Biomedical subjects

C F Allex

Publications and source records attributed to C F Allex.

3 recordsLinked to original sources

PubMed1999

Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies.

MOTIVATION: Given inputs extracted from an aligned column of DNA bases and the underlying Perkin Elmer Applied Biosystems (ABI) fluorescent traces, our goal is to train a neural network to determine correctly the consensus base for the column. Choosing an appropriate network input representation is critical to success in this task. We empirically compare five representations; one uses only base calls and the others include trace information. RESULTS: We attained the most accurate results from networks that incorporate trace information into their input representations. Based on estimates derived from using 10-fold cross-validation, the best network topology produces consensus accuracies ranging from 99.26% to >99.98% for coverages from two to six aligned sequences. With a coverage of six, it makes only three errors in 20 000 consensus calls. In contrast, the network that only uses base calls in its input representation has over double that error rate: eight errors in 20 000 consensus calls. CONTACT: allex@cs.wisc.edu

Base Sequence↗

PubMed1997

Increasing consensus accuracy in DNA fragment assemblies by incorporating fluorescent trace representations.

We present a new method for determining the consensus sequence in DNA fragment assemblies. The new method, Trace-Evidence, directly incorporates aligned ABI trace information into consensus calculations via our previously described representation, Trace-Data Classifications. The new method extracts and sums evidence indicated by the representation to determine consensus calls. Using the Trace-Evidence method results in automatically produced consensus sequences that are more accurate and less ambiguous than those produced with standard majority-voting methods. Additionally, these improvements are achieved with less coverage than required by the standard methods-using Trace-Evidence and a coverage of only three, error rates are as low as those with a coverage of over ten sequences.

Algorithms↗

PubMed1996

Improving the quality of automatic DNA sequence assembly using fluorescent trace-data classifications.

Virtually all large-scale sequencing projects use automatic sequence-assembly programs to aid in the determination of DNA sequences. The computer-generated assemblies required substantial hand-editing to transform them into submissions for GenBank. As the size of sequencing projects increases, it becomes essential to improve the quality of the automated assemblies so that this time consuming hand-editing may be reduced. Current ABI sequencing technology uses base calls made from fluorescently-labeled DNA fragments run on gels. We present a new representation for the fluorescent trace data associated with individual base calls. This representation can be used before, during, and after fragment assembly to improve the quality of assemblies. We demonstrate one such use-end-trimming of sub-optimal data-that results in a significant improvement in the quality of subsequent assemblies.

Algorithms↗