dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular findings Localization of variants and implicated amino acids are displayed in Fig. 1. Family 1: We performed array-CGH in the proband (the sister) and found a 3p26. 1-168 Kb deletion including exon 8 of GRM7 (Fig. 2 ) which was not in frame and which was not the last nor the penultimate exon and was therefore predicted to lead to haploinsufficiency by nonsense mediated decay (NMD) mechanism. This deletion was inherited from the mother. However, GRM7 was not known as a morbid gene at that time and this deletion was classified as a variant of unknown significance. We performed trio WES three years later and found a heterozygous missense variant NM_000844. 4:c. 1180G > A located in the ligand binding domain, inherited from the father. It was absent from the control variant databases gnomAD (gnomADv3) and LOVD (LOVDv.
3. 0). Prediction scores for pathogenicity were in favor of a    Glu394Lys ) variant on the protein neighborhood was studied with SWISS-MODEL software and is depicted in Fig. 2. The lysine substitution was predicted to rearrange the hydrogen bonds of this area located near the ligand binding domain and then destabilize the region and the binding. We suggest that this variation, in trans with a pathogenic deletion and with high in silico prediction of pathogenicity, could reach the PM3 and the PM1 (as suggested by recent literature  (Pejaver et al. , 2022; Ghosh et al. , 2017; Cheng et al. , 2023) ) ACMG criteria  (Richards et al. , 2015) , respectively. Family 2: Exome sequencing showed a homozygous 1-bp deletion, NM_000844. 4:c. 2067del, in both affected siblings. It was absent from control variant databases gnomAD (gnomAD v3) and LOVD (LOVDv.
Among them, ELFN1  (Tomioka et al. , 2014; Dursun et al. , 2021; Matsunaga and Aruga, 2021)  and PICK1  (Bertaso et al. , 2008)  have been yet described. Looking for variants in these two genes in our local exome sequencing database of patients with epilepsy who were negative for testing with our 145-gene panel for Mendelian epilepsies, we did not find any relevant variation. Overall, we report 5 additional patients from 3 unrelated families with GRM7 bi allelic variations. Other observations are needed to explore further the complex genotype-phenotype relationship in this severe epileptic and neurodevelopmental syndrome.
Funding This work was supported by  LABEX (excellence laboratory , program investment for the future)  DISTALZ  ( Development of Innovative Strategies for a Transdisciplinary approach to Alzheimer's disease)  (FC and LB),  IDEX UCAJedi :  Appel Jeunes Chercheurs   2022  (MC and FE),  France Alzheimer  (Grant #  8217 ) (MC),  Université Côte d'Azur  (Living-systems-complexity-and- diversity ) (MC), and  São Paulo Research Foundation (FAPESP , Grant # 2018/14289-6 ) (PFK and MC).
This GRN inference algorithm was applied to a dataset of single-cell RTqPCR data obtained on differentiating chicken erythrocytic cells. As expected, it did not produce a single GRN but rather a collection of 364 candidate GRNs equally well suited for reproducing experimental data when simulated. It is therefore the ideal playground for the development of a Design of Experiment strategy able to efficiently reduce the number of candidate GRNs previously generated by a GRN inference algorithm. To do this, we introduce TopoDoE, an iterative method for the in silico identification of the most informative perturbation -that is eliminating as many incorrect candidate GRNs as possible from the data gathered in one experiment. That method is a 4 step process in which : 1. a topological analysis is performed on the set of candidate GRNs to identify the most promising gene targets.
Then, in-silico data was obtained for all 6 conditions (reference data with no perturbation and each of the 5 selected genes knocked-out independently). For each, all 364 candidate GRNs were simulated for 100 hours, so that they had enough time to reach a new stable state after perturbation. Data was recorded at the end of the 100 hours to obtain mRNA count matrices of 200 cells each. Counts of mRNA molecules after each perturbation were compared to the reference dataset using one-sided t-tests for both 'greater' and 'less' mean value hypothesis. for which we measured significant expression variation is reported (p-value < 0. 01).
Step 3: In vitro perturbations To test experimentally our in-silico predictions, we devised a dedicated strategy to obtain single cell transcriptomics data on cells that had been validated for the KO of FNIP1 (Fig 3. A ). One of the main challenges we had to face was that the T2ECs being primary cells, they have a finite lifetime of 30 days  [9]  during which the cells had to be transfected, cloned, amplified, molecularly validated, and seeded in 96 wells plate for subsequent scRTqPCR analysis. Following FNIP1 's KO, we acquired single cell transcriptomics data for 61 KO cells and for 12 cells transfected with an empty plasmid which we used as control (Fig 3. B ). We recovered expression data for 45 of the 49 genes in the GRNs. Genes ABCG2, LDHA and GAB1 displayed poor quality data and were removed from the dataset.
