Avoiding Ambiguity and Assessing Uniqueness in Minisatellite Alignment

Several algorithms have been suggested for minisatellite alignment. Their time complexity is high -- close to O(n^3) -- due to the necessary reconstruction of duplication histories. We investigate the uniqueness of optimal alignments computed under the common single-copy duplication model. To this extent, it is necessary to avoid ambiguity in the algorithm employed. We re-code the ARLEM algorithm in the form of a grammar, and apply a disambiguation technique which uses a mapping to a canonical representation of minisatellite alignments. Having arrived at a non-ambiguous algorithm this way, we demonstrate that the underlying model -- independent of the algorithm -- gives rise to an exorbitant number of different, co-optimal alignments when applied to real-world data. We conclude that alignment-free methods should be considered for minisatellite comparison

Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics

BACKGROUND: The general problem of RNA secondary structure prediction under the widely used thermodynamic model is known to be NP-complete when the structures considered include arbitrary pseudoknots. For restricted classes of pseudoknots, several polynomial time algorithms have been designed, where the O(n(6))time and O(n(4)) space algorithm by Rivas and Eddy is currently the best available program. RESULTS: We introduce the class of canonical simple recursive pseudoknots and present an algorithm that requires O(n(4)) time and O(n(2)) space to predict the energetically optimal structure of an RNA sequence, possible containing such pseudoknots. Evaluation against a large collection of known pseudoknotted structures shows the adequacy of the canonization approach and our algorithm. CONCLUSIONS: RNA pseudoknots of medium size can now be predicted reliably as well as efficiently by the new algorithm

A comprehensive comparison of comparative RNA structure prediction approaches

BACKGROUND: An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms. RESULTS: Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. CONCLUSIONS: We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research

mkESA: enhanced suffix array construction tool

Summary: We introduce the tool mkESA, an open source program for constructing enhanced suffix arrays (ESAs), striving for low memory consumption, yet high practical speed. mkESA is a user-friendly program written in portable C99, based on a parallelized version of the Deep-Shallow suffix array construction algorithm, which is known for its high speed and small memory usage. The tool handles large FASTA files with multiple sequences, and computes suffix arrays and various additional tables, such as the LCP table (longest common prefix) or the inverse suffix array, from given sequence data

Significant speedup of database searches with HMMs by search space reduction with PSSM family models

Motivation: Profile hidden Markov models (pHMMs) are currently the most popular modeling concept for protein families. They provide sensitive family descriptors, and sequence database searching with pHMMs has become a standard task in today's genome annotation pipelines. On the downside, searching with pHMMs is computationally expensive

XenDB: Full length cDNA prediction and cross species mapping in Xenopus laevis

BACKGROUND: Research using the model system Xenopus laevis has provided critical insights into the mechanisms of early vertebrate development and cell biology. Large scale sequencing efforts have provided an increasingly important resource for researchers. To provide full advantage of the available sequence, we have analyzed 350,468 Xenopus laevis Expressed Sequence Tags (ESTs) both to identify full length protein encoding sequences and to develop a unique database system to support comparative approaches between X. laevis and other model systems. DESCRIPTION: Using a suffix array based clustering approach, we have identified 25,971 clusters and 40,877 singleton sequences. Generation of a consensus sequence for each cluster resulted in 31,353 tentative contig and 4,801 singleton sequences. Using both BLASTX and FASTY comparison to five model organisms and the NR protein database, more than 15,000 sequences are predicted to encode full length proteins and these have been matched to publicly available IMAGE clones when available. Each sequence has been compared to the KOG database and ~67% of the sequences have been assigned a putative functional category. Based on sequence homology to mouse and human, putative GO annotations have been determined. CONCLUSION: The results of the analysis have been stored in a publicly available database XenDB . A unique capability of the database is the ability to batch upload cross species queries to identify potential Xenopus homologues and their associated full length clones. Examples are provided including mapping of microarray results and application of 'in silico' analysis. The ability to quickly translate the results of various species into 'Xenopus-centric' information should greatly enhance comparative embryological approaches. Supplementary material can be found at

Efficient computation of absent words in genomic sequences

Herold J, Kurtz S, Giegerich R. Efficient computation of absent words in genomic sequences. BMC Bioinformatics. 2008;9(1): 167.Background: Analysis of sequence composition is a routine task in genome research. Organisms are characterized by their base composition, dinucleotide relative abundance, codon usage, and so on. Unique subsequences are markers of special interest in genome comparison, expression profiling, and genetic engineering. Relative to a random sequence of the same length, unique subsequences are overrepresented in real genomes. Shortest words absent from a genome have been addressed in two recent studies. Results: We describe a new algorithm and software for the computation of absent words. It is more efficient than previous algorithms and easier to use. It directly computes unwords without the need to specify a length estimate. Moreover, it avoids the space requirements of index structures such as suffix trees and suffix arrays. Our implementation is available as an open source package. We compute unwords of human and mouse as well as some other organisms, covering a genome size range from 109 down to 105 bp. Conclusion: The new algorithm computes absent words for the human genome in 10 minutes on standard hardware, using only 2.5 Mb of space. This enables us to perform this type of analysis not only for the largest genomes available so far, but also for the emerging pan- and meta-genome data

Fine-tuning structural RNA alignments in the twilight zone

Bremges A, Schirmer S, Giegerich R. Fine-tuning structural RNA alignments in the twilight zone. BMC Bioinformatics. 2010;11(1): 222

Complete probabilistic analysis of RNA shapes

BACKGROUND: Soon after the first algorithms for RNA folding became available, it was recognised that the prediction of only one energetically optimal structure is insufficient to achieve reliable results. An in-depth analysis of the folding space as a whole appeared necessary to deduce the structural properties of a given RNA molecule reliably. Folding space analysis comprises various methods such as suboptimal folding, computation of base pair probabilities, sampling procedures and abstract shape analysis. Common to many approaches is the idea of partitioning the folding space into classes of structures, for which certain properties can be derived. RESULTS: In this paper we extend the approach of abstract shape analysis. We show how to compute the accumulated probabilities of all structures that share the same shape. While this implies a complete (non-heuristic) analysis of the folding space, the computational effort depends only on the size of the shape space, which is much smaller. This approach has been integrated into the tool RNAshapes, and we apply it to various RNAs. CONCLUSION: Analyses of conformational switches show the existence of two shapes with probabilities approximately [Formula: see text] vs. [Formula: see text] , whereas the analysis of a microRNA precursor reveals one shape with a probability near to 1.0. Furthermore, it is shown that a shape can outperform an energetically more favourable one by achieving a higher probability. From these results, and the fact that we use a complete and exact analysis of the folding space, we conclude that this approach opens up new and promising routes for investigating and understanding RNA secondary structure