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4 results for “NanoVar”
Simulated data for Correspondence on NanoVar's performance outlined by Jiang T. et al.
<p>Simulated data were generated as described by Jiang et al. 2021 with several modifications due to the encountering of errors. Please refer to https://github.com/cytham/nv_benchmark_jiang/blob/main/commands.md for the commands to replicate the analysis.</p>
NanoVar Simulation Datasets
<p>The data stored here were used for benchmarking long-read structural variant (SV) callers in the publication:</p> <p>Tham, CY., Tirado-Magallanes, R., Goh, Y. <em>et al.</em> NanoVar: accurate characterization of patients’ genomic structural variants using low-depth nanopore sequencing. <em>Genome Biol</em> <strong>21, </strong>56 (2020). https://doi.org/10.1186/s13059-020-01968-7</p> <p>The data consist of six simulated SV genomes generated using various open source tools and comprise also of simulated long-read FASTA files and ground truth SV information. More information can be found in the README.md.</p>
Dataset for "NanoVar: a Comprehensive Workflow for Structural Variant Detection to uncover the Genome's Hidden Patterns"
<h2><strong>Output Files for Long-Read Structural Variant and Repeat Analysis in Colorectal Cancer Samples (HRR698464, HRR698460, C586, C588)</strong></h2> <h3>Description:</h3> <p>This Zenodo dataset includes comprehensive output files generated during the application of a long-read sequencing analysis protocol for structural variant (SV) detection and repeat element characterization in colorectal cancer samples. The dataset is organized into two main directories:</p> <p><strong>1. HRR698464_MSI-H_Tumor</strong><br>This directory contains all primary output files generated from the analysis pipeline applied to the MSI-H tumor sample HRR698464 (also referred to as patient C586.T). Each subdirectory corresponds to a specific stage in the protocol:</p> <ul> <li>NanoPlot_output<br>Output from Stage 1 – Quality assessment of raw reads using NanoPlot.</li> <li>SAMtools_output<br>BAM file processing outputs from Stage 2 – Alignment of long reads to the reference genome using SAMtools.</li> <li>NanoVar_output<br>Output from Stage 3 – Structural variant calling using NanoVar.</li> <li>VCF_filtering_output<br>Output from Stage 4 – Filtering of structural variants using SURVIVOR and BCFtools; includes the filtered VCF files.</li> <li>NanoINSight_output<br>Output from Stage 5 – Characterization of repeat elements using NanoINSight.</li> <li>VEP_output<br>Output from Stage 6 – Annotation of structural variants using Ensembl Variant Effect Predictor (VEP).</li> </ul> <p> </p> <p><strong>2. Additional_output_files</strong><br>This directory contains supplementary output files used for comparison and visualization in Figures 4–7 of the associated publication. These include:</p> <ul> <li>HRR698460.NanoPlot.report.html<br>NanoPlot quality summary of a lower-quality tumor sample (HRR698460), used in Figure 4 for comparison with HRR698464.</li> <li>C586.N.nanovar.pass.vcf<br>NanoVar VCF output for the matched normal sample of patient C586, used to filter somatic calls in Stage 4.</li> <li>C586.N.nanovar.pass.report.html<br>NanoVar summary report of the normal sample of C586; used in Figure 5a.</li> <li>C588.N.nanovar.pass.vcf<br>NanoVar VCF output of the MSS normal sample (C588) for comparison with the MSI-H patient (C586).</li> <li>C588.N.nanovar.pass.report.html<br>NanoVar summary report of the MSS normal sample; used in Figure 5b.</li> <li>C588.T.nanovar.pass.vcf<br>NanoVar VCF output of the MSS tumor sample (C588); used in comparative analyses with the MSI-H sample.</li> <li>C588.T.nanovar.pass.report.html<br>NanoVar summary report of the MSS tumor sample; used in Figure 5b.</li> <li>MSS.tumor.unique.vcf<br>VCF file of somatic SVs in the MSS sample, generated by comparing matched tumor and normal pairs.</li> <li>MSS.tumor.unique.RepeatMasker.tbl<br>RepeatMasker output annotating somatic insertions in the MSS tumor sample; used in Figure 6.</li> <li>MSS.tumor.unique.vep.html<br>Ensembl VEP annotation report of somatic SVs in the MSS patient; used in Figures 7a and 7b.</li> <li>This dataset supports reproducibility and transparency of the protocol and offers a valuable resource for researchers interested in long-read-based SV detection, repeat annotation, and comparative cancer genomics.</li> </ul>
The data used in correspondence on the article entitled "Reply: Correspondence on NanoVar's performance outlined by Jiang T. et al. in 'Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation'."
<p>The data used in correspondence on the article entitled “Reply: Correspondence on NanoVar’s performance outlined by Jiang T. et al. in ‘Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation’.”. It contains the benchmarking results evaluated by Truvari. The running log files of NanoVar are also included in this repository.</p>
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