ArticleNucleic acids research2011
Computational identification of insertional mutagenesis targets for cancer gene discovery.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 30 citations in OpenAlex.
- NetCIS: a network-based common insertion site pipeline for case-control Sleeping Beauty screens.Briefings in bioinformatics · 2026Article
- Restoration of T and B Cell Differentiation after RAG1 Gene Transfer in Human RAG1 Defective Hematopoietic Stem Cells.Biomedicines · 2024Article
- Transmicron: accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens.Nucleic acids research · 2023Article
- Forward and Reverse Genetics of B Cell Malignancies: From Insertional Mutagenesis to CRISPR-Cas.Frontiers in immunology · 2021Review
- LUMI-PCR: an Illumina platform ligation-mediated PCR protocol for integration site cloning, provides molecular quantitation of integration sites.Mobile DNA · 2020Article
- Mouse models in the era of large human tumour sequencing studies.Open biology · 2018Review
- Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates.Nature communications · 2018Article
- Insertional mutagenesis identifies drivers of a novel oncogenic pathway in invasive lobular breast carcinoma.Nature genetics · 2017Article
- Identifying transposon insertions and their effects from RNA-sequencing data.Nucleic acids research · 2017Article
- Article
- A Graph Based Framework to Model Virus Integration Sites.Computational and structural biotechnology journal · 2016Article
- 3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes.Nature communications · 2015Article
- Identifying regulatory mechanisms underlying tumorigenesis using locus expression signature analysis.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Chromatin landscapes of retroviral and transposon integration profiles.PLoS genetics · 2014Article
- Insertional mutagenesis and deep profiling reveals gene hierarchies and a Myc/p53-dependent bottleneck in lymphomagenesis.PLoS genetics · 2014Article
- Evaluating risks of insertional mutagenesis by DNA transposons in gene therapy.Translational research : the journal of laboratory and clinical medicine · 2013Review
- Article
- Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.BMC bioinformatics · 2013Article
- Applications of DNA integrating elements: Facing the bias bully.Mobile genetic elementsReview
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genome-wide evaluation of the association of insertion occurrences with aberrant gene expression of the predicted targets in both retroviral and transposon data sets. We demonstrate the efficiency of KC-RBM by showing its superior performance over existing approaches in recovering true positives from a list of independently, manually curated cancer genes. The results of this work will significantly enhance the accuracy and speed of cancer gene discovery in forward genetic screens. KC-RBM is available as R-package.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.