dataesr/scientific-paragraphs-categorization huggingface dataset:
4 ). Additionally, we found that ~92% of the total transcripts in each of four assemblies were orthologous with at least one transcript from the Atlantic salmon RefSeq protein dataset (Additional file 2: Table  S2 ). The marked level of sequence overlap observed between the four current transcriptomes, as well as between the published set of Atlantic salmon RefSeq proteins, further validates the completeness and quality of the assemblies presented here. This statement is further supported by the additional OrthoFinder analyses we performed comparing the orthogroup distribution size of the current salmon assembly (at all four filtering steps: unfiltered, after TransDecoder single-best ORF prediction, after CD-Hit clustering at 100% identity and after Trinity full-length transcript analysis (e.
However, our Atlantic salmon transcriptome predictably mapped with the highest success (99. 5%) to the conspecific reference genome. This is consistent with the recent Atlantic salmon reference genome publication, which found that 98% of the NCBI mRNA sequences for Atlantic salmon aligned to the genome  [12]. The comparable results demonstrated here indicated that we were able to successfully recover a set of high-confidence protein-coding genes in all four species' transcriptomes. Previous transcriptome comparisons First we assessed how successfully transcripts from the new and previous assemblies had been reconstructed to full (100% coverage) or near full (> 70% coverage) length compared to the NCBI protein database for Atlantic salmon. Of the seven assemblies, we found that the number of transcripts reconstructed to full-length was highest in the PhyloFish brown trout  [35]  assembly (19,404 transcripts), followed by the four current assemblies (11,099 to 13,546 transcripts), then the PhyloFish European whitefish  [35]  assembly (5073 transcripts), with the lowest number of full-length transcripts recovered in the Magnanou et al.
Our analysis suggests that there are a higher proportion of fragmented transcripts in both the previous charr and trout assemblies, as well as higher levels of false duplicates and/or mis-assembled transcripts in all three previous assemblies. This further supports the quality of the new assemblies presented here and the relevance of their contribution to the currently available resources for salmonids. Secondly, we used BLAST tools to compare sequence similarity between the current and previous assemblies for Arctic charr (current vs. [34] ), brown trout (current vs. [35] ) and European whitefish (current vs. [35] ). Unexpectedly, we observed little overlap in the assembled transcripts between the current and previous charr assemblies (Fig. 5a ). A total of 8038 sequences were identified as overlapping between the charr transcriptomes, which is representative of around 24% of the current assembly and 23% of the Magnanou et al.
[34]  assembly. For brown trout and European whitefish, the level of sequence similarity between the current and previous assemblies was considerably higher (Fig. 5b, c ). In brown trout, 30,945 transcripts overlapped, representative of ~86% of the total transcripts from the current assembly and ~41% of the total PhyloFish transcripts. Similarly, for European whitefish we found sequence overlap for 28,499 transcripts, representative of ~85% of the total transcripts from the current assembly and ~38% of the PhyloFish transcripts. Here we made no direct assessment of the cause of the differences between the charr assemblies and additionally the high proportion of transcripts that were unique to all three previous assemblies, however we offer several possible explanations. First and foremost, the data for the four current and PhyloFish assemblies were generated by Illumina sequencing platforms, whereas Magnanou et al.
Gene activation and expression fluctuates throughout an organism's life cycle, therefore the variation between the current and previous assemblies could be explained by the differences in the tissues used to generate them. In the current study RNA was extracted from whole organism samples of juvenile fish (~5 months), whereas both Magnanou et al. [34]  and Pasquier et al. [35]  used multiple tissues from mature Table  5  Comparison of full-length transcript reconstruction between the four current assemblies and three previously published transcriptomes for Arctic charr  [34] , brown trout  [35]  and European whitefish  [35]. The table shows the number (percent) of transcripts from each assembly that aligned to the NCBI protein database for Atlantic salmon (GCF_000233375. adults, and additionally, Pasquier et al.
