dataesr/scientific-paragraphs-categorization huggingface dataset:
In our experience, these algorithms were not reliable enough to classify individual VUS with confidence  [Hoogeveen-Westerveld et al. , 2011]. However, this approach may still be applicable where high-quality alignments of multiple species are utilized. Conservation analysis has been utilized as a secondary line of evidence in the classification of VUS in the BRCA1/2 genes  [Eggington et al. , 2014]. The TSC1 and TSC2 Leiden Open Variation Databases (TSC LOVD) were made publicly available in 2006 and the TSC2 LOVD now displays 2,232 different variants  (November 13, 2015) , with a further 300 variants available on querying. Of these 2,532 different variants, about half are considered to be certainly or probably pathogenic. All 41 coding TSC2 exons, except exons 25 and 31, include both obviously truncating variants and fully confirmed missense variants that cause tuberous sclerosis.
TSC2 Exon and Variant Nomenclature TSC2 (GenBank NG 005895. 1 GI:125662814) consists of 42 exons, including a 5' noncoding exon [ftp://ftp. ebi. edu. au/pub/ databases/lrgex/LRG_487. xml]. However, the TSC community usually number TSC2 exons from the start of the protein coding sequence making 41 exons in total, with the 5' noncoding exon numbered 1a. This convention is used here. Nucleotide numbering corresponds to the TSC2 cDNA sequence (GenBank NM 000548. 3, GI:116256351) with +1 as A of the ATG translation initiation codon in the reference sequence, and the initiation codon as codon 1.
Conservation Analysis and Pathogenicity of Variants in TSC2 Exons 25 and 31 TSC2 exons 25 and 31 are conserved in vertebrates down to the lamprey (Supp. Fig. S1, A  and B ). The TSC2 LOVD contains 17 different variants (excluding contiguous exon deletions) mapping to TSC2 exon 25, or to the adjacent intronic sequences; 14 of these (five intronic, two silent, and seven missense variants) are classified as not pathogenic (not causing TSC) and three missense variants (p. K954R, p. S960F, and p. E984Q) are unclassified. One of the three unclassified missense variants (p. K954R) occurs naturally in 24/161 vertebrate species analyzed, including 11/83 mammals, whereas codons 960 (22/161) and 984 (19/161) in the alignment have different substituted amino acids to those found in our database (Supp.
, 1995] , whereas human fetal muscle had more transcripts containing exon 25 (+ex25)  [Xu et al. , 1995]. A quantitative study  [Olsson et al. , 1996]  estimated the ratio of +ex25:-ex25 in human fetal brain at 1:1; in three other fetal tissues, the -ex25 transcript was predominant. Human fetal kidney and fetal brain were found to have mainly the transcript lacking exon 31  [Xiao et al. , 1995]. We searched two databases with TSC2 transcript information: The Ensembl Genome Browser at http://www. ensembl. org/ and the National Center for Biotechnology Information Gene database at http://www. ncbi. nlm. nih. gov/gene/7249. Six of the 26 different TSC2 transcripts listed in Ensembl were aligned using Clustal Omega to confirm the exact sequences that were absent from each transcript. The remaining 20 transcripts were not analyzed as these are too short to produce a functional protein.
In the NCBI database (Homo sapiens annotation release 106), tracks of intron spanning TSC2 RNA-sequencing (RNA-seq) reads generated from alignments of RNA-seq data from the Bodymap2 dataset (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE30611) of 16 adult tissue samples were analyzed. Each tissue in the Bodymap2 dataset is derived from a different single individual. To obtain evidence for tissue-specific alternative splicing of TSC2 exons 25 and 31, we examined tracks of intron spanning RNA-seq reads for these exons in each Bodymap2 tissue sample. Analysis of the BodyMap2 tracks showed evidence for the expected sequence joining each pair of adjacent exons in the majority of reads, except those involving exons 25 and 31 where a minority of reads (ß6% and ß11%, respectively) showed a join between adjacent exons, indicating that transcripts encoding exons 25 and/or 31 are relatively scarce.
