Overview of Omics Data
Omics data analysis has revolutionized the field of biology by providing a comprehensive understanding of biological systems at different molecular levels. With advances in high-throughput technologies, large amounts of omics data are being generated, such as genomics, proteomics, transcriptomics, and metabolomics. The size of these data files, as well as the differences in terminology between these data types, make it challenging to integrate these multidimensional omics data into a biologically meaningful context. These challenges include differences in data cleaning, normalization, biomolecular identification, data downscaling, biological contextualization, statistical validation, data storage and processing, sharing, and data archiving. Various computational methods such as data mining, machine-learning, deep learning, statistical methods, meta-heuristics, etc. have attracted attention in processing, standardizing, integrating, and analyzing omics data as key aids in extracting meaningful insights from omics data.
Fig. 1. Omics data analysis process. (Kaur P, et al., 2021)
Types of Omics Data
Omics data, a treasure trove of biological information, encompasses diverse types of biomolecules that offer unique glimpses into the intricate machinery of cellular processes. This section provides a comprehensive elucidation of the four principal types of omic data, each yielding invaluable insights:
Genomics
Genomics, an intricate discipline, delves into the comprehensive study of an organism's entire DNA sequence. By scrutinizing this vast genetic blueprint, researchers gain profound understanding of genetic variation and its intricate impact on phenotype. Genomics unravels the intricate interplay between nucleotide sequences, genomic architecture, and the manifestation of biological traits. Through the lens of genomics, researchers explore the fascinating landscape of DNA variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations, thereby illuminating the complex tapestry of genetic diversity and its contributions to phenotypic variation.
Proteomics
Proteomics, a captivating realm of study, embarks on a journey to unravel the intricate world of proteins, the workhorses of biological systems. By meticulously identifying and quantifying these molecular entities, proteomics illuminates their multifaceted functions, dynamic interactions, and ever-changing states. This multidimensional discipline unravels the complex orchestration of post-translational modifications, protein-protein interactions, and protein localization, all of which collectively govern cellular processes, signaling cascades, and regulatory networks. Through proteomics, researchers unlock the secrets of protein abundance, turnover rates, and subcellular localization, shedding light on the complex choreography that underpins biological systems.
Transcriptomics
Transcriptomics, a captivating frontier, unlocks the realm of gene expression, the dynamic process by which genetic information is transcribed into functional RNA molecules. This intricate discipline transcends mere genetic blueprints and delves into the nuanced world of gene regulation, cellular processes, and the delicate dance of molecular orchestration. Transcriptomics scrutinizes the delicate balance between gene activation and repression, unraveling the complex web of transcription factors, enhancers, repressors, and non-coding RNAs that intricately govern gene expression programs. By analyzing the abundance and dynamics of transcripts, researchers gain insights into the intricate regulatory networks that underlie developmental processes, cellular responses, and the etiology of diseases.
Metabolomics
Metabolomics, a captivating and ever-evolving field, embarks on a quest to decipher the intricate language of small molecule metabolites, the end products and intermediates of cellular metabolism. This intricate discipline illuminates the dynamic interplay between environmental cues, genetic predispositions, and metabolic homeostasis. Metabolomics unveils the rich tapestry of metabolic pathways, unveiling the delicate balance between anabolic and catabolic processes that sustain cellular life. By scrutinizing the abundance, flux, and interactions of metabolites, researchers unravel the intricate web of metabolic signatures, biomarkers, and metabolic fingerprints that serve as hallmarks of physiological states, disease phenotypes, and therapeutic responses.
Fig. 2. Multi-omics data types of different biological levels. (Kaur P, et al., 2021)
Using Machine Learning and Deep Learning for Omics Data Analysis
The integration of machine learning and deep learning methodologies has garnered substantial attention and acclaim within the realm of omics data analysis. These cutting-edge computational techniques possess an extraordinary capacity to handle voluminous datasets and untangle the intricate tapestry of patterns that pervade biological systems. By harnessing the power of these methodologies, researchers can embark on an unprecedented exploration of biomarkers, disease prognoses, and the convoluted web of molecular interactions that underpin the intricate machinery of life.
Machine learning algorithms, including Support Vector Machines (SVMs), Random Forests, and Neural Networks, stand as stalwarts within the landscape of omics data analysis. Through the employment of these algorithms, researchers gain the ability to classify samples based on their omicsal profiles with an exceptional degree of accuracy. As these algorithms ingest and comprehend labeled data, they acquire a discerning eye for patterns and relationships deeply embedded within the omic data. This prowess enables precise categorization and comprehensive characterization of biological entities, thereby catalyzing advancements in diagnostics, prognostics, and personalized medicine.
Deep learning, an illustrious branch of machine learning, has garnered unparalleled recognition within the realm of omics data analysis. By harnessing deep neural networks with their unparalleled capacity for hierarchical representation learning, researchers delve into the enigmatic realms of raw omicsal data, unravelling the convoluted tapestry of molecular intricacies. These neural networks, operating autonomously, unravel intricate patterns and interactions, illuminating the underlying complexities that govern the labyrinthine biological systems. Within the domain of omic imaging data, particularly radiomics, convolutional neural networks (CNNs) have emerged as an indomitable force. Leveraging the hierarchical representation capabilities of CNNs, researchers unlock the power to identify disease-specific features concealed within omic imaging data. This breakthrough propels diagnostic accuracy to unprecedented heights, empowering clinicians and researchers alike to chart precise treatment plans and interventions.
Using Imaging Features for Omics Data Analysis
Omic imaging data, particularly radiomics, assumes a critical role in disease diagnosis, treatment planning, and monitoring. Computational technologies facilitate the extraction of quantitative imaging features from medical images, which can be integrated with omics data, enabling comprehensive analysis.
Image Segmentation
Image segmentation techniques enable the depiction of regions of interest within medical images, facilitating the extraction of imaging features. These features capture crucial information about tissue properties, including shape, intensity, texture, and spatial characteristics, providing valuable insights into disease characteristics.
Comprehensive Analysis
By combining imaging features with omics data, researchers can uncover novel biomarkers and develop predictive models for disease prognosis. This integration enables a holistic understanding of complex diseases, revealing intricate interactions between molecular entities and identifying key biomarkers or pathways associated with diseases.
Fig. 3. Taxonomy of omics data analysis. (Kaur P, et al., 2021)
Challenges for Omics Data Analysis
Omics data analysis poses several challenges that necessitate advanced methodologies and techniques. This section explores key challenges:
- Heterogeneous Datasets
- Complex Datasets
- Temporal Data
- Missing Data
- Curse of Dimensionality
- Unbalanced Data
Reference
- Kaur P, Singh A, Chana I. Computational techniques and tools for omics data analysis: state-of-the-art, challenges, and future directions[J]. Archives of Computational Methods in Engineering, 2021, 28: 4595-4631.
