Large-scale Sequencing Projects Research Articles

Bioinformatics in Russia: history and present-day landscape.

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction.

Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphisms identified in genome-wide association studies. However, most deep learning methods rely on supervised learning, which requires DNA sequences associated with functional data, and whose amount is severely limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is growing exponentially due to ongoing large-scale sequencing projects, but in most cases without functional data. To alleviate the limitations of supervised learning, we propose a novel semi-supervised learning (SSL) based on pseudo-labeling, which allows to exploit unlabeled DNA sequences from numerous genomes during model pre-training. We further improved it incorporating principles from the Noisy Student algorithm to predict the confidence in pseudo-labeled data used for pre-training, which showed improvements for transcription factor with very few binding (very small training data). The approach is very flexible and can be used to train any neural architecture including state-of-the-art models, and shows in most cases strong predictive performance improvements compared to standard supervised learning. Moreover, small models trained by SSL showed similar or better performance than large language model DNABERT2.

Rare germline genetic variation in PAX8 transcription factor binding sites and susceptibility to epithelial ovarian cancer.

Common genetic variation throughout the genome together with rare coding variants identified to date explain about a half of the inherited genetic component of epithelial ovarian cancer risk. It is likely that rare variation in the non-coding genome will explain some of the unexplained heritability, but identifying such variants is challenging. The primary problem is lack of statistical power to identifying individual risk variants by association as power is a function of sample size, effect size and allele frequency. Power can be increased by using burden tests which test for association of carriers of any variant in a specified genomic region. This has the effect of increasing the putative effect allele frequency. PAX8 is a transcription factor that plays a critical role in tumour progression, migration and invasion. Furthermore, regulatory elements proximal to target genes of PAX8 are enriched for common ovarian cancer risk variants. We hypothesised that rare variation in PAX8 binding sites are also associated with ovarian cancer risk, but unlikely to be associated with risk of breast, colorectal or endometrial cancer. We have used publicly available, whole-genome sequencing data from the UK 100,000 Genomes Project to evaluate the burden of rare variation in PAX8 binding sites across the genome. Data were available for 522 ovarian cancers, 2,984 breast cancers, 2,696 colorectal cancers, 836 endometrial cancers and 2253 non-cancer controls. Active binding sites were defined using data from multiple PAX8 and H3K27 ChIPseq experiments. We found no association between the burden of rare variation in PAX8 binding sites (defined in several ways) and risk of ovarian, breast or endometrial cancer. An apparent association with colorectal cancer was likely to be a technical artefact as a similar association was also detected for rare variation in random regions of the genome. Despite the null result this study provides a proof-of -principle for using burden testing to identify rare, non-coding germline genetic variation associated with disease. Larger sample sizes available from large-scale sequencing projects together with improved understanding of the function of the non-coding genome will increase the potential of similar studies in the future.

Oncogenic Enhancers in Leukemia.

Although the study of leukemogenesis has traditionally focused on protein-coding genes, the role of enhancer dysregulation is becoming increasingly recognized. The advent of high-throughput sequencing, together with a better understanding of enhancer biology, has revealed how various genetic and epigenetic lesions produce oncogenic enhancers that drive transformation. These aberrations include translocations that lead to enhancer hijacking, point mutations that modulate enhancer activity, and copy number alterations that modify enhancer dosage. In this review, we describe these mechanisms in the context of leukemia and discuss potential therapeutic avenues to target these regulatory elements. Significance: Large-scale sequencing projects have uncovered recurrent gene mutations in leukemia, but the picture remains incomplete: some patients harbor no such aberrations, whereas others carry only a few that are insufficient to bring about transformation on their own. One of the missing pieces is enhancer dysfunction, which only recently has emerged as a critical driver of leukemogenesis. Knowledge of the various mechanisms of enhancer dysregulation is thus key for a complete understanding of leukemia and its causes, as well as the development of targeted therapies in the era of precision medicine.

COPO - Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

Natural antisense transcripts as versatile regulators of gene expression.

Long non-coding RNAs (lncRNAs) are emerging as a major class of gene products that have central roles in cell and developmental biology. Natural antisense transcripts (NATs) are an important subset of lncRNAs that are expressed from the opposite strand of protein-coding and non-coding genes and are a genome-wide phenomenon in both eukaryotes and prokaryotes. In eukaryotes, a myriad of NATs participate in regulatory pathways that affect expression of their cognate sense genes. Recent developments in the study of NATs and lncRNAs and large-scale sequencing and bioinformatics projects suggest that whether NATs regulate expression, splicing, stability or translation of the sense transcript is influenced by the pattern and degrees of overlap between the sense-antisense pair. Moreover, epigenetic gene regulatory mechanisms prevail in somatic cells whereas mechanisms dependent on the formation of double-stranded RNA intermediates are prevalent in germ cells. The modulating effects of NATs on sense transcript expression make NATs rational targets for therapeutic interventions.

Species-aware DNA language models capture regulatory elements and their evolution

BackgroundThe rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution.ResultsHere, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution. Investigating their ability to predict masked nucleotides from context, we show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. Owing to their flexibility, DNA language models capture conserved regulatory elements over much further evolutionary distances than sequence alignment would allow. Remarkably, DNA language models reconstruct motif instances bound in vivo better than unbound ones and account for the evolution of motif sequences and their positional constraints, showing that these models capture functional high-order sequence and evolutionary context. We further show that species-aware training yields improved sequence representations for endogenous and MPRA-based gene expression prediction, as well as motif discovery.ConclusionsCollectively, these results demonstrate that species-aware DNA language models are a powerful, flexible, and scalable tool to integrate information from large compendia of highly diverged genomes.

Differentiated adaptative genetic architecture and language-related demographical history in South China inferred from 619 genomes from 56 populations

BackgroundThe underrepresentation of human genomic resources from Southern Chinese populations limited their health equality in the precision medicine era and complete understanding of their genetic formation, admixture, and adaptive features. Besides, linguistical and genetic evidence supported the controversial hypothesis of their origin processes. One hotspot case was from the Chinese Guangxi Pinghua Han people (GPH), whose language was significantly similar to Southern Chinese dialects but whose uniparental gene pool was phylogenetically associated with the indigenous Tai-Kadai (TK) people. Here, we analyzed genome-wide SNP data in 619 people from four language families and 56 geographically different populations, in which 261 people from 21 geographically distinct populations were first reported here.ResultsWe identified significant population stratification among ethnolinguistically diverse Guangxi populations, suggesting their differentiated genetic origin and admixture processes. GPH shared more alleles related to Zhuang than Southern Han Chinese but received more northern ancestry relative to Zhuang. Admixture models and estimates of genetic distances showed that GPH had a close genetic relationship with geographically close TK compared to Northern Han Chinese, supporting their admixture origin hypothesis. Further admixture time and demographic history reconstruction supported GPH was formed via admixture between Northern Han Chinese and Southern TK people. We identified robust signatures associated with lipid metabolisms, such as fatty acid desaturases (FADS) and medically relevant loci associated with Mendelian disorder (GJB2) and complex diseases. We also explored the shared and unique selection signatures of ethnically different but linguistically related Guangxi lineages and found some shared signals related to immune and malaria resistance.ConclusionsOur genetic analysis illuminated the language-related fine-scale genetic structure and provided robust genetic evidence to support the admixture hypothesis that can explain the pattern of observed genetic diversity and formation of GPH. This work presented one comprehensive analysis focused on the population history and demographical adaptative process, which provided genetic evidence for personal health management and disease risk prediction models from Guangxi people. Further large-scale whole-genome sequencing projects would provide the entire landscape of southern Chinese genomic diversity and their contributions to human health and disease traits.

Clinical application of tumour-in-normal contamination assessment from whole genome sequencing

The unexpected contamination of normal samples with tumour cells reduces variant detection sensitivity, compromising downstream analyses in canonical tumour-normal analyses. Leveraging whole-genome sequencing data available at Genomics England, we develop a tool for normal sample contamination assessment, which we validate in silico and against minimal residual disease testing. From a systematic review of 771\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$771$$\\end{document} patients with haematological malignancies and sarcomas, we find contamination across a range of cancer clinical indications and DNA sources, with highest prevalence in saliva samples from acute myeloid leukaemia patients, and sorted CD3+ T-cells from myeloproliferative neoplasms. Further exploration reveals 108 hotspot mutations in genes associated with haematological cancers at risk of being subtracted by standard variant calling pipelines. Our work highlights the importance of contamination assessment for accurate somatic variants detection in research and clinical settings, especially with large-scale sequencing projects being utilised to deliver accurate data from which to make clinical decisions for patient care.

GSC: efficient lossless compression of VCF files with fast query.

