dataesr/scientific-paragraphs-categorization huggingface dataset:
| Genomic analysis module The Genomic Analysis module is the analytical core of the RD-Connect GPAP, where researchers and clinicians analyze their own RD cases through the integration of the phenotypic information they have supplied, and the application of filters based on multiple annotations, tools and resources built into, or accessible from, the platform. The primary use case for the analysis is to discover diseasecausing variants in a submitted index case or family structure, and the system supports variant analysis in both a "diagnostic" paradigm (known variants or variants in known genes) and a "discovery" paradigm (potentially damaging variants in genes not previously associated with disease). F I G U R E 1 Flow of genome-phenome data in the RD-Connect Genome-Phenome Analysis Platform.
Clinical scientists submit their data to the RD-Connect GPAP wherein it is processed through a standard analysis pipeline. The variants identified are returned to the user via a user-friendly interface where they can undertake filtration and prioritization to diagnose their rare disease cases. This effort is supported by the integration of data from a large variety of external resources, and through live links via APIs to other resources. When an inconclusive but interesting result is found, patient matchmaking may be performed using the Matchmaker Exchange API to query other similar resources around the world To begin an analysis, the user selects the samples they want to analyze and defines the mode of inheritance they wish to investigate. Following this, a range of filters can be applied within four broad categories: variant effect  (McLaren et al.
To ease further exploration of variants of interest, hyperlinks to the appropriate record in popular genomic web servers are provided at both co-ordinate and gene level for example, OMIM, Ensembl  (Howe et al. , 2021) , gnomAD, the Human Gene Mutation database  (Stenson et al. , 2020) , and the University of California at Santa Cruz Genome Browser  (Kent et al. , 2002). A full list of resources used in the platform is provided in Table  S2. Furthermore, the RD-Connect GPAP provides fully detailed annotations for each variant in an extended results section, including the functional effect of the variant at the level of each Ensembl transcript, variant quality metrics, protein pathway, and interaction data, amongst others.
There are a number of features within the platform that facilitate this extended investigation. The simplest of these is the extended results view which indicates if genes are known to be involved in certain pathways, regulatory networks, or implicated in disease through annotations provided by databases such as Wikipathways, Reactome, and DisGeNet, and so on (see Table  S2 ). A more advanced feature is the search-across-all tool which allows a user to search across all shared samples, or a user-defined sub-cohort of samples, to see if a particular variant, or a similar type of variant in the same gene is found in other affected individuals in the RD-Connect GPAP, for example, a HIGH impact homozygous alternative variant in gene X. If such variants are commonly found in other cases, this would imply that the initial variant is less likely to be clinically relevant.
Gene discovery Internal data discovery | 725 The RD-Connect GPAP is designed to facilitate data sharing and foster collaboration between RD researchers across the world in a number of ways: 1) Query parameters can be saved through the generation of unique URLs, thus allowing users to return to their analyses at any point in the future, and share results easily with other authorized users in the system. 2) Variants of interest can be tagged and user interpretation applied, which is then visible to all other users. Furthermore, when a user confirms a variant as disease causing within the platform, a similar set of information to that required by ClinVar is requested to provide the necessary context and evidence. 3) The search-across-all feature allows users who have identified a variant of interest in a certain gene to search across all genomic datasets available to them within the RD-Connect GPAP to attempt to discover participants with similar or identical variants.
| Impact on rare disease diagnosis Since its initial release in 2015, the RD-Connect GPAP has been involved in the primary analysis and reanalysis of thousands of RD cases. Unfortunately, it is not possible to provide an accurate count of how many cases have been solved since the successful resolution of cases is not always updated in the system or communicated by the submitter. The total number of shared genome-phenome datasets has grown steadily and now stands at over 22,000, including affected participants and relatives. Currently, the total number of affected participants is 13,676, of which 5,736 are discoverable through MME. The majority of submitted datasets have come from large European consortia projects, but in some cases, data from just a few sequenced individuals or trios from independent research groups have been uploaded and cases solved (e.
g. ,  Oktay et al. , 2020; Owen et al. , 2018; Permanyer et al. , 2020; Topf et al. , 2020; Yaramis et al. , 2020). Before the initiation of the Solve-RD project, the three largest projects which had used the system for primary analysis were   Kurul et al. , 2021). Currently, the Solve-RD project is using the RD-Connect GPAP as a key resource for the collection and reanalysis of 19,000 previously unsolved genome-phenome datasets  (Zurek et al. , 2021). In addition, the RD-Connect GPAP is further developed and made available to all European researchers through the EJP-RD Virtual Platform, and also as an ELIXIR-ES resource. The EU FP7 NeurOmics project, funded in parallel to RD-Connect, focussed on the investigation of cases of rare neuromuscular and neurodegenerative diseases, and was the first large project to upload genome-phenome data to the RD-Connect GPAP and use it as one of their analysis tools.
