Computational solutions for omics data

Abstract

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.

Parallel Computing in Genome-Wide Association Studies

“…This might yield a substantial gain in computation speed, even with the added cost of using extra information in the data. In addition, computational hardware has been drastically improved in recent years [1], meaning that we have the computational power necessary to parallelize computations, even in the analysis of large genomic data sets.…”

“…One technique used to speed up computation (i.e. make computation more efficient) is parallelization, where multiple tasks are performed simultaneously on multiple cores or threads within a machine [1]. Even with computational and methodological limitations, analysis of genome-wide association study (GWAS) data has led to the inference of connections among several phenotypes and SNPs [2,3].…”

Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

“…In the current study, the data were obtained from the experiment of Samavat et al on urinary protein profile of patients with IgA nephropathy and healthy individuals using nanoscale liquid chromatography with tandem mass spectrometry (nLC-MS system) (11). The case group in the mentioned study consisted of 13 patients with approved IgA nephropathy disease by biopsy and 8 healthy volunteers without any nephropathy disease, considered as the control group.…”

Mis-specication Model Related Instabilities in High Dimensional Linear Models

“…Often more applicable but time consuming is the use of algorithmic search techniques [11]. These cycle through all possible value combinations for the elements of the unknown β vector until an optimal choice is found, minimizing the sum of squared errors subject to the sparsity restrictions.…”