Dataset Information

Increasing sequence search sensitivity with transitive alignments.

ABSTRACT: Sequence alignment is an important bioinformatics tool for identifying homology, but searching against the full set of available sequences is likely to result in many hits to poorly annotated sequences providing very little information. Consequently, we often want alignments against a specific subset of sequences: for instance, we are looking for sequences from a particular species, sequences that have known 3d-structures, sequences that have a reliable (curated) function annotation, and so on. Although such subset databases are readily available, they only represent a small fraction of all sequences. Thus, the likelihood of finding close homologs for query sequences is smaller, and the alignments will in general have lower scores. This makes it difficult to distinguish hits to homologous sequences from random hits to unrelated sequences. Here, we propose a method that addresses this problem by first aligning query sequences against a large database representing the corpus of known sequences, and then constructing indirect (or transitive) alignments by combining the results with alignments from the large database against the desired target database. We compare the results to direct pairwise alignments, and show that our method gives us higher sensitivity alignments against the target database.

SUBMITTER: Malde K

PROVIDER: S-EPMC3573025 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increasing sequence search sensitivity with transitive alignments.

Malde Ketil K Furmanek Tomasz T

PloS one 20130214 2

Sequence alignment is an important bioinformatics tool for identifying homology, but searching against the full set of available sequences is likely to result in many hits to poorly annotated sequences providing very little information. Consequently, we often want alignments against a specific subset of sequences: for instance, we are looking for sequences from a particular species, sequences that have known 3d-structures, sequences that have a reliable (curated) function annotation, and so on. ...[more]

PMID: 23457449

Similar Datasets

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.

Dataset Information

Increasing sequence search sensitivity with transitive alignments.

Publications

Increasing sequence search sensitivity with transitive alignments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets