dataesr/scientific-paragraphs-categorization huggingface dataset:
Updated annotation identifies putative clinically-relevant variants Given these considerations, we decided to investigate the clinical impact of the novel annotations without initial regard to their expression, conservation, or proteomics metrics. Firstly, we compared the overlap of pre-existing and updated annotation with five public variation datasets (dbSNP, ClinVar, ESP, HGMD-PUBLIC and PhenCode). We note that human mutation catalogues are currently biased towards 'known' gene sequences in terms of content, i. e. disease-associated mutations are less likely to be found in regions that are less well studied (or have been resequenced less often). Nonetheless, the updated annotation reclassifies 294 variants as exonic, and demonstrates that a further 70 intronic variants are found within 8bp of newly identified splice-sites (Table  1 ).
To quantify the significance of de novo mutation enrichment in the novel 450bp of CDS sequences (sum of features 7, 9, 12, and 14 from Table  2 ) and to estimate the probability of identifying 2 or more de novo variants in a cohort of 122 sequenced probands, we performed a de novo mutation enrichment statistical test using the fitDNM package  31. This package returns the Poisson unweighted P-value based on expected mutability rate and shows that it is improbable to observe 2 de novo variants among 122 individuals along this stretch of 450bp of CDS (unadjusted p = 9. 5x10  -7  ). Even after conservative exome-wide multiple testing correction for 18,000 possible protein-coding genes, this remains significant (p = 0. 017), confirming a significant enrichment of de novo variants in the novel SCN1A sequences.
Until it is possible to confidently generate a de novo genome sequence for a patient, in conjunction with a complete transcriptome (and also proteome etc), researchers and clinicians must rely upon data that is available in public databases. As such, our investigation shows that the current set of human transcripts, while being expertly curated and maintained, cannot be considered complete. To illustrate this, we have used human brain-transcriptomic data to rigorously interrogate the human reference transcriptome for missing transcriptional features. Interestingly, the extended transcriptional footprint of these genes immediately allowed for 294 intronic or intergenic variants, found in human mutation databases, to be reclassified as exonic, while a further 70 intronic variants were reclassified as splice-site proximal.
Selection of genes 191 genes associated with deleterious variants implicated in epilepsy in general and EIEE, in particular, were included in this study. These data were aligned to the individual BAC clones that make up the reference genome sequence using BLAST  43  with a subsequent realignment of transcript data by Est2Genome  44. In addition, SLR-RNA-Seq data  45  mapped using Gmap  46  , PACBIO  47  reads mapped using STAR  48  , and fetal and infant RNA-Seq data  17  mapped using cufflinks  49.
Seq data Illumina data from Jaffe et al  17  was re-mapped for fetal and infant transcriptome to GRCh38 to identify novel transcriptional features. Fastq files from the following datasets were downloaded from ENA: SRR1554537, SRR1554538, SRR1554541, SRR1554544, SRR1554546, SRR1554549, SRR1554551, SRR1554553, SRR1554554, SRR1554566, SRR1554567 and SRR1554568. Data were mapped to GRCh38 with TopHat (tophat-2. 0. 13)  50. Reads mapping to the gene regions that were studied, were merged into two files containing fetal and infant alignments using SAMtools  51. Transcript models were generated from the fetal and infant BAM files using Cufflinks (cufflinks-2. 2. 1)  50. Novel introns and splice sites were identified, a BED file generated and passed to manual annotators for checking.
Quantification of gene expression at exon level Raw reads from Jaffe et al  17  were available via study accession SRP045638. The 36 pairedend libraries were analyzed using the iRAP pipeline (https://github. com/nunofonseca/irap). First, raw reads in the original FASTQ files underwent quality assessment and filtering  52. They were then aligned against the GRCh38 genome reference using Tophat2  53  , with the option: '--min-intron-length 6'.
