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Abstract Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High‐throughput technologies for assessment and quantification of genome‐wide gene expression patterns have enabled systems‐level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information‐rich RNA‐Seq datasets of the model plantArabidopsis thalianato construct a gene co‐expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co‐expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition inArabidopsisonto the co‐expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the firstA. thalianaco‐expression network constructed using the entire mRNA‐Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for theArabidopsisresearch community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.
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Combining spectral clustering and large cut algorithms to find compensatory functional modules from yeast physical and genetic interaction data with GLASS
Various algorithmic and statistical approaches have been proposed to uncover functionally coherent network motifs consisting of sets of genes that may occur as compensatory pathways (called Between Pathway Modules, or BPMs) in a high-throughput S. Cerevisiae genetic interaction network. We extend our previous Local-Cut/Genecentric method to also make use of a spectral clustering of the physical interaction network, and uncover some interesting new fault-tolerant modules.
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- Award ID(s):
- 1934553
- PAR ID:
- 10350061
- Date Published:
- Journal Name:
- BCB '22: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3535508.3545509
