Omics Data Analysis

The field of omics has revolutionized our understanding of biological systems, offering unprecedented insights into the complex molecular processes that govern life. As high-throughput technologies continue to advance, the volume and complexity of omics data have grown exponentially, presenting both opportunities and challenges for researchers. This introduction explores the world of omics data analysis, its significance, the current landscape, and the complexities involved in deriving meaningful insights from these vast datasets.

The omics revolution has played a critical role in transforming scientific research along two key facets. One, the ability to combine data generated by distinct omics disciplines into a holistic molecular perspective of biological systems paved the way for a shift from the tradition of reductionism to a more integrative systems biology approach to biomedical research. And two, the big data characteristics (volume, variety, velocity, etc.) of omics data helped shift the scientific mindset towards exploratory data analysis compared to the time-consuming and expensive approaches of conventional data-scarce, hypothesis-driven experiments.

As the omics disciplines continue to expand and evolve, concurrent advancements in sophisticated computing technologies, such as cloud and AI/ML, have helped amplify the value of a data-driven, integrated multi-omics model of biomedical and life sciences research. Constant improvements in the accuracy, throughput, and cost-effectiveness of next-generation data generation technologies combined with the advanced pattern recognition, prediction, and data interpretation capabilities of modern AI techniques are enabling researchers to study complex interactions and processes at molecular levels and generate a holistic understanding of biological systems.

What is omics data analysis?

Omics, derived from the suffix "-ome," refers to a group of scientific disciplines to comprehensively assess and analyze the totality of molecules or biological processes within biological systems. Each omics discipline employs specialized high-throughput techniques for the comprehensive assessment of a specific set of molecules, such as RNA (transcriptomics), proteins (proteomics), metabolites (metabolomics), etc. Multi-omics data analysis enables researchers to obtain a detailed snapshot of underlying biology at an unprecedented resolution for a more holistic understanding of complex biological processes and interactions.

Read more on our blog: Why we love multi-omics analysis

Why is omics data important in scientific research?

Since the genomics revolution, the field of multi-omics disciplines has continued to evolve, enabling a multi-layered understanding of biological systems at different levels, from molecular interactions to organism-wide processes.

Here’s a partial list of omics disciplines and their associated techniques.

• Structural (genome mapping, genome sequencing, genome sequence assembly, genome annotation) and functional (genetic interaction mapping, microarray technology, SAGE) genomics. 
• Transcriptomics (RNA-Seq, microarray analysis, single-cell RNA sequencing, long-read RNA sequencing)
• Proteomics (mass spectrometry, protein microarrays, two-dimensional gel electrophoresis, MALDI-TOF analysis)
• Metabolomics (NMR spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS))
• Lipidomics (shotgun, targeted and untargeted lipidomics)
• Epigenomics (ChIP-seq, bisulfite sequencing, ATAC-seq, Hi-C)
• Glycomics (glycan microarrays, mass spectrometry)
• Interactomics (yeast two-hybrid screening, protein-fragment complementation assays, co-immunoprecipitation)
• Phenomics (high-throughput phenotyping, automated image analysis)
• Pharmacogenomics (SNP genotyping, whole genome association studies)
• Single-cell omics
• Microbiomics (16S rRNA sequencing, shotgun metagenomics)
• Spatial omics

The significance of omics data lies in its ability to provide deeper and richer biological insights that are critical for an array of diverse applications including molecular profiling, biomarker discovery, drug discovery and development, and precision medicine, to name just a few.

Read more on our blog: The future of precision medicine

The current landscape of omics data analysis

The field of omics data analysis is rapidly evolving, driven by advancements in both experimental technologies and computational methods. Here are the key highlights of the current multi-omics data analysis landscape.

Next-generation sequencing (NGS) technologies continue to improve in terms of accuracy, throughput, and cost-effectiveness. The rise of automation in mass spectrometry (MS), the gold standard for qualitative and quantitative protein analysis, protein identification, and quantification, has helped expand and accelerate the scope of research by enabling more complex experiments, improving precision, and increasing throughput. 

The exponential increase in the quantity and quality of raw data has necessitated a disruptive transformation in downstream multi-omics data integration and analysis pipelines. Cloud-first omics and big data technologies have emerged as the default for at-scale storage, processing, and analysis of omics big data.

