Most existing methods for structural variant detection focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced structural variants with no gain or loss of genomic segments, for example, inversions and translocations, is a particularly challenging task. Furthermore, there are very few algorithms to predict the insertion locus of large interspersed segmental duplications and characterize translocations. Here, we propose novel algorithms to characterize large interspersed segmental duplications, inversions, deletions, and translocations using linked-read sequencing data. We redesign our earlier algorithm, VALOR, and implement our new algorithms in a new software package, called VALOR2. Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Many algorithms aimed at characterizing genomic structural variation (SV) have been developed since the inception of high-throughput sequencing. However, the full spectrum of SVs in the human genome is not yet assessed. Most of the existing methods focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced SVs with no gain or loss of genomic segments (e.g., inversions) is particularly a challenging task. Long read sequencing has been leveraged to find short inversions but there is still a need to develop methods to detect large genomic inversions. Furthermore, currently there are no algorithms to predict the insertion locus of large interspersed segmental duplications.Here we propose novel algorithms to characterize large (>40Kbp) interspersed segmental duplications and (>80Kbp) inversions using Linked-Read sequencing data. Linked-Read sequencing provides long range information, where Illumina reads are tagged with barcodes that can be used to assign short reads to pools of larger (30-50 Kbp) molecules. Our methods rely on split molecule sequence signature that we have previously described [