dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of new genes In order to identify regions of the Arabidopsis genome that could encode new genes expressed under our experimental nitrate-treatment conditions, we searched for clusters of sequences that match the genome uniquely in regions without annotation (see Methods). These clusters could overlap annotated genes but in anti-sense orientation. Average exon length in the Arabidopsis genome (TAIRv10) is 298 nt, therefore we only considered clusters of 300 nt or more. We found 17 clusters with Reads were mapped to the Arabidopsis genome and regulated genes were determined using DESeq. We identified protein coding genes that were not represented on the Affymetrix ATH1 microarray. We show the log 2 (KNO 3 /KCl) value for 2 biological replicates. these criteria (Additional file 10), 4 of which were located in the complementary strand of annotated genes and might represent natural antisense transcripts (NATs).
Two clusters have been previously reported as cis-NATs in an analysis of Arabidopsis full-length cDNAs, At5g49440 and At3g19380  [56]. We found that one of the 17 clusters was induced by nitrate treatments. We labeled this cluster TCP23as as it is antisense to the TCP transcription factor TCP23 (At1g35560) (Figure  3A ). We also found sRNAs matching the same region both in sense and antisense orientation (Figure  3B ), however we did not find a correlation between their expression and TCP23as regulation by nitrate, suggesting these sequences most likely represent degradation products of TCP23 and TCP23as. TCP23 was found not to be regulated by nitrate in our RNA-sequencing data (Additional file 5). In order to validate expression of this putative antisense transcript, we reverse-transcribed root RNA using strand-specific primers for TCP23 and for its antisense transcript and performed PCR with gene specific primers.
The miRCat program was able to predict 51 new miRNA sequences when compared with miRBase v17 (Table  2 ). From these 51 new miRNA sequences our studies uncovered, 12 were recently cross-validated by other groups, suggesting the veracity of our results  [47, [60] [61] [62] [63]. 21 putative new miRNA sequences were found in intergenic regions, likely representing new transcriptional units (Table  2 ). 10 miRNA sequences were found inside introns and 2 were found in the 5'UTR of protein coding genes suggesting they are transcribed along with the gene they overlap and 1 miRNA was found in a pseudogene (Table  2 ). 17 new miRNA sequences were located inside the region coding for the stem loop of known miRNAs (Table  2 ). Sequences that map onto miRNA precursors and that do not correspond to the mature miRNA or miRNA* sequences have been previously reported in Arabidopsis and are potentially functional miRNAs that are generated by the miRNA pathway  [64, 65].
All these new miRNAs have low expression levels, most of them being sequenced less than 50 times in our libraries, which probably explains why they have not been reported previously. A novel nitrate-responsive miRNA/target regulatory module (AtPPC3/miR5640) In order to further characterize the role of the novel miRNAs in the root nitrate response, we predicted target genes for new miRNAs sequenced in our libraries using the target prediction tool Target finder from the UEA sRNA toolkit, (Additional file 11). The program is based on a set of rules determined specifically for plant miRNA/TARGET interactions  [66, 67]. We looked in the target list for genes that could be related to N metabolism or to root growth regulation and that were either induced or repressed by nitrate based on our Illumina results.
Discussion High throughput sequencing approaches have become powerful tools to identify the transcriptome of Arabidopsis and other systems. Besides the ability to  Sequences from the Illumina libraries were queried for sequences representing putative new miRNAs with the miRCat program from the UEA sRNA toolkit. We show the genomic location and sequences of these miRNAs. Presence of reads corresponding to miRNA* are indicated (Yes or No). miRNAs that are not included in miRBase 17 but that have been cross-validated by other groups are indicated. The name of mature sequences derived from the same arm of known hairpin precursor were named with the suffix. 1 or. 2. If the sequences derive from the opposite arm from the previously annotated sequence, were named with the suffix -5p, -3p. profile novel genes expressed at low levels which could not be identified by traditional cloning and sequencing approaches, the high depth of sequencing obtained by these techniques allows for the absolute quantification of genes, and the comparison of gene expression under different experimental conditions  [38, 73, 74].