At the same time, methods for integrating data from different omics layers have also become increasingly sophisticated. The advent of scalable, user-friendly, automated, and integrated data-ingestion-to-insight multi-omics analysis platforms has accelerated the shift towards a systems biology approach to biomedical research.

Advanced statistical techniques, including multivariate analysis, machine learning, and deep learning approaches, have become critical to extracting meaningful patterns from high-dimensional omics data and translating them into actionable information. Sophisticated ML/artificial intelligence (AI) algorithms are becoming the standard for integrated multi-omics data analysis that enables more accurate predictions and novel discoveries. Graphical and interactive visualization tools have helped expand the complex field of multi-omics data exploration and interpretation even to users with little computational expertise. 

And finally, there are the two key drivers to accelerating innovation and collaboration in multi-omics analysis. First, there is a growing emphasis on the critical role of data standardization and governance practices to ensure reproducibility and facilitate data sharing across the scientific community. And two, a wealth of open-source ML/AI tools and curated omics data repositories, like EMBL-EBI and The Cancer Genome Atlas, are helping democratize multi-omics research.

Read more on our blog: Why omics data analysis needs to be remade

What makes omics data complex to analyze

There are several factors that contribute to the complexity of omics data analysis including the integration of high-volume heterogeneous multi-omics data and the challenges of maintaining data quality.

Over and above the inherent big data characteristics (volume, variety, velocity, etc.) of omics datasets, data curation and integration pose a significant challenge. Any effective integration strategy will have to account not only for the regulatory relationships between datasets from different omics layers but also for the unique parameters, instruments, and characteristics used by different platforms to generate raw omics datasets.

Multi-omics data is exceptionally heterogeneous, reflecting both the complexity of biological systems as well as different high-throughput sources, which requires sophisticated data preprocessing.

The high dimensionality of omics data, and the disparity between the exceedingly high number of variables in a dataset vis-a-vis the number of can also pose significant challenges for statistical analysis and interpretation. This curse of dimensionality can result in ML algorithms tending to overfit high-dimensional datasets thereby decreasing the generalizability of new data.

Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics datasets. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. Since AI/ML models typically require complete data, this could lead to the need to discard valuable data entirely or to institute additional mechanisms to impute the missing values.

Over and above all this, there are AI/ML-related challenges, such as transparency, explainability, and interpretability, that have to be addressed. 

Sequencing methods

Advancements in sequencing technologies are typically mapped to three distinct generations starting with Sanger sequencing in the 1970s. This first-generation approach, also called the chain termination method, is based on the natural process of DNA replication.

The second generation, Next Generation Sequencing (NGS), emerged in the early 2010s with a more powerful short-read sequencing technique that far out-scaled the capabilities of the previous generation. This technique, however, could get hugely complicated for larger fragments and complex sequences.

Third-generation long-read sequencing methodologies enabled the sequencing of ultra-long fragments of DNA and facilitated the assembly of complex genomes. Though long-read sequencing addresses several limitations associated with the previous generation's technology, a comparatively higher sequencing cost has resulted in a shift towards a hybrid approach to achieve high accuracy without the high cost. 

Mass spectrometry (for proteomics)

Advances in mass spectrometry (MS) over the past two decades have helped revolutionize mass spectrometry-based proteomics and enabled the in-depth characterization and quantification of proteins and peptides in a biological system. Despite the availability of alternative methods, MS has emerged as the most common method for large-scale and comprehensive proteome analysis for diverse biological research applications.

Though early mass spectrometers lacked the sensitivity and resolution needed for complex protein samples, continuous technological advancements in the form of high-resolution and high-mass accuracy mass spectrometers have enabled precise mass measurements and improved peptide identification. Innovations in ionization techniques, particularly electrospray ionization and matrix-assisted laser desorption/ionization, have allowed the sequencing of nucleic acids at single-nucleotide resolution.

Coupled with liquid chromatography (LC), LC-MS systems are the most popular technique for the comprehensive identification and quantification of proteins.

Primary analysis

Primary analysis forms the foundation of omics data processing, focusing on data acquisition, storage, and management. This stage also necessitates a comprehensive understanding of experimental conditions, sample complexity, and detailed instrumentation specifications apart from generation and quality assessments of raw data. This phase establishes robust data management systems, ensuring the quality and integrity of data and efficient storage and retrieval of information.

