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author:("Yin, yanbian")
1.  Bi-Factor Analysis Based on Noise-Reduction (BIFANR): A New Algorithm for Detecting Coevolving Amino Acid Sites in Proteins 
PLoS ONE  2013;8(11):e79764.
Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.
doi:10.1371/journal.pone.0079764
PMCID: PMC3835919  PMID: 24278175
2.  Computational analyses of transcriptomic data reveal the dynamic organization of the Escherichia coli chromosome under different conditions 
Nucleic Acids Research  2013;41(11):5594-5603.
The circular chromosome of Escherichia coli has been suggested to fold into a collection of sequentially consecutive domains, genes in each of which tend to be co-expressed. It has also been suggested that such domains, forming a partition of the genome, are dynamic with respect to the physiological conditions. However, little is known about which DNA segments of the E. coli genome form these domains and what determines the boundaries of these domain segments. We present a computational model here to partition the circular genome into consecutive segments, theoretically suggestive of the physically folded supercoiled domains, along with a method for predicting such domains under specified conditions. Our model is based on a hypothesis that the genome of E. coli is partitioned into a set of folding domains so that the total number of unfoldings of these domains in the folded chromosome is minimized, where a domain is unfolded when a biological pathway, consisting of genes encoded in this DNA segment, is being activated transcriptionally. Based on this hypothesis, we have predicted seven distinct sets of such domains along the E. coli genome for seven physiological conditions, namely exponential growth, stationary growth, anaerobiosis, heat shock, oxidative stress, nitrogen limitation and SOS responses. These predicted folding domains are highly stable statistically and are generally consistent with the experimental data of DNA binding sites of the nucleoid-associated proteins that assist the folding of these domains, as well as genome-scale protein occupancy profiles, hence supporting our proposed model. Our study established for the first time a strong link between a folded E. coli chromosomal structure and the encoded biological pathways and their activation frequencies.
doi:10.1093/nar/gkt261
PMCID: PMC3675479  PMID: 23599001
3.  Integration of sequence-similarity and functional association information can overcome intrinsic problems in orthology mapping across bacterial genomes 
Nucleic Acids Research  2011;39(22):e150.
Existing methods for orthologous gene mapping suffer from two general problems: (i) they are computationally too slow and their results are difficult to interpret for automated large-scale applications when based on phylogenetic analyses; or (ii) they are too prone to making mistakes in dealing with complex situations involving horizontal gene transfers and gene fusion due to the lack of a sound basis when based on sequence similarity information. We present a novel algorithm, Global Optimization Strategy (GOST), for orthologous gene mapping through combining sequence similarity and contextual (working partners) information, using a combinatorial optimization framework. Genome-scale applications of GOST show substantial improvements over the predictions by three popular sequence similarity-based orthology mapping programs. Our analysis indicates that our algorithm overcomes the intrinsic issues faced by sequence similarity-based methods, when orthology mapping involves gene fusions and horizontal gene transfers. Our program runs as efficiently as the most efficient sequence similarity-based algorithm in the public domain. GOST is freely downloadable at http://csbl.bmb.uga.edu/~maqin/GOST.
doi:10.1093/nar/gkr766
PMCID: PMC3239196  PMID: 21965536

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