dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimize re-assembly of draft genomes and quality evaluation To increase the size of contigs and scaffolds, we performed an optimized de novo re-assembly procedure after the clustering procedure. The contig pools were used to recruit reads from their Illumina HiSeq 2000 sequencing pool, by alignment to the contigs using BWA  [61]. The recruited reads were corrected using Quake  [62]. A new de novo assembly was performed for each cluster using only the reads that were remapped on them, using Velvet  [63]. Scaffold gaps were filled using SOAPdenovo GAPCloser  [58]. When a close species reference genome was available in the NCBI database, we used it for an assisted assembly procedure. This procedure used the NCBI contigs in combination with the cluster contigs to recruit the reads. This may recover the missing regions that were lost during the first assembly and clustering procedures.
Genomic drafts passing the core-ratio HMP criterion and 328 NCBI reference genomes were used for phylogenic classification (see Additional file 10: Table  S5 , genome references phylogeny sheet). For each draft, 40 markers commonly used for phylogeny classification and corresponding to 40 essential proteins  [40] , were detected using BLASTP procedure and a marker reference database of about 1500 complete genomes. The best hit was selected with at least 50% identity and 50% coverage. Each marker protein was aligned to reference markers using MUSCLE  [66]  and the 40 individual alignments were concatenated to a single alignment. The missing markers were replaced by gapped lines, not used for the distance calculation. Only the drafts with at least 10 markers out of the 40 described above were selected for the tree.
Metagenomic sample mapping Three DNA samples from cheeses were sequenced using SOLiD technology, which yielded between 11 and 19 million single reads of 50 nucleotides length. The identification of species was done in two steps: a first mapping was done on a catalog reference of 5990 genomes, which included the 117 draft sequenced in the present study, and a second mapping with more detailed analyses on a selection of genomes (see Additional file 10: Table  S5 , genomes metagenomic analyse sheet). The first mapping was performed using Bowtie aligner  [60]  (with parameters: first 35 nucleotides mapped; 3 mismatches allowed; 10000 matches by read allowed), on the whole database. This mapping allowed a first selection of genomes. Genomes with no annotation were cut into fragments of 1000 bases.
Genomes sequenced in the present study (1)  or from the NCBI database (0). (b) Species known to be components of cheese making commercial cultures. (c) Number of reads mapped on CDS from the reference genome with three or less mismatches on 35 nucleotides. (d) Number of CDS in the genome. CDS corresponding to insertion sequences, prophages and potential repeated and transferable elements were removed. (e) Percentage of CDS covered with at least one read. (f) Percentage of sequence covered by at least one read (sequence is restrained to the selected CDS). (g) Number of reads aligned with this genome divided by the number of good quality reads. (h) Length of sequence covered by perfect match reads (with no mismatch on the 35 nt length alignment) divided by the length of the sequence covered by reads.
Acknowledgements M. A. was supported by a grant from the  Ministère de la Recherche et de l'Education Nationale (France). This work was mainly supported by the  ANR  grant " Food Microbiomes " ( ANR-08-ALIA-007-02 ) coordinated by P. R. and additional funding from the  Centre National Interprofessionnel de l'Economie Laitière (France). Computing was facilitated by the funds obtained from the  OpenGPU FUI  collaborative research projects, with funding from  DGCIS. Optimized re-assembly draft has been processed with  CBS computing facility (Denmark). We thank  Junjie Qin  for the experimental advices on the pool preparations,  Cécile Callon  for her help for strain cultivation and DNA extraction, and  Henrik Bjørn Nielsen  for discussion on co-abundance clustering methods. We also thank  Serge Casaregola  and  Joëlle Dupont  for providing an early access to the genomes of  Geotrichum candidum CLIB 918, Penicillium roqueforti FM 164 and Penicillium camemberti FM 013.