Secondary analysis

Secondary analysis bridges raw data and interpretable results through data preprocessing and workflow automation pipelines and workflows. Critical processes, such as mapping, alignment, variant calling, quantification, annotation, data normalization, and batch correction, collectively refine raw data into structured, analyzable formats for downstream analysis. Secondary analysis is the critical link between raw data and actionable scientific knowledge and therefore requires precision data manipulation and automated, reproducible workflows.

Multiple sequence alignment

Sequence alignment is a foundational process in bioinformatics to compare two (pairwise sequence alignment) or more (multiple sequence alignment) biological sequences in order to identify regions of similarity that could help to elucidate functional, structural, and evolutionary relationships. 

Multiple sequence alignment (MSA) reveals more biological information, compared to pairwise alignments, and helps to identify homologous sequences, detects conserved sequence patterns and motifs, predicts 3D protein structures, and infers the function of uncharacterized sequences.

Common algorithmic approaches to MSA can be classified into progressive alignment methods (e.g., ClustalW, T-Coffee), iterative methods (e.g., MUSCLE), consistency-based methods (e.g., MAFFT), and hidden Markov model-based methods (e.g., HMMER). In recent years, deep learning (DL) and NLP-based approaches have emerged as “highly accurate, comparable and sometimes better than state-of-the-art” alternatives to conventional MSA techniques.

De Novo assembly

De novo assembly is a bioinformatics technique used to reconstruct longer fragments, called contigs, from short DNA or RNA sequencing reads without using a reference genome.

Since de novo genome assembly is not constrained by a reference genome, unlike reference-based assembly, it is essential to study non-model organisms without a pre-existing reference genome and is critical to several areas of research including disease identification, gene identification, and evolutionary biology. De novo assembly algorithms include overlap-layout-consensus and de-Bruijn-graph, string-graph based assembly, and hybrid approaches. Geometric deep learning frameworks are now being applied to de novo genome assembly to address some of the limitations associated with existing algorithmic methods.

Variant calling

Variant calling is the process of using NGS data to identify genetic variations between a sample genome and a reference genome. Genetic variation information can include Single Nucleotide Polymorphism (SNP), insertion and deletion sites (InDel), structural variation sites (SV), copy number variation (CNV), etc.. Variant calling is a critical step and its accuracy impacts all further downstream analysis and interpretation. Deep learning-based variant calling is quickly becoming the standard with one benchmarking study touting their superior accuracy.

Tertiary analysis

Tertiary analysis focuses on data discovery, exploration, analysis, interpretation and visualization. Today, this typically involves advanced AI/ML technologies to uncover patterns and generate hypotheses. The emphasis is converting processed data into biological insights and actionable knowledge.

Quality control in processing omics data

Quality control (QC) is a critical capability in the preprocessing of raw, inherently diverse, complex, and high-dimensional omics data. Additionally, there is also the challenge of inter- and intra-experimental quality heterogeneity arising from the multiplicity of technology platforms, experimental protocols, public & private data sources, etc. Effective QC strategies, techniques, and tools help:

• Ensure data reliability, by eliminating low-quality/contaminated data
• Improve reproducibility and standardize data processing across experiments.
• Identify experimental and technical biases, batch effects or systematic errors.
• Reduce false positives/negatives to enhance data interpretation and quality of downstream analyses
• Optimize resource usage by focusing solely on high quality data. 

Key multi-omics data quality processes include read quality assessment, adapter trimming, contamination screening, GC content analysis, coverage analysis, batch effect detection and correction, principal component analysis (PCA) for dimensionality reduction, normalization techniques, and base quality score recalibration.

Some of the most widely used QC tools and techniques include FastQC, to check the quality of high-throughput sequence data, MultiQC, for a holistic view of metrics, Trimmomatic, for trimming and filtering NGS data, PRINSEQ++, to filter, reformat or trim sequence data, Picard, for manipulating high-throughput sequencing data, RSeQC and RNA-SeQC for RNA sequences, GATK (Genome Analysis Toolkit) for variant discovery,  Qualimap, to evaluate sequencing alignment data. Preseq, to predict sequencing library complexity, and FASTX-Toolkit, for Short-Reads FASTA/FASTQ files preprocessing.