Identification of variants in updated annotation The collection of variants available under "All phenotype-associated -short variants (SNPs and indels)" in Ensembl release 90 (August 2017) was intersected separately with the EIEErelated gene exons in GENCODE 20 and in the current annotation, using a custom Perl script and the Ensembl API. The variants overlapping the current annotation but not GENCODE 20 were reported. A second round was carried out considering only the 8-nt exon flanking sequences in both annotation sets. As a proof-of-concept, we screened a cohort of 122 patients with Dravet syndrome or a clinically similar severe myoclonic epilepsy phenotype, for the 125 novel regions identified from this study of all genes that have previously been associated with Dravet syndrome (SCN1A, SCN2A, SCN1B, GABRA1, GABRG2, HCN1, CHD2 and PCDH19), using an amplicon targeted amplification assay (Agilent, https://www.
Analysis was performed in-house with a standardized pipeline integrated in genomecomb  58. The pipeline used fastq-mcf (http://code. google. com/p/ea-utils) for adapter clipping. Reads were then aligned using bwa mem  59  and the resulting sam file converted to bam using Samtools  51. Bam files were sorted using biobambam  60. Realignment in the neighbourhood of indels was performed with GATK  61. Amplicon primers were clipped using genomecomb  58. Variants were called at all positions with a total coverage >=5 using both GATK  61  and Samtools  51. At this initial stage, positions with a coverage <5 or a score <30 were considered unsequenced. The resulting variant sets of different individuals were combined and annotated and filtered using genomecomb  58. Variants with a coverage above 7 and a GATK quality score above 50, that were identified by both variant callers (GATK and Samtools) and were absent in publicly available databases (EXaC 62 , gnomAD  62  , 1000 Genomes  63  , Exome Variant Server (http://evs.
gs. washington. edu/EVS/)) and our in house database, were extracted. Furthermore, variants present in homopolymer regions (>8 homopolymers) or simple repeat regions, were excluded. The effect of the variant was predicted with the Ensembl Variant Effect Predictor (VEP)  64  , using the manually annotated GENCODE dataset as custom gene annotation. Variants were validated and the segregation was checked using bi-directional Sanger sequencing. For de novo variants, maternity and paternity was confirmed using an in-house developed multiplex PCR panel consisting of 16 STR-markers scattered over the genome, including the X and Y chromosome. To predict the effect of the de novo variants on splicing efficiency, we used the quick mutation analysis from human splice finder  65  , using the CDS from the NMD transcripts as custom sequence input.
Acknowledgments We thank the following: all patients and their families who participated in this study, as well as the teams who were involved in recruiting patients and gathering samples and data at the respective study sites;  Dr Gautam Ambegaonkar ,  Ms Margo Elsworth ,  Dr Jill Gordon ,  Dr Alasdair Parker ,  Dr Elizabeth Radford ,  Ms Kuldeep Stohr  ( NHS England ) for clinical support and advice;  Dr Andrew Jaffe  ( Lieber Institute for Brain Development, USA ) for advice with utilising the 6 different life-stage transcriptomic datasets used in this study;  Dr Matthew Hurles  ( Wellcome Sanger Institute, UK ) for initial discussions regarding this study. We also thank  Imogen  and  Jasper Steward , both of whom have rare neurological disorders and have been instrumental in driving this study.
Funding This work was supported in part by the  National Human Genome Research Institute (NHGRI)  ( 2U41HG007234 ),  Wellcome Trust  ( WT108749/Z/15/Z ) and the  European Molecular Biology Laboratory. The work was partly undertaken at  UCLH/UCL , which received a proportion of funding from the  Department of Health's NIHR Biomedical Research Centres funding scheme. SMS is partly supported by the  Epilepsy Society. JR is funded by the  Agency for Innovation by Science and Technology, IWT. H. S. is a PhD fellow of the Fund for Scientific Research Flanders (FWO,  1125416N ). SW is partly supported by the  BOF-University of Antwerp  ( FFB180053 ) and  FWO  ( 1861419N ).
