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Jinlin Xu

Publications and source records attributed to Jinlin Xu.

2 recordsLinked to original sources

Relationships among stop codon usage bias, its context, isochores, and gene expression level in various eukaryotes.

It is well known that stop codons play a critical role in the process of protein synthesis. However, little effort has been made to investigate whether stop codon usage exhibits biases, such as widely seen for synonymous codon usage. Here we systematically investigate stop codon usage bias in various eukaryotes as well as its relationships with its context, GC3 content, gene expression level, and secondary structure. The results show that there is a strong bias for stop codon usage in different eukaryotes, i.e., UAA is overrepresented in the lower eukaryotes, UGA is overrepresented in the higher eukaryotes, and UAG is least used in all eukaryotes. Different conserved patterns for each stop codon in different eukaryotic classes are found based on information content and logo analysis. GC3 contents increase with increasing complexity of organisms. Secondary structure prediction revealed that UAA is generally associated with loop structures, whereas UGA is more uniformly present in loop and stem structures, i.e., UGA is less biased toward having a particular structure. The stop codon usage bias, however, shows no significant relationship with GC3 content and gene expression level in individual eukaryotes. The results indicate that genomic complexity and GC3 content might contribute to stop codon usage bias in different eukaryotes. Our results indicate that stop codons, like synonymous codons, exhibit biases in usage. Additional work will be needed to understand the causes of these biases and their relationship to the mechanism of protein termination.

Base Sequence↗

Refined phylogenetic profiles method for predicting protein-protein interactions.

MOTIVATION: The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method. RESULTS: In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.

Algorithms↗