Differential gene expression (DGE) analysis

Differential gene expression (DGE) analysis is a fundamental technique for analyzing RNA-sequencing (RNA-seq) data and identifying genes expressed differentially with respect to a trait of interest across different samples or conditions. DGE analysis sets the stage for further functional enrichment analysis for a better understanding of molecular mechanisms and pathways.

Statistical techniques play a critical role in DGE analysis with the most commonly used tools including EdgeR, DESeq2, limma, and EBSeq.

Pathway analysis

Pathway analysis, also pathway enrichment analysis,  is a technique to analyze and interpret the complex interactions, or pathways, between genes, proteins, metabolites, and other molecular entities in order to gain new insights into biological mechanisms. It is widely used to identify pathways associated with differentially expressed genes from RNA-Seq analysis results.   

Key approaches to pathway analysis include over-representation analysis (ORA) using tools like DAVID or Enrichr, functional class scoring (FCS) methods like ebGSEA, and pathway topology-based approaches like SPIA.

Gene Set Enrichment Analysis (GSEA)

Gene Set Enrichment Analysis is a FCS-based method widely used for pathway analysis.

Bayesian statistics

Bayesian approaches for the integrative analysis of multi-omics data provide frameworks that combine prior knowledge with data-derived evidence for prediction and classification.

Applications of this approach include gene regulatory network (GRN) inference using co-expression-based methods such as WGCNA, ARACNe, multi-omics data integration with iCluster or MOFA, and causal inference using Bayesian networks.

Machine learning algorithms

Machine learning technologies have emerged as the new standard in multi-omics analysis and are being used widely to integrate multimodal omics data, identify data patterns and make connections,  investigate and interpret these connections, and automate information extraction. ML algorithms play an important role in dimensionality reduction techniques, such as PCA, t-SNE, and UMAP, that facilitate the extraction of meaningful insights from scRNA-seq datasets, clustering methodologies, classification and prediction using Random Forests, Support Vector Machines, Neural Networks, etc., and deep learning autoencoder-based approaches, like DeepProg, with superior predictive accuracy.

Heat maps

Heat maps have been used as a statistical visualization method since the 1990s and are currently the standard for visualizing omics‐level data. Key features for a heat map in the context of multi-omics data analysis include:

• Versatility to integrate various types of omics data into one cohesive visual representation.
• Provide compact and unified data views.
• Capabilities for users to generate and customize based on specific data clustering, interactivity, and aesthetic requirements.
• Facilitate data discovery, exploration, and analysis through an easy-to-use interface.

There are several tools for creating heat maps including R Programming Language resources, like ComplexHeatmap, Python libraries, like Seaborn and Matplotlib, web-based tools, like Morpheus and Heatmapper, and specialized software, like GraphPad Prism.

Scatter plots

Scatter plots are a valuable tool in multi-omics data analysis to help visualize relationships between different types of omics data, for example, transcriptomics and proteomics, and identify patterns or correlations. They are used for a range of data visualization scenarios including in sample comparison to identify clusters and surface outliers, visual correlation analysis of different molecular entities, and as a subtype called lag plot for time series analysis. Some specific applications include PCA plots, which visualize similarities between groups of samples in a data set, volcano plots, which plot the results of statistical tests as the magnitude of change on one axis against the statistical significance of this change, and 

MA plots, to compare expression levels between two conditions.

Pathway maps

Pathway maps are powerful tools to visualize and interpret omics data in the context of biological processes by integrating complex molecular data within the framework of known biological pathways. They facilitate the concurrent visualization of multiple types of omics data, mapped onto known biological processes, to facilitate more accurate hypothesis generation for further advanced analysis.

Common pathway databases and tools include KEGG (Kyoto Encyclopedia of Genes and Genomes), Reactome, WikiPathways, PathVisio, and Cytoscape.

Network-based visualization

Network-based visualization is a powerful tool for deriving actionable insights from large, complex data sets that can be visualized as a network of relationships and interactions. This approach uses graph theory to model networks with, for example, molecules (e.g., genes, proteins, metabolites) as Nodes, their interactions/relationships as Edges, and additional data (e.g., expression levels, interaction strength) mapped to nodes/edges as Attributes. 

