Biomedical subjects

Francis Y L Chin

Publications and source records attributed to Francis Y L Chin.

5 recordsLinked to original sources

PubMed2006

Finding motifs from all sequences with and without binding sites.

MOTIVATION: Finding common patterns, motifs, from a set of promoter regions of coregulated genes is an important problem in molecular biology. Most existing motif-finding algorithms consider a set of sequences bound by the transcription factor as the only input. However, we can get better results by considering sequences that are not bound by the transcription factor as an additional input. RESULTS: First, instead of using the simple hyper-geometric analysis, we propose to calculate the likelihood based on a more precise probabilistic analysis which considers motif length, sequence length and number of binding sites as input parameters for testing whether motif is found. Second, we adopt an heuristic algorithm bases on our analysis to find motifs. For the simulated and real datasets, our algorithm ALSE compares favorably against common motif-finding programs such as SeedSearch and MEME in all cases and performs very well, especially when each input sequence contains more than one binding site. AVAILABILITY: ALSE is available for download at the homepage http://alse.cs.hku.hk CONTACT: cmleung2@cs.hku.hk.

Algorithms↗

PubMed2006

Algorithms for challenging motif problems.

Pevzner and Sze(19) have introduced the Planted (l,d)-Motif Problem to find similar patterns (motifs) in sequences which represent the promoter regions of co-regulated genes, where l is the length of the motif and d is the maximum Hamming distance around the similar patterns. Many algorithms have been developed to solve this motif problem. However, these algorithms either have long running times or do not guarantee the motif can be found. In this paper, we introduce new algorithms to solve this motif problem. Our algorithms can find motifs in reasonable time for not only the challenging (9, 2), (11, 3), (15, 5)-motif problems but for even longer motifs, say (20, 7), (30, 11) and (40, 15), which have never been seriously attempted by other researchers because of the large time and space required. Besides, our algorithms can be extended to find more complicated motifs structure called cis-regulatory modules (CRM).

Algorithms↗

PubMed2005

Finding exact optimal motifs in matrix representation by partitioning.

MOTIVATION: Finding common patterns, or motifs, in the promoter regions of co-expressed genes is an important problem in bioinformatics. A common representation of the motif is by probability matrix or PSSM (position specific scoring matrix). However, even for a motif of length six or seven, there is no algorithm that can guarantee finding the exact optimal matrix from an infinite number of possible matrices. RESULTS: This paper introduces the first algorithm, called EOMM, for finding the exact optimal matrix-represented motif, or simply optimal motif. Based on branch-and-bound searching by partitioning the solution space recursively, EOMM can find the optimal motif of size up to eight or nine, and a motif of larger size with any desired accuracy on the principle that the smaller the error bound, the longer the running time. Experiments show that for some real and simulated data sets, EOMM finds the motif despite very weak signals when existing software, such as MEME and MITRA-PSSM, fails to do so.

Algorithms↗

PubMed2005

Efficient constrained multiple sequence alignment with performance guarantee.

The constrained multiple sequence alignment problem is to align a set of sequences of maximum length n subject to a given constrained sequence, which arises from some knowledge of the structure of the sequences. This paper presents new algorithms for this problem, which are more efficient in terms of time and space (memory) than the previous algorithms, and with a worst-case guarantee on the quality of the alignment. Saving the space requirement by a quadratic factor is particularly significant as the previous O(n4)-space algorithm has limited application due to its huge memory requirement. Experiments on real data sets confirm that our new algorithms show improvements in both alignment quality and resource requirements.

Algorithms↗

PubMedDate not supplied

Finding motifs with insufficient number of strong binding sites.

A molecule called transcription factor usually binds to a set of promoter sequences of coexpressed genes. As a result, these promoter sequences contain some short substrings, or binding sites, with similar patterns. The motif discovering problem is to find these similar patterns and motifs in a set of sequences. Most existing algorithms find the motifs based on strong-signal sequences only (i.e., those containing binding sites very similar to the motif). In this paper, we use a probability matrix to represent a motif to calculate the minimum total number of binding sites required to be in the input dataset in order to confirm that the discovered motifs are not artifacts. Next, we introduce a more general and realistic energy-based model, which considers all sequences with varying degrees of binding strength to the transcription factors (as measured experimentally). By treating sequences with varying degrees of binding strength, we develop a heuristic algorithm called EBMF (Energy-Based Motif Finding Algorithm) to find the motif, which can handle sequences ranging from those that contain more than one binding site to those that contain none. EBMF can find motifs for datasets that do not even have the required minimum number of binding sites as previously derived. EBMF compares favorably with common motif-finding programs AlignACE and MEME. In particular, for some simulated and real datasets, EBMF finds the motif when both AlignACE and MEME fail to do so.

Algorithms↗