scholarly journals Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

2019 ◽  
Author(s):  
Christina Stangl ◽  
Sam de Blank ◽  
Ivo Renkens ◽  
Tamara Verbeek ◽  
Jose Espejo Valle-Inclan ◽  
...  
Keyword(s):  
Fusion Gene ◽  
Diagnostic Methods ◽  
Partner Choice ◽  
Fusion Partner ◽  
Nanopore Sequencing ◽  
Fusion Genes ◽  
Sequencing Data ◽  
Gene Detection ◽  
Detection Assay ◽  
Long Read

AbstractFusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment possibilities. The promiscuity of fusion genes with respect to partner choice and exact breakpoint-positions restricts their detection in the diagnostic setting, even for known and recurrent fusion gene configurations. To accurately identify these gene fusions in an unbiased manner, we developed FUDGE: a FUsion gene Detection assay from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for enrichment and detection of fusion gene drivers (e.g. BRAF, EWSR1, KMT2A/MLL) - without prior knowledge of fusion partner or breakpoint-location - to long-read Nanopore sequencing. FUDGE encompasses a dedicated bioinformatics approach (NanoFG) to detect fusion genes from Nanopore sequencing data. Our strategy is flexible with respect to target choice and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in a single sequencing run. We observe on average a 508 fold on-target enrichment and identify fusion breakpoints at nucleotide resolution - all within two days. We demonstrate that FUDGE effectively identifies fusion genes in cancer cell lines, tumor samples and on whole genome amplified DNA irrespective of partner gene or breakpoint-position in 100% of cases. Furthermore, we show that FUDGE is superior to routine diagnostic methods for fusion gene detection. In summary, we have developed a rapid and versatile fusion gene detection assay, providing an unparalleled opportunity for pan-cancer detection of fusion genes in routine diagnostics.

scholarly journals Partner independent fusion gene detection by multiplexed CRISPR-Cas9 enrichment and long read nanopore sequencing

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Christina Stangl ◽  
Sam de Blank ◽  
Ivo Renkens ◽  
Liset Westera ◽  
Tamara Verbeek ◽  
...  
Keyword(s):  
Fusion Gene ◽  
Nanopore Sequencing ◽  
Gene Detection ◽  
Long Read

scholarly journals Identification of a Novel Oncogenic Fusion Gene SPON1-TRIM29 in Clinical Ovarian Cancer That Promotes Cell and Tumor Growth and Enhances Chemoresistance in A2780 Cells

2022 ◽  
Vol 23 (2) ◽  
pp. 689
Author(s):  
Saya Nagasawa ◽  
Kazuhiro Ikeda ◽  
Daisuke Shintani ◽  
Chiujung Yang ◽  
Satoru Takeda ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Rna Sequencing ◽  
Anticancer Drugs ◽  
Fusion Gene ◽  
Xenograft Model ◽  
Fusion Transcript ◽  
Serous Carcinoma ◽  
Fusion Genes ◽  
Sequencing Data ◽  
Chromosome 11

Gene structure alterations, such as chromosomal rearrangements that develop fusion genes, often contribute to tumorigenesis. It has been shown that the fusion genes identified in public RNA-sequencing datasets are mainly derived from intrachromosomal rearrangements. In this study, we explored fusion transcripts in clinical ovarian cancer specimens based on our RNA-sequencing data. We successfully identified an in-frame fusion transcript SPON1-TRIM29 in chromosome 11 from a recurrent tumor specimen of high-grade serous carcinoma (HGSC), which was not detected in the corresponding primary carcinoma, and validated the expression of the identical fusion transcript in another tumor from a distinct HGSC patient. Ovarian cancer A2780 cells stably expressing SPON1-TRIM29 exhibited an increase in cell growth, whereas a decrease in apoptosis was observed, even in the presence of anticancer drugs. The siRNA-mediated silencing of SPON1-TRIM29 fusion transcript substantially impaired the enhanced growth of A2780 cells expressing the chimeric gene treated with anticancer drugs. Moreover, a subcutaneous xenograft model using athymic mice indicated that SPON1-TRIM29-expressing A2780 cells rapidly generated tumors in vivo compared to control cells, whose growth was significantly repressed by the fusion-specific siRNA administration. Overall, the SPON1-TRIM29 fusion gene could be involved in carcinogenesis and chemotherapy resistance in ovarian cancer, and offers potential use as a diagnostic and therapeutic target for the disease with the fusion transcript.

