dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Structure Database. Our database to search for loop candidates consists of the entire set of protein structures available in the Protein Data Bank (PDB). In March 2017, it consisted of 123,417 PDB entries, corresponding to 338,613 chains in total. Each chain was split into segments that correspond to consecutive regions separated by gaps or non-standard residues, but accepting seleno-methionines. This led to a database of 758,143 protein segments.
Template-based test sets. To assess the performance of our approach, we have used three test sets. The first one (HOMSTRAD) was taken from the study by  32. It consists of 23 loops with sizes between 6 and 11 residues. The two other ones correspond to the targets of the CASP11 (http://predictioncenter. org/casp11/) and CASP12 (http://predictioncenter. org/casp12/) experiments  51, 52. For each CASP target, templates were identified using HHsearch 53 against the PDB70 database (02-04-2016), considering a maximum sequence identity cutoff of 50% between template and target. In case of multiple, non-overlapping templates, they were combined into a template set. For each target, the template set was aligned to the target using TM-align  54  , and the template set with the highest TM-score was selected.
Loop candidate search. We previously introduced the BCLoopSearch protocol, to mine large protein structure datasets and retrieve loop candidates, given two disjoint fragments (loop flanks)  49. It is based on a Binet-Cauchy (BC) kernel and a Rigidity score: = BC X Y det X Y det X X det Y Y ( , ) ( ) ( ) ( ) (1) T T T where X and Y are C α coordinates of the flanks and dataset fragments, respectively and they are centered at the origin. Note that a BC score of 1 indicates a perfect match. Rigidity score R(X, Y) is defined as: ′ = | - | ≤ ≤ R X Y max X Y ( , ) (2) i N i i 1 = ′ | - - - | R X Y max R X Y X X Y Y ( , ) { ( , ), } (3) N N 1 1 where X i and Y i are C α coordinates of the ith residues of the flanks and dataset fragments and ||⋅|| is the euclidean norm. Rigidity score is the maximum variation of intra-distances between: (i) residues and geometric center and (ii) intra-distances between terminal C α.
In addition, we also measured the RMSD between query and candidate flanks for the fragments returned. In total, four cut-offs values related to (i) flank size, (ii) flank BC score, (iii) flank Rigidity and (iv) flank RMSD, have been considered to limit the number of loop candidates. In this study we used: a flank size of 4 residues, Rigidity ≤ 3 and flank RMSD ≤ 4Å. The minimal flank BC score cut-off was set depending on the size of the loop to be modeled: 0. 9 for loops of at most 8 residues and 0. 8 for longer loops. For each target protein, prior to the loop modeling homologous proteins with more than 70% chain sequence identity were excluded from our search database.
Candidate filtering. In most cases the number of candidates returned by BCLoopSearch is too large to be tractable, which implies to limit their number. Three filters were sequentially applied in our protocol to this aim: Sequence similarity. The sequence similarity of a loop candidate with the query loop sequence using BLOSUM62 score. Candidates with negative scores were discarded. Geometrical clustering. We used the python Numpy library to measure the pairwise distances (RMSD) between all the candidates  56. In addition, we used the python Scipy package to perform hierarchical clustering  57. A RMSD cut-off of 1Å was used to group similar loop candidates. To consider memory constraints, we applied an iterative clustering over subsets of 25,000 candidates, until at most 25,000 clusters were obtained.
Finally, one representative loop candidate with the highest sequence similarity to the query loop was selected for each cluster. The computational time of our clustering protocol is in the range of 1-5 minutes, however it depends directly on the number of candidates detected by BCLoopSearch. In extreme cases, the needed time may increase up to 10-15 minutes. Local conformation. Previously, Shen et al. have shown that local conformation profiles predicted from sequence and profile-profile comparison can be employed to accurately distinguish similar structural fragments  58. Consequently, we pre-computed a collection of profiles for all the protein chains in the structure dataset, and for all proteins of the test sets. For each loop candidate, it is thus possible to extract the sub-profiles P and Q, corresponding to the query and candidate loop, and to measure the Jensen Shannon divergence (JS(P, Q)) between these profiles: = + JS P Q D P M D Q M ( , ) 1 2 ( , ) 1 2 ( , ) (4) KL KL where M corresponds to 1/2(P + Q) and D KL is the Kullback-Leibler divergence: ∑ = ≤ ≤ D P Q Pi ln P i Q i ( , ) () ( ( )/ ( )) (5) KL i 1 27 P(i) is the probability of SA letter i.
