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Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes
<p>Supplementary Tables and Supplementary Methods for the research article "Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes" published in Database.</p>
A phased genome of the highly heterozygous 'Texas' almond uncovers patterns of allele-specific expression linked to heterozygous structural variants
<h2># Genomic datasets associated to the publication: </h2> <h3># Gene-ID conversion with previous genome version</h3> <p>Texasv3_vs_Texasv2_GeneID.txt -- gene ID conversion between Texasv3 and Texasv2 (https://www.rosaceae.org/analysis/295)</p> <p>pdulcis26_to_F1_liftoff_polished.gff3 -- Texasv2 gene annotation liftoff on Phase-1 assembly (Phase-1 coordinates)</p> <h3># Phase-1</h3> <p>Texas_F1_K80_chr.fasta -- genome asssembly, phase-1 <br>Texas_F1_gene_models.gff3 -- phase-1 gene annotation (de novo annotation)<br>Functional_annotation_TexasF1.csv -- phase-1 gene functions <br>Texas_F1_ref_SV.vcf --- Structural variations relative to phase-0 (This file uses Phase-1 as reference)</p> <h3># Phase-0</h3> <p>Texas_F0_K80_chr.fasta -- genome asssembly, phase-0 <br>Texas_F0_gene_models.gff3 -- phase-0 gene annotation (liftoff from Phase-1)<br>Functional_annotation_TexasF0.csv -- phase-0 gene functions <br>Texas_F0_ref_SV.vcf --- Structural variations relative to phase-1 (This file uses Phase-0 as reference)</p> <h3># Transposable element annotation</h3> <p>Texas_F0_HiConf_TE_v3.gff3 --- TE annotation in Phase-0<br>Texas_F1_HiConf_TE_v3.gff3 --- TE annotation in Phase-1<br>Texasv3_TElib.fa --- TE library of TexasV3 (non-redundant repeat consensuses taking the account the two genome phases)</p> <h3># Gene sequences in fasta</h3> <p>Transcript, CDS and protein sequences in fasta format for phase-0 (F0) and phase-1 (F1) </p>
Population-level variant frequencies in segmental duplication regions
<p>Variant frequencies in segmental duplication regions analyzed by <a href="https://github.com/PacificBiosciences/paraphase">Paraphase</a> in five ancestral populations.</p>
Data and code for: A naturally occurring mitochondrial genome variant confers broad protection from infection in Drosophila (doi: https://doi.org/10.1101/2024.03.28.587162)
<p><strong> </strong>Raw data and R code for:</p> <p>Tiina S. Salminen, Laura Vesala, Yuliya Basikhina, Megan Kutzer, Tea Tuomela, Ryan Lucas, Katy Monteith, Arun Prakash, Tilman Tietz<sup> </sup>and Pedro F. Vale. A naturally occurring mitochondrial genome variant confers broad protection from infection in <em>Drosophila </em>(<strong>doi:</strong> https://doi.org/10.1101/2024.03.28.587162)</p> <p><em> </em></p> <p><strong>Data files:</strong></p> <p>CFU_stats.R: statistics for CFU data</p> <p>Prettgeri_CFU.csv: colony forming units (CFUs) in <em>P. rettgeri</em>-infected flies</p> <p>Saureus_CFU.csv: colony forming units (CFUs) in <em>S. aureus</em>-infected flies</p> <p> </p> <p>gene exp_mitotypes-stats.R: statistics for the qPCR data</p> <p>gene exp_mitotypes.csv: data on the relative gene expression of <em>FucTC</em>, <em>AANATL3</em>, <em>Acp1</em> and <em>CG3397</em> among mitotypes</p> <p> </p> <p>copynumber_stats.R: statistics for the copynumber data</p> <p>copynumber.csv: Mitochondrial copy number as copies of the mtDNA target gene <em>16S</em> relative to nuclear target gene <em>RpL32 </em></p> <p> </p> <p>respirometry_stats.R: statistics for the respirometry data</p> <p>respirometry.csv: Mitochondrial respiration measured in uninfected male flies by measuring oxygen consumption of the OXPHOS complexes I, III and IV</p> <p> </p> <p>ROS_stats.R: statistics for ROS</p> <p>ROS.csv: hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) quantification (whole flies)</p> <p> </p> <p>wasp_stats.R: Statistics on the proportion of melanised and unmelanised wasp larvae</p> <p>Wasps_data.csv: Data on proportion of melanised (killed) and unmelanised (living) <em>L. boulardi</em> parasitoid wasp larvae found in <em>Drosophila</em> larvae</p> <p> </p> <p>CC_stats.R: statistics on crystal cell counts</p> <p>CC.csv: amount of hemocytes called crystal cell in the larvae</p> <p> </p> <p>HC_stats.R: statistics on hemocyte counts</p> <p>hc_uninf.csv: total and activated hemocyte (blood cell) counts in uninfected