This consisted of a total of 671 exome sequencing datasets, and 446 genome sequencing datasets, from a total of 375 families. By the end of the project in December 2017, 104 families (28%) had been solved, and over 100 new gene-disease relationships described  (Lochmüller et al. , 2018). The (43%) fully analyzed families have been solved  (Ivanovski et al. , 2020; Maini et al. , 2018 Maini et al. , , 2021;; Saredi et al. , 2019; Urreizti et al. , 2020). The Consequitur project focussed on progeny from consanguineous marriages who have a RD. 626 samples from 190 families were analyzed using the RD-Connect GPAP. Here the implementation of an algorithm to detect runs of homozygosity was particularly valuable as most causative variants in such families are likely to be found in regions identical by descent  (Matalonga et al.
Some of these methods should facilitate the provision of aggregated or summary information to non-registered users to let them know, for example, if there is data from individuals with a certain disease or phenotype (or even the number of available datasets of each kind). We are constantly looking for novel methods to aid in RD diagnosis, and with this in mind we are evaluating how other -omics data (e. g. , transcriptomics, proteomics, metabolomics, epigenomics, etc. ) could be incorporated in a systematic and user-friendly manner to help improve diagnostic yield. Similarly, as long-read sequencing becomes more common, we will also develop workflows to accept.
received funding from  EU projects Solve-RD, EJP-RD  (grant numbers  H2020 779257 ,  H2020 825575 ),  Instituto de Salud Carlos III  (Grant numbers  PT13/0001/0044 ,  PT17/0009/0019 ;  Instituto Nacional de Bioinformática, INB) ,  ELIXIR-EXCELERATE  (Grant number  EU H2020 #676559 ) and ELIXIR Implementation Studies (Remote real-time visualization of human rare disease genomics data (RD-Connect) stored at the EGA ELIXIR. 2017-2018; ELIXIR IT-2017- INTEGRATION, Rare Disease Infrastructure ELIXIR ,  2019-2020  and the  Beacon ELIXIR ,  2019-2021 ). The RD-Connect GPAP has leveraged developments funded through project  VEIS  ( 001-P-001647  co-financed by the  European Regional Development Fund of the European Union  in the framework of the Operational Program FEDER of Catalonia 2014-2020 with the support of the  Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya ) and  URD-Cat  ( PERIS SLT002/16/ 00174 ,  Departament de Salut, Generalitat de Catalunya ).
Ribosome Footprinting Human neuronal progenitor cells (hNPCs) (n = 2) were infected with ZIKV-Ibh and DV-2 16,681 (72 h) followed by cycloheximide treatment for 10 min. Total RNA and ribosomeprotected fragments were isolated following the published protocol  [22]. Small RNA libraries were generated using the SMARTer smRNA kit from Illumina. Deep sequencing libraries were generated from two independent clones in replicates (n = 2) and sequenced on the HiSeq 2000 platform. Genome annotation was from the human genome sequence GRCh37 downloaded from Ensembl public database: http://www. ensembl. org, accessed on 17 October 2018. For the virus genome, we used the reference genomes for ZIKV-Ibh and DV-2 16,681 downloaded from the NCBI database  [23].
ucsc. edu/cgi-bin/hgTables accessed on 17 October 2018). After removing the reads aligned to the ribosome RNAs, RF reads were mapped to the human genome sequence GRCh37 downloaded from Ensembl public database: http://www. ensembl. org accessed on 17 October 2018 using HISAT2 with default parameters  [25, 26]. We only used the uniquely aligned reads for further analysis. Total mRNA sequencing reads were first aligned to the virus genome as done for the ribosome footprint reads. Then the unmapped reads were aligned to the GRCh37 reference using HISAT2  [25, 26]. Similarly, as RF reads alignment, we performed the splice alignment for the paired-end mRNA-seq datasets with the default parameters. We only kept the uniquely aligned reads for the downstream analysis. The virus genome alignment quantification was done using featureCounts with the virus annotation (for both RF and mRNA sequencing in both ZIKV and DENV genome)  [27].
