dataesr/scientific-paragraphs-categorization huggingface dataset:
ANK TPR LRR Figure  1 : Repeat proteins are elongated objects with internal symmetry. Representative structures of members of the repeat proteins families studied. On left, ankyrin repeat (PDB id:1N0R  [19] ), center tetratricopeptide-like repeats (PDB id:1NA0  [11] ) and right leucine-rich repeats (PDB id:4IM6). The defined repeated unit is highlighted in orange. zero between 85 and 91% of the J ij parameters which, when they are free to vary, only reach small values (Suppl. Fig. 3 ). We bound the maximum error permitted in the frequency estimations to 0. 02. Refer to Methods for more details. In the three families studied, the parameters obtained allow us to generate ensembles of sequences which reproduce natural f i (a i ), f ij (a i , b j ) and p id (Fig. 2A ). Notice that most frequencies are fitted with an error of an order of magnitude lower than the maximum bound imposed (Suppl.
Frequency calculations Our model fits the occurrence of amino acids in every position, which we call the marginal frequency of residue a i at position i of the alignment and denote f i (a i ), and the joint occurrence of two amino acids a i and b j simultaneously at two different positions of the alignment, f ij (a i , b j ). To avoid biases by the overrepresentation of some proteins in the database, we used CD-HIT  [54]  to choose representative sequences which differ between them in more than 90% of identity percentage. Finally, we computed by counting the f i (a i ) and f ij (a i , b j ), and divided by the total number of sequences.
Finally, the parameters λ Id are updated according to: λ t+1 Id ← λ t Id + ID p id (%Id) -p model id (%Id)  (7)  We iterated until the maximum difference between the predicted frequencies and the natural sequences was below 0. 02. The code was written in C++ and is available at GitHub. Inferring repeat protein energetics from evolutionary information Espada R, Parra RG, Mora T, Walczak AM, Ferreiro DU Supplementary information qqq q q q q q q q q q q q q q q q qq qqq q q q q q q q q qqq ANK 0 10 20 30 0. 0 0. 1 q Natural With λ id Without λ id   At center, contact map (grey indicates position in contact, white not in contact on the native structure). On blue, pairs of positions involed in highest Jij(ai, bj), red lowest Jij(ai, bj). At right, pairs of amino acids involved in the highest Jij(ai, bj) parameters (on blue) and in the lowest Jij(ai, bj) (on red).
SAMPLE SELECTION Our sample was selected from the APOGEE survey, making use of Sloan Digital Sky Survey-IV (SDSS-IV) Data Release 13 (DR13, SDSS  Collaboration et al. 2016; Majewski et al. 2017). APOGEE DR13 provides chemical and kinematical information of about 150,000 Galactic stars through the analysis of high-resolution (R ∼22,500) H-band λ= 1. 51 -1. 69µm spectra  (Zasowski et al. 2013). We focus our search in the low-metallicity regime (-1. 8 <[Fe/H]< -0. 7), where stars from the halo and thick disk are expected to dominate the Galactic metallicity distribution  (Hawkins et al. 2015; Martell et al. 2016; Hayes et al. 2017). We impose a minimal signalto-noise (S/N) ratio per pixel of 70 to ensure good quality spectra. In order to identify abundance anomalies in MW field stars, we proceed as follows: From our initial sample (4,611 stars) we selected stars with SG-like chemical patterns in the [Mg/Fe] versus [Al/Fe] plane by means of a clustering analysis.
2016). However, we do not adopt the surface gravity (log g) provided by ASPCAP, since it is affected by a systematic effect that overestimates the log g values  (Holtzman et al. , in preparation). We estimate surface gravity from 10 Gyr PARSEC  (Bressan et al. 2012 ) isochrones (10 Gyr is the typical age of Galactic GCs; Harris 2010). The line list used in this work is the latest internal DR13 atomic/molecular linelist (linelist. 20150714), and the line-by-line analysis was done using the 1D spectral synthesis code Turbospectrum  (Alvarez & Plez 1998)  and MARCS model atmospheres  (Gustafsson et al. 2008). In particular, a mix of heavily CN-cycle and αpoor MARCS models were used. The same molecular lines adopted by  Smith et al. (2013)  and  Souto et al. (2016)  were employed to determine the C, N, and O abundances.
