Types Of Biological Data Research Articles

Data sharing by biologists: A comparative study of genome sequence data and lab experiment data

Whether biologists share data depends on data type. This study employed an integrated theoretical framework combining theory of planned behavior and institutional theory to investigate individual motivations, technological resources, and institutional pressures affecting data sharing intentions between biologists depending on whether they were sharing genome sequence data or regular lab experiment data. A series of statistical analyses including principal component factor analysis, hierarchical regression, and independent sample t-test were conducted on a total of 342 survey responses from biologists. The results suggest that biologists' data sharing intentions are influenced by individual motivations, technological resources, and institutional pressures, and that the intensities of those factors are considerably different for genome sequence data and lab experiment data. This study suggests that data sharing by biologists varies depending on the type of data they produce, and that different policies and technological supports should be applied to facilitate the sharing of diverse biological data types.

In silico identification of anti-aging pharmaceutics from community knowledge

In this era of Big Data, the volume of biological data is growing exponentially. Systematic profiling and analysis of these data will provide a new insight into biology and human health. Among diverse types of biological data, gene expression data closely mirror both the static phenotypes and the dynamic changes in biological systems. Drug-to-drug or drug-to-disease comparison of gene expression signature allows repurposing/repositioning of existing pharmaceutics to treat additional diseases that, in turn, provides a rapid and cost-effective approach for drug discovery. Thanks to technological advances, gene expression profiling by mRNA-seq became a routine tool to address all aspects of the problem in modern biological research. Here, we present how drug repositioning using published mRNA-seq data can provide unbiased and applicable pharmaco-chemical intervention strategies to human diseases and aging. In specifics, we profiled over a half-million gene expression profiling data generated from various contexts, and using this, we screened conditions that can suppress age-associated gene expression changes. As a result, our analysis identified various previously validated aging intervention strategies as positive hits. Furthermore, our analysis also predicted a novel group of chemicals that has not been studied from an aging context, and this indeed significantly extended the life span in model animals. Taken together, our data demonstrate that our community knowledge-guided in silico drug-discovery pipeline provides a useful and effective tool to identify the novel aging intervention strategy.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi.

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

Network propagation-based prioritization of long tail genes in 17 cancer types

BackgroundThe diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development.ResultsWe identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets “upward mobility genes” and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes.ConclusionOur analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.

A guide to machine learning for biologists.

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are alsodiscussed.

An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data

Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feature selection algorithms, and four cross validation methods was used to discriminate patients with schizophrenia from healthy controls. Our results show that the support vector machine classifier without feature selection using the input features of multi-biological data achieved the best performance, with an accuracy of 91.7% and an AUC of 96.5% (p < 0.05). These results indicate that multi-biological data showed better discriminative capacity for patients with schizophrenia than single biological data. The top 5% discriminative features selected from the optimal model include the gut microbiota features (Lactobacillus, Haemophilus, and Prevotella), the blood features (superoxide dismutase level, monocyte-lymphocyte ratio, and neutrophil count), and the electroencephalogram features (nodal local efficiency, nodal efficiency, and nodal shortest path length in the temporal and frontal-parietal brain areas). The proposed integrated framework may be helpful for understanding the pathophysiology of schizophrenia and developing biomarkers for schizophrenia using multi-biological data.

Tripal, a community update after 10years of supporting open source, standards-based genetic, genomic and breeding databases.

Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data. Tripal software development is managed by a shared, inclusive governance structure including both project management and advisory teams. Here, we report on the most important and innovative aspects of Tripal after 11 years development, including integration of diverse types of biological data, successful collaborative projects across member databases, and support for implementing FAIR principles.

A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms.

Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite.

The secretive behavior and life history of snakes makes studying their biology, distribution, and the epidemiology of venomous snakebite challenging. One of the most useful, most versatile, and easiest to collect types of biological data are photographs, particularly those that are connected with geographic location and date-time metadata. Photos verify occurrence records, provide data on phenotypes and ecology, and are often used to illustrate new species descriptions, field guides and identification keys, as well as in training humans and computer vision algorithms to identify snakes. We scoured eleven online and two offline sources of snake photos in an attempt to collect as many photos of as many snake species as possible, and attempt to explain some of the inter-species variation in photograph quantity among global regions and taxonomic groups, and with regard to medical importance, human population density, and range size. We collected a total of 725,565 photos—between 1 and 48,696 photos of 3098 of the world's 3879 snake species (79.9%), leaving 781 “most wanted” species with no photos (20.1% of all currently-described species as of the December 2020 release of The Reptile Database). We provide a list of most wanted species sortable by family, continent, authority, and medical importance, and encourage snake photographers worldwide to submit photos and associated metadata, particularly of “missing” species, to the most permanent and useful online archives: The Reptile Database, iNaturalist, and HerpMapper.