Exon 25 was just detectable in heart, lymph node, skeletal muscle, and thyroid tissue samples, but not detectable in adipose, adrenal, brain, breast, colon, kidney, liver, lung, ovary, prostate, testes, or white blood cells. Exon 31 was detectable in transcripts from adipose, adrenal, brain, breast, colon, kidney, lung, prostate, skeletal muscle, testes, thyroid, and white blood cells, but not in heart, liver, lymph node, or ovary. A similar analysis was performed for 27 different tissues that are reported in a study of normal adult tissues taken from 95 different individuals  [Fagerberg et al, 2014] , and are added to the TSC2 dataset at NCBI (Homo sapiens Annotation Release 107) [https://goo. gl/Gu0JWq]. Some of the tissues examined in the BodyMap2 dataset are not included in this study, for example, skeletal muscle, breast, and white blood cells.
The same tissue type was analyzed from at least two individuals and exons 25/31 are also just detectable in some individuals and not evident at all in others. Aggregate reads show that the inclusion of exons 25 and 31 is 7. 8% and ß23% of transcripts, respectively, supporting the abundance of the -ex25 and -ex31 transcripts in adult tissues. Since the tracks in both studies are from short reads, it was not possible to determine whether the observation of both exons, for example, in skeletal muscle and thyroid tissue, indicated that both exons are present in the same transcript in those tissues. No other TSC2 exons showed evidence for alternative splicing in these datasets at NCBI. Analysis of a third RNA-seq dataset from 175 individuals in the genotype-tissue expression (GTEx) project  [GTEx Consortium, 2015 ] also confirmed the relatively low abundance of TSC2 transcripts containing exons 25 and/or 31 in 51 different tissues from adults (Supp.
, 2011 , 2013]. Our analysis indicates that variants within TSC2 exon 25 or 31 do not inactivate the TSC complex and are unlikely to cause TSC. Coincidentally, just prior to submission of this manuscript, a batch of data submitted to the TSC databases was found to contain the same exon 31 variant (TSC2 c. 3846 3855delinsG) as in case 3. This in-frame deletion co-occurs with a definite TSC-causing variant in TSC2 exon 5 (TSC2 c. 569dup, p. Y190 * ) in an infant who fulfils the diagnostic criteria for TSC (cortical tubers, SEGA, subependymal nodules, hypomelanotic macules, renal angiomyolipoma, epilepsy). The exon 5 and exon 31 variants (TSC2 c. 569dup and TSC2 c. 3846 3855delinsG) are both inherited from a parent who also has clinical TSC (hypomelanotic macules, facial angiofibromas, cortical tubers, epilepsy, intellectual disability, and renal angiomyolipomas).
, 2006]. It is possible that these exons alter the activity or response to stimuli of the TSC complex, particularly during fetal development. More sensitive assays of TSC complex activity are required to compare the activities of the different TSC2 protein splice isoforms and variants in more detail. The findings reported here are specific to TSC2 exons 25 and 31, as stop variants that definitely cause TSC have been identified in all other TSC2 exons. Additional evidence that variants within exon 31 do not cause TSC comes from reports of three truncating variants (two frameshift and one nonsense) in the EVS and ExAC population databases (Supp. Table  S4 ). Although phenotype information is not available, the population frequencies are quite high (1:532-1:1,443 individuals), which supports our conclusion that these truncating variants do not cause TSC.
Acknowledgments We wish to thank  Johan den Dunnen ,  Peter E. M. Taschner , and  Ivo Fokkema  ( LOVD Team at Leiden ) for hosting and maintaining the TSC1 and TSC2 variation databases. Our thanks also goes to  Terence Murphy (NCBI)  for help with the RNA-sequence track reads, to  Matthew Hurles  ( Wellcome Trust Sanger Institute ) for help with accessible information on the UK10K study, and to  Elizabeth Shephard  ( UCL ) and  Christine Orengo  ( UCL ) for useful discussions. This study makes use of data generated by the  UK10K Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www. UK10K. org.
