To address slow loading, IGV defaults to sampling a subset of reads and stops displaying alignment data when viewing broad regions, both of which further complicate SV interpretation. The software often loads slowly for large variants which require plotting large numbers of reads. While IGV can be configured for SV viewing (i.e., viewing reads as pairs, sorting by insert size), visualizing large variants is difficult. IGV is optimized for single-nucleotide variant visualization, making it easy to zoom into particular loci to identify base mismatches in read pileups. Tools such as the Integrative Genomics Viewer (IGV), bamsnap, and svviz enable visual review of SVs, but they can be cumbersome or complicated, slowing down the review process and often limiting the number of SVs that can be considered. This study highlights the essential step of removing false positives from SV calls and the effectiveness of visual review to identify the real variants. However, the false-positive rate plummeted to 7% (according to long-read sequence validation) subsequent to visual inspection. For example, a recent study of SVs in 465 Salmon samples found that 91% of SVs reported using Illumina paired-end sequencing data were false positives. As the human eye excels at pattern recognition, visual inspection of sequence alignments in a variant region can quickly identify erroneous calls, making manual curation a powerful part of the validation process. While filtering and annotation tools can help, tuning these filters to remove only false positives is still quite difficult. Unfortunately, state-of-the-art SV discovery tools still report large numbers of false positives. Structural variants, which include mobile elements, deletions, duplications, inversions, and translocations larger than 50bp, can have serious consequences for human health and development and are a primary source of genetic diversity. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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