Search

Background: The introduction of next-generation sequencing (NGS) into molecular cancer diagnostics has led to an increase in the data available for the identification and evaluation of driver mutations and for defining personalized cancer treatment regimens. The meaningful combination of omics data, ie, pathogenic gene variants and alterations with other patient data, to understand the full picture of malignancy has been challenging., Objective: This study describes the implementation of a system capable of processing, analyzing, and subsequently combining NGS data with other clinical patient data for analysis within and across institutions., Methods: On the basis of the already existing NGS analysis workflows for the identification of malignant gene variants at the Institute of Pathology of the University Hospital Erlangen, we defined basic requirements on an NGS processing and analysis pipeline and implemented a pipeline based on the GEMINI (GEnome MINIng) open source genetic variation database. For the purpose of validation, this pipeline was applied to data from the 1000 Genomes Project and subsequently to NGS data derived from 206 patients of a local hospital. We further integrated the pipeline into existing structures of data integration centers at the University Hospital Erlangen and combined NGS data with local nongenomic patient-derived data available in Fast Healthcare Interoperability Resources format., Results: Using data from the 1000 Genomes Project and from the patient cohort as input, the implemented system produced the same results as already established methodologies. Further, it satisfied all our identified requirements and was successfully integrated into the existing infrastructure. Finally, we showed in an exemplary analysis how the data could be quickly loaded into and analyzed in KETOS, a web-based analysis platform for statistical analysis and clinical decision support., Conclusions: This study demonstrates that the GEMINI open source database can be augmented to create an NGS analysis pipeline. The pipeline generates high-quality results consistent with the already established workflows for gene variant annotation and pathological evaluation. We further demonstrate how NGS-derived genomic and other clinical data can be combined for further statistical analysis, thereby providing for data integration using standardized vocabularies and methods. Finally, we demonstrate the feasibility of the pipeline integration into hospital workflows by providing an exemplary integration into the data integration center infrastructure, which is currently being established across Germany., (©Julian Gruendner, Nicolas Wolf, Lars Tögel, Florian Haller, Hans-Ulrich Prokosch, Jan Christoph. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.10.2020.)

Background: The literature abounds with increasing numbers of research studies using genomic data in combination with health data (eg, health records and phenotypic and lifestyle data), with great potential for large-scale research and precision medicine. However, concerns have been raised about social acceptability and risks posed for individuals and their kin. Although there has been public engagement on various aspects of this topic, there is a lack of information about public views on data access models., Objective: This study aimed to address the lack of information on the social acceptability of access models for reusing genomic data collected for research in conjunction with health data. Models considered were open web-based access, released externally to researchers, and access within a data safe haven., Methods: Views were ascertained using a series of 8 public workshops (N=116). The workshops included an explanation of benefits and risks in using genomic data with health data, a facilitated discussion, and an exit questionnaire. The resulting quantitative data were analyzed using descriptive and inferential statistics, and the qualitative data were analyzed for emerging themes., Results: Respondents placed a high value on the reuse of genomic data but raised concerns including data misuse, information governance, and discrimination. They showed a preference for giving consent and use of data within a safe haven over external release or open access. Perceived risks with open access included data being used by unscrupulous parties, with external release included data security, and with safe havens included the need for robust safeguards., Conclusions: This is the first known study exploring public views of access models for reusing anonymized genomic and health data in research. It indicated that people are generally amenable but prefer data safe havens because of perceived sensitivities. We recommend that public views be incorporated into guidance on models for the reuse of genomic and health data., (©Kerina H Jones, Helen Daniels, Emma Squires, David V Ford. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.08.2019.)

Inherited neuromuscular diseases (NMDs) are genetic disorders that affect the skeletal muscles or the nerves controlling muscle function. With a new generation of diagnostic options and recent advances in translational research improving the opportunities for therapy development for these rare conditions, capturing patient information in databases collecting a range of clinical and genetic data together with contact details has assumed an increasingly important role in trial planning and recruitment as well as natural history data collection. Here we provide an overview of a decade of patient registration activities in the NMD field, with a particular focus on patient registries set up with trial readiness in mind. A summary is provided of databases collecting precise genetic information focused on confirming the causative mutation and their evolution into registries that combine genetic data with additional clinical information useful for trial feasibility and recruitment. Use of these systems for a range of purposes beyond trial recruitment, including natural history assessment, care standards monitoring, genotype-phenotype correlation and disease burden evaluation is also described within the context of research networks (TREAT-NMD) and European Reference Networks (ERN-EURO-NMD). New initiatives including registries using controlled vocabularies for computational accessibility that focus on phenotypic data capture for gene discovery are analysed, and examples of the lessons learned at every stage are provided in order to allow new patient registration initiatives to benefit from the extensive experience gained.