Determination of differentially expressed genes To evaluate differential gene expression between KNO 3 and KCl treated samples, we used sequence counts corresponding to sRNAs or annotated elements as input for the DESeq package version 1. 1. 6 [55]  available from Bioconductor (www. bioconductor. org). This tool uses a negative binomial distribution model to test for differential gene expression  [55]. We found correlation values of 0. 91 and 0. 96 for controls and treatments respectively for sRNA-seq and of 0. 99 for controls and treatments for RNA-seq data. Replicates were used independently for statistical analysis of gene expression. We adjusted for multiple testing using FDR correction  [93]  and filtered genes whose expression changed with corrected p-values ≥ 0. 05.
Acknowledgements This work was supported by  International Early Career Scientist program  from  Howard Hughes Medical Institute  to  R. A. G. ,  Fondo de Desarrollo de Areas Prioritarias (FONDAP) Center for Genome Regulation  [15090007 to R. A. G. ],  Millennium Nucleus Center for Plant Functional Genomics  [ P10-062-F  to R. A. G. ],  Fondo Nacional de Desarrollo Científico y Tecnológico  [ 1100698  to R. A. G. ],  Comisión Nacional de Investigación Científica y Tecnológica-ANR program  [ ANR-007  to R. A. G. ], the  National Institutes of Health  [ GM032877  to G. M. C. ] and  National Science Foundation  [ MCB-0929338  to G. M. C. ],  Agence Nationale de la Recherche  [ NitroNet :  ANR 11 PDOC 020 01  to  G. K. ]  and  Centre National de la Recherche Scientifique  [PEPS Bio math Info 2012-2013: SuperRegNet to G.
, 2012; Bik et al. , 2012). As a result of this development, the assembly and analysis of nucleotide data has become routine methodology in most biological disciplines, including marine biodiversity (e. g. Glöckner, 2012; Teeling and Glöckner, 2012; DeLong, 2005; Karsenti et al. , 2011; Roger et al. , 2012). Following this trend, the methods of genomic analysis are being continuously modified and refined in order to serve new purposes and applications in conservation biology and monitoring programs (e. g. the projects FishPoptrace (https://fishpoptrace. jrc. ec. europa. eu/) and DEVOTES (http://www. devotes-project. eu)). This process is closely coordinated with the development of bioinformatic and e-science tools that integrate genomic information into conventional data streams (e. g. BiSciCol (http://biscicol.
Genomic methods relevant to assess marine health New tools based on genomic methods could be used to address the bottlenecks in assessing marine health, and can therefore be applied to improve current practices; see examples from casestudies world-wide in Table  3. DNA barcoding consists in assigning a specimen or sample (e. g. a piece of tissue or contents of a gut) to species by sequencing a standardized short DNA fragment (the 'DNA barcode') and comparing it against a reference database  (Hebert et al. , 2003). This technique has the advantage of being independent of the user's taxonomic expertise and makes it possible to assign species names to specimens or samples that are challenging (or impossible) to identify any other way. Importantly, this applies not only individual organisms (or tissues from those organisms, like a fin clip from a fish or leg from a crab), but also to environmental or 'bulk' samples, from which the target gene/barcode can be sequenced.
The approach consisting in sequencing a DNA fragment from a whole environmental sample is sometimes called metagenetics or metabarcoding (for example, see:  Taberlet et al. , 2012). The essential prerequisite for DNA barcoding (and metabarcoding) is the creation of a reference database consisting of a library of species names linked to the DNA barcodes. Building the reference library requires an expert taxonomist to name a representative specimen for each species (usually deposited in a natural history museum or herbarium) and to sequence the specimen for the appropriate barcode gene (or genes) designated by the international Consortium for the Barcode of Life (CBOL). The reference library (usually created from adult life stages) serves as a tool for robust and reproducible species identification for assigning biological material (any sample with DNA) to species so long as the DNA barcode can be sequenced from the sample and is present in the reference library.
