216 research outputs found
A Coverage Criterion for Spaced Seeds and its Applications to Support Vector Machine String Kernels and k-Mer Distances
Spaced seeds have been recently shown to not only detect more alignments, but
also to give a more accurate measure of phylogenetic distances (Boden et al.,
2013, Horwege et al., 2014, Leimeister et al., 2014), and to provide a lower
misclassification rate when used with Support Vector Machines (SVMs) (On-odera
and Shibuya, 2013), We confirm by independent experiments these two results,
and propose in this article to use a coverage criterion (Benson and Mak, 2008,
Martin, 2013, Martin and No{\'e}, 2014), to measure the seed efficiency in both
cases in order to design better seed patterns. We show first how this coverage
criterion can be directly measured by a full automaton-based approach. We then
illustrate how this criterion performs when compared with two other criteria
frequently used, namely the single-hit and multiple-hit criteria, through
correlation coefficients with the correct classification/the true distance. At
the end, for alignment-free distances, we propose an extension by adopting the
coverage criterion, show how it performs, and indicate how it can be
efficiently computed.Comment: http://online.liebertpub.com/doi/abs/10.1089/cmb.2014.017
A Coverage Criterion for Spaced Seeds and its Applications to Support Vector Machine String Kernels and k-Mer Distances
Spaced seeds have been recently shown to not only detect more alignments, but
also to give a more accurate measure of phylogenetic distances (Boden et al.,
2013, Horwege et al., 2014, Leimeister et al., 2014), and to provide a lower
misclassification rate when used with Support Vector Machines (SVMs) (On-odera
and Shibuya, 2013), We confirm by independent experiments these two results,
and propose in this article to use a coverage criterion (Benson and Mak, 2008,
Martin, 2013, Martin and No{\'e}, 2014), to measure the seed efficiency in both
cases in order to design better seed patterns. We show first how this coverage
criterion can be directly measured by a full automaton-based approach. We then
illustrate how this criterion performs when compared with two other criteria
frequently used, namely the single-hit and multiple-hit criteria, through
correlation coefficients with the correct classification/the true distance. At
the end, for alignment-free distances, we propose an extension by adopting the
coverage criterion, show how it performs, and indicate how it can be
efficiently computed.Comment: http://online.liebertpub.com/doi/abs/10.1089/cmb.2014.017
Lossless seeds for searching short patterns with high error rates
International audienceWe address the problem of approximate pattern matching using the Levenshtein distance. Given a text T and a pattern P , find alllocations in T that differ by at most k errors from P . For that purpose, we propose a filtration algorithm that is based on a novel type of seeds,combining exact parts and parts with a fixed number of errors. Experimental tests show that the method is specifically well-suited for short patterns with a large number of error
Estimating seed sensitivity on homogeneous alignments
We address the problem of estimating the sensitivity of seed-based similarity
search algorithms. In contrast to approaches based on Markov models [18, 6, 3,
4, 10], we study the estimation based on homogeneous alignments. We describe an
algorithm for counting and random generation of those alignments and an
algorithm for exact computation of the sensitivity for a broad class of seed
strategies. We provide experimental results demonstrating a bias introduced by
ignoring the homogeneousness condition
Improved hit criteria for DNA local alignment
BACKGROUND: The hit criterion is a key component of heuristic local alignment algorithms. It specifies a class of patterns assumed to witness a potential similarity, and this choice is decisive for the selectivity and sensitivity of the whole method. RESULTS: In this paper, we propose two ways to improve the hit criterion. First, we define the group criterion combining the advantages of the single-seed and double-seed approaches used in existing algorithms. Second, we introduce transition-constrained seeds that extend spaced seeds by the possibility of distinguishing transition and transversion mismatches. We provide analytical data as well as experimental results, obtained with the YASS software, supporting both improvements. CONCLUSIONS: Proposed algorithmic ideas allow to obtain a significant gain in sensitivity of similarity search without increase in execution time. The method has been implemented in YASS software available at
Novel computational techniques for mapping and classifying Next-Generation Sequencing data
Since their emergence around 2006, Next-Generation Sequencing technologies have been revolutionizing biological and medical research. Quickly obtaining an extensive amount of short or long reads of DNA sequence from almost any biological sample enables detecting genomic variants, revealing the composition of species in a metagenome, deciphering cancer biology, decoding the evolution of living or extinct species, or understanding human migration patterns and human history in general. The pace at which the throughput of sequencing technologies is increasing surpasses the growth of storage and computer capacities, which creates new computational challenges in NGS data processing.
