Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to facilitate their exploration and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure etc. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.

Pathway Multi-Omics Simulated Data

These are synthetic variations of the TCGA COADREAD data set (original data available at http://linkedomics.org/data_download/TCGA-COADREAD/). This data set is used as a comprehensive benchmark data set to compare multi-omics tools in the manuscript "pathwayMultiomics: An R package for efficient integrative analysis of multi-omics datasets with matched or un-matched samples".

There are 100 sets (stored as 100 sub-folders, the first 50 in "pt1" and the second 50 in "pt2") of random modifications to centred and scaled copy number, gene expression, and proteomics data saved as compressed data files for the R programming language. These data sets are stored in subfolders labelled "sim001", "sim002", ..., "sim100". Each folder contains the following contents: 1) "indicatorMatricesXXX_ls.RDS" is a list of simple triplet matrices showing which genes (in which pathways) and which samples received the synthetic treatment (where XXX is the simulation run label: 001, 002, ...), (2) "CNV_partitionA_deltaB.RDS" is the synthetically modified copy number variation data (where A represents the proportion of genes in each gene set to receive the synthetic treatment [partition 1 is 20%, 2 is 40%, 3 is 60% and 4 is 80%] and B is the signal strength in units of standard deviations), (3) "RNAseq_partitionA_deltaB.RDS" is the synthetically modified gene expression data (same parameter legend as CNV), and (4) "Prot_partitionA_deltaB.RDS" is the synthetically modified protein expression data (same parameter legend as CNV).

Supplemental Files

The file "cluster_pathway_collection_20201117.gmt" is the collection of gene sets used for the simulation study in Gene Matrix Transpose format. Scripts to create and analyze these data sets available at: https://github.com/TransBioInfoLab/pathwayMultiomics_manuscript_supplement

The Multiomics Market offers a range of products, including instruments, consumables, software, and services. Instruments include sequencing systems, mass spectrometers, and flow cytometers. Consumables encompass reagents, kits, and microarrays. Software solutions provide data analysis and visualization capabilities. Services include sample preparation, data analysis, and interpretation. Recent developments include: September 2023: The chromium single-cell gene expression flex assay manufactured by 10x Genomics Inc. now offers high throughput multi-omic cellular profiling as a commercially available capability thanks to the introduction of a new kit. Researchers and their options may detect simultaneous gene and protein expression, which can be expanded at a greater scale thanks to the new kit, which makes the multi-omic characterization of cell populations simple and efficient. The company's product portfolio was able to grow due to this technique., February 2023: Becton, Dickinson, and Company introduced the Rhapsody HT Xpress System, a high-throughput single-cell multiomics platform, to broaden the field of scientific research. With up to eight times more cells per sample than previous BD single-cell analyzers, this innovative technology allows scientists to extract, label, and analyze individual cells at a high sample throughput. This plan should assist the business in expanding its product's uses and serving more clients.. Notable trends are: Rising integration of multi-omics data is driving the market growth.

This data characterizes gene expression levels in THP-1 macrophages. The data was generated using RNA sequencing and analyzed with DeSeq2 (version 1.24.0). The analysis included raw count data and normalized count matrices obtained from DESeq2's dds_deseq objects.This data describes the methylation levels of individual CpG sites in THP-1 macrophages. The data was obtained using the Infinium MethylationEPIC v2.0 Kit (Illumina) and analyzed with the minfi package (version 1.46). Specifically, the data underwent quantile normalization using the preprocessQuantile function within minfi. Only CpG sites with a detection p-value less than 0.05 were included to obtain MatrixProcessedGEO.txt file. The beta values (bValues.xlsx) were obtained using the function “getBeta” from the same package, considering each time point individually.The macrophages were exposed to phorbol 12-myristate 13-acetate (PMA) for 48 hours, followed by treatment with either a combination of LPS (10 pg/ml) and interferon-gamma (IFNγ) (20 ng/ml) or a combination of interleukins 13 (IL-13) (20 ng/ml) and 4 (IL-4) (20 ng/ml) for 24, 48, and 72 hours.

