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Sin Lam Tan

dblp:55/5548 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene regulation
0.112006
Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures · Bioinform. 2006
Bioinformatics and computational biology › gene regulation › promoter analysis
promoter modeling
0.112006
Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures · Bioinform. 2006
Bioinformatics and computational biology › gene regulation
cis-regulatory element analysis
0.012006
Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures · Bioinform. 2006

Methods — techniques the papers use, named apart from their topics

motif feature analysis · 0.1bayesian network · 0.1
YearPublicationVenuePosition
2006 Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures
abstract
UNLABELLED: Dragon Promoter Mapper (DPM) is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers. AVAILABILITY: http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Manual for using DPM web server is provided at http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/html/manual/manual.htm.
Rajesh Chowdhary, Sin Lam Tan, R. Ayesha Ali, Brent Boerlage, Limsoon Wong, Vladimir B. Bajic
Bioinform.2
2006 Computational promoter analysis of mouse, rat and human antimicrobial peptide-coding genes
abstract
BACKGROUND: Mammalian antimicrobial peptides (AMPs) are effectors of the innate immune response. A multitude of signals coming from pathways of mammalian pathogen/pattern recognition receptors and other proteins affect the expression of AMP-coding genes (AMPcgs). For many AMPcgs the promoter elements and transcription factors that control their tissue cell-specific expression have yet to be fully identified and characterized. RESULTS: Based upon the RIKEN full-length cDNA and public sequence data derived from human, mouse and rat, we identified 178 candidate AMP transcripts derived from 61 genes belonging to 29 AMP families. However, only for 31 mouse genes belonging to 22 AMP families we were able to determine true orthologous relationships with 30 human and 15 rat sequences. We screened the promoter regions of AMPcgs in the three species for motifs by an ab initio motif finding method and analyzed the derived promoter characteristics. Promoter models were developed for alpha-defensins, penk and zap AMP families. The results suggest a core set of transcription factors (TFs) that regulate the transcription of AMPcg families in mouse, rat and human. The three most frequent core TFs groups include liver-, nervous system-specific and nuclear hormone receptors (NHRs). Out of 440 motifs analyzed, we found that three represent potentially novel TF-binding motifs enriched in promoters of AMPcgs, while the other four motifs appear to be species-specific. CONCLUSION: Our large-scale computational analysis of promoters of 22 families of AMPcgs across three mammalian species suggests that their key transcriptional regulators are likely to be TFs of the liver-, nervous system-specific and NHR groups. The computationally inferred promoter elements and potential TF binding motifs provide a rich resource for targeted experimental validation of TF binding and signaling studies that aim at the regulation of mouse, rat or human AMPcgs.
Manisha Brahmachary, Christian Schönbach, Enli Huang, Sin Lam Tan, Rajesh Chowdhary, S. P. T. Krishnan, Chin-Yo Lin, David A. Hume, Chikatoshi Kai, Jun Kawai, Piero Carninci, Yoshihide Hayashizaki, Vladimir B. Bajic
BMC Bioinform.5
2005 Comparison of core promoters in Fugu rubripes and human
Sin Lam Tan, Vidhu Choudhary, Alan Christoffels, Byrappa Venkatesh, Vladimir B. Bajic
APBC1