( a ) a Genomes sequenced in the present study (1)  or from the NCBI database (0). (b) Species known to be components of cheese making commercial cultures. (c) Number of reads mapped on CDS from the reference genome with three or less mismatches on 35 nucleotides. (d) Number of CDS in the genome. CDS corresponding to insertion sequences, prophages and potential repeated and transferable elements were removed. (e) Percentage of CDS covered with at least one read. (f) Percentage of sequence covered by at least one read (sequence is restrained to the selected CDS). (g) Number of reads aligned with this genome divided by the number of good quality reads. (h) Length of sequence covered by perfect match reads (with no mismatch on the 35 nt length alignment) divided by the length of the sequence covered by reads.
One way to detect positive selection is by analyzing nucleotide substitution patterns at the codon level in a phylogenetic framework. Example of a protein tree as it can be found on the GreenPhylDB. The Arabidopsis sequences 7 -12 (cluster B) are only related by duplication (nodes with red boxes) and are therefore ultraparalogs (=UP; red lines). Sequences only related by speciation are superorthologs (=SO; blue lines). Those are sequences 1 -6 (cluster A), 13 and 14 (cluster C), 15 -17 (cluster D). For example, sequences 13 and 15 are paralogs (as they are related by duplication) but not ultraparalogs (as a speciation event occurred after duplication). We used only clusters containing at least six sequences (clusters A and B, bold) for further analysis. The dashed lines indicate that SO and UP clusters can come from the same GreenPhyl tree (SO1 and UP1 datasets) or from separate trees (SO2 and UP2 datasets).
Dataset description We investigated whole genomes of five monocots (Musa acuminata, O. sativa, Brachypodium distachyon, Zea mays, Sorghum bicolor) and five dicots (Vitis vinifera, A. thaliana, Populus trichocarpa, Glycine max, Medicago truncatula). From the GreenPhyl database  [41]  we extracted ultraparalog clusters (UPsequences only related by duplication) which represent our LSE gene set. As a single-copy gene reference, we chose a superortholog gene set (SOsequences only related by speciation). To address the question of whether or not positive selection is more frequent during LSE events, we compared the results obtained on UPs with those obtained on SO gene sets. The SO gene set was then divided in two subsets. The first one, SO1, contains SO genes extracted from GreenPhyl protein trees in which at least one UP cluster was also identified.
This means that all the trees from which an SO1 was extracted contain at least one UP cluster. The second SO set (SO2) is the complement of SO1, i. e. it is composed of SO genes extracted from GreenPhyl trees in which no UP clusters were found. Likewise, the UP1 dataset represents UP clusters extracted from Green-Phyl trees also containing SO clusters and the UP2 dataset represents UP clusters from GreenPhyl trees from which no SO clusters were extracted. We subdivided the dataset as we expected a "family effect". This effect may be caused by an accelerated evolutionary rate in some families which are more prone to gene duplication and/or retention than others, e. g. due to their function or base composition. If one GreenPhyl tree contained more than one SO or UP cluster, we kept only one cluster randomly (see Methods for details).
A detailed overview of the workflow can be found in Figure  1. Our final dataset for codeml analysis comprised 160 UP1, 1,512 UP2, 167 SO1, and 1,203 SO2 clusters (Table  1 ). The mapNH analysis was performed on 154 UP1, 1,435 UP2, 167 SO1, and 1,203 SO2 clusters (Table  1 ) and 1,257 UP1, 14,326 UP2, 1,807 SO1, and 13,374 SO2 branches (Table  1 ). The median length of the UP1 alignments is 1,272 bp (base pairs), 1,220 bp for the UP2, 1,230 bp for SO1, and 987 bp for SO2 alignments (Table  1 , Figure  2 ). The UP alignments are significantly longer than the SO alignments (Mann-Whitney test: p < 0. 001). This can be partially explained by the fact that GUIDANCE introduces gaps instead of aligning ambiguous sites  [42]. Therefore, UP geneswhich are frequently under less selective constraintmay produce longer alignments due to the introduction of gaps.
The median number of sequences in an alignment (i. e. median cluster size) is 7 for UP and SO alignments (Table  1 , Figure  2 ). We found that the cluster sizes for the SO datasets are significantly smaller than for the UP datasets (Mann-Whitney test: p < 0. 001) which was expected because the number of sequences a superortholog cluster can contain is at most ten (=number of species used in this study) whereas it is not bounded for UP clusters. As this divergence time between one species and its closest relative increases, one might expect that the number of detected UPs could also increase when compared to a distantly related species than to a closely related one. Therefore, we tested if the divergence time and the number of identified UP clusters correlated. Note that we always used the divergence time relative to the most closely related species in the GreenPhyl database, no matter if we analysed this species later (divergence times can be found in Additional file 1: Figure  S1 ).