Introduction: Genomic studies of Legacy African Americans have a tangled and convoluted history in western science. In this review paper, core issues affecting African American genomic studies are addressed and two case studies, the New York African Burial Ground and the Gullah Geechee peoples, are presented to highlight the current status of genomic research among Africa Americans. Methods: To investigate our target population's core issues, a metadatabase derived from 22 publicly accessible databases were reviewed, evaluated, and synthesized to identify the core bioethical issues prevalent during the centuries of the African American presence in North America. The sequence of metadatabase development included 5 steps: identification of information, record screening and retention of topic relevant information, identification of eligibility via synthesis for concept identifications, and inclusion of studies used for conceptual summaries and studies used for genetic and genomic summaries. To these data we added our emic perspectives and specific insights from our case studies. Results: Overall, there is a paucity of existing research on underrepresent African American genomic diversity. In every category of genomic testing (i.e., diagnostic, clinical predictive, pharmacogenomic, direct-to-consumer, and tumor testing), African Americans are disproportionately underrepresented compared to European Americans. The first of our case studies is from the New York African Burial Ground Project where genomic studies of grave soil derived aDNA yields insights into the causes of death of 17th and 18th Century African Americans. In the second of our case studies, research among the Gullah Geechee people of the Carolina Lowcountry reveals a connection between genomic studies and health disparities. Discussion: African Americans have historically borne the brunt of the earliest biomedical studies used to generate and refine primitive concepts in genetics. As exploited victims these investigations, African American men, women, and children were subjected to an ethics-free western science. Now that bioethical safeguards have been added, underrepresented and marginalized people who were once the convenient targets of western science, are now excluded from its health-related benefits. Recommendations to enhance the inclusion of African Americans in global genomic databases and clinical trials should include the following: emphasis on the connection of inclusion to advances in precision medicine, emphasis on the relevance of inclusion to fundamental questions in human evolutionary biology, emphasis on the historical relevance of inclusion for Legacy African Americans, emphasis on the ability of inclusion to foster expanded scientific expertise in the target population, ethical engagement with their descendants, and increase the number of science researchers from these communities. [ABSTRACT FROM AUTHOR]

Human papillomavirus 31 (HPV31) is the fourth most frequent high-risk HPV (HR-HPV) genotype identified in cervical cancer (CC) worldwide and in Mexico. It has been recently classified into three lineages (A, B, and C) and eight sublineages (A1, A2, B1, B2, and C1 – C4). Here, we report the complete genomic sequences of 14 HPV31 isolates from cervical samples, and these were compared with viral genome sequences from the GenBank database for phylogenetic and genetic distance analysis. The formation of two novel clades within the C lineage (proposed as C5 and C6) was observed, with a well-defined variant-specific mutational pattern. The smallest average pairwise distance was 0.71% for lineages A and B, 0.94% for lineages A and C, and 1.01% for lineages B and C, and between sublineages, these values were 0.21% for clade A, 0.29% for clade B, and 0.24% for clade C. The isolates were grouped into the sublineages A1, B2, C1-C3, and C6. This is the first report on the whole-genome diversity of HPV31 in Mexico. [ABSTRACT FROM AUTHOR]

The reefs at Palmyra Atoll, a small outlying atoll in the equatorial Pacific, have been undergoing a phase shift from scleractinian corals to a corallimorph-dominated benthos. It has been unclear whether there has been cryptic speciation or morphological plasticity leading to different ecotypes of Rhodactis howesii. Here, we use mitochondrial genomic analysis to assess species validation and underlying cause of morphological variation across the atoll. We mapped sequenced reads to Rhodactis indosinensis, R. howesii's closest recorded genomic taxon. In addition to one individual from American Sāmoa, we assessed phylogenetic relationships of published corallimorph genomes with those from Palmyra. There was no identifiable population structure within Palmyra, and available dinoflagellate symbiont communities were consistent among the sequenced individuals. There were noticeable differences in symbiont communities between Palmyra and American Sāmoa individuals, as well as six fixed nucleotide differences. We conclude that the lack of taxonomically validated genetic reference material together with vague species descriptions, morphological plasticity and overlap among morphological characters, combine to raise doubts about the validity of the currently accepted species name, R. howesii. Comparison of our results to all currently available genetic data for corallimorpharians suggests that the species at Palmyra is most closely related to an unidentified species of Rhodactis from Okinawa. However, taxonomically confirmed R. howesii is absent from genetic databases, so no firm conclusions about species identification can yet be drawn. It seems clear that this group is in need of additional taxonomic work and a broad phylogenetic survey of taxa with geographic distribution would further our understanding of marine biodiversity, conservation, and invasion dynamics of this understudied group. [ABSTRACT FROM AUTHOR]

Motivation Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. Results To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases. Availability and implementation The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

From the 1980s onwards, the Roslin Institute and its predecessor organizations faced budget cuts, organizational upheaval and considerable insecurity. Over the next few decades, it was transformed by the introduction of molecular biology and transgenic research, but remained a hub of animal geneticists conducting research aimed at the livestock-breeding industry. This paper explores how these animal geneticists embraced genomics in response to the many-faceted precarity that the Roslin Institute faced, establishing it as a global centre for pig genomics research through forging and leading the Pig Gene Mapping Project (PiGMaP); developing and hosting resources, such as a database for genetic linkage data; and producing associated statistical and software tools to analyse the data. The Roslin Institute leveraged these resources to play a key role in further international collaborations as a hedge against precarity. This adoption of genomics was strategically useful, as it took advantage of policy shifts at the national and European levels towards funding research with biotechnological potential. As genomics constitutes a set of infrastructures and resources with manifold uses, the development of capabilities in this domain also helped Roslin to diversify as a response to precarity. [ABSTRACT FROM AUTHOR]