Given the limited knowledge of the biological impact of spaceflight a multi-omics systems biology approach was used to investigate NASA xe2 x80 x99s GeneLab data and astronaut biomedical profiles. These data consist of hundreds of samples flown in space human metrics from 59 astronauts and confirmatory data from NASA xe2 x80 x99s Twin Study analyzed together for consistent transcriptomic proteomic metabolomic and epigenetic response to spaceflight. Pathway analysis showed significant enrichment of mitochondrial activity and innate immunity. Muscle and liver tissues showed that chronic inflammation may be a response to mitochondrial dysfunction. Additional pathways altered in spaceflight included cell cycle circadian rhythm and olfactory activity pathways all of which are known to have interactions with mitochondrial activity. Evidence of altered mitochondrial function was also found in the urine and blood metabolic data compiled from the astronaut cohort and NASA Twin Study data all of which indicate mitochondrial stress as a consistent phenotype of spaceflight.

MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases.

To illustrate the use of MIntO, a set of 91 human fecal metagenomes from the Inflammatory Bowel Disease Multi’omics Database was selected (IBDMDB). We selected six participants diagnosed as non-IBD (P6018 (nIBD1), M2072 (nIBD2)); Crohn’s disease (H4006 (CD1) and H4020 (CD2)); and ulcerative colitis (H4019 (UC1) and H4035 (UC2)) that were followed for one year each.

Here, we present the results from the genome-based assembly-dependent mode, where we used 91 metagenomic high-quality reads to recover 163 high-quality MAGs, which constituted a set of non-redundant genomes.

This repository is a part of the supplementary document in Park et al., "Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis" published in iScience 2022.

Abstract: To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). We uncover associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of FAM3D, which has been previously implicated in gastrointestinal inflammation. Additionally, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of UBD, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cells in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our integrative analysis provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.

Global omics lab services market size & share estimated to cross USD 245.69 billion by 2032, to grow at a CAGR of 13.4% during the forecast period.