PD analyzes of the information from large-scale DNA barcoding programs can provide a range of biodiversity assessment and monitoring applications  (Faith and Baker, 2006). Smith and Fisher (2009)  demonstrated that PD applied to phylogenetic patterns derived from DNA barcoding provided good estimates of species richness and species-level ''complementarity'' values -measures of biodiversity gains or losses (see also  Zhou et al. , 2009; Krishnamurthy and Francis, 2012). Finally, DNA sequences are 'born digital' and are easily (and freely) retained in public databases where they can be retrieved and reinterpreted as necessary (e. g. if a group is subject to taxonomic revision). Traditional approaches to species identification, by contrast, often rely on specialist knowledge and it can be hard to verify the decisions made even when detailed records (photographs and specimens) are kept.
DNA barcoding is also able to leverage many web-based tools (including those generated originally for biomedical purposes) that can greatly increase its potential usage. While informatics challenges remain in the tracking of DNA sequences and retaining linkage to related biodiversity data and metadata (e. g. photos, specimens, species names) across projects and institutions, and public repositories, pipelines are becoming increasingly robust and advances in semantic web technology are helping to improve tracking and discoverability of specimens and digital biodiversity data (e. g. the BiSciCol project).
The Moorea Biocode Project The Moorea Biocode Project  (Check, 2006 ) is a textbook example of a comprehensive DNA barcoding project. It compiles voucher specimens, digital photographs, high-quality DNA extractions, and genetic sequences (minimally DNA barcodes) for almost all species (adult stage >1 mm) in marine, freshwater, and terrestrial habitats on the island of Moorea (136 km 2 ) French Polynesia. So far, the project has amassed >42,000 specimens and >18,000 sequences from >7000 species: this is already an unparalleled database for a tropical ecosystem. Moorea Biocode is also developing an IT platform to support this research: a standards-based informatics infrastructure connecting scientific data, and tracking Access and.
Technical challenges of metagenomics While shotgun metagenomics has considerable advantages over amplicon metagenetics (e. g. it does not involve PCR amplification or primer biases), it also has some notable limitations. Firstly, some studies have reported that the abundance of taxa and their functional genes in a metagenomic library do vary depending on the DNA extraction protocol used to acquire the nucleic acid from the environmental sample. Secondly, metagenomic datasets are often only sequenced to a low depth compared with the quantity of DNA in a sample, which results in only the extremely dominant populations being observed. Thirdly, it is difficult to annotate the function or taxonomy of a short sequence fragment, resulting in a large portion of data lacking an appropriate annotation.
DNA-sequencing of the microbial communities as part of Micro B3's OSD will provide insights into the fundamental rules describing microbial diversity and function and will contribute to the blue economy through the identification of novel, ocean-derived biotechnologies. Micro B3's OSD is working closely with the Genomic Observatories Network, the Earth Microbiome Project and the Smithsonian's Global Genome Initiative to take this project forward. The long-term aim is to build an OSD Consortium to continue building a global time-series data set as part of the world's Ocean Observatories.
Opportunities offered by SNP's Genotyping using SNP markers is clearly more rapid than previous DNA based technologies such as microsatellites. High numbers of SNPs can be genotyped simultaneously using array based methods. Current custom SNP arrays can simultaneously genotype 1 million individual SNPs. Firstly, using SNP markers that are putatively under selection allows populations to be delineated on much smaller scales than were previously possible. Secondly, a big advantage of SNP markers over size-based DNA methods (e. g. microsatellites) is the digital nature of the outputs (presence or absence of a particular allele). This means extensive cross-calibration among labs is not necessary and results from published research can be easily compared. Moreover, a database can be established that contains baseline allele frequencies of different populations.
By using gene-associated single nucleotide polymorphisms, it was shown that individual marine fish can be assigned back to population of origin with unprecedented high levels of precision. By applying high differentiation single nucleotide polymorphism assays, in four commercial marine fish, on a pan-European scale, 93-100% of individuals could be correctly assigned to origin in policy-driven case studies. The authors show how case-targeted single nucleotide polymorphism assays can be created and forensically validated, using a centrally maintained and publicly available database. The results demonstrate how application of gene-associated markers will likely revolutionize origin assignment and become highly valuable tools for fighting illegal fishing and mislabelling worldwide  (Nielsen et al.
