SABmark—a benchmark for sequence alignment that covers the entire known fold space

Walle, Ivo Van; Lasters, Ignace; Wyns, Lode

doi:10.1093/bioinformatics/bth493

Cited by 204 publications

(181 citation statements)

References 10 publications

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“…Unlike protein alignments, where structural information can be used to generate reference alignments (Van Walle et al 2005), or the prediction of transcription factor-binding sites, where experimental data can be used to define bound and unbound sites (Tompa et al 2005), no such "gold standards" exist for genomic sequence alignments. The challenge is to define measurements for alignment specificity (i.e., fraction of orthology predictions that are correct) and sensitivity (i.e., fraction of all truly orthologous relationships that are correctly predicted).…”

Section: Generation Of Multisequence Alignmentsmentioning

confidence: 99%

Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome

Margulies¹,

Cooper²,

Asimenos³

et al. 2007

Genome Res.

190

224

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A key component of the ongoing ENCODE project involves rigorous comparative sequence analyses for the initially targeted 1% of the human genome. Here, we present orthologous sequence generation, alignment, and evolutionary constraint analyses of 23 mammalian species for all ENCODE targets. Alignments were generated using four different methods; comparisons of these methods reveal large-scale consistency but substantial differences in terms of small genomic rearrangements, sensitivity (sequence coverage), and specificity (alignment accuracy). We describe the quantitative and qualitative trade-offs concomitant with alignment method choice and the levels of technical error that need to be accounted for in applications that require multisequence alignments. Using the generated alignments, we identified constrained regions using three different methods. While the different constraint-detecting methods are in general agreement, there are important discrepancies relating to both the underlying alignments and the specific algorithms. However, by integrating the results across the alignments and constraint-detecting methods, we produced constraint annotations that were found to be robust based on multiple independent measures. Analyses of these annotations illustrate that most classes of experimentally annotated functional elements are enriched for constrained sequences; however, large portions of each class (with the exception of protein-coding sequences) do not overlap constrained regions. The latter elements might not be under primary sequence constraint, might not be constrained across all mammals, or might have expendable molecular functions. Conversely, 40% of the constrained sequences do not overlap any of the functional elements that have been experimentally identified. Together, these findings demonstrate and quantify how many genomic functional elements await basic molecular characterization.

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Section: Generation Of Multisequence Alignmentsmentioning

confidence: 99%

Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome

Margulies¹,

Cooper²,

Asimenos³

et al. 2007

Genome Res.

190

224

View full text Add to dashboard Cite

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“…(BENCH itself is a selection of benchmarks from BAliBASE (Bahr et al, 2001), OXBench (Russell and Barton, 1992), and SABRE (Van Walle et al, 2005).) Both BENCH and PALI consist of protein multiple sequence alignments mainly induced by structural alignment of the known three-dimensional structures of the proteins.…”

Section: Resultsmentioning

confidence: 99%

Accuracy Estimation and Parameter Advising for Protein Multiple Sequence Alignment

Kececioglu

DeBlasio

2013

Journal of Computational Biology

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We develop a novel and general approach to estimating the accuracy of multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new task that we call parameter advising: the problem of choosing values for alignment scoring function parameters from a given set of choices to maximize the accuracy of a computed alignment.For protein alignments, we consider twelve independent features that contribute to a quality alignment. An accuracy estimator is learned that is a polynomial function of these features; its coefficients are determined by minimizing its error with respect to true accuracy using mathematical optimization. Compared to prior approaches for estimating accuracy, our new approach (a) introduces novel feature functions that measure nonlocal properties of an alignment yet are fast to evaluate, (b) considers more general classes of estimators beyond linear combinations of features, and (c) develops new regression formulations for learning an estimator from examples; in addition, for parameter advising, we (d) determine the optimal parameter set of a given cardinality, which specifies the best parameter values from which to choose.Our estimator, which we call Facet (for ''feature-based accuracy estimator''), yields a parameter advisor that on the hardest benchmarks provides more than a 27% improvement in accuracy over the best default parameter choice, and for parameter advising significantly outperforms the best prior approaches to assessing alignment quality.

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“…The test is performed on a more limited dataset comprising of 12 protein families which were either studied in previous works [8,10,15,17] or chosen from the SABmark dataset [60]. The alignment quality is quantified in terms of N rms , N dist and N 3.5 .…”

Section: Resultsmentioning

confidence: 99%

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

2012

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Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string.A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition.Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.3

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SABmark—a benchmark for sequence alignment that covers the entire known fold space

Cited by 204 publications

References 10 publications

Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome

Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome

Accuracy Estimation and Parameter Advising for Protein Multiple Sequence Alignment

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

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