Paper reading (二十二)：Integrated omics: tools, advances and future approaches
论文题目：Integrated omics: tools, advances and future approaches
scholar 引用：12
页数：25
发表时间：2018.07
发表刊物：Journal of Molecular Endocrinology
作者：Biswapriya B Misra, Carl Langefeld, Michael Olivier and Laura A Cox
摘要：Key Words: integrated, omics, genomics, transcriptomics, proteomics, metabolomics, network, statistics, Bayesian, machine learning, principal component analysis, correlation, clustering
With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a d aily basis. These data file
sizes, together with differences in nomenclature(命名法) among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just ‘omics’, the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. The ultimate goal is toward the holistic realization of a ‘systems biology’ understanding of the biological question. Commonly used approaches are currently limited by the 3 i’s – integration, interpretation and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics(糖组学), lipidomics(脂类组学), microbiomics and phenomics(表型组学), an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.
结论：
- no single approach exists for processing, analyzing and interpreting all data from different -omes.
- the community needs to embrace challenges posed from these complex datasets to standardize sample quality, sample analysis pipelines, data analysis pipelines and data formats for public data availability.
- Integrated omics is not just a collage of tools, but a cohesive paradigm for insightful biological interpretation of multi-omics datasets that will potentially reveal novel insights into basic biology, as well as health and disease.
Introduction：
- Access to large-scale omics datasets has revolutionized biology and led to the emergence of systems approaches to advance our understanding of biological processes.
- These multilayered, multifactorial approaches are computationally challenging and difficult to display and comprehend
visually. - Broad experimental challenges in these integrated omics approaches include, but are not limited to：
- understanding the statistical behavior of readouts from each omics regime independently
- recognizing non-obvious relationships that exist between omics regimes within their original biological context
- capitalizing on time resolution in omics data
正文组织架构：
1. Introduction
2. Strengths and challenges of individual omics
2.1 Genomics and transcriptomics
2.2 Proteomics
2.3 Metabolomics
2.4 Unique challenges to specific omics platforms
2.4.1 Linking genotype to phenotype
2.4.2 Quantification of the proteome
2.4.3 Quantification of the metabolome
2.5 Issues shared among the omics platforms
2.5.1 Data handling
2.5.2 Annotation
2.5.3 Study design and analytic assumptions
2.5.4 Statistical power
2.5.5 Data archiving and sharing
3. Tools available for integration of multi-omics data
4. Recent examples of integration in real world datasets
4.1 Complex diseases
4.2 Immunity and infection
4.3 Cancer
4.4 Host microbiome interactions
5. Statistical approaches for current challenges
5.1 Number of samples vs number of molecules
5.2 Dimension reduction
5.3 Data integration
6. Current challenges and looking to future
6.1 Experimental challenges
6.1.1 Challenges in sample preparation
6.1.2 Optimizing, documenting and sharing workflows
6.1.3 Data processing
6.1.4 Time course studies
6.2 Individual omics datasets – normalization, transformation of different omics data types
6.3 Integration issues – data scaling, false positives and unknowns
6.4 Data issues – data archiving and sharing
6.5 Hurdles in implementing multi-omics approaches in the clinic for diagnostic/prognostic purposes
6.6 Biological knowledge – data interpretation
7. Conclusions
正文部分内容摘录：
2. Strengths and challenges of individual omics
2.1 Genomics and transcriptomics
- Genomics and transcriptomics have been applied to various aspects of research and clinical applications.
2.2 Proteomics
- Proteomics is used to quantify proteins in multiple sample types using both shotgun and targeted approaches.
- proteomics is advancing our understanding in biomedical research, including diagnosis, protein-based biomarker development and therapeutics.
2.3 Metabolomics
- Metabolites are often end products of complex biochemical cascades that can link the genome, transcriptome and
proteome to phenotype, providing an important key tool for discovery of the genetic basis of metabolic variation.