In this thesis, we present novel computational techniques for read mapping and taxonomic classification. With more than a hundred of published mappers, read mapping might be considered fully solved. However, the vast majority of mappers follow the same paradigm and only little attention has been paid to non-standard mapping approaches. Here, we propound the so-called dynamic mapping that we show to significantly improve the resulting alignments compared to traditional mapping approaches. Dynamic mapping is based on exploiting the information from previously computed alignments, helping to improve the mapping of subsequent reads. We provide the first comprehensive overview of this method and demonstrate its qualities using Dynamic Mapping Simulator, a pipeline that compares various dynamic mapping scenarios to static mapping and iterative referencing.
An important component of a dynamic mapper is an online consensus caller, i.e., a program collecting alignment statistics and guiding updates of the reference in the online fashion. We provide Ococo, the first online consensus caller that implements a smart statistics for individual genomic positions using compact bit counters. Beyond its application to dynamic mapping, Ococo can be employed as an online SNP caller in various analysis pipelines, enabling SNP calling from a stream without saving the alignments on disk.
Metagenomic classification of NGS reads is another major topic studied in the thesis. Having a database with thousands of reference genomes placed on a taxonomic tree, the task is to rapidly assign a huge amount of NGS reads to tree nodes, and possibly estimate the relative abundance of involved species. In this thesis, we propose improved computational techniques for this task. In a series of experiments, we show that spaced seeds consistently improve the classification accuracy. We provide Seed-Kraken, a spaced seed extension of Kraken, the most popular classifier at present. Furthermore, we suggest ProPhyle, a new indexing strategy based on a BWT-index, obtaining a much smaller and more informative index compared to Kraken. We provide a modified version of BWA that improves the BWT-index for a quick k-mer look-up
Efficient seeding techniques for protein similarity search
We apply the concept of subset seeds proposed in [1] to similarity search in
protein sequences. The main question studied is the design of efficient seed
alphabets to construct seeds with optimal sensitivity/selectivity trade-offs.
We propose several different design methods and use them to construct several
alphabets.We then perform an analysis of seeds built over those alphabet and
compare them with the standard Blastp seeding method [2,3], as well as with the
family of vector seeds proposed in [4]. While the formalism of subset seed is
less expressive (but less costly to implement) than the accumulative principle
used in Blastp and vector seeds, our seeds show a similar or even better
performance than Blastp on Bernoulli models of proteins compatible with the
common BLOSUM62 matrix
Efficient seeding techniques for protein similarity search
We apply the concept of subset seeds proposed in [1] to similarity search in
protein sequences. The main question studied is the design of efficient seed
alphabets to construct seeds with optimal sensitivity/selectivity trade-offs.
We propose several different design methods and use them to construct several
alphabets.We then perform an analysis of seeds built over those alphabet and
compare them with the standard Blastp seeding method [2,3], as well as with the
family of vector seeds proposed in [4]. While the formalism of subset seed is
less expressive (but less costly to implement) than the accumulative principle
used in Blastp and vector seeds, our seeds show a similar or even better
performance than Blastp on Bernoulli models of proteins compatible with the
common BLOSUM62 matrix
Progressive Mauve: Multiple alignment of genomes with gene flux and rearrangement
Multiple genome alignment remains a challenging problem. Effects of
recombination including rearrangement, segmental duplication, gain, and loss
can create a mosaic pattern of homology even among closely related organisms.
We describe a method to align two or more genomes that have undergone
large-scale recombination, particularly genomes that have undergone substantial
amounts of gene gain and loss (gene flux). The method utilizes a novel
alignment objective score, referred to as a sum-of-pairs breakpoint score. We
also apply a probabilistic alignment filtering method to remove erroneous
alignments of unrelated sequences, which are commonly observed in other genome
alignment methods. We describe new metrics for quantifying genome alignment
accuracy which measure the quality of rearrangement breakpoint predictions and
indel predictions. The progressive genome alignment algorithm demonstrates
markedly improved accuracy over previous approaches in situations where genomes
have undergone realistic amounts of genome rearrangement, gene gain, loss, and
duplication. We apply the progressive genome alignment algorithm to a set of 23
completely sequenced genomes from the genera Escherichia, Shigella, and
Salmonella. The 23 enterobacteria have an estimated 2.46Mbp of genomic content
conserved among all taxa and total unique content of 15.2Mbp. We document
substantial population-level variability among these organisms driven by
homologous recombination, gene gain, and gene loss. Free, open-source software
implementing the described genome alignment approach is available from
http://gel.ahabs.wisc.edu/mauve .Comment: Revision dated June 19, 200
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