ACKNOWLEDGEMENTS J. G. F-T would like to dedicate this work in memory of his father,  José Gregorio Fernández Rangel  ( 1959-2017 ). We are grateful to the referee for a prompt and constructive report. J. G. F-T is especially grateful for the technical expertise and assistance provided by the  Instituto de Astrofísica de Canarias (IAC). J. G. F-T, D. G, and B. T gratefully acknowledge support from the  Chilean BASAL Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA)  grant  PFB-06/2007. S. V gratefully acknowledges the support provided by  Fondecyt reg. n. 1170518. D. A. G. H. was funded by the  Ramón y Cajal  fellowship number  RYC-2013-14182. D. A. G. H. and O. Z. acknowledge support provided by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant  AYA-2014-58082-P.
In the last decade, high throughput sequencing and transcriptome-wide approaches led to a continuous accumulation of complete genomic data in public databases and contributed to the discovery of hundreds of putative and confirmed new sRNAs in many bacteria such as Escherichia coli  [12] , Salmonella  [13] , Bacillus subtilis  [14, 15]  and Listeria  [5, 6, [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] , giving rise to large-scale comparative analyses and sRNA evolutionary studies. Existing studies on that topic focused on Gramnegative species, including Escherichia coli and related genomes  [8, [28] [29] [30]. Phyletic analysis of E. coli sRNAs  [29]  led to the first insights into the distribution of sRNAs in gamma-proteobacteria, greatly improving our understanding of the origin of sRNA-mediated regulation and the underlying mechanisms at the source of sRNA acquisition.
A comparative transcriptomics approach was also used to compare the expression of non-coding RNAs in L. monocytogenes and L. innocua species, which revealed conservation across most transcripts, but significant divergence between the species in a subset of non-coding sRNAs  [22]. In this article, we present a robust phylogenomics approach that extends and improves existing strategies dedicated to the study of sRNA evolutionary dynamics. We use it to provide the first evolutionary dynamics study of 79 complete genomes of the Listeria genus with regards to protein-coding genes, and a selected set of 112 sRNA loci experimentally identified in the pathogenic strain L. monocytogenes EGD-e. This dataset includes intergenic trans-encoded sRNAs assumed to target independently expressed and distant mRNAs.
The detection of correlated evolution events relies on a phylogenetic-statistical method based on continuous-time Markov modeling of trait evolution developed by M. Pagel  [39]. It compares the statistical likelihood of the observed data (in this case, sRNAs, 5'UTRs and CDS presence/absence patterns) under two alternative scenarii: one in which the two features are allowed to evolve independently on the phylogeny, and another where they coevolve together. This strategy was applied on 112 L. monocytogenes EGD-e putative trans sRNAs, all screened on 79 Listeria genomes (see Additional file 1: Table  S1 ) obtained from the Listeriomics database  [36]. To deal with the remaining paralogs in the dataset, sRNAs exhibiting overlapping positions on the EGD-e reference genome were merged in 15 sRNA loci (see Additional file 2: Table  S2  and the Methods section for details).
Interestingly, nine out of 11 of these individual clusters include evolving partners that are close on the genome (< 8 kb). Two individual coevolving groups [rli28-3/lmo0035] and [rli5-rliA_rli121/ lmo2309-lmo2407] were found with distant coevolution partners (distance >800 kb for both clusters). To summarize, our results reveal an sRNA hub including mainly distantly located coevolving 5'UTRs and CDS regions, some of them exhibiting functions related to Listeria pathogenicity. Most of the remaining coevolution clusters include pairs of sRNA and 5'UTRs/CDS that exhibit very close genomic positions, i. e. a distance between the start of their sRNA and the start of their 5'UTR/CDS under 8 kb. We investigated the functional classes of genes coevolving with Listeria regulatory sRNAs by using annotations from the Clusters of Orthologous Groups (COGs) database  [42]  retrieved from the Listeriomics website  [36].
monocytogenes common ancestor. Rli133 cannot be found in other Listeria species, indicating that this sRNA is specific to L. monocytogenes species. Nevertheless, rli133 is lost in four strains of lineage III (L. monocytogenes FSLF2-208, L. monocytogenes LM850658, L. monocytogenes M7, L. monocytogenes L99) and in the L. monocytogenes FSLJ1-208 strain of lineage IV. In these five strains, the corresponding intergenic region is missing due to the insertion of two genes. These genes appear to be specific of these five strains and do not have any homolog in public databases (see Additional file 5: Figure  S1 ). In genomes where rli133 is present, the corresponding sequence is well-conserved and includes few mutation events, i. e. , six transitions, two transversions and two indels events corresponding to 12 variables sites out of 126 (9.
