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Allen Chong

Publications and source records attributed to Allen Chong.

8 recordsLinked to original sources
PubMed2006

Reverse transcriptase template switching and false alternative transcripts.

Reverse transcriptase (RT) can switch from one template to another in a homology-dependent manner. In the study of eukaryotic transcripts, this propensity of RT can produce an artificially deleted cDNA, which can be wrongly interpreted as an alternative transcript. Here, we have investigated the presence of such template-switching artifacts in cDNA databases, by scanning a collection of human splice sites (Information for the Coordinates of Exons, ICE database). We have confirmed several cases at the experimental level. Artifacts represent a significant portion of apparently spliced sequences using noncanonical splice signals but are rare in the context of the whole database. However, care should be taken in the annotation of alternative transcripts, especially when the RT used is poorly thermostable and when the putative intron is flanked by direct repeats, which are the substrate for template switching.

Alternative Splicing↗
PubMed2004

Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells.

BACKGROUND: Estrogens and their receptors are important in human development, physiology and disease. In this study, we utilized an integrated genome-wide molecular and computational approach to characterize the interaction between the activated estrogen receptor (ER) and the regulatory elements of candidate target genes. RESULTS: Of around 19,000 genes surveyed in this study, we observed 137 ER-regulated genes in T-47D cells, of which only 89 were direct target genes. Meta-analysis of heterogeneous in vitro and in vivo datasets showed that the expression profiles in T-47D and MCF-7 cells are remarkably similar and overlap with genes differentially expressed between ER-positive and ER-negative tumors. Computational analysis revealed a significant enrichment of putative estrogen response elements (EREs) in the cis-regulatory regions of direct target genes. Chromatin immunoprecipitation confirmed ligand-dependent ER binding at the computationally predicted EREs in our highest ranked ER direct target genes, NRIP1, GREB1 and ABCA3. Wider examination of the cis-regulatory regions flanking the transcriptional start sites showed species conservation in mouse-human comparisons in only 6% of predicted EREs. CONCLUSIONS: Only a small core set of human genes, validated across experimental systems and closely associated with ER status in breast tumors, appear to be sufficient to induce ER effects in breast cancer cells. That cis-regulatory regions of these core ER target genes are poorly conserved suggests that different evolutionary mechanisms are operative at transcriptional control elements than at coding regions. These results predict that certain biological effects of estrogen signaling will differ between mouse and human to a larger extent than previously thought.

Binding Sites↗
PubMed2004

Information for the Coordinates of Exons (ICE): a human splice sites database.

We present a comprehensive database, Information for the Coordinates of Exons (ICE), of genomic splice sites (SSs) for 10,803 human genes. ICE contains 91,846 pairs of donor acceptor sites, supported by the alignment of "full-length" human mRNAs (including transcript variants) on human genomic sequences. ICE represents the largest collection of human SSs known to date and provides a significant resource to both molecular biologists and bioinformaticians alike. A user can visualize and extract genomic sequences around SSs of the donor acceptor pairs and can also visualize the primary structure of individual genes. We list in this article the 22 most frequently found canonical and noncanonical splice sites. The top four most represented donor acceptor pairs (GT-AG, GC-AG, AT-AC, and GT-GG) accounted for 99.16% of our data set. In addition, we calculated the SS matrix models for the three most common donor acceptor pairs. The database is focused on providing SSs and surrounding sequence information, associated SS and sequence characteristics, and relation to overall transcript structure. It allows targeted search and presents evidence for the gene structure.

Computational Biology↗
PubMed2003

Dragon ERE Finder version 2: A tool for accurate detection and analysis of estrogen response elements in vertebrate genomes.

We present a unique program for identification of estrogen response elements (EREs) in genomic DNA and related analyses. The detection algorithm was tested on several large datasets and makes one prediction in 13 300 nt while achieving a sensitivity of 83%. Users can further investigate selected regions around the identified ERE patterns for transcription factor binding sites based on the TRANSFAC database. It is also possible to search for candidate human genes with a match for the identified EREs and their flanking regions within EPD annotated promoters. Additionally, users can search among the extended promoter regions of approximately 11 000 human genes for those that have a high degree of similarity to the identified ERE patterns. Dragon ERE Finder version 2 is freely available for academic and non-profit users (http://sdmc.lit.org.sg/ERE-V2/index).

Algorithms↗
PubMed2003

FIE2: A program for the extraction of genomic DNA sequences around the start and translation initiation site of human genes.

FIE2 (5' end Information Extraction v2) is a web-based program for easy identification and extraction of nucleotide sequence around the start of genes (promoter region) and their translation initiation site (TIS). Using information provided by the National Center for Biotechnology Information's (NCBI's) LocusLink, FIE2 identifies the 5'-most end of a gene on its respective chromosome based on alignment of a selected set of mRNAs representative of the gene. FIE2 then uses currently available human genome sequence information to extract the desired sequences. The accuracy of the information extracted is therefore limited by the accuracy and completeness of the sequence annotation and sequence alignment provided by LocusLink. In addition, multiple TIS positions are also occasionally presented, for example, as a result of multiple alignments of transcript variants. One of the key criteria of FIE2 is that it should extract only the correct information or attempt no extraction at all. To date, the authors are not aware of any publicly available web-based tool that uses the human genomic sequence to extract pertinent promoter- and TIS-region information in this fashion. FIE2 is freely available at http://sdmc.lit.org.sg/FIE2.0.

Base Sequence↗
PubMed2003

Computer model for recognition of functional transcription start sites in RNA polymerase II promoters of vertebrates.

This paper introduces a new computer system for recognition of functional transcription start sites (TSSs) in RNA polymerase II promoter regions of vertebrates. This system allows scanning complete vertebrate genomes for promoters with significantly reduced number of false positive predictions. It can be used in the context of gene finding through its recognition of the 5' end of genes. The implemented recognition model uses a composite-hierarchical approach, artificial intelligence, statistics, and signal processing techniques. It also exploits the separation of promoter sequences into those that are C+G-rich or C+G-poor. The system was evaluated on a large and diverse human sequence-set and exhibited several times higher accuracy than several publicly available TSS-finding programs. Results obtained using human chromosome 22 data showed even greater specificity than the evaluation set results. The system has been implemented in the Dragon Promoter Finder package, which can be accessed at http://sdmc.krdl.org.sg:8080/promoter/.

Animals↗
PubMed2002

Dragon Promoter Finder: recognition of vertebrate RNA polymerase II promoters.

Dragon Promoter Finder (DPF) locates RNA polymerase II promoters in DNA sequences of vertebrates by predicting Transcription Start Site (TSS) positions. DPF's algorithm uses sensors for three functional regions (promoters, exons and introns) and an Artificial Neural Network (ANN). Results on a large and diverse evaluation set indicate that DPF exhibits a superior predicting ability for TSS location compared to three other promoter-finding programs.

Algorithms↗