Acknowledgments We would like to thank all members of the SBDM, Dracula and Vidium teams for their valuable advice and fruitful discussions. We also thank  Alice Hugues ,  Emma Risson  and  Kaushik Karambelkar  for participating as interns to the very early phases of that work. We thank the  BioSyL Federation  and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the  University of Lyon  for inspiring scientific events.
Availability of data and materials The datasets analysed during the current study are available in the OSF repository, https://osf. io/r3ujs/. Additionally, TopoDoE is made available as a Python library which can be downloaded from github at https://github. com/Vidium/topodoe. The simulated annealing algorithm modified for noisy cost functions that we used for balancing our simulations is also available as a Python library. Source code can be downloaded from github at https://github. com/Vidium/josiann. The library can also be installed with pip by running 'pip install josiann'.
CNV Carriers and Intraindividual Brain Differences Working Group core dataset and the UK Biobank across 61 scanner sites. Each CNV carrier was matched with 5 noncarriers based on age, sex, scanner site, and intracranial volume using the MatchIt package in R  (26). This resulted in 4 subsets (sample characteristics are presented in Tables  1  and 2 ; see also Note 1 in Supplement 1).
MRI-Derived Features, CNVs, and Quality Control Neuroimaging data were obtained from the UK Biobank, as described elsewhere  (27) , and from the ENIGMA-CNV core dataset. The ENIGMA-CNV neuroimaging measures were collected from several sites (see Appendix 1 in Supplement 2 for details) and analyzed using the standardized ENIGMA protocol (https://enigma. ini. usc. edu/protocols/imagingprotocols/). Details of the quality control of MRI are provided in Note 2 in Supplement 1. Briefly, the MRI data from the ENIGMA-CNV Working Group were subjected to the ENIGMA cortical quality control procedures (https://enigma. ini. usc. edu/protocols/imaging-protocols/), where the 68 cortical and 14 subcortical regions were extracted using the Desikan-Killiany atlas. For the UK Biobank sample, we used the Euler number as a proxy for image quality  (28)  and removed all participants with Euler numbers below minus 4 standard deviations from downstream analyses (n = 437).
To account for site effects in the samples, we ran each of the 4 subsets through ComBat, an instrument for data harmonization  (29). CNV calling in ENIGMA-CNV was based on previous publications  (15, 16). For the UK Biobank sample, we identified CNVs based on the returned dataset from Crawford et al. (30). All participants with a CNV as defined in previous publications  (15, 16, 30)  were removed from downstream analyses except for the individuals flagged with the 1q21. 1 distal or the 15q11. 2 BP1-BP2 CNV.
However, this is somewhat counterbalanced by the ENIGMA-CNV dataset that likely increases the heterogeneity in the study sample (although some datasets are likely to have similar bias toward healthy individuals as the UK Biobank). Indeed, the variability observed in brain structure within individuals underscores the heterogeneity between and within individuals in the sample. Future studies with larger sample sizes are needed to examine the phenotypic heterogeneity observed in CNV carriers. The results of the current study aid understanding of 1q21. 1 distal and 15q11. 2 BP1-BP2 CNV brain profiles by identifying regional differences using intraindividual variability metrics, which has the potential to give better insight into the neuronal mechanisms in neurodevelopment and risk for psychiatric diseases.
ACKNOWLEDGMENTS AND DISCLOSURES 1000BRAINS. This work was supported by the  Institute of Neuroscience and Medicine (INM-1) ,  Research Centre Julich ,  European Union 's  Horizon 2020 Research and Innovation Programme  (Grant No. 945539 [HBP SGA3 ; SC]), and  Joint Lab Supercomputing and Modeling for the Human Brain. We thank the  Heinz Nixdorf Foundation  for the generous support of the  Heinz Nixdorf Study. We also thank the scientists and the study staff of the Pomerania. Genome-wide data in SHIP have been supported by the  Federal Ministry of Education and Research  (Grant No. 03ZIK012 ) and a joint grant from  Siemens Healthineers  and the  Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from  Siemens Healthineers  and the  Federal State of Mecklenburg-West Pomerania.