Highlighted and numbered regions correspond to predicted interaction zones according to the RNAplex software. c Predicted structure of the sRNA rli133. Structure was generated using LocaRNA software and a multiple alignment of all conserved rli133 sRNAs in the genomic dataset. Highlighted numbered regions correspond to Lmo0333-5'UTR predicted interaction zones of Fig. 5b. Representation of structure was performed with FoRNA. d Predicted structure of the 5'UTR region of Lmo0333. Structure was generated using LocaRNA and a multiple alignment of all conserved Lmo0333 5'UTRs in the genomic dataset. Highlighted numbered regions correspond to rli133 predicted interaction zones of Fig. 5b. Representation of structure was performed with FoRNA during translation initiation, suggesting a potential inhibitory action of rli133 at posttranscriptional level on these genes.
Discussion We built a robust workflow that provided new insights on Listeria sRNA evolution and coevolution patterns. First, the screening of sRNA presence-absence patterns suggests that 60 out of 112 L. monocytogenes EGD-e sRNAs (53%) shape the Listeria sRNA core set, in the sense that they were found to be present and conserved in all Listeria genomes. These 60 sRNAs were hence inferred present in the common ancestor of Listeria, suggesting that they were present early during the evolutionary history of the genus. This is a lower proportion compared to the 60 out of 83 E. coli K12 sRNAs (72%) that were found present and conserved in a dataset of 27 complete genomes of E. coli-Shigella  [29]. However, this is consistent with the higher number of genomes and the wider evolutionary scale (genus) used in our analysis compared to the species level of the E.
coli-Shigella study. The 52 remaining L. monocytogenes EGD-e sRNA loci constitute the variable sRNA set that is part of the Listeria accessory genome. This number is higher than the 43 accessory Listeria sRNAs previously identified by Kuenne et al. [38]  in a smaller dataset of 11 genomes restricted to L. monocytogenes species. We found a higher proportion of them exhibiting complex paraphyletic distribution compared to the E. coli/Shigella study: 47 variable sRNAs out of 52 were shown to have paraphyletic pattern (90%) compared to 25 out of 32 (78%) in the 27 genomes of E. coli-Shigella  [29]. Only three and two sRNAs have monophyletic and polyphyletic patterns, respectively. This indicates complex and various evolutionary histories underlying diverse origins and a potentially wide panel of sRNA acquisition and loss mechanisms in Listeria.
The presence of compatible interacting regions is not a feature specific to genes coevolving with rli133, but taking together the observations of coevolution pattern and the presence of a consistent interacting zone argue in favor of a functional link. Moreover, we looked for the presence of the nine 5'UTR-interacting zones of the genes co-evolving with rli133 in 5'UTRs and CDS that do not coevolve with rli133 and found only two similar regions for the inlP (lmo2470) interacting zone: one located in another internalin 5'UTR region (inlP/lmo2027) and one located in the 5'UTR region of the lmo0974 gene that is involved in LPS synthesis and conserved in all the genomes of the dataset. This argues for quite a good specificity of the rli133 predicted interacting zones. For coevolving partners in which no clear mechanism were highlighted, such as sepA (lmo2157)  [51] , a well-known stress response gene involved in septum formation, coevolution patterns may correspond to presence of direct interaction at post-translational level or indirect functional links involving intermediate genes.
This indicates that the presence of rli133 is not an absolute hallmark for pathogenesis and that other, yet unannotated sRNAs may interact with internalin genes in pathogenic strains of lineage IV. Additional genomes and sRNA experimental datasets are needed in this clade to fully understand the role of sRNAs and internalin coding genes in Listeria pathogenicity. The Listeria coevolution network also pointed out 11 sRNAs exhibiting correlated evolution, mostly with close 5'UTRs and CDS regions. Screening for distances between coevolving sRNAs and genes indeed revealed two trends concerning gene location: on one hand, genes close to the corresponding sRNA (putative cis-regulated genes closer than 8 kb), and on the other hand, genes found at distant locations (putative trans-regulated genes with distances higher than 40 kb) (see Additional file 7: Figure  S3 ).
The entire workflow is built on an open source frame that is flexible, optimized and implements parallel and distributed computation, while however remaining computationally demanding. Several features may be proposed in the future to enhance the proposed strategy. First, as we currently only consider presence, absence and unknown states, it constitutes an oversimplification of the way functional elements are defined, also undermining paralogy for sRNA or coding genes. Consequently, an enhancement of our strategy would be to deal with the occurrence of sRNAs and coding genes for both evolution and coevolution analysis. Second, another useful extension of the current strategy would be to include the analysis of mutation patterns and coevolving sites also for the core sRNAs and coding genes present in all the genomes of the dataset as well.