With the rise of large-scale genome sequencing projects, genotyping of thousands of samples has produced immense variant call format (VCF) files. It is becoming increasingly challenging to store, transfer, and analyze these voluminous files. Compression methods have been used to tackle these issues, aiming for both high compression ratio and fast random access. However, existing methods have not yet achieved a satisfactory compromise between these 2 objectives. To address the aforementioned issue, we introduce GSC (Genotype Sparse Compression), a specialized and refined lossless compression tool for VCF files. In benchmark tests conducted across various open-source datasets, GSC showcased exceptional performance in genotype data compression. Compared with the industry's most advanced tools (namely, GBC and GTC), GSC achieved compression ratios that were higher by 26.9% to 82.4% over GBC and GTC on the datasets, respectively. In lossless compression scenarios, GSC also demonstrated robust performance, with compression ratios 1.5× to 6.5× greater than general-purpose tools like gzip, zstd, and BCFtools-a mode not supported by either GBC or GTC. Achieving such high compression ratios did require some reasonable trade-offs, including longer decompression times, with GSC being 1.2× to 2× slower than GBC, yet 1.1× to 1.4× faster than GTC. Moreover, GSC maintained decompression query speeds that were equivalent to its competitors. In terms of RAM usage, GSC outperformed both counterparts. Overall, GSC's comprehensive performance surpasses that of the most advanced technologies. GSC balances high compression ratios with rapid data access, enhancing genomic data management. It supports seamless PLINK binary format conversion, simplifying downstream analysis.

Comparative RNA Genomics.

Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

The Hong Kong genome project: building genome sequencing capacity and capability for advancing genomic science in Hong Kong

Aim: The Hong Kong Genome Project (HKGP) is the first large-scale genome sequencing (GS) project in the Hong Kong Special Administrative Region. The Hong Kong Genome Institute (HKGI) is entrusted with the task of implementing the HKGP. With the aim to sequence 45,000-50,000 genomes in five years, it is the project’s goal to provide participants with more precise diagnosis and personalised treatment, and to drive the application and integration of genomic medicine into routine clinical care. Methods: The HKGI Laboratory’s hardware and software components were customised to tailor to the needs of the project. Sample handling and storage protocol, DNA extraction, and PCR-free GS workflow were developed and optimised. Quality control indicators and metrics for assessing the quality of samples, sequencing libraries and sequencing data were established. Results: The Laboratory is designed to facilitate a unidirectional GS workflow to minimise the risk of contamination. The Sample Manager system handles laboratory data generated from the HKGP samples and biobank. The Laboratory handles and analyses approximately 350-500 samples per week, the majority of which are whole blood. During the first 24 months since the launch of the HKGP, 12,937 participants and their family members (6,680 genomes) have been recruited and sequenced. The sequencing capacity of the Laboratory has been further enhanced to include the latest technologies, such as long-read sequencing and multi-omics in order to meet the target of the HKGP. Conclusion: HKGI Laboratory established a robust GS workflow for the HKGP. The clinical utility of GS will bring precision medicine into routine clinical practice.

COPO - Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project.

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

TP53 mutations in Romanian patients with colorectal cancer

BackgroundColorectal cancer (CRC) has been ranked as the second most deadly cancer and the third most diagnosed cancer cases for the year 2020. Specifically for Romania, the number of CRC-related deaths in 2019 was estimated at 6307 people, with a standardized mortality rate of 33.8 per 100,000 inhabitants. Although the tumor protein 53 (TP53) gene is intensively studied, there are few data on TP53 mutations in Romanian CRC. Furthermore, since genetic alterations may show geographical differences, our study aimed to analyze the clinical status and TP53 somatic variation in Romanian CRC patients.Subjects and methodsDNA from 40 randomly selected cases of CRC was extracted from formalin-fixed paraffin-embedded tissues and sequenced using direct Sanger sequencing techniques, and variants were annotated according to the recommendations of the Human Genome Variation Society. Novel variants were analyzed using MutationTaster2021 to predict their effects.ResultsThe mean age was 63.6 years (range 33–85 years) with a male to female ratio of 2.3. More than 45% (18/40) had an advanced cancer stage (≥ stage III). Mutations were found in 21/40 cases (52.5%), with one case having two mutations, giving a total of twenty-two mutations in the TP53 coding DNA. These mutations include 3 (13.6%) insertion-deletion mutations, two of which are novel frameshift mutations: c.165delT (in exon 4) and c.928_935dup (in exon 9), both of which are predicted to lead to nonsense-mediated mRNA decay and are classified as deleterious. The remaining 19 (86.36%) were substitution mutations: 1 nonsense and 18 (81.8%) missense mutations, with G > A (n = 7/19; 36.8%) and C > T (n = 6/19; 31.5%) transitions being the most common. The G > T transversion was found in 21.05% (4/19) of the substitution mutations.ConclusionWe have described two novel frameshift mutations in TP53. The discovery of novel mutations following the efforts of The Cancer Genome Atlas and other large-scale cancer genome sequencing projects may be further evidence of the heterogeneous nature of mutations in cancer and may indicate that the identification of carcinogenic mutations is not yet saturated. Further sequencing is therefore needed, especially in less studied populations. Importantly, consideration of their geographical environment will shed light on population-specific carcinogenesis.