39ese included sixty-six genes from the Great Ormond EIEE gene panel, as well as 14 genes from the DDD study39and 53 genes from literature searching. The full list can be found in the Supplemental Data 2. ESTs and mRNAs) and protein sequence data from GenBank 41 and Uniprot 42. Street Hospital (GOSH) for Children, London, UK, EIEE gene-panel (http://www. labs. gosh. nhs. uk/media/759010/eiee_v7. pdf), selected as established causes of early-onset seizures and/or severe developmental delay in patients without frequent major structural brain anomalies. Genes leading to neurometabolic disorders with readily identifiable blood/urine/cerebrospinal fluid (CSF) biomarkers were not included. An additional fifty eight genes were included from the Addenbrooke's Hospital, Cambridge, UK, Gene annotation Manual re-annotation of the 191 genes was performed on GRCh38 (https://www.
were also used to identify novel transcripts and splice junctions. Data are available at www. gencodegenes. org/releases/. Updated annotation of the 191 genes described in this study on GRCh38 is represented in GENCODE releases from v27 (August 2017) onwards. In addition, the updated annotation is available remapped to GRCh37 [https://github. com/diekhans/gencode-backmap] here: http://www. gencodegenes. org/releases/grch37_mapped_releases. html.
Genomics The use of DNA markers promises to lower selection age even more, reducing the need for continuous phenotypic assessment, including wood properties  [28, 29]. In general terms, phenotypic data are used to predict genetic values  [30] , which are deregressed  [31] , and then a variant of the method proposed by Meuwissen et al. [32]  is used to build a linear predictive model of genetic performance based on DNA markers. Furthermore, long-term use of genomics requires recalibrating the prediction equations after a few cycles of selection, which will, again, need a substantial number of phenotypic assessments for newer material in the breeding program  [33]. However, this approach still needs relatively large datasets for building the statistical models, a task for which NDE is well-placed.
NIR spectroscopy was intended as a fourth component but this was only partially implemented/integrated and continues to be a topic of investigation. Measured data are exported both as radial profiles at 25-micron intervals and as tables of annual ring widths and statistical properties (mean, median, percentiles, and standard deviation) for each wood characteristic. From these primary measurements, many other properties (coarseness, wall thickness, specific surface area, MOE, modulus of rupture, longitudinal shrinkage, paper sheet density, growth rate, EW/LW ratio, compression wood, tension wood) may be estimated, depending on the sample type. NIR spectroscopy was intended as a fourth component but this was only partially implemented/integrated and continues to be a topic of investigation. Measured data are exported.
Reflected light imaging has been performed using a wide range of wavelengths. The system currently uses 450 nm (blue) but green, red, and infrared have all been employed over the last 25 years. It would be possible to use readily available UV diodes in SilviScan's illumination system for reflected light imaging and fluorescence studies, given the appropriate optics and camera. When lighting systems are changed, SilviScan corrects for residual chromatic aberration and its effect on magnification and focal plane position. Both the software and hardware in SilviScan have been designed to follow technological and scientific advances. This has allowed us to take advantage of huge increases in computer power, to follow the development of cameras and of light-emitting diode (LED) lighting. In addition, as operating systems, drivers, and other third-party software evolve, SilviScan has been adapted.
Except for a few fundamental principles, SilviScan-3 bears very little resemblance to SilviScan-1. Both the software and hardware in SilviScan have been designed to follow technological and scientific advances. This has allowed us to take advantage of huge increases in computer power, to follow the development of cameras and of light-emitting diode (LED) lighting. In addition, as operating systems, drivers, and other third-party software evolve, SilviScan has been adapted. Except for a few fundamental principles, SilviScan-3 bears very little resemblance to SilviScan-1. 6. 1. 8. SilviScan Applications Some of the ways in which wood property information is used are discussed briefly below. The reference list is far from exhaustive; there are many others, including conference papers, confidential reports and works-in-progress.
Every identifiable source of variation was included giving a high-level of confidence in predictions, but model applicability to a new or different population should be questioned and it is often an obstacle to adopting NIR technology. Continued inclusion of new samples into calibration models seems prudent since new sources of variation can be used to re-calibrate models and improve predictions  [230]. NIR spectroscopy has also been employed to examine the within-tree variation of wood properties. Again, wood chemistry and related properties have been a focus with studies examining radial variation in pulp yield  [231] [232] [233] , cellulose and various wood sugars  [232] [233] [234] , extractives (acetone-soluble and hot-water), lignin, and total phenolics  [235]. Radial variation in fiber collapse, density, MFA, MOE, and tracheid properties have also been explored based on NIR-predicted data  [204, 234, 236].
