Dataset Information

Fast and efficient Drosophila melanogaster gene knock-ins using MiMIC transposons.

ABSTRACT: Modern molecular genetics studies necessitate the manipulation of genes in their endogenous locus, but most of the current methodologies require an inefficient donor-dependent homologous recombination step to locally modify the genome. Here we describe a methodology to efficiently generate Drosophila knock-in alleles by capitalizing on the availability of numerous genomic MiMIC transposon insertions carrying recombinogenic attP sites. Our methodology entails the efficient PhiC31-mediated integration of a recombination cassette flanked by unique I-SceI and/or I-CreI restriction enzyme sites into an attP-site. These restriction enzyme sites allow for double-strand break-mediated removal of unwanted flanking transposon sequences, while leaving the desired genomic modifications or recombination cassettes. As a proof-of-principle, we mutated LRRK, tau, and sky by using different MiMIC elements. We replaced 6 kb of genomic DNA encompassing the tau locus and 35 kb encompassing the sky locus with a recombination cassette that permits easy integration of DNA at these loci and we also generated a functional LRRK(HA) knock in allele. Given that ~92% of the Drosophila genes are located within the vicinity (<35 kb) of a MiMIC element, our methodology enables the efficient manipulation of nearly every locus in the fruit fly genome without the need for inefficient donor-dependent homologous recombination events.

SUBMITTER: Vilain S

PROVIDER: S-EPMC4267933 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross-validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un-annotated genes. A total of approximately 5043 different genes, or about one-third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un-annotated. RESULTS:39 Gene Ontology Biological Process (GO-BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO-BP term for 1422 previously un-annotated genes or about 77% of the un-annotated genes represented on the microarray and about 19% of all of the un-annotated genes in the D. melanogaster genome. CONCLUSIONS:Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure.

Dataset Information

Fast and efficient Drosophila melanogaster gene knock-ins using MiMIC transposons.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure