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Dinucleotide controlled null models for comparative RNA gene prediction.

ABSTRACT: BACKGROUND: Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. RESULTS: We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. CONCLUSION: SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. AVAILABILITY: SISSIz is available as open source C code that can be compiled for every major platform and downloaded here: http://sourceforge.net/projects/sissiz.

SUBMITTER: Gesell T

PROVIDER: S-EPMC2453142 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Dinucleotide controlled null models for comparative RNA gene prediction.

Gesell Tanja T Washietl Stefan S

BMC bioinformatics 20080527

<h4>Background</h4>Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. ...[more]

PMID: 18505553

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Project description:We are reporting qualitative and quantitative changes of the extracellular matrix (ECM) and associated receptor proteomes, occurring during the transition from liver fibrosis and steatohepatitis to hepatocellular carcinoma (HCC). We compared two mouse models relevant to human HCC: PDGFC transgenic (Tg) and Pten null mice, models of disease progression from fibrosis and steatohepatitis to HCC. Using mass spectrometry, we identified in the liver of both models proteins for 26 collagen-encoding genes, providing the first evidence of expression at the protein level for 16 collagens. We also identified post-transcriptional protein variants for six collagens and lysine hydroxylation modifications for 14 collagens. Tumor-associated collagen proteomes were similar in both models with increased expression of collagens type IV, VI, VII, X, XIV, XV, XVI, and XVIII. Splice variants for Col4a2, Col6a2, Col6a3 were co-upregulated while only the short form of Col18a1 increased in the tumors. We also identified tumor specific increases of nidogen 1, decorin, perlecan, and of six laminin subunits. The changes in these non-collagenous ECM proteins were similar in both models with the exception of laminin β3, detected specifically in the Pten null tumors. Pdgfa and Pdgfc mRNA expression was increased in the Pten null liver, a possible mechanism for the similarity in ECM composition observed in the tumors of both models. In contrast and besides the strong up-regulation of integrin α5 protein observed in the liver tumors of both models, the expression of the six other integrins identified was specific to each model, with integrins α2b, α3, α6, and β1 up-regulated in Pten null tumors and integrins α8 and β5 up-regulated in the PDGFC Tg tumors. In conclusion, HCC-associated ECM proteins and ECM-integrin networks, common or specific to HCC subtypes, were identified, providing a unique foundation to using ECM composition for HCC classification, diagnosis, prevention, or treatment.

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Dinucleotide controlled null models for comparative RNA gene prediction.

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Dinucleotide controlled null models for comparative RNA gene prediction.

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