scholarly journals Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Davide Bolognini ◽  
Alberto Magi
Keyword(s):  
Variant Calling ◽  
Research Report ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Factors Affecting ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Sequencing Studies ◽  
Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

scholarly journals Cas9-Assisted Targeting of CHromosome segments (CATCH) for targeted nanopore sequencing and optical genome mapping

2017 ◽  
Author(s):  
Tslil Gabrieli ◽  
Hila Sharim ◽  
Yael Michaeli ◽  
Yuval Ebenstein
Keyword(s):  
Single Molecule ◽  
Genome Mapping ◽  
Single Point ◽  
Read Length ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Whole Genome Analysis ◽  
Long Read

ABSTRACTVariations in the genetic code, from single point mutations to large structural or copy number alterations, influence susceptibility, onset, and progression of genetic diseases and tumor transformation. Next-generation sequencing analysis is unable to reliably capture aberrations larger than the typical sequencing read length of several hundred bases. Long-read, single-molecule sequencing methods such as SMRT and nanopore sequencing can address larger variations, but require costly whole genome analysis. Here we describe a method for isolation and enrichment of a large genomic region of interest for targeted analysis based on Cas9 excision of two sites flanking the target region and isolation of the excised DNA segment by pulsed field gel electrophoresis. The isolated target remains intact and is ideally suited for optical genome mapping and long-read sequencing at high coverage. In addition, analysis is performed directly on native genomic DNA that retains genetic and epigenetic composition without amplification bias. This method enables detection of mutations and structural variants as well as detailed analysis by generation of hybrid scaffolds composed of optical maps and sequencing data at a fraction of the cost of whole genome sequencing.

scholarly journals Evaluating nanopore sequencing data processing pipelines for structural variation identification

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Anbo Zhou ◽  
Timothy Lin ◽  
Jinchuan Xing
Keyword(s):  
Detection Accuracy ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Structural Variations ◽  
Human Genomes ◽  
Data Assessment ◽  
Machine Learning Approach ◽  
Long Read ◽  
The Impact

Abstract Background Structural variations (SVs) account for about 1% of the differences among human genomes and play a significant role in phenotypic variation and disease susceptibility. The emerging nanopore sequencing technology can generate long sequence reads and can potentially provide accurate SV identification. However, the tools for aligning long-read data and detecting SVs have not been thoroughly evaluated. Results Using four nanopore datasets, including both empirical and simulated reads, we evaluate four alignment tools and three SV detection tools. We also evaluate the impact of sequencing depth on SV detection. Finally, we develop a machine learning approach to integrate call sets from multiple pipelines. Overall SV callers’ performance varies depending on the SV types. For an initial data assessment, we recommend using aligner minimap2 in combination with SV caller Sniffles because of their speed and relatively balanced performance. For detailed analysis, we recommend incorporating information from multiple call sets to improve the SV call performance. Conclusions We present a workflow for evaluating aligners and SV callers for nanopore sequencing data and approaches for integrating multiple call sets. Our results indicate that additional optimizations are needed to improve SV detection accuracy and sensitivity, and an integrated call set can provide enhanced performance. The nanopore technology is improving, and the sequencing community is likely to grow accordingly. In turn, better benchmark call sets will be available to more accurately assess the performance of available tools and facilitate further tool development.

