Dataset Information

Mining endonuclease cleavage determinants in genomic sequence data.

ABSTRACT: Homing endonucleases have great potential as tools for targeted gene therapy and gene correction, but identifying variants of these enzymes capable of cleaving specific DNA targets of interest is necessary before the widespread use of such technologies is possible. We identified homologues of the LAGLIDADG homing endonuclease I-AniI and their putative target insertion sites by BLAST searches followed by examination of the sequences of the flanking genomic regions. Amino acid substitutions in these homologues that were located close to the target site DNA, and thus potentially conferring differences in target specificity, were grafted onto the I-AniI scaffold. Many of these grafts exhibited novel and unexpected specificities. These findings show that the information present in genomic data can be exploited for endonuclease specificity redesign.

SUBMITTER: Szeto MD

PROVIDER: S-EPMC3173205 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mining endonuclease cleavage determinants in genomic sequence data.

Szeto Mindy D MD Boissel Sandrine J S SJ Baker David D Thyme Summer B SB

The Journal of biological chemistry 20110721 37

Homing endonucleases have great potential as tools for targeted gene therapy and gene correction, but identifying variants of these enzymes capable of cleaving specific DNA targets of interest is necessary before the widespread use of such technologies is possible. We identified homologues of the LAGLIDADG homing endonuclease I-AniI and their putative target insertion sites by BLAST searches followed by examination of the sequences of the flanking genomic regions. Amino acid substitutions in the ...[more]

PMID: 21778233

Similar Datasets

Project description:Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.

Dataset Information

Mining endonuclease cleavage determinants in genomic sequence data.

Publications

Mining endonuclease cleavage determinants in genomic sequence data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets