dataesr/scientific-paragraphs-categorization huggingface dataset:
Political preferences To investigate the question of political preferences, we exploit the French Electoral Study conducted in 2017 by  Gougou and Sauger (2017). This study comprises 1830 face-to-face interviews. Respondents had to answer a long list of political questions. According to our review of the literature, this survey is the only dataset in France released after the suspension of military service that includes a question on conscription status. In the sample, 436 respondents have performed military service. Because we have individual data on political preferences and military service, a first attempt to assess the effect of conscription is to estimate the following specification: Y i = α + βS i + δ 1 Age i + δ 2 X i + i. (6) Where Y i is the reported political preference of individual i, S i is a dummy variable equal to 1 if the individual declares that he has done military service, and X i is a set of control variables.
Acknowledgments We are grateful to the Direction du service national, the  Service historique de la Défense , the  Observatoire économique de la défense  and in particular to  Jean-Michel Oudot. We also thank  Emeric Henry ,  Jean Barthelemy ,  Julia Cagé ,  Pierre Cotterlaz-Carraz ,  Félix De Monts ,  Arthur Guillouzouic-Le Corff ,  Sergei Guriev ,  Nicole Kliewer ,  Guy Laroque ,  Thierry Mayer ,  Riccardo Zago  as well as a co-editor and two anonymous referees for their valuable contributions. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Background: Sequence-binning techniques enable the recovery of an increasing 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. Results: We present here a scalable pre-assembly binning scheme (i. e. , operating on unassembled short reads) enabling latent genome 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 = 1,135, >10 10 reads). Conclusion: 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.
This shift towards contig binning was mainly driven by the increase in data throughput, as the first read-level binning methods were designed at the time of 454 (Roche) and even Sanger sequencing (both providing longer reads) to process individual samples. They were thus not designed to scale to large multisample 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  [13]  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.
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 LifeLines DEEP cohort  [18]  and either assembled these individually (i. e. , on a sampleby-sample basis) with metaSPAdes (v3. 13. 0) followed by contig binning across samples with the MetaBat2 adaptive algorithm  [17]  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 7 using the bin-first method.
Enhanced accuracy of sparse coding-based read binning versus state of the art read binning Besides the 2 pioneering read-binning methods already mentioned (AbundanceBin  [11]  and MetaCluster  [12] ), we could also mention CompostBin  [20] , which is a principal component analysis-based read-level-binning algorithm that was designed and tested on Sanger reads. BiMeta  [21]  and MetaProb  [22]  are other tools that operate at the read level but describe themselves as "assembly-assisted," meaning that they rely on the detection of read overlaps. BiMeta was tested on 454 reads simulating bacterial communities of a dozen different genomes at most and on the Acid Mine Drainage dataset  [23] , which is of low complexity and consists of Sanger reads. MetaProb shares some principles with BiMeta: it is also "assembly-assisted" 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 of 43 million reads, but only after filtering the latter down to 2 million reads. Thus, all the above methods were designed to operate on individual samples, at a time when 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 because 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 resources. In this respect, there is only 1 competing method left, Latent Strain Analysis  [13] , 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 Fvalue metrics; see Methods) with that of the original LSA method by using previously described benchmark datasets  [24]  (downloadable from the GigaScience database  [25] ), for which read to genome assignments were known (  [24]  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 algorithm.
96% average nucleotide identity [ANI]; Table  2 ) strains that were deliberately included in the genome mixtures of the virtual cohort used in the test experiments. Fig. 2  illustrates 2 practical examples of partial strain separation achieved with the method. Fig. 2A  illustrates a partial separation of 7 strains of the species Bacillus amyloliquefaciens (whose ANI ranged from 94. 18 to 99. 96; Table  2 ), while Fig. 2B  shows similar results for 8 strains of Sulfolobus islandicus (whose ANI ranged from 97. 84 to 99. 59). Because 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).
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 abil- ity 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 >10 10 reads (∼10 TB of raw sequence data) derived from 1,135 gut microbiomes of healthy Dutch individuals from the LifeLines DEEP cohort  [18]. The pre-assembly binning of the cohort's reads resulted in 983 partitions, which were then assembled individually using the SPAdes engine  [27]  (Methods). The distribution of assembly sizes is shown in Fig. 3 , making apparent that almost all partitions are bacterial-genome 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 lowabundance 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 LifeLines DEEP 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 Fig.
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 (>50% complete with <5% contamination, accessible via GigaDB  [25] ) against several reference genome libraries. We first compared the genomes against the Kraken 2 [29] database built from NCBI's Refseq bacteria, archaea, and viral libraries (accessed October 2018). Only 21 of the 164 genomes compared had ≥1 fragment classified against this reference database (Methods). We also compared the genomes against the "Global Human Gastrointestinal Bacteria Genome Collection" (HGG  [6] ), which represents one of the most comprehensive resources of gastrointestinal bacterial reference sequences currently available. Less than half (72 of 164) of the genomes displayed convincing similarity to the HGG genome catalogue (Methods).