Mission: Dynamically evolve sequencing, finishing, annotation and analysis processes, exploit new technologies, and develop expertise to deliver high quality and high throughput sequence-based microbial science by listening to and responding to DOE Users and scientific community needs. GOALS 1. Expand product catalog and increase sample throughput while maintaining highest quality The MGP has been expanding its product catalog beyond a finished microbial genome and has projected to significantly up ramp throughput for the majority of its current products namely Draft Genomes, Single Cell Genomes, Quick Draft Genomes, Resequencing projects and RNAseq Project. This projected increase in microbial genomes is going hand-in-hand with and has been stimulated by new high throughput technologies and capabilities (de novo microbial Illumina assemblies, single cell genomics, Genologic sample tracking). The increased throughput will support the user community as well as JGI scientists by enabling DOE-relevant science at a grander scale. As the Program aims to generate hundreds of microbial genomes per year, our goal is to scale our production efficiency and maintain our trademark quality to best support our science mission. 2. Expand sequence space One of the ongoing missions of the MGP is to expand the coverage of the phylogenomic sequence space by generating reference genome datasets from highly diverse braches in bacterial and archaeal tree of life. The value of such effort includes the generation of phylogenetic anchors for metagenomic datasets, the improvement of annotation, an increased insight into phylogenetic distribution of functions, the discovery of novel genes, protein families, pathways and a better understanding on evolutionary diversication. 3. Make Single Cell Genomes a robust User product As the vast majority of microbes are uncultured to date, single cell genomics will be a crucial component of the MGP over the next several years to drive not only JGI science but also User community proposed single cell research. Going hand-in-hand are R&D efforts in selective single cell isolations, testing the effects of fixation of single cell sequencing, as well as single cell transcriptomics. 4. Sequence Pangenomes Combining similar genomes together creating pangenomes will allow more compact genome sequence storage and visualization and expedite analysis and annotation. Moreover, the pangenome as a representation of the whole group of organisms may be more representative of a given species within the environment. The MGP thus thrives to enable the sequencing and analysis of pangenomes. Current technology allows the sequencing of one organism strain at a time. Assuming that for most cases, several dozen strains may need to be sequenced in order to generate a more accurate pangenome for every microbial species, it becomes evident that the cost for doing so may be prohibitively high. Our goal here will be to explore new approaches and technologies for generating these pangenomes at a very low cost and analogous to what is the cost today for a single strain. 5. Expand and improve microbial annotation using transcriptomic data To improve annotation of gene structure, establish accurate transcription level and timing, provide information on gene regulation and generate information for expanding understanding of systems biology, the MGP thieves to generate transcriptomics data for larger sets of Bacteria and/or Archaea. This will enable the identification of novel regulator RNAs, as well as facilitate the understanding of uncharacterized protein families. 6. Maintain and evolve a top quality data management system To enable state of the art and world class comparative analysis of internal and external scientific projects, the JGI data integration and visualization management system for comparative analysis of microbial genomes, namely IMG, needs to be maintained and continuously evolved. The system needs to be able to support and integrate all data generated by JGI (WGS, reseq, RNAseq, -other omics data), as well as by the user community, enabling annotation and manual curation of the annotation, comparative analysis, gene-centric and pathway centric analyzes. The system should also facilitate the interation of associated metadata, enable data sharing and distribution, as well as automated data GenBank submissions. Lastly, the system needs to have the ability to scale enabling the annotation of thousands of genomes per year. 7. Drive Flagship projects To stay at the forefront of microbial genomic research, be recognized as such and enable the development new methods and tools, the MGP aims to drive DOE mission relevant flagship projects. Novel tools and methods developed will ultimately serve the user community if proven useful and implemented as part of a larger pipeline. MGP flagship projects are the GEBA and GEBA uncultured projects, as well as the GEBA-RNB, the proposed Microbial Earth and the Microbial Dark Matter Projects.

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Spatial Genomics And Transcriptomics Market Size 2025-2029

The spatial genomics and transcriptomics market size is forecast to increase by USD 732.3 million at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth due to the increasing adoption of spatial genomics in drug discovery and development. This technology allows for a more precise knowledge of the spatial arrangement of genes and their expression levels within cells, providing valuable insights into complex biological processes and potential therapeutic targets. Another key driver is the increasing use of spatial omics for biomarker identification, enabling the development of personalized medicine and diagnostics. In therapeutic areas like neurological disorders, infectious diseases, neuroscience, immunology, genomics, and proteomics. However, the market faces challenges, including the lack of workforce expertise in spatial genomics. As the technology continues to evolve and gain traction, there is a growing need for skilled professionals to analyze and interpret the data generated.
Companies seeking to capitalize on this market opportunity must invest in building a talented workforce and collaborating with academic institutions and industry partners to stay at the forefront of innovation. Navigating these challenges effectively will require strategic planning, investment in research and development, and a focus on building partnerships and collaborations to drive growth in the market.

What will be the Size of the Spatial Genomics And Transcriptomics Market during the forecast period?

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The market encompasses innovative technologies enabling the analysis of the three-dimensional organization of genetic material and gene expression within cells and tissues. This market is gaining significant traction due to its potential applications in various fields, including neurodegenerative disorders and cancer research. Spatial genomic analysis offers insights into the complex interplay of genetic and environmental factors, providing novel biomarkers for pathology diagnosis and personalized medicine. In cancer research, spatial transcriptomics is revolutionizing the comprehension of tumor interfaces and tertiary lymphoid structures, offering improved cancer detection tools and prognostic variables.
Technologies such as spatial phenotyping, immunohistochemistry, flow cytometry, and mass spectrometry are driving the market's growth, enabling the creation of a human genetic map beyond primary sequence information. These advancements contribute to the development of more effective therapies for chronic diseases and improved patient outcomes. The next generation sequencing cancer detection tool, coupled with customized access programs, enables biopharmaceutical players to enhance staining assays, molecular profiling, and precision medicine, while advancing spatial biology and cellular interactions.

