Data integration and predictive modeling methods for multi-omics datasets

Kim, Minseung; Tagkopoulos, Ilias

doi:10.1039/c7mo00051k

Cited by 80 publications

(65 citation statements)

References 207 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, they may reveal issues such as batch effects or outliers in a given dataset. For a more detailed view on the predictive modeling and analytics approaches, the readers are advised to consult a recent review by Kim and Tagkopoulos (2018).…”

Section: Dimension Reductionmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

392

240

View full text Add to dashboard Cite

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 daily basis. These data file sizes, together with differences in nomenclature 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 towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts 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, lipidomics, microbiomics, and phenomics, 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.

show abstract

Section: Dimension Reductionmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

392

240

View full text Add to dashboard Cite

show abstract

“…Concomitantly, the current computational models are unable to capture the complex nonlinear dynamics that take place in such environments, hence constraining our options to a trial-and-error approach. As such, creating an accurate computational model that can generalize its predictions in novel environments is one of the grand challenges in computational biotechnology (Carrera et al 2014;Kim et al 2016;Kim and Tagkopoulos 2018;Kim et al 2015;Mozhayskiy and Tagkopoulos 2013;Wang et al 2018).…”

mentioning

confidence: 99%

Population collapse and adaptive rescue during long‐term chemostat fermentation

Rai

Huynh

Kim

et al. 2019

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

Microbial fermentation is an essential process for research and industrial applications, yet our understanding of cellular dynamics during long‐term fermentation is limited. Here, we report a reproducible phenomenon of abrupt population collapse followed by a rapid population rescue that was observed during long‐term chemostat cultivations, for various strains of Escherichia coli in minimal media. Through genome resequencing and whole‐genome transcriptional profiling of replicate runs over time, we identified that changes in the tRNA and carbon catabolic genes are the genetic basis of this phenomenon. Since current fermentation models are unable to capture the observed dynamics, we present an extended model that takes into account critical biological processes during fermentation, and we further validated carbon source predictions through forward experimentation. This study extends the predictability of current models for microbial fermentation and adds to our system‐level knowledge of cellular adaptation during this crucial biotechnological process.

show abstract

“…Normalization is the process that aims to make samples more comparable, allowing a decrease in intragroup variation between technical and/or biological replicates. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis (Bantscheff et al ., ; Kim & Tagkopoulos, ). For example, a study carried out by Välikangas et al .…”

Section: Considerations For Proteomic Analysis Of Apoplastic Fluidmentioning

confidence: 99%

What proteomic analysis of the apoplast tells us about plant–pathogen interactions

et al. 2018

View full text Add to dashboard Cite

Plant pathogens have developed different strategies during their evolution to infect and colonize their hosts. In the same way, plants have evolved different mechanisms acting against potential pathogens trying to infect and colonize their tissues. Regulation of a wide variety of proteins is required in order to perceive the pathogen and to activate the plant defence mechanisms. The apoplast is the first compartment where these recognition phenomena occur in most plantpathogen interactions, allowing the exchange of different molecules and facilitating inter-and intracellular communication in plant cells. Proteomic analysis of the apoplast in recent years has found the initial biochemical responses involved in pathogen recognition and early defence responses. However, this proteomic approach requires some specific experimental conditions to obtain an extract free of cytoplasmic proteins and nonprotein contaminants that affect the subsequent stages of separation and quantification. Obtaining the highest proportion of proteins from the apoplastic space in infected tissues requires different steps such as extraction of apoplastic washing fluids and preparation of total secreted proteins (protein precipitation, solubilization, separation and digestion). Protein identification using mass spectrometry techniques and bioinformatics tools identifying peptides for the extracellular exportation is required to confirm the apoplastic location. This review compiles the most commonly used techniques for proteomic studies, focusing on the early biochemical changes occurring in the apoplast of plants infected by a wide range of pathogens. The scope of this approach to discover the molecular mechanisms involved in the plant-pathogen interaction is discussed.

show abstract

Data integration and predictive modeling methods for multi-omics datasets

Cited by 80 publications

References 207 publications

Integrated omics: tools, advances and future approaches

Integrated omics: tools, advances and future approaches

Population collapse and adaptive rescue during long‐term chemostat fermentation

What proteomic analysis of the apoplast tells us about plant–pathogen interactions

Contact Info

Product

Resources

About