larvae</p> <p>hc_inf.csv: total and activated hemocyte (blood cell) counts in infected larvae (48 h after infection by <em>L. boulardi</em> parasitoid wasps)</p> <p> </p> <p>PPO3.R: statistics on PPO3 expression levels</p> <p>PPO3.csv: data on gene expression level of prophenoloxidase (PPO3)</p> <p> </p> <p>MMP.R: statistics on MMP</p> <p>cybridMMP.csv: mitochondrial membrane potential (MMP) measured from larval hemocytes using the TMRM dye</p> <p> </p> <p>CellROX.R: statistics on CellROX<sup>TM </sup>signal</p> <p>CellROX.csv: Reactive oxygen species (ROS) measured from larval hemocytes using CellROX<sup>TM</sup> green reagent</p> <p> </p> <p>Hazard.Rmd: hazard ratios</p> <p>Combined.xlsx: data on hazard ratios Hazard ratios of survival post <em>P. rettgeri, S. aureus, </em>Kallithea and DCVinfections</p> <p> </p> <p>Mito_priming.R: Statistics on the effect of priming</p> <p>mito_priming.csv: data on the effect of immune priming with heat-killed bacteria prior to infection</p> <p> </p> <p>hc_stats_supplementary.R: statistics for sex-specific differences in hemocyte counts (data: hc_uninf.csv & hc_inf.csv )</p> <p>PI_hc_stats.R: statistics on dead (Propidium iodide, PI-positive) vs. living (PI-negative) larval hemocytes (data: hc_uninf.csv & hc_inf.csv )</p> <p> </p> <p>melanisation_stats.R: statistics on melanisation response</p> <p>melanisation_males.csv: melanisation response in hemolymph</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Tracking SARS-CoV-2 variants in wastewater in San Pedro de la Paz, Chile
<p>Various studies have shown the presence of SARS-CoV-2 RNA in the feces of patients<br>with COVID-19, both symptomatic and asymptomatic. This allowed determining the<br>viral load in wastewater samples from Wastewater Treatment Plants (WWTPs),<br>carrying out wastewater-based surveillance (WBS) of the virus in the community, as a<br>complement to person-to-person testing. The appearance of SARS-CoV-2 variants,<br>which can increase transmissibility and/or immune evasion, creates an imperative<br>need to implement specific and permanent surveillance methods to control the COVID19 pandemic. For variant detection, we performed a real-time RT-qPCR assay with a<br>commercial kit to detect five virus variants (Alpha, Beta, Gamma, Lambda, and Delta)<br>in the municipality of San Pedro de la Paz, Chile, from January to November 2021.<br>Detection of variants in wastewater was consistent with available clinical data and<br>provided additional information for community surveillance, identifying lambda and<br>delta variants as the most frequently detected during the second and third wave of<br>infections in the population of this area. Furthermore, in some cases we detected<br>specific variants in wastewater before local authorities confirmed the first clinical cases.<br>The study demonstrates that WBS is a tool that allows a rapid and cost-effective<br>detection of specific mutations associated with SARS-CoV-2 variants using RT-qPCR.<br>However, Illumina amplicon sequencing confirms that there are more optimal methods<br>to sequence this type of matrices. This method can be used to complement clinical<br>data during outbreaks and is especially useful when clinical care is insufficient or<br>collapsed and/or cost is very high, as is the case in many countries.</p>
LYCEUM: Learning to call copy number variants on low coverage ancient genomes
<p>Copy number variants (CNVs) are pivotal in driving phenotypic variation that facilitates species adaptation. They are significant contributors to various disorders, making ancient genomes crucial for uncovering the genetic origins of disease susceptibility across populations. However, detecting CNVs in ancient DNA (aDNA) samples poses substantial challenges due to several factors: (i) aDNA is often highly degraded; (ii) contamination from microbial DNA and DNA from closely related species introduce additional noise into sequencing data; and finally, (iii) the typically low coverage of aDNA renders accurate CNV detection particularly difficult. Conventional CNV calling algorithms, which are optimized for high coverage read-depth signals, underperform under such conditions. To address these limitations, we introduce LYCEUM, the first machine learning-based CNV caller for aDNA. To overcome challenges