VisuaLife: library for interactive visualization in rich web applications.

MotivationVisualization is a powerful tool to analyze, understand and present big data. Computational biology, bioinformatics and molecular modeling require dedicated tools, tailored to very complex, highly multidimensional data. Over the recent years, numerous tools have been developed for online presentation, but new challenges like the COVID-19 pandemic require new libraries which will guarantee fast development of online tools for a better understanding of biomedical data/results.ResultsVisuaLife is a Python library that provides a new approach to visualization in a web browser. It offers 2D and 3D plotting capabilities as well as widgets designed to display the most common biological data types: nucleotide or protein sequences, 3D biomolecular structures and multiple sequence alignments. Components provided by the VisuaLife library can be assembled into a web application to create an analysis tool tailored to provide multidimensional analysis of a specific research problem. VisuaLife, to our best knowledge, is the most modern solution that allows one to implement such a client-side interactivity in Python.Availability and implementationThe git repository of the library is hosted at BitBucket: https://bitbucket.org/dgront/visualife/. PyPI distribution is also provided for MacOS and Linux. While basic examples are provided in the supporting materials, the full documentation is available at ReadTheDocs website: https://visualife.readthedocs.io/.Supplementary information Supplementary data are available at Bioinformatics online.

The international political process around Digital Sequence Information under the Convention on Biological Diversity and the 2018–2020 intersessional period

Societal Impact StatementThe international conservation of biological diversity is addressed under the Convention on Biological Diversity (CBD) and goals for the next decade will be discussed at the next Conference of the Parties. One issue under negotiation in the CBD is Digital Sequence Information (DSI), which has created tension between parties calling for preserving open access to DSI who also note its importance in addressing biodiversity and the UN Sustainable Development Goals and those parties calling for fair and equitable benefit sharing from DSI. This article introduces scientists to the current debate and political process on DSI within the CBD.SummaryMost biologists take open access to sequence data for granted. This open system, while a hallmark of innovation and collaboration for the scientific community, is being called into question as some parties to the Convention on Biological Diversity (CBD) assert that this access undermines their sovereign rights over their genetic resources and corresponding benefit sharing. The governance of sequence data and potentially other types of biological data, known in international policy circles as “Digital Sequence Information” (DSI), a placeholder term invented by negotiators, could be dramatically altered and ultimately change the way scientific research and publishing on sequence data is conducted. Many sequence‐using scientists are unfamiliar with the international political processes around DSI even though it could lead to irreversible decisions that might have significant impacts on research. This paper bridges that gap by providing an overview of the ongoing political process with a focus on the most recent studies on DSI commissioned by the Secretariat of the Convention on Biological Diversity (SCBD) and what these studies forecast about the political debate. With this information in hand, the scientific community can hopefully better engage with the political process and proactively promote evidence‐based decisions or even solutions that can bridge the demand for benefit sharing with the scientific need for open access to DSI.

A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases.

One important challenge in the post-genomic era is to explore disease mechanisms by efficiently integrating different types of biological data. In fact, a single disease is usually caused through multiple genes products such as protein complexes rather than single gene. Therefore, it is meaningful for us to discover protein communities from the protein-protein interaction network and use them for inferring disease-disease associations. In this article, we propose a new framework including protein-protein networks, disease-gene associations and disease-complex pairs to cluster protein complexes and infer disease associations. Complexes discovered by our approach is superior in quality (Sn, PPV and ACC) and clustering quantity than other four popular methods on three PPI networks. A systematic analysis shows that disease pairs sharing more protein complexes (such as Glucose and Lipid Metabolic Disorders) are more similar and overlapping proteins may have different roles in different diseases. These findings can provide clinical scholars and medical practitioners with new ideas on disease identification and treatment.