Background: One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model. Results: We presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics database and L1000 landmark genes, and used their expression and mutations as genomic features in model training. We obtain the cheminformatics features for compounds from PubChem or ChEMBL. Our finding is that combining gene expression, genetic mutation, and cheminformatics features greatly enhances the predictive performance. Conclusion: We implemented an extended Graph Neural Network for molecular graphs and Convolutional Neural Network for gene features. With the employment of multi-tasking and self-attention functions to monitor the similarity between compounds, our model outperforms recently published methods using the same training and testing datasets. [ABSTRACT FROM AUTHOR]

Marinobacter is the abundant and important algal-associated and hydrocarbon biodegradation bacteria in the ocean. However, little knowledge about their phages has been reported. Here, a novel siphovirus, vB_MalS-PS3, infecting Marinobacter algicola DG893(T), was isolated from the surface waters of the western Pacific Ocean. Transmission electron microscopy (TEM) indicated that vB_MalS-PS3 has the morphology of siphoviruses. VB_MalS-PS3 was stable from −20 to 55°C, and with the latent and rise periods of about 80 and 10 min, respectively. The genome sequence of VB_MalS-PS3 contains a linear, double-strand 42,168-bp DNA molecule with a G + C content of 56.23% and 54 putative open reading frames (ORFs). Nineteen conserved domains were predicted by BLASTp in NCBI. We found that vB_MalS-PS3 represent an understudied viral group with only one known isolate. The phylogenetic tree based on the amino acid sequences of whole genomes revealed that vB_MalS-PS3 has a distant evolutionary relationship with other siphoviruses, and can be grouped into a novel viral genus cluster with six uncultured assembled viral genomes from metagenomics, named here as Marinovirus. This study of the Marinobacter phage vB_MalS-PS3 genome enriched the genetic database of marine bacteriophages, in addition, will provide useful information for further research on the interaction between Marinobacter phages and their hosts, and their relationship with algal blooms and hydrocarbon biodegradation in the ocean. [ABSTRACT FROM AUTHOR]

Background: Leukodystrophies are genetic diseases affecting the white matter and leading to early death. Our objective was to determine leukodystrophy incidence, using genomics sequencing databases allele frequencies of disease-causing variants.Methods: From 49 genes, representing the standardly defined group of leukodystrophies, we identified potential disease-causing variants from publications in the Human Genetic Mutation Database and from predictions in the Genome Aggregation Database. Allele frequencies were estimated from Genome Aggregation Database. Allele frequencies for each gene were summed to generate a super allele frequency and we used the Hardy-Weinberg equation to calculate overall expected live birth incidence associated with the gene in question.Results: We identified 4564 pathogenic variants for 25 discrete leukodystrophies. The largest effect was from GALC variants (Krabbe disease), which had a predicted incidence of one in 12,080 live births, 8.3 times higher than published estimates. The second most frequently predicted leukodystrophy was the RNA polymerase III-related disorders, which had an incidence of 1:26,160. Overall, we found a leukodystrophy incidence of 1 in 4733 live births, significantly higher than previous estimates.Conclusions: Our data are consistent with a significant underdiagnosis of leukodystrophy patients. An intriguing additional consideration is that there may be genetic modifiers that lead to weaker, absent, or adult-onset disease phenotypes. [ABSTRACT FROM AUTHOR]

The article offers a research which present the initial phase of the Korean Genome Project (Korea1K), including 1094 whole genomes along with data of 79 quantitative clinical traits. Topics discussed include genome-wide association study illustrated the power of whole-genome sequences for analyzing clinical traits; and Korea1K can be a useful genotypic and phenotypic resource for clinical and ethnogenetic studies.

Recent advances in technology have significantly improved the accuracy of genetic testing and analysis, and substantially reduced its cost, resulting in a dramatic increase in the amount of genetic information generated, analysed, shared, and stored by diverse individuals and entities. Given the diversity of actors and their interests, coupled with the wide variety of ways genetic data are held, it has been difficult to develop broadly applicable legal principles for genetic privacy. This article examines the current landscape of genetic privacy to identify the roles that the law does or should play, with a focus on federal statutes and regulations, including the Health Insurance Portability and Accountability Act (HIPAA) and the Genetic Information Nondiscrimination Act (GINA). After considering the many contexts in which issues of genetic privacy arise, the article concludes that few, if any, applicable legal doctrines or enactments provide adequate protection or meaningful control to individuals over disclosures that may affect them. The article describes why it may be time to shift attention from attempting to control access to genetic information to considering the more challenging question of how these data can be used and under what conditions, explicitly addressing trade-offs between individual and social goods in numerous applications. [ABSTRACT FROM AUTHOR]

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree. [ABSTRACT FROM AUTHOR]

Motivation Genomic data-sharing beacons aim to provide a secure, easy to implement and standardized interface for data-sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his/her single-nucleotide polymorphisms (SNPs). Here, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Results Using the proposed attack, even if the victim systematically hides informative SNPs, it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use linkage disequilibrium and a high-order Markov chain-based algorithm for inference. We show that in a simulated beacon with 65 individuals from the European population, we can infer membership of individuals with 95% confidence with only 5 queries, even when SNPs with MAF <0.05 are hidden. We need less than 0.5% of the number of queries that existing works require, to determine beacon membership under the same conditions. We show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants. Availability and implementation Software is available at http://ciceklab.cs.bilkent.edu.tr/beacon%5fattack. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at . [ABSTRACT FROM AUTHOR]

Background: The massive quantities of genetic data generated by high-throughput sequencing pose challenges to data storage, transmission and analyses. These problems are effectively solved through data compression, in which the size of data storage is reduced and the speed of data transmission is improved. Several options are available for compressing and storing genetic data. However, most of these options either do not provide sufficient compression rates or require a considerable length of time for decompression and loading. Results: Here, we propose TRCMGene, a lossless genetic data compression method that uses a referential compression scheme. The novel concept of two-step compression method, which builds an index structure using K-means and k-nearest neighbours, is introduced to TRCMGene. Evaluation with several real datasets revealed that the compression factor of TRCMGene ranges from 9 to 21. TRCMGene presents a good balance between compression factor and reading time. On average, the reading time of compressed data is 60% of that of uncompressed data. Thus, TRCMGene not only saves disc space but also saves file access time and speeds up data loading. These effects collectively improve genetic data storage and transmission in the current hardware environment and render system upgrades unnecessary. TRCMGene, user manual and demos could be accessed freely from . The data mentioned in this manuscript could be downloaded from: . [ABSTRACT FROM AUTHOR]

A database organizes information, but since information is produced by actors, it also coordinates the different actors involved with data. Here, focusing on the newly created ClinVar, a genomic clinical variant database, we will see how it helps the government, academia, and industry (represented mainly by the company Illumina) find their positions relative to one another. [ABSTRACT FROM AUTHOR]

Abstract: Climate change and increasing habitat loss greatly impact species survival, requiring range shifts, phenotypic plasticity and/or evolutionary change for long‐term persistence, which may not readily occur unaided in threatened species. Therefore, defining conservation actions requires a detailed assessment of evolutionary factors. Existing genetic diversity needs to be thoroughly evaluated and spatially mapped to define conservation units (CUs) in an evolutionary context, and we address that here. We also propose a multidisciplinary approach to determine corridors and functional connectivity between CUs by including genetic diversity in the modelling while controlling for isolation by distance and phylogeographic history. We evaluate our approach on a Near Threatened Iberian endemic rodent by analysing genotyping‐by‐sequencing (GBS) genomic data from 107 Cabrera voles (Microtus cabrerae), screening the entire species distribution to define categories of CUs and their connectivity: We defined six management units (MUs) which can be grouped into four evolutionarily significant units (ESUs) and three (putatively) adaptive units (AUs). We demonstrate that the three different categories of CU can be objectively defined using genomic data, and their characteristics and connectivity can inform conservation decision‐making. In particular, we show that connectivity of the Cabrera vole is very limited in eastern Iberia and that the pre‐Pyrenean and part of the Betic geographic nuclei contribute the most to the species genetic diversity. We argue that a multidisciplinary framework for CU definition is essential and that this framework needs a strong evolutionary basis. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we present an iterative method for delimiting species under the general lineage concept (GLC) based on the multivariate clustering of morphological, ecological and genetic data. Our rationale is that distinct multivariate groups correspond to evolutionarily independent metapopulation lineages because they reflect the common signal of different secondary defining properties (environmental and genetic distinctiveness, phenotypic diagnosability, etc.) that imply the existence of barriers preventing or limiting gene exchange. We applied this method to study a group of endangered poison frogs, the Oophaga histrionica complex. In our study case, we used next‐generation targeted amplicon sequencing to obtain a robust genetic data set that we combined with patterns of morphological and ecological features. Our analyses revealed the existence of at least five different species in the histrionica complex (three, new to science), some of them, occurring in small isolated populations outside any protected areas. The lineage delimitation proposed here has important conservation implications as it revealed that some of the Oophaga species should be considered among the most vulnerable of the Neotropical frogs. More broadly, our study exemplifies how multiple‐amplicon and multivariate statistical techniques can be integrated to successfully identify species and their boundaries. [ABSTRACT FROM AUTHOR]

The recent increase in accessibility and scale of genetic data available through next-generation sequencing (NGS) technology has transformed biological inquiry. As a direct result, the application and analysis of NGS data has quickly become an important skill for future scientists. However, the steep learning curve for applying NGS technology to biological questions, including the complexity of sample preparation for sequencing and the analysis of large data sets, are deterrents to the integration of NGS into undergraduate education. Here, we present a course-based undergraduate research experience (CURE) designed to aid in overcoming these limitations through NGS investigations of prokaryotic diversity. Specifically, we use 16S rRNA sequencing to explore patterns of diversity stemming from student-directed hypothesis development. This CURE addresses three learning objectives: (1) it provides a forum for experimental design hypothesis generation, (2) it introduces modern genomic tools through a hands-on experience generating an NGS data-set, and (3) it provides students with an introductory experience in bioinformatics. [ABSTRACT FROM AUTHOR]

Connectivity is frequently cited as a vital component of Marine Protected Area (MPA) networks and was formally identified as one of five key principles for marine network design in European waters. Yet, without the ability to demonstrate connectivity, it is impossible to be certain that sites designated within a MPA network do in fact constitute a network, when they may –irrespective of the diversity and rarity of the taxa within them– be in reality a set of unlinked habitats and associated species assemblages. However, the process of assessing connectivity between MPAs, and which taxa to include in assessments of connectivity, is often difficult and can be dependent on a variety of factors that can be outside the control of managers, stakeholders and policymakers. Among the many methods that have been used to assess connectivity, genetic markers are often used to infer connectivity indirectly by estimating the degree of genetic differentiation between populations of a species or by inferring the origin(s) of migrants using assignment methods. While modern molecular methods can be extremely robust and are now routinely used to address conservation issues, genetic data are, to the authors’ knowledge, rarely used to inform designation of MPA networks. In this paper, several biological and methodological factors are highlighted, consideration of which may help to inform the selection of species for assessments of connectivity between MPAs in a network, and this paper suggests ways in which genetic data may be interpreted to inform MPA design and policy. [ABSTRACT FROM AUTHOR]