Infrastructures for genomic monitoring Marine biodiversity is not only changing at large scales of time and space, but also at smaller scales relevant for local or regional management (e. g. Marine Spatial Planning;  Ehler and Douvere, 2009). To understand these changes effectively, a major effort is required to build biodiversity monitoring and research infrastructures in the future  (Basset and Los, 2012). Such infrastructures will consist of three principal components: the data generation layer (including sensors, monitoring programs, research, etc. ), the data storage layer (including databases, data curation, archives, and repositories), and the analytical layer (including interoperability systems, analytical resources). The genomic components will be integrated simultaneously on all three levels, and this process is coordinated by the Genomic Observatories infrastructure initiative.
Data storage and curation The link between genomic data and national, regional or commercial data centers for marine monitoring data is relatively straightforward, as genomics databases, due to their large data volumes, are very well structured. In the future, all genetic data generated by monitoring activities will be deposited in one of the existing archives. The databases for genetic information are: the European Nucleotide Archive (ENA), an open access, annotated collection of publicly available nucleotide sequences and their protein translations; the U. S. National Center for Biotechnology Information (NCBI); and the DNA Data Bank of Japan (DDBJ). The link among them is maintained via the International Nucleotide Sequence Database Collaboration (INSDC), the central authority that manages genetic libraries globally.
Through INSDC also a large number of specific archives can be accessed, such as dbSNP for single nucleotide polymorphisms (SNP's) and short tandem repeats (STR's), dbEST for expressed sequence tags (EST), or SRA for raw sequence reads. All INSDC databases are furthermore coupled to NCBI's Taxonomy database. An elaborate service set of BLAST and alignment functions is coupled to these libraries allowing for initial data inspection, exploration, and some basic analytical functions.
Analytical approaches Workflows-powerful analytical pipelines which access distributed computing resources-are being constructed through the Bio-VeL project to address the needs of the biodiversity research community. Micro B3 and BioVel have agreed to join forces to develop metagenome workflows of OSD. Additional workflows are being designed to process metagenetic data from environmental samples (e. g. DNA metabarcoding), to enable identification of species from a metagenetic sample by matching them to databases and reference libraries, and to provide measures of phylogenetic or alpha and beta diversity between samples. These analysis pipelines are complementary to tools that translate genomic data into indicator metrics that can be used for decision making, which are being developed through the DEVOTES project.
(2) Standardized manuals and protocols for sampling and sample processing. (3) Analytical pipelines and technologies integrating genomic data with other data sources (remote sensing, mapping tools, taxonomy databases). (4) Molecular indicators and /or methods to translate the results from molecular analyzes into indicator metrics for use in policy and decision making (e. g. status assessment). (5) Dissemination to and facilitation of adoption by monitoring bodies. tion, call FP7-KBBE-2010-4) Grant No. 266055. Special thanks go to Bernard Kloareg and Damien Guiffant for coordinating the project. Angel Borja and Naiara Rodríguez-Ezpeleta are supported by the project DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status) funded by the EU 7th FP 'The Ocean for Tomorrow' Theme (Grant agreement No.
Benefit Sharing (ABS) agreements, across disparate sites, research teams, labs, collections, and data repositories. As the Moorea reference database is populated, researchers are carrying out innovative projects (e. g. on marine plankton and food web dynamics) to demonstrate the applications of DNA barcoding in a system with a comprehensive reference library. Increasingly, these studies employ next generation sequencing technologies and metagenomics (e. g. in gut content analyzes). They also connect to microbial surveys and the physical and ecological time-series data collected on Moorea's coral reefs (e. g. by CNRS-EPHE CRIOBE since 1971 and the NSF MCR-LTER since 2004). Model ecosystems, like Moorea, are thus becoming 'Genomic Observatories', contributing to the emerging field of biodiversity genomics and mainstreaming genetic data into Earth Observing Systems (see GEO BON http:// www.
