dataesr/scientific-paragraphs-categorization huggingface dataset:
Global data include increased at which they operate. Pollination and pest uncertainty as they are often estimated or control are dependent on small-scale land- modelled (e. g. FAO livestock data). Un- scape elements making it difficult to map certainty can be addressed with sensitivity them accurately globally. Furthermore, cul- analyses (see Chapter 6. 3), but is not of- tural services such as aesthetic value, rec- ten done in practice. Last but not least, it reation and tourism have a subjective and remains difficult to validate global datasets local character which makes them difficult due to differences in temporal and spatial to generalise. As these ES do not have a di- consistencies and classification systems, rect market value either, it is more difficult amongst others. to prepare a monetary value map for them.
Background: While eukaryotic noncoding RNAs have recently received intense scrutiny, it is becoming clear that bacterial transcription is at least as pervasive. Bacterial small RNAs and antisense RNAs (sRNAs) are often assumed to be noncoding, due to their lack of long open reading frames (ORFs). However, there are numerous examples of sRNAs encoding for small proteins, whether or not they also have a regulatory role at the RNA level. Methods: Here, we apply flexible machine learning techniques based on sequence features and comparative genomics to quantify the prevalence of sRNA ORFs under natural selection to maintain protein-coding function in 14 phylogenetically diverse bacteria. Importantly, we quantify uncertainty in our predictions, and follow up on them using mass spectrometry proteomics and comparison to datasets including ribosome profiling.
RNAcode  [24]  handles multiple alignment issues like insertions and deletions intelligently, but because it does not take into account phylogenetic structure it relies on careful selection of orthologous species to yield relevant results, making it difficult to apply on a large scale. Warren et al. [8]  used a clever BLASTbased approach to quickly find new genes, but this is less sensitive than D n /D s , which is aware of phylogeny and mutations at the DNA level. Other methods are either adhoc and difficult to apply to other species  [22]  and/or do not incorporate both sequence features and comparative genomics  [21]. Short proteins can rarely be predicted with nearly 100% confidence because of limited evidence, but most standard gene annotation tools do not provide an estimated false discovery rate (FDR) for marginal predictions, instead choosing ad-hoc cutoffs for amino acid length or coding score.
Most bacterial sRNAs have open reading frames Hypothesizing that many bacterial sRNAs contain unannotated short ORFs that could code for small proteins, we retrieved sRNA annotations from BSRD  [3] , supplemented by recent published datasets not included in the BSRD database  [26, 27] , and existing ORF annotations from Genbank. sRNAs were defined as any RNA expressed outside of annotated ORFs, including those found by small-scale experimental validation, RNA-seq, microarrays, or by sequence and structural conservation to known sRNAs. The sRNA definitions could be heterogeneous depending on the species, as they were identified by diverse methods. We focused first on a Gram-positive model species with particularly good sRNA annotations, Bacillus subtilis 168. We observed a sharp decrease in annotated coding ORFs shorter than around 50 amino acids (Fig.
We constructed positive training sets based on subsets of annotated coding ORFs, and negative sets based on mock ORFs in likely noncoding sequence, controlling for relevant properties of sRNA ORFs (see Methods). We trained several types of classifiers on these datasets and selected a bootstrap aggregated (bagged) decision tree classifier, which had consistently high performance on all species, for further analysis (Additional file 1: Figure  S4 ). In general, higher scores for each feature increased the likelihood of predicting an ORF to be coding (Fig. 3a ). However, the classifier takes into account some complexities, such as the fact that an SD sequence is not always required for coding, and that negative log likelihoods for the D n /D s test (i. e. more non-synonymous than synonymous mutations) can be evidence for coding as well.