related to data quality and scarcity, we employ a two-step training strategy. First, the model is pre-trained on whole genome sequencing data from the 1000 Genomes Project, teaching it CNV-calling capabilities similar to conventional methods. Next, the model is fine-tuned using high- confidence CNV calls derived from only a few existing high-coverage aDNA samples. During this stage, the model adapts to making CNV calls based on the downsampled read depth signals of the same aDNA samples. LYCEUM achieves accurate detection of CNVs even in typically low-coverage ancient genomes. We also observe that the segmental deletion calls made by LYCEUM show correlation with the demographic history of the samples and exhibit patterns of negative selection inline with natural selection.</p>
Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast
<p><strong>Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast</strong></p> <p>This dataset contains mutation effect predictions for 22,169 <em>Drosophila melanogaster</em> protein isoforms, classifying over <strong>293 million </strong>amino acid substitutions as neutral, uncertain, or impactful. The predictions were generated using the evolution-based <strong>GEMME </strong>model (E.Laine et <em>al.</em> MBE 2019) with multiple sequence alignments (MSAs) from the highly efficient <strong>ColabFold</strong> protocol (M.Mirdita et<em> al.</em> NatMet 2022, Abakarova et al. <em>GBE</em> 2023). To ensure reliability, we provide<strong> </strong>global (per-protein) and local (per-residue) confidence metrics, since the predictions are sensitive to the input MSA quality.</p> <p>Predictions were validated using natural polymorphisms from the Drosophila Genetic Reference Panel (DGRP) and Drosophila Evolution over Space and Time (DEST2) datasets, as well as FlyBase’s developmentally lethal and hypomorphic mutations. Additionally, the dataset includes sensitivity data for post-translational modifications (PTMs) and short linear motifs (SLiMs), aiding functional site identification.</p> <p>All this data can be visualized at <a href="https://proteocast.ijm.fr/drosophiladb/">proteocast.ijm.fr</a>. </p> <p>Readme:</p> <ol> <li><strong>Drosophila_ProteoCast.tar.gz </strong>- this archive contains ProteoCast predictions and analysis for each unique proteoform, with folder names corresponding to IDs listed in the mapping_database.csv file. A detailed description of the folder structure and contents can be found in ReadMe.txt. </li> <li><strong>data.tar.gz </strong>- this archive contains the data used in this study, sourced from FlyBase, DGRP2, and DEST2.</li> <li><strong>Dmel6.44PredictionsRecap.csv</strong> - this summary file provides detailed information for each proteoform, all FlyBase protein IDs (<em>FBpp_ID</em>) included. It contains the following data: <ul> <li><strong>Identifiers</strong>: FlyBase protein ID (<em>FBpp_ID</em>), protein symbol (Protein_symbol), gene ID (<em>FBgn_ID</em>), transcript ID (<em>FBtr_ID</em>), and UniProt ID if available (<em>UniProt_ID</em>).</li> <li><strong>Protein Characteristics</strong>: Sequence length (<em>Length</em>) and whether the proteoform is representative (<em>Representative_FBpp</em>).</li> <li><strong>MSA and GEMME Predictions</strong>: Fraction of observed mutations (<em>F_obs</em>) and number of sequences (<em>Nb_seq_MSA</em>) in the ColabFold MSA, presence or absence of GEMME predictions (<em>GEMME_prediction</em>), and global confidence score (<em>GlobalConfidence</em>).</li> <li><strong>Mutation Classification</strong>: Thresholds for defining mutations as neutral, uncertain, or impactful (<em>GMM3_uncertain</em>, <em>GMM3_impactful</em>).</li> <li><strong>Genomic Information</strong>: DNA strand (<em>Strand</em>) and exon coordinates (<em>Exons_coordinates</em>).</li> <li><strong>Structural Data</strong>: 3D structure file name if available (<em>Structure_3D_file, Structure_3D</em>).</li> <li><strong>Mutation Counts</strong>: Number of analyzed mutations and affected residues, labelled as lethal, hypomorphic on FlyBase, or from the DEST2 and DGRP datasets (<em>n_Lethal, n_Lethal_res, n_Hypomorphic, n_Hypomorphic_res, n_DEST2, n_DEST2_res, n_DGRP, n_DGRP_res, n_DEST_DGRP_union, n_DEST_DGRP_union_res</em>).</li> </ul> </li> <li><strong>csv.tar.gz </strong>-<strong> </strong>this archive contains the files generated in this study. A detailed description of the folder structure and contents can be found in ReadMe.txt.</li> </ol>