A pipeline for sample tagging of whole genome bisulfite sequencing data using genotypes of whole genome sequencing

BackgroundIn large-scale high-throughput sequencing projects and biobank construction, sample tagging is essential to prevent sample mix-ups. Despite the availability of fingerprint panels for DNA data, little research has been conducted on sample tagging of whole genome bisulfite sequencing (WGBS) data. This study aims to construct a pipeline and identify applicable fingerprint panels to address this problem.ResultsUsing autosome-wide A/T polymorphic single nucleotide variants (SNVs) obtained from whole genome sequencing (WGS) and WGBS of individuals from the Third China National Stroke Registry, we designed a fingerprint panel and constructed an optimized pipeline for tagging WGBS data. This pipeline used Bis-SNP to call genotypes from the WGBS data, and optimized genotype comparison by eliminating wildtype homozygous and missing genotypes, and retaining variants with identical genomic coordinates and reference/alternative alleles. WGS-based and WGBS-based genotypes called from identical or different samples were extensively compared using hap.py. In the first batch of 94 samples, the genotype consistency rates were between 71.01%-84.23% and 51.43%-60.50% for the matched and mismatched WGS and WGBS data using the autosome-wide A/T polymorphic SNV panel. This capability to tag WGBS data was validated among the second batch of 240 samples, with genotype consistency rates ranging from 70.61%-84.65% to 49.58%-61.42% for the matched and mismatched data, respectively. We also determined that the number of genetic variants required to correctly tag WGBS data was on the order of thousands through testing six fingerprint panels with different orders for the number of variants. Additionally, we affirmed this result with two self-designed panels of 1351 and 1278 SNVs, respectively. Furthermore, this study confirmed that using the number of genetic variants with identical coordinates and ref/alt alleles, or identical genotypes could not correctly tag WGBS data.ConclusionThis study proposed an optimized pipeline, applicable fingerprint panels, and a lower boundary for the number of fingerprint genetic variants needed for correct sample tagging of WGBS data, which are valuable for tagging WGBS data and integrating multi-omics data for biobanks.

Genofunc: genome annotation and identification of genome features for automated pipelining analysis of virus whole genome sequences

BackgroundViral genomics and epidemiology have been increasingly important tools for analysing the spread of key pathogens affecting daily lives of individuals worldwide. With the rapidly expanding scale of pathogen genome sequencing efforts for epidemics and outbreaks efficient workflows in extracting genomic information are becoming increasingly important for answering key research questions.ResultsHere we present Genofunc, a toolkit offering a range of command line orientated functions for processing of raw virus genome sequences into aligned and annotated data ready for analysis. The tool contains functions such as genome annotation, feature extraction etc. for processing of large genomic datasets both manual or as part of pipeline such as Snakemake or Nextflow ready for down-stream phylogenetic analysis. Originally designed for a large-scale HIV sequencing project, Genofunc has been benchmarked against annotated sequence gene coordinates from the Los Alamos HIV database as validation with downstream phylogenetic analysis result comparable to past literature as case study.ConclusionGenofunc is implemented fully in Python and licensed under the MIT license. Source code and documentation is available at: https://github.com/xiaoyu518/genofunc.

Identification of prolactin receptor variants with diverse effects on receptor signalling.