Discussion Abundance 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 that is successfully recovered via read-level binning (Supplementary Table ). When applied to the >10 10 reads from the LifeLines DEEP cohort's metagenomes, our bin-first protocol recovers hundreds of metagenome-derived genomes, including from consistently less abundant organisms (Figs  4  and 5B  and D ).
By increasing the number of distinct abundance profiles that can be generated, larger sample numbers increase both the sensitivity and resolution of covariance-based methods; one may therefore anticipate further gains in the application of such methods in relation to future increases in the scale of sequence data generated (i. e. , increased cohort sizes). We need however to acknowledge several important limitations that impede the overall performance and applicability of our bin-first framework. First, we already mentioned a limitation arising from the natural occurrence of strain-level variation at the origin of differential segregation of core and variable fractions of species pangenomes (Fig. 2 ). The large number of incomplete but otherwise uncontaminated partitions/bins in the LifeLines DEEP analysis partly reflects the widespread occurrence of this type of variation in natural habitats.
However, it should be noted that neither are assembly-based approaches 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, e. g. ,  [ 30] ). Sample-by-sample assembly limits the risk of strain mix-up, but at the expense of focusing on those genomes reaching high coverage (∼10×). 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 targetin an unsupervised way-low-coverage genomes in a strainresolved 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 (LSH) scheme will inherently increase the probability of collision for similar sequences. Thus, we face here another trade-off: besides efficient in-memory indexing, the same LSH trick that allows convenient handling of sequencing errors (noise) can also put a limit on the power to separate very similar sequences (e. g. , strains). The observation that 4 of 7 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. ,  [33] ), assembling large multi-terabyte 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 high-performing and userfriendly software  [19, [34] [35] [36] , some of which have shown capabilities to recover genomes as low as 0. 6% (10 -3 ) in relative abundance  [35]. 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 control experiments used the dataset described in Gkanogiannis et al. [24]  (and accessible from GigaScience's GigaDB  [25] ) 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 10× (see  [24]  for details). 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 ) of Meyer et al. [38]  (see section "Comparison of read binning algorithms"), with precision corresponding to what the authors refer to as purity and recall corresponding to completeness.
Real dataset: LifeLines-DEEP metagenomes The LifeLines-DEEP cohort features 1,135 individuals (474 men and 661 women) from the general Dutch population, whose gut microbiomes were shotgun sequenced using the Illumina short-read technology, generating an average of 32 million reads per sample (see  [18]  and EBI dataset accession No. EGAD00001001991).
Regarding the choice of a k-mer length, the key requirement is that k-mers should be sufficiently long so that most of them will be specific to each genome, thereby capturing genuine abundance patterns of individual genomes. In our experiments, the k-mer length (31) was chosen to be close to the value used by Cleary et al. [13]  to analyze their largest (terabase-sized) dataset. Some limited experiments with varying k-mer length values were carried out on smaller subsets of the data to check that small variations in k-mer size did not result in disproportionate differences in clustering outputs. In choosing the k-mer length, we were also guided by the observations in  [39]  that k-mer similarity between genomes at different k approximates various degrees of taxonomic similarity, with k = 31 appearing to correspond to species-level similarity.
First experiment for comparing assembly-first versus bin-first protocols An experimental set-up was designed to illustrate the ability of read binning to cluster rare reads from a target genome across samples, while assembly-first protocols are inoperable because the low coverage of the target genome prevents the generation of any kilobase-sized contig from the assembly of the individual samples. The dataset consisted of 18 samples each containing a subset of 20,000 reads sampled from the 18 metagenomic libraries analyzed in Sharon et al. [42]  and randomly spiked with mock reads from a Bacillus thuringiensis plasmid (NG 035027. 1) as in the test data used in the original LSA paper  [13]. However, as the number of spiked reads (up to 4,000) distributed among the samples in LSA's test dataset was sufficient to yield contigs covering a large fraction of the plasmid genome upon assembly, we derived a new dataset only containing 0-100 paired reads (14 samples contained 100 paired reads while 4 were entirely devoid of plasmid reads) and used the latter for this experiment.
This dataset is available on the repository associated with this publication  [25]. After checking that no kilobase-sized contig could be assembled in any of the samples-thus precluding the application of contig binning-the dataset was processed by our pre-assembly pipeline using the following settings: a k-mer length of 30 and a hash size of 22 were used to build the k-mer abundance matrix; the latter was decomposed by SVD and the columns of the eigen-k-mer matrix were clustered using a cosine similarity threshold of 0. 25, followed by read assignment and assembly (using SPAdes) of the partitions. More than 99% (2,782 of 2,800) of the plasmid-derived reads ended up in a single partition (Supplementary Table ), leading to the recovery of the complete target genome sequence upon assembly.