The UPps clusters have a longer median length (1,314 bp) and larger median cluster size (8) than the other UP and SO clusters (Table  1 ). ω at the branch level The analysis of selective pressures at the branch level was performed using mapNH on the same dataset as the codon analysis. If ω at a branch is larger than 1. 2 we consider this a strong indicator of positive selection (simply defining ω > 1 as an indicator of positive selection might lead to false positives as in a neutral scenario ω rather fluctuates around 1 than being exactly 1). The mean ω of the branches is significantly (p < 0. 001) higher in UP2 (0. 62) than in SO2 (0. 29) and the distribution shows a larger variance for UP2 than for SO2 (Figure  4 , Table  3 ). As compared to SO2, in UP2 we observe: (i) a higher proportion of branches with ω > 1.
Effect of evolutionary time and polymorphism To see if our results are biased by divergence discrepancies between UP and SO, we sorted the ω value of each branch by their synonymous substitution rate (d S ). To rule out the effect of polymorphism, we excluded ("young") external branches from the dataset and compared the remaining ("old") internal branches (UPint) to the SO dataset. We found a significant difference between the ω of SO and UPint in d S intervals ranging from 0. 01 to 0. 21 (Figure  5a+b ). There is no significant difference in the first d S interval (Figure  5a ), most likely because of residual polymorphism and/or a low mutation rate in SO and UP clusters. This interval harbors, however, more than 50% of the dataset. These results indicate thatexcept for very low d S valuesthe difference between SO and UP cluster cannot be explained solely by divergence discrepancies or residual polymorphism.
As annotation is ongoing and remains under constant modification, a comprehensive analysis of the potential function of the clusters with codons under selection would not lead to reliable results. However, some trends can be observed: (i) the most abundant molecular function is "protein binding" (21. 57% of all annotated molecular functions in the dataset) followed by "transferase activity" (9. 80%). This is especially true in the Level 2 dataset (i. e. clusters derived from large GreenPhyl trees) whereas potential molecular functions seem to be more diverse in the Level 1 dataset (Additional file 2). (ii) The most common predicted biological functions are "metabolic process" (23. 53% of all annotated biological processes in the dataset) and "oxidation-reduction process" (20. 59%.
Discussion The important role of duplicated genes in plant adaptation has been argued theoretically (reviewed by  [43] ). To assess whether lineage specific expanded (LSE) genes show more evidence for positive selection than single-copy genes we analyzed LSE gene families from ten angiosperm genomes using a d N /d S -based test. We found positive selection footprints moderately frequently at the codon level in LSE genes (5. 38% in average among the different species) but did not find any positive selection footprints on singlecopy genes after manual curation. The number of codons under positive selection is also found higher in LSE than in single copy genes for different cluster size categories and thus cannot be explained solely by a difference of power to detect positive selection between the two datasets.
When d N /d S was computed on all the branches of the same dataset as for site analyses, we detected a stronger effect of positive selection on LSE genes compared to singlecopy genes. Therefore, we argue that LSE genes are a much more important substrate for positive selection to act on than single-copy genes. This isto our knowledgethe first genome-scale study to empirically demonstrate that LSE genes fuel adaptation in angiosperms. Among the vast literature dealing with population genetic models of duplicated gene evolution, a crucial point is whether natural selection plays a role in it  [18]. Positive selection is expected to act either on the fixation process of the duplication itself or at new mutations occurring after fixation of the copy in the species (or at both levels successively).
