Dataset Information

MAVL/StickWRLD: analyzing structural constraints using interpositional dependencies in biomolecular sequence alignments.

ABSTRACT: The increasing availability of structurally aligned protein families has made it possible to use statistical methods to discover regions of interpositional dependencies of residue identity. Such dependencies amongst residues often have structural or functional implications, and their discovery can supply valuable constraints that assist in the refinement of measured, or predicted molecular structure assignments. Multiple Alignment Variation Linker (MAVL) and StickWRLD [W. Ray (2004) Nucleic Acids Res., 32, W59-W63] were developed to analyze and visualize nucleic acid and protein alignments, to discover and illuminate position/location relationships to the user. The original system analyzed users' data from a web-form submission and presented the user with a static VRML diagram describing their data. We are pleased to report that MAVL/StickWRLD has been completely redesigned and rewritten. MAVL/StickWRLD now functions as a platform-independent Java applet, with real-time dynamic controls that enable much more intuitive exploration and interaction with the data. The system has also been upgraded to enable visualization of a range of aggregate residue properties, and an extensive database of pre-computed StickWRLD diagrams based on PFAM families is now available directly from the interface. The Java StickWRLD applet is available via the WWW at http://www.microbial-pathogenesis.org/stickwrld/.

SUBMITTER: Ozer HG

PROVIDER: S-EPMC1538782 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature

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MAVL/StickWRLD: analyzing structural constraints using interpositional dependencies in biomolecular sequence alignments.

Ozer Hatice Gulcin HG Ray William C WC

Nucleic acids research 20060701 Web Server issue

The increasing availability of structurally aligned protein families has made it possible to use statistical methods to discover regions of interpositional dependencies of residue identity. Such dependencies amongst residues often have structural or functional implications, and their discovery can supply valuable constraints that assist in the refinement of measured, or predicted molecular structure assignments. Multiple Alignment Variation Linker (MAVL) and StickWRLD [W. Ray (2004) Nucleic Acid ...[more]

PMID: 16844976

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Project description:BackgroundWhile the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.ResultsWe compared near-optimal protein sequence alignments produced by the Zuker algorithm and a set of probabilistic alignments produced by the probA program with structural alignments produced by four different structure alignment algorithms. There is significant overlap between the solution spaces of structural alignments and both the near-optimal sequence alignments produced by commonly used scoring parameters for sequences that share significant sequence similarity (E-values < 10-5) and the ensemble of probA alignments. We constructed a logistic regression model incorporating three input variables derived from sets of near-optimal alignments: robustness, edge frequency, and maximum bits-per-position. A ROC analysis shows that this model more accurately classifies amino acid pairs (edges in the alignment path graph) according to the likelihood of appearance in structural alignments than the robustness score alone. We investigated various trimming protocols for removing incorrect edges from the optimal sequence alignment; the most effective protocol is to remove matches from the semi-global optimal alignment that are outside the boundaries of the local alignment, although trimming according to the model-generated probabilities achieves a similar level of improvement. The model can also be used to generate novel alignments by using the probabilities in lieu of a scoring matrix. These alignments are typically better than the optimal sequence alignment, and include novel correct structural edges. We find that the probA alignments sample a larger variety of alignments than the Zuker set, which more frequently results in alignments that are closer to the structural alignments, but that using the probA alignments as input to the regression model does not increase performance.ConclusionsThe pool of suboptimal pairwise protein sequence alignments substantially overlaps structure-based alignments for pairs with statistically significant similarity, and a regression model based on information contained in this alignment pool improves the accuracy of pairwise alignments with respect to structure-based alignments.

Project description:BackgroundGenomic sequence data cannot be fully appreciated in isolation. Comparative genomics--the practice of comparing genomic sequences from different species--plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program.ResultsWe tested the GenAlignRefine algorithm by running it on a Linux cluster to refine sequences from a simulation, as well as refine a multiple alignment of 15 Orthopoxvirus genomic sequences approximately 260,000 nucleotides in length that initially had been aligned by Multi-LAGAN. It took approximately 150 minutes for a 40-processor Linux cluster to optimize some 200 fuzzy (poorly aligned) regions of the orthopoxvirus alignment. Overall sequence identity increased only slightly; but significantly, this occurred at the same time that the overall alignment length decreased--through the removal of gaps--by approximately 200 gapped regions representing roughly 1,300 gaps.ConclusionWe have implemented a genetic algorithm in parallel mode to optimize multiple genomic sequence alignments initially generated by various alignment tools. Benchmarking experiments showed that the refinement algorithm improved genomic sequence alignments within a reasonable period of time.

Dataset Information

MAVL/StickWRLD: analyzing structural constraints using interpositional dependencies in biomolecular sequence alignments.

Publications

MAVL/StickWRLD: analyzing structural constraints using interpositional dependencies in biomolecular sequence alignments.

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