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102
Optimal Word Sizes for Dissimilarity Measures and Estimation of the Degree of Dissimilarity Between DNA Sequences Running Head: Optimal word size and degree of dissimilarity
, 2005
"... Motivation: Several measures of DNA sequence dissimilarity have been developed. The purpose of this paper is threefold. Firstly, we compare the performance of sev-eral word-based or alignment-based methods. Secondly, we give a general guideline for choosing the window size and determine the optimal ..."
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Cited by 14 (0 self)
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Motivation: Several measures of DNA sequence dissimilarity have been developed. The purpose of this paper is threefold. Firstly, we compare the performance of sev-eral word-based or alignment-based methods. Secondly, we give a general guideline for choosing the window size and determine the optimal word sizes for several word-based measures at different window sizes. Thirdly, we use a large-scale simulation to simulate data from the distribution of SK-LD (symmetric Kullback-Leibler discrepancy). These simulated data can be used to estimate the degree of dissimilarity β between any pair of DNA sequences. Results: Our study shows (i) for whole sequence similiarity/dissimilarity identifica-tion the window size should be taken as large as possible, but probably not larger than 3000, as restricted by CPU time in practice, (ii) for each measure the optimal word size increases with window size, (iii) when the optimal word size is used, SK-LD performs superiorly in both simulation and real data analysis, (iv) the estimate ˆ β of β based on SK-LD can be used to filter out quickly a large number of dissimilar sequences and speed alignment-based database search for similar sequences and (v) ˆ β is also applica-ble in local similarity comparison situations. For example, it can help in selecting oligo probes with high specificity and therefore has potential in probe design for microarrays. Availability: The algorithm SK-LD, estimate ˆ β and simulation software are imple-mented in MATLAB code, and are available at
Estimation of pairwise sequence similarity of mammalian enhancers with word neighbourhood counts
- Bioinformatics
, 2012
"... Motivation: The identity of cells and tissues is to a large degree governed by transcriptional regulation. A major part is accomplished by the combinatorial binding of transcription factors at regulatory sequences, such as enhancers. Even though binding of transcription factors is sequence-specific, ..."
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Cited by 13 (0 self)
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Motivation: The identity of cells and tissues is to a large degree governed by transcriptional regulation. A major part is accomplished by the combinatorial binding of transcription factors at regulatory sequences, such as enhancers. Even though binding of transcription factors is sequence-specific, estimating the sequence similarity of two functionally similar enhancers is very difficult. However, a similarity measure for regulatory sequences is crucial to detect and understand functional similarities between two enhancers and will facilitate large-scale analyses like clustering, prediction and classification of genome-wide datasets. Results: We present the standardized alignment-free sequence similarity measure N2, a flexible framework that is defined for word neighbourhoods. We explore the usefulness of adding reverse complement words as well as words including mismatches into the neighbourhood. On simulated enhancer sequences as well as functional enhancers in mouse development, N2 is shown to outperform previous alignment-free measures. N2 is flexible, faster than competing methods and less susceptible to single sequence noise and the occurrence of repetitive sequences. Experiments on the mouse enhancers reveal that enhancers active in different tissues can be separated by pairwise comparison using N2. Conclusion: N2 represents an improvement over previous alignment-free similarity measures without compromising speed, which makes it a good candidate for large-scale sequence comparison of regulatory sequences.
A Genomic Distance Based on MUM Indicates Discontinuity between Most Bacterial Species
- JOURNAL OF BACTERIOLOGY
, 2009
"... The fundamental unit of biological diversity is the species. However, a remarkable extent of intraspecies diversity in bacteria was discovered by genome sequencing, and it reveals the need to develop clear criteria to group strains within a species. Two main types of analyses used to quantify intras ..."
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The fundamental unit of biological diversity is the species. However, a remarkable extent of intraspecies diversity in bacteria was discovered by genome sequencing, and it reveals the need to develop clear criteria to group strains within a species. Two main types of analyses used to quantify intraspecies variation at the genome level are the average nucleotide identity (ANI), which detects the DNA conservation of the core genome, and the DNA content, which calculates the proportion of DNA shared by two genomes. Both estimates are based on BLAST alignments for the definition of DNA sequences common to the genome pair. Interestingly, however, results using these methods on intraspecies pairs are not well correlated. This prompted us to develop a genomic-distance index taking into account both criteria of diversity, which are based on DNA maximal unique matches (MUM) shared by two genomes. The values, called MUMi, for MUM index, correlate better with the ANI than with the DNA content. Moreover, the MUMi groups strains in a way that is congruent with routinely used multilocus sequence-typing trees, as well as with ANI-based trees. We used the MUMi to determine the relatedness of all available genome pairs at the species and genus levels. Our analysis reveals a certain consistency in the current notion of bacterial species, in that the bulk of intraspecies and intragenus values are clearly separable. It also confirms that some species are much more diverse than most. As the MUMi is fast to calculate, it offers the possibility of measuring genome distances on the whole database of available genomes.
