With wide applications of genomics, transcriptomics, proteomics and metabolomics in the post-genome era, functional explanation has become the central task in life science research, and multi-omics data integrative analysis has become an indispensable strategy for uncovering the underlying biological mechanism. This special issue aimed to introduce the related research advances and applications in multi-omics by inviting the domestic experts. In total, 28 papers have been collected in this issue, for researcher's reference in multi-omics.
Genomics ; Proteomics ; Metabolomics ; Transcriptome

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Cluster Analysis ; Transcriptome

No abstract available.
Gene Expression Profiling* ; RNA ; Transcriptome*

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems ; Proteins ; Transcriptome

Humans ; Transcriptome ; Gene Expression Profiling

Hemoglobin A2 ; genetics ; Humans ; Thalassemia ; genetics ; Transcriptome

Panax quinquefolium is one of the most common medicinal plants worldwide. Ginsenosides are the major pharmaceutical components in P. quinquefolium. The biosynthesis of ginsenosides in different tissues of P. quinquefolium remained largely unknown. In the current study, an integrative method of transcriptome and metabolome analysis was used to elucidate the ginsenosides biosynthesis pathways in different tissues of P. quinquefolium. Herein, 22 ginsenosides in roots, leaves, and flower buds showed uneven distribution patterns. A comprehensive P. quinquefolium transcriptome was generated through single molecular real-time (SMRT) and second-generation sequencing (NGS) technologies, which revealed the ginsenoside pathway genes and UDP-glycosyltransferases (UGT) family genes explicitly expressed in roots, leaves, and flower buds. The weighted gene co-expression network analysis (WGCNA) of ginsenoside biosynthesis genes, UGT genes and ginsenoside contents indicated that three UGT genes were positively correlated to pseudoginsenoside F11, notoginsenoside R1, notoginsenoside R2 and pseudoginsenoside RT5. These results provide insights into ginsenoside biosynthesis in different tissues ofP. quinquefolium.
Ginsenosides ; Panax ; Plant Roots ; Plants, Medicinal ; Transcriptome

Objective Circular RNAs (circRNAs) are non-coding RNAs (ncRNA) circularized without a 3' polyadenylation [poly-(A)] tail or a 5' cap, resulting in a covalently closed loop structure. circRNAs were first discovered in RNA viruses in the 1970s, but only a small number of circRNAs were discovered at that time due to limitations in traditional polyadenylated transcriptome analyses. With the development of specific biochemical and computational methods, recent studies have shown the presence of abundant circRNAs in eukaryotic transcriptomes. circRNAs play vital roles in many physiological and pathological processes, such as acting as miRNA sponges, binding to RNA-binding proteins (RBPs), acting as transcriptional regulatory factors, and even serving as translation templates. Current evidence has shown that circRNAs can be potentially used as excellent biomarkers for diagnosis, therapeutic effect evaluation, and prognostic assessment of a variety of diseases, and they may also provide effective therapeutic targets due to their stability and tissue and development-stage specificity. This review focuses on the properties of circRNAs and their immune relationship to disease, and explores the role of circRNAs in immune-related diseases and the directions of future research.
Biomarkers ; MicroRNAs/genetics* ; RNA, Circular ; Transcriptome

No abstract available.
Gene Expression ; Humans ; Keratinocytes ; Skin ; Transcriptome

Acclimatization ; Acinetobacter ; metabolism ; Cold Temperature ; Transcriptome