Using phylogeny to improve genome-wide distant homology recognition

Characterization of rubber tree microRNA in phytohormone response using large genomic DNA libraries, promoter sequence and gene expression analysis

The para rubber tree is the most widely cultivated tree species for producing natural rubber (NR) latex. Unfortunately, rubber tree characteristics such as a long life cycle, heterozygous genetic backgrounds, and poorly understood genetic profiles are the obstacles to breeding new rubber tree varieties, such as those with improved NR yields. Recent evidence has revealed the potential importance of controlling microRNA (miRNA) decay in some aspects of NR regulation. To gain a better understanding of miRNAs and their relationship with rubber tree gene regulation networks, large genomic DNA insert-containing libraries were generated to complement the incomplete draft genome sequence and applied as a new powerful tool to predict a function of interested genes. Bacterial artificial chromosome and fosmid libraries, containing a total of 120,576 clones with an average insert size of 43.35 kb, provided approximately 2.42 haploid genome equivalents of coverage based on the estimated 2.15 gb rubber tree genome. Based on these library sequences, the precursors of 1 member of rubber tree-specific miRNAs and 12 members of conserved miRNAs were successfully identified. A panel of miRNAs was characterized for phytohormone response by precisely identifying phytohormone-responsive motifs in their promoter sequences. Furthermore, the quantitative real-time PCR on ethylene stimulation of rubber trees was performed to demonstrate that the miR2118, miR159, miR164 and miR166 are responsive to ethylene, thus confirmed the prediction by genomic DNA analysis. The cis-regulatory elements identified in the promoter regions of these miRNA genes help augment our understanding of miRNA gene regulation and provide a foundation for further investigation of the regulation of rubber tree miRNAs.

E value cutoff and eukaryotic genome content phylogenetics

Genome content analysis has been used as a source of phylogenetic information in large prokaryotic tree of life studies. Recently the sequencing of many eukaryotic genomes has allowed for the similar use of genome content analysis for these organisms too. In this communication we examine the utility of genome content analysis for recovering phylogenetic patterns in several eukaryotic groups. By constructing multiple matrices using different e value cutoffs we examine the dynamics of altering the e value cutoff on five eukaryotic genome data sets. Our analysis indicates that the e value cutoff that is used as a criterion in the construction of the genome content matrix is a critical factor in both the accuracy and information content of the analysis. Strikingly, genome content by itself is not a reliable or accurate source of characters for phylogenetic analysis of the taxa in the five data sets we analyzed. We discuss two problems--small genome attraction and genome duplications as being involved in the rather poor performance of genome content data in recovering eukaryotic phylogeny.

Integrating overlapping structures and background information of words significantly improves biological sequence comparison

Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models.

Protein sequence comparison and fold recognition: progress and good-practice benchmarking

Protein sequence comparison methods have grown increasingly sensitive during the last decade and can often identify distantly related proteins sharing a common ancestor some 3 billion years ago. Although cellular function is not conserved so long, molecular functions and structures of protein domains often are. In combination with a domain-centered approach to function and structure prediction, modern remote homology detection methods have a great and largely underexploited potential for elucidating protein functions and evolution. Advances during the last few years include nonlinear scoring functions combining various sequence features, the use of sequence context information, and powerful new software packages. Since progress depends on realistically assessing new and existing methods and published benchmarks are often hard to compare, we propose 10 rules of good-practice benchmarking.