Emerging Resistance to Novel b-Lactam b-Lactamase Inhibitor Combinations in Klebsiella pneumoniae bearing KPC Variants
<p><em><span><span>Klebsiella pneumoniae</span></span></em><span><span> carbapenemase (KPC) variants, predominantly KPC-2 and KPC-3, are a significant global resistance mechanism in <em><span>K. pneumoniae</span></em>. KPC-2 and KPC-3 confer resistance to a broad range of β-lactams, including carbapenems, while remaining susceptible to ceftazidime-avibactam (CZA). Lately, new KPC variants have emerged and developed resistance to CZA, often through mutations, insertions, or deletions in key regions such as the Omega loop, the 237–243 loop, and the 266–275 loop. In the present work we aim to understand the role of these mutations in KPC in the emergence of cross-resistance to last-resort antibiotics like cefiderocol (FDC) and cefepime/zidebactam (FPZ). Fifteen clinical isolates of KPC-producing <em><span>Klebsiella</span></em> spp. were analyzed, representing 15 distinct KPC variants. CZA resistance was confirmed in 12 of the 15 KPC variants tested. Cross-resistance to FDC was observed in eight isolates, with five of these exhibiting spontaneous resistant subpopulations, including one originally categorized as FDC-susceptible. Six FDC-resistant strains carried mutations in the 266–275 loop. Cross-resistance to FPZ was observed in five KPC variants, particularly those with mutations in the 266–275 loop, although many omega loop and 237–243 loop mutants remained susceptible to FPZ. WGS of FDC-resistant subpopulations revealed additional mutations in genes such as <em><span>ompC</span></em>, <em><span>rpoC</span></em>, <em><span>dksA</span></em>, and <em><span>cirA</span></em>.<br>This study demonstrates that KPC variants show resistance to both CZA and FDC, with cross-resistance to FPZ observed to a lesser degree. The emergence of cross-resistance in strains that had not been exposed to these antibiotics raises concerns about the spread of resistance. The identification of mutations in <em><span>bla</span></em><sub>KPC</sub>, <em><span>cirA</span></em>, and other novel genes highlights the importance of understanding the molecular mechanisms driving resistance to develop effective therapeutic strategies. </span></span></p>
AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia
<p>A preprint paper describing scenarios which generated this dataset can be accessed here: https://arxiv.org/abs/2107.06617. Please cite this work when using the dataset:<br> S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, arXiv: 2107.06617, 2021.</p> <p>Abstract. An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing"). Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model successfully predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.</p> <p>The AMTraC-19 source code (v7.7d) is released on Zenodo: https://zenodo.org/record/5778218</p>
Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California
<p>This repository contains the pertinent datasets used in the manuscript, <em>Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California</em>.</p>
Data Integration to Identify and Prioritize Potential Regulatory Variants for Rheumatoid Arthritis in a Post-GWAS era
<p>The Data set contain data related to the SNPs in linkage disequilibrium with published GWAS rheumatoid arthritis SNPs and the subsequent annotation and pathway analysis files performed to identify putative 'missed' variants of rheumatoid arthritis. </p>
Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation
<p>During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.</p> <p>This dataset comprises of summary stats for eQTLs identified in the study (parquet files) and the ones which passed significance threshold (tensor_out.tar.gz archive). Files are described by cell subset (CD4 Naive, CD 4 Memory, TEMRA, TCM, etc.), time since activation (16h, 4h, 5days) as described in the publication (preprint https://doi.org/10.1101/2021.12.06.470953)</p>
Ensemble Refinement of Stable-5-LOX and AlphaFold2 predictions including variants
