Dataset Information

GENE-IS: Time-Efficient and Accurate Analysis of Viral Integration Events in Large-Scale Gene Therapy Data.

ABSTRACT: Integration site profiling and clonality analysis of viral vector distribution in gene therapy is a key factor to monitor the fate of gene-corrected cells, assess the risk of malignant transformation, and establish vector biosafety. We developed the Genome Integration Site Analysis Pipeline (GENE-IS) for highly time-efficient and accurate detection of next-generation sequencing (NGS)-based viral vector integration sites (ISs) in gene therapy data. It is the first available tool with dual analysis mode that allows IS analysis both in data generated by PCR-based methods, such as linear amplification method PCR (LAM-PCR), and by rapidly evolving targeted sequencing (e.g., Agilent SureSelect) technologies. GENE-IS makes use of trimming strategies, customized reference genome, and soft-clipped information with sequential filtering steps to provide annotated IS with clonality information. It is a scalable, robust, precise, and reliable tool for large-scale pre-clinical and clinical data analysis that provides users complete flexibility and control over analysis with a broad range of configurable parameters. GENE-IS is available at https://github.com/G100DKFZ/gene-is.

SUBMITTER: Afzal S

PROVIDER: S-EPMC5363413 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

GENE-IS: Time-Efficient and Accurate Analysis of Viral Integration Events in Large-Scale Gene Therapy Data.

Afzal Saira S Wilkening Stefan S von Kalle Christof C Schmidt Manfred M Fronza Raffaele R

Molecular therapy. Nucleic acids 20161210

Integration site profiling and clonality analysis of viral vector distribution in gene therapy is a key factor to monitor the fate of gene-corrected cells, assess the risk of malignant transformation, and establish vector biosafety. We developed the Genome Integration Site Analysis Pipeline (GENE-IS) for highly time-efficient and accurate detection of next-generation sequencing (NGS)-based viral vector integration sites (ISs) in gene therapy data. It is the first available tool with dual analysi ...[more]

PMID: 28325279

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Project description:BACKGROUND: ESTs and full-length cDNAs represent an invaluable source of evidence for inferring reliable gene structures and discovering potential alternative splicing events. In newly sequenced genomes, these tasks may not be practicable owing to the lack of appropriate training sets. However, when expression data are available, they can be used to build EST clusters related to specific genomic transcribed loci. Common strategies recently employed to this end are based on sequence similarity between transcripts and can lead, in specific conditions, to inconsistent and erroneous clustering. In order to improve the cluster building and facilitate all downstream annotation analyses, we developed a simple genome-based methodology to generate gene-oriented clusters of ESTs when a genomic sequence and a pool of related expressed sequences are provided. Our procedure has been implemented in the software EasyCluster and takes into account the spliced nature of ESTs after an ad hoc genomic mapping. METHODS: EasyCluster uses the well-known GMAP program in order to perform a very quick EST-to-genome mapping in addition to the detection of reliable splice sites. Given a genomic sequence and a pool of ESTs/FL-cDNAs, EasyCluster starts building genomic and EST local databases and runs GMAP. Subsequently, it parses results creating an initial collection of pseudo-clusters by grouping ESTs according to the overlap of their genomic coordinates on the same strand. In the final step, EasyCluster refines the clustering by again running GMAP on each pseudo-cluster and groups together ESTs sharing at least one splice site. RESULTS: The higher accuracy of EasyCluster with respect to other clustering tools has been verified by means of a manually cured benchmark of human EST clusters. Additional datasets including the Unigene cluster Hs.122986 and ESTs related to the human HOXA gene family have also been used to demonstrate the better clustering capability of EasyCluster over current genome-based web service tools such as ASmodeler and BIPASS. EasyCluster has also been used to provide a first compilation of gene-oriented clusters in the Ricinus communis oilseed plant for which no Unigene clusters are yet available, as well as an evaluation of the alternative splicing in this plant species.

Project description:BackgroundRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.ResultsIn this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [Formula: see text] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [Formula: see text] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [Formula: see text], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [Formula: see text] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [Formula: see text]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc .

Dataset Information

GENE-IS: Time-Efficient and Accurate Analysis of Viral Integration Events in Large-Scale Gene Therapy Data.

Publications

GENE-IS: Time-Efficient and Accurate Analysis of Viral Integration Events in Large-Scale Gene Therapy Data.

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