Toward global integration of biodiversity big data: a harmonized metabarcode data generation module for terrestrial arthropods.
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ABSTRACT:
SUBMITTER: Arribas P
PROVIDER: S-EPMC9295367 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.Highlights
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Project description:BACKGROUND:The immense geologic and ecological complexity of the Caribbean has created a natural laboratory for interpreting when and how organisms disperse through time and space. However, competing hypotheses compounded with this complexity have resulted in a lack of unifying principles of biogeography for the region. Though new data concerning the timing of geologic events and dispersal events are emerging, powerful new analytical tools now allow for explicit hypothesis testing. Arthropods, with varying dispersal ability and high levels of endemism in the Caribbean, are an important, albeit understudied, biogeographic model system. Herein, we include a comprehensive analysis of every publicly available genetic dataset (at the time of writing) of terrestrial Caribbean arthropod groups using a statistically robust pipeline to explicitly test the current extent of biogeographic hypotheses for the region. RESULTS:Our findings indicate several important biogeographic generalizations for the region: the South American continent is the predominant origin of Caribbean arthropod fauna; GAARlandia played a role for some taxa in aiding dispersal from South America to the Greater Antilles; founder event dispersal explains the majority of dispersal events by terrestrial arthropods, and distance between landmasses is important for dispersal; most dispersal events occurred via island hopping; there is evidence of 'reverse' dispersal from islands to the mainland; dispersal across the present-day Isthmus of Panama generally occurred prior to 3 mya; the Greater Antilles harbor more lineage diversity than the Lesser Antilles, and the larger Greater Antilles typically have greater lineage diversity than the smaller islands; basal Caribbean taxa are primarily distributed in the Greater Antilles, the basal-most being from Cuba, and derived taxa are mostly distributed in the Lesser Antilles; Jamaican taxa are usually endemic and monophyletic. CONCLUSIONS:Given the diversity and deep history of terrestrial arthropods, incongruence of biogeographic patterns is expected, but focusing on both similarities and differences among divergent taxa with disparate life histories emphasizes the importance of particular qualities responsible for resulting diversification patterns. Furthermore, this study provides an analytical toolkit that can be used to guide researchers interested in answering questions pertaining to Caribbean biogeography using explicit hypothesis testing.
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