Real-Time PCR Assay Total RNA was extracted using AllPrep DNA/RNA/Protein Mini Kit (Qiagen, Hilden, Germany, 80004). cDNAs were synthesized from 1 µg of total RNA using SuperScript III First-Strand (Invitrogen, Waltham, MA, USA, 18080-400) and were amplified using Taqman Universal Master Mix II, no UNG (Applied Biosystems, Waltham, MA, USA, 4427788). Analysis was performed by ∆∆Ct. Applied Biosystems Taqman Gene Expression Assays: human SHMT2 Hs01059263_g1, PIM1 Hs01065498-m1, ATF3 Hs00231069-m1, and Beta-Actin 4332645. Relative mRNA expression was evaluated after normalization for Beta-Actin expression. Data show results from three independent experiments.
Transcriptional Changes Induced by ZIKV and DENV We simultaneously sequenced total RNA and ribosome-protected RNA fragments from uninfected and virus-infected human neuronal progenitor cells (hNPCs) (Figure  1A , the complete dataset is submitted to GEO). Briefly, two independent clones of hNPCs were differentiated from hiPSC (n = 2) and infected with ZIKV (IbH isolate) and DENV-2 (strain 16681) (referred to as ZIKV and DENV from here on) with an MOI of 1  [23]. hNPCs were differentiated from hiPSC from different healthy donors (n = 2) and complete differentiation was characterized by immunostaining for Nestin and Sox9 (Figure  S1A ). Consistent with prior observations, we found that ZIKV was more infective than DENV in hNPCs [30] and therefore we optimized the infection conditions to achieve equal infection rates as indicated by immunostaining for E-protein for both ZIKV and DENV (Figure  S1B ).
Measuring Translational Effects of Flavivirus Infection We measured effects on host cell mRNA translation by ribosome profiling on ZIKV and DENV infected hNPCs (MOI = 1) at 72 h post-infection in duplicates. Briefly, we used our published method of RiboDiff to measure the translation efficiency of transcripts that are differentially regulated upon virus infection  [28]. A summary of reading counts mapped to ribosomal RNAs, virus genome, and the human genome is provided in Supplementary Table  S4. On average, 4. 4 million RF reads mapped to the coding region of the human genome in uninfected cells, 4. 9 million in ZIKV infected, and 4. 8 million in DENV infected hNPC samples, corresponding to coverage across 19,821 protein-coding genes. Quality control analysis of replicates showed significant correlations among the replicates with a Pearson coefficient >0.
RNA Regulatory Motifs Enriched in Translationally Dysregulated mRNAs To identify cis-regulatory RNA motifs in the 5 UTRs of mRNAs that are translationally affected by ZIKV and DENV, we compared the TE down and TE up groups for both ZIKV and DENV datasets. We included RNAs with annotated 5 UTRs and compared the groups to each other and to a background list of equally expressed and annotated mRNAs that showed no significant change in their translation compared to the uninfected control sample. For ZIKV, the groups were TE up (n = 83 at p < 0. 05, q < 0. 3), TE down (n = 228 at p < 0. 05, q < 0. 3), and background (n = 302); for DENV TE, up (n = 69 at p < 0. 05, q < 0. 3), TE down (n = 67 at p < 0. 05, q < 0. 3), and background (n = 208). Despite the relatively small size of groups, we identified four significant (p < 1.
