Large-scale Omics Datasets Research Articles

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.

Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.

Comprehensive phage display viral antibody profiling using VirScan: potential applications in chronic immune-mediated disease.

Phage immunoprecipitation sequencing (PhIP-Seq) is a high-throughput platform that uses programmable phage display for serology. VirScan, a specific PhIP-Seq library encoding viral peptides from all known human viruses, enables comprehensive quantification of past viral exposures. We review its use in immune-mediated diseases (IMDs), highlighting its utility in identifying viral exposures in the context of IMD development. Finally, we evaluate its potential for precision medicine by integrating it with other large-scale omics data sets.

Network analysis of the proteome and peptidome sheds light on human milk as a biological system

Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.

OmicsTIDE: interactive exploration of trends in multi-omics data.

The increasing amount of data produced by omics technologies has enabled researchers to study phenomena across multiple omics layers. Besides data-driven analysis strategies, interactive visualization tools have been developed for a more transparent analysis. However, most state-of-the-art tools do not reconstruct the impact of a single omics layer on the integration result. We developed a data classification scheme focusing on different aspects of multi-omics datasets for a systemic understanding. Based on this classification, we developed the Omics Trend-comparing Interactive Data Explorer (OmicsTIDE), an interactive visualization tool for the comparison of gene-based quantitative omics data. The tool consists of a computational part that clusters omics datasets to determine trends and an interactive visualization. The trends are visualized as profile plots and are connected by a Sankey diagram that allows for an interactive pairwise trend comparison to discover concordant and discordant trends. Moreover, large-scale omics datasets are broken down into small subsets that can be analyzed functionally using Gene Ontology enrichment within few analysis steps. We demonstrate the interactive analysis using OmicsTIDE with two case studies focusing on different experimental designs. OmicsTIDE is a web tool available via http://omicstide-tuevis.cs.uni-tuebingen.de/. Supplementary data are available at Bioinformatics Advances online.

Integrated multi-omics approaches to understand microbiome assembly in Jiuqu, a mixed-culture starter.

The use of Jiuqu as a saccharifying and fermenting starter in the production of fermented foods is a very old biotechnological process that can be traced back to ancient times. Jiuqu harbors a hub of microbial communities, in which prokaryotes and eukaryotes cohabit, interact, and communicate. However, the spontaneous fermentation based on empirical processing hardly guarantees the stable assembly of the microbiome and a standardized quality of Jiuqu. This review describes the state of the art, limitations, and challenges towards the application of traditional and omics-based technology to study the Jiuqu microbiome and highlights the need for integrating meta-omics data. In addition, we review the varieties of Jiuqu and their production processes, with particular attention to factors shaping the microbiota of Jiuqu. Then, the potentials of integrated omics approaches used in Jiuqu research are examined in order to understand the assembly of the microbiome and improve the quality of the products. A variety of different approaches, including molecular and mass spectrometry-based techniques, have led to scientific advances in the analysis of the complex ecosystem of Jiuqu. To date, the extensive research on Jiuqu has mainly focused on the microbial community diversity, flavor profiles, and biochemical characteristics. An integrative approach to large-scale omics datasets and cultivated microbiota has great potential for understanding the interrelation of the Jiuqu microbiome. Further research on the Jiuqu microbiome may explain the inherent property of compositional stability and stable performance of a complex microbiota coping with environmental perturbations and provide important insights to reconstruct synthetic microbiota and develop modern intelligent manufacturing procedures for Jiuqu.

Towards Phytopathogen Diagnostics? Coconut Bud Rot Pathogen Phytophthora palmivora Mycelial Proteome Analysis Informs Genome Annotation.

Planetary agriculture stands to benefit immensely from phytopathogen diagnostics, which would enable early detection of pathogens with harmful effects on crops. For example, Phytophthora palmivora is one of the most destructive phytopathogens affecting many economically important tropical crops such as coconut. P. palmivora causes diseases in over 200 host plants, and notably, the bud rot disease in coconut and oil palm, which is often lethal because it is usually detected at advanced stages of infection. Limited availability of large-scale omics datasets for P. palmivora is an important barrier for progress toward phytopathogen diagnostics. We report here the mycelial proteome of P. palmivora using high-resolution mass spectrometry analysis. We identified 8073 proteins in the mycelium. Gene Ontology-based functional classification of detected proteins revealed 4884, 4981, and 3044 proteins, respectively, with roles in biological processes, molecular functions, and cellular components. Proteins such as P-loop, NTPase, and WD40 domains with key roles in signal transduction pathways were identified. KEGG pathway analysis annotated 2467 proteins to various signaling pathways, such as phosphatidylinositol, Ca2+, and mitogen-activated protein kinase, and autophagy and cell cycle. These molecular substrates might possess vital roles in filamentous growth, sporangia formation, degradation of damaged cellular content, and recycling of nutrients in P. palmivora. This large-scale proteomics data and analyses pave the way for new insights on biology, genome annotation, and vegetative growth of the important plant pathogen P. palmivora. They also can help accelerate research on future phytopathogen diagnostics and preventive interventions.

MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis.

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

SGI: automatic clinical subgroup identification in omics datasets.

SummaryThe ‘Subgroup Identification’ (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. In this article, we first describe the functionality of the toolbox and then demonstrate its capabilities through application examples on a type 2 diabetes metabolomics study as well as two copy number variation datasets from The Cancer Genome Atlas.Availability and implementationSGI is an open-source package implemented in R. Package source codes and hands-on tutorials are available at https://github.com/krumsieklab/sgi. The QMdiab metabolomics data is included in the package and can be downloaded from https://doi.org/10.6084/m9.figshare.5904022.Supplementary information Supplementary data are available at Bioinformatics online.

Data-driven approaches to advance research and clinical care for pediatric cancer

Pediatric cancer is a rare disease with a distinct etiology and mutational landscape compared with adult cancer. Multi-omics profiling of retrospective and prospective cohorts coupled with innovative computational analysis have been instrumental in uncovering mechanisms of tumorigenesis and drug resistance that are now informing pediatric cancer clinical therapy. In this review we present the major data resources of pediatric cancer and actionable insights into pediatric cancer etiology stemming from the identification of oncogenic gene fusions, mutational signature analysis, systems biology, cancer predisposition and survivorship studies – that have led to improved clinical diagnosis, discovery of new drug-targets, pharmacological therapy, and screening for genetic predisposition. Ultimately, integration of large-scale omics datasets generated through international collaboration is required to maximize the power of data-driven approaches to advance pediatric cancer research informing clinical therapy.

Identification of stem cells from large cell populations with topological scoring.

Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed a Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

Ontology-driven integrative analysis of omics data through Onassis

Public repositories of large-scale omics datasets represent a valuable resource for researchers. In fact, data re-analysis can either answer novel questions or provide critical data able to complement in-house experiments. However, despite the development of standards for the compilation of metadata, the identification and organization of samples still constitutes a major bottleneck hampering data reuse. We introduce Onassis, an R package within the Bioconductor environment providing key functionalities of Natural Language Processing (NLP) tools. Leveraging biomedical ontologies, Onassis greatly simplifies the association of samples from large-scale repositories to their representation in terms of ontology-based annotations. Moreover, through the use of semantic similarity measures, Onassis hierarchically organizes the datasets of interest, thus supporting the semantically aware analysis of the corresponding omics data. In conclusion, Onassis leverages NLP techniques, biomedical ontologies, and the R statistical framework, to identify, relate, and analyze datasets from public repositories. The tool was tested on various large-scale datasets, including compendia of gene expression, histone marks, and DNA methylation, illustrating how it can facilitate the integrative analysis of various omics data.

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology—type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types—single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.

Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

Motivation: Understanding and predicting an individual’s response in a clinical trial is the key to better treatments and cost-effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.Methods: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response.Results: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.Contact: daniel.ziemek@pfizer.comSupplementary information: Supplementary data are available at Bioinformatics online.

Prioritization of cancer marker candidates based on the immunohistochemistry staining images deposited in the human protein atlas.

Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in the cancer type of interest, which is related to sensitivity, and the specificity of the marker among cancer groups, are the most critical considerations. Protein expression profiling on the basis of immunohistochemistry (IHC) staining images is a technique commonly used during such filtering procedures. To systematically investigate the protein expression in different cancer versus normal tissues and cell types, the Human Protein Atlas is a most comprehensive resource because it includes millions of high-resolution IHC images with expert-curated annotations. To facilitate the filtering of potential biomarker candidates from large-scale omics datasets, in this study we have proposed a scoring approach for quantifying IHC annotation of paired cancerous/normal tissues and cancerous/normal cell types. We have comprehensively calculated the scores of all the 17219 tested antibodies deposited in the Human Protein Atlas based on their accumulated IHC images and obtained 457110 scores covering 20 different types of cancers. Statistical tests demonstrate the ability of the proposed scoring approach to prioritize cancer-specific proteins. Top 100 potential marker candidates were prioritized for the 20 cancer types with statistical significance. In addition, a model study was carried out of 1482 membrane proteins identified from a quantitative comparison of paired cancerous and adjacent normal tissues from patients with colorectal cancer (CRC). The proposed scoring approach demonstrated successful prioritization and identified four CRC markers, including two of the most widely used, namely CEACAM5 and CEACAM6. These results demonstrate the potential of this scoring approach in terms of cancer marker discovery and development. All the calculated scores are available at http://bal.ym.edu.tw/hpa/.

