MetageneCluster: a Python package for filtering conflicting signal trends in metagene plots

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 21 - 9
• Main Authors: Carter, Clayton, Saporito, Aaron, Douglass, Stephen M.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.01.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Annotations; Arrays; Bioinformatics; Biomedical and Life Sciences; Cluster analysis; Clustering; Clustering (Computers); Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data mining; Datasets; DNA methylation; Gene expression; Gene loci; Genomics; k-means clustering; Life Sciences; Metagene plot; Methods; Microarrays; Python (Programming language); RNA sequencing; Sequencing data; Software; Trends; Vector quantization; Visualization; United States; Visualization; k-means clustering; Sequencing data; Metagene plot
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05647-3

Background Metagene plots provide a visualization of biological signal trends over subsections of the genome and are used to perform high-level analysis of experimental data by aggregating genome-level data to create an average profile. The generation of metagene plots is useful for summarizing the results of many sequencing-based applications. Despite their prevalence and utility, the standard metagene plot is blind to conflicting signals within data. If multiple distinct trends occur, they can interact destructively, creating a plot that does not accurately represent any of the underlying trends. Results We present MetageneCluster, a Python tool to generate a collection of representative metagene plots based on k-means clustering of genomic regions of interest. Clustering the data by similarity allows us to identify patterns within the features of interest. We are then able to summarize each pattern present in the data, rather than averaging across the entire feature space. We show that our method performs well when used to identify conflicting signals in real-world genome-level data. Conclusions Overall, MetageneCluster is a user-friendly tool for the creation of metagene plots that capture distinct patterns in underlying sequence data.