Abstracts Keynote & Plenary

More documents

Recommendations

Info

PO-019 Evaluating Post-translational Modification Identification by InspecT Hong Li1,2, Sujun Li2, Qingrun Li2, Rong Zeng2, Yu Shyr3, Lu Xie1§ 1. Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai 200235, P.R.China 2. Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, P.R. China er, Nashville, TN, USA c.cn SJL: sjli@sibs.ac.cn QRL: qrli@sibs.ac.cn g scale is important, due to the slational modifications, InspecT, evaluation, proteomics, machine learning form microarray data integration combining meta-analysis andgene set enrichment 2, 1*, Jian Yu Miaoxin Li 1, Yajun Yi 3 , Yu Shyr 3 , Yixue Li 1 , 2 § , Lu Xie1 § 3. Cancer Biostatistics Center, Vanderbilt-Ingram Cancer Cent §Corresponding author: xielu@scbit.org Email addresses: HL: lihong@sibs.a RZ: zr@sibs.ac.cn YS: yu.shyr@vanderbilt.edu LX: xielu@scbit.or Understanding post-translational modifications (PTMs) on a proteomic universal and complex functions of PTMs; however, reliable and unrestrictive PTM identification is still one of the biggest challenges in proteomics. InspecT is an algorithm with a broad range of applications in the identification of PTMs, especially in unrestrictive searching of PTMs. In this paper, we propose a strategy for evaluating the PTM identification results of InspecT. We employed three evaluation methods (false discovery rate, principal component analysis, and support vector machine) on three InspecT search types (unmodified peptides, phosphorylation peptides, and unrestrictive PTM searching). The proposed evaluation strategy has been implemented as a web server for InspecT users (http://www.biosino.org/Validation/). Similar approaches can be used to evaluate PTM identification by other algorithms. Key Words: post-tran PO-020 Cross-plat analysis Jun Wu1 , USA * , 1. Shanghai Center for Bioinformation Technology, 200235 Shanghai, China 2. College of Life Science, Tongji University, 200092 Shanghai, China 3. Cancer Biostatistics Center, Vanderbilt University, 37232 Nashville, TN *Jun Wu and Jian Yu contributed equally to this work. §Correspondence to: yxli@scbit.org, or xielu@scbit.org Email addresses: Jun Wu: wujun@scbit.org Jian Yu: yujian@scbit.org Miaoxin Li: limx54@yahoo.com Yajun Yi: andrew.yi@vanderbilt.edu Yixue Li: yxli@scbit.org Lu Xie: xielu@scbit.org Integrative analysis of microarray data has always been both fascinating and challenging. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to the pathway level; however, GSEA does not allow for integrating multiple original microarray datasets. The objective of this study is to construct an integrative analysis approach to extract consistent expression pattern change data from multiple microarray datasets at the pathway level. In this article, two pipelines were developed. Pipeline I, combining meta-analysis and gene set enrichment analysis, was established to integrate data from similar microarray platforms. For
analysis across different platforms, we modified the algorithm from an advanced version of GSEA, analysis of sample set enrichment scores (ASSESS). The modified algorithm (ASSESS’) was linked to meta-analysis to form Pipeline II for the integration of data from different microarray platforms. Pipeline I was able to identify 20 changed pathways that could not be identified by GSEA in single breast cancer datasets; one pathway was validated in another breast cancer dataset. Pipeline II was able to identify six changed pathways that could not be found based on combining the GSEA analyses of single liver cancer dataset. Our work provides simple and effective strategies to integrate multiple gene expression profiles for similar research objects at the pathway level, which allows pathway ranking analysis based on utilizing a larger sample size. It may also allow biological system overview