Dataset Information

Improved repeat identification and masking in Dipterans.

ABSTRACT: Repetitive sequences are a major constituent of many eukaryote genomes and play roles in gene regulation, chromosome inheritance, nuclear architecture, and genome stability. The identification of repetitive elements has traditionally relied on in-depth, manual curation and computational determination of close relatives based on DNA identity. However, the rapid divergence of repetitive sequence has made identification of repeats by DNA identity difficult even in closely related species. Hence, the presence of unidentified repeats in genome sequences affects the quality of gene annotations and annotation-dependent analyses (e.g. microarray analyses). We have developed an enhanced repeat identification pipeline using two approaches. First, the de novo repeat finding program PILER-DF was used to identify interspersed repetitive elements in several recently finished Dipteran genomes. Repeats were classified, when possible, according to their similarity to known elements described in Repbase and GenBank, and also screened against annotated genes as one means of eliminating false positives. Second, we used a new program called RepeatRunner, which integrates results from both RepeatMasker nucleotide searches and protein searches using BLASTX. Using RepeatRunner with PILER-DF predictions, we masked repeats in thirteen Dipteran genomes and conclude that combining PILER-DF and RepeatRunner greatly enhances repeat identification in both well-characterized and un-annotated genomes.

SUBMITTER: Smith CD

PROVIDER: S-EPMC1945102 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Improved repeat identification and masking in Dipterans.

Smith Christopher D CD Edgar Robert C RC Yandell Mark D MD Smith Douglas R DR Celniker Susan E SE Myers Eugene W EW Karpen Gary H GH

Gene 20061012 1

Repetitive sequences are a major constituent of many eukaryote genomes and play roles in gene regulation, chromosome inheritance, nuclear architecture, and genome stability. The identification of repetitive elements has traditionally relied on in-depth, manual curation and computational determination of close relatives based on DNA identity. However, the rapid divergence of repetitive sequence has made identification of repeats by DNA identity difficult even in closely related species. Hence, th ...[more]

PMID: 17137733

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Project description:BackgroundMethods of alignment masking, which refers to the technique of excluding alignment blocks prior to tree reconstructions, have been successful in improving the signal-to-noise ratio in sequence alignments. However, the lack of formally well defined methods to identify randomness in sequence alignments has prevented a routine application of alignment masking. In this study, we compared the effects on tree reconstructions of the most commonly used profiling method (GBLOCKS) which uses a predefined set of rules in combination with alignment masking, with a new profiling approach (ALISCORE) based on Monte Carlo resampling within a sliding window, using different data sets and alignment methods. While the GBLOCKS approach excludes variable sections above a certain threshold which choice is left arbitrary, the ALISCORE algorithm is free of a priori rating of parameter space and therefore more objective.ResultsALISCORE was successfully extended to amino acids using a proportional model and empirical substitution matrices to score randomness in multiple sequence alignments. A complex bootstrap resampling leads to an even distribution of scores of randomly similar sequences to assess randomness of the observed sequence similarity. Testing performance on real data, both masking methods, GBLOCKS and ALISCORE, helped to improve tree resolution. The sliding window approach was less sensitive to different alignments of identical data sets and performed equally well on all data sets. Concurrently, ALISCORE is capable of dealing with different substitution patterns and heterogeneous base composition. ALISCORE and the most relaxed GBLOCKS gap parameter setting performed best on all data sets. Correspondingly, Neighbor-Net analyses showed the most decrease in conflict.ConclusionsAlignment masking improves signal-to-noise ratio in multiple sequence alignments prior to phylogenetic reconstruction. Given the robust performance of alignment profiling, alignment masking should routinely be used to improve tree reconstructions. Parametric methods of alignment profiling can be easily extended to more complex likelihood based models of sequence evolution which opens the possibility of further improvements.

Dataset Information

Improved repeat identification and masking in Dipterans.

Publications

Improved repeat identification and masking in Dipterans.

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