Robust Data Integration Method for Classification of Biomedical Data

We present a protocol for integrating two types of biological data – clinical and molecular – for more effective classification of patients with cancer. The proposed approach is a hybrid between early and late data integration strategy. In this hybrid protocol, the set of informative clinical features is extended by the classification results based on molecular data sets. The results are then treated as new synthetic variables. The hybrid protocol was applied to METABRIC breast cancer samples and TCGA urothelial bladder carcinoma samples. Various data types were used for clinical endpoint prediction: clinical data, gene expression, somatic copy number aberrations, RNA-Seq, methylation, and reverse phase protein array. The performance of the hybrid data integration was evaluated with a repeated cross validation procedure and compared with other methods of data integration: early integration and late integration via super learning. The hybrid method gave similar results to those obtained by the best of the tested variants of super learning. What is more, the hybrid method allowed for further sensitivity analysis and recursive feature elimination, which led to compact predictive models for cancer clinical endpoints. For breast cancer, the final model consists of eight clinical variables and two synthetic features obtained from molecular data. For urothelial bladder carcinoma, only two clinical features and one synthetic variable were necessary to build the best predictive model. We have shown that the inclusion of the synthetic variables based on the RNA expression levels and copy number alterations can lead to improved quality of prognostic tests. Thus, it should be considered for inclusion in wider medical practice.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

Public awareness of and attitudes towards research biobanks in Latvia.

BackgroundPublic awareness and engagement are among the main prerequisites for protecting the rights of research participants and for successful and sustainable functioning of research biobanks. The aim of our study was to analyse public awareness and attitudes towards research biobanks in Latvia, and to compare these data with the results of the 2010 Eurobarometer study. We also analysed the influence of awareness and attitudes towards biobanks on willingness to participate in biobank studies and on preferred type of informed consent.MethodsWe developed a 12-question survey repeating seven questions about biobanks from the 2010 Eurobarometer questionnaire and adding five others. After describing the study variables, we performed a two-stage analysis of the results. In the first stage we analysed differences between the answers from 2010 and 2019 and conducted univariate analyses of relationships among particular variables, and between those variables and the socio-demographic characteristics of participants. In the second stage we investigated multivariable associations of willingness to participate and type of consent with awareness, trust and the socio-economic characteristics of participants.ResultsAccording to our study, the general public in Latvia is still not well informed about research biobanks. Fewer respondents have heard about research biobanks than in 2010. At the same time, the number of respondents who are willing to donate biological samples and personal data to a biobank has increased, e.g. the number of respondents who would definitely or probably be willing to provide information about themselves has increased from 25.8.% to 40.7 since 2010. Overall, concerns about the donation of different types of biological samples and data to a biobank have slightly decreased.ConclusionsPublic awareness about biobanks is important for their sustainability. It needs to be increased not only by traditional methods of informing the public, but also by more innovative and participatory approaches, e.g. by citizen science projects. There is a need to strengthen the public visibility and trustworthiness of ethics committees in Latvia in the field of biobanking.

Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

BackgroundDrugs achieve pharmacological functions by acting on target proteins. Identifying interactions between drugs and target proteins is an essential task in old drug repositioning and new drug discovery. To recommend new drug candidates and reposition existing drugs, computational approaches are commonly adopted. Compared with the wet-lab experiments, the computational approaches have lower cost for drug discovery and provides effective guidance in the subsequent experimental verification. How to integrate different types of biological data and handle the sparsity of drug-target interaction data are still great challenges.ResultsIn this paper, we propose a novel drug-target interactions (DTIs) prediction method incorporating marginalized denoising model on heterogeneous networks with association index kernel matrix and latent global association. The experimental results on benchmark datasets and new compiled datasets indicate that compared to other existing methods, our method achieves higher scores of AUC (area under curve of receiver operating characteristic) and larger values of AUPR (area under precision-recall curve).ConclusionsThe performance improvement in our method depends on the association index kernel matrix and the latent global association. The association index kernel matrix calculates the sharing relationship between drugs and targets. The latent global associations address the false positive issue caused by network link sparsity. Our method can provide a useful approach to recommend new drug candidates and reposition existing drugs.