2.4 Unique challenges to specific omics platforms
2.4.1 Linking genotype to phenotype
- Combining data from proteomics and metabolomics with genomics and transcriptomics helps to overcome this limitation by providing molecular information that links genetic and epigenetic variation with phenotypic variation.
2.4.2 Quantification of the proteome
- advances in instrument sensitivity, and the development of effective isotopic labeling tools for tissue samples have
significantly improved the accuracy and reproducibility of peptide and protein quantification using MS.
2.4.3 Quantification of the metabolome
- 相关challenges的解决方法：standardization, annotation of metabolites, interoperability of protocols and methods and statistical considerations.
2.5 Issues shared among the omics platforms
2.5.1 Data handling
- it is essential that every analysis pipeline is well documented, including versions of software used for each step in the pipeline and rationale for parameters implemented.
2.5.2 Annotation
- 主要是non-standard organisms的annotation数据存在更多问题。
2.5.3 Study design and analytic assumptions
- Too often the large number of variables is viewed as making assumption validation impossible or not worth the investment.
- An important step in a proper analysis is to clearly understand from the experimental question whether the omics variable is a predictor or an outcome.
- aligning the analytic approach to match the outcome is important so that the proper variance is estimated for the test and interval estimates.
2.5.4 Statistical power
- Major statistical challenges：the number of samples in a study versus the number of molecules quantified in each sample；analysis of time series data and treatment of data for targeted and untargeted (unbiased) approaches；the
large variation in the number of observations per sample
2.5.5 Data archiving and sharing
- the lack of a standardized nomenclature, data formatting and eventual public access to datasets.
3. Tools available for integration of multi-omics data
4. Recent examples of integration in real world datasets
- 集成组学在以下四个领域近几年取得的一些研究成果的介绍，反映了集成组学的重要性。
4.1 Complex diseases
4.2 Immunity and infection
4.3 Cancer
4.4 Host microbiome interactions
5. Statistical approaches for current challenges
5.1 Number of samples vs number of molecules
- Sampling directly impacts the appropriate statistical tools employed and must be defined prior to sampling for a given study.
5.2 Dimension reduction
- Dimension reduction is one strategy to reduce the computational burden while also addressing multiple testing concerns.
5.3 Data integration
- Most methods implemented for data integration have relied on PCA, correlation or Bayesian or non-Bayesian networkbased methods.
6. Current challenges and looking to future
6.1 Experimental challenges
6.1.1 Challenges in sample preparation
- the unified sample preparation workflows are in their infancy with current methods typically providing unequal sample
quality; significant work is required to achieve universal applications for diverse biological matrices.
6.1.2 Optimizing, documenting and sharing workflows
- It is essential to define and document all steps in the data handling workflow, including generation of individual omics datasets and integration of omics datasets.
6.1.3 Data processing
- Analysis tools chosen for integrative efforts also have a significant impact on outcomes.
6.1.4 Time course studies
- Sampling time courses are important for understanding integrated network dynamics.
6.2 Individual omics datasets – normalization, transformation of different omics data types
- 不同的dataset，比如说0的含义也不一样。
- imputation of missing values must be addressed differently for the different types of datasets.
6.3 Integration issues – data scaling, false positives and unknowns
- Tools for scaling datasets and addressing false positives from three or more independent platforms for integration and subsequent analysis have not yet been developed.
- A key strength of unbiased omics approaches is the ability to identify novel molecules that impact biological function.
- A major limitation of omics analyses is the ability to annotate unknowns.
6.4 Data issues – data archiving and sharing
- There is a growing urgency for reproducible research using integrated omics, similar to all disciplines in science.
- although public databases for archiving individual omics datasets exist, no such archive exists for integrated omics datasets.
6.5 Hurdles in implementing multi-omics approaches in the clinic for diagnostic/prognostic purposes
- there is the need for not only quantification of different types of biological variants, but integration of these data in ways that inform our understanding health and disease which will translate to clinical practice.
6.6 Biological knowledge – data interpretation
- The largest hurdle for any omics dataset remains ‘making sense of the data’.
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