Annotation and GO analysis To provide comprehensive annotation of these four new transcriptomes, we conducted sequence homology searches against two different annotation resources. Using BLAST tools, we first compared transcripts against the NCBI Atlantic salmon protein database, as this represents the most established set of reference proteins that are publically available for salmonids at present. For this reason, successful alignment to the salmon protein database was also used to determine which transcripts were retained or discarded from our assemblies during optimisation (as detailed in Methods). Given that successful alignment to the NCBI Atlantic salmon protein database was used as part of our filtering pipeline, 100% of the final set of transcripts for all four assemblies are annotated to the salmon database (Table  6 ).
Transcripts were further characterised by performing BLASTP searches against the UniProtKB/SwissProt curated proteins. Significant alignment (e-value 1e-3) for 95 to 96% of transcripts was found across our four assemblies (Table  6 ). The consistently complete or near complete annotation obtained across both protein databases gives us high confidence in the accuracy of the assembled transcripts. The annotation statistics obtained for the four assemblies we present here are higher than those reported for previously published salmonid transcriptomes. Respectively, in the published lake whitefish  [32] , coho salmon  [33]  and Arctic charr  [34]  transcriptomes, 54, 40 and 48% of the transcripts were unannotated. Higher annotation success was obtained for the six salmonid species included in the PhyloFish database, with unannotated transcripts comprising just 9 to 15% of the assemblies  [35].
Specifically, with regards to annotation against the well curated SwissProt database, we yielded significantly greater annotation (95 to 96%) compared to those obtained for the previous assemblies of our focal species; 3, 5. 5 and 5. 5% for Arctic charr  [34] , Brown trout  [35]  and European whitefish  [35] , respectively. The observed difference in annotation success between the current and previous assemblies is most likely due to the fact that, unlike previous studies, here we specifically filtered the assemblies to retain only protein-coding transcripts, with the aim of generating robust molecular resources to improve efficiency and accuracy of downstream genetic a b Fig. 5  Venn diagrams showing the number of overlapping sequences between the current and previously published transcriptome assemblies for a Arctic charr (current vs.
ref. [34] ), b brown trout (current vs. ref. [35] ) and c European whitefish (current vs. ref. [35] ) analyses. SwissProt/UniProtKB accessions are one of the most widely recognised by GO analysis softwares, therefore the high level of annotation against the SwissProt database makes our four assemblies very useful for future comparative analyses and downstream applications. Transcripts were functionally annotated based on their assigned UniProtKB/SwissProt gene symbols. We identified a wide range of GO terms in each assembly, indicating that molecular functions, biological processes and cellular components were well represented (Fig. 6  and Additional file 6: Table  S4 ). High uniformity in GO profiles was observed across the four transcriptomes. These findings agree with previous research that reported high consistency of GO terms across multiple species, as well as across multiple phyla  [62, 64, [66] [67] [68].
Consistency across the assemblies further indicates accuracy the assemblies and the assigned annotations. We performed a separate GO analysis on the 'assembly-specific' transcripts and observed no difference in the number and assortment of the gene ontology terms compared to the complete dataset (Fig. 6 , Additional file 7: Figure  S3 ). Further, the distribution of GOs for 'assembly-specific' transcript subsets was comparable to those obtained for the previous coho salmon  [33]  and Arctic charr  [34]  transcriptomes, and therefore further justifies their inclusion within the final assemblies. Direct comparisons between the GOs assigned to the current transcriptomes and previously published transcriptomic data for salmonids is somewhat limited, given that the majority of transcriptomic research to date has focussed on subsets of GOs, related to specific biological and/or ecological questions  [69] [70] [71] [72] [73] [74] [75].
Identification of paralogous sequences Ancestral genome duplication increases the complexity of genetic studies in salmonids. Consequently, the need to distinguish between paralogous sequences and allelic variation presents a major challenge in generating comprehensive molecular resources for these species  [10, 12, 33, 76]. Here we applied two methods to distinguish between orthologous and paralogous sequences in our transcriptome assemblies and by combining the results generate a robust approximation of the number of paralogous sequences. First, using the BUSCO tool, we found that 34 to 37% of the singlecopy orthologs detected in our assemblies were duplicated (Table  3 ). Second, using OrthoFinder algorithms, we were able to estimate the total number of paralogous sequences present in our transcriptomic datasets by calculating the number of self-BLAST hits identified between transcripts within a single species (Table  7 ).
Of the total number of transcripts in each of the species' assemblies, we identified 37% in Atlantic salmon, 36% in brown trout, 34% in Arctic charr and 34% in European whitefish as putative paralogous transcripts. The high consistency in the proportion of paralogs detected by both methods applied here, BUSCO and OrthoFinder, increased our confidence that we were able to successfully identify 'true' paralogous sequences within our final assemblies. Further, to the best of our knowledge, the results presented here represent the most comprehensive identification of true paralogs within de novo assembled trancriptomes for salmonids, demonstrating a considerably higher capture rate than reported previously for the coho salmon transcriptome, where 29% of the assembled transcripts were identified as duplicates  [33].
Acknowledgements We thank  Alex Lyle ,  Stuart Wilson ,  Simon McKelvey , and  Oliver Hooker  for their valuable contributions to generating, rearing and sampling of the specimens used in this study. This research was funded by the  BBSRC-Westbio  DTP studentship awarded to MC with CEA and KRE ( BB/J013854/1 ),  Wellcome Trust-Glasgow Polyomics ISSF Catalyst to KRE (Wellcome Trust  [ 097821/Z/11/Z ]), and  Marie Curie   CIG 321999  to KRE.
Patients Group and Description of Biomodelling. Three groups of patients who underwent TAVR procedure were evaluated using biomodelling: patients without thrombosis (group 1), patients with THVT (group 2), and patients with THVT and dislodgment (group 3) (Figures  3 4 5 6 7 8 ). All recipients were symptomatic for severe aortic stenosis and included in the prohibitive-high-risk group. The baseline characteristics and preoperative data are reported in Table  1. The catheter-based aortic valve implantation was performed using transfemoral access in accordance with established preoperative planning including contrast enhanced CT imaging, which ensures complete information on the size of the ascending aorta, aortic root and aortic annulus for an exact sizing of the prosthesis. Furthermore, CT images were used to assess the suitability of peripheral vessel access to accommodate the relatively large sheaths necessary to introduce the prosthesis.
The CT data obtained were compared with preoperative echocardiographic findings. They underwent an uncomplicated transcatheter aortic valve replacement (TAVR) procedure and were implanted with balloon and selfexpandable prosthesis. Postprocedural angiography showed satisfactory results with satisfactory positioning and function of the valve (Video 1). Patients were followed up 1-year after procedure. In 546 cases the TAVR showed excellent functioning. 61 patients had thrombus formation of catheterbased aortic valve and 1 patient developed an acute coronary syndrome.
Computed Biomodelling Study. The combined data presented in the case described were used to create an advanced computed biomodelling model. The adopted computational framework to simulate transcatheter aortic valve implantation can be roughly divided into four main phases: Phase 1: processing of medical images. Phase 2: establishing suitable models for analysis. Phase 3: simulation of the entire clinical procedure after analysis of the acquired data. Phase 4: postprocessing of the simulation results and comparison with follow-up data. The commercial finite element solver Abaqus 6. 14 by Dassault Systèmes (Simulia, Providence, RI, USA) was used to create an aortic valve Finite Element Model (FEM) and perform all simulations (stent crimping and prosthesis implantation in the native root). Preoperative CT images were used as a starting point to create a patient-specific geometrical model of the aortic valve complex, consisting of aortic root wall, native leaflets, and calcific plaques.
Native Aortic Root Model. ITK-Snap 3. 6 software (www. itksnap. org) was used to capture the main anatomical features of interest and generate the three-dimensional reconstructions: the aortic wall surface was extracted from DICOM images by semiautomatic segmentation. The STL representation of the aortic root, outcome of the segmentation procedure, was used as input for an in-house developed Matlab code (v. R2017a, Mathworks Inc, Natick, MA, USA) that allowed the generation of the outer profile of the wall with a constant thickness of 2. 5mm, for simplicity. The output file contained Rhinoceros 5. 0 commands (McNeel & associates, Seattle, WA, USA) and used to get an IGES file representing the final 3D CAD geometry of the aortic root. The obtained volume model of the aortic root was then imported to Abaqus for the discretization using C3D4 tetrahedral elements.
