Statistical contributions to bioinformatics: Design, modelling, structure learning and integration

Abstract

The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modeling, and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all availble information to uncover new biological insights.

Detection of suspicious interactions of spiking covariates in methylation data

“…Hence, it is important that the researcher decides, if possible detected suspicious interaction will violate the linearity assumption of the regression analysis or if the independent assumption of the bioinformatical pipeline can not hold. We would state, that a flipped or misleading effect is much more problematic than a lower statistical power [1,16].…”

“…The bioinformatical analysis of high dimensional omics data is run in a pipeline fashion [14,15]. This is feasible for the preprocessing and quality control of the samples until the differential analysis step begins [16]. In the case of an epigenome-wide association study (EWAS), not one CpG site is analyzed but hundreds of thousands.…”

First the seed: Genomic advances in seed science for improved crop productivity and food security

“…Equally noteworthy developments have taken place in the field of bioinformatics and statistical models which are providing the tools to store, retrieve, share and analyze large phenotyping and genotyping data sets, thus allowing researchers to interpret these judicially in selection of promising candidates to support crop breeding. Linking traits to genebank accessions and aligning them to known functional genes may accelerate discovery of allelic variants associated with beneficial traits (Anglin, Amri, Kehel, & Ellis, 2018;D'Argenio, 2018;Morris & Baladandayuthapani, 2017).…”

“…Linking traits to gene bank accessions and aligning them to known functional genes may accelerate discovery of allelic variants associated with beneficial traits (Anglin et al. 2018; D'Argenio, 2018; Morris & Baladandayuthapani, 2017).…”

The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

“…Reproducibility is a key feature of scientific research; high-throughput data are challenging in this regard due to the high variability of the samples analyzed and/or of the experimental procedures, and the complexity of the data and the use of not properly validated and/or standardized pipelines. Statistician-derived methods may be useful in this context by supporting experimental design and reproducibility, preprocessing, structure learning, and data integration [ 65 ]. The information is in the data: the methodology for their correct interpretation must be widely validated and standardized to ensure laboratory data harmonization and be sure that significant differences in a specific sample, or in a population, are really due to a relevant biological alteration and not to biases attributable to the used analytical approaches (both at molecular and bioinformatics levels).…”