Biomedical subjects

Ziliang Qian

Publications and source records attributed to Ziliang Qian.

3 recordsLinked to original sources

PubMed2006

A novel computational method to predict transcription factor DNA binding preference.

Transcription factor binds to sequence specific sites in regulatory region to control nearby gene's expression. It is termed as the major regulator of transcription. However, identifying DNA binding preference of transcription factors systematically is still a challenge. By using the nearest neighbor algorithm, a novel computational approach was developed to predict transcription factor DNA binding preference based on the gene ontology [M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K. Dolinski, S.S. Dwight, J.T. Eppig, M.A. Harris, D.P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J.C. Matese, J.E. Richardson, M. Ringwald, G.M. Rubin, G. Sherlock, Gene Ontology: tool for the unification of biology, Nat. Genet. 25 (2000) 25-29.] and 0/1 encoding system of nucleotide. The overall success rate of Jackknife cross-validation test for our predictor reaches 76.6%, which indicates the DNA binding preference is closely correlated with its biological functions and computational method developed in this contribution could be a powerful tool to investigate transcription factor DNA binding preference, especially for those novel transcription factors with little prior knowledge on its DNA binding preference.

Algorithms↗

PubMed2006

Automatic transcription factor classifier based on functional domain composition.

To understand the transcriptional regulatory mechanism, it is indispensable to identify transcription factors (TF) from the whole genome and to classify transcription factors into different classes. New computational approaches have been developed to identify TFs/non-TFs, and furthermore to classify TFs into four different classes, based on the protein functional domain composition [K.C. Chou, Y.D. Cai, Using functional domain composition and support vector machines for prediction of protein subcellular location, J. Biol. Chem. 277 (2002) 45765-45769]. We trained and tested our method on a non-redundancy dataset consisting of 74 transcription factors collected from TRANSFAC v7.0 [V. Matys, O.V. Kel-Margoulis, E. Fricke, I. Liebich, S. Land, A. Barre-Dirrie, I. Reuter, D. Chekmenev, M. Krull, K. Hornischer, N. Voss, P. Stegmaier, B. Lewicki-Potapov, H. Saxel, A.E. Kel, E. Wingender, TRANSFAC(R) and its module TRANSCompel(R): transcriptional gene regulation in eukaryotes, Nucleic Acids Res. 34 (2006) D108-D110] and 1558 non-transcription factors from UniProtKB/Swiss-Prot Release 49.3 of 21-Mar-2006. The overall success rates of jackknife cross-validation tests reached 98.4% for TF/non-TF identification and 97.2% for classifications of TF classes: basic domains, zinc-coordinating DNA-binding domains, helix-turn-helix, and beta-scaffold factors.

Artificial Intelligence↗

PubMed2006

Genomic characterization of ribitol teichoic acid synthesis in Staphylococcus aureus: genes, genomic organization and gene duplication.

BACKGROUND: Staphylococcus aureus or MRSA (Methicillin Resistant S. aureus), is an acquired pathogen and the primary cause of nosocomial infections worldwide. In S. aureus, teichoic acid is an essential component of the cell wall, and its biosynthesis is not yet well characterized. Studies in Bacillus subtilis have discovered two different pathways of teichoic acid biosynthesis, in two strains W23 and 168 respectively, namely teichoic acid ribitol (tar) and teichoic acid glycerol (tag). The genes involved in these two pathways are also characterized, tarA, tarB, tarD, tarI, tarJ, tarK, tarL for the tar pathway, and tagA, tagB, tagD, tagE, tagF for the tag pathway. With the genome sequences of several MRSA strains: Mu50, MW2, N315, MRSA252, COL as well as methicillin susceptible strain MSSA476 available, a comparative genomic analysis was performed to characterize teichoic acid biosynthesis in these S. aureus strains. RESULTS: We identified all S. aureus tar and tag gene orthologs in the selected S. aureus strains which would contribute to teichoic acids sythesis. Based on our identification of genes orthologous to tarI, tarJ, tarL, which are specific to tar pathway in B. subtilis W23, we also concluded that tar is the major teichoic acid biogenesis pathway in S. aureus. Further analyses indicated that the S. aureus tar genes, different from the divergon organization in B. subtilis, are organized into several clusters in cis. Most interesting, compared with genes in B. subtilis tar pathway, the S. aureus tar specific genes (tarI,J,L) are duplicated in all six S. aureus genomes. CONCLUSION: In the S. aureus strains we analyzed, tar (teichoic acid ribitol) is the main teichoic acid biogenesis pathway. The tar genes are organized into several genomic groups in cis and the genes specific to tar (relative to tag): tarI, tarJ, tarL are duplicated. The genomic organization of the S. aureus tar pathway suggests their regulations are different when compared to B. subtilis tar or tag pathway, which are grouped in two operons in a divergon structure.

Amino Acid Sequence↗