Native leaflets were geometrically reconstructed and modelled using 4-node shell elements with reduced integration (approximately 7. 000 S4R elements) and under the assumption of a uniform thickness measuring 0. 5mm  [43]. Abaqus was used for the definition of the attachment lines (between the leaflets and the root) and Rhinoceros software was then employed to define the free margin of the leaflets to reconstruct the surface in the open configuration.
Calcifications. In order to accurately reenact the TAVR procedure, calcifications were included. Although attributing specific material properties to a patient's calcifications from CT data was difficult, calcifications may drastically affect the efficacy of TAVI procedure  [43]. In this case, the software ITK-Snap was used to extract and segment relevant calcium deposits from preoperative CT images, using a threshold of 800 Hounsfield units. The STL file of calcifications was then imported to a software called ParaView v. 5. 4. 1 (open source multiple-platform application for scientific visualization, geometries modification and data analysis) and processed using VMTK (Vascular Modelling ToolKit, www. vmtk. org) to obtain a regular tetrahedral volume mesh. Each calcific block is treated as a single entity and imported as a new part in the Abaqus model.
Prosthetic Model. An accurate geometrical model of the self-expandable Medtronic CoreValve size 26 (Medtronic Inc. , Minneapolis, MN, USA) is obtained initially from highresolution micro-CT images of the actual device after its expansion, modelled using Nitinol constitutive laws  [44]. The CAD model of stent frame was built using Rhinoceros 5. 0 and Matlab to obtain the entire description of the device. Abaqus Explicit was used to compute a finite element simulation of the crimping step. The whole model consisted of two parts: the CoreValve stent and the catheter. The stent frame model was meshed using approximately 70. 000 solid brick elements ("C3D8R") with reduced integration. Leaflets made of pericardial tissue were not included because they did not affect the mechanical stent performance and its interactions with the aortic root wall.
Participants Data were acquired in two different cohorts in North America and Europe. Enrollment in the Simons Variation in Individuals Project  (22)  included referral by clinical genetic centers or webbased networks, or active online registration of families, while in the European 16p11. 2 consortium the families were directly recruited by the referring physician. Carriers were ascertained regardless of clinical diagnoses or age. The CNV carriers were either probands (n = 76) referred to the genetic clinic for the investigation of neurodevelopmental and psychiatric disorders, or their relatives (parents [n = 49], siblings [n = 14], and other relatives [n = 10]). Familial control subjects were relatives who do not carry a 16p11. 2 CNV. All families participated in a larger phenotyping project, as previously reported  (10, 13, (20) (21) (22).
Full description of cognitive and psychiatric assessment is available in the Supplemental Methods and Materials. We analyzed data from 78 16p11. 2 (breakpoints 4-5) deletion carriers, 71 duplication carriers, 72 familial control subjects, and 140 unrelated control subjects, including data not previously analyzed at the regional level on 64 deletion carriers, 54 duplication carriers, 51 familial control subjects, and 138 unrelated control subjects. The latter were selected among volunteers from the general population who had neither a major DSM-5 diagnosis nor a relative with a neurodevelopmental disorder. The study was approved by the institutional review boards of each consortium. Signed informed consent was obtained from the participants or legal representatives. Full description of participants is available in Table  1 , Supplemental Table  S1 , and the Supplemental Methods and Materials.
