Discussion Covariance-based binning has the power to identify biologically meaningful associations between metagenomic sequences that could go unnoticed by analyses based on sequence overlap (assembly) or nucleotide signatures. This is illustrated in the present study by a preliminary experiment using a synthetic dataset spiked with low abundance sequences from a target genome that does not reach a sufficient coverage to yield kilobase-sized fragments after assembly in any individual sample (thus precluding the application of contig binning), but which is successfully recovered via read-level binning (Supplementary Table ). When applied to the > 10 10 reads from the LifeLinesDeep cohort's metagenomes, our bin-first protocol recovers hundreds of metagenome-derived genomes, including from consistently less abundant organisms (Figure  4  and right panels of Figure  5 ).
However, it should be noted that assembly-based approaches are neither immune to this type of variation, frequently discarding it when building "flattened" consensus contigs. This type of polymorphism is difficult to handle in a de novo way, and current methods for strain-level surveys of metagenomes typically rely on reference databases of strain-specific nucleotide polymorphisms (see for example ref  [24] ). Sample by sample assembly limits the risk of strain mix-up, but at the expense of focusing on those genomes reaching high-coverage (around 10x). Our approach aimed at relaxing the latter constraint, but by doing so through the aggregation of lower abundance reads across samples, it becomes vulnerable to extensive strain-level variation. To the best of our knowledge, a method that could target -in an unsupervised way-low-coverage genomes in a strain resolved manner is not available today, and working towards this goal is clearly a promising research area.
It should be noted however that, to some extent, the degree of similarity that one wishes to distinguish can be tuned through the choice of the k-mer length and hash size. Increasing the k-mer size would increase the separation of closely related sequences, but only to some extent because the locality sensitive hashing scheme will inherently increase the probability of collision for similar sequences. Thus, we face here another benefit versus disadvantage trade-off: besides efficient inmemory indexing, LSH allows to conveniently handle sequencing errors (noise), but at the same time can also put a limit on the power to separate very similar sequences (e. g. strains). The observation that four out of seven genomes retrieved in the preliminary experiment based on 18 samples were not among the set of MAGs identified by analyzing the full dataset is indicative of a lack of stability of the algorithm.
One should note however that, even though this can leverage developments in efficient k-mer counting and graph compaction (e. g. [10] ), assembling large multi-terabytes datasets can remain problematic in the first place. Nevertheless, most of the recent development efforts in the field of metagenomic sequence binning remain directed toward assembly-first approaches, which have already delivered a vast array of performent and user-friendly software  [1, 3, 23, 40] , some of which have shown capabilities to recover genomes as low as 0. 6 % (10 -3 ) in relative abundance  [40]. However, we have shown that the method presented here is able to recover genomes by sequence enrichments of the order of up to 10 -6 (10 -7 for some plasmid sequences), and therefore believe that it could be a useful adjunct to existing more mainstream approaches, especially for targeting more rare organisms.
Control datasets The dataset described in ref  [15]  (and accessible from cns. fr/SCdata/vc50/) corresponding to a virtual cohort of 50 individuals each harboring a microbiome of 100 distinct bacterial genomes sampled under a power-law abundance distribution (with power parameter α = 1. 0) from a pool of 750 fully-sequenced genomes at an average depth of 10x (see ref  [15]  for details), was used in the control experiments. We call these datasets semi-synthetic because they are made of real bacterial genome sequences assembled into artificial mixtures. The read to genome assignments (ground truth) being known in advance for all the reads, the precision and recall metrics were computed from the read clustering output as in equations (  10 ) and (  11 ) from ref  [27]  (see section "Comparison of read binning algorithms"), with precision corresponding to what the authors refer to as purity and recall corresponding to completeness.