MYBS: a comprehensive web server for mining transcription factor binding sites in yeast.

Correct interactions between transcription factors (TFs) and their binding sites (TFBSs) are of central importance to gene regulation. Recently developed chromatin-immunoprecipitation DNA chip (ChIP-chip) techniques and the phylogenetic footprinting method provide ways to identify TFBSs with high precision. In this study, we constructed a user-friendly interactive platform for dynamic binding site mapping using ChIP-chip data and phylogenetic footprinting as two filters. MYBS (Mining Yeast Binding Sites) is a comprehensive web server that integrates an array of both experimentally verified and predicted position weight matrixes (PWMs) from eleven databases, including 481 binding motif consensus sequences and 71 PWMs that correspond to 183 TFs. MYBS users can search within this platform for motif occurrences (possible binding sites) in the promoters of genes of interest via simple motif or gene queries in conjunction with the above two filters. In addition, MYBS enables users to visualize in parallel the potential regulators for a given set of genes, a feature useful for finding potential regulatory associations between TFs. MYBS also allows users to identify target gene sets of each TF pair, which could be used as a starting point for further explorations of TF combinatorial regulation. MYBS is available at http://cg1.iis.sinica.edu.tw/~mybs/.

Method for identifying transcription factor binding sites in yeast.

MOTIVATION: Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit. RESULTS: We develop a TFBS identification method, TFBSfinder, which utilizes several data sources, including DNA sequences, phylogenetic information, microarray data and ChIP-chip data. For a TF, TFBSfinder rigorously selects a set of reliable target genes and a set of non-target genes (as a background set) to find overrepresented and conserved motifs in target genes. A new metric for measuring the degree of conservation at a binding site across species and methods for clustering motifs and for inferring position weight matrices are proposed. For synthetic data and yeast cell cycle TFs, TFBSfinder identifies motifs that are highly similar to known consensuses. Moreover, TFBSfinder outperforms well-known methods. AVAILABILITY: http://cg1.iis.sinica.edu.tw/~TFBSfinder/.

SinicView: a visualization environment for comparisons of multiple nucleotide sequence alignment tools.

BACKGROUND: Deluged by the rate and complexity of completed genomic sequences, the need to align longer sequences becomes more urgent, and many more tools have thus been developed. In the initial stage of genomic sequence analysis, a biologist is usually faced with the questions of how to choose the best tool to align sequences of interest and how to analyze and visualize the alignment results, and then with the question of whether poorly aligned regions produced by the tool are indeed not homologous or are just results due to inappropriate alignment tools or scoring systems used. Although several systematic evaluations of multiple sequence alignment (MSA) programs have been proposed, they may not provide a standard-bearer for most biologists because those poorly aligned regions in these evaluations are never discussed. Thus, a tool that allows cross comparison of the alignment results obtained by different tools simultaneously could help a biologist evaluate their correctness and accuracy. RESULTS: In this paper, we present a versatile alignment visualization system, called SinicView, (for Sequence-aligning INnovative and Interactive Comparison VIEWer), which allows the user to efficiently compare and evaluate assorted nucleotide alignment results obtained by different tools. SinicView calculates similarity of the alignment outputs under a fixed window using the sum-of-pairs method and provides scoring profiles of each set of aligned sequences. The user can visually compare alignment results either in graphic scoring profiles or in plain text format of the aligned nucleotides along with the annotations information. We illustrate the capabilities of our visualization system by comparing alignment results obtained by MLAGAN, MAVID, and MULTIZ, respectively. CONCLUSION: With SinicView, users can use their own data sequences to compare various alignment tools or scoring systems and select the most suitable one to perform alignment in the initial stage of sequence analysis.

A comparative method for identification of gene structures and alternatively spliced variants.

MOTIVATION: Alternative splicing (AS) serves as a mechanism to create diversity among functional proteins. Increasing evidence indicates that a large portion of genes have AS forms. Hence AS variants should be considered while analyzing gene structures. RESULTS: A new cross-species gene identification and AS analysis system, PSEP, has been developed. The system is based on expressed sequence tag (EST)-to-genome and genome-to-genome comparisons and is implemented in two steps: sequence alignment and a series of post-alignment processes, including progressive signal extraction and patching. For gene identification, these post-alignment processes serve as noise filters and enable PSEP to eliminate approximately 88% of potential overprediction. The overall accuracy of PSEP is better than or comparable to that of other well-known cross-species gene prediction programs, including the ROSETTA program, TWINSCAN, SGP-1/-2 and SLAM, when tested on three benchmark datasets (the ELN gene region, the HoxA cluster and the ROSETTA set). In addition, 76.2 and 76.0% of multiple-exon genes in the ROSETTA dataset and human chromosome 20, respectively, are found to have AS forms. Approximately 23% of the 210 elementary alternatives identified in the ROSETTA dataset are not conserved between the human and mouse genomes, and none of the 210 transcripts is found in the RefSeq annotation. With its dual functions in cross-species conserved sequence analysis and AS analysis, PSEP is highly suitable for studying the evolution of AS patterns and for finding unidentified gene expression features.