A network-based approach is ideal for the visualization of a variety of biological networks, such as protein-protein interaction networks, gene regulatory networks, metabolic networks, co-expression networks, etc.

Leading tools and platforms include NetworkX (Python package), igraph (R package), Neo4j (graph database), and Gephi (visualization and exploration).

Batch effects

Multi-omics data analysis is prone to batch effects, resulting from a combination of biological, technical, and computational variations, that can mask true biological distinctions and thereby skew analysis, bias outcomes, and affect reliability and reproducibility. This is further amplified in multi-omic datasets, where combining different batches of diverse omics data modalities from multiple sources can result in more technical noise and systematic biases.  

The key challenge, therefore, is to identify and correct the distinct batch effect patterns specific to each omics dataset while ensuring that there is no over-correction, which removes genuine biological signals, or under-correction, which leaves residual batch effects.

There are several models for batch-effect correction, each with its own advantages and limitations. The two mainstream approaches can be categorized as location-scale (LS) matching methods — which include the more widely used ComBat,  batch mean-centering, and cross-platform normalization, and matrix factorization (MF) methods — such as surrogate variable analysis (SVA)], remove unwanted variation (RUV), and LEAPP.

Today, autoencoders and deep learning models are being used to address the challenges of batch effect identification and correction with frameworks like DESC (unsupervised deep embedding algorithm to remove complex batch effect, preserve biological variations), ABC (autoencoder-based batch correction for integrating single-cell sequencing datasets), BERMAD (multi-layer adaptation autoencoder to address the under-and over-correction), BERNN (batch effect removal neural networks for large liquid chromatography- mass spectrometry experiments), and DB-AAE (dynamic batching adversarial autoencoder for denoising scRNA-seq datasets).

Multiple hypothesis testing

Multiple hypothesis testing is a critical yet challenging aspect of analyzing high-throughput, high-dimensional, heterogeneous biological data. It is critical because it helps prioritize and allocate resources to further experiments and ensures statistical credibility and reproducibility.

In multiple hypothesis testing, the probability of making an error increases proportionally to the number of hypotheses to be tested. The challenge, therefore, is to maximize true discoveries, apposite rejections of null hypotheses, while controlling for false discoveries, incorrect rejections of a true null hypothesis.

Familywise error rate (FWER), the probability of making one false discovery, and false discovery rate (FDR), the expected proportion of false positives, are two of the most commonly used error control measures in scientific studies. FDR control methods, such as Benjamini-Hochberg and Storey's q-value and resampling-based bootstrapping statistics, provide important statistical guarantees for signal identification. FWER procedures, which include Bonferroni correction, Holm-Bonferroni, and Westfall-Young permutation, seek to prevent the occurrence of even a single false positive.

Multiple hypothesis testing, which was traditionally offline, is stepping up to modern data analysis by moving online. This online model is distinguished by its sequential nature,  where an incoming stream of hypotheses is continuously evaluated and decisions to reject the current null hypothesis are based solely on previous decisions and the evidence against the current hypothesis. In effect, online multiple testing could possibly test an infinite sequence of hypotheses with no information about the hypotheses that are yet to arrive for evaluation.

Importance and benefits

Multi-omics integration combines data from different biomolecular layers to provide researchers with a holistic understanding of complex biological systems. This integrative approach reveals novel insights that may not be apparent from analyzing individual omics datasets. The ability to holistically analyze relationships and interactions across multiple omics layers results in improved predictive capabilities for complex traits, diseases, and biological processes. It also enables a deeper understanding of individual genetic and molecular profiles that are foundational for the development of personalized healthcare solutions. The enhanced predictive capabilities and accuracy of integrated multi-omics data analysis can help boost the efficiency and productivity of biomedical R&D, reduce costs, and accelerate development.

Read more on our blog: Making Sense of Multi-Omics Data

Challenges in integration

Biological complexity

In biotechnology and biotherapeutics, non-linear, context-dependent relationships between molecular levels require sophisticated approaches. We use advanced machine learning to model these complex interactions, supporting drug discovery with enhanced predictive insights.

