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The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform the public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This portal has been coupled with other resources, such as Viral AI, and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this portal (https://virusseq-dataportal.ca/), including its contextual data not available elsewhere, and the Duotang (https://covarr-net.github.io/duotang/duotang.html), a web platform that presents key genomic epidemiology and modelling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the portal (COVID-MVP, CoVizu), are all open source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Aim: The study aimed to evaluate and compare the short and long-term outcomes of doppler-guided (DG) hemorrhoidal artery ligation and rectoanal repair (HAL-RAR) using a wireless-doppler-guided probe and stapled hemorrhoidopexy (SH) for treatment of II-III hemorrhoids., Methods: This cohort study included patients who underwent HAL-RAR (n = 89) or SH (n = 174) for grade II-III hemorrhoids between January 2020 and December 2021. After propensity score matching at a 1:1 ratio, 76 patients for each group were analyzed. Short and long-term outcomes were collected. Pain was measured using a Visual Analogue Scale (VAS) at POD1, POD 10, 1 month, and 6 months after surgery. The enrolled patients completed the Hemorrhoidal Disease Symptom Score and Short Health ScaleHD quality of life (HDSS/SHS QoL) questionnaire preoperatively and during a regular follow-up visit at 24 months after surgery., Results: Groups exhibited comparable overall postoperative complication rates (23% HAL-RAR/ 21% SH; p = 0.295). Postoperative pain via VAS showed median scores of 4, 3, 1, 1 for HAL-RAR and 6, 4, 2, 1 for SH at POD1, POD10, 1 month, and 6 months, respectively (p =  < 0.001, 0.004, 0.025, 0.019). At a median follow-up of 12 months, the recurrence rate was 10.5% in the HAL-RAR group and 9.2% in the SH group (p = 0.785), respectively. At 24 months, 15.7% of HAL-RAR patients and 19.7% of SH patients remained symptomatic (p = 0.223). Median post-op QoL index was 1 (HAL-RAR) and 0.92 (SH), p = 0.036., Conclusions: HAL-RAR is a safe and feasible technique in treating grade II-III hemorrhoids showing better outcomes in terms of postoperative pain and QoL., Significance: This paper adds a new perspective in comparing the HAL-RAR and SH, focusing the attention on the patients and not surgical techniques. A long and difficult follow-up was completed to fully understand the long-term results and the impact on the QoL of the patients who underwent these procedures., (© 2024. The Author(s).)

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic., Competing Interests: Declaration of interests E. Anagnostou has received consultation fees from Roche, Quadrant, and Oron; grant funding from Roche; in-kind supports from AMO Pharma and CRR; editorial honoraria from Wiley; and book royalties from APPI and Springer. She co-holds a patent for the device Anxiety Meter (patent # US20160000365A1). S.W.S. is on the Scientific Advisory Committee of Population Bio and serves as a Highly Cited Academic Advisor for King Abdulaziz University, and intellectual property from aspects of his research held at The Hospital for Sick Children are licensed to Athena Diagnostics and Population Bio. These relationships did not influence data interpretation or presentation during this study but are disclosed for potential future considerations., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource., Competing Interests: DECLARATION OF INTERESTS M. Brudno holds financial interest in PhenoTips. E.B. is a consultant to Oxford Nanopore Technologies and Dovetail Inc. and a member of the Cell Genomics advisory board. H.L.R. is a member of the Cell Genomics advisory board. All other authors have no interests to declare.

In the version of this article initially published, Lena Dolman's second affiliation was given as Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK. The correct second affiliation is Ontario Institute for Cancer Research, Toronto, Ontario, Canada. The error has been corrected in the HTML and PDF versions of the article.

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use. However, under current practices, the data is fragmented into many distinct datasets, and researchers must go through a separate application process for each dataset. This is time-consuming both for the researchers and the data stewards, and it reduces the velocity of research and new discoveries that could improve human health. We propose to simplify this process, by introducing a standard Library Card that identifies and authenticates researchers across all participating datasets. Each researcher would only need to apply once to establish their bona fides as a qualified researcher, and could then use the Library Card to access a wide range of datasets that use a compatible data access policy and authentication protocol.