An imaging spectrograph fitted with an NIR camera (900-1700 nm) is used to image the transverse face of discs at a radial resolution of approximately 2 mm  [239, 240]. These NIR spectral datasets are processed using a chemometric model to produce estimates of the variation in lignin, glucose, and galactan content in radiata pine trees. MOE is predicted from information on wood density and MFA  [161]. Wood density is estimated from measurements made with an X-ray line camera (150 kV X-ray source) at approximately 0. 5 mm resolution. MFA is predicted from ultrasonic time of flight measurements made with a pair of transducers that roll over the sample. A series of parallel paths 5 mm apart are traced across the sample to provide complete coverage. Linear potentiometers measure the thickness of the disc so that the velocity of the ultrasonic wave can be calculated.
By taking multiple discs from within a single stem, intra-stem wood property maps can be produced that can then be used as inputs into numerical product performance simulators  [252, 258, 265]. These data and the subsequent simulations enable the effects of silvicultural practices, environment, and genetics on intra-stem wood property patterns and their impacts on endproduct performance to be determined. This will provide greater insights into the full impact of these  To estimate the variation in stiffness and shrinkage behavior of wood within a stem, the DiscBot collects information on chemical composition (primarily cellulose, lignin, and hemicellulose content), MFA, grain angle, and wood density. The DiscBot has five sensors that capture information on the properties of disc samples that are 20-30 mm thick and have been conditioned to achieve an equilibrium moisture content of approximately 12%.
Discs are mounted in a frame that moves them past the five sensors and precisely records their position. A distortion-free RGB image is captured of the disc using a high-quality camera, which provides a permanent record and also enables features such as knots, resin pockets, intra-ring checks, and compression wood to be identified. An imaging spectrograph fitted with an NIR camera (900-1700 nm) is used to image the transverse face of discs at a radial resolution of approximately 2 mm  [239, 240]. These NIR spectral datasets are processed using a chemometric model to produce estimates of the variation in lignin, glucose, and galactan content in radiata pine trees. MOE is predicted from information on wood density and MFA  [161]. Wood density is estimated from measurements made with an X-ray line camera (150 kV X-ray source) at approximately 0.
Acknowledgments: Joseph Dahlen-members and collaborators of the  Wood Quality Consortium. Jan Van den Bulcke-The Special Research Fund of Ghent University (BOF-UGent ) is acknowledged for the financial support of the  UGCT Center of Expertise  ( BOF. EXP. 2017. 0007 ). The authors also acknowledge  Eduardo Cappa (Argentina) ,  Greg Dutkowski ,  Milos Ivkovic ,  Dominic Kain ,  David Pilbeam , and  Dean Williams (Australia) ,  Claudio Balocchi  and  Fernando Droppelman  (Chile),  Annie Raffin  (France),  Mark Paget  ( New Zealand ),  Steve Verryn  ( South Africa ),  Fikret Isik ,  Keith Jayawickrama , and  Gary Peter  ( USA ) for information related to criteria and tools frequently used for early selection (Table 1).
, 2004)  or snow cover area from NSIDC MODIS  (Hall and Riggs, 2016) , and (d) catchment attributes such as the Global Streamflow Indices and Metadata Archive  (Do et al. , 2018)  or national datasets such as GAGES-II  (Falcone, 2011)  and CAMELS Published by Copernicus Publications on behalf of the European Geosciences Union. (Addor et al. , 2017). Together, these datasets have facilitated the investigation of many catchments as well as interdisciplinary research linking meteorology, climatology, hydrology, and the earth sciences. In addition to the availability of large-scale data archives, the increase in computational power and uptake of programming languages have also been a major driver of change in the discipline. Increasingly, hydrologists are using data science approaches to derive process insights from large and complex datasets (e.
