A measure of the similarity of sets of sequences not requiring sequence alignment.

Blaisdell, B. Edwin

doi:10.1073/pnas.83.14.5155

Cited by 311 publications

(201 citation statements)

References 46 publications

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“…More will be said about these methods, but they have been applied to gene and genome sequences with varying levels of success (e.g. Blaisdell 1986Blaisdell , 1989Höhl et al 2006;Ferragina et al 2007). They have not yet been applied to ESS.…”

Section: Introductionmentioning

confidence: 99%

The perils of plenty: what are we going to do with all these genes?

Rodrigo

Bertels

Heled

et al. 2008

Phil. Trans. R. Soc. B

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This new century's biology promises more of everything-more genes, more organisms, more species and, in short, more data. The flood of data challenges us to find better and quicker ways to summarize and analyse. Here, we present preliminary results and proofs of concept from three of our research projects that are motivated by our search for solutions to the perils of plenty. First, we discuss how models of evolution can accommodate change to better reflect the dynamics of sequence diversity, particularly when it is becoming a lot easier to obtain sequences at different times and across intervals where the probability of new mutations contributing to this diversity is high. Second, we describe our work on the use of a single locus for species delimitation; this research targets the new DNA-barcoding approach that aims to catalogue the entirety of life. We have developed a single-locus test based on the coalescent that tests the null hypothesis of panmixis. Finally, we discuss new sequencing technologies, the types of data available and the efficacy of alignment-free methods to estimate pairwise distances for phylogenetic analyses.

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Section: Introductionmentioning

confidence: 99%

The perils of plenty: what are we going to do with all these genes?

Rodrigo

Bertels

Heled

et al. 2008

Phil. Trans. R. Soc. B

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“…In the mid-1980s, n-grams were adapted for use in comparing gene sequences [24], an approach typically called "alignmentfree". In this approach, similarity among individual sequences is gauged by comparing the frequency of all n-grams [14,29].…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the use of n-gram-statistics is computationally less expensive, and therefore remains relevant to both linguistics and genomics. The application of n-gram statistics in genomics may be particularly useful for genomics, as the structure of the genome is possibly a finite state.In the mid-1980s, n-grams were adapted for use in comparing gene sequences [24], an approach typically called "alignmentfree". In this approach, similarity among individual sequences is gauged by comparing the frequency of all n-grams [14,29].…”

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confidence: 99%

Genomic Taxonomy Boost by Lexical Clustering

Gramatikoff¹

2014

JIG

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SummaryMultiple sequence alignment is a foundational technique in bioinformatics, and is often the first step in DNA and protein sequence analyses. However, it can be a slow step for genomic scale datasets, a problem that will only get worse as the sheer scale of biological sequence analyses continues to increase. Sequence alignment is also potentially inappropriate when there have been many small-and large-scale rearrangements among the sequences to be aligned, and subsequent analyses may be sensitive to uncertainties in the alignment. In this paper, we propose an alignment-free methodology for sequence comparison, based on n-gram frequency vectors, and demonstrate its ability to detect ontological relationships in biological literature and DNA sequence families (specifically kinases, Alu repeats and promoter sequences of co-expression networks). The methodology is versatile for clustering methods such as classical hierarchical clustering, as well as non-negative matrix factorization. It is also highly efficient in terms of computational time and space requirements, and we foresee it becoming an indispensable tool in genomic sequence analysis. IntroductionAugust Schleicher, Ernst Haeckel, and other 19 th century AbstractIn the post-genomic era, drawing inferences from multiple massive data sets is a ubiquitous challenge in the computational life sciences. Multiple sequence alignment has played a key role in genomics (and other "omics") as a means of summarizing and representing relationships between sequences. However, two problems with alignment-based strategies are apparent: the computational expense of constructing alignments and the sensitivity of subsequent analyses to alignment uncertainties.Here we present a novel alignment-free alternative. We use frequency profiles (or n-gram vectors) for sequence comparison, a method inspired by lexical statistics. Such profiles can be used to infer relationships between texts or between biological sequences, and we demonstrate that two statistical techniques -hierarchical clustering (HC) and non-negative matrix factorization (NMF) -provide invaluable insights in both contexts.We present four case studies. First, we show that bigram frequency profiles can be used to reconstruct the ontology of 102,402 PubMed titles selected for their relevance to nine drugs and nine therapeutic proteins. Second, we apply the same methodology to classify 63 protein kinase coding DNA sequences into functional categories, based on trigram frequency profiles. The two major classes (Tyr vs Ser/Thr) are correctly identified. Third, and similarly, we show that Alu subfamilies can be identified in 58,122 Alu sequences, in perfect agreement with the accepted topology of the Alu phylogeny, again based only on trigram frequency profiles. Fourth, we clustered 8,885 human promoters using trigram frequency profiles for ab initio discovery of co-expression networks associated with disease.We demonstrate that "lexical" statistics offers a viable alignment-free approach to identifying and representing...

