*Bioinformatics ; 36(2): 380-387, 2020 Jan 15.*

**| MEDLINE**| ID: mdl-31287494

##### RESUMO

MOTIVATION: Simple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases and applications in biotechnology. There is an immediate need for an accurate tool for detecting microsatellites in newly sequenced genomes. The current available tools are either sensitive or specific but not both; some tools require adjusting parameters manually. RESULTS: We propose Look4TRs, the first application of self-supervised hidden Markov models to discovering microsatellites. Look4TRs adapts itself to the input genomes, balancing high sensitivity and low false positive rate. It auto-calibrates itself. We evaluated Look4TRs on 26 eukaryotic genomes. Based on F measure, which combines sensitivity and false positive rate, Look4TRs outperformed TRF and MISA-the most widely used tools-by 78 and 84%. Look4TRs outperformed the second and the third best tools, MsDetector and Tantan, by 17 and 34%. On eight bacterial genomes, Look4TRs outperformed the second and the third best tools by 27 and 137%. AVAILABILITY AND IMPLEMENTATION: https://github.com/TulsaBioinformaticsToolsmith/Look4TRs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

*Genome Biol ; 20(1): 144, 2019 07 25.*

**| MEDLINE**| ID: mdl-31345254

##### RESUMO

BACKGROUND: Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. RESULTS: Here, we present a community resource (http://afproject.org) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference, and reconstruction of species trees under horizontal gene transfer and recombination events. CONCLUSION: The interactive web service allows researchers to explore the performance of alignment-free tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-the-art tools, accelerating the development of new, more accurate AF solutions.

##### Assuntos

Análise de Sequência , Benchmarking , Transferência Genética Horizontal , Internet , Filogenia , Sequências Reguladoras de Ácido Nucleico , Alinhamento de Sequência , Análise de Sequência de Proteína , Software*Brief Bioinform ; 20(4): 1222-1237, 2019 07 19.*

**| MEDLINE**| ID: mdl-29220512

##### RESUMO

MOTIVATION: Since the dawn of the bioinformatics field, sequence alignment scores have been the main method for comparing sequences. However, alignment algorithms are quadratic, requiring long execution time. As alternatives, scientists have developed tens of alignment-free statistics for measuring the similarity between two sequences. RESULTS: We surveyed tens of alignment-free k-mer statistics. Additionally, we evaluated 33 statistics and multiplicative combinations between the statistics and/or their squares. These statistics are calculated on two k-mer histograms representing two sequences. Our evaluations using global alignment scores revealed that the majority of the statistics are sensitive and capable of finding similar sequences to a query sequence. Therefore, any of these statistics can filter out dissimilar sequences quickly. Further, we observed that multiplicative combinations of the statistics are highly correlated with the identity score. Furthermore, combinations involving sequence length difference or Earth Mover's distance, which takes the length difference into account, are always among the highest correlated paired statistics with identity scores. Similarly, paired statistics including length difference or Earth Mover's distance are among the best performers in finding the K-closest sequences. Interestingly, similar performance can be obtained using histograms of shorter words, resulting in reducing the memory requirement and increasing the speed remarkably. Moreover, we found that simple single statistics are sufficient for processing next-generation sequencing reads and for applications relying on local alignment. Finally, we measured the time requirement of each statistic. The survey and the evaluations will help scientists with identifying efficient alternatives to the costly alignment algorithm, saving thousands of computational hours. AVAILABILITY: The source code of the benchmarking tool is available as Supplementary Materials.

*Nucleic Acids Res ; 46(14): e83, 2018 08 21.*

**| MEDLINE**| ID: mdl-29718317

##### RESUMO

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 MeShClust's ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.