PAFIP (Programa de Atención a las Fases Iniciales de Psicosis) : This work was supported by the  Instituto de Salud Carlos III  (Grant Nos. 00/3095 ,  01/3129 ,  PI020499 ,  PI14/00639 ,  PI17/01056 , and  PI14/00918 ),  SENY Fundació  (Research Grant No. CI2005 0308007 ), and  Fundación Marqués de Valdecilla Instituto de Investigación Sanitaria Valdecilla  (Grant Nos. A/02/07 ,  NCT0235832 , and  NCT02534363 ). Osaka: This work was supported by the  Japan Agency for Medical Research and Development  (Grant Nos. JP21wm0425012 ,  JP18dm0307002 ,  JP22wm0525019 , and  JP22dk0207060 ) and  Japan Society for the Promotion of Science KAKENHI  (Grant Nos. JP20H03611 ,  JP22H04926 , and  20K15778 ). Some computations were performed at the  Research Center for Computational Science, Okazaki, Japan  (Project:  NIPS ,  18-IMS-C162 ,  19-IMS-C181 ,  20-IMS-C162 ,  21-IMS-C179 ,  22-IMS-C195 ).
U54 EB020403 , supported by a cross- NIH  alliance that funds  Big Data to Knowledge Centres of Excellence , and Grant No. 1R56AG058854-01 ),  National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London ,  ERANID  ( Understanding the   Interplay Between Cultural, Biological and Subjective Factors in Drug Use Pathways)  (Grant No. PR-ST-0416-10004 ),  BRIDGET  ( JPND :  BRain Imaging, cognition Dementia and next generation GEnomics)  (Grant No. MR/N027558/1 ),  Human Brain Project  ( HBP SGA 2 ) (Grant No. 785907 ),  FP7  project  MATRICS  (Grant No. 603016 ),  Medical Research Council Grant Consortium on Vulnerability to Externalizing Disorders and Addictions  (Grant No. MR/N000390/1 ),  Bundesministerium für Bildung und Forschung  (Grant Nos.
01GS08152 ,  01EV0711 , Forschungsnetz  AERIAL 01EE1406A , and  01EE1406B ),  Deutsche Forschungsgemeinschaft  (Grant Nos. SM 80/7-2 ,  SFB 940/2 , and  NE 1383/14-1 ),  Agence Nationale de la Recherche (ANR)  (Grant Nos. ANR-12-SAMA-0004  and  AAPG2019 [GeBra ]),  Eranet Neuron  (Grant Nos. AF12-NEUR0008-01 [WM2NA ],  ANR-18-NEUR00002-01  [ ADORe ], and  ANR-12-SAMA-0004 ),  Fondation de France  (Grant No. 00081242 ),  Fondation pour la Recherche Médicale  (Grant No. DPA20140629802 ),  Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives ,  Assistance Publique Hôpitaux de Paris  and  Institut National de la Santé et de la Recherche Médicale (Interface Grant) ,  Paris Sud University IDEX 2012 ,  Fondation de l'Avenir  (Grant No. AP-RM-17-013 ), and  Fédération pour la Recherche sur le Cerveau.
OSR ( Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffele) -Milan: This work was supported by the  European Union   H2020  ( EU. 3. 1. 1  Grant No. 754740 MOODSTRATIFICATION. EU),  Italian Ministry of Health  (Grant No. RF-2018-12367249 ) and  Italian Ministry of University and Scientific Research  (Grant No. A_201779W93T ). Dublin: This work was supported by the  European Research Council  (Grant No. ERC-2015-STG-677467  [to GD]) and  Science Foundation Ireland  (Grant No. SFI-16/ERCS/3787  [to GD]). Brain Imaging Genetics (BIG): This work makes use of the BIG database, first established in Nijmegen, the Netherlands, in 2007. This resource is now part of Cognomics (www. cognomics. nl), a joint initiative by researchers from the  Donders Centre for Cognitive Neuroimaging, Human Genetics and Cognitive Neuroscience departments of Radboud University Medical Centre , and  Max Planck Institute for Psycholinguistics in Nijmegen.
The  Cognomics Initiative  is supported by the participating departments and centers and  Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) ,  Hersenstichting Nederland , and  NWO. The research leading to these results also receives funding from the NWO (Gravitation Grant No. 024. 001. 006 Language in Interaction), European Community's Seventh Framework Programme (FP7/2007-2013) (Grant Nos. 602450 IMAGEMEND, 278948 TACTICS, and 602805 Aggressotype), European Community's Horizon 2020 programme (Grant Nos. 643051 MiND, and ERC-2010-AdG 268800-NEUROSCHEMA). In addition, the work was supported by the ENIGMA Consortium (Grant No. U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. UK Biobank: This research has been conducted using the UK Biobank Resource under Application Number 27412.
ECHO-DEFINE: This work was supported by the  Wellcome Trust  ( Institutional Strategic Support Fund  [to MBMvdB] and  Clinical Research Training Fellowship  Grant No. 102003/Z/13/Z [ to JLD]),  Waterloo Foundation  (Grant No. WF 918-1234  [to MBMvdB]),  Baily Thomas Charitable Fund  (Grant No. 2315/1  [to MBMvdB]),  NIMH  (Grant Nos. 5UO1MH101724  and  U01MH119738  [to MBMvdB]),  IMAGINE-ID  and  IMAGINE-2 studies  (funded by  Medical Research Council  Grant Nos. MR/N022572/1  and  MR/T033045/ 1  [to MBMvdB]), and  Medical Research Council (Centre  Grant No. MR/ P005748/1  [to MJO]). The DEFINE study was supported by a  Wellcome Trust   Strategic Award  (Grant No. 100202/Z/12/Z  [to MJO]). FOR2107  Marburg and Münster: This work is part of the  German multicenter  consortium " Neurobiology of Affective Disorders: A Translational Perspective on Brain Structure and Function ," supported by the  German Research Foundation (Deutsche Forschungsgemeinschaft ; Forschungsgruppe/Research Unit  FOR2107 ).
Methylation calling RRBS. The methylation analyses were conducted using the Bismark software (v0. 20. 0)  (Krueger and Andrews, 2011). Initially, the sample was aligned to the reference genome (ARS-UCD1. 2) using Bowtie 1. 2. 1  (Langmead and Salzberg, 2012). CpGs in unplaced scaffolds were excluded. The remaining CpGs were assigned a methylation percentage using Bismark's methylation calling algorithm. Further details can be found in  (Costes et al. , 2022). Nanopore. The analyses were executed using Nanopolish (v0. 13. 2)  (Loman et al. , 2015) , following the recommended pipeline by its author. The fast5 and fastQ files obtained were indexed and then aligned against the Bos taurus reference genome (ARS-UCD1. 2) using minimap2 v2. 17-r941  (Li, 2018). Methylation sites were subsequently identified using the call_methylation function from Nanopolish, and the frequencies were extracted using the calculate_methylation_frequency.
Differentially methylated sites The same differential methylation analysis was performed for all samples using the DSS package  (Park and Wu, 2016)  in R software. A filter was set to remove positions that were not present or enough covered in every analysed sample. These analyses were conducted at minimum coverage thresholds of 4x and 7x. Each cytosine was required to be covered above these thresholds in all the 6 samples individually, or filtered out otherwise. The list of regions present in every sample was then annotated to remove positions located more than 50 kb away from a transcription start site  (Porto-Neto et al. , 2014). To estimate the mean methylation frequencies for each group, the smoothing option was selected as 'True' as recommended by the authors for whole-genome analyses.
Comparison of called CPGS ChipSeeker  (Yu et al. , 2015)  was used to plot the CpGs with a coverage ≥ 4x surrounding the TSS to study the density of those around promoter sites. The distance of the CpGs to the closest TSS and the genomic features associated to them were also estimated. A kernel density estimation (KDE) was used to study the location of the CpGs in the chromosome. The correlation between the methylation frequency of those CpGs sites detected by ONT and RRBS, was calculated using the aforementioned mentioned coverage thresholds of 4x and 7x. The agreement of genome-wide methylation detected by both techniques was analysed using the R package ChipSeeker without filtering by coverage. For this, methylation percentage in genomic regions, concordance between detected genes and detection of CpGs close to promoters were studied.
Promoter regions were defined as -3000 and +3000 bp from ATG transcription-start codon. ChipSeeker was used to find the proximity of the DMCs from each technique to TSS through the function tagHeatmap, the genomic annotation via plotAnnoBar and the density of DMCs in the proximal regions of the gene bodies using plotPeakProf2. Minimum coverages of 4x and 7x were tested. The DMCs present in quantitative trait loci (QTL) were described using the files available in the Cattle QTL database  (Hu et al. , 2022). The QTLs were grouped by trait and plotted together to investigate the possible role of the epigenetic marks detected by each technique  (Liao et al. , 2019).
QTL associated with the DMCs The possible role of the methylation of these DMCs was studied by positioning the detected DMC searched in the animal QTLdatabase. A total of 211 different QTLs out of 1579 were found associated to the DMCs detected by ONT4x (14 %), 117 out of 513 by ONT7x (23 %), 185 QTLs out of 1209 by RRBS4x (15 %) and 175 QTLs out of 1143 by RRBS7x (15 %). The majority of the QTLs annotated were related to traits of relevance in cattle production such as body and carcass weight, marbling score, milk yield, feed intake and calving easy. The QTLs detected are shown in Fig. 12. Out of the total 211 QTLs previously described, 30 were directly associated with bull fertility. Table  2  summarises the number of QTLs associated to different male fertility traits called by each technique.