Background: The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. We describe genomic variation identified in the initial recruitment cohort of 56 volunteers., Methods: Volunteers were screened for eligibility and provided informed consent for open data sharing. Using blood DNA, we performed whole genome sequencing and identified all possible classes of DNA variants. A genetic counsellor explained the implication of the results to each participant., Results: Whole genome sequencing of the first 56 participants identified 207 662 805 sequence variants and 27 494 copy number variations. We analyzed a prioritized disease-associated data set ( n = 1606 variants) according to standardized guidelines, and interpreted 19 variants in 14 participants (25%) as having obvious health implications. Six of these variants (e.g., in BRCA1 or mosaic loss of an X chromosome) were pathogenic or likely pathogenic. Seven were risk factors for cancer, cardiovascular or neurobehavioural conditions. Four other variants - associated with cancer, cardiac or neurodegenerative phenotypes - remained of uncertain significance because of discrepancies among databases. We also identified a large structural chromosome aberration and a likely pathogenic mitochondrial variant. There were 172 recessive disease alleles (e.g., 5 individuals carried mutations for cystic fibrosis). Pharmacogenomics analyses revealed another 3.9 potentially relevant genotypes per individual., Interpretation: Our analyses identified a spectrum of genetic variants with potential health impact in 25% of participants. When also considering recessive alleles and variants with potential pharmacologic relevance, all 56 participants had medically relevant findings. Although access is mostly limited to research, whole genome sequencing can provide specific and novel information with the potential of major impact for health care., Competing Interests: Competing interests: Stephen Scherer serves on the Scientific Advisory Committees of Population Bio and Deep Genomics. Sherilyn Bell, Jo-Anne Herbrick, Jennifer Howe, Ann Joseph-George, Barbara Kellam, Chao Lu, Jeffrey MacDonald, Christian Marshall, Thomas Nalpathamkalam, Rohan Patel, Tara Paton, Giovanna Pellecchia, Sergio Pereira, Miriam Reuter, Stephen Scherer, Lisa Strug, Wilson Sung, Bhooma Thiruvahindrapuram, Susan Walker, Zhuozhi Wang, John Wei, Joe Whitney, Richard Wintle and Ryan Yuen have received grants from Genome Canada/Ontario Genomics; Canada Foundation for Innovation; McLaughlin Centre, University of Toronto; the Government of Ontario, Canadian Institutes of Health Research (CIHR); and the The Hospital for Sick Children Foundation during the conduct of the study. James Ellis, Matthew Hildebrandt, Hin Lee, Peter Pasceri and Wei Wei have received a grant from the McLaughlin Centre, University of Toronto. Daniele Merico is an employee of Deep Genomics. Brett Trost has received a postdoctoral fellowship from CIHR. No other competing interests were declared., (© 2018 Joule Inc. or its licensors.)

A systematic way of recording data use conditions that are based on consent permissions as found in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA).

High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r(2) = 0.94) with the predicted abundances.

The development of high-throughput sequencing (HTS) technologies has opened the door to novel methods for detecting copy number variants (CNVs) in the human genome. While in the past CNVs have been detected based on array CGH data, recent studies have shown that depth-of-coverage information from HTS technologies can also be used for the reliable identification of large copy-variable regions. Such methods, however, are hindered by sequencing biases that lead certain regions of the genome to be over- or undersampled, lowering their resolution and ability to accurately identify the exact breakpoints of the variants. In this work, we develop a method for CNV detection that supplements the depth-of-coverage with paired-end mapping information, where mate pairs mapping discordantly to the reference serve to indicate the presence of variation. Our algorithm, called CNVer, combines this information within a unified computational framework called the donor graph, allowing us to better mitigate the sequencing biases that cause uneven local coverage and accurately predict CNVs. We use CNVer to detect 4879 CNVs in the recently described genome of a Yoruban individual. Most of the calls (77%) coincide with previously known variants within the Database of Genomic Variants, while 81% of deletion copy number variants previously known for this individual coincide with one of our loss calls. Furthermore, we demonstrate that CNVer can reconstruct the absolute copy counts of segments of the donor genome and evaluate the feasibility of using CNVer with low coverage datasets.

Motivation: The advent of high-throughput sequencing (HTS) technologies has made it affordable to sequence many individuals' genomes. Simultaneously the computational analysis of the large volumes of data generated by the new sequencing machines remains a challenge. While a plethora of tools are available to map the resulting reads to a reference genome, and to conduct primary analysis of the mappings, it is often necessary to visually examine the results and underlying data to confirm predictions and understand the functional effects, especially in the context of other datasets., Results: We introduce Savant, the Sequence Annotation, Visualization and ANalysis Tool, a desktop visualization and analysis browser for genomic data. Savant was developed for visualizing and analyzing HTS data, with special care taken to enable dynamic visualization in the presence of gigabases of genomic reads and references the size of the human genome. Savant supports the visualization of genome-based sequence, point, interval and continuous datasets, and multiple visualization modes that enable easy identification of genomic variants (including single nucleotide polymorphisms, structural and copy number variants), and functional genomic information (e.g. peaks in ChIP-seq data) in the context of genomic annotations., Availability: Savant is freely available at http://compbio.cs.toronto.edu/savant.

The development of Next Generation Sequencing technologies, capable of sequencing hundreds of millions of short reads (25-70 bp each) in a single run, is opening the door to population genomic studies of non-model species. In this paper we present SHRiMP - the SHort Read Mapping Package: a set of algorithms and methods to map short reads to a genome, even in the presence of a large amount of polymorphism. Our method is based upon a fast read mapping technique, separate thorough alignment methods for regular letter-space as well as AB SOLiD (color-space) reads, and a statistical model for false positive hits. We use SHRiMP to map reads from a newly sequenced Ciona savignyi individual to the reference genome. We demonstrate that SHRiMP can accurately map reads to this highly polymorphic genome, while confirming high heterozygosity of C. savignyi in this second individual. SHRiMP is freely available at http://compbio.cs.toronto.edu/shrimp.