<p>Ensemble Refinement of Stable-5-LOX in "closed" (PDB code: 7TTK) and "open" (PDB code: 7TTJ) conformations. AlphaFold2 models of Stable-5-LOX and variants from the research article "Helical remodeling augments 5-lipoxygenase activity."</p>
A complete list of filtered variants and gene lists of frequently and recurrently mutated genes in DLBCL, MCL, T-NHL, and BL, as well as pre-assembled CNV gene list
<p>A complete list of variants which passed filtering described in supplemental methods, that were found in both PDX model sample and patient’s sample from which it was derived (S1A) and variants which were gained (S1B) or lost (S1C) during PDX model derivation. Gene lists for filtration of variants in genes of special interest are included in the table (S1D). Gene list for filtration of CNV changes in genes of special interest is included in the table (S1E). Chr - Chromosome, REF - Reference allele, ALT - Alternative allele, AA change - Amino acid change, Patient AF - Allele frequency in the patient’s sample, Patient Depth – Read depth in patient’s sample, PDX AF- Allele frequency in PDX model sample, PDX Depth - Read depth in PDX sample, DLBCL - Diffuse Large B-cell lymphoma, MCL - Mantle Cell Lymphoma, TCL - T-cell lymphoma, BL – Burkitt Lymphoma, and CNV - Copy Number Variation.</p>
Data accompanying MetaChrom and "Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning"
<p>This is the data accompanying the paper " Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning" and the GitHub repository https://github.com/bl-2633/MetaChrom. </p> <p><strong>/data/bed_files/ </strong>contains the unprocessed bed file used in analysis</p> <p><strong>/data/seq_data/</strong> contains processed data from the bed files with corresponding partition and labels for each sequence segment.</p> <p><strong>/trained_model/MetaChrom_model/</strong> contains the pre-trained MetaChrom model on neural developmental context</p> <p><strong>/trained_models/MetaFeat_model/ </strong>contains the MetaFeat model used in training</p> <p><strong>/tool/</strong> contains files and software necessary for the processing pipeline.</p>
Interaction of Surface Glycoprotein of SARS-CoV-2 Variants of Concern with Potential Drug Candidates: A Molecular Docking Study
<p>This dataset contains additional docking images of SARS-CoV-2 Variants of Concern with drugs under study. It also contains a spreadsheet containing binding residues with different types of bonds. Further it includes the raw spike protein sequences, 3D model of spike proteins of VOCs (variant of concern) and potential drugs used in this study. </p>
Confocal images of rDRG neurons overexpressing human GDAP1 and its R120W and H123R variants
<p>Original confocal images for panels in Figure 6 in Sutinen et al. (2022). The images correspond to rDRG primary neurons overexpressing RFP or human GDAP1 (wild-type, R120W, H123R variants). Included is a Zip file with 16 TIFF files, one for each panel. </p>
Neutralizing immunity induced against the Omicron BA.2 variant in vaccinated and unvaccinated individuals infected by Omicron BA.1
<p>This repository contains the pertinent data files used in the manuscript, <em>Neutralizing immunity induced against the Omicron BA.2 variant in vaccinated and unvaccinated individuals infected by Omicron BA.1</em>.</p>
Patch clamp dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
<p><span>Many genes, including <em>KCNH2</em>, contain </span><span>'hotspot' domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of <em>KCNH2, </em>a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death<em>. </em>Forty-two percent of <em>KCNH2</em> exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT <em>KCNH2</em> allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this 'hotspot' domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.</span></p>
Trafficking dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
<p><span>Many genes, including <em>KCNH2</em>, contain </span><span>'hotspot' domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of <em>KCNH2, </em>a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death<em>. </em>Forty-two percent of <em>KCNH2</em> exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT <em>KCNH2</em> allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this 'hotspot' domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.