Search Results (3)

Mutations that provide environment-dependent selective advantages drive adaptive divergence among species. Many phenotypic differences among related species are more likely to result from gene expression divergence rather than from non-synonymous mutations. In this regard, cis-regulatory mutations play an important part in generating functionally significant variation. Some proposed mechanisms that explore the role of cis-regulatory mutations in gene expression divergence involve microsatellites. Microsatellites exhibit high mutation rates achieved through symmetric or asymmetric mutation processes and are abundant in both coding and non-coding regions in positions that could influence gene function and products. Here we tested the hypothesis that microsatellites contribute to gene expression divergence among species with 50 individuals from five closely related Helianthus species using an RNA-seq approach. Differential expression analyses of the transcriptomes revealed that genes containing microsatellites in non-coding regions (UTRs and introns) are more likely to be differentially expressed among species when compared to genes with microsatellites in the coding regions and transcripts lacking microsatellites. We detected a greater proportion of shared microsatellites in 5′UTRs and coding regions compared to 3′UTRs and non-coding transcripts among Helianthus spp. Furthermore, allele frequency differences measured by pairwise F_ST at single nucleotide polymorphisms (SNPs), indicate greater genetic divergence in transcripts containing microsatellites compared to those lacking microsatellites. A gene ontology (GO) analysis revealed that microsatellite-containing differentially expressed genes are significantly enriched for GO terms associated with regulation of transcription and transcription factor activity. Collectively, our study provides compelling evidence to support the role of microsatellites in gene expression divergence. Full article

The rapid developments in high-throughput sequencing technologies have allowed researchers to analyze the full genomic sequence of organisms faster and cheaper than ever before. An important application of such advancements is to identify the impact of single nucleotide polymorphisms (SNPs) on the phenotypes and genotypes of the same species by discovering the factors that affect the occurrence of SNPs. The focus of this study is to determine whether climate factors such as the main climate, the precipitation, and the temperature affecting a certain geographical area might be associated with specific variations in certain ecotypes of the plant Arabidopsis thaliana. To test our hypothesis we analyzed 18 genes that encode Forkhead-Associated domain-containing proteins. They were extracted from 80 genomic sequences gathered from within 8 Eurasian regions. We used k-means clustering to separate the plants into distinct groups and evaluated the clusters using an innovative scoring system based upon the Köppen-Geiger climate classification system. The methods we used allow the selection of candidate clusters most likely to contain samples with similar polymorphisms. These clusters show that there is a correlation between genomic variations and the geographic distribution of those ecotypes. Full article

Ensemble clustering is a promising approach that combines the results of multiple clustering algorithms to obtain a consensus partition by merging different partitions based upon well-defined rules. In this study, we use an ensemble clustering approach for merging the results of five different clustering algorithms that are sometimes used in bioinformatics applications. The ensemble clustering result is tested on microarray data sets and compared with the results of the individual algorithms. An external cluster validation index, adjusted rand index (C-rand), and two internal cluster validation indices; silhouette, and modularity are used for comparison purposes. Full article