based on statistical integration of multiple gene expression studies. A web server for Pipeline I is available at: http://lifecenter.sgst.cn/pipeline1/home.htm PO-021 Xie, Lu PO-022 Sequence-discrimination by PspGI Keith D. Lunnen, Elizabeth Lang, and Geoffrey G. Wilson New England Biolabs, Inc., 240 County Road, Ipswich, MA 01938, USA PspGI is a thermostable restriction enzyme from the archaeon Pyrococcus [1]. PspGI recognizes the sequence CCWGG in double‐strand DNA, and cleaves before the first C in each strand to produce fragments with 5‐base, 5’‐overhangs. The crystal structure of PspGI bound to its recognition sequence has been solved [2]. The enzyme acts as a homodimer, encircling the DNA and contacting the base pairs in both the major‐ and the minorgrooves. We are investigating the molecular mechanism by which PspGI discriminates the central base pair of its recognition sequence. From the crystal structure, discrimination of the C:G base pairs occurs by majorgroove contacts with the invariant amino acids Arg164, Glu165, and Arg166 of each subunit, but discrimination of the central base pair—which can be either A:T or T:A (i.e. W:W) but not G:C or C:G (i.e. not S:S)—is unclear and remains the subject of experimentation [3]. We tested whether this discrimination occurs passively, by steric clashes in the minor groove. A:T and T:A base pairs are smaller than G:C and C:G base pairs in the minor groove due to the presence of the 2‐amino group that protrudes from the ring of Guanine but not from Adenine. If the space in the DNA‐binding site needed to accommodate this group were occupied by an amino acid instead, then G:C and C:G would not fit into the site, whereas A:T and T:A would fit. From the crystal structure, two amino acids that might clash with Guanine in this way were identified: Tyr67 and Phe97. Each was changed to other amino acids by mutagenesis, and the resulting variant enzymes were assayed to see if any now cleaved CCSGG in addition to CCWGG. All of the catalytically active Phe97 mutants examined to date continue to cleave only CCWGG, suggesting that Phe97 is not involved in sequencediscrimination.The Tyr67 mutants also continue to cleave only CCWGG, but many now appear to bind—although not to cleave—CCSGG as well,suggesting that Tyr67 is at leasta factor in sequence-discrimination. [1] Morgan, R., Xiao, J.-P. and Xu, S.-Y., Appl. Environ. Microbiol. 1998, 64, 3669. [2] Szczepanowski, R.H., Carpenter, M.A., Czapinska, H., Zaremba, M., Tamulaitis, G., Siksnys, V., Bhagwat, A.S. and Bochtler, M., Nucleic Acids Res. 2008, 36, 6109. [3] Tamulaitis, G., Zaremba, M., Szczepanowski, R.H., Bochtler, M. and Siksnys, V., Nucleic Acids Res. 2008, 36, 6101.
Page 1 and 2: Abstracts Keynote & Plenary KN-001
Page 3 and 4: The nonlinear dynamical interaction
Page 5 and 6: Lim., Carmay Our research interests
Page 7 and 8: Email: jtus@phys.ualberta.caBackgro
Page 9 and 10: Formula drug or nutrient Any LMN is
Page 11 and 12: present study provide support to th
Page 13 and 14: Keywords: Amino acid index, non-cla
Page 15 and 16: mainly stabilized by the H-bonds fr
Page 17 and 18: generalized by Glazier and Graner [
Page 19 and 20: Profile-profile alignment may be th
Page 21 and 22: natural structure is coded in its a
Page 23 and 24: information will be presented. Assi
Page 25 and 26: Seasonal trade-off between water- a
Page 27 and 28: fluorescent protein ng, W.-D. Cheng
Page 29 and 30: 2. Email: xpgao@xtu.edu.cn Classifi
Page 31: * To whom correspondence should be
Page 35 and 36: 1.D. Q. Wei and G. N. Patey, Phys.
Page 37 and 38: 2. Göteborg University, Department
Page 39 and 40: Pushkal Samadhiya and S.K. Srivasta
Page 41 and 42: conformations may convert to each o
Page 43 and 44: and imidopropyl with 1,2,3-benzotri

Abstracts Keynote & Plenary

Create successful ePaper yourself

Delete template?

Save as template?