Integration of large-scale multi-omic datasets: A protein-centric view

Abstract

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

Regulatory start-stop elements in 5’ untranslated regions pervasively modulate translation

“…The complexity of life depends on the ability to engage intricate systems that respond to many situations ( 36, 37 ), either intrinsic, e.g. through development, or externally inflicted, e.g.…”

Translational remodeling by RNA‐binding proteins and noncoding RNAs

“…On the other hand, RNA-sequencing based techniques are uniquely useful for dissecting translational mechanisms, such as measuring mRNA translation efficiency and transcript level changes concurrently from the same samples by RNA sequencing of ribosome density fractions Ho, Balukoff, et al, 2020), translation initiation start site usage by GTI-/QTI-seq (Gao et al, 2015;Lee et al, 2012), translation kinetics and ribosome positional information by ribosome profiling (Ingolia et al, 2012;Ingolia et al, 2019), and related techniques including TCP-seq (Archer et al, 2016;Shirokikh et al, 2017), RCP-seq (Giess et al, 2020), RiboLace (Clamer et al, 2018), and transcript isoform utilization by TrIP-seq (Floor & Doudna, 2016). Overall, the future of translational discovery will rely on the integrative, protein-centric approach of quantitative modeling to establish relationships between multi-omic datasets, rather than simple parallel analyses of different omic-level data (Buccitelli & Selbach, 2020;Gawron et al, 2014;Rendleman et al, 2018;Vitrinel et al, 2019). ANALYSIS As discussed above, global translatome remodeling events under most circumstances are driven by stress-induced changes to the protein synthesis architecture, which includes the machinery and translatome remodelers.…”

An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types

“…A large number of prior studies have inferred the relations of genes or molecular alterations. Many of them integrate the information from multi-omics data probing the same cohort or cancer type (vertical integration according to [21], see review articles such as [22][23][24][25][26]), or combine the single-omics data probing multiple cancer types (horizontal integration). Vertical integration methods are widely employed to cluster patients or samples [27][28][29][30][31], predict clinical outcomes [32][33][34], identify biomarkers or driver genes [35][36][37][38], and infer pathway or subnetwork activities [39][40][41][42][43].…”