How is the Spatial Genomics and Transcriptomics Industry segmented?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Consumables
  Instruments


End-user

  Translational research
  Academic customers
  Diagnostic customers
  Pharmaceutical manufacturer


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  Asia
  Rest of World (ROW)

By Product Insights

The consumables segment is estimated to witness significant growth during the forecast period. Spatial genomics and transcriptomics are advanced technologies that facilitate the investigation of gene expression and genomic information about spatial organization within tissues. Consumables are essential components of these techniques, ensuring experiment accuracy and efficiency. In spatial genomics, specialized slides or chips with spatially barcoded features are employed, enabling the capture and analysis of gene expression patterns at distinct tissue locations. These consumables incorporate microfluidics or imaging technologies, enabling precise in situ mapping of gene activity. In spatial transcriptomics, spatially barcoded beads or oligonucleotides capture RNA molecules at specific positions within a tissue sample. These technologies are instrumental in the identification of novel biomarkers for various diseases, including neurodegenerative disorders and cancer.

They contribute significantly to pathology diagnosis, cancer prognostic variables, and personalized medicine. Spatial genomic methods, such as single-cell analysis and nucleic acid sequencing, offer spatial multi-omics information through cell imaging and disease morphology analysis. Precision therapies, automated sample processing, and biomarker-specific gene panels are a

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Cancer genomics unveils many cancer-related mutations, including some chromosome 20 (Chr.20) genes. The mutated messages have been found in the corresponding mRNAs; however, whether they could be translated to proteins still requires more evidence. Herein, we proposed a transomics strategy to profile the expression status of human Chr.20 genes (555 in Ensembl v72). The data of transcriptome and translatome (the mRNAs bound with ribosome, translating mRNAs) revealed that ∼80% of the coding genes on Chr.20 were detected with mRNA signals in three liver cancer cell lines, whereas of the proteome identified, only ∼45% of the Chr.20 coding genes were detected. The high amount of overlapping of identified genes in mRNA and RNC-mRNA (ribosome nascent-chain complex-bound mRNAs, translating mRNAs) and the consistent distribution of the abundance averages of mRNA and RNC-mRNA along the Chr.20 subregions in three liver cancer cell lines indicate that the mRNA information is efficiently transmitted from transcriptional to translational stage, qualitatively and quantitatively. Of the 457 genes identified in mRNAs and RNC-mRNA, 136 were found to contain SNVs with 213 sites, and >40% of these SNVs existed only in metastatic cell lines, suggesting them as the metastasis-related SNVs. Proteomics analysis showed that 16 genes with 20 SNV sites were detected with reliable MS/MS signals, and some SNVs were further validated by the MRM approach. With the integration of the omics data at the three expression phases, therefore, we are able to achieve the overall view of the gene expression of Chr.20, which is constructive in understanding the potential trend of encoding genes in a cell line and exploration of a new type of markers related to cancers.

