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Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.

ABSTRACT: Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

SUBMITTER: Manzoni C

PROVIDER: S-EPMC6018996 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.

Manzoni Claudia C Kia Demis A DA Vandrovcova Jana J Hardy John J Wood Nicholas W NW Lewis Patrick A PA Ferrari Raffaele R

Briefings in bioinformatics 20180301 2

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomi ...[more]

PMID: 27881428

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Project description:BackgroundRapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.ResultsA simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.ConclusionsSimulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

Project description:In Europe, Ixodes ricinus ticks are the most important vectors of diseases threatening humans, livestock, wildlife and companion animals. Nevertheless, genomic sequence information is missing and functional annotation of transcripts and proteins is limited. This lack of information is restricting studies of the vector and its interactions with pathogens and hosts. Here we present and integrate the first analysis of the I. ricinus genome with the transcriptome and proteome of the unfed I. ricinus midgut.Whole genome sequencing was performed on I. ricinus ticks and the sequences were de novo assembled. In parallel, I. ricinus ticks were dissected and the midgut transcriptome sequenced. Both datasets were integrated by transcript discovery analysis to identify putative genes and genome contigs were screened for homology. An alignment-based and a motif-search-based approach were combined for the annotation of the midgut transcriptome. Additionally, midgut proteins were identified and annotated by mass spectrometry with public databases and the in-house built transcriptome database as references and results were cross-validated.The de novo assembly of 1 billion DNA sequences to a reference genome of 393 Mb length provides an unprecedented insight into the I. ricinus genome. A homology search revealed sequences in the assembled genome contigs homologous to 89% of the I. scapularis genome scaffolds indicating coverage of most genome regions. We identified moreover 6,415 putative genes. More than 10,000 transcripts from naïve midgut were annotated with respect of predicted function and/or cellular localization. By combining an alignment-based with a motif-search-based annotation approach, we doubled the number of annotations throughout all functional categories. In addition, 574 gel spots were significantly identified by mass spectrometry (p<0.05) and 285 distinct proteins expressed in the naïve midgut were annotated functionally and/or for cellular localization. Our systems approach reveals a midgut metabolism of the unfed tick that is prepared to sense and process an anticipated blood meal.This multiple-omics study vastly extends the publicly available DNA and RNA databases for I. ricinus, paving the way for further in-depth analysis of the most important European disease vector and its interactions with pathogens and hosts.

Dataset Information

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.

Publications

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.

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