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“…But MSA is not without limitations. MSA, based on heuristic algorithms 62 , provides alignment scores for which relevance to homology can be difficult to assess statistically 63 .…”

Section: Research Problemmentioning

confidence: 99%

“…Dot-matrix methods were prefigured by Walter Fitch 63 and others prior to their formal description by Gibbs and MacIntyre 64 . Adrian Gibbs and colleagues considered the dot-matrix to subsume the sliding-window approach 62 , and to be "similar in principle" 64 to a method explained by Saul…”

Section: Oligonucleotides and K-mersmentioning

confidence: 99%

Scalable alignment-free approaches in microbial phylogenomics

Bernard¹

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In the 1970s Carl Woese and colleagues discovered the third domain of life by comparing oligonucleotide catalogs of 16S/18S rRNAs. Four decades later, phylogenetic studies are mostly based on multiple sequence alignment (MSA) approaches. However, genome evolution in microbes involves highly dynamic molecular mechanisms including genome rearrangement and lateral genetic transfer (LGT). These mechanisms can potentially violate the implicit assumption of full-length contiguity in MSA. Furthermore, commonly used MSA-based approaches can necessitate the use of heuristic methods, e.g. Bayesian inference, in reconstructing phylogenies, and these may not be scalable to the quantity of existing and forthcoming genome data. In recent years, alignment-free (AF) methods have been developed as an alternative strategy to infer evolutionary relatedness based on shared subsequences of fixed length, known as k-mers, similarly to Woese's preliminary work. In this thesis, I aimed to study the complex evolution of microbial genomes with the development of novel AF approaches, and systematic assessment of the AF methods' potential for phylogenetic inference. This could potentially provide new insight onto microbial evolution and change the way we do phylogenomics, i.e. potentially lead to the development of "next-generation phylogenomics".The thesis starts with a brief overview of the diversity of microbial life, and the difficulties in understanding microbial evolution due to complex phenomena such as LGT or rearrangement. I explain how phylogenomic approaches can be used to understand microbial evolution, and describe distinct approaches based on MSA and AF.The second chapter is a literature review of the conceptual foundations of alignment-free approaches for the inference of phylogenetic relationships of genome sequences. I discuss the limitations of MSA-based approaches, introduce the concept of k-mers, present in detail the different families of alignment-free approaches and describe their applications to infer vertical and lateral phylogenetic signal among microbial genomes.The three result chapters are presented in the form of research papers, each with its own introduction, methods, results and discussion.

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A measure of the similarity of sets of sequences not requiring sequence alignment.

Cited by 311 publications

References 46 publications

The perils of plenty: what are we going to do with all these genes?

The perils of plenty: what are we going to do with all these genes?

Genomic Taxonomy Boost by Lexical Clustering

Scalable alignment-free approaches in microbial phylogenomics

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