In clinical practice and in research, there is an ongoing debate on how to return incidental and secondary findings of genetic tests to patients and research participants. Previous investigations have found that most of the people most of the time are in favor of full disclosure of results. Yet, the option to reject disclosure, based on the so-called right not to know, can be valuable especially for some vulnerable subgroups of recipients. In the present study we investigated variations in informational preferences in the context of genetic testing in a large and diverse German sample. This survey examined health care professionals, patients, participants of genetic counseling sessions and members of the general population (N = 518). Survey participants were assessed regarding their openness to learning about findings under various hypothetical scenarios, as well as their attitudes about the doctor-patient-relationship in a disclosure situation and about informational transfer to third parties. While the majority of participants wanted to learn about their findings, the extent of support of disclosure varied with features of the hypothetical diagnostic scenarios (e.g., controllability of disease; abstract vs. concrete scenario description) and demographic characteristics of the subjects. For example, subjects with higher levels of education were more selective with regards to the kind of information they want to receive than those with lower levels of education. We discuss implications of these findings for the debate about the right not to know and for the clinical practice of informed consent procedures. [ABSTRACT FROM AUTHOR]

Motivation: Recent technological developments have enabled the possibility of genetic and genomic integrated data analysis approaches, where multiple omics datasets from various biological levels are combined and used to describe (disease) phenotypic variations. The main goal is to explain and ultimately predict phenotypic variations by understanding their genetic basis and the interaction of the associated genetic factors. Therefore, understanding the underlying genetic mechanisms of phenotypic variations is an ever increasing research interest in biomedical sciences. In many situations, we have a set of variables that can be considered to be the outcome variables and a set that can be considered to be explanatory variables. Redundancy analysis (RDA) is an analytic method to deal with this type of directionality. Unfortunately, current implementations of RDA cannot deal optimally with the high dimensionality of omics data (p» n). The existing theoretical framework, based on Ridge penalization, is suboptimal, since it includes all variables in the analysis. As a solution, we propose to use Elastic Net penalization in an iterative RDA framework to obtain a sparse solution. Results: We proposed sparse redundancy analysis (sRDA) for high dimensional omics data analysis. We conducted simulation studies with our software implementation of sRDA to assess the reliability of sRDA. Both the analysis of simulated data, and the analysis of 485 512 methylation markers and 18,424 gene-expression values measured in a set of 55 patients with Marfan syndrome show that sRDA is able to deal with the usual high dimensionality of omics data. [ABSTRACT FROM AUTHOR]

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time consuming and expensive process. Second, it requires large-scale computation and storage systems to process genomic sequences. Third, genomic databases are often owned by different organizations, and thus, not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 Single Nucleotide Polymorphisms (SNPs) in a database of 20 000 records takes around 100 and 150 s, respectively. [ABSTRACT FROM AUTHOR]

The Genomic Observatories Metadatabase (GeOMe, ) is an open access repository for geographic and ecological metadata associated with biosamples and genetic data. Whereas public databases have served as vital repositories for nucleotide sequences, they do not accession all the metadata required for ecological or evolutionary analyses. GeOMe fills this need, providing a user-friendly, web-based interface for both data contributors and data recipients. The interface allows data contributors to create a customized yet standard-compliant spreadsheet that captures the temporal and geospatial context of each biosample. These metadata are then validated and permanently linked to archived genetic data stored in the National Center for Biotechnology Information’s (NCBI’s) Sequence Read Archive (SRA) via unique persistent identifiers. By linking ecologically and evolutionarily relevant metadata with publicly archived sequence data in a structured manner, GeOMe sets a gold standard for data management in biodiversity science. [ABSTRACT FROM AUTHOR]

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so. [ABSTRACT FROM AUTHOR]

We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g., exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g., consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. Therefore, we introduce a composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data. [ABSTRACT FROM AUTHOR]

Background: In the past decade many Genome-wide Association Studies (GWAS) were performed that discovered new associations between single-nucleotide polymorphisms (SNPs) and various phenotypes. Imputation methods are widely used in GWAS. They facilitate the phenotype association with variants that are not directly genotyped. Imputation methods can also be used to combine and analyse data genotyped on different genotyping arrays. In this study we investigated the imputation quality and efficiency of two different approaches of combining GWAS data from different genotyping platforms. We investigated whether combining data from different platforms before the actual imputation performs better than combining the data from different platforms after imputation. Methods: In total 979 unique individuals from the AMC-PAS cohort were genotyped on 3 different platforms. A total of 706 individuals were genotyped on the MetaboChip, a total of 757 individuals were genotyped on the 50K gene-centric Human CVD BeadChip, and a total of 955 individuals were genotyped on the HumanExome chip. A total of 397 individuals were genotyped on all 3 individual platforms. After pre-imputation quality control (QC), Minimac in combination with MaCH was used for the imputation of all samples with the 1,000 genomes reference panel. All imputed markers with an r2 value of <0.3 were excluded in our post-imputation QC. Results: A total of 397 individuals were genotyped on all three platforms. All three datasets were carefully matched on strand, SNP ID and genomic coordinates. This resulted in a dataset of 979 unique individuals and a total of 258,925 unique markers. A total of 4,117,036 SNPs were available when imputation was performed before merging the three datasets. A total of 3,933,494 SNPs were available when imputation was done on the combined set. Our results suggest that imputation of individual datasets before merging performs slightly better than after combining the different datasets. Conclusions: Imputation of datasets genotyped by different platforms before merging generates more SNPs than imputation after putting the datasets together. [ABSTRACT FROM AUTHOR]