g. Guo, 2017). The ability to explore and mine these datasets has facilitated a move from in-depth experiments in single catchments towards large-sample studies (e. g. Villarini et al. , 2009; Berghuijs et al. , 2014; Slater et al. , 2015; Archfield et al. , 2016; Blöschl et al. , 2017; Blum et al. , 2017; Harrigan et al. , 2018). By applying an analysis to many catchments, or grouping those with similar characteristics, hydrologists have been able to test the broad applicability of hydrological theories and draw systematic insights about hydrological processes (e. g. Blöschl et al. , 2013). Largesample studies are increasingly being employed to develop novel (conceptual and/or physical) models that are applicable across diverse catchment conditions, thereby improving process understanding and leading to more robust predictions (e.
As a free multiplatform language, R is highly ver-satile and has a wide range of uses, including data acquisition and provisioning, manipulation, analysis, modelling, statistics, visualization, and even well-developed geospatial and geographic information system (GIS) applications. R can be used for generating reports, making interactive presentations for teaching or conferences, or even prototyping dashboards and web applications. One of the greatest strengths of R is its extremely active community of users, who, in the past 25 years, have developed and released in the public domain more than 14 000 packages spanning many scientific disciplines. The Comprehensive R Archive Network (CRAN; https://cran. r-project. org) is the main repository for these packages, hosted by a network of FTP and web servers around the world.
Democratizing open science and numerical literacy One of the principal advantages of R is its ease of use, resulting from typically detailed documentation, a large number of online resources, object-oriented programming (the language is organized around objects with unique attributes), functional programming (the code can be written with functions to facilitate modularity and avoid changing-state data), and the availability of the source code under the open-source license. Further, R can be run on all major operating systems (i. e. Microsoft Windows, macOS, and Linux), making it ideal for institutional or personal use. In contrast with compiled languages, such as C or Fortran, R is an interpreted language, which means that the code can be written and executed line by line. In practice this means that achieving a basic hydrological analysis can be as simple as writing a sequence of commands for reading a file, cleaning the data, and plotting a graph.
These sequences of commands are typically   2 ) date of last publication -i. e. most recent publication date of packages that are currently available on CRAN (black bars; https://cran. r-project. org/web/packages/available_packages_by_date. html). In both cases each package is counted only once; for example the package hydroTSM was first released in 2010 (grey), but its most recent update was in 2017 (black). The script to produce this figure is provided in the Supplement. Data were downloaded from CRAN on 23 June 2019; thus the year 2019 is incomplete. collated in an R Script; together, multiple scripts may constitute a hydrological workflow (see Fig. 3 ). A high level of documentation and support exists for each of the tasks within a hydrological workflow, including a diverse range of packages (see Sect.
g. different catchments, different types of gridded data, or the same data at different points in time. Thus, scripts written in R (or other languages) have wide-ranging benefits, as they facilitate the testing and quality control of the scientific workflow, can be shared and improved by a team of users, lessen the risk of making manual mistakes, and significantly enhance the speed with which analyses can be conducted and updated. Recent developments in R have contributed to enhancing open science and numerical literacy in the hydrological sciences. R's ease of access and use has improved what might be termed "scientific computing literacy" within the hydrological community. Volunteer projects such as the Software Carpentry  (Wilson, 2006 (Wilson, , 2014) )  have been teaching basic computing skills to researchers since 1998, and R now forms a central part of their training.
Over the past decade or so, R has become one of the core tools for scientific computation in hydrology. Hosted instances of R allow the user to run R and RStudio ® (an integrated development environment -IDE; described in greater detail in Sect. 4. 2) in the cloud, i. e. in a web browser rather than locally on one's own computer. These hosted instances have made the language more accessible to non-specialists due to the large range of pre-installed packages. Importantly, the RStudio Cloud (https://rstudio. cloud/) has recently been developed to make it easy for professionals, hobbyists, trainers, teachers, and students to do, share, teach, and learn data science using R and provides many learning materials, including interactive tutorials covering the basics of data science, cheat sheets for working with popular R packages, and a guide to using RStudio Cloud.