The prolactin receptor (PRLR) signals predominantly through the JAK2-STAT5 pathway regulating multiple physiological functions relating to fertility, lactation, and metabolism. However, the molecular pathology and role of PRLR mutations and signalling are incompletely defined, with progress hampered by a lack of reported disease-associated PRLR variants. To date, two common germline PRLR variants are reported to demonstrate constitutive activity, with one, Ile146Leu, overrepresented in benign breast disease, while a rare activating variant, Asn492Ile, is reported to be associated with an increased incidence of prolactinoma. In contrast, an inactivating germline heterozygous PRLR variant (His188Arg) was reported in a kindred with hyperprolactinaemia, while an inactivating compound heterozygous PRLR variant (Pro269Leu/Arg171Stop) was identified in an individual with hyperprolactinaemia and agalactia. We hypothesised that additional rare germline PRLR variants, identified from large-scale sequencing projects (ExAC and GnomAD), may be associated with altered in vitro PRLR signalling activity. We therefore evaluated >300 previously uncharacterised non-synonymous, germline PRLR variants and selected 10 variants for in vitro analysis based on protein prediction algorithms, proximity to known functional domains and structural modelling. Five variants, including extracellular and intracellular domain variants, were associated with altered responses when compared to the wild-type receptor. These altered responses included loss- and gain-of-function activities related to STAT5 signalling, Akt and FOXO1 activity, as well as cell viability and apoptosis. These studies provide further insight into PRLR structure-function and indicate that rare germline PRLR variants may have diverse modulating effects on PRLR signalling, although the pathophysiologic relevance of such alterations remains to be defined.

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results.

The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00-4.53]) and premature CHD (odds ratio, 3.68 [2.34-5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. In a multisite cohort of electronic health record-linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record-linked biobanks to detect FH.

Genomes on a Tree (GoaT): A versatile, scalable search engine for genomic and sequencing project metadata across the eukaryotic tree of life.

As genomic data transform our understanding of biodiversity, the Earth BioGenome Project (EBP) has set a goal of generating reference quality genome assemblies for all ~1.9 million described eukaryotic taxa. Meeting this goal requires coordination among many individual regional and taxon-focussed projects working under the EBP umbrella. Large-scale sequencing projects require ready access to validated genome-relevant metadata, such as genome sizes and karyotypes, but these data are dispersed across the literature, and directly measured values are lacking for most taxa. To meet these needs, we have developed Genomes on a Tree (GoaT), an Elasticsearch-powered datastore and search index for genome-relevant metadata and sequencing project plans and statuses. GoaT indexes publicly available metadata for all eukaryotic species and interpolates missing values through phylogenetic comparison. GoaT also holds target priority and sequencing status information for many projects affiliated to the EBP to aid project coordination. Metadata and status attributes in GoaT can be queried through a mature API, a web front end, and a command line interface. The web front end additionally provides summary visualisations for data exploration and reporting (see https://goat.genomehubs.org). GoaT currently holds direct or estimated values for over 70 taxon attributes and over 30 assembly attributes across 1.5 million eukaryotic species. The depth and breadth of curated data, frequent updates, and a versatile query interface make GoaT a powerful data aggregator and portal to explore and report underlying data for the eukaryotic tree of life. We illustrate this utility through a series of use cases from planning through to completion of a genome-sequencing project.

MutFusVAE: Mutational Fusion Variational Autoencoder for Predicting Primary Sites of Cancer

The metastatic propensity of malignant primary tumors is a recurring theme when it comes to the cause of mortality in cancer. Establishing the primary site of a metastatic cancer is a significant but challenging task. There are ∼3% of metastatic cancer cases diagnosed as cancer of unknown primary (CUP), and the conventional diagnostic process fails to detect the primary site for 80% of CUP patients. Benefiting from the explosion of the information available from large-scale tumor DNA sequencing projects, it became favorable to predict the cancer primary sites from genomic perspective. The existing methods on the task intensively studied the mutational and oncogenic features with assists of machine learning (ML) and deep learning (DL) techniques, yet lack of development of a model architecture tailored for the mutational features. To address the gap, in this research, we aimed to develop a DL methodology specialized for the mutational data. A mutational fusion variational autoencoder (MutFusVAE) deep architecture is proposed to actualize the idea1. We downloaded mutational profiles meeting our criteria of 2,603 tumor samples, which get split into 2,082 training samples and 521 (20%) held-out testing samples, from the International Cancer Genome Consortium (ICGC) database. The proposed methods achieved 94% overall classification accuracy for differentiating among seven primary sites on the held-out testing set. The results show the discriminative power brought by a specialized design of deep models for the mutational data and gain insights to facilitate DL-based genomic diagnostics for cancer from a modeling view.