Imputation of human leukocyte antigen (HLA) alleles from SNP-level data is attractive due to importance of HLA alleles in human disease, widespread availability of genome-wide association study (GWAS) data, and expertise required for HLA sequencing. However, comprehensive evaluations of HLA imputations programs are limited. We compared HLA imputation results of HIBAG, SNP2HLA, and HLA*IMP:02 to sequenced HLA alleles in 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository. We performed four-digit HLA sequencing for HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1 using long-read 454 FLX sequencing. All samples were genotyped using both the Illumina HumanExome BeadChip platform and a GWAS platform. Call rates and concordance rates were compared by platform, frequency of allele, and race/ethnicity. Overall concordance rates were similar between programs in European Americans (EA) (0.975 [SNP2HLA]; 0.939 [HLA*IMP:02]; 0.976 [HIBAG]). SNP2HLA provided a significant advantage in terms of call rate and the number of alleles imputed. Concordance rates were lower overall for African Americans (AAs). These observations were consistent when accuracy was compared across HLA loci. All imputation programs performed similarly for low frequency HLA alleles. Higher concordance rates were observed when HLA alleles were imputed from GWAS platforms versus the HumanExome BeadChip, suggesting that high genomic coverage is preferred as input for HLA allelic imputation. These findings provide guidance on the best use of HLA imputation methods and elucidate their limitations. [ABSTRACT FROM AUTHOR]

In high-throughput molecular profiling studies, genotype labels can be wrongly assigned at various experimental steps; the resulting mislabeled samples seriously reduce the power to detect the genetic basis of phenotypic variation. We have developed an approach to detect potential mislabeling, recover the “ideal” genotype and identify “best-matched” labels for mislabeled samples. On average, we identified 4% of samples as mislabeled in eight published datasets, highlighting the necessity of applying a “data cleaning” step before standard data analysis. [ABSTRACT FROM AUTHOR]

We evaluated the fraction of variation in HIV-1 set point viral load attributable to viral or human genetic factors by using joint host/pathogen genetic data from 541 HIV infected individuals. We show that viral genetic diversity explains 29% of the variation in viral load while host factors explain 8.4%. Using a joint model including both host and viral effects, we estimate a total of 30% heritability, indicating that most of the host effects are reflected in viral sequence variation. [ABSTRACT FROM AUTHOR]

Antarctic krill (Euphausia superba) is a key species in the Southern Ocean with an estimated biomass between 100 and 500 million tonnes. Changes in krill population viability would have catastrophic effect on the Antarctic ecosystem. One looming threat due to elevated levels of anthropogenic atmospheric carbon dioxide (CO2) is ocean acidification (lowering of sea water pH by CO2 dissolving into the oceans). The genetics of Antarctic krill has long been of scientific interest for both for the analysis of population structure and analysis of functional genetics. However, the genetic resources available for the species are relatively modest. We have developed the most advanced genetic database on Euphausia superba, KrillDB, which includes comprehensive data sets of former and present transcriptome projects. In particular, we have built a de novo transcriptome assembly using more than 360 million Illumina sequence reads generated from larval krill including individuals subjected to different CO2 levels. The database gives access to: 1) the full list of assembled genes and transcripts; 2) their level of similarity to transcripts and proteins from other species; 3) the predicted protein domains contained within each transcript; 4) their predicted GO terms; 5) the level of expression of each transcript in the different larval stages and CO2 treatments. All references to external entities (sequences, domains, GO terms) are equipped with a link to the appropriate source database. Moreover, the software implements a full-text search engine that makes it possible to submit free-form queries. KrillDB represents the first large-scale attempt at classifying and annotating the full krill transcriptome. For this reason, we believe it will constitute a cornerstone of future approaches devoted to physiological and molecular study of this key species in the Southern Ocean food web. [ABSTRACT FROM AUTHOR]

The number of publications on research of male infertility is increasing. Technologies used in research of male infertility generate complex results and various types of data that need to be appropriately managed, arranged, and made available to other researchers for further use. In our previous study, we collected over 800 candidate loci for male fertility in seven mammalian species. However, the continuation of the work towards a comprehensive database of candidate genes associated with different types of idiopathic human male infertility is challenging due to fragmented information, obtained from a variety of technologies and various omics approaches. Results are published in different forms and usually need to be excavated from the text, which hinders the gathering of information. Standardized reporting of genetic anomalies as well as causative and risk factors of male infertility therefore presents an important issue. The aim of the study was to collect examples of diverse genomic loci published in association with human male infertility and to propose a standardized format for reporting genetic causes of male infertility. From the currently available data we have selected 75 studies reporting 186 representative genomic loci which have been proposed as genetic risk factors for male infertility. Based on collected and formatted data, we suggested a first step towards unification of reporting the genetics of male infertility in original and review studies. The proposed initiative consists of five relevant data types: 1) genetic locus, 2) race/ethnicity, number of participants (infertile/controls), 3) methodology, 4) phenotype (clinical data, disease ontology, and disease comorbidity), and 5) reference. The proposed form for standardized reporting presents a baseline for further optimization with additional genetic and clinical information. This data standardization initiative will enable faster multi-omics data integration, database development and sharing, establishing more targeted hypotheses, and facilitating biomarker discovery. [ABSTRACT FROM PUBLISHER]