MRI Data Acquisition and Processing The MRI data included T1-weighted (T1w) anatomical images acquired at seven sites using different 3T whole-body scanners: Philips Achieva (Philips Healthcare, Andover, MA) and Siemens Prisma Syngo and TIM Trio (Siemens Corp. , Erlangen, Germany). Four sites used multiecho sequences for 264 participants (52 deletion carriers, 51 duplication carriers, 21 familial control subjects, and 140 unrelated control subjects), and three sites used single-echo sequences for 97 participants (26 deletion carriers, 20 duplication carriers, and 51 familial control subjects). Thirty-four scans were excluded from the analysis based on standardized visual inspection, which identified significant artifacts potentially compromising the accurate tissue classification and boundary detection (details in Supplemental Methods and Materials).
fil. ion. ac. uk/spm). T1w images were classified in different brain tissue classes using the "unified segmentation"  (35)  and an enhanced set of brain tissue priors  (36). Aiming at optimal spatial registration, we applied the diffeomorphic registration algorithm DARTEL  (37)  followed by a Gaussian spatial smoothing with 8-mm full width at half maximum. Of note, total intracranial volume computed by SPM is referred to as TIV. Regions of interest were extracted using maximum probability tissue labels (http://www. neuromorphometrics. com) within SPM12 using data from the OASIS project (http://www. oasis-brains. org). All MRI scanning parameters and processing are detailed in the Supplemental Methods and Materials.
In addition to the linear effect of gene dosage, we investigated the quadratic term to identify nonreciprocal effects of both CNVs. Post hoc analyses comparing deletion carriers and control subjects as well as duplication carriers and control subjects identified regions predominantly altered by each CNV. We analyzed the interaction of the genetic groups with the regressors (age, sex, MRI site, NVIQ) as well as the MRI parameters (single-echo vs. multiecho) and three other clinical variables: SRS, phonological processing (nonword repetition), and psychiatric diagnoses. NVIQ did not show any significant effect and was removed from the analyses on the whole dataset. Dice index was computed to estimate the overlap between 16p11. 2-related alterations and statistical maps obtained from a large cross-disorder neuroimaging meta-analysis (http://anima.
fz-juelich. de) (43). We computed the rate of overlap between both maps. Finally, to motivate future hypotheses we relied on the Neurosynth database (http:// neurosynth. org) to meta-analytically decode the functional association of the structural alterations observed in the gene dosage analyses of the 16p11. 2 CNV carriers. All these analyses are detailed in the Supplemental Methods and Materials. Linear models on global metrics and regions of interest were performed in R, version 3. 2. 5 (http://www. r-project. org; R Project for Statistical Computing, Vienna, Austria), and voxeland surface-based analyses in MATLAB 2016b (The Math-Works, Inc. , Natick, MA).
A total of 20 of 25 European (EU) cohort deletion carriers (80%) had at least one psychiatric diagnosis: 11 had one diagnosis and 9 had several diagnoses (between two and five); 17 of 23 EU cohort duplication carriers (74%) had at least one psychiatric diagnosis: 7 had one diagnosis and 10 had several diagnoses (two or three); 9 of 45 familial control subjects (20%) had at least one psychiatric diagnosis: 6 had one diagnosis and 3 had two diagnoses. In the Simons Variation in Individuals Project (SVIP) dataset, 50 of 53 deletion carriers (94. 3%) had at least one psychiatric diagnosis: 6 had one diagnosis and 44 had several diagnoses (between two and eight); 32 of 48 SVIP duplication carriers (66. 6%) had at least one psychiatric diagnosis: 10 had one diagnosis and 22 had several diagnoses (between two and five); 9 of 27 familial control subjects (33%) had at least one psychiatric diagnosis: 4 had one diagnosis and 5 had two diagnoses.
Limitations The broad age range of our dataset (6-63 years of age) is a potential limitation. However, we did not find any interaction between age and effects of gene dosage. The global and regional alterations remain unchanged in age-specific subgroups, with the caveat of a significant decrease in power (Supplemental Figures  S2  and S4A ). The developmental onset of global and regional differences in 16p11. 2 CNV carriers remains unknown, but the insula, striatum, and thalamus are also altered in a 7-day-old 16p11. 2 deletion mouse model  (54, 55) , suggesting an early developmental effect. However, specific anatomical effects are difficult to interpret between humans and mouse models. The multisite data represents another limitation and can introduce false-positive findings. However, investigating the impact of sites using main as well as random effects did not identify any biases introduced by the different scanners: this means that the effect of the CNV may be more important than the noise introduced by the multiple MRI sites.