Several examples of non-canonical binding (CDS or 5′UTR) have been reported lately for miRNAs proven to be functional  [3]. The role of miRNAs has been studied in several physiological and physiopathological processes. They are involved in key animal developmental processes, such as maternal transcript clearance  [4]  or axial patterning  [5]. Moreover, miRNAs are also involved in disease and 163 pathologies reported in miR2Disease database have been associated with misregulation of 349 miRNA genes or dysfunction of miRNA/mRNA target interaction  [6]. In addition, miR-NAs can be associated with economical traits. In sheep, a mutation in myostatin 3′-UTR creates a new target site for miRNA and affects muscularity  [7]. miRNAs are often expressed in a tissue-enriched manner  [8]. They can control both temporal and spatial gene expression and are believed to regulate 30 to 70 % of the proteincoding genes  [3].
All above miRNAs have been discovered by next-generation sequencing. This database is however limited for salmonid species despite the reports of several miRNAs in several tissues of Atlantic salmon  [12, 13]  or rainbow trout  [14, 15]  and in rainbow trout eggs  [16]. Salmonids are economically and environmentally important species for both wild fisheries and worldwide aquaculture production. Genomic resources for rainbow trout and Atlantic salmon are advanced but our knowledge of salmonid miRNAs repertoire remains incomplete. The aim of the current study was to discover and characterize rainbow trout (Oncorhynchus mykiss) miR-NAs in a large number of tissues using next-generation sequencing in order to provide an extensive repertoire of rainbow trout miRNAs. A total of 38 different samples corresponding to 16 different tissues were individually sequenced and analyzed independently in order to identify a large number of miRNAs with high confidence.
Results and discussion Overview of small RNA sequencing Independent small RNA libraries were constructed for 38 samples representing 16 different tissues including gonadal and somatic tissue. A total of 3,484,155,614 reads were generated from the 38 libraries using a HiSeq-1000. The number of reads per sample ranged from 29,326,345 to 126,937,431 (Additional file 1). The processing procedure applied to this dataset is described in Fig. 1. A cleaning procedure was first used that removed adapters sequences and eliminated reads of low complexity. Only the 42,118 non redundant sequences, representing 1,158,211,340 reads with a size in the range of 16-28 nucleotides and a global expression level higher than 1000 were kept for mapping. The mapping procedure allowed to align 75. 5 % of the 42,118 non redundant sequences in their full length at 100 % identity onto the rainbow trout genome  [17].
A maximum of 15 alignments was reported for each sequence. From the 149,685 alignments that were generated 47,283 loci were built and submitted to the miRNA prediction procedure. 3271 loci were predicted as containing putative pre-miRNA. All the pre-miRNA predicted loci were annotated against ncRNA databases including miRBase 21. 0  [11] , RFAM 11. 0  [18, 19] , Silva  [20, 21]  and GtRNAdb  [22]. miRNA from miRBase and RFAM were merged before annotation in order to have a unique miRNA database. Considering the 3271 miRNA predictions, 303 were not considered because they had a perfect match to known repeat elements in rainbow trout (145) or were annotated as other RNA species than miRNA (158), including for example snoRNAs. Three of them were common to repeat elements and ncRNA other than miRNAs.
2 ). Mis-predicted loci have mainly a score below 250: among the mis-predicted miRNAs loci, only 5 have a score above 800. This threshold of 800 will be further used to select high confidence novel miRNAs among predicted loci. Considering predicted loci annotated as miRNA only (in miRBase, Rfam or both), 352 (79. 1 %) are located in intergenic regions, 85 (19. 1 %) are in introns, and 8 (1. 8 %) are in 5′UTR, 3′UTR or in exons. We observe similar structural annotation percentages for loci predicted as miRNA but not annotated in any database (Fig. 1 ): 2043 such loci are located in intergenic regions (81. 7 %), 416 (16. 6 %) are in introns and 42 (1. 7 %) are in exons. A complete list of all miRNA loci with structural and functional annotation is provided in Additional file 2. Fig. 1  Identification and annotation pipeline of rainbow trout miRNAs using sRNA-sequencing.
