IS-Seq: a novel bioinformatics pipeline for integration sites analysis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE203211 | GEO | 2023/08/08
REPOSITORIES: GEO
Similar Datasets
Project description:IS-Seq: a novel bioinformatics pipeline for integration sites analysis
| PRJNA839059 | ENA
Project description:BackgroundIntegration site (IS) analysis is a fundamental analytical platform for evaluating the safety and efficacy of viral vector based preclinical and clinical Gene Therapy (GT). A handful of groups have developed standardized bioinformatics pipelines to process IS sequencing data, to generate reports, and/or to perform comparative studies across different GT trials. Keeping up with the technological advances in the field of IS analysis, different computational pipelines have been published over the past decade. These pipelines focus on identifying IS from single-read sequencing or paired-end sequencing data either using read-based or using sonication fragment-based methods, but there is a lack of a bioinformatics tool that automatically includes unique molecular identifiers (UMI) for IS abundance estimations and allows comparing multiple quantification methods in one integrated pipeline.ResultsHere we present IS-Seq a bioinformatics pipeline that can process data from paired-end sequencing of both old restriction sites-based IS collection methods and new sonication-based IS retrieval systems while allowing the selection of different abundance estimation methods, including read-based, Fragment-based and UMI-based systems.ConclusionsWe validated the performance of IS-Seq by testing it against the most popular analytical workflow available in the literature (INSPIIRED) and using different scenarios. Lastly, by performing extensive simulation studies and a comprehensive wet-lab assessment of our IS-Seq pipeline we could show that in clinically relevant scenarios, UMI quantification provides better accuracy than the currently most widely used sonication fragment counts as a method for IS abundance estimation.
| S-EPMC10354991 | biostudies-literature
Project description:The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential information available through these methods, we developed an optimized bioinformatics pipeline to analyze NGS clonality datasets. We found that short-read aligners, specifically designed to manage NGS datasets, provide increased speed, significantly reducing processing time and decreasing the computational burden. This is achieved while also accounting for sequencing base quality. We demonstrate the utility of an additional trimming step in the workflow, which adjusts for the number of reads supporting each insertion site. In addition, we developed a recall procedure to reduce bias associated with proviral integration within low complexity regions of the genome, providing a more accurate estimation of clone abundance. Finally, we recommend the application of a "clean-and-recover" step to clonality datasets generated from large cohorts and longitudinal studies. In summary, we report an optimized bioinformatics workflow for NGS clonality analysis and describe a new set of steps to guide the computational process. We demonstrate that the application of this protocol to the analysis of HTLV-1 and bovine leukemia virus (BLV) clonality datasets improves the quality of data processing and provides a more accurate definition of the clonal landscape in infected individuals. The optimized workflow and analysis recommendations can be implemented in the majority of bioinformatics pipelines developed to analyze LAM-PCR-based NGS clonality datasets.
| S-EPMC7606357 | biostudies-literature
Project description:Castanet pipeline validation study
| PRJEB77004 | ENA
Project description:We developed a Transcriptomic Analysis Pipeline (TAP) as a flexible workflow for comprehensive transcriptome analysis from any species with a reference genome. We tested TAP in a case study to compare polyA+ and rRNA-depletion RNA-seq library protocols using Drosophila melanogaster following different thermal stress temperatures. TAP provides a flexible and complete pipeline to enable researchers to extract more biologically relevant interpretations by integrated and interactive transcriptome analysis.
2023-11-09 | GSE229448 | GEO
Project description:Transcription factor partners can cooperatively bind to DNA composite elements to augment gene transcription. Here, we report a novel protein-DNA binding screening pipeline, termed Spacing Preference Identification of Composite Elements (SPICE), that can systematically predict protein binding partners and DNA motif spacing preferences. SPICE de novo predicted known composite elements, including AP1-IRF composite elements (AICE) and STAT5 tetramers, and also predicted a range of novel binding partners, including JUN-IKZF1 composite elements. We confirmed cooperative binding of JUN and IKZF1 to an upstream conserved noncoding region, CNS9, in the human IL10 gene, that contains a non-canonical IKZF1 site, and the activity of an IL10-luciferase reporter construct depended on both this site and the AP1 binding site within this composite element in primary B and T cells. Our findings reveal an unappreciated global association of IKZF1 and AP1, and establish SPICE as a valuable new pipeline for predicting novel transcription binding complexes.
2023-11-05 | GSE230035 | GEO
Project description:RNA-seq dynamic transcriptomic pipeline
| PRJNA524471 | ENA
Project description:BACKGROUND:Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. RESULTS:Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). CONCLUSIONS:We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a?>?6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 ( http://openserver.itb.cnr.it/vispa/ ) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository ( https://bitbucket.org/andreacalabria/vispa2 ).
| S-EPMC5702242 | biostudies-literature
Project description:We developed a bioinformatics approach called the Read-Split-Walk (RSW) pipeline, and evaluated it using two Ire1α heterozygous and two Ire1α-null samples. The 26nt non-canonical splice site in Xbp1 was detected as the top hit by our RSW pipeline in heterozygous samples but not in the negative control Ire1α knockout samples. We compared the Xbp1 results from our approach with results using the alignment program BWA, STAR, Exonerate and the Unix “grep” command. We then applied our RSW pipeline to RNA-Seq data from the SKBR3 human breast cancer cell line. RSW reported a large number of non-canonical spliced regions for 108 genes in chromosome 17, which were identified by an independent study.
2014-07-29 | GSE54631 | GEO
Project description:We developed a bioinformatics approach called the Read-Split-Walk (RSW) pipeline, and evaluated it using two Ire1? heterozygous and two Ire1?-null samples. The 26nt non-canonical splice site in Xbp1 was detected as the top hit by our RSW pipeline in heterozygous samples but not in the negative control Ire1? knockout samples. We compared the Xbp1 results from our approach with results using the alignment program BWA, STAR, Exonerate and the Unix “grep” command. We then applied our RSW pipeline to RNA-Seq data from the SKBR3 human breast cancer cell line. RSW reported a large number of non-canonical spliced regions for 108 genes in chromosome 17, which were identified by an independent study. Identification of non-canonical spliced regions for mouse MEF samples (two Ire1? heterozygous and two Ire1?-null samples)
2014-07-29 | E-GEOD-54631 | biostudies-arrayexpress