Listeria genome dataset Seventy-nine complete public genomes of Listeria were obtained from GenBank (release 211). A full description of the 79 Listeria genomes is available in Additional file 1: Table  S1 ). The dataset includes 70 complete and 9 draft genomes representing five different Listeria species (L. monocytogenes, L. ivanovii, L. inoccua, L. welshimeri and L. seeligeri). L. monocytogenes genomes were the most represented (73 genomes corresponding to 92% of our dataset).
Listeria monocytogenes EGD-e sRNA A set of 304 experimentally validated sRNAs from L. monocytogenes EGD-e was extracted from the Listeriomics database  [36]. We focused on the 154 sRNAs annotated as putative trans sRNAs which are known as important regulators of gene expression in bacteria acting on independently expressed targets. A group of 19 sRNAs were excluded because they recently have been found to include small ORFs  [6]. Overlapping sRNAs and sRNAs harboring paralogs in the L. monocytogenes EGD-e genome were processed using the following procedure. sRNAs were aligned on the L. monocytogenes EGD-e genome sequence using BLASTN+  [59]. Overlapping hits were merged considering a minimal overlap length of 15 pb, independently of their orientation. Finally, 112 sRNA loci were used as input sequences in the following analyses, including 15 loci built from merged hits and 97 original sequences.
sRNA and coding gene coevolution strategy The strategy we developed is implemented in a Snakemake workflow  [60]  that consists in four main steps (see Fig. 1 ). Step 1: Phylogenetic reference tree. PanOCT, version 3. 23  [61] , was used to build groups of orthologs from annotated genomes. PanOCT is able to deal with recently diverging paralogs by using neighborhood gene information (synteny). All the parameters were set to default values except for the length ratio to discard shorter protein fragments when a protein is split due to a frameshift or other mechanisms was set to 1. 33 as recommended by the authors. Amino-acid sequences of ortholog families were then aligned using ProbCons, version 1. 12  [62] , and resulting alignments were postprocessed using GBLOCKS, version 0. 91b  [63] , using the following parameters: the minimum number of sequences for a conserved position was set to (n/2) + 1, the minimum number of sequences for a flank position to (n/2) + 1 (where n is the total number of sequences in the aligned dataset), the maximum number of contiguous non-conserved positions was set to 20, the minimum length for a block to 5, and gap positions were allowed  [8].
The reference tree was built using the syntenic core gene families corresponding to the PanOCT clusters with a single unique ortholog in each genome of our dataset. The corresponding nucleic acid alignments were obtained from all these core families filtered amino-acid alignments and concatenated into a single superalignment to compute a maximum likelihood tree using FastTree2, version 2. 1. 9 [64]. The following parameters were used for FastTree2: the Generalized Time-Reversible model (GTR) was chosen, the likelihood was reported under the Gamma model using 20 categories of sites, the exhaustive search mode ("-slow" option) was selected to obtain a more accurate reconstruction, NNI and SPR heuristics were used to browse the tree space. Support analyses were performed using Shimodaira Hasegawa test (SH) and 1000 resampling steps of site likelihood.
Step 2: sRNA and coding gene presence-absence matrix. Presence-absence patterns were inferred from BLAST analyses with different parameters for sRNAs and coding genes. L. monocytogenes EGD-e sRNAs were aligned on the genome dataset using BLASTN+, version 2. 2. 29  [59]. Resulting hits were filtered using two criteria: an e-value <10 -2 and a coverage related to the query sequence ≥70%. Only sRNAs meeting these two criteria were considered as present in the targeted genomes. For coding genes, we analyzed separately 5'UTRs and CDS regions. L. monocytogenes EGD-e CDS were retrieved from GenBank annotations and aligned against all Listeria genomes using BLASTP+, version 2. 2. 29  [59]. Resulting hits were filtered using the following criteria: an e-value <10 -2 , a coverage relative to the query sequence ≥70%, an identity rate ≥ 60% and a bitscore ≥50.