A novel alignment-free method for comparing transcription factor binding site motifs
- PLoS One
, 2010
"... Background: Transcription factor binding site (TFBS) motifs can be accurately represented by position frequency matrices (PFM) or other equivalent forms. We often need to compare TFBS motifs using their PFMs in order to search for similar motifs in a motif database, or cluster motifs according to th ..."
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Cited by 8 (0 self)
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Background: Transcription factor binding site (TFBS) motifs can be accurately represented by position frequency matrices (PFM) or other equivalent forms. We often need to compare TFBS motifs using their PFMs in order to search for similar motifs in a motif database, or cluster motifs according to their binding preference. The majority of current methods for motif comparison involve a similarity metric for column-to-column comparison and a method to find the optimal position alignment between the two compared motifs. In some applications, alignment-free methods might be preferred; however, few such methods with high accuracy have been described. Methodology/Principal Findings: Here we describe a novel alignment-free method for quantifying the similarity of motifs using their PFMs by converting PFMs into k-mer vectors. The motifs could then be compared by measuring the similarity among their corresponding k-mer vectors. Conclusions/Significance: We demonstrate that our method in general achieves similar performance or outperforms the existing methods for clustering motifs according to their binding preference and identifying similar motifs of transcription
APPROXIMATE WORD MATCHES BETWEEN TWO RANDOM SEQUENCES
, 801
"... Given two sequences over a finite alphabet L, the D2 statistic is the number of m-letter word matches between the two sequences. This statistic is used in bioinformatics for expressed sequence tag database searches. Here we study a generalization of the D2 statistic in the context of DNA sequences, ..."
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Cited by 7 (4 self)
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Given two sequences over a finite alphabet L, the D2 statistic is the number of m-letter word matches between the two sequences. This statistic is used in bioinformatics for expressed sequence tag database searches. Here we study a generalization of the D2 statistic in the context of DNA sequences, under the assumption of strand symmetric Bernoulli text. For k < m, we look at the count of m-letter word matches with up to k mismatches. For this statistic, we compute the expectation, give upper and lower bounds for the variance and prove its distribution is asymptotically normal. 1. Introduction. Methods
Pattern-Based Phylogenetic Distance Estimation and Tree Reconstruction
, 2006
"... We have developed an alignment-free method that calculates phyloge netic distances using a maximum-likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive compariso ..."
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Cited by 5 (1 self)
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We have developed an alignment-free method that calculates phyloge netic distances using a maximum-likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at
Spaced words and kmacs: fast alignment-free sequence comparison based on inexact word matches
- Nucleic Acids Res
, 2014
"... In this article, we present a user-friendly web inter-face 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 be-tween the input sequences. By contrast, our new algorithm ..."
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Cited by 4 (0 self)
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In this article, we present a user-friendly web inter-face 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 be-tween 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)’:
Characterising the D2 statistic: word matches in biological sequences
, 909
"... Word matches are often used in sequence comparison methods, either as a measure of sequence similarity or in the first search steps of algorithms such as BLAST or BLAT. The D2 statistic is the number of matches of words of k letters between two sequences. Recent advances have been made in the charac ..."
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Cited by 3 (2 self)
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Word matches are often used in sequence comparison methods, either as a measure of sequence similarity or in the first search steps of algorithms such as BLAST or BLAT. The D2 statistic is the number of matches of words of k letters between two sequences. Recent advances have been made in the characterisation of this statistic and in the approximation of its distribution. Here, these results are extended to the case of approximate word matches. We compute the exact value of the variance of the D2 statistic for the case of a uniform letter distribution, and introduce a method to provide accurate approximations of the variance in the remaining cases. This enables the distribution of D2 to be approximated for typical situations arising in biological research. We apply these results to the identification of cis-regulatory modules, and show that this method detects such sequences with a high accuracy. The ability to approximate the distribution of D2 for both exact and approximate word matches will enable the use of this statistic in a more precise manner for sequence comparison, database searches, and identification of transcription factor binding sites. 1
Efficient Influenza A Virus Origin Detection
"... ABSTRACT-This research describes a novel, alignment-free method of genomic sequence comparisons based on absent nucleotide words and expression levels. Testing this method on Influenza A virus isolates, three classifications are presented which successfully identify; 1) the geographic origins of dom ..."
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Cited by 2 (2 self)
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ABSTRACT-This research describes a novel, alignment-free method of genomic sequence comparisons based on absent nucleotide words and expression levels. Testing this method on Influenza A virus isolates, three classifications are presented which successfully identify; 1) the geographic origins of domestic bird H5N1 isolates through China and Southeast Asia during 2006, 2) the country of human H5N1 isolates crossing over from domestic bird hosts and, 3) the
