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PubMed1998

Assembling genes from predicted exons in linear time with dynamic programming.

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

Algorithms↗
PubMed1995

Correcting sequencing errors in DNA coding regions using a dynamic programming approach.

This paper presents an algorithm for detecting and 'correcting' sequencing errors that occur in DNA coding regions. The types of sequencing errors addressed are insertions and deletions (indels) of DNA bases. The goal is to provide a capability which makes single-pass or low-redundancy sequence data more informative, reducing the need for high-redundancy sequencing for gene identification and characterization purposes. This would permit improved sequencing efficiency and reduce genome sequencing costs. The algorithm detects sequencing errors by discovering changes in the statistically preferred reading frame within a putative coding region and then inserts a number of 'neutral' bases at a perceived reading frame transition point to make the putative exon candidate frame consistent. We have implemented the algorithm as a front-end subsystem of the GRAIL DNA sequence analysis system to construct a version which is very error tolerant and also intend to use this as a testbed for further development of sequencing error-correction technology. Preliminary test results have shown the usefulness of this algorithm and also exhibited some of its weakness, providing possible directions for further improvement. On a test set consisting of 68 human DNA sequences with 1% randomly generated indels in coding regions, the algorithm detected and corrected 76% of the indels. The average distance between the position of an indel and the predicted one was 9.4 bases. With this subsystem in place, GRAIL correctly predicted 89% of the coding messages with 10% false message on the 'corrected' sequences, compared to 69% correctly predicted coding messages and 11% falsely predicted messages on the 'corrupted' sequences using standard GRAIL II method (version 1.2).(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1993

MASCOT: multiple alignment system for protein sequences based on three-way dynamic programming.

A multiple alignment methodology that can produce high-quality alignment is extremely important for predicting the structure of unknown proteins. Nearly all the methodologies developed so far have employed two-way alignment only. Although these methods are fast, the alignments they produce lose reliability as the similarity of sequences reduces. We developed the MASCOT multiple alignment system. MASCOT can sustain the reliability of alignment even when the similarity of sequences is low. MASCOT achieves high-quality alignment by employing three-way alignment in addition to two-way alignment. The resultant alignments are refined by simulated annealing to higher quality. We also use a cluster analysis of sequences to produce highly reliable alignments.

Algorithms↗