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Revealing hidden complexities of genomic rearrangements generated with Cas9.

ABSTRACT: Modelling human diseases caused by large genomic rearrangements has become more accessible since the utilization of CRISPR/Cas9 in mammalian systems. In a previous study, we showed that genomic rearrangements of up to one million base pairs can be generated by direct injection of CRISPR/Cas9 reagents into mouse zygotes. Although these rearrangements are ascertained by junction PCR, we describe here a variety of anticipated structural changes often involving reintegration of the region demarcated by the gRNAs in the vicinity of the edited locus. We illustrate here some of this diversity detected by high-resolution fibre-FISH and conclude that extensive molecular analysis is required to fully understand the structure of engineered chromosomes generated by Cas9.

SUBMITTER: Boroviak K

PROVIDER: S-EPMC5634419 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Revealing hidden complexities of genomic rearrangements generated with Cas9.

Boroviak Katharina K Fu Beiyuan B Yang Fengtang F Doe Brendan B Bradley Allan A

Scientific reports 20171009 1

Modelling human diseases caused by large genomic rearrangements has become more accessible since the utilization of CRISPR/Cas9 in mammalian systems. In a previous study, we showed that genomic rearrangements of up to one million base pairs can be generated by direct injection of CRISPR/Cas9 reagents into mouse zygotes. Although these rearrangements are ascertained by junction PCR, we describe here a variety of anticipated structural changes often involving reintegration of the region demarcated ...[more]

PMID: 28993641

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Project description:Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1].

Dataset Information

Revealing hidden complexities of genomic rearrangements generated with Cas9.

Publications

Revealing hidden complexities of genomic rearrangements generated with Cas9.

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