Scene localizer set 1 The stimuli included six different categories: familiar indoor scenes, unfamiliar human and monkey faces, unfamiliar head-less human and monkey bodies, and familiar objects. Examples of the stimuli are shown in the first row of Fig. 1a. The stimuli were chromatic, static, luminance matched images presented on a black background. Images of familiar scenes were taken from the training, scanning and housing facilities. The familiar object condition contained images of objects from the same environments, such as toys, cage enrichment, water bottles, etc. Different viewpoints and postures were shown for the faces and bodies. Human and monkey faces were selected from Markus Weber's Frontal face database, MIT-CBCL face recognition database, the SiblingsDB  (Vieira et al. , 2014) , and the PrimFace database (https://visiome.
ne uroinf. jp/primface/), respectively. Human bodies were selected from the MPII human pose dataset, the SUN database  (Xiao et al. , 2010) , and the Microsoft clipart image base. Monkey bodies were created from images taken from the other monkey groups in the lab. Each category contained 20 different images, each image was shown for 750 ms and was repeated twice in one block. Each run lasted 825 s, and contained 275 functional volumes. Data from 11 days were acquired for M1 (7-14 runs per day) and 5 days for M2 (6-12 runs per day).
To better match the visual complexity (e. g. the visual clutter and quantity of objects) of scenes  (Rajimehr et al. , 2009) , face mosaics and object mosaics stimuli were added as different conditions. The face mosaics were extracted from group photographs, each containing multiple people adjacent to each other (https://github. com/qdraw/tensorflow-faceobject-detector-tutorial). The object mosaics contained multiple objects never seen by the subjects (e. g. , kitchen tools and tableware), randomly positioned within each image of this stimulus condition. All the images were luminance matched, and embedded in a luminance-matched pink noise background. Each category contained 10 different images, and each image was shown for 750 ms and repeated four times within each block. Each run lasted 825 s, and contained 275 functional volumes.
Resting-state The subjects were asked to maintain fixation (within a 2 • × 2 • window) at a small red fixation point on a uniform gray background during the entire run. In addition, the subjects were also requested to have both their hands placed on reponse buttons within a response box in front of the chair (without moving their hands). Each run lasted 658 s, containing 231 functional volumes. Data from 2 days were acquired in M1 (12 and 17 runs per day) and 3 days for M2 (11-14 runs per day).
Surface segmentation and reconstruction We followed the non-human primates version of the Human Connectome Project (HCP) pipeline  (Autio et al. , 2020) , which includes FSL, Freesurfer and the Connectome Workbench (https://www. humanc onnectome. org/software/get-connectome-workbench). As our reference anatomical images were all acquired in a single session while the subject was anesthetized and fixed by its headpost, there is virtually no movement between the T1-w and T2-w images. Therefore, we skipped the motion correction step to avoid unnecessary smoothing. T2-w images were used to remove the effect of cerebrospinal fluid and pial veins during surface segmentation.
Time course analysis Percent signal changes were calculated from the second scene localizer experiment using ROIs defined on half of the data of the first scene experiment. Only the runs using the first sequence order were included, which includes 5 runs from subject M1 and 4 runs from subject M2. The signals were averaged inside each ROI across both hemispheres, after detrending and regressing of nuisance signals derived from the GLM denoise algorithm and converted in units of percent signal changes. The resulting signals were further averaged across runs and subjects. Because MION produces an activity-related signal decrease, the signal has been inverted. The time course was not smoothed in any extra step.
Eye movement analysis We only included runs with a mean fixation performance exceeding 90% of the run duration in all the fMRI analyses. The eye movement variances across different categories were compared based on eye data acquired from the second experiment. The eye movement variance was calculated using the median absolute deviation for each run, separately for each condition, and a non-parametric one-way analysis of variance (ANOVA) (Kruskal-Wallis) was conducted across runs to quantify the differences in eye movement variability between different conditions. The data from the two animals were tested separately.
FC analysis based on resting-state fMRI data Besides all the steps mentioned in "fMRI Image Reconstruction and Preprocessing", several additional preprocessing steps were conducted on the resting-state data for FC analysis  (Mantini et al. , 2011; Schwiedrzik et al. , 2015; Vincent et al. , 2007). Specifically, we included slice timing correction and the signal was high-pass filtered at 0. 0025 Hz and low-pass filtered at 0. 05 Hz. The signal from white matter and ventricle ROIs, the motion correction regressors, reward and eye movement related regressors, and their first derivatives were used to regress out nuisance effects. No global mean regression was performed to avoid discarding any underlying neural components  (Schölvinck et al. , 2010). The representative time course of each ROI was obtained by averaging the signals across voxels.
For scene versus body systems, the selectivity pattern was calculated using the contrast familiar scenes versus monkey and human bodies. For scene versus face systems, we contrasted familiar scenes versus monkey and human faces. The connectivity-selectivity correlations were converted to z-scores using Fisher's r-to-z transformation per run  (Zar, 1999)  and were averaged across runs and hemispheres for each subject to regenerate transformed r-matrices per subject (Fig. S12 ). To compute the r-matrix pooled across the two subjects, the z-scores were averaged across runs, hemispheres and subjects, and then back transformed to r (Figs. 7b  and S13b ). Two-tailed t-tests were performed on the original run-wise z-scores to threshold the r-matrices (p < 0. 05, FDR corrected). The NetworkX package was used to draw a surface representation of the functionally relevant network, in which the arrow lines were draw from the seeds to targets.
4 ). Unlike observations in humans  (Kamps et al. , 2016) , we found significantly higher responses for intact scenes in all core and non-core scene patches, including mOPA (Fig. S7 ; for all scene-selective areas: p-value < 10 -105 , FDR corrected). Hence, all monkey scene-selective patches are involved in processing coherent images portraying 3-dimensional spaces compared to local scene elements presented in the same pictures. Fig. 5  shows the average fMRI time course for each of the core scene-selective patches, averaged over both hemispheres of the two monkeys. Although only one-twelfth of the entire dataset could be used to calculate the time course, due to the randomization of 12 possible stimulus sequence orders, the higher response for scenes than control stimuli is apparent in these patches, corroborating the bar plots of Fig.
Link between functional connectivity and category selectivity in the scene network Next, we examined the functional relevance of the intrinsic scene network connectivity. Specifically, we asked whether coupling exists between FC and voxel-wise scene-selectivity within the scene processing network of monkeys, and whether the frontal and inferotemporal scene patches belong to the FRN specific for processing scenes. To this end, we included regions from other-domain specific networks and calculated task-independent FC between all possible source nodes to each voxel within a specific target node. FC was calculated using the same independent resting-state data and partial correlations as explained above (regressing out effects from all other source nodes). This yielded a FC fingerprint for each target node.
Acknowledgements The authors thank  A. Coeman ,  C. Fransen ,  P. Kayenbergh ,  I. Puttemans ,  S. De Pril ,  A. Hermans ,  G. Meulemans ,  W. Depuydt ,  C Ulens ,  S. Verstraeten , and  M. Depaep  for technical and administrative support. We are indebted to  J. Polimeni  and  T. Witzel  for their assistance in finetuning the high-resolution MR-sequences. This work was supported by the  Exploratory Program of CEA , the  French Alternative Energies and Atomic Energy Commission  (Grant No. 20P28 );  ANR , the  French National Agency for Research  (Grant No. ANR-20-CE37-0005 );  FWO G0C1920N ,  G0E0520N ; The European Union's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement No  945539  ( Human Brain  Project  SGA3 );  KU LeuvenC14/17/109 ,  C3/21/027, IDN/20/016  and  C14/21/111.
This is also an acute problem for Archaea  (13) , as well as certain eukaryotic taxa, including plants  (14, 15). In general, non-model organisms remain poorly annotated with issues identified in the early days of whole-genome sequencing  (16) , such as limited curation resources for integrating experimental data, pollution of databases with legacy annotations, inconsistent propagation of known annotations, widely propagated errors/overprediction due to shared superfamily membership and high proportions of true unknowns remaining unresolved  (9, 17, 18). Systems and synthetic biology approaches remain obstructed from reaching their full potential if the functions of biological parts continue to be left unknown or unannotated  (6, 19).
Bringing together three communities to better understand the issues at play This meeting, one of the first that brought together three communities (biocurators, experimentalists and computational biologists) within the same venue that rarely interact in triune, showed that significant technological advances to support protein functional annotation have been made; however, these methodologies have yet to be aligned and effectively applied across databases and communities. As discussed in this meeting report, many different groups and consortia have worked to develop ontologies, create machine-readable representations of enzyme reactions and metabolic pathways, implement high-throughput (HTP) experimental methods and construct comparative genomic and modeling tools that, all together, have the potential to increase the holistic knowledge of protein function; yet many of these tools have been used exclusively for model organisms, and therein, the acquired knowledge is unable to flow between different research silos.