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Degree of Freedom of Gene Expression in Saccharomyces cerevisiae.

Zhen YangFeng XuAijuan XueHong LvYungang He
Published in: Microbiology spectrum (2022)
The complexity of genome-wide gene expression has not yet been adequately addressed due to a lack of comprehensive statistical analyses. In the present study, we introduce degree of freedom (DOF) as a summary statistic for evaluating gene expression complexity. Because DOF can be interpreted by a state-space representation, application of the DOF is highly useful for understanding gene activities. We used over 11,000 gene expression data sets to reveal that the DOF of gene expression in Saccharomyces cerevisiae is not greater than 450. We further demonstrated that various degrees of freedom of gene expression can be interpreted by different sequence motifs within promoter regions and Gene Ontology (GO) terms. The well-known TATA box is the most significant one among the identified motifs, while the GO term "ribosome genesis" is an associated biological process. On the basis of transcriptional freedom, our findings suggest that the regulation of gene expression can be modeled using only a few state variables. IMPORTANCE Yeast works like a well-organized factory. Each of its components works in its own way, while affecting the activities of others. The order of all activities is largely governed by the regulation of gene expression. In recent decades, biologists have recognized many regulations for yeast genes. However, it is not known how closely the regulation links each gene together to make all components of the cell work as a whole. In other words, biologists are very interested in how many independent control factors are needed to operate an artificial "cell" that works the same as a real one. In this work, we suggested that only 450 control factors were sufficient to represent the regulation of all 5800 yeast genes.
Keyphrases
  • gene expression
  • genome wide
  • dna methylation
  • saccharomyces cerevisiae
  • copy number
  • single cell
  • preterm infants
  • bone marrow
  • stem cells
  • electronic health record
  • big data
  • binding protein
  • amino acid
  • neural network