It is now clear that major malignancies are heterogeneous diseases associated with diverse molecular properties and clinical outcomes, posing a great challenge for more individualized therapy. In the last decade, cancer molecular subtyping studies were mostly based on transcriptomic profiles, ignoring heterogeneity at other (epi-)genetic levels of gene regulation. Integrating multiple types of (epi)genomic data generates a more comprehensive landscape of biological processes, providing an opportunity to better dissect cancer heterogeneity. Here, we propose sparse canonical correlation analysis for cancer classification (SCCA-CC), which projects each type of single-omics data onto a unified space for data fusion, followed by clustering and classification analysis. Without loss of generality, as case studies, we integrated two types of omics data, mRNA and miRNA profiles, for molecular classification of ovarian cancer (n = 462), and breast cancer (n = 451). The two types of omics data were projected onto a unified space using SCCA, followed by data fusion to identify cancer subtypes. The subtypes we identified recapitulated subtypes previously recognized by other groups (all P- values < 0.001), but display more significant clinical associations. Especially in ovarian cancer, the four subtypes we identified were significantly associated with overall survival, while the taxonomy previously established by TCGA did not (P- values: 0.039 vs. 0.12). The multi-omics classifiers we established can not only classify individual types of data but also demonstrated higher accuracies on the fused data. Compared with iCluster, SCCA-CC demonstrated its superiority by identifying subtypes of higher coherence, clinical relevance, and time efficiency. In conclusion, we developed an integrated bioinformatic framework SCCA-CC for cancer molecular subtyping. Using two case studies in breast and ovarian cancer, we demonstrated its effectiveness in identifying biologically meaningful and clinically relevant subtypes. SCCA-CC presented a unique advantage in its ability to classify both single-omics data and multi-omics data, which significantly extends the applicability to various data types, and making more efficient use of published omics resources.

Inflammation-associated chronic pain is a global clinical problem, affecting millions of people worldwide. However, the underlying mechanisms that mediate inflammation-associated chronic pain remain unclear. A rat model of cutaneous inflammation induced by Complete Freund’s Adjuvant (CFA) has been widely used as an inflammation-induced pain hypersensitivity model. We present the transcriptomics profile of CFA-induced inflammation in the rat dorsal root ganglion (DRG) via an approach that targets gene expression, DNA methylation, and post-transcriptional regulation. We identified 418 differentially expressed mRNAs, 120 differentially expressed microRNAs (miRNAs), and 2,670 differentially methylated regions (DMRs), which were all highly associated with multiple inflammation-related pathways, including nuclear factor kappa B (NF-κB) and interferon (IFN) signaling pathways. An integrated analysis further demonstrated that the activator protein 1 (AP-1) network, which may act as a regulator of the inflammatory response, is regulated at both the transcriptomic and epigenetic levels. We believe our data will not only provide drug screening targets for the treatment of chronic pain and inflammation but will also shed light on the molecular network associated with inflammation-induced hyperalgesia.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

We aimed to study the molecular mechanisms of chronic obstructive pulmonary disease (COPD) caused by cigarette smoke more comprehensively and systematically through different perspectives and aspects and to explore the role of protein acetylation modification in COPD. We established the COPD model by exposing C57BL/6J mice to cigarette smoke for 24 weeks, then analyzed the transcriptomics, proteomics, and acetylomics data of mouse lung tissue by RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and associated these omics data through unique algorithms. This study demonstrated that the differentially expressed proteins and acetylation modification in the lung tissue of COPD mice were co-enriched in pathways such as oxidative phosphorylation (OXPHOS) and fatty acid degradation. A total of 19 genes, namely, ENO3, PFKM, ALDOA, ACTN2, FGG, MYH1, MYH3, MYH8, MYL1, MYLPF, TTN, ACTA1, ATP2A1, CKM, CORO1A, EEF1A2, AKR1B8, MB, and STAT1, were significantly and differentially expressed at all the three levels of transcription, protein, and acetylation modification simultaneously. Then, we assessed the distribution and expression in different cell subpopulations of these 19 genes in the lung tissues of patients with COPD by analyzing data from single-cell RNA sequencing (scRNA-seq). Finally, we carried out the in vivo experimental verification using mouse lung tissue through quantitative real-time PCR (qRT-PCR), Western blotting (WB), immunofluorescence (IF), and immunoprecipitation (IP). The results showed that the differential acetylation modifications of mouse lung tissue are widely involved in cigarette smoke-induced COPD. ALDOA is significantly downregulated and hyperacetylated in the lung tissues of humans and mice with COPD, which might be a potential biomarker for the diagnosis and/or treatment of COPD.

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.