Dataset Information

SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations.

ABSTRACT:

Background

Single Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development. Several approaches have been developed to identify highly mutated regions, but these do not take into account the specific genomic context, and thus likelihood of mutation, of CREs.

Results

Here, we present SMuRF (Significantly Mutated Region Finder), a user-friendly command-line tool to identify these significantly mutated regions from user-defined genomic intervals and SNVs. We demonstrate this using publicly available datasets in which SMuRF identifies 72 significantly mutated CREs in liver cancer, including known mutated gene promoters as well as previously unreported regions.

Conclusions

SMuRF is a helpful tool to allow the simple identification of significantly mutated regulatory elements. It is open-source and freely available on GitHub ( https://github.com/LupienLab/SMURF ).

SUBMITTER: Guilhamon P

PROVIDER: S-EPMC6258448 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations.

Guilhamon Paul P Lupien Mathieu M

BMC bioinformatics 20181126 1

<h4>Background</h4>Single Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development. Several approaches have been developed to identify highly mutated regions, but these do not take into account the specific genomic context, and thus likelihood of mutation, of CREs.<h4>Results</h4>Here, we present SMuRF (Significantly Mutated Region Finder), a use ...[more]

PMID: 30477433

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Project description:BackgroundAccurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed.ResultsWe developed a novel method to identify composite regulatory elements in promoters using a library of known examples. In depth investigation of regularities encoded in known composite elements allowed us to introduce a new characteristic measure and to improve the specificity compared with other methods. Tests on an established benchmark and real genomic data show that our method outperforms other available methods based either on known examples or statistical evaluations. In addition to better recognition, a practical advantage of this method is first the ability to detect a high number of different types of composite elements, and second direct biological interpretation of the identified results. The program is available at http://gnaweb.helmholtz-hzi.de/cgi-bin/MCatch/MatrixCatch.pl and includes an option to extend the provided library by user supplied data.ConclusionsThe novel algorithm for the identification of composite regulatory elements presented in this paper was proved to be superior to existing methods. Its application to tissue specific promoters identified several highly specific composite elements with relevance to their biological function. This approach together with other methods will further advance the understanding of transcriptional regulation of genes.

Project description:BackgroundOccult Hepatitis B Infection (OBI) is characterized by absence of serum HBsAg and persistence of HBV-DNA in liver tissue, with low to undetectable serum HBV-DNA. The mechanisms underlying OBI remain to be clarified. To evaluate if specific point mutations of HBV genome may be associated with OBI, we applied an approach based on bioinformatics analysis of complete genome HBV sequences. In addition, the feasibility of bioinformatics prediction models to classify HBV infections into OBI and non-OBI by molecular data was evaluated.Methods41 OBI and 162 non-OBI complete genome sequences were retrieved from GenBank, aligned and subjected to univariable analysis including statistical evaluation. Their S coding region was analyzed for Stop codon mutations too, while S amino acid variability could be evaluated for genotype D only, due to the too small number of available complete genome OBI sequences from other genotypes.Prediction models were derived by multivariable analysis using Logistic Regression, Rule Induction and Random Forest approaches, with extra-sample error estimation by Multiple ten-fold Cross-Validation (MCV). Models were compared by t-test on the Area Under the Receiver Operating Characteristic curve (AUC) distributions obtained from the MCV runs for each model against the best-performing model.ResultsVariations in seven nucleotide positions were significantly associated with OBI, and occurred in 11 out of 41 OBI sequences (26.8%): likely, other mutations did not reach statistical significance due to the small size of OBI dataset. All variations affected at least one HBV coding region, but none of them mapped to regulative elements. All viral proteins, with the only exception of the X, were affected. Stop codons in the S, that might account for absence of serum HBsAg, were not significantly enriched in OBI sequences. In genotype D, amino acid variability in the S was higher in OBI than non-OBI, particularly in the immunodominant region. A Random Forest prediction model showed the best performance, but all models were not satisfactory in terms of specificity, due to the small sample size of OBIs; however results are promising in the perspective of a broader dataset of complete genome OBI sequences.ConclusionsData suggest that point mutations rarely occur in regulative elements of HBV, if ever, and contribute to OBI by affecting different viral proteins, suggesting heterogeneous mechanisms may be responsible for OBI, including, at least in genotype D, an escape mutation mechanism due to imperfect immune control. It appears possible to derive prediction models based on molecular data when a larger set of complete genome OBI sequences will become available.

Dataset Information

SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations.

Background

Results

Conclusions

Publications

SMuRF: a novel tool to identify regulatory elements enriched for somatic point mutations.

Similar Datasets

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