A N Habowski1, T J Habowski2, M L Waterman2. 1. Department of Microbiology and Molecular Genetics, University of California Irvine, Irvine, CA, 92697, USA. habowski@uci.edu. 2. Department of Microbiology and Molecular Genetics, University of California Irvine, Irvine, CA, 92697, USA.
Abstract
BACKGROUND: Due to continued advances in sequencing technology, the limitation in understanding biological systems through an "-omics" lens is no longer the generation of data, but the ability to analyze it. Importantly, much of this rich -omics data is publicly available waiting to be further investigated. Although many code-based pipelines exist, there is a lack of user-friendly and accessible applications that enable rapid analysis or visualization of data. RESULTS: GECO (Gene Expression Clustering Optimization; http://www.theGECOapp.com ) is a minimalistic GUI app that utilizes non-linear reduction techniques to rapidly visualize expression trends in many types of biological data matrices (such as bulk RNA-seq or proteomics). The required input is a data matrix with samples and any type of expression level of genes/protein/other with a unique ID. The output is an interactive t-SNE or UMAP analysis that clusters genes (or proteins/other unique IDs) based on their expression patterns across the multiple samples enabling visualization of expression trends. Customizable settings for dimensionality reduction, data normalization, along with visualization parameters including coloring and filters, ensure adaptability to a variety of user uploaded data. CONCLUSION: This local and cloud-hosted web browser app enables investigation of any -omic data matrix in a rapid and code-independent manner. With the continued growth of available -omic data, the ability to quickly evaluate a dataset, including specific genes of interest, is more important than ever. GECO is intended to supplement traditional statistical analysis methods and is particularly useful when visualizing clusters of genes with similar trajectories across many samples (ex: multiple cell types, time course, dose response). Users will be empowered to investigate -omic data with a new lens of visualization and analysis that has the potential to uncover genes of interest, cohorts of co-regulated genes programs, and previously undetected patterns of expression.
BACKGROUND: Due to continued advances in sequencing technology, the limitation in understanding biological systems through an "-omics" lens is no longer the generation of data, but the ability to analyze it. Importantly, much of this rich -omics data is publicly available waiting to be further investigated. Although many code-based pipelines exist, there is a lack of user-friendly and accessible applications that enable rapid analysis or visualization of data. RESULTS: GECO (Gene Expression Clustering Optimization; http://www.theGECOapp.com ) is a minimalistic GUI app that utilizes non-linear reduction techniques to rapidly visualize expression trends in many types of biological data matrices (such as bulk RNA-seq or proteomics). The required input is a data matrix with samples and any type of expression level of genes/protein/other with a unique ID. The output is an interactive t-SNE or UMAP analysis that clusters genes (or proteins/other unique IDs) based on their expression patterns across the multiple samples enabling visualization of expression trends. Customizable settings for dimensionality reduction, data normalization, along with visualization parameters including coloring and filters, ensure adaptability to a variety of user uploaded data. CONCLUSION: This local and cloud-hosted web browser app enables investigation of any -omic data matrix in a rapid and code-independent manner. With the continued growth of available -omic data, the ability to quickly evaluate a dataset, including specific genes of interest, is more important than ever. GECO is intended to supplement traditional statistical analysis methods and is particularly useful when visualizing clusters of genes with similar trajectories across many samples (ex: multiple cell types, time course, dose response). Users will be empowered to investigate -omic data with a new lens of visualization and analysis that has the potential to uncover genes of interest, cohorts of co-regulated genes programs, and previously undetected patterns of expression.
Authors: Spas Dimitrov-Markov; Javier Perales-Patón; Bruno Bockorny; Ana Dopazo; Manuel Muñoz; Natalia Baños; Victoria Bonilla; Camino Menendez; Yolanda Duran; Ling Huang; Sofia Perea; Senthil K Muthuswamy; Fatima Al-Shahrour; Pedro P Lopez-Casas; Manuel Hidalgo Journal: Mol Cancer Ther Date: 2020-06-04 Impact factor: 6.261
Authors: Ana Conesa; Pedro Madrigal; Sonia Tarazona; David Gomez-Cabrero; Alejandra Cervera; Andrew McPherson; Michal Wojciech Szcześniak; Daniel J Gaffney; Laura L Elo; Xuegong Zhang; Ali Mortazavi Journal: Genome Biol Date: 2016-08-26 Impact factor: 13.583
Authors: Naim Al Mahi; Mehdi Fazel Najafabadi; Marcin Pilarczyk; Michal Kouril; Mario Medvedovic Journal: Sci Rep Date: 2019-05-20 Impact factor: 4.379
Authors: Amber N Habowski; Jessica L Flesher; Jennifer M Bates; Chia-Feng Tsai; Kendall Martin; Rui Zhao; Anand K Ganesan; Robert A Edwards; Tujin Shi; H Steven Wiley; Yongsheng Shi; Klemens J Hertel; Marian L Waterman Journal: Commun Biol Date: 2020-08-19