Plant MetGenMAP: An Integrative Analysis System for Plant Systems Biology

The information and resources generated from diverse "omics" technologies provide opportunities for producing novel biological knowledge. It is essential to integrate various kinds of biological information and large-scale omics data sets through systematic analysis in order to describe and understand complex biological phenomena. For this purpose, we have developed a Web-based system, Plant MetGenMAP, which can comprehensively integrate and analyze large-scale gene expression and metabolite profile data sets along with diverse biological information. Using this system, significantly altered biochemical pathways and biological processes under given conditions can be retrieved rapidly and efficiently, and transcriptional events and/or metabolic changes in a pathway can be easily visualized. In addition, the system provides a unique function that can identify candidate promoter motifs associated with the regulation of specific biochemical pathways. We demonstrate the functions and application of the system using data sets from Arabidopsis (Arabidopsis thaliana) and tomato (Solanum lycopersicum), respectively. The results obtained by Plant MetGenMAP can aid in a better understanding of the mechanisms that underlie interesting biological phenomena and provide novel insights into the biochemical changes associated with them at the gene and metabolite levels. Plant MetGenMAP is freely available at http://bioinfo.bti.cornell.edu/tool/MetGenMAP.

OmicsViz: Cytoscape plug-in for visualizing omics data across species

Motivation: OmicsViz is a Cytoscape plug-in for mapping and visualizing large-scale omics datasets across species, including those with many-to-many mappings between homologs. This allows users to map their data onto pathways of related model organisms. Mapping schemas across species or different experimental protocols allow users to comparatively analyze the omics data. The data can also be visualized in parallel-coordinate plots.Availability: The latest version of OmicsViz with documentation, tutorial and jar files can be downloaded from http://metnet.vrac.iastate.edu/MetNet_fcmodeler.htmContact: julied@iastate.edu

High-throughput bioinformatics with the Cyrille2 pipeline system

BackgroundModern omics research involves the application of high-throughput technologies that generate vast volumes of data. These data need to be pre-processed, analyzed and integrated with existing knowledge through the use of diverse sets of software tools, models and databases. The analyses are often interdependent and chained together to form complex workflows or pipelines. Given the volume of the data used and the multitude of computational resources available, specialized pipeline software is required to make high-throughput analysis of large-scale omics datasets feasible.ResultsWe have developed a generic pipeline system called Cyrille2. The system is modular in design and consists of three functionally distinct parts: 1) a web based, graphical user interface (GUI) that enables a pipeline operator to manage the system; 2) the Scheduler, which forms the functional core of the system and which tracks what data enters the system and determines what jobs must be scheduled for execution, and; 3) the Executor, which searches for scheduled jobs and executes these on a compute cluster.ConclusionThe Cyrille2 system is an extensible, modular system, implementing the stated requirements. Cyrille2 enables easy creation and execution of high throughput, flexible bioinformatics pipelines.

Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae: Hughes JD, Estep PW, Tavazoie S, Church GM: J Mol Biol 2000, 296:1205–1214

In the last few years, research in plant sciences has seen an unprecedented growth of high dimensional omics data sets and its application in functional genomics studies. These studies have used the individual or integrative omics analysis approach for establishing a network of relationships between biomolecules within a system. The expansion of knowledge base–derived and the generated omics data sets offer a unique opportunity to use deep learning approaches to derive plant metabolic models. The deep learning algorithms require large data sets to identify important associations that regulate a biological process and to achieve high accuracy against expected outcomes. Legacy omics data sets could serve as input for such deep learning algorithms, while knowledge base serves as its expected outcome around which the algorithm attempts to achieve high prediction accuracy. Therefore, structured metadata submission associated with omics data sets and knowledge derived from it is valuable to derive next-generation toolsets for functional genomics in plant sciences. Here, we have reviewed recent advances in the functional genomics knowledge base driven by results from genomics, transcriptomics, metabolomics analysis, and their integration. We have particularly focused on studies that have generated large-scale omics data sets that will be suitable for deriving the plant metabolic models. We conclude our review with a brief discussion on requirements for legacy data to derive metabolic models by using machine learning and deep learning approaches.