Data heterogeneity

The variety in data types, distributions, and volumes presents a significant challenge in multi-omics integration. Our LENS^ai platform provides harmonization frameworks and standardized pipelines to streamline data integration for R&D teams.

Missing data

Incomplete datasets are common in biological research, which can hinder analysis. We address this with imputation methods that improve data quality, supporting more comprehensive multi-omics integration.

Computational challenges

High-dimensional data can lead to overfitting and strain computational resources. Our dimensionality reduction techniques, such as PCA and t-SNE, in combination with Biostrand's HYFT "fingerprint" technology, help manage complex datasets while preserving accuracy and reducing computational load.

Technological biases

Variability across omics platforms may introduce biases in analysis. We address this with cross-platform normalization and integrated quality control, providing more consistent, reliable insights across diverse multi-omics datasets.

Read more on our blog: Challenges in multi-omics data integration

Methods for integration

As ML models continue to evolve as the standard for analyzing complex multi-omics data, a number of data integration methods/ frameworks have emerged based on the following five distinct integration strategies:

Early integration

This is based on the concatenation of every dataset into a single large matrix. Despite some clear advantages, which include ease of implementation and the ability to directly uncover inter-layer interactions, this approach could result in a more complex, noisy and high dimensional matrix that misdirects ML algorithms.

Mixed integration

This strategy addresses the key shortcomings of the previous approach by using kernel-based, graph-based, and deep learning-based transformation methods to create less dimensional and noisy representations of omics datasets that can be analyzed by ML models.

Intermediate integration

These are methods that integrate multi-omics datasets and reduce dimensionality and complexity without the need for prior transformation or concatenation. They are generally used after feature selection and robust pre-processing to address data heterogeneity. Intermediate integration typically involves variations and combinations of the following methods: joint dimensionality reduction (jDR), multi-omics factor analysis (MOFA), probabilistic/Bayesian Models (PR), network-based integration (NB), etc.

Late integration

In this strategy, ML models are independently applied to each dataset with a second model combining the discrete predictions. This approach facilitates the use of omics-specific ML tools thereby representing a simple approach to integrating different kinds of data.

Hierarchical integration

Most integration strategies tend to ignore the hierarchical organization, the underlying biological relationships, and the information flow within biological systems that are reflected in multi-omics datasets. A hierarchical strategy grounds integration on prior knowledge of inter-layer regulatory relationships to explicitly model multi-level information transmission. Examples of hierarchical integration methods include Bayesian analysis of genomics data (iBAG), linear regulatory modules (LRMs), Assisted Robust Marker Identification (ARMI), and Robust Network.

Reproducibility has emerged as one of the biggest challenges in omics research. Best practices for reproducible analysis include:

• Analytical considerations such as adequate quality control of raw data to ensure data quality and the imperative to include metadata in computational analyses.
• Defining a multi-stage framework that spans pre-analysis data/metadata management practices to post-analysis data/code sharing practices that are critical to reproducible research.
• Applying a severe testing framework (STF) that is aimed at falsifying rather than confirming results to alleviate some reproducibility-related issues.
• Leveraging the latest advancements in data, code, workflow, and environment sharing that enable the FAIRification of multi-omics analysis and enhance reproducibility.
• The ever-expanding applications of AI/ML technologies in multi-omics and biomedical research need to be stabilized against robust and reproducible ML/DL model development.

Read more on our blog: The importance of reproducibility in in-silico drug discovery

Drug discovery

Multi-omics data analysis offers a comprehensive approach to understanding biological systems that continue to transform drug discovery. Potential applications include:

• The discovery and validation of drug targets based on molecular signatures revealed by multi-omics data, constructing molecular networks to understand disease/drug mechanisms of action, and prioritizing and validating potential drug targets.
• The prediction of drug toxicity, drug sensitivity, and drug adverse reactions, and optimizing drug responses based on multiple indicators such as efficacy, safety, toxicity, adverse effects, etc.
• Multiple omics datasets enable the discovery of reliable biomarkers that provide a better understanding of the biology of complex diseases and therefore the development of more effective treatments.
• Multi-omics data integration for drug repurposing and drug repositioning.