DNA shearing for ONT sequencing is optional and this fragmentation does not generate a bias, as it only generates fragments of a desired size, without targeting any specific region. However, the targeted fragmentation of RRBS poses a limitation to this study, as regions with a large proportion of CpGs (e. g. CpG islands) will be overrepresented in contrast to regions with fewer CpGs (e. g. distal intergenic regions). This difficults the comparison of CpGs located away from   promoter or genic regions between the methylation frequency estimated by RRBS and ONT. A future ideal approach would add sequencing depth to the ONT data and would include WGBS data to correctly compare intergenic regions. Here, we proposed the complementarity of RRBS and ONT, where RRBS increases the coverage in promoter regions and ONT explores other regions of the genome unreachable to RRBS due to its chemistry.
In almost half of the clones sequenced (13 of 30), the insertion of 1 nucleotide (insþ1) at the predicted target site (43. 3%) was found. Twelve clones among 30 showed from 1 to 12 nucleotide deletions (del-1 to del-12) (40%). One clone showed an SNP (3. 3%), and 4 clones were found unedited (13. 3%) (Supplementary Figure  S2c ). Assessment of off-targets in edited primary RDEB-K and RDEB-F The off-target activity of the gRNA T1 targeting intron 79 was assessed on in silico predicted top off-target sites predicted by COSMID and http://crispor. tefor. net databases. (e) Summary of the cleavage activity of gRNAs T1, T2, and T3 estimated by TIDE assay. Each experiment was repeated 3 times. Fw, forward; gRNA, guide RNA; NHEJ, non-homologous end joining; RDEB, recessive dystrophic epidermolysis bullosa; RDEB-F, recessive dystrophic epidermolysis bullosa fibroblast; RDEB-K, recessive dystrophic epidermolysis bullosa keratinocyte; rev, reverse; RNP, ribonucleoprotein; TIDE, tracking of insertion/deletions by decomposition.
Currently, several clinical trials using CRISPR/Cas9 technology are underway to treat genetic and chronic disorders (https://clinicaltrials. gov). Very recently, an ex vivo clinical trial was conducted on 2 patients with genetic blood disorders, transfusion-dependent b-Thalassemia, and sickle cell disease  (Frangoul et al, 2021; Sharma et al, 2023). Other ex vivo clinical trials were also successfully completed or are ongoing to treat blood and lung cancers using chimeric antigen receptor T-cell and PD-1 combinatorial approaches  (Lu et al, 2020; Stadtmauer et al, 2020). Overall, these early-stage clinical trials have already investigated the efficacy, safety, and tolerability issues related to CRISPR/Cas9 and demonstrated the compatibility of this technology with the clinic. For RDEB, different CRISPR-Cas9ebased preclinical studies involving HDR, exon reframing, double nicking, or base editing strategies have been conducted during these last 5 years to demonstrate the feasibility and functionality of COL7A1 correction  (March et al, 2020; Titeux et al, 2020).
MATERIALS AND METHODS gRNA design To demonstrate the feasibility of COL7A1 editing in primary RDEB cells using the CRISPR/Cas9 approach, we designed gRNAs to correct a causative null variant in exon 80 seen in several subjects with RDEB (c. 6508C>T; p. Gln2170*). Three gRNAs (T1, T2, and T3) targeting the c. 6508C>T variant or sequences in close vicinity of the variant were identified and designed using the ZiFiT Targeter 4. 2 (http://zifit. partners. org) online software (Supplementary Table  S1 ). No other sequence within the human genome showed 100% identity to these target sites. The fourth gRNA (T4) targeting the sequences in close vicinity of the c. 425A>G variant was identified and designed similarly (Supplementary Table  S1 ).
The PCR program is as follows: initial denaturation at 95 C for 10 minutes, followed by 40 cycles of melting at 95 C for 30 seconds and annealing per extension at 60 C for 1 minute (rampe rate at 2 C/ sec) and final enzyme deactivation at 98 C for 10 minutes. After cycling, the 96-well plate was immediately transferred to BioRad QX-100 Droplet Reader, where flow cytometric analysis determined the fraction of PCR-positive droplets (blue and green dots) versus the number of PCR-negative (gray dots) droplets in the sample. Data were analyzed by QuantaSoft Software (Bio-Rad Laboratories) using the RED (rare events detection) option. The concentration of target (corrected) versus reference (mutated) DNA was automatically determined and corrected for Poisson distribution. All error bars were generated by the software and represent 95% confidence interval.