Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants.

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

Panomics reveals patient individuality as the major driver of colorectal cancer progression.

BACKGROUND: Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity. METHODS: We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results. RESULTS: Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes. CONCLUSION: Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future.

Artefact Profiling: Panomics Approaches for Understanding the Materiality of Written Artefacts.

This review explains the strategies behind genomics, proteomics, metabolomics, metallomics and isotopolomics approaches and their applicability to written artefacts. The respective sub-chapters give an insight into the analytical procedure and the conclusions drawn from such analyses. A distinction is made between information that can be obtained from the materials used in the respective manuscript and meta-information that cannot be obtained from the manuscript itself, but from residues of organisms such as bacteria or the authors and readers. In addition, various sampling techniques are discussed in particular, which pose a special challenge in manuscripts. The focus is on high-resolution, non-targeted strategies that can be used to extract the maximum amount of information about ancient objects. The combination of the various omics disciplines (panomics) especially offers potential added value in terms of the best possible interpretations of the data received. The information obtained can be used to understand the production of ancient artefacts, to gain impressions of former living conditions, to prove their authenticity, to assess whether there is a toxic hazard in handling the manuscripts, and to be able to determine appropriate measures for their conservation and restoration.

A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.

BACKGROUND: Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. RESULTS: We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. CONCLUSIONS: We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence.

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

PANOMICS meets germplasm.

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

"Right-to-Try" experimental drugs: an overview.

The "Right-to-Try" experimental drugs act passed by Donald Trump in 2018 provides an opportunity of early access to experimental drugs for the treatment of life-threatening diseases and a potential boon to many young and under-capitalized biotechnology or pharmaceutical companies. The pros and cons of experimental drugs, including a number of "cutting edge" scientific, clinical, and a number of synergistic approaches such as artificial intelligence, machine learning, big data, data refineries, electronic health records, data driven clinical decisions and risk mitigation are reviewed.

Precision radiotherapy for non-small cell lung cancer.

Precision medicine is becoming the standard of care in anti-cancer treatment. The personalized precision management of cancer patients highly relies on the improvement of new technology in next generation sequencing and high-throughput big data processing for biological and radiographic information.Systemic precision cancer therapy has been developed for years. However, the role of precision medicine in radiotherapy has not yet been fully implemented. Emerging evidence has shown that precision radiotherapy for cancer patients is possible with recent advances in new radiotherapy technologies, panomics, radiomics and dosiomics.This review focused on the role of precision radiotherapy in non-small cell lung cancer and demonstrated the current landscape.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era.

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes.

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Exploring the Potential of Multiomics and Other Integrative Approaches for Improving Waterlogging Tolerance in Plants.

Soil flooding has emerged as a serious threat to modern agriculture due to the rapid global warming and climate change, resulting in catastrophic crop damage and yield losses. The most detrimental effects of waterlogging in plants are hypoxia, decreased nutrient uptake, photosynthesis inhibition, energy crisis, and microbiome alterations, all of which result in plant death. Although significant advancement has been made in mitigating waterlogging stress, it remains largely enigmatic how plants perceive flood signals and translate them for their adaptive responses at a molecular level. With the advent of multiomics, there has been significant progress in understanding and decoding the intricacy of how plants respond to different stressors which have paved the way towards the development of climate-resistant smart crops. In this review, we have provided the overview of the effect of waterlogging in plants, signaling (calcium, reactive oxygen species, nitric oxide, hormones), and adaptive responses. Secondly, we discussed an insight into past, present, and future prospects of waterlogging tolerance focusing on conventional breeding, transgenic, multiomics, and gene-editing approaches. In addition, we have also highlighted the importance of panomics for developing waterlogging-tolerant cultivars. Furthermore, we have discussed the role of high-throughput phenotyping in the screening of complex waterlogging-tolerant traits. Finally, we addressed the current challenges and future perspectives of waterlogging signal perception and transduction in plants, which warrants future investigation.

Recent insights into metabolic and signalling events of directional root growth regulation and its implications for sustainable crop production systems.

Roots are sensors evolved to simultaneously respond to manifold signals, which allow the plant to survive. Root growth responses, including the modulation of directional root growth, were shown to be differently regulated when the root is exposed to a combination of exogenous stimuli compared to an individual stress trigger. Several studies pointed especially to the impact of the negative phototropic response of roots, which interferes with the adaptation of directional root growth upon additional gravitropic, halotropic or mechanical triggers. This review will provide a general overview of known cellular, molecular and signalling mechanisms involved in directional root growth regulation upon exogenous stimuli. Furthermore, we summarise recent experimental approaches to dissect which root growth responses are regulated upon which individual trigger. Finally, we provide a general overview of how to implement the knowledge gained to improve plant breeding.

Global serum profiling: an opportunity for earlier cancer detection.

The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a 'simple' aggregate of mutant cells and focusing on detecting key cell changes leading to the tumorigenesis, the understanding of cancer has broadened to consider it as a complex organ interacting with its close and far surroundings through tumor and non-tumor cells, metabolic mechanisms, and immune processes. Metabolism and the immune system have been linked to tumorigenesis and malignancy progression along with cancer-specific genetic mutations. However, most technologies developed to overcome the barriers to earlier detection are focused solely on genetic information. The concept of cancer as a complex organ has led to research on other analytical techniques, with the quest of finding a more sensitive and cost-effective comprehensive approach. Furthermore, artificial intelligence has gained broader consensus in the oncology community as a powerful tool with the potential to revolutionize cancer diagnosis for physicians. We herein explore the relevance of the concept of cancer as a complex organ interacting with the bodily surroundings, and focus on promising emerging technologies seeking to diagnose cancer earlier, such as liquid biopsies. We highlight the importance of a comprehensive approach to encompass all the tumor and non-tumor derived information salient to earlier cancer detection.

