rasbhari: Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison

Abstract

Many algorithms for sequence analysis rely on word matching or word statistics. Often, these approaches can be improved if binary patterns representing match and don’t-care positions are used as a filter, such that only those positions of words are considered that correspond to the match positions of the patterns. The performance of these approaches, however, depends on the underlying patterns. Herein, we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set. We propose a modified hill-climbing algorithm to optimize pattern sets for database searching, read mapping and alignment-free sequence comparison of nucleic-acid sequences; our implementation of this algorithm is called rasbhari. Depending on the application at hand, rasbhari can either minimize the overlap complexity of pattern sets, maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison. We show that, for database searching, rasbhari generates pattern sets with slightly higher sensitivity than existing approaches. In our Spaced Words approach to alignment-free sequence comparison, pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used. Finally, we used rasbhari to generate patterns for short read classification with CLARK-S. Here too, the sensitivity of the results could be improved, compared to the default patterns of the program. We integrated rasbhari into Spaced Words; the source code of rasbhari is freely available at http://rasbhari.gobics.de/

Multi-SpaM: A Maximum-Likelihood Approach to Phylogeny Reconstruction Using Multiple Spaced-Word Matches and Quartet Trees

“…The results of these programs therefore depend on the underlying patterns. Both programs use the software rasbhari [24] to calculate patterns. rasbhari uses a probabilistic algorithm, so different program runs usually return different patterns and, as a result, different program runs with FSWM and Multi-SpaM may produce slightly different distance estimates, even if the same parameter setting is used.…”

“…by default the pattern has 10 match positions and 100 don't-care positions, but other values for and w can be chosen by the user. Given these parameters, P is calculated by running our previously developed software tool rasbhari [24]. As a basis for phylogeny reconstruction, we are using four-way alignments consisting of occurrences of the same spaced word with respect to P in four different sequences.…”

Prot-SpaM: Fast alignment-free phylogeny reconstruction based on whole-proteome sequences

“…By default, our program uses a set of 5 patterns. To find good patterns sets, we integrated the tool rasbhari [20] into our implementation. rasbhari uses a hill climbing algorithm to reduce the overlap complexity [28] of pattern sets.…”

Improving on hash-based probabilistic sequence classification using multiple spaced seeds and multi-index Bloom filters

“…Fig S3) according to metrics like overlap complexity (46), yet we still have not yet scratched the surface in terms of optimal spaced seed design. Optimal seed sensitivity computation NP-hard (47), and although faster approximations exist (48), they are still quite slow and infeasible because our seed can be any length. Optimal design for sequence classification is a function of sequencing error rate, homology detection tolerance, sequence length, and mutation/error types.…”