Advances in genetic sequencing technologies and techniques have made large, genome-wide data sets comprised of hundreds or even thousands of individuals and loci the norm rather than the exception even for nonmodel organisms. While such data present new opportunities for evaluating population structure and demographic processes, the large size of these genomic data sets brings new computational challenges for researchers needing to parse, convert and manipulate data often into a variety of software-specific formats required of genomic analyses. We developed genepopedit as a flexible tool for the manipulation of multilocus molecular data sets. Functionality can be divided among diagnostic-, manipulation-, sampling-, simulation-, and transformation-based tools. Metadata from large genomic data sets can be efficiently extracted, without the need to view data in a text-editing program. genepopedit provides tools to manipulate loci, individual samples and populations included in genomic data sets, in addition to the ability to convert directly to a variety of software formats. Functions are compiled as an R package, which can integrate into existing analysis workflows. Importantly, genepopedit provides a simple yet robust code-based tool for repeatable genomic data manipulation, which has been proven to be stable for data sets in excess of 200 000 SNPs. The latest version of the package and associated documentation are available on Github (github.com/rystanley/genepopedit). [ABSTRACT FROM AUTHOR]

Population genetics and genomics have developed and been treated as independent fields of study despite having common roots. The continuous progress of sequencing technologies is contributing to (re-)connect these two disciplines. We review the challenges faced by data analysts and software developers when handling very big genetic data sets collected on many individuals. We then expose how r, as a computing language and development environment, proposes some solutions to meet these challenges. We focus on some specific issues that are often encountered in practice: handling and analysing single-nucleotide polymorphism data, handling and reading variant call format files, analysing haplotypes and linkage disequilibrium and performing multivariate analyses. We illustrate these implementations with some analyses of three recently published data sets that contain between 60 000 and 1 000 000 loci. We conclude with some perspectives on future developments of r software for population genomics. [ABSTRACT FROM AUTHOR]

Next-generation sequencing has significantly reduced the cost of genome-sequencing projects, resulting in an expressive increase in the availability of genomic data in public databases. The cheaper and easier is to sequence new genomes, the more accurate the annotation steps have to be to avoid both the loss of information and the accumulation of erroneous features that may affect the accuracy of further analysis. In the case of bacteria genomes, a range of web annotation software has been developed; however, many applications have yet to incorporate the steps required to improve their result, including the removal of false-positive/spurious and a more complete identification of non-coding features. We present Genix, a new web-based bacterial genome annotation pipeline. A comparison of the results generated by Genix for four reference genomes against those generated by other annotation tools indicated that our pipeline is able to provide results that are closer to the reference genome annotation, with a smaller amount of false-positive proteins and missing functional annotated proteins. Additionally, the metrics obtained by Genix were slightly better than those obtained by Prokka, a state-of-art standalone annotation system. Our results indicate that Genix is a useful tool that is able to provide a more refined result, and may be a user-friendly way to obtain high-quality results. [ABSTRACT FROM AUTHOR]

Background: Next-generation sequencing (NGS) informs many biological questions with unprecedented depth and nucleotide resolution. These assays have created a need for analytical tools that enable users to manipulate data nucleotide-by-nucleotide robustly and easily. Furthermore, because many NGS assays encode information jointly within multiple properties of read alignments - for example, in ribosome profiling, the locations of ribosomes are jointly encoded in alignment coordinates and length - analytical tools are often required to extract the biological meaning from the alignments before analysis. Many assay-specific pipelines exist for this purpose, but there remains a need for user-friendly, generalized, nucleotide-resolution tools that are not limited to specific experimental regimes or analytical workflows. Results: Plastid is a Python library designed specifically for nucleotide-resolution analysis of genomics and NGS data. As such, Plastid is designed to extract assay-specific information from read alignments while retaining generality and extensibility to novel NGS assays. Plastid represents NGS and other biological data as arrays of values associated with genomic or transcriptomic positions, and contains configurable tools to convert data from a variety of sources to such arrays. Plastid also includes numerous tools to manipulate even discontinuous genomic features, such as spliced transcripts, with nucleotide precision. Plastid automatically handles conversion between genomic and featurecentric coordinates, accounting for splicing and strand, freeing users of burdensome accounting. Finally, Plastid's data models use consistent and familiar biological idioms, enabling even beginners to develop sophisticated analytical workflows with minimal effort. Conclusions: Plastid is a versatile toolkit that has been used to analyze data from multiple NGS assays, including RNA-seq, ribosome profiling, and DMS-seq. It forms the genomic engine of our ORF annotation tool, ORF-RATER, and is readily adapted to novel NGS assays. Examples, tutorials, and extensive documentation can be found at https://plastid.readthedocs.io. [ABSTRACT FROM AUTHOR]