LoopIng and Sphinx, the two methods relying on a databank search, with average improvements of 1. 83 and 1. 5Å, respectively. It performs slightly better than NGK and Galaxy with improvements of 0. 21 and 0. 47Å, respectively. One observes some outliers among the predictions of NGK, GalaxyLoop-PS2, Sphinx and DaReUS-Loop. Indeed, DaReUS-Loop can model almost all the long loops in the test sets and its failure rate is 3% (1/37) compared to 6% (2/37) for Sphinx, 7% (1/15) for GalaxyLoop-PS2 and 9% (3/34) for NGK. Excluding those cases, the performance of DaReUS-Loop remains better than Sphinx by 0. 81Å n while, NGK and GalaxyLoop-PS2 perform better by 0. 11 and 0. 63Å. Note that, this is an average performance and in some cases, DaReUS-Loop is able to provide solutions when NGK and GalaxyLoop-PS2 fail.
Availability of materials and data The set of all gapped models for CASP11 and CASP12 generated and analysed during the current study are available with the sequence of the targets at http://bioserv. rpbs. univ-paris-diderot. fr/public/DaReUS-Loop. tgz. It contains, the top 10 predictions of every method (DaReUS-Loop, Rosetta NGK, GalaxyLoop-PS2, LoopIng and Sphinx) and the corresponding RMSD values. It also includes a script that can be used to measure the RMSD values, as well as a detailed description (README. txt) on the data and how to use the script.
outperforms LoopIng and Sphinx, the two methods relying on a databank search, with average improvements of 1. 83 and 1. 5Å, respectively. It performs slightly better than NGK and Galaxy with improvements of 0. 21 and 0. 47Å, respectively. One observes some outliers among the predictions of NGK, GalaxyLoop-PS2, Sphinx and DaReUS-Loop. Indeed, DaReUS-Loop can model almost all the long loops in the test sets and its failure rate is 3% (1/37) compared to 6% (2/37) for Sphinx, 7% (1/15) for GalaxyLoop-PS2 and 9% (3/34) for NGK. Excluding those cases, the performance of DaReUS-Loop remains better than Sphinx by 0. 81Å n while, NGK and GalaxyLoop-PS2 perform better by 0. 11 and 0. 63Å. Note that, this is an average performance and in some cases, DaReUS-Loop is able to provide solutions when NGK and GalaxyLoop-PS2 fail.
Diatoms are one of the most successful and ecologically important groups of eukaryotic phytoplankton in the modern ocean. Deciphering their genomes is a key step towards better understanding of their biological innovations, evolutionary origins, and ecological underpinnings. Here, we have used 90 RNA-Seq datasets from different growth conditions combined with published expressed sequence tags and protein sequences from multiple taxa to explore the genome of the model diatom Phaeodactylum tricornutum, and introduce 1,489 novel genes. The new annotation additionally permitted the discovery of extensive alternative splicing in diatoms, including intron retention and exon skipping, which increase the diversity of transcripts generated in changing environments. In addition, we have used upto-date reference sequence libraries to dissect the taxonomic origins of diatom genes.
Results and Discussion Structural re-annotation of the P. tricornutum genome reveals numerous new gene models. To generate a new annotation of the P. tricornutum nuclear genome, our approach combined high-throughput RNA sequencing data along with ESTs and protein sequences. Several mapping pipelines (see Methods) were used, allowing the prediction of 12,233 gene models with an average gene length of 1,624 bp and ~1. 7 exons per gene. The predicted Phatr3 gene models were then compared to the Phatr2 gene models (http://genome. jgi. doe. gov/Phatr2/Phatr2. home. html) and their structural differences can be grouped into the following categories (Table  1 , Supplementary Table  S1 ): (1) New gene models, genes that are newly discovered and are not present in the Phatr2 gene set. (2) Unchanged gene models, gene whose structural annotation remains the same as in Phatr2.