Mass spectrometry confirms small proteins from sRNA ORFs Many sRNA short ORFs could be occasionally translated without accumulation of a protein product to appreciable levels if the protein were quickly degraded. Conversely, detection of sRNA ORF protein products in cells by methods with limited sensitivity would be strong evidence against this possibility. Therefore, we took advantage of a mass spectrometry dataset from an experiment specifically designed to find small proteins to search for sRNA ORF products in Helicobacter pylori. For the purposes of this search, we included the widest set of sRNA ORF predictions possible, filtering neither for predicted coding score nor potential overlaps with annotated full-length ORFs. This search resulted in 25 peptide hits from 17 sRNA ORFs at FDR less than 0.
sRNA ORFs are enriched for certain functional annotations Some of the sRNAs considered in our study are already known to encode functional peptides in addition to their non-coding functions. For example, the transfermessenger RNA gene SsrA has tRNA-like properties but also codes for a short peptide, which is rarely annotated. We predicted the coding peptide for SsrA in S. pneumoniae at a q-value of 0. 10, and in two other species at a coding score p ≥ 0. 5. Most other sRNAs with known coding ORFs were already annotated correctly, for example SgrT in E. coli and SR1P/YkzW in B. subtilis, so they were skipped by our methods. To systematically find other annotations for our predicted sRNA coding ORFs, we searched their translated sequences against known proteins using protein BLAST. S3 ). Many of these BLAST hits were annotated in the same genus as the sRNA ORF, suggesting that they are direct orthologs that have escaped annotation so far.
In one case (sbsu22741. 1), a B. subtilis sRNA ORF matched a type I toxin-antitoxin system annotated in the same species but added to genbank after we performed our analysis. Another notable example is a gene with BLAST hits to many proteins annotated as phenolsoluble modulins, that was nevertheless unannotated in S. aureus N315 (Additional file 4: Table  S3 ). Phenol-soluble modulins are a crucial virulence factor for methicillinresistant S. aureus  [36] , so it is surprising that the protein in S. aureus N315 was never annotated as such based on homology alone. Still, the 43 sRNA ORFs with informative BLAST hits leave more than 350 new predicted coding ORFs with no known homolog. To identify potential functions of our sRNA predicted coding ORFs lacking annotated homologs, we examined a large compendium of B.
Web Server We anticipate that this collection of predicted bacterial sRNA coding ORFs with quantified uncertainty will be of broad utility for experimental microbiology. To make these results as accessible as possible, we created a userfriendly web site providing all of our coding ORF predictions for searching or browsing. A parser was written in Python to browse the results of all analyses, formatting and storing them in a couchdb database (http://couchdb. apache. org/). NoSQL technologies were used to dynamically display bioinformatics results. The resulting website is available at the URL http://disco-bac. web. pasteur. fr. For each species (cf. Table  1 ), a summary of the coding sRNA ORF search statistics is available. From this first page, the user may view the detailed characteristics including coding score for each sRNA ORF, or may expand the search to all intergenic ORFs.
RNAcode  [24]  handles multiple alignment issues like insertions and deletions intelligently, but because it does not take into account phylogenetic structure it relies on careful selection of orthologous species to yield relevant results, making it difficult to apply on a large scale. Warren et al. [8]  used a clever BLAST-based approach to quickly find new genes, but this is less sensitive than D n /D s , which is aware of phylogeny and mutations at the DNA level. Other methods are either ad-hoc and difficult to apply to other species  [22]  and/or do not incorporate both sequence features and comparative genomics  [21]. A key advantage of our analysis is the effective aggregation of predictions with weak confidence (high FDR) to make statistically accurate statements about the set of sRNA ORFs as a whole.
In addition to presenting our detailed predictions here (Additional file 5: Table  S4 ), we distributed our predictions in a user-friendly web database (http://disco-bac. web. pasteur. fr) combining summary results, individual ORF characteristics, and tools for sorting and visualizing genomic context. These easily-accessible predictions should hasten the experimental characterization of sRNA ORFs. With an average of over a dozen new protein-coding genes predicted in each bacterial species, we expect that future experiments will elucidate novel functions for sRNA ORFs for years to come.
Genomic data collection Bacterial genomes and ORF annotations were downloaded from Genbank, with accession numbers found in Table  1. Bacterial sRNA annotations were downloaded from BSRD version 1. 2  [3]. BSRD annotations were supplemented by sRNA annotations that were not in BSRD from two recently published studies on B. subtilis 168 (Table  S6. 2 of  [26] ) and S. aureus NCTC8325 (Table  S6  of  [27] ).