Only CDS meeting these three criteria were considered as present in the targeted genomes. L. monocytogenes EGD-e 5'UTRs were retrieved using the following procedure. When available, we used experimental data indicating 5'UTR positions  [36]  to extract the corresponding DNA sequence. When not available, 5'UTR positions were defined arbitrarily as the 100 nearest 5' nucleotides upstream from each L. monocytogenes EGD-e CDS start codon of intergenic region. Only 5'UTRs with a minimum size of 15 bp were kept. 5'UTR sequences were then used as queries for BLASTN+  [59]  alignments against all genomes. Resulting BLASTN+ hits were filtered using the following criteria: an e-value <10 -2 , and a minimal identity percentage and coverage adjusted to the 5'UTR sequence lengths as follows. For 5'UTRs with lengths from 15 to 20 bp, the minimum identity percentage was set to 90% and the minimum coverage percentage to 100%.
For 20-50 bp long 5'UTRs, both identity percentage and coverage were set to a minimum of 80%. For 50-100 bp and >100 bp long 5'UTRs, the minimal identity percentage was set to 80% and the minimal coverage was set to 50% and 25%, respectively. 5'UTRs meeting these criteria in subject genomes were considered as present. A binary vector (0/1) corresponding to the absence/ presence profile in the whole genome dataset was finally generated using BLASTN+ results and filters defined above. Due to their lack of informative value, sRNAs, 5'UTRs and CDS found in all genomes were not taken into account in subsequent analyses. To avoid absence mispredictions corresponding to unsequenced regions of draft genomes, absence events of non-coding elements (sRNAs and 5'UTRs) were systematically checked as follows: for queries without hit in a given genome, 5′ and 3′ adjacent genes of the query element were screened for putative homologs in the same genome.
We used the 'rayDISC' function of the 'corHMM' R package version 1. 20  [17]  and selected the 'ARD' transition model, i. e. independent transition rates. Internal node states were inferred using maximum likelihood estimation and joint probabilities. The root state probabilities were inferred using the method of Fitzjohn and Maddison  [66]. Initial transition rates were estimated using the results of PanOCT orthologs obtained in Step 1 and computed using the 'DiscML' function of the 'DiscML' R package, version 1. 0. 1  [67] , and the 'ARD' transition model (assuming independent transition events, in this case, gain and loss events, for each element). This step results in a matrix containing the binary presence/absence (0/1) pattern for each internal node of the reference tree and for the three analysed features (sRNAs, CDS, 5'UTRs).
Finally, gain and loss events were determined as follows: if the feature was absent (present) in a given node but present (absent) in its ancestor, it was considered as lost (gained) along the corresponding branch linking both nodes. Step 4: Detection of coevolution events. Our strategy allows the detection of coevolution between a sRNA and a 5'UTR or CDS using a reference phylogenetic tree and both observed and ancestral presence-absence patterns. We used the 'corDISC' function of the 'corHMM' R package  [65]  to identify putative coevolutionary relationships. This function fits Pagel's models of independency and dependency  [39]  to identify dependent evolution between two binary characters (in this case, the presence or absence of sRNAs, CDS/ 5'UTRs) and related to a phylogenetic tree. The first model supports an independent relationship between both binary traits: sRNA and 5'UTR/CDS (the null hypothesis).
Interacting regions prediction Possible physical interactions between sRNAs and coding genes identified as coevolving partners using our method were predicted using several pieces of software: Ssearch (implementation from Wisconsin Package), version 6. 1, IntaRNA, version 2. 0. 2, RIsearch, version 1. 1, RNAcofold, version 2. 3. 4 and RNAplex, version 2. 3. 4  [72] [73] [74] [75] [76] , which are all included in the sRNAtabac resource  [77]. We used an extended region including 100 bp before and after the start codon to identify putative interactions between a sRNA region and the extended 5'UTR region of mRNAs (original regions were used for CDS). Only interactions containing a minimum of six successive interacting matches were selected and considered as valid. Homologous sequences of rli133 previously identified (see Step 2 for details) in L.
monocytogenes genomes were extracted. Homologous sequences of lmo0333 (inlI) 5'UTR (see Step 2 for details) were extended up to 60 nucleotides after the start codon. Rli133 and lmo0333extended 5'UTR sequences were processed using LocARNA software, version 1. 8. 9  [73]. LocARNA is a tool that allows simultaneous folding and alignment of input RNA sequences. LocARNA default alignment accuracy was increased using match probabilities and probabilistic consistency transformation. Additional parameters were used since it is recommended by the authors in the software documentation for aligning up to about 15 sequences of lengths up to a few hundred nucleotides. The weight of base pair match contribution was set to 400. An iterative refinement of the progressive alignment was performed using two iterations.