Fastq files were first cleaned from low quality and adapter sequence. The redundancy was removed from the 38 sample files for the annotation process (Panel 1). Unique sequences were then blasted against rRNA, tRNA and RNA database to remove already known RNA sequences that are not miRNA (Panel 2). A Blast against miRBase database predicts the already identified miRNAs in other species (Panel 2). In parallel, reads were mapped onto the rainbow trout genome to eliminate any contaminant sequences and characterize the miRNA loci. Novel pre-miRNA were also characterized with the specific miRNAs features (Panel 3) For most samples in the present study, the length distribution of the reads is as expected for a miRNA study, with a peak at 22 nt, which corresponds to the expected miRNA size (Fig. 3A  and Additional file 3).
Identification of conserved rainbow trout miRNAs Many miRNAs have been conserved during evolution and the sequence identity is extremely high even among evolutionary distant species  [25] [26] [27]. In the present study, we identified rainbow trout miRNAs based on their sequence similarity with other species and used miRBase database (version 21. 0) and Rfam database as reference datasets. A total of 445 conserved putative precursors were discovered at unique genome locations that correspond to 123 mature conserved miRNAs. The annotation of miRNAs showed that we have identified 111 evolutionary conserved miRNA families in our dataset (Additional file 2). Several isoforms in sequence and size were found for each miRNA family. This number of identified miRNAs is consistent (i. e in the same range) with miRNA repertoires characterized in other fish species such as salmon  [12, 13] , zebrafish  [28] , and medaka  [29].
Tissue distribution of rainbow trout miRNAs Relative abundance of miRNA in the dataset was characterized (Fig. 4 ). In the global dataset, two miRNAs, miR-21 and miR-146, represent almost half of the reads with 33 and 15 % of the total number of reads in the complete dataset, respectively. Gene expression datasets from several tissues usually exhibit a different pattern: each gene has a small contribution to the total but none of them are overrepresented like it is the case for miR-NAs. miR-21 is a well studied miRNA that is involved in different types of cancer  [30, 31]  and also in the development of heart disease  [32]. miR-146 is thought to be a mediator of inflammation and is upregulated by inflammatory factors such as interleukins and tumor necrosis factor-alpha  [33]. Eight miRNAs (miR-21, miR-146, miR-30, miR-3596, let-7, miR-101, miR-1 and miR-192) account for more than 1 % each and collectively represent 70 % of the total number of reads.
In mammals, it was demonstrated that miRNAs are expressed in a tissue-dependent manner    [7]  and rainbow trout miRNAs are clearly following the same rule. This miRNA distribution was validated by RT-qPCR for several selected miRNAs. As shown in Fig. 6 , sequencing and RT-qPCR data are highly correlated for the 9 miRNAs studied (Pearson correlation > 0. 74). The 9 miRNAs exhibit different patterns of expression (uniquitous vs. organ/tissue enriched expression) and different level of expression. Hereby, we show that sRNAsequencing can be used for characterizing miRNA repertoire and that expression profiles are confirmed by RT-qPCR. This indicates that the over-representation of some miRNAs does not bias the expression dataset. Another set of samples was used for the RT-qPCR analysis in order to exclude any RNA-seq library preparation bias and to extend the validation to a larger set of samples.
Further analyses were performed for high confidence loci (Additional file 6). Sequencing data were used to build the novel miRNA loci as illustrated in Fig. 7  for one example (panel A and B). Information about the predicted locus are shown in Fig. 7A : it includes structure and sequence data such as freeenergy of the hairpin, hairpin sequence and nucleotides distribution. In Fig. 7B , the reads are shown as "stacks" below the precursor sequence and align to both ends of the precursor (both -3p and -5p). The count per base is shown in the top of the B panel. As found in known miRNAs, several isoforms in length are found in the sequencing dataset with a frame shift of 1 or 2 nucleotides in both ends. Expression profiles were also studied for the novel miRNAs (Fig. 7C ). As expected most of them Each row represents a miRNA isoform (total of 1946 rows) and each column a tissue RNA sample.