Sequence features sRNA ORFs between 10 and 50 amino acids in length were found assuming ATG as the only start codon. The 50 amino acid maximum length was applied to account for full-length ORFs that would be found by conventional algorithms but were not included in a particular genome's Genbank annotations. When an ORF extended past the 3 end of an annotated sRNA, it was extended until the nearest in-frame stop codon, allowing for inaccurate sRNA 3 end annotations. sRNA ORFs having in-frame overlap in the same sense as an annotated coding ORF were filtered out. When all intergenic ORFs were considered, no overlap was allowed with annotated coding ORFs, regardless of the frame. The number of ORFs expected by chance (Fig. 1b ) was generated by shuffling the sRNA sequences one thousand times and counting the resulting ORFs using the same procedure as above.
In many instances, multiple start codons could be matched to the same stop codon, corresponding to overlapping in-frame protein sequences. In all predictions of coding ORFs (Figs. 3, 4 , and 6, these in-frame overlaps were merged, resulting in only the longest ORF, which avoids double-counting coding ORFs. The ORF with the highest coding score was retained in these cases. Shine-Dalgarno sequence strength (the SD score) was calculated as in Ma & Karlin  [31]  using their published anti-SD sequences or when unavailable, the corresponding section of the species' 16S rRNA sequence downloaded from Genbank. For each ORF or mock ORF, ensemble free energies of SD binding were calculated by hybridizing the twenty basepairs upstream of the start codon to the anti-SD sequence using the "hybrid" program from the UNAFold package  [44].
Comparative genomics Orthologous sequences were selected with a blastn search against all complete bacterial genomes in Genbank with an E-value cutoff of 0. 05 and parameters -word_size 7 -gapopen 5 -gapextend 2 -penalty -3 -reward 2. After running blastn for all full-length annotated ORFs in the reference species, species with at least 50% of the maximum number of hits were included as orthologous species; in other words, 50% of the maximum was our adhoc threshold for the number of total genes shared with species to be included in the tree. For this analysis, plasmids were included in the search and counted towards the number of hits for a species. To construct a phylogenetic tree, annotated ORFs in the reference sequence were truncated to a maximum of 5000 nucleotides, re-blasted against the orthologous species, and aligned using Clustal Omega  [46]  with default parameters.
The alignments were then concatenated and analyzed with the dnaml program of the PHYLIP package using default options  [47]. To limit memory usage and running time, we randomly subsampled the sites to yield a maximum of 5 million total nucleotides including all species. For species having more than fifty members of the tree at this stage, we pruned the tree using a greedy algorithm to remove redundant leaves and decrease computational complexity, i. e. we iteratively removed one random species from the pair with the shortest intervening branch length until only fifty leaves remained. At this point, multiple alignments were recreated as above and the phylogenetic tree recalculated using the fifty remaining orthologous species. Any gap positions in the reference organism sequence were spliced out of the multiple alignment, and in-frame stop codons in non-reference species were replaced by gaps.
These steps were intended to maximize the amount of data to the D n /D s tests while remaining robust to indels and substitutions in the genome assembly and alignment errors. Misaligned sequences statistically add equally to D n and D s , thus providing evidence against proteincoding potential, and therefore making the method more conservative with respect to predicting functional coding sequences. Likewise, we did not explicitly test for the presence of start or stop codons in the orthologous sequences. This choice was also deliberately made to provide more information, while making the method more conservative with respect to predicting coding. D n /D s tests were performed using the codeml program of the PAML package using equal amino acid distance and one site type  [48]. Codeml was run once with a fixed omega of one and once with a variable omega, and the reported log likelihood is the difference between the log likelihood of the two models (or zero if the fixed-omega model was more likely than the variable-omega model).
We did not perform the D n /D s test on sRNA ORFs overlapping with annotated coding ORFs on either strand, as, in these cases, any signal for selection could not be solely attributed to the sRNA ORF. To search for homologs to amino acid sequences, command-line BLASTP was used to search the nonredundant protein database (Nr) with the parameterstask blastp-short -evalue 0. 25. The search was performed both with and without composition-based statistics (-comp_based_stats 0), and all matches were filtered to be within a two-fold difference in length to the predicted short protein. Any matches with E-value <0. 25 in either search were considered homologs. Homologs with descriptions containing "hypothetical", "uncharacterized", "unknown", "unnamed", "predicted", "undefined function", or beginning with "conserved domain protein" were considered to be hypothetical proteins.
