BACKGROUND: Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method. RESULTS: omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset. CONCLUSIONS: omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

MOTIVATION: Mutual information (MI) is a quantity that measures the dependence between two arbitrary random variables and has been repeatedly used to solve a wide variety of bioinformatic problems. Recently, when attempting to quantify the effects of sampling variance on computed values of MI in proteins, we encountered striking differences among various novel estimates of MI. These differences revealed that estimating the 'true' value of MI is not a straightforward procedure, and minor variations of assumptions yielded remarkably different estimates. RESULTS: We describe four formally equivalent estimates of MI, three of which explicitly account for sampling variance, that yield non-equal values of MI given exact frequencies. These MI estimates are essentially non-predictive of each other, converging only in the limit of implausibly large datasets. Lastly, we show that all four estimates are biologically reasonable estimates of MI, despite their disparity, since each is actually the Kullback-Leibler divergence between random variables conditioned on equally plausible hypotheses. CONCLUSIONS: For sparse contingency tables of the type universally observed in protein coevolution studies, our results show that estimates of MI, and hence inferences about physical phenomena such as coevolution, are critically dependent on at least three prior assumptions. These assumptions are: (i) how observation counts relate to expected frequencies; (ii) the relationship between joint and marginal frequencies; and (iii) how non-observed categories are interpreted. In any biologically relevant data, these assumptions will affect the MI estimate as much or more-so than observed data, and are independent of uncertainty in frequency parameters.

Motivation: Mutual information (MI) is a quantity that measures the dependence between two arbitrary random variables and has been repeatedly used to solve a wide variety of bioinformatic problems. Recently, when attempting to quantify the effects of sampling variance on computed values of MI in proteins, we encountered striking differences among various novel estimates of MI. These differences revealed that estimating the ‘true’ value of MI is not a straightforward procedure, and minor variations of assumptions yielded remarkably different estimates. Results: We describe four formally equivalent estimates of MI, three of which explicitly account for sampling variance, that yield non-equal values of MI given exact frequencies. These MI estimates are essentially non-predictive of each other, converging only in the limit of implausibly large datasets. Lastly, we show that all four estimates are biologically reasonable estimates of MI, despite their disparity, since each is actually the Kullback–Leibler divergence between random variables conditioned on equally plausible hypotheses. Conclusions: For sparse contingency tables of the type universally observed in protein coevolution studies, our results show that estimates of MI, and hence inferences about physical phenomena such as coevolution, are critically dependent on at least three prior assumptions. These assumptions are: (i) how observation counts relate to expected frequencies; (ii) the relationship between joint and marginal frequencies; and (iii) how non-observed categories are interpreted. In any biologically relevant data, these assumptions will affect the MI estimate as much or more-so than observed data, and are independent of uncertainty in frequency parameters. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Compensating alterations during the evolution of protein families give rise to coevolving positions that contain important structural and functional information. However, a high background composed of random noise and phylogenetic components interferes with the identification of coevolving positions. RESULTS: We have developed a rapid, simple and general method based on information theory that accurately estimates the level of background mutual information for each pair of positions in a given protein family. Removal of this background results in a metric, MIp, that correctly identifies substantially more coevolving positions in protein families than any existing method. A significant fraction of these positions coevolve strongly with one or only a few positions. The vast majority of such position pairs are in contact in representative structures. The identification of strongly coevolving position pairs can be used to impose significant structural limitations and should be an important additional constraint for ab initio protein folding. AVAILABILITY: Alignments and program files can be found in the Supplementary Information.

MOTIVATION: Some functionally important protein residues are easily detected since they correspond to conserved columns in a multiple sequence alignment (MSA). However important residues may also mutate, with compensatory mutations occurring elsewhere in the protein, which serve to preserve or restore functionality. It is difficult to distinguish these co-evolving sites from other non-conserved sites. RESULTS: We used Mutual Information (MI) to identify co-evolving positions. Using in silico evolved MSAs, we examined the effects of the number of sequences, the size of amino acid alphabet and the mutation rate on two sources of background MI: finite sample size effects and phylogenetic influence. We then assessed the performance of various normalizations of MI in enhancing detection of co-evolving positions and found that normalization by the pair entropy was optimal. Real protein alignments were analyzed and co-evolving isolated pairs were often found to be in contact with each other. AVAILABILITY: All data and program files can be found at http://www.biochem.uwo.ca/cgi-bin/CDD/index.cgi