scholarly journals Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

2019 ◽  
Author(s):  
Christian Brandt ◽  
Erik Bongcam-Rudloff ◽  
Bettina Müller
Keyword(s):  
Microbial Communities ◽  
Fold Increase ◽  
Green Energy ◽  
Dramatic Improvement ◽  
Metagenomic Sequencing ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Long Reads ◽  
Long Read ◽  
Water Reactors

Abstract Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and not cultivable, which makes abundance tracking difficult. Developments in nanopore sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacterial composition, was established. Samples from 20 different biogas/waste-water reactors were investigated and a median of 20 Gb sequencing data per flow cell were retrieved for each reactor. Using the GTDB index allowed sufficient characterisation of abundance of bacteria and archaea in biogas reactors. A dramatic improvement (1.8- to 13-fold increase) in taxonomic classification was achieved using the GTDB-based index compared with the RefSeq index. Ongoing efforts in GTDB to achieve more phylogenetically coherent taxonomic species definitions, including meta-assembled genomes, give a clear advantage over conventional classification databases such as RefSeq. Unlike conventional 16S rRNA studies, metagenomic read classification allows abundance of the unknown microbial fraction to be monitored.

scholarly journals A comparative analysis of computational tools for the prediction of epigenetic DNA methylation from long-read sequencing data

2021 ◽  
Author(s):  
Shruta Sandesh Pai ◽  
Aimee Rachel Mathew ◽  
Roy Anindya
Keyword(s):  
Dna Methylation ◽  
Dna Modification ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Computational Tools ◽  
Specific Location ◽  
Oxford Nanopore ◽  
Long Read ◽  
Dna Methylations ◽  
Higher Eukaryotes

AbstractRecent development of Oxford Nanopore long-read sequencing has opened new avenues of identifying epigenetic DNA methylation. Among the different epigenetic DNA methylations, N6-methyladenosine is the most prevalent DNA modification in prokaryotes and 5-methylcytosine is common in higher eukaryotes. Here we investigated if N6-methyladenosine and 5-methylcytosine modifications could be predicted from the nanopore sequencing data. Using publicly available genome sequencing data of Saccharomyces cerevisiae, we compared the open-access computational tools, including Tombo, mCaller, Nanopolish and DeepSignal for predicting 6mA and 5mC. Our results suggest that Tombo and mCaller can predict DNA N6-methyladenosine modifications at a specific location, whereas, Tombo dampened fraction, Nanopolish methylation likelihood and DeepSignal methylation probability have comparable efficiency for 5-methylcytosine prediction from Oxford Nanopore sequencing data.

scholarly journals Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

2020 ◽  
Author(s):  
Christian Brandt ◽  
Erik Bongcam-Rudloff ◽  
Bettina Müller
Keyword(s):  
Microbial Communities ◽  
Illumina Sequencing ◽  
Fold Increase ◽  
Green Energy ◽  
Dramatic Improvement ◽  
Metagenomic Sequencing ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Long Read ◽  
Genus Richness

Abstract Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.

scholarly journals Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

2020 ◽  
Author(s):  
Christian Brandt ◽  
Erik Bongcam-Rudloff ◽  
Bettina Müller
Keyword(s):  
Microbial Communities ◽  
Illumina Sequencing ◽  
Fold Increase ◽  
Green Energy ◽  
Dramatic Improvement ◽  
Metagenomic Sequencing ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Long Read ◽  
Genus Richness

Abstract Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.

scholarly journals DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yang Liu ◽  
Wojciech Rosikiewicz ◽  
Ziwei Pan ◽  
Nathaniel Jillette ◽  
Ping Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Molecule ◽  
Evaluation Criteria ◽  
Systematic Evaluation ◽  
Whole Genome ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Long Read ◽  
Genome Scale ◽  
Analytical Tools

Abstract Background Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies. Results We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database (https://nanome.jax.org) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts. Conclusions Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing.

Sign in / Sign up

Export Citation Format

Share Document