Papers by Sebastian Horwege
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Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16, 2016
CubeLendar
Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16, 2016
Algorithms for molecular biology : AMB, 2015
Alignment-free methods are increasingly used to calculate evolutionary distances between DNA and ... more Alignment-free methods are increasingly used to calculate evolutionary distances between DNA and protein sequences as a basis of phylogeny reconstruction. Most of these methods, however, use heuristic distance functions that are not based on any explicit model of molecular evolution. Herein, we propose a simple estimator d N of the evolutionary distance between two DNA sequences that is calculated from the number N of (spaced) word matches between them. We show that this distance function is more accurate than other distance measures that are used by alignment-free methods. In addition, we calculate the variance of the normalized number N of (spaced) word matches. We show that the variance of N is smaller for spaced words than for contiguous words, and that the variance is further reduced if our spaced-words approach is used with multiple patterns of 'match positions' and 'don't care positions'. Our software is available online and as downloadable source code at:...
Alignment-free methods are increasingly used for genome analysis and phylogeny reconstruction sin... more Alignment-free methods are increasingly used for genome analysis and phylogeny reconstruction since they circumvent various difficulties of traditional approaches that rely on multiple sequence alignments. In particular, they are much faster than alignment-based methods. Most alignmentfree approaches work by analyzing the k-mer composition of sequences. In this paper, we propose to use 'spaced k-mers', i.e. patterns of deterministic and 'don't care' positions instead of contiguous k-mers. Using simulated and real-world sequence data, we demonstrate that this approach produces better phylogenetic trees than alignment-free methods that rely on contiguous k-mers. In addition, distances calculated with spaced k-mers appear to be statistically more stable than distances based on contiguous k-mers.
Estimating Evolutionary Distances from Spaced-Word Matches
Lecture Notes in Computer Science, 2014
ABSTRACT Alignment-free methods are increasingly used to estimate distances between DNA and prote... more ABSTRACT Alignment-free methods are increasingly used to estimate distances between DNA and protein sequences and to reconstruct phylogenetic trees. Most distance functions used by these methods, however, are heuristic measures of dissimilarity, not based on any explicit model of evolution. Herein, we propose a simple estimator of the evolutionary distance between two DNA sequences calculated from the number of (spaced) word matches between them. We show that this distance function estimates the evolutionary distance between DNA sequences more accurately than other distance measures used by alignment-free methods. In addition, we calculate the variance of the number of (spaced) word matches depending on sequence length and mismatch probability.
Nucleic Acids Research, 2014
In this article, we present a user-friendly web interface for two alignment-free sequence-compari... more In this article, we present a user-friendly web interface for two alignment-free sequence-comparison methods that we recently developed. Most alignment-free methods rely on exact word matches to estimate pairwise similarities or distances between the input sequences. By contrast, our new algorithms are based on inexact word matches. The first of these approaches uses the relative frequencies of so-called spaced words in the input sequences, i.e. words containing 'don't care' or 'wildcard' symbols at certain pre-defined positions. Various distance measures can then be defined on sequences based on their different spaced-word composition. Our second approach defines the distance between two sequences by estimating for each position in the first sequence the length of the longest substring at this position that also occurs in the second sequence with up to k mismatches. Both approaches take a set of deoxyribonucleic acid (DNA) or protein sequences as input and return a matrix of pairwise distance values that can be used as a starting point for clustering algorithms or distance-based phylogeny reconstruction. The two alignment-free programmes are accessible through a web interface at 'Göttingen Bioinformatics Compute Server (GOBICS)': http://spaced.gobics.de http://kmacs.gobics.de and the source codes can be downloaded.
Bioinformatics, 2014
Motivation Alignment-free methods for sequence comparison are increasingly used for genome analys... more Motivation Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well known problem with these methods is that neighbouring word matches are far from independent. Results To reduce the statistical dependency between adjacent word matches, we propose to use 'spaced words', defined by patterns of 'match' and 'don't care' positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spacedword frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words.
CubeLendar
Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16, 2016
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Papers by Sebastian Horwege