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Waterman, M. S.

Publications and source records attributed to Waterman, M. S..

2 recordsLinked to original sources

Benchmarking of alignment-free sequence comparison methods

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. 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. 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.

bioinformatics↗

A new statistic for efficient detection of repetitive sequences

Detecting sequences containing repetitive regions is a basic bioinformatics task with many applications. Several methods have been developed for various types of repeat detection tasks. An efficient generic method for detecting all types of repetitive sequences is still desirable.\n\nInspired by the excellent properties and successful applications of the D2 family of statistics in comparative analyses of genomic sequences, we developed a new statistic [Formula] that can efficiently discriminate sequences with or without repetitive regions. Using the statistic, we developed an algorithm of linear complexity in both computation time and memory usage for detecting all types of repetitive sequences in multiple scenarios, including finding candidate CRISPR regions from bacterial genomic or metagenomics sequences. Simulation and real data experiments showed that the method works well on both assembled sequences and unassembled short reads.

bioinformatics↗