| Autor: |
Maleana G. Khoury, Kenneth S. Berenhaut, Katherine E. Moore, Edward E. Allen, Alexandria F. Harkey, Joëlle K. Muhlemann, Courtney N. Craven, Jiayi Xu, Suchi S. Jain, David J. John, James L. Norris, Gloria K. Muday |
| Rok vydání: |
2022 |
| Popis: |
Transcriptome studies which provide temporal information can be valuable for identifying groups of similarly-behaving transcripts and provide insight into overarching gene regulatory networks. Nevertheless, inferring meaningful biological conclusions is challenging, in part because it is difficult to holistically consider both local relationships and global structure of these complex and overlapping transcriptional responses. To address this need, we employed the recently-developed method, Partitioned Local Depth (PaLD), which reveals community structure in large data sets, to examine four time-course transcriptomic data sets generated using the model plant Arabidopsis thaliana. As this is the first paper in systems biology to implement the PaLD approach, we provide a self-contained description of the method and show how it can be used to make predictions about gene regulatory networks based on temporal responses of transcripts. The analysis provides a global network representation of the data from which graph partitioning methods and neighborhood analysis can reveal smaller, more well-defined groups of like-responding transcripts. These groups of transcripts that change in response to hormone treatment (auxin and ethylene) and salt treatment were shown to be enriched in common biological function and/or binding of transcription factors that were not identified with prior analyses of this data using other clustering methods. These results reveal the potential of PaLD to predict gene regulatory networks within large transcriptomic data sets.Author SummaryThis paper applies a newly developed approach to identify community structure in distance-based data and applies it for the first time to time-course transcriptomic datasets. The application of Partitioned Local Depth analysis (PaLD) to four previously published datasets measuring transcript abundance changes over time in response to hormone or stress treatment identified unique features of these datasets. This method was used to build networks revealing relationships between transcripts based on temporal responses and to identify groups of transcripts with similar temporal responses. These groups were examined revealing enrichment in functional annotation and in targets of specific transcription factors that provided additional insight beyond commonly used clustering approaches. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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