seq++: analyzing biological sequences with a range of Markov-related models

Miele, Vincent; Bourguignon, Pierre-Yves; Robelin, David; Richard, Hugues

doi:10.1093/bioinformatics/bti389

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…5. Additional computational experiments based on generation of artificial sequences using Markov models of order 2–5 constructed using the seq++ package (Miele et al ., 2005) and trained on the DC3000 genome suggest that this cut‐off corresponds to a false positive rate of three to six hits per genome equivalent for model A. This false positive rate is consistent with the low number of hits (data not shown) in the genomes of the non‐fluorescent pseudomonad strains, Pseudomonas stutzeri A1501 and P. mendocina ymp (Holt et al ., 1994).…”

Section: Methodsmentioning

confidence: 99%

Characterization of the PvdS‐regulated promoter motif in Pseudomonas syringae pv. tomato DC3000 reveals regulon members and insights regarding PvdS function in other pseudomonads

Swingle

Thete

Moll

et al. 2008

Molecular Microbiology

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SummaryBacteria that survive under variable conditions possess an assortment of genetic regulators to meet these challenges. The group IV or extracytoplasmic function (ECF) sigma factors regulate gene expression in response to specific environmental signals by altering the promoter specificity of RNA polymerase. We have undertaken a study of PvdS, a group IV sigma factor encoded by Pseudomonas syringae pv. tomato DC3000 (DC3000), a plant pathogen that is likely to encounter variations in nutrient availability as well as plant host defences. The gene encoding PvdS was previously identified by sequence similarity to the Pseudomonas aeruginosa orthologue, which directs transcription of genes encoding the biosynthesis of pyoverdine, a siderophore involved in iron acquisition, and is responsible for the characteristic fluorescence of the pseudomonads. We identified 15 promoters regulated by PvdS in DC3000 and characterized the promoter motif using computational analysis. Mutagenesis of conserved nucleotides within the motif interfered with promoter function and the degree of the effect was different depending on which region of the motif was mutated. Hidden Markov models constructed from alignments of sequence motifs extracted from DC3000 and PAO1 were used to query genomes of DC3000 and other fluorescent pseudomonads for similar motifs. We conclude that the role of PvdS as a regulator of pyoverdine synthesis is conserved among the fluorescent pseudomonads, but the promoters recognized by PvdS orthologues may differ subtly from species to species.

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Section: Methodsmentioning

confidence: 99%

Characterization of the PvdS‐regulated promoter motif in Pseudomonas syringae pv. tomato DC3000 reveals regulon members and insights regarding PvdS function in other pseudomonads

Swingle

Thete

Moll

et al. 2008

Molecular Microbiology

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“…We used Markov model (MM) in seq++ package (27) to generate the negative data set. From CpG island sequences, the Markov model parameters were estimated and the negative sequences were generated using the Markov model.…”

Section: Resultsmentioning

confidence: 99%

Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes

Chae

Park

Lee

et al. 2013

Nucleic Acids Research

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CpG islands are GC-rich regions often located in the 5′ end of genes and normally protected from cytosine methylation in mammals. The important role of CpG islands in gene transcription strongly suggests evolutionary conservation in the mammalian genome. However, as CpG dinucleotides are over-represented in CpG islands, comparative CpG island analysis using conventional sequence analysis techniques remains a major challenge in the epigenetics field. In this study, we conducted a comparative analysis of all CpG island sequences in 10 mammalian genomes. As sequence similarity methods and character composition techniques such as information theory are particularly difficult to conduct, we used exact patterns in CpG island sequences and single character discrepancies to identify differences in CpG island sequences. First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes. Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers. In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.

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“…This software is written in ANSI C++ and developed on x86 GNU/Linux systems with GCC 3.4, and successfully tested with GCC latest versions on Sun and Apple Mac OSX systems. It relies on seq++ library (Miele et al 2005) and will soon be integrated on seq++ library. Compilation and installation are compliant with the GNU standard procedure.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences

Vergne¹

2008

Statistical Applications in Genetics and Molecular Biology

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In this article, we introduce the drifting Markov models (DMMs) which are inhomogeneous Markov models designed for modeling the heterogeneities of sequences (in our case DNA or protein sequences) in a more flexible way than homogeneous Markov chains or even hidden Markov models (HMMs). We focus here on the polynomial drift: the transition matrix varies in a polynomial way. To show the reliability of our models on DNA, we exhibit high similarities between the probability distributions of nucleotides obtained by our models and the frequencies of these nucleotides computed by using a sliding window. In a further step, these DMMs can be used as the states of an HMM: on each of its segments, the observed process can be modeled by a drifting Markov model. Search of rare words in DNA sequences remains possible with DMMs and according to the fits provided, DMMs turn out to be a powerful tool for this purpose. The software is available on request from the author. It will soon be integrated on seq++ library (http://stat.genopole.cnrs.fr/seqpp/).

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seq++: analyzing biological sequences with a range of Markov-related models

Cited by 7 publications

References 4 publications

Characterization of the PvdS‐regulated promoter motif in Pseudomonas syringae pv. tomato DC3000 reveals regulon members and insights regarding PvdS function in other pseudomonads

Characterization of the PvdS‐regulated promoter motif in Pseudomonas syringae pv. tomato DC3000 reveals regulon members and insights regarding PvdS function in other pseudomonads

Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes

Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences

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