PANOMICS at the interface of root-soil microbiome and BNI.

Nitrification and denitrification are soil biological processes responsible for large nitrogen losses from agricultural soils and generation of the greenhouse gas (GHG) N2O. Increased use of nitrogen fertilizer and the resulting decline in nitrogen use efficiency (NUE) are a major concern in agroecosystems. This nitrogen cycle in the rhizosphere is influenced by an intimate soil microbiome-root exudate interaction and biological nitrification inhibition (BNI). A PANOMICS approach can dissect these processes. We review breakthroughs in this area, including identification and characterization of root exudates by metabolomics and proteomics, which facilitate better understanding of belowground chemical communications and help identify new biological nitrification inhibitors (BNIs). We also address challenges for advancing the understanding of the role root exudates play in biotic and abiotic stresses.

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.

Branched-chain ketoacids derived from cancer cells modulate macrophage polarization and metabolic reprogramming.

Macrophages are prominent immune cells in the tumor microenvironment that can be educated into pro-tumoral phenotype by tumor cells to favor tumor growth and metastasis. The mechanisms that mediate a mutualistic relationship between tumor cells and macrophages remain poorly characterized. Here, we have shown in vitro that different human and murine cancer cell lines release branched-chain α-ketoacids (BCKAs) into the extracellular milieu, which influence macrophage polarization in an monocarboxylate transporter 1 (MCT1)-dependent manner. We found that α-ketoisocaproate (KIC) and α-keto-ß-methylvalerate (KMV) induced a pro-tumoral macrophage state, whereas α-ketoisovalerate (KIV) exerted a pro-inflammatory effect on macrophages. This process was further investigated by a combined metabolomics/proteomics platform. Uptake of KMV and KIC fueled macrophage tricarboxylic acid (TCA) cycle intermediates and increased polyamine metabolism. Proteomic and pathway analyses revealed that the three BCKAs, especially KMV, exhibited divergent effects on the inflammatory signal pathways, phagocytosis, apoptosis and redox balance. These findings uncover cancer-derived BCKAs as novel determinants for macrophage polarization with potential to be selectively exploited for optimizing antitumor immune responses.

Panomics: New Databases for Advancing Cardiology.

The multifactorial nature of cardiology makes it challenging to separate noisy signals from confounders and real markers or drivers of disease. Panomics, the combination of various omic methods, provides the deepest insights into the underlying biological mechanisms to develop tools for personalized medicine under a systems biology approach. Questions remain about current findings and anticipated developments of omics. Here, we search for omic databases, investigate the types of data they provide, and give some examples of panomic applications in health care. We identified 104 omic databases, of which 72 met the inclusion criteria: genomic and clinical measurements on a subset of the database population plus one or more omic datasets. Of those, 65 were methylomic, 59 transcriptomic, 41 proteomic, 42 metabolomic, and 22 microbiomic databases. Larger database sample sizes and longer follow-up are often better suited for panomic analyses due to statistical power calculations. They are often more complete, which is important when dealing with large biological variability. Thus, the UK BioBank rises as the most comprehensive panomic resource, at present, but certain study designs may benefit from other databases.

Applications of Multi-Omics Technologies for Crop Improvement.

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Recognizing the salivary panomics for the clinical application in oral potentially malignant disorders.

Oral cancer arises as a result of multistep carcinogenic progress from precursor lesion to oral squamous cell carcinoma through collective mutational process occur in the stem cells of mucosal epithelium. The detection of such oral potentially malignant disorders (OPMDs)/cancer in subclinical level will greatly improve the prognosis of a patient. The highly specific and sensitive salivary biomarkers have functioned in detection, prediction, surveillance and therapeutic monitoring of the diseases of interest. The aim of the review is to appraise various salivary biomarkers for the clinical utility in OPMDs. An electronic web-supported search was performed via PubMed, ScienceDirect and Google Scholar search engine since the year 2015-2019. A total of 28 research articles were selected for the review after screening and assessment. The various genomic, transcriptomic, proteomic, metabolomic and miscellaneous markers were analyzed and their characteristics and clinical application in OPMD patients were discussed. miR-21, miR-31, miR-84, H3F3A mRNA + IL-8P, matrix metalloproteinase-9, chemerin, tumor necrosis factor-alpha, cytokeratin-10, ornithine + O-hydroxybenzoate + R5F, 8-hydroxy-2-deoxyguanosine, malondialdehyde, Vitamin E and Vitamin C are identified as potential markers for OPMD patients. Scientifically validated, reliable and economical clinical biomarkers in OPMDs would serve as evidence-based treatment from patient point of view. Further longitudinal studies are needed to verify the accuracy and validate the applicability of these diagnostic/prognostic markers. Saliva has been reported as a valuable noninvasive valuable tool in biomarker identification. Recent advancements in salivary biomarker identification techniques lead to various potential biomarkers with precise outcome. The utilization of these biomarkers for the clinical application in OPMDs depends on the feasibility and personal choice of the clinician.

Analysis of Pan-omics Data in Human Interactome Network (APODHIN).

Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein-protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.