Biology subjects

James, B. T.

Publications and source records attributed to James, B. T..

3 recordsLinked to original sources

bioRxiv2018

Look4TRs: A de-novo tool for detecting simple tandem repeats using self-supervised hidden Markov models.

Simple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases, and applications in biotechnology. Sequences of thousands of species will be available soon. There is immediate need for an accurate tool for detecting microsatellites in the new genomes. The current available tools have limitations. As a remedy, we proposed Look4TRs, which is the first application of self-supervised hidden Markov models to discovering microsatellites. It adapts itself to the input genomes, balancing high sensitivity and low false positive rate. It auto-calibrates itself, freeing the user from adjusting the parameters manually, leading to consistent results across different studies. We evaluated Look4TRs on eight genomes. Based on F-measure, which combines sensitivity and false positive rate, Look4TRs outperformed TRF and MISA -- the most widely-used tools -- by 106% and 82%. Look4TRs outperformed the second best tool, MsDetector or Tantan, by 11%. Look4TRs represents technical advances in the annotation of microsatellites.

bioinformatics↗

bioRxiv2018

FASTCAR: Rapid alignment-free prediction of sequence alignment identity scores

MotivationPairwise alignment is a predominant algorithm in the field of bioinformatics. This algorithm is quadratic -- slow especially on long sequences. Many applications utilize identity scores without the corresponding alignments. For these applications, we propose FASTCAR. It produces identity scores for pairs of DNA sequences using alignment-free methods and two self-supervised general linear models. ResultsFor the first time, the new tool can predict the pair-wise identity score in linear time and space. On two large-scale sequence databases, FASTCAR provided the best compromise between sensitivity and precision while being faster than BLAST by 40% and faster than USEARCH by 6-10 times. Further, FASTCAR is capable of producing the pair-wise identity scores of long DNA sequences -- millions-of-nucleotides-long bacterial genomes; this task cannot be accomplished by any alignment-based tool. AvailabilityFASTCAR is available at https://github.com/TulsaBioinformaticsToolsmith/FASTCAR and as the Supplementary Dataset 1. Contacthani-girgis@utulsa.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

MeShClust: an intelligent tool for clustering DNA sequences

Sequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster. Often times, a biologist may not know the exact sequence similarity. Therefore, clusters produced by these tools do not likely match the real clusters comprising the data if the provided parameter is inaccurate. To overcome this limitation, we adapted the mean shift algorithm, an unsupervised machine-learning algorithm, which has been used successfully thousands of times in fields such as image processing and computer vision. The theory behind the mean shift algorithm, unlike the greedy approaches, guarantees convergence to the modes, e.g. cluster centers. Here we describe the first application of the mean shift algorithm to clustering DNA sequences. MeShClust is one of few applications of the mean shift algorithm in bioinformatics. Further, we applied supervised machine learning to predict the identity score produced by global alignment using alignment-free methods. We demonstrate MeShClusts ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.

bioinformatics↗