Conservation of identified rainbow trout miRNAs miRNAs are known to be conserved among species to a certain extend  [42]  and therefore we checked whether the identified rainbow trout miRNAs were conserved among fish species. We compared our rainbow trout dataset (all predicted miRNAs in the study, i. e 445 annotated miRNAs plus 2501 putative novel miRNAs) to 8 fish genomes: Atlantic salmon, Atlantic cod, zebrafish, medaka, stickleback, sea bass, tetraodon and fugu and looked for miRNA sequence conservation (Additional file 2). As shown in Fig. 8a, 7  % of the rainbow trout miRNAs are found in all the 8 other selected species, while 67 % of the trout miRNAs are found in only one other species (mainly Atlantic salmon). Those 7 % of miRNAs found in all 8 fish species are common miR-NAs also expressed in most vertebrates.
Cleaning and redundancy removal The reads of length 36 nt contain part of the adapter in their 3′ end. We used CutAdapt  [46]  to remove the part of the adapter occurring at the 3′ end of reads and keep only relevant sequences for further analysis. The trimming was realized on all datasets with options -a ATCTCGTATGCCGTCTTCTGCTTG to remove the Illumina 3′ adapter. Resulting reads in the range 16 to 28 were obtained by setting parameters -m 16 and -M 28. This allowed us to keep reads in an enlarged expected range of miRNA size. Sequences containing 'N' and those of low complexity (less than 3 different nucleotides) were removed. Redundancy was then removed by pooling together all datasets and keeping only unique sequences. Finally sequences for which the number of occurrence was lower than 1000 were removed from the datasets.
A score is assigned to each predicted pre-miRNA locus considering previous characteristics. Each predicted pre-miRNA locus is then submitted to the annotation procedure by aligning it against known ncRNA databases with Blast +  [47]  allowing mismatches. We selected SILVA  [21] , GtRNAdb  [22] , miRBase  [11]  and Rfam  [19]  to annotate sequences matching respectively known SSU and LSU rRNA, tRNA, miRNA and ncRNA from other families (Additional file 3). All predicted loci were mapped (Bowtie2  [48] , no mismatch) against a repeat elements database  [17]  to remove transposons sequences from the putative predicted miRNA loci. Conservation analysis was performed by blasting the identified rainbow trout miRNAs dataset against 8 fish genomes: Atlantic salmon (ICSASG_v1), Atlantic cod (gadMor1), zebrafish (Zv9), medaka (HdrR), seabass, stickleback (BROAD S1), tetraodon (TETRADON 8.
After a 30-s incubation step at 95 °C, amplification was performed using the following cycle: 95 °C, 3 s; 60 °C, 30 s; 40 times. The relative abundance of target cDNA within sample set was calculated from a serially diluted cDNA pool using the Step One Plus software. After amplification, a fusion curve was obtained in order to ensure that a single PCR product had been generated using the following protocol: 1 s holding followed by a 0. 5 °C increase, from 60 to 95 °C. QPCR signal was normalized using 18S expression which is stable when comparing different tissues.
Acknowledgements The authors are grateful to the experimental facility staff of the  INRA LPGP laboratory  for fish rearing and to  Thaovi Nguyen  for excellent technical assistance. The authors thank the MGX sequencing platform and especially  Laurent Journot ,  Hugues Parrinello  and  Grégory Baronian  for the sRNA-sequencing. The project was founded by  INRA  (AIP Bioressources 2010 « microTrout » and PHASE division grant « microFish »). OR was supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contrat  ANR-10-INBS-09 ).
Availability of supporting data All the sequencing data are deposited in SRA under the Bioproject accession number PRJNA227065 (http:// www. ncbi. nlm. nih. gov/sra/?term=PRJNA227065). A website located at "ngspipelines. toulouse. inra. fr:9064" allows to explore all predicted miRNA providing complete information including functional and structural annotation, locus view and expression visualization by library (Additional file 8).
The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125km up to 16km). The project includes more than 120 simulations in both a historical scenario  and a climate change projection (2039-2068), together with coupled transient runs. A total of 20. 4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1. 5 PBytes of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Center (LRZ) in Garching, Germany.
About 140 TBytes of post-processed data are stored on the CINECA supercomputing center archives and are freely accessible to the community thanks to an EUDAT Data Pilot project. This paper presents the technical and scientific setup of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1. 0 protocol. In addition, an overview of preliminary results is given: an improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increases is observed. It is also shown that including stochastic parameterisation in the low resolution runs helps to improve some aspects of the tropical climate -specifically the Madden-Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).