Two main functions are described for LRR-RLKs: development and defense  [55]. LRR-RLKs involved in defense are predominantly found in LSE gene clusters whereas LRR-RLKs involved in development are mostly found in non-expanded groups  [55]. It was also discovered that the LRR domains are significantly less conserved than the remaining domains of the LRR-RLK genes  [55]. In addition, a study on four plant genomes showed that LRR-RLK genes from LSE gene clusters show significantly more indication of positive selection or relaxed constraint than LRR-RLKs from non-expanded groups  [55]. Therefore, it is not surprising that F-box and LRR domains are the most abundant domains we found in the LSE clusters with codons under positive selection. First, proteins containing these domains constitute large gene families and are therefore likely to show up in our LSE datasetespecially when coming from the GreenPhyl Level 2 dataset as it comprises of large trees.
We used PRANK and GUIDANCE to align and clean the sequence clusters. Those recent methods have been found to produce the most reliable alignments for downstream analysis using the PAML software  [57, 58]. Third, we curated the alignments for which we detected positive selection manually. As this is a great deal of work in large datasets many studies fail to do this. However, we argue that this step is crucial to produce reliable results as we found around 50% alignment errors and therefore false positives. The manual validation of all the positively selected sites is a major strength of our study. Fourth, the power for d N /d S analysis is related to the number of sequences aligned. In our dataset the difference in sequence number was significant between the LSE and the singlecopy dataset.
This means that the more a gene family is prone to duplication/retention the less probable a single-copy gene cluster will be found. Here, we give an argument in favor of the hypothesis that the initial level of selective constraint partially conditions the frequency of duplication/retention. We detect a family effect in different trees but the d N /d S difference between LSE clusters and single-copy gene sets remains significant when controlling for this effect by comparing clusters extracted from the same gene trees. Finally, when analyzing very recent duplicates it is possible that the differences between copies are still segregating within populations which violates basic assumptions of d N /d S -based tests  [59]. Our LSE dataset may include genes where differences are still polymorphic which can lead to an overestimation of positive selection  [59, 60].
Conclusions In this paper we conduct one of the largest studies on the role of recurrent gene duplication on adaptation in angiosperms so far. Indeed, most of the former studies either dealt with candidate families in a broad taxonomical range (e. g. [35] [36] [37] ) or whole genomes for a maximum of four plant species (e. g. [11, 12] ). We searched duplicated genes from ten angiosperm genomes for footprints of positive selection and our results provide candidates for further functional or population genetic studies. In general, we used a very conservative approach to detect positive selection footprints at LSE genes and might therefore miss many true positives. Still, because of the inherent differences between LSE and single-copy datasets, our results must be interpreted with caution. As the number and quality of sequenced genomes is increasing daily, our analysis can be expanded to many more plant species in the future.
We used the information provided by the Green-Phyl v3 database (http://www. greenphyl. org) which uses a tree reconciliation approach  [70]  to identify orthologs (genes related by speciation) and paralogs (genes related by duplication) in protein trees. This database contains protein families' composition and phylogenies for a broad variety of green plants whose genomes have been completely sequenced  [41]. Based on their sequence similarity, the GreenPhylDB clusters gene families at different levels from the less stringent (large clusters of relatively similar sequences at Level 1) to the most stringent (small clusters of highly similar sequence at Level 4). First, we extracted 3,330 protein clusters from Level 1. As large gene families (>500 sequences) are not further analyzed in GreenPhyl, we extracted 2,238 protein clusters from Level 2 for these gene familie.
These are two separate datasets and Level 2 trees are not nested in Level 1 tress (see GreenPhyl homepage for details: http://www. greenphyl. org/). We extracted ultraparalog clusters (UPsequences only related by duplication) from the GreenPhylDB trees on which duplication and speciation events were positioned according to the tree reconciliation approach cited previously (Figure  1a ). Those clusters represent our LSE gene set. As a single-copy gene reference, we chose a superortholog gene set (SOsequences only related by speciation). We ignored clusters with less than six sequences. The SO clusters were divided into clusters coming from the same tree as UP clusters (SO1) or from trees exclusively harboring SO clusters (SO2). Likewise, UP clusters were divided in clusters coming from trees containing SO clusters (UP1) or from trees with only UP clusters (UP2).
