| 1. |
- Dress, Andreas, et al.
(författare)
-
Hereditarily Optimal Realizations of Consistent Metrics.
- 2006
-
Ingår i: Annals of Combinatorics. - : Springer Science and Business Media LLC. - 0218-0006 .- 0219-3094. ; 10:1, s. 63-76
-
Tidskriftsartikel (refereegranskat)abstract
- One of the main problems in phylogenetics is to find good approximations of metrics by weighted trees. As an aid to solving this problem, it could be tempting to consider optimal realizations of metrics—the guiding principle being that, the (necessarily unique) optimal realization of a tree metric is the weighted tree that realizes this metric. And, although optimal realizations of arbitrary metrics are, in general, not trees, but rather weighted networks, one could still hope to obtain a phylogenetically informative representation of a given metric, maybe even more informative than the best approximating tree. However, optimal realizations are not only difficult to compute, they may also be non-unique. Here we focus on one possible way out of this dilemma: hereditarily optimal realizations. These are essentially unique, and can be described in a rather explicit way. In this paper, we recall what a hereditarily optimal realization of a metric is and how it is related to the 1-skeleton of the tight span of that metric, and we investigate under what conditions it coincides with this 1-skeleton. As a consequence, we will show that hereditarily optimal realizations for consistent metrics, a large class of phylogentically relevant metrics, can be computed in a straight-forward fashion.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Freyhult, Eva, et al.
(författare)
-
Boltzmann probability of RNA structural neighbors and riboswitch detection
- 2007
-
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 23:16, s. 2054-2062
-
Tidskriftsartikel (refereegranskat)abstract
- Motivation: We describe algorithms implemented in a new software package, RNAbor, to investigate structures in a neighborhood of an input secondary structure of an RNA sequence s. The input structure could be the minimum free energy structure, the secondary structure obtained by analysis of the X-ray structure or by comparative sequence analysis, or an arbitrary intermediate structure. Results: A secondary structure of s is called a -neighbor of if and differ by exactly base pairs. RNAbor computes the number (N), the Boltzmann partition function (Z) and the minimum free energy (MFE) and corresponding structure over the collection of all -neighbors of . This computation is done simultaneously for all m, in run time O (mn3) and memory O(mn2), where n is the sequence length. We apply RNAbor for the detection of possible RNA conformational switches, and compare RNAbor with the switch detection method paRNAss. We also provide examples of how RNAbor can at times improve the accuracy of secondary structure prediction.
|
|
| 5. |
- Freyhult, Eva, et al.
(författare)
-
Fisher: a program for the detection of H/ACA snoRNAs using MFE secondary structure prediction and comparative genomics - assessment and update
- 2008
-
Ingår i: BMC Research Notes. - : Springer Science and Business Media LLC. - 1756-0500. ; 1:49, s. 1-8
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundThe H/ACA family of small nucleolar RNAs (snoRNAs) plays a central role in guiding the pseudouridylation of ribosomal RNA (rRNA). In an effort to systematically identify the complete set of rRNA-modifying H/ACA snoRNAs from the genome sequence of the budding yeast, Saccharomyces cerevisiae, we developed a program - Fisher - and previously presented several candidate snoRNAs based on our analysis [1]. FindingsIn this report, we provide a brief update of this work, which was aborted after the publication of experimentally-identified snoRNAs [2] identical to candidates we had identified bioinformatically using Fisher. Our motivation for revisiting this work is to report on the status of the candidate snoRNAs described in [1], and secondly, to report that a modified version of Fisher together with the available multiple yeast genome sequences was able to correctly identify several H/ACA snoRNAs for modification sites not identified by the snoGPS program [3]. While we are no longer developing Fisher, we briefly consider the merits of the Fisher algorithm relative to snoGPS, which may be of use for workers considering pursuing a similar search strategy for the identification of small RNAs. The modified source code for Fisher is made available as supplementary material. ConclusionOur results confirm the validity of using minimum free energy (MFE) secondary structure prediction to guide comparative genomic screening for RNA families with few sequence constraints.
|
|
| 6. |
|
|
| 7. |
- Freyhult, Eva, et al.
(författare)
-
Predicting RNA structure using mutual information.
- 2005
-
Ingår i: Applied Bioinformatics. - 1175-5636. ; 4:1, s. 53-59
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB((R)) toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. RESULTS: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. CONCLUSION: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. AVAILABILITY: MIfold is freely available from http://www.lcb.uu.se/~evaf/MIfold/
|
|
| 8. |
- Freyhult, Eva, et al.
(författare)
-
RNAbor : a web server for RNA structural neighbors
- 2007
-
Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 35:Suppl. S: Web Server issue, s. W305-W309
-
Tidskriftsartikel (refereegranskat)abstract
- RNAbor provides a new tool for researchers in the biological and related sciences to explore important aspects of RNA secondary structure and folding pathways. RNAbor computes statistics concerning delta-neighbors of a given input RNA sequence and structure (the structure can, for example, be the minimum free energy (MFE) structure). A delta-neighbor is a structure that differs from the input structure by exactly delta base pairs, that is, it can be obtained from the input structure by adding and/or removing exactly d base pairs. For each distance delta RNAbor computes the density of delta-neighbors, the number of delta-neighbors, and the MFE structure, or MFEd structure, among all delta-neighbors. RNAbor can be used to study possible folding pathways, to determine alternate low-energy structures, to predict potential nucleation sites and to explore structural neighbors of an intermediate, biologically active structure. The web server is available at http://bioinformatics.bc.edu/clotelab/RNAbor.
|
|
| 9. |
- Freyhult, Eva, et al.
(författare)
-
Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling
- 2005
-
Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 6, s. 50-
-
Tidskriftsartikel (refereegranskat)abstract
- Background Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis. Results A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small. Conclusion The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.
|
|
| 10. |
|
|