Ribosome profiling data Processed ribosome profiling data from  [25]  was downloaded in processed WIG format from the NCBI Gene Expression Omnibus (Accession GSE35641). Analysis shown is for samples GSM872393 and GSM872397, although similar results were obtained from the other replicates. We began by visualizing the rates of reads mapping in all sRNA ORFs and mock ORFs. We found that a large fraction of ORFs had no reads mapping at all, and when reads were mapped they preferentially accumulated at the start and stop codons. Therefore, a normalization by ORF length would disadvantage longer ORFs. Instead, we counted ORFs as being ribosome-bound if they had ribosome profiling signal in the correct strand within 3 codons of the start or stop codon, the range reported by Li et al. [25]. ORFs were compared to 20 sets of mock ORFs chosen in the same way as the negative controls for the nucleotide composition analysis.
Data Analysis Proteome Discoverer (version 1. 4. 1. 14, Thermo Scientific. , Bremen, Germany) was utilized for peptide identification. The database search engines MS Amanda  [51]  and Sequest  [52]  were applied for peptide and protein identification using a concatenated database containing all proteins of H. pylori strain 26695 from NCBI (1596 entries) as well as 89 predicted proteins from an sRNA short ORF with coding p > 0. 5. The search was conducted allowing a precursor mass tolerance of 10 ppm and a fragment mass tolerance of 0. 5 Da. Up to two proteolytic missed cleavages were allowed. Carbamidomethylation of cysteine was defined as fixed modification, whereas oxidation of methionine was set as variable modification for both proteases. AspN specificity was defined to cleave at the N-terminal side of aspartic acid and glutamic acid.
20  negative training examples were selected for each positive example. For each amino acid sequence, physicochemical properties were predicted using various methods: In vitro aggregation, α-helix, β-strand, and β-turn conformation, and helical aggregation propensity were calculated using TANGO version 2. 2  [55]  with the parameters ct = N nt = N ph = 7 te = 298 io = 0. 1. Isoelectric point and hydropathicity were calculated using Bio::Tools::pICalculator and Bio::Tools::SeqStats from Bioperl 1. 006923. A random forest classifier was trained on this data using Weka 3-7-9 with parameters -I 10 -K 0 -S 1 -num-slots 1. Data from  [40]  were downloaded from the sequence read archive (SRA, accessions SRR064320, SRR064321, and SRR064325). Short RNA reads were trimmed to remove 3 adapter sequences as in  [40] , and mapped to the S.
Funding RCF was funded by a  Pasteur Foundation   Postdoctoral Fellowship  and  ERC  grant  ERC-2009-AG-232798-HOMEOPITH. MvB acknowledges funding from  DFG   Priority programme 2002. MC acknowledges funding from  DFG CRC Aquadiva. None of the funding bodies had a role in study design, analysis, interpretation of data, or in writing the manuscript.
Acknowledgments I am most grateful to  Adriano Kury  and  Alessandro Giupponi ,  Museu Nacional/UFRJ ,  Rio de Janeiro , for arranging facilities for the study of the material and for donating a paratype to the collections of the MNHN, Paris; to  Adriano Kury  and  Franck Brehier (Balagueres, France)  for the use of several photos from the field; to  Adriano Kury  for useful comments to previous versions of the text; to  Lucienne Wilme, Missouri Botanical Garden  for the preparation of the map and to E ´lise- Anne Leguin ,  MNHN, Paris , for the preparation of several photos and plates.
Following the breeding, the adult fish were replaced in DU contaminated water for a total time of 10 days. Fish behaviour and feeding were observed daily during the whole time of the experiment to detect any sign of distress or suffering. No death, behavioural differences or sign of suffering were observed in the DU-exposed fish group compared to controls. All fish were euthanized at the end of the experiment by immersion in ice cold water. Measurement of body mass and length didn't reveal any difference between the exposed and control group (data not shown). Dissection of brain, ovaries and testis was conducted under the binocular (Leica, France). The main entry of uranium in the body is through the gills  [25]. The DU-bioaccumulation in brain, gonads, two-cells stage embryos and 96 hpf larvae was analysed on mineralized samples using a 7500Cx spectrometer (ICP-MS, Agilent) with a detection limit of 0.
