Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology

Sjölander, Kimmen; Karplus, Kevin; Brown, Michael P.; Hughey, Richard; Krogh, Anders; Mian, I. Saira; Haussler, David

doi:10.1093/bioinformatics/12.4.327

Cited by 243 publications

(283 citation statements)

References 34 publications

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“…Sean Eddy and collaborators added Dirichlet mixtures (Sjölander et al 1996) and effective sequence number scalings to the algorithm, which resulted in a significant performance boost for both the 5 and 20 sequence data sets (see Supplemental Tables 1 and 2).…”

Section: Structure-enhanced Homology Searchmentioning

confidence: 99%

“…2) allows for insertions and deletions in the model, and, in addition, deletions can be modeled in a position-dependent manner. To account for overrepresented sequences in the input alignment, tree-weighting schemes can be used (Durbin et al 1998), and there are schemes to avoiding over-fitting and to account for unobserved data in the input (Sjölander et al 1996). …”

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confidence: 99%

“…Figure 2. Additional features added during this investigation are an "effective sequence number" weighting scheme and Dirichlet mixture priors (Sjölander et al 1996). URL: infernal.janelia.org 0.7 (Eddy 2002) RaveNnA Converts a CM generated by Infernal into a profile HMM.…”

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confidence: 99%

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Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA

Freyhult¹,

Bollback²,

Gardner³

2006

Genome Res.

121

100

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Homology search is one of the most ubiquitous bioinformatic tasks, yet it is unknown how effective the currently available tools are for identifying noncoding RNAs (ncRNAs). In this work, we use reliable ncRNA data sets to assess the effectiveness of methods such as BLAST, FASTA, HMMer, and Infernal. Surprisingly, the most popular homology search methods are often the least accurate. As a result, many studies have used inappropriate tools for their analyses. On the basis of our results, we suggest homology search strategies using the currently available tools and some directions for future development.[Supplemental material is available online at www.genome.org and http://www.binf.ku.dk/ ∼ pgardner/bralibase/ bralibase3/.]Compared with the relatively trivial task of protein homology search, ncRNA homology search is more challenging because of the fact that intra-and intermolecular base pairs are, in evolutionary terms, preserved to a higher degree than the sequence. The wobble GU and other noncanonical base pairs allow RNA sequences to evolve seemingly unrelated sequences along nearly neutral paths through structure space (e.g., A · U ↔ G · U ↔ G · C). Thus, specialized homology search techniques, such as nucleotide specific scoring schemes (States et al. 1991), profile hidden Markov model (profile HMMs) (Haussler et al. 1993;Krogh et al. 1994), and covariance models (CMs) (Eddy and Durbin 1994), are necessary for accurate ncRNA homology search.The goal of this study is to identify programs that balance sensitivity (true predictions) and specificity (false predictions) for practical ncRNA homology search situations. We use large highquality ncRNA data sets and randomized control data sets to test the 12 homology search programs summarized in Table 1. Briefly, sequences are sampled from each ncRNA data set and then used as input sequences for each algorithm against the original (true homologs) and randomized data sets. Our test data sets are composed of a subclass of ncRNAs that tend to be highly structured, and therefore there is more information for homology detection than for unconstrained ncRNAs. The algorithms that do not perform well on these data sets are not likely to perform better on more challenging classes of ncRNAs. To ensure our results reflect practical scenarios, we have used both predicted alignments and secondary structures to generate input data for the alignment and structure-based methods.Homology search programs fall into one of three classes: sequence based methods, profile HMM methods, and structure enhanced methods (Fig. 1). In addition to evaluating homology search programs, we extend the use of ancestral sequence reconstructions (ASR) and introduce the novel phylogeny-based predictive sequence reconstruction (PSR) method for use in homology searches (Collins et al. 2003;McCormack 2003;Qian and Goldstein 2003;Cai et al. 2004) to the RNA homology search problem (see Supplemental Fig. 1). Briefly, we discuss each of these in turn.The most popular homology search methods are sequence based....

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Section: Structure-enhanced Homology Searchmentioning

confidence: 99%

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confidence: 99%

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Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA

Freyhult¹,

Bollback²,

Gardner³

2006

Genome Res.

121

100

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“…It will be demonstrated that these classifications are not quite fine enough later in this paper. In order to automatically uncover groups of mutation, deletion, or insertion patterns that tend to be observed together, these generic priors are estimated as a Dirichlet mixture [7] in recent versions of the Infernal [8] suite of programs for covariance-model-based RNA family analysis and search.…”

Section: Introductionmentioning

confidence: 99%

Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search

Smith

2010

Pattern Recognition in Bioinformatics

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Abstract. The effect of more detailed modeling of the interface between stem and loop in non-coding RNA hairpin structures on efficacy of covariance-model-based non-coding RNA gene search is examined. Currently, the prior probabilities of the two stem nucleotides and two loop-end nucleotides at the interface are treated the same as any other stem and loop nucleotides respectively. Laboratory thermodynamic studies show that hairpin stability is dependent on the identities of these four nucleotides, but this is not taken into account in current covariance models. It is shown that separate estimation of emission priors for these nucleotides and joint treatment of substitution probabilities for the two loop-end nucleotides leads to improved non-coding RNA gene search.Keywords: sequence analysis, RNA gene search, covariance models IntroductionCovariance models are an effective method of capturing the joint probability information inherent in the intramolecularly base-paired positions of a non-coding RNA molecule [1,2]. Unlike profile hidden Markov models [3,4], which have a set of four emission probabilities over the possible nucleotides at each consensus sequence position, covariance models allow consensus base pairs to be assigned sixteen joint probabilities over the possible ordered nucleotide pairs. Covariance models also allow the probability of insertion or deletion of a base pair to be different than the sum of the marginal probabilities of insertion or deletion of the individual nucleotides. The profile hidden Markov model can be viewed as a special form of a covariance model with no base pairs specified.

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“…5,6 Since profile HMMs were first introduced for modeling multiple sequence alignments, considerable refinements have been carried out on different aspects of building the HMMs. Improvements have, for example, been done to weighting schemes, 7 null models, 8 prior emission probabilities 9,10 and modeling strategies. 5,6 Database libraries of HMMs 6,11 -13 have been developed to aid annotation of genomes and to study relationships between sequences and protein families.…”

Section: Introductionmentioning

confidence: 99%

Improving Profile HMM Discrimination by Adapting Transition Probabilities

Wistrand

Sonnhammer

2004

Journal of Molecular Biology

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Profile hidden Markov models (HMMs) are used to model protein families and for detecting evolutionary relationships between proteins. Such a profile HMM is typically constructed from a multiple alignment of a set of related sequences. Transition probability parameters in an HMM are used to model insertions and deletions in the alignment. We show here that taking into account unrelated sequences when estimating the transition probability parameters helps to construct more discriminative models for the global/local alignment mode. After normal HMM training, a simple heuristic is employed that adjusts the transition probabilities between match and delete states according to observed transitions in the training set relative to the unrelated (noise) set. The method is called adaptive transition probabilities (ATP) and is based on the HMMER package implementation. It was benchmarked in two remote homology tests based on the Pfam and the SCOP classifications. Compared to the HMMER default procedure, the rate of misclassification was reduced significantly in both tests and across all levels of error rate.

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Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology

Cited by 243 publications

References 34 publications

Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA

Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA

Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search

Improving Profile HMM Discrimination by Adapting Transition Probabilities

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