IS-Seq: a novel bioinformatics pipeline for integration sites analysis
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA839059 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19243487_1.fastq.gz | Fastqsanger.gz | |||
| SRR19243487_2.fastq.gz | Fastqsanger.gz | |||
| SRR19243488_1.fastq.gz | Fastqsanger.gz | |||
| SRR19243488_2.fastq.gz | Fastqsanger.gz | |||
| SRR19243489_1.fastq.gz | Fastqsanger.gz |
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Project description:IS-Seq pipeline is developed,and 4 data sets are used to compare different abundance estimation methods of this pipeline
2023-08-08 | GSE203211 | GEO
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.
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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
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.
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Project description:COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data. It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values. COHCAP is currently the only DNA methylation package that can handle integration with gene expression data, and the results of this study show that COHCAP can identify regions of differential methylation with ~50% concordance with gene expression. COHCAP is scalable for analysis of both cell line data and heterogeneous patient data, and it can identify known cancer biomarkers as well as intriguing new roles of epigenetic regulation in cancer (such as methylation of estrogen receptor in breast cancer patients). This study also uses cell line data to show that COHCAP is capable of analyzing Illumina methylation array and targeted bisulfite sequencing data, with either 1-group or 2-group study designs. The accuracy of COHCAP is accessed using qPCR, EpiTect, and comparison of COHCAP regions of differential methylation with MIRA peaks. This software is freely available at https://sourceforge.net/projects/cohcap/. The following third-party datasets were utilized in the paper: BS-Seq data: GSE26826 Additional Microarray Data: GSE29290 This SuperSeries is composed of the SubSeries listed below. Refer to individual Series.
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