An integrated approach (CLuster Analysis Integration Method) to combine expression data and protein-protein interaction networks in agrigenomics: application on Arabidopsis thaliana

Multi-View Kernel Learning for Identification of Disease Genes

Gene expression data sets and protein-protein interaction (PPI) networks are two heterogeneous data sources that have been extensively studied, due to their ability to capture the co-expression patterns among genes and their topological connections. Although they depict different traits of the data, both of them tend to group co-functional genes together. This phenomenon agrees with the basic assumption of multi-view kernel learning, according to which different views of the data contain a similar inherent cluster structure. Based on this inference, a new multi-view kernel learning based disease gene identification algorithm, termed as DiGId, is put forward. A novel multi-view kernel learning approach is proposed that aims to learn a consensus kernel, which efficiently captures the heterogeneous information of individual views as well as depicts the underlying inherent cluster structure. Some low-rank constraints are imposed on the learned multi-view kernel, so that it can effectively be partitioned into k or fewer clusters. The learned joint cluster structure is used to curate a set of potential disease genes. Moreover, a novel approach is put forward to quantify the importance of each view. In order to demonstrate the effectiveness of the proposed approach in capturing the relevant information depicted by individual views, an extensive analysis is performed on four different cancer-related gene expression data sets and PPI network, considering different similarity measures.

Scalable Non-Linear Graph Fusion for Prioritizing Cancer-Causing Genes

In the past few decades, both gene expression data and protein-protein interaction (PPI)networks have been extensively studied, due to their ability to depict important characteristics of disease-associated genes. In this regard, the paper presents a new gene prioritization algorithm to identify and prioritize cancer-causing genes, integrating judiciously the complementary information obtained from two data sources. The proposed algorithm selects disease-causing genes by maximizing the importance of selected genes and functional similarity among them. A new quantitative index is introduced to evaluate the importance of a gene. It considers whether a gene exhibits a differential expression pattern across sick and healthy individuals, and has a strong connectivity in the PPI network, which are the important characteristics of a potential biomarker. As disease-associated genes are expected to have similar expression profiles and topological structures, a scalable non-linear graph fusion technique, termed as ScaNGraF, is proposed to learn a disease-dependent functional similarity network from the co-expression and common neighbor based similarity networks. The proposed ScaNGraF, which is based on message passing algorithm, efficiently combines the shared and complementary information provided by different data sources with significantly lower computational cost. A new measure, termed as DiCoIN, is introduced to evaluate the quality of a learned affinity network. The performance of the proposed graph fusion technique and gene selection algorithm is extensively compared with that of some existing methods, using several cancer data sets.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Background

Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states.

Results

In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis.

Conclusions

The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.

Supplementary Information

The online version contains supplementary material available at (doi:10.1186/s12864-020-07144-2).

Proteomics and bioinformatics analysis of Fasciola hepatica somatic proteome in different growth phases

Fasciola hepatica (F. hepatica) is a well-known zoonotic parasite that is crucial for economic and public health worldwide. Quantitative proteomics studies have been performed on proteins expressed by F. hepatica to investigate the differential expression of proteomes in different growth phases. And the screening of several marker proteins for use as early diagnostic antigens is essential. In this study, high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) was conducted to analyze the differences in the expression of F. hepatica somatic proteins in different growth phases. Furthermore, gene ontology (GO) functional annotation, KEGG metabolic pathway, and clustering analyses were also performed. LC-MS/MS identified 629, 2286, 2254, and 2192 proteins in metacercariae, juvenile flukes 28dpi, immature flukes 59dpi, and adult phases, respectively. GO analysis revealed that differentially expressed proteins (DEPs) were mainly involved in transport, localization, metabolism, enzyme regulation, protein folding and binding, and nucleoside and nucleotide binding. The DEPs were enriched in cells, intracellular components, organelles, cytoplasm, vesicles, and membranes. KEGG pathway annotation results showed that the DEPs were involved in metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, and other processes. These findings provide a theoretical basis for vaccine development and establishing early diagnostic methods in the future.

An Integrated Approach for Identification of Functionally Similar MicroRNAs in Colorectal Cancer

Colorectal cancer (CRC) is one of the most prevalent cancers around the globe. However, the molecular reasons for pathogenesis of CRC are still poorly understood. Recently, the role of microRNAs or miRNAs in the initiation and progression of CRC has been studied. MicroRNAs are small, endogenous noncoding RNAs found in plants, animals, and some viruses, which function in RNA silencing and posttranscriptional regulation of gene expression. Their role in CRC development is studied and they are found to be potential biomarkers in diagnosis and treatment of CRC. Therefore, identification of functionally similar CRC related miRNAs may help in the development of a prognostic tool. In this regard, this paper presents a new algorithm, called μSim. It is an integrative approach for identification of functionally similar miRNAs associated with CRC. It integrates judiciously the information of miRNA expression data and miRNA-miRNA functionally synergistic network data. The functional similarity is calculated based on both miRNA expression data and miRNA-miRNA functionally synergistic network data. The effectiveness of the proposed method in comparison to other related methods is shown on four CRC miRNA data sets. The proposed method selected more significant miRNAs related to CRC as compared to other related methods.

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

Unveiling network-based functional features through integration of gene expression into protein networks

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.

Significance and Functional Similarity for Identification of Disease Genes

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.

Fusion of expression values and protein interaction information using multi-objective optimization for improving gene clustering

One of the crucial problems in the field of functional genomics is to identify a set of genes which are responsible for a particular cellular mechanism. The current work explores the usage of a multi-objective optimization based genetic clustering technique to classify genes into groups with respect to their functional similarities and biological relevance. Our contribution is two-fold: firstly a new quality measure to compute the goodness of gene-clusters namely protein-protein interaction confidence score is developed. This utilizes the confidence scores of the protein-protein interaction networks to measure the similarity between genes of a particular cluster with respect to their biochemical protein products. Secondly, a multi-objective based clustering approach is developed which intelligently uses integrated information of expression values of microarray dataset and protein-protein interaction confidence scores to select both statistically and biologically relevant genes. For that very purpose, some biological cluster validity indices, viz. biological homogeneity index and protein-protein interaction confidence score, along with two traditional internal cluster validity indices, viz. fuzzy partition coefficient and Pakhira-Bandyopadhyay-Maulik-index, are simultaneously optimized during the clustering process. Experimental results on three real-life gene expression datasets show that the addition of new objective capturing protein-protein interaction information aids in clustering the genes as compared to the existing techniques. The observations are further supported by biological and statistical significance tests.