Long-RangePeriodicPatternsinMicrobialGenomesIndicate长距离的微生物基因组中周期模式显示.ppt

1、Long-Range Periodic Patterns in MicrobialGenomes Indicate Significant Multi-ScaleChromosomal Organization(Allen et. al., UCSD),presented by Suvrajit Maji,Background Materials/Method Results & Discussion Summary and Conclusion,Background,Genes in bacterial nucleoids are arranged along the long axis o

2、f the cell , to preserve the linear order of the genes along the chromosome. Wavelet analysis has been used to detect such long-range periodic patterns in inherently noisy chromosome position-dependent data Gene orientation DNA-bending profiles, and gene expression data in prokaryotes, GC/AT skew os

3、cillations in human chromosomes were studied.,Approach for Detecting Genome Position-Dependent Patterns,Materials/Method,Chromosome position-dependent data were analyzed for 151 prokaryotic organisms (163 chromosomes in 16 archaeal and 135 bacterial organisms): 1) GC/AT content averaged in kilobase

4、bins, 2) gene orientation (i.e., strand), 3) fractional gene density (number or fractions of genesper kilobase), 4) codon adaptation index (CAI) per gene. CAI measures the tendency of one of the available codons to be preferentially used for coding a specific amino acid residue.,The filter function

5、used in this study was the Morlet wavelet, defined as,Ordered data - f(x) x - nucleotide position along the chromosome. g - family of filter functions W - transform value a - filter widths (scales),Scalogram is the plot of the sq. of the magnitude of transform value :,More on Wavelets ,Wavelet analy

6、sis is applied to time series for which traditional methods (e.g Fourier Transform ) appear unsatisfactory. Wavelet analysis does for unsteady and/or intermittent systems what FT has done for multi-periodic systems. FT has no time resolution , not very useful on non-stationary signals. Localization

7、in both time and frequency,More on Wavelets ,complex wavelet , decomposed in two parts :,The wavelet defined by Morlet is:,Descriptive Statistics for Pattern Strengths in GC/AT Content, Gene Density, and CAI across 163 Prokaryotic Chromosomes,Results & Discussions,The high SDs indicate that signific

8、ant chromosome position-dependent patterns vary extensively for different organisms,Pattern Strengths of Sequenced Prokaryotic Organisms,Generality of Chromosome Position-Dependent Patterns in Sequence Properties for 163 Prokaryotic Chromosomes,Chromosomes containing the strongest and weakest patter

9、ns for each parameter, and the scalograms corresponding to the strongest patterns are indicated in the left column.,The relative lack of patterning in gene density is a result of the low positional variability due to the short intergenic regions found in the generally gene-dense prokaryotic organism

10、s,Organisms Exhibiting Either very High or very Low Chromosome Position-Dependent Patterns in Sequence-Derived Data,Correlation of Pattern Strengths to Organism-Specific Properties,Correlations between Sequence-Derived Properties for 163 Prokaryotic Chromosomes,Correlation between Pattern Strength i

11、n CAI and Organism Taxon, Gram Staining, Cell Shape, and the Presence of Known Motility and Nucleoid Proteins,Correlation of Specific Chromosome Position-Dependent Patterns in E. coli Functional Properties,Overlay Plots of Significant Regions of Wavelet Scalograms for Various E. coli Parameters,Comp

12、arison of E. coli Gene Expression, Essentiality, and Evolutionary Retention at 600650-kb Length Scale with Experimentally Identified Chromosome Macrodomains,Summary and Conclusions,presence of macro-domains in 163 prokaryotic chromosomes which vary in properties such as GC content, CAI, and gene density. 1. strong correlation between the degree of patterning and genome size, GC content. 2. evolutionary retention index, essentiality, and gene expression exhibit periodic pattern at the scale of 650kb leng