Second experiment for comparing assembly-first versus bin-first strategies The raw sequence data from 18 (randomly chosen) individuals of the LifeLines DEEP cohort were either assembled 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  [17] , or the raw reads were clustered using our read-level binning pipeline, followed by metaSPAdes assembly of the resulting partitions/bins. The raw reads were first mapped to the assembled contigs using bwa-mem  [43]  using default parameters. MetaBat2 was then invoked in the following way: first, the jgi summarize bam contig depths script was called to compute contig abundance statistics from the read mapping bam files, with the default options (minimum percent identity for a mapped read: 0.
97; minimum contig length: 1,000; minimum contig depth: 1). The metabat2 program was then called using the default parameters (minCV 1. 0, minCVSum 1. 0, maxP 95%, minS 60, and maxEdges 200) and the previously generated coverage statistics file, leading to the generation of 225 bins covering 694,000,907 bases. For the comparison, our sparse coding pipeline was then executed under the same settings as in the full cohort analysis (hash size and k-mer size equal to 30 and 31, respectively, and default parameters for the dictionary learning and sparse decomposition of the abundance matrix), with the exception of the number of components that was matched to the number of bins (225) generated by MetaBat2. To generate Fig. 1 , the complete genomes retrieved using both approaches were aligned (using nucmer  [44]  with default parameters) to individual assemblies from all the samples, and the number of distinct contig hits (≥99% identity and ≥2,500 bp) was recorded.
Comparison of read-binning algorithms The virtual cohort dataset described above and in Gkanogiannis et al. [24]  was used to compare the clustering accuracies of the original LSA  [13]  and sparse coding methods, as well as the performance of directly clustering the columns of the abundance matrix using a k-means algorithm as a baseline. The read to genome memberships being comprehensively known in these controlled genome mixtures, clustering accuracy metrics (precision, recall, and F-measure) could be quantified as in Meyer et al. [38]  (Table  1 ). Briefly, each bin is first mapped to its most abundant (in terms of number of reads) genome (note that if each bin is mapped to a single genome, a given genome can be mapped to multiple bins). Precision is defined as the ratio of reads originating from the mapped genome to all the bin's reads.
Evaluation of read enrichment levels To assess whether we could identify genomes segregating at consistently low abundance levels in real-life datasets, we characterized the abundance of a dozen MAGs reconstructed from the LifeLines DEEP cohort analysis by directly mapping the raw reads from the original samples against them. Given the large size of the cohort (and the significant amount of computer resources associated with this analysis), and given that our objective was to establish whether consistently rare genomes can be identified by the method, this analysis was performed on a limited number of genomes. Relative enrichment levels were estimated by mapping the original reads (after removal of duplicated reads) to the genomeresolved partitions using bwa-mem  [43]  with default parameters.
Comparison of genome-resolved partitions to reference genomes To assess the novelty of the genomes assembled from individual partitions produced by our pipeline through the analysis of the LifeLines DEEP cohort, we screened them against 2 reference libraries. First, the genomes were compared to the Kraken2 (v1) database  [46]  built from NCBI's Refseq bacteria, archaea, and viral libraries (accessed October 2018), using the Kraken2 classifier  [29]  and a confidence score threshold of 0. 2. Second, the same genomes were compared against the Human Gastrointestinal Bacteria Genome Collection  [6]  (HGG, encompassing >100 GB of sequence data) using the nucmer aligner  [44]  with default parameters. A genome was marked as already known if it shared ≥10 distinct 99% identity alignments of length ≥5 kb to any HGG entry.
Binning implementation Code for the pipeline used to perform the analysis of the Life-Lines DEEP cohort can be cloned from https://gitlab. com/kyrgy zov/lsa slurm, while a more lightweight implementation of key algorithms (including sparse non-negative matrix factorization [NMF]) is available from  [47] ; they draw on the code base of the LSA tool (  [13]  and  [48] ) and on the SPAMS library that can be downloaded from  [40]. The analysis of the metagenomes from the LifeLines DEEP cohort was carried out on a Bullion S6130 octo module server equipped with 2 Intel Xeon Haswell E7-4890 v3 CPU (18 cores) per module, 8 TB of RAM, and 35 TB storage. Most of the tasks being embarassingly parallelizable, they were run through a Slurm workload manager. The analysis took ∼3 weeks wall time, with the sparse decomposition of the k-mer abundance matrix taking <1 day.