The assessment of biological replicates quality by hierarchical clustering led to the selection of 22 samples (S1 Fig). Data normalization and differential expression analysis were performed with DESeq2  [27]. Genes with a log2 fold change > ±1 and adjusted p-value (FDR) < 0. 01 were considered as differentially expressed. Hierarchical clustering was performed in R with the hclust package using Pearson correlation and the complete linkage method on a set of 6140 genes with rlog normalized expression (from DESeq2) consistently > = 11 in the biological replicates for at least one condition within the following condition: brain, testis, ovaries, two-cells stage embryos, 96 hpf larvae. Five-ways Venn-diagram was constructed with BEG online tools (http://bioinformatics. psb. ugent. be/cgi-bin/liste/Venn/calculate_venn.
Gene Ontology and diseases analysis Gene Ontology (GO) enrichment analysis were performed on zebrafish Ensembl identifiers with the R package TopGO  [28]. Unique human orthologues were collected from biomart when the % of identity between the zebrafish and human genes was > 30%. Zebrafish and human gene-set provided similar enrichment of GO terms with TopGO or Gene Set Enricher available from CTD website (http://ctdbase. org/tools/enricher. go) (S5 Table and data not shown). To infer the diseases enrichment, human orthologues were submitted to the CTD Enriched diseases tool (http://ctdbase. org/tools/enricher. go). GO pathways and MESH terms (disease names) with p-values < 0. 01 and < 0. 001 respectively (exact Fisher's test) were considered as significantly enriched. Upstream regulators were identified using human orthologues as a query to ChEA2 (http://amp.
Microscopic analyses The 96 hpf larvae were fixed with 2. 5% (w/v) glutaraldehyde in 0. 1 M, pH 7. 4 sodium cacodylate buffer for two days at 4˚C. The samples were washed three times 5 min in the same buffer. Samples were treated to 1% (w/v) osmium tetroxyde in cacodylate buffer for 1h, then dehydrated through a graded ethanol series, and finally embedded in monomeric resin (Epon 812). All chemicals used for histological preparation were purchased from Electron Microscopy Sciences (Hatfield, USA). Sections for optical and electron microscope of 500 and 80 nm respectively were obtained with an ultramicrotome UCT (Leica Microsystems GmbH, Wetzlar, Germany). The semi-thin sections were stained with aqueous blue toluidine and examined under a light microscope (Leica, DM750) equipped with a Leica camera ICC50 and the LAS EZ Software.
001) at 96 hpf were also enriched at the two-cells stage (S3A Fig). But despite this overlap the number of genes implicated in biological disorders and co-regulated at two-cells and 96 hpf remained low (2%) (S3B Fig and  S7 Table). These results suggest that although the different transcriptomic patterns observed by mRNAseq at the two-cells stage and the 96 hpf stage are different, they nonetheless converge towards the same disease effects. We then carried out an analysis of the transcriptional regulators potentially controlling the differentially expressed genes. In this analysis the known gene regulation networks were used to identify the transcriptional regulators that better explain the differential expression observed in the mRNAseq datasets. This identification of upstream-regulators (transcription factors and chromatin remodelers) was performed using the human orthologous genes to benefit from the rich functional annotations of the human genome.
Hatching delays could also be caused by developmental delays. A detailed analysis of the morphological abnormalities in the larvae obtained from DUexposed fish was not done here, but the observed decreased expression of the hatching enzymes in this study could explain the effects on hatching rate. The direct comparison of the transcriptomics changes induced by DU between the five mRNAseq datasets showed no overlap, which suggests that the molecular response of DU in the different stages and tissues are mostly different. The comparison between the brain and the testis was the most significant with 132 genes regulated in common. Like the brain, the testis is a lipid-rich organ and both organs are immunologically privileged, which could explain some similarities in the response to DU  [42, 43].
Acknowledgments We want to thank  Christelle Etard  and  Volker Middel  ( KIT, Germany ) for their help in the analysis of the electron microscopy results. We thank  Jacqueline Garnier Laplace  and  Francois Paquet  for the critical reading of the manuscript. We also thank  Pr. Uwe Stra  ¨hle and  Victor Gourain  ( KIT, Germany) at the Institute of Toxicology and Genetics  for the sequencing of the NGS libraries. This work was supported by  IRSN , the European project  COMET , 7 th  PCRD EURATOM (Fission-2012-3. 4. 1-604794 ) and  NEEDS-Environment.