Thus, our identification and filtering criteria must rely on the common features of miRNAs shared by the organisms investigated to date. These features are as follows: (i) the primary transcript is processed into a precursor called the pre-miR. Alternatively, pre-miRs can derive from introns of protein-coding genes  (17, 18). In any case, the pre-miR is folded as a stable hairpin, which exhibits specific structural features  (19) ; (ii) this structured RNA is recognized and processed by an enzyme called DICER  (20) , which excises a short (possibly imperfect) duplex made of the miRNA and its complementary strand: the miRNA*. The cutting points may vary by one or two nucleotides, thus allowing the precursor to generate alternative duplexes  (21) ; (iii) the miRNA strand is specifically incorporated into a ribonucleoproteic complex called RNA-induced silencing complex (RISC, including the protein Argonaute), and serves as a guide to bind the complex to the target mRNA, using an imperfect sequence complementarity.
Data collection and preparation The genome sequences (super-contigs with length >2 kb), primary annotation and protein sequences (version June 2010) were retrieved from the E. siliculosus annotation Web site (https://bioinformatics. psb. ugent. be/gdb/ecto carpus/). These data were processed to obtain a suitable partition of the sequences (shown as 're-assignation' in Figure  1 ). We identified all the ribosomal RNAs (rRNAs), including those that were not annotated, using BLAST  (27)  to search for sequences similar to the EMBL entries EF990201 [partial rRNA gene of E. siliculosus  (28) ] and D16558 [complete rRNA gene of the closely related species Scytosiphon lomentaria  (29) ]. We also identified the transfer RNAs (tRNAs) using the tRNAsan-SE 1. 23 software  (30)  downloaded from the author's Web site (http://lowelab.
Constitution of a large set of potential miRNAs The program findMiRNA  (31)  was downloaded from its author's Web site (http://sundarlab. ucdavis. edu/mirna/). We introduced a small modification to the code to predict the best secondary structure at a temperature of 13 C [the usual temperature for E. siliculosus cultures, see  (32)  instead of the default 37 C. This adapted version of findMiRNA was used to process (i) the 'intergenic' sequences (on both strands) and (ii) the introns (on the transcript strand only), both using the mRNAs as a reference (shown as 'findMiRNA' on Figure  1 ).
Reference RNA folding parameters To build a set of reference values for the structural and topological properties of folded RNAs in E. siliculosus, we isolated local substructures from rRNAs and mRNAs. To do so, we predicted their complete optimal folding at 13 C (see above) using the RNAfold 1. 8 software (33) downloaded from the Vienna RNA Package Web site (http:// www. tbi. univie. ac. at/$ivo/RNA/). Substructures made of 40-200 nt were extracted out of these complete structures. As tRNA sizes lie within this range, we used their fulllength sequences. We determined the structural and topological properties of the complete tRNAs and of the rRNA and mRNA substructures using the genRNAStats and RNAspectral programs  (19)  downloaded from their author's Web site (http://web. bii. a-star. edu.
Selection of miRNA candidates We computed the values of the five parameters described above on the predicted secondary structures of the 697 657 potential pre-miRs resulting from the search with findMiRNA. Each candidate was retained in the pre-miR90 set only if its values of Nmfe, NQ, ND and NF were, respectively, lower than Nmfe90, NQ90, ND90 and NF90, and its value of Nbp was higher than Nbp90. These thresholds are displayed as 'filter 90' in Table  1. Similarly, we used the values of Nmfe95, NQ95, ND95, NF95 and Nbp95 to design the 'filter 95' (Table  1 ) and to obtain the pre-miR95 subset of candidates. This step is shown as 'filter' on Figure  1. A Support Vector Machine (SVM) approach was implemented using the R package e1071 (http://cran. rproject. org/web/packages/e1071) to filter the potential pre-miRs, using a discriminant kernel function adjusted for maximal separation between known pre-miRNAs (1000 animal and plant precursors from miRBase) and non-miRNA (1000 tRNAs, rRNA and mRNA hairpins).
Expression level analysis We sorted the sequences (tiles) of the E. siliculosus highresolution transcription map (tiling array, Gene Expression Omnibus GSE19912) into seven sets: Exon, tRNA, Intergenic not candidate, Intergenic miR candidate, Intronic not candidate, Intronic miR candidate, Others (discarded from further analysis). The expression level for each tile was computed as the logarithm of its RNA expression normalized to its DNA expression signal. The statistical significance of the difference between 'miR candidate' and 'not candidate' sets was tested using the Student's t-test implemented in R  (34).