- Sing-Hoi Sze
- Associate Professor of Computer Science and Engineering
- HRBB / Room 328B
- Undergraduate Education
- B.Sc. Chinese University of Hong Kong (1990)
- Graduate Education
- M.S. Pennsylvania State University (1995)
- Ph.D. University of Southern California (2000)
- Postdoc. University of California-San Diego (2001-02)
- Joined Texas A&M in 2002
Bioinformatics / Computational Biology
Our work focuses on the application of various computer science techniques to solve computational problems in molecular biology. Our current research projects cover diverse areas in bioinformatics, including motif finding algorithms and their applications, computational approaches to model transcription factor binding sites, and algorithms for EST sequence assembly and enumeration of alternatively spliced variants of a gene.
The motif finding problem can be formulated as follows: given a set of sequences, find a pattern (motif) shared by these sequences. The major biological application of this computational problem is to identify transcription factor binding sites given a set of upstream sequences of genes that are believed to be co-regulated. Existing motif finding approaches usually make simplifying assumptions in modeling these sites and we are on a constant quest to develop better models. Recently, we work with a few groups of biologists on designing experiments to verify our predictions.
Another active research project is the identification of alternatively spliced variants of a gene from EST sequences. The traditional approach to this problem is to assemble EST sequences that represent fragments of a gene into a longer linear sequence which represents the most dominant form of the gene. In order to better model the splicing structure, we develop an algorithm to assemble the given set of EST sequences into a non- linear graph structure, so that each alternatively spliced variant of a gene is represented as a path in the graph.
Qiu, C, Erinne, OC, Dave, JM, Cui, P, Jin, H, Muthukrishnan, N et al.. High-Resolution Phenotypic Landscape of the RNA Polymerase II Trigger Loop. PLoS Genet. 2016;12 (11):e1006321.
Yuan, Y, Zhang, Y, Fu, S, Crippen, TL, Visi, DK, Benbow, ME et al.. Genome Sequence of a Proteus mirabilis Strain Isolated from the Salivary Glands of Larval Lucilia sericata. Genome Announc. 2016;4 (4):.
Fu, S, Tarone, AM, Sze, SH. Heuristic pairwise alignment of de Bruijn graphs to facilitate simultaneous transcript discovery in related organisms from RNA-Seq data. BMC Genomics. 2015;16 Suppl 11 :S5.
Edman, RM, Linger, RJ, Belikoff, EJ, Li, F, Sze, SH, Tarone, AM et al.. Functional characterization of calliphorid cell death genes and cellularization gene promoters for controlling gene expression and cell viability in early embryos. Insect Mol. Biol. 2015;24 (1):58-70.
Sze, SH, Tarone, AM. A memory-efficient algorithm to obtain splicing graphs and de novo expression estimates from de Bruijn graphs of RNA-Seq data. BMC Genomics. 2014;15 Suppl 5 :S6.
Radulović, ŽM, Kim, TK, Porter, LM, Sze, SH, Lewis, L, Mulenga, A et al.. A 24-48 h fed Amblyomma americanum tick saliva immuno-proteome. BMC Genomics. 2014;15 :518.
Hsieh, MF, Sze, SH. Finding alignments of conserved graphlets in protein interaction networks. J. Comput. Biol. 2014;21 (3):234-46.
Fan, JH, Chen, J, Sze, SH. Identifying complexes from protein interaction networks according to different types of neighborhood density. J. Comput. Biol. 2012;19 (12):1284-94.
Yi, G, Thon, MR, Sze, SH. Supervised protein family classification and new family construction. J. Comput. Biol. 2012;19 (8):957-67.
Sze, SH, Dunham, JP, Carey, B, Chang, PL, Li, F, Edman, RM et al.. A de novo transcriptome assembly of Lucilia sericata (Diptera: Calliphoridae) with predicted alternative splices, single nucleotide polymorphisms and transcript expression estimates. Insect Mol. Biol. 2012;21 (2):205-21.