Note that when a GreenPhyl tree harbors several SO and/or UP clusters, all were extracted. We downloaded the corresponding complete CDS of the species of interest (links on GreenPhylDB Documentation section). In case of alternative spliceforms, the longest one is kept in the GreenPhylDB pipeline; it is thus the one we downloaded. Most GreenPhyl trees are too large and/or too divergent to create reliable nucleotide alignments and perform d N /d S -based tests on the whole tree alignment. This is especially true for the most interesting cases where trees contain both UP and SO clusters (the UP1/SO1 dataset). We therefore chose to analyze each UP and SO cluster independently. In GreenPhyl trees harboring several UP and/or SO clusters i. e. in gene families in which gene duplication/ retention might be more frequent, one might expect selective constraint to be different, in particular more relaxed.
Therefore, some gene families might be overrepresented when several clusters from the same tree are analyzed separately. To avoid this, an additional step of selection was added to the initial dataset as we randomly kept only one cluster each time several clusters of UP or several clusters of SO were identified from a same tree and removed all other clusters from our analysis. Here, we present the results for this final sub-dataset. However, we performed our analysis on three additional sub-datasets: (i) the whole dataset without removing clusters from trees harboring more than one cluster, (ii) a dataset which contains clusters from GreenPhyl trees with only one UP and/or one SO cluster, (iii) a dataset where only clusters from trees harboring more than one cluster were kept. The results for these sub-datasets can be found in Additional file 1.
e. length not divisible by three). Those clusters were also removed from the analysis. Additionally, for 18 UP clusters codeml failed to run (probably due to insufficient sequences overlap). We retrieved 167 UP1 and 167 SO1 as well as 1,656 UP2 and 1,203 SO2 clusters. After cleaning, our final dataset for codeml analysis comprised 160 UP1, 1,512 UP2, 167 SO1, and 1,203 SO2 clusters for the codeml analysis (Table  1 ). As alignment errors can create false positives in the detection of positive selection footprints, each cluster suggested to be under positive selection was again checked both automaticallyusing muscle  [72]  and trimAL  [73]  for creating and cleaning alignments (muscle-trimAL method; see Additional file 1)and manually for alignment errors. We found that our initial alignment and cleaning procedure using PRANK  [71]  and GUIDANCE  [42]  is superior to the muscle-trimAL method.
Detecting codons under positive selection We used codeml site model implemented in the PAML4 software  [74]  to infer positive selection on codons under several substitution models. For these analyses, we extensively relied on the egglib package  [75]  to implement the following pipeline: First, for every alignment the maximum likelihood phylogeny was inferred at the nucleotide level using PhyML 3. 0  [76]  under the GTR-Γ substitution model (Figure  1c ). Second, different codeml site models were run (Figure  1d ). The nearly neutral models (M1a and M8a) assume codons to evolve either neutrally or under purifying selection whereas the positive selection models (M2a and M8) assume positive selection acting on some codons. Third, likelihood ratio tests (LRTs) were performed using R  [77]  to compare nearly neutral and positive selection models and hence to detect clusters for which models including positive selection are significantly more likely than models that do not.
Determining effects of time and polymorphism SO and UP clusters are different by definition. First, the divergence times between sequences are not expected to be the same. Specifically, divergence in a given SO cluster should range between minimum and maximum divergence time of the species included in this cluster. Divergence in UP clusters should range from null (for very recent duplications) to the last speciation event. It has been shown that d N /d S -based tests are strongly influenced by d S  [59]. To test whether our results are biased due to divergence discrepancies between UP and SO, we sorted the ω value of each branch by their synonymous substitution rate (d S ). Second, in the UP dataset some duplications could have occurred very recently. It is likely that some differences between those young paralogs are still segregating in populations and should therefore be considered as polymorphism instead of divergence.
Inferring selection using d N /d S in such a scenario has been shown to be incorrect  [60]. To rule out effects of polymorphism on UP clusters, we excluded external branches from the dataset and compared the remaining internal branches to the SO dataset. To test if ω differs significantly between types of clusters, we performed a Mann-Whitney test using R  [77]. When ω is analyzed according to d S , Mann-Whitney tests were performed in a sliding window of 0. 01 d S. The calculation was done when a window contained at least 100 values by group studied.