Prospects have never seemed better for a truly global approach to science to improve human health, with leaders of national initiatives laying out their vision of a worldwide network of related projects. An extensive literature addresses obstacles to global genomic data sharing, yet a series of public polls suggests that the scientific community may be overlooking a significant barrier: potential public resistance to data sharing across national borders. In several large United States surveys, university researchers in other countries were deemed the least acceptable group of data users, and a just-completed US survey found a marked increase in privacy and security concerns related to data access by non-US researchers. Furthermore, diminished support for sharing beyond national borders is not unique to the US, although the limited data from outside the US suggest variation across countries as well as demographic groups. Possible sources of resistance include apprehension about privacy and security protections. Strategies for building public support include making the affirmative case for global data sharing, addressing privacy, security, and other legitimate concerns, and investigating public concerns in greater depth. [ABSTRACT FROM AUTHOR]

Genetic testing has far-reaching consequences, not only in terms of immediate patient management and the wider implications for the patient and their families, but also with respect to disclosure to insurance companies. The focus of this review is the controversial but important topic of the use of genetic data in private medical insurance. We discuss the current legal regulation of genetic data in the context of Swiss insurance, what type(s) of information is relevant to insurance companies, and why 'genetic exceptionalism' (the notion that genetic data has special status) persists. Furthermore, we discuss the sensitive area of handling genetic data from children. The consequences of legal regulation of disclosure of genetic information are considered, particularly from the economic perspective. Finally, we examine how legal conditions correspond to current insurance practice and contrast the Swiss system with the handling of genetic data in other countries in the context of private insurance. Switzerland has adopted fairly 'laissez-faire' regulations compared to other countries, and the public need education on the potential effects of genetic testing on their insurance, especially with respect to direct-to-consumer genetic testing, where there is no consultation from a qualified doctor, or when minors are involved. [ABSTRACT FROM AUTHOR]

Understanding when species diverged aids in identifying the drivers of speciation, but the end of gene flow between populations can be difficult to ascertain from genetic data. We explore the use of pairwise sequential Markovian coalescent (PSMC) modelling to infer the timing of divergence between species and populations. PSMC plots generated using artificial hybrid genomes show rapid increases in effective population size at the time when the two parent lineages diverge, and this approach has been used previously to infer divergence between human lineages. We show that, even without high coverage or phased input data, PSMC can detect the end of significant gene flow between populations by comparing the PSMC output from artificial hybrids to the output of simulations with known demographic histories. We then apply PSMC to detect divergence times among lineages within two real datasets: great apes and bears within the genus Ursus. Our results confirm most previously proposed divergence times for these lineages, and suggest that gene flow between recently diverged lineages may have been common among bears and great apes, including up to one million years of continued gene flow between chimpanzees and bonobos after the formation of the Congo River. This article is part of the themed issue 'Dating species divergences using rocks and clocks'. [ABSTRACT FROM AUTHOR]

The significantly related diseases of sequences could play an important role in understanding the functions of these sequences. In this paper, we introduced BLAT2DOLite, an online system for annotating human genes and diseases and identifying the significant relationships between sequences and diseases. Currently, BLAT2DOLite integrates Entrez Gene database and Disease Ontology Lite (DOLite), which contain loci of gene and relationships between genes and diseases. It utilizes hypergeometric test to calculate P-values between genes and diseases of DOLite. The system can be accessed from: . The corresponding web service is described in: . [ABSTRACT FROM AUTHOR]

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets. [ABSTRACT FROM AUTHOR]

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. [ABSTRACT FROM AUTHOR]

Background: Precision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data. Results: We developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants. Conclusions: In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations. [ABSTRACT FROM AUTHOR]

Data visualization methods are necessary during the exploration and analysis activities of an increasingly data-intensive scientific process. There are few existing visualization methods for raw nucleotide sequences of a whole genome or chromosome. Software for data visualization should allow the researchers to create accessible data visualization interfaces that can be exported and shared with others on the web. Herein, novel software developed for generating DNA data visualization interfaces is described. The software converts DNA data sets into images that are further processed as multi-scale images to be accessed through a web-based interface that supports zooming, panning and sequence fragment selection. Nucleotide composition frequencies and GC skew of a selected sequence segment can be obtained through the interface. The software was used to generate DNA data visualization of human and bacterial chromosomes. Examples of visually detectable features such as short and long direct repeats, long terminal repeats, mobile genetic elements, heterochromatic segments in microbial and human chromosomes, are presented. The software and its source code are available for download and further development. The visualization interfaces generated with the software allow for the immediate identification and observation of several types of sequence patterns in genomes of various sizes and origins. The visualization interfaces generated with the software are readily accessible through a web browser. This software is a useful research and teaching tool for genetics and structural genomics. [ABSTRACT FROM AUTHOR]

This article reviews international privacy norms governing human genomic biobanks and databases, and how they address issues related to consent, secondary use, de- identification, access, security, and governance. A range of international instruments were identified, varying in substance - e.g., human rights, data protection, research ethics, biobanks, and genetics - and legal character. Some norms detail processes for broad consent, namely, that even where potential participants cannot consent to specific users and uses, they should be given clear information on access policies, procedures, and governance structures. Some also give guidance about the conditions under which secondary use of data and samples without consent is appropriate, e.g., where consent is impracticable. International norms exhibit a confusing range of terminology relating to de-identification. They also continue to rely heavily on consent and anonymity as the basis for privacy protection, though governance is becoming more prominent. It may not be fatal that such a plurality of norms apply to biobanking; what is essential is that governance be built on shared values, our common interest in the success of genomic research, and practical tools that incentivize responsible, global sharing. [ABSTRACT FROM AUTHOR]