Read more on our blog: Improving drug safety with adverse event detection using NLP

Precision medicine

• The integration of multi-omics and clinical data provides a holistic view of disease pathophysiology thereby enhancing patient outcomes and paving the way for precision medicine.
• Combining multi-omics data, deep phenotyping, and translational approaches with predictive analysis facilitates precision medicine.
• Multi-omics network medicine is helping elucidate the heterogeneity of human diseases and guide tailored drug treatments.

Read more on our blog: Omics data analysis and precision medicine

Biomarker identification

• Multi-omics have facilitated the identification of novel disease biomarkers for the efficient diagnosis, monitoring, and management of complex diseases.
• Multi-omics approaches for biomarker discovery have helped identify novel biomarkers that help in early disease diagnosis even in the absence of specific symptoms.

Read more on our blog: Accelerate biomarker discovery with artificial intelligence

The field of omics analysis is evolving rapidly, driven by technological advancements in data generation, integration and analysis, and reshaping understanding of biological systems. Key emerging trends in this field include:

Single-cell omics

Multi-omics approaches tend to obscure crucial cellular nuances by averaging signals across heterogeneous cell populations. Single-cell omics enable the observation of individual cells at a molecular level, and at high resolution and sensitivity, to reveal diversity, heterogeneity, dynamics states, and unique molecular characteristics of different cell populations. Combining single-cell data with multi-omics data provides a holistic view of cellular processes including complex cellular interactions, regulatory networks, and molecular mechanisms.

Advances in single-cell technologies have enabled the cellular-level analysis of genomes, transcriptomes, open chromatin landscapes,  DNA methylation, individual histone modifications, etc., and helped transform the landscape of molecular and cellular biology research.

Spatial Omics:

Spatial omics refers to a range of technologies used to profile the molecular make-up of tissues along with information about spatial organization. This approach identifies molecular subpopulations of cells while also providing information on their location and proximity to each other. This is achieved through four main steps - Detection (probes bind to specific molecules), Identification (identify biomolecules with next-generation sequencing or mass spectrometry or tag on the detection probe), Measurement, (measure signal intensity of probes), and Localization (assign biomolecules to spatial locations).

High-throughput spatial omics technologies are driving a potential revolution in the diagnosis, prognosis, and treatment of complex diseases like cancer. The capabilities of these technologies to decipher the heterogeneity of highly intricate tumor microenvironments while preserving cell–environment interactions have opened up novel opportunities in cancer immunology research.

Continuing advancements in spatial omics technologies, spanning spatial transcriptomics, spatial proteomics, spatial metabolomics, spatial epigenomics, spatial multi-omics, etc., has resulted in the generation of huge volumes of multiple types of multidimensional data with spatial context. A new generation of spatially informed methods and tools are now emerging to allow researchers to fully capitalize on the potential of spatial multi-omics data.

Time-series Omics:

Advances in high-throughput techniques now enable the creation of omics data sets with an embedded temporal dimension. This facilitates the integrated analysis of data sets from different points in time to build a more dynamic view of biological phenomena.

Time-series data has already been applied successfully across numerous real-world applications notably for improved cancer detection, integrated time-dose modeling of toxicogenomics data, investigating the temporal dynamics of the microbiome, and inferring cell velocity, growth, and cellular dynamics.

Time-series omics data, however, comes with its own set of challenges including missing or incomplete data shortage, lack of alignment between different time-series datasets, as well as the limitations of conventional omics integration and analysis tools in handling time-series dependencies in the data.

• The field of multi-omics data analysis has revolutionized our understanding of biological systems, offering unprecedented insights into the complex molecular processes that govern life.
• Concurrent advancements in next-generation data generation technologies and in AI-driven pattern recognition, prediction, and data interpretation capabilities continue to expand the horizon for data-driven, integrated multi-omics data analysis.
• Multi-omics data has become essential in numerous biomedical research applications, especially as next-generation, high-throughput technologies generate increasingly intricate datasets, including single-cell and spatial data. Advanced solutions like BioStrand's LENS^ai platform are paving the way for more streamlined data integration and analysis, aligning with the needs of biotechnology and pharmaceutical enterprises engaged in drug discovery and development.
• The future of multi-omics data integration and analysis will be driven by the development of advanced computational tools and algorithms that can seamlessly combine and analyze diverse datasets; by effectively addressing issues related to data standardization and reproducibility; and by emphasizing usability and accessibility.