Additional file 1 of Genome-wide analysis on the maize genome reveals weak selection on synonymous mutations
Creators: name: Chu, Duan
name: Wei, Lai
Publisher: name: figshare
Year: 2020
Subjects: subject: Biophysics
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Evolutionary Biology
subject: 69999 Biological Sciences not elsewhere classified
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences

description: Additional file 1: Figure S1. Inference of ancestral state of the mutations. For polymorphic mutations (called from the deep sequenced RNA-seq data) and fixed mutations (obtained from the CDS alignment) in maize, the ancestral state is inferred from the orthologous nucleotide in wheat and carrot. Only the sites with identical bases between wheat and carrot are used. Take the case (of polymorphic mutations) shown in the graph as an example, the orthologous sites in two outgroups are C, and the reference genome of maize is also C, while in the RNA-seq data, both C and another nucleotide are detected. Furthermore, any potential polymorphic sites in wheat or carrot (that are detectable in RNA-seq in wheat or carrot) are discarded. Figure S2. Locations of the CDS sequence files and RNA-seq data of the plant species used in this study and the basic statistics of the maize genome. Figure S3. Fixed to polymorphic ratios of different mutation categories. Highly expressed and lowly expressed genes are classified. These mutation categories include nonsynonymous and nonsense mutations as well as the synonymous mutations that increase or decrease the tAI. Figure S4. DAF and MAF spectrum of polymorphic mutations of different mutation categories. Highly expressed and lowly expressed genes are classified. These mutation categories include nonsynonymous and nonsense mutations as well as the synonymous mutations that increase or decrease the tAI. P values are calculated from Wilcoxon rank sum tests. Figure S5. Relationships between tAI, GC content and RNA folding. a Spearman correlation between tAI and GC content of genes. P value &lt;2e-16. b Spearman correlation between GC content and the percentage of RNA structured regions of genes. P value &lt;1e-3. c GC contents in versus out of RNA structured regions. P value is calculated from Fisher’s exact test. d Spearman correlation between tAI and the percentage of RNA structured regions of genes. P value &lt;1e-3. Figure S6. DAF and MAF spectrum of polymorphic mutations of different mutation categories. Genes with high or low GC contents are classified. These mutation categories include nonsynonymous and nonsense mutations as well as the synonymous mutations that increase or decrease the tAI. P values are calculated from Wilcoxon rank sum tests. Figure S7. Distance (bp) between the nearby “tAI-up” and “tAI-down” synonymous mutations within each gene. Each dot represents a gene. These genes are ranked by the median distance of the “tAI-up” and “tAI-down” mutations within them. Error bar represents 25 to 75% quantile. Figure S8. DAF and MAF spectrum of different mutation sets. For a “tAI-up” mutation, if its nearest mutation is also a “tAI-up” mutation, we name it “up near up”. If its nearest mutation is a “tAI-down” mutation, we name it “up near down”. Similarly, we define “down near up” and “down near down” mutations. No significant difference is detected using Wilcoxon rank sum test.