A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data

BackgroundSingle-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes in imputation and integrate multiple types of imputation data to identify cell types.ResultsWe present a new framework, NetImpute, towards the identification of cell types from scRNA-seq data by integrating multiple types of biological networks. We employ a statistic method to detect the noise data items in scRNA-seq data and develop a new imputation model to estimate the real values of data noise by integrating the PPI network and gene pathways. Meanwhile, based on the data imputed by multiple types of biological networks, we propose an integrated approach to identify cell types from scRNA-seq data. Comprehensive experiments demonstrate that the proposed network-based imputation model can estimate the real values of noise data items accurately and integrating the imputation data based on multiple types of biological networks can improve the identification of cell types from scRNA-seq data.ConclusionsIncorporating the prior gene associations in biological networks can potentially help to improve the imputation of noisy scRNA-seq data and integrating multiple types of network-based imputation data can enhance the identification of cell types. The proposed NetImpute provides an open framework for incorporating multiple types of biological network data to identify cell types from scRNA-seq data.

Integrative Biological Network Analysis to Identify Shared Genes in Metabolic Disorders.

Identification of common molecular mechanisms in interrelated diseases is essential for better prognoses and targeted therapies. However, complexity of metabolic pathways makes it difficult to discover common disease genes underlying metabolic disorders; and it requires more sophisticated bioinformatics models that combine different types of biological data and computational methods. Accordingly, we built an integrative network analysis model to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks by combining gene expression, protein-protein interaction, and gene ontology data from multiple sources. For 90 different configurations of disease networks, we detected the significant modules by using MCL, SPICi, and Linkcomm graph clustering algorithms. We also performed a comparative evaluation on disease modules to determine the best method providing the highest biological validity. By overlapping the disease modules, we identified 22 shared genes for MS-CAD and T2D-CAD. Moreover, 19 out of these genes were directly or indirectly associated with relevant diseases in the previous medical studies. This study does not only demonstrate the performance of different biological data sources and computational methods in disease-gene discovery, but also offers potential insights into common genetic mechanisms of the metabolic disorders.

Breast Cancer Candidate Gene Detection Through Integration of Subcellular Localization Data With Protein-Protein Interaction Networks.

Due to technological advances the quality and availability of biological data has increased dramatically in the last decade. Analysing protein-protein interaction networks (PPINs) in an integrated way, together with subcellular compartment data, provides such biological context, helps to fill in the gaps between a single type of biological data and genes causing diseases and can identify novel genes related to disease. In this study, we present BCCGD, a method for integrating subcellular localization data with PPINs that detects breast cancer candidate genes in protein complexes. We achieve this by defining the significance of the compartment, constructing edge-weighted PPINs, finding protein complexes with a non-negative matrix factorization approach, generating disease-specific networks based on the known disease genes, prioritizing disease candidate genes with a WDC method. As a case study, we investigate the breast cancer but the techniques described here are applicable to other disorders. For the top genes scored by BCCGD approach, we utilize the literature retrieving method to test the correlations of them with the breast cancer. The results show that BCCGD discover some novel breast cancer candidate genes which are valuable references for the biomedical scientists.

Coupling landscape graph modeling and biological data: a review

Landscape graphs are widely used to model networks of habitat patches. As they require little input data, they are particularly suitable for supporting conservation decisions (and decisions about other issues as e.g. disease spread) taken by land planners. However, it may be problematic to use these methods in operational contexts without validating them with empirical data on species or communities. Since little is known about methodological alternatives for coupling landscape graphs with biological data, we have made an exhaustive review of these methods to analyze links between the main purposes of the studies, the way landscape graphs are constructed and used, the type of field data, and the way these data are integrated into the analysis. We systematically describe a corpus of 71 scientific papers dealing with terrestrial species, with particular emphasis on methodological choices and contexts of the studies. Despite a great variability of types of biological data and coupling strategies, our analyses reveal a dichotomy according to the objective of the studies, between (i) approaches aimed at improving ecological knowledge, mainly based on land-cover maps and using biological data to test the influence of landscape connectivity on biological responses, and (ii) approaches with an operational aim, in which biological data are directly integrated into the graph construction and assuming a positive effect of connectivity. Beyond these main contrasts, the review shows that landscape graphs can benefit from field data of different types at varying scales. The great variability of approaches adopted reveals the flexible nature of these tools.