(3) Modified gene models, genes whose structural annotation has a different 5′ end, 3′ end or both 5′ and 3′ ends with respect to Phatr2. Thirty of these genes with a different N terminus in Phatr3 compared to Phatr2 were validated by their presence within a previously constructed multi-gene reference dataset of aligned plastid-targeted proteins that are well conserved across ochrophyte lineages (Supplementary File S1, panel A)  10. The N-terminus identified by the Phatr3 gene model in each alignment broadly matches the N-termini identified for orthologous sequences from other ochrophytes (Supplementary File S1, panel B). Furthermore, RT-PCR analysis of six randomly chosen genes within this dataset amplified genes with product length predicted by Phatr3 (Supplementary File S1, panel C). (4) Merged gene models, genes that are formed by merging two or more Phatr2 genes into one Phatr3 gene model.
This is substantially greater than the ~60% of P. tricornutum genes previously identified to have orthologues in other groups  7  , underlining the importance of dataset size and taxonomic sampling when considering gene conservation  11. Up to 251 different conservation patterns were identified across the entire genome, thirteen of which each accounted for 100 genes or more (Fig. 1 , Supplementary Table  S2 ). Many of the genes were found to have broad distributions across the tree of life. We still found, with the expanded dataset, that many genes within the P. tricornutum genome have limited evolutionary conservation, with 5750 genes (47. 2%) having originated within the recent vertical history of the stramenopile lineage. A total of 3,170 genes (26. 0%) were found to be specific to P. tricornutum, 1,929 (15.
In our second analysis, we aimed to reassess the evolutionary origins of the P. tricornutum genome (see methods for details). In particular, we wished to validate the presence of genes derived from green algae and from prokaryotes, which have previously been controversial  7, 9, 11. Prokaryotic genes. Across the entire dataset, 584 genes yielded top BLAST hits against prokaryotes (Supplementary Table  S3 ), which is similar to the number of prokaryotic genes (587) identified in the initial publication of the P. tricornutum genome  7. These genes resolved with multiple sub-categories, consistent with independent transfer events from different prokaryotic lineages (Supplementary Table  S4 , Supplementary File S3, panels A-C). Similarly to the initial genome publication, the prokaryotic sub-category that produced the most top hits (235) was the proteobacteria (Supplementary Table  S3 ; Fig.
S2A ). Nine other sub-categories (cyanobacteria, firmicutes, chlorobi, archaea, actinobacteria, chlamydiae, chloroflexi, the Deinococcus-Thermus clade, and planctomycetes) contributed more than ten hits each (Fig. S2A ; Supplementary Table  S3 ). We considered whether the prokaryotic genes present in the P. tricornutum genome are recent acquisitions (e. g. , species-specific), or are more ancient acquisitions, e. g. by the common ancestors of diatoms, ochrophytes, or earlier (Supplementary File S4; Supplementary Table  S3 ). Instead of originating at a single point, we find that there has been a continuous flux of genes from prokaryotes into different ancestors of Phaeodactylum (Supplementary File S4). For example, we detected 22 genes of specifically prokaryotic origin using the full reference dataset, hence were specifically acquired by P.
tricornutum in its recent evolutionary history. Using the dataset excluding pennate diatoms, we not only detected these 22 genes, but found a further 47 genes, annotated with the full reference dataset as being either of vertical or uncertain origin, which displayed clear prokaryotic affinities (Table  S3 ; Supplementary File S4). These might correspond to genes transferred from prokaryotes into an ancestor of the pennate diatom lineage, prior to the radiation of Phaeodactylum from other pennate species in the reference dataset. We similarly detected prokaryotic gene transfers that specifically occurred into common ancestors of the diatom lineage including centric species (133), the ochrophytes (236), the stramenopiles (138) and the SAR clade as a whole (19) (Supplementary File S4). In addition, different transfer events appear to have occurred with prokaryotes at different points in diatom evolutionary history.
For example, 10 of the 15 gene transfers involving members of the Deinococcus-Thermus clade are detectable either using the full reference dataset or the dataset excluding pennate diatoms, in contrast to the prokaryotic genes as a whole, which predominantly show more ancient origins. This lineage has not previously been reported to have specifically exchanged genes with an ancestor of P. tricornutum  7. The 10 genes of purported recent origin from the Deinococcus-Thermus clade encode metabolic processes including (1) cobalamin-independent synthase which is essential for growth and survival of diatoms in the absence of vitamin B12 (2) proteolysis exemplified by 8 proteins of the subtilase gene family which is important in symbiosis and host pathogen interactions. Interestingly, bacteria of the Deinococcus genus were shown to have an expansion of extracellular proteases including subtilases compared to other thermophilic bacteria species  22.
These genes form a cluster on chromosome 17 and an unassembled contig most likely for co-regulation purposes. Red algal genes. Across the entire dataset, 459 genes produced BLAST top hits against members of the red algae, consistent with the red algal ancestry of the diatom plastid  4, 23  (Fig. 2A ; Supplementary File S3, panel D-E). This is broadly equivalent to the number of red genes identified in previous studies of diatom plastids  24. The majority of the red genes (353/459) are exposed following the removal of all ochrophyte sequences, with only smaller numbers found following the removal of diatoms (53) or the removal of plastid lacking SAR group members (i. e. oomycetes, labyrinthulomycetes, slopalinids) (53). This is consistent with a red gene transfer happening after the ochrophytes diverged from oomycetes, supporting a late acquisition of the ochrophyte plastid (Supplementary File S3, panel D-E)  10.
S3 ). Secondly, green gene transfers appear to have occurred at a different time point to the red algal gene transfers. Although the largest number of putative green genes (805) were identified with the dataset from which all ochrophyte groups were removed (Fig. 2B ), nearly as many (691) were identified following the removal of plastid lacking stramenopiles from the dataset (Fig. 2A ). Finally, the vast majority of the green gene transfers (1491) involved members of the chlorophyte algae, indicative of one or more gene transfer events from members of this clade, as opposed to vertical inheritance and differential loss of green genes (Supplementary Table  S3 ; Supplementary File S3, panels F-H)  10. In summary, the presence of green, red and prokaryotic genes in the P. tricornutum genome, which appear to have arisen at different points in its evolutionary history, confirms that it is an evolutionary mosaic, reflects examples of inferred gene transfer events in other major eukaryotic algal lineages  10, 25, 26  , and underlines the significance of progressive horizontal gene transfer in the evolution and diversification of modern algae  27.
1 , Supplementary Fig S5, Supplementary Table  S5 ). Interestingly, a high number of Pt-specific genes encode the DNA integration GO category, which may indicate a permissive way for integration of genetic material from diverse sources, thus creating novel genetic diversity. Regulation of transcription is one of the important functional categories of diatom-specific genes, perhaps reflecting differences in promoter architecture compared to other eukaryote. Next, we used ASAFind and HECTAR to predict the sub-cellular targeting of the Phatr3 proteome (Supplementary Table  S6 ). Across Phatr3, 3196 proteins (26. 3% total; Supplementary Fig. S6A ) were predicted to have a targeting sequence of some description by ASAFind, and 4067 proteins (33. 3%; Supplementary Fig. S5B ) were predicted to have a targeting sequence by HECTAR.
The right-hand graph (II) shows similar values calculated for reference libraries from which all algae with a suspected history of secondary endosymbiosis other than ochrophytes were removed. Note that in this case a separate value for a reference library excluding SAR and CCTH clade taxa is not provided, as all it is not known when secondary endosymbioses arose within CCTH clade lineages  21  , hence all CCTH clade taxa were excluded from the analysis. (B) Comparison of the proportion of genes that yield top hits against five different categories (all prokaryotes, the Deinococcus-Thermus clade only, all red algae, and green groups, and all amorphea) following the removal of different groups from the complex algae-free dataset. Total number of genes within each category is indicated beneath it in round brackets.
Data labels for each value are provided in the corresponding segment; where the segment is too small to accommodate the corresponding value, the value is placed outside the chart, and shaded to match the color of the segment. The largest value recorded for each gene category corresponds to the most probable evolutionary time point at which genes were acquired; for example, the largest number of genes of red algal affinity were recovered following the removal of all algae with plastids of secondary or higher endosymbiotic derivation from the reference dataset, indicating a large-scale donation of red algal genes into algae with plastids of secondary or higher endosymbiotic derivation. We also considered the expression dynamics of each gene using quartile approach. Most of the novel genes (~70%) are expressed at below the median level inferred for all other genes in the genome (Fig.
Methods Gene discovery and annotation. Structural re-annotation. The P. tricornutum version 2 genome (JGI), published in the year 2008  7  , was comprehensively structurally re-annotated using multiple RNA sequencing libraries, non-redundant ESTs and updated repertoires of stramenopile proteomes. 42 RNA-Seq libraries generated using an Illumina sequencing platform (Bio-sample accession numbers SAMN04488978-SAMN04489007 and SAMN06350641-SAMN06350652) were mapped to the genome using Genomic Short-read Nucleotide Alignment Program, GSNAP  53  , integrated within mapping pipeline of Ensembl Genomes  54. The remaining 49 SoLiD sequence libraries (SRA: SRP069841) were aligned to the genome in color space  55. The alignment file for each mapped library can be accessed from ftp://ftp. ensemblgenomes.
org/pub/misc_data/bam/protists/phatr3/. The mapping percentage was estimated using Samtools and varied between 70-96% (Table  S9 ). Transcript different (two sample T-test with unequal variance, P-value < 0. 002), compared to the expression of unmarked genes. Out of 1,489 genes, 1,388 are considered in the analysis, as expression for the other 101 genes was not calculable without errors. (C) Different chromatin states maintained genome wide, based on the association of protein coding genes with repressive, active or both repressive and active chromatin modifiers (Repressive modifications: CG, CHG, CHH, H3K27me3, H3K9me2, H3K9me3; Active modifications: H3K4me2, H3K9_14Ac). Numbers and percentages (in square brackets) in the Venn diagram reflects the absolute number of genes and the relative percentage of the total Phatr3 genes.
assembly was further executed using Cufflinks 56 with default parameters. The resulting unfiltered transcripts along with EST libraries and protein sequences from stramenopile UniProt Reference Clusters (UniRef90)  57  were then used for the genome re-annotation. The structural units derived were finally used to train SNAP  58  and Augustus 59 gene prediction programs using default parameters of the MAKER2 annotation pipeline  60. Functional re-annotation. All predicted gene models were annotated for protein function using InterProScan  61  as part of the Ensembl protein features pipeline  62. The results are available at http://protists. ensembl. org/ Phaeodactylum_tricornutum/Info/Index/. We also used CLADE and DAMA to enhance the protein domain predictions (see supplementary methods for details).
The presence of organelle signaling signatures within the entire Phatr3 gene repertoire was further investigated using ASAFind and HECTAR, under the default conditions as specified in the original publications for each program  13, 63. HECTAR was run remotely, using the Galaxy integrated server provided by the Roscoff Culture Collection (http://webtools. sb-roscoff. fr/). Distribution of epigenetic marks. Data corresponding to histone H3 post-translation modification marks, H3K27me3; H3K9me2/3; H3K14_Ac; H3K4me2 and DNA methylation (CG, CHH, CHG), were taken from  15  and  16, 64  , respectively. RNA-Seq data for Pt18. 6 (normal growth condition) was also downloaded from the same resource. Distribution of all marks along with the expression were then checked over the new genes and new transposable element (TE) models by adapting the methodology applied in  15  and  16.
This analysis was performed for seven different reference libraries, one containing all possible reference sequences (Phatr3), and six deducting relatives of P. tricornutum (all pennate diatoms, all diatoms, all ochrophytes, all stramenopiles, all SAR clade members, and all SAR + CCTH clade members). Tabulated outputs for each analysis are provided in Table  S3. The results obtained by this pipeline were compared to a subset of 324 Phat3 genes for which single-gene tree topologies generated using the expanded reference library have previously been published  10  , and found to give broadly equivalent results (see Results; Supplementary Fig. S9 ; Supplementary Table  S10 ). Alternative splicing. To explore the set of genes undergoing alternative splicing, exon skipping or intron retention, 17 RNA-Seq samples prepared under different conditions of nutrient availability (Biosample accessions: SAMN06350643, SAMN06350647, SAMN04488978-SAMN04488992) were compared.
05. To gain insights into the role of alternate splicing in regulating the molecular physiology of the cells, the expression patterns of AS candidates predicted to undergo IR and/or ES under nitrogen replete conditions (Biosample accessions: SAMN04488981, SAMN04488985, SAMN04488979, SAMN04488989) were compared at different time-points (T15min, T45min, T90min and T18hrs (referred as Tend) to the wild-type (Biosample accession: SAMN06350643). RNA-Seq expression values were calculated using Eoulsan  65  , and additionally normalized to eliminate biases caused by the restructuring of alternatively spliced transcripts. Annotation of repetitive elements. The REPET v2. 2 package 69 was used to detect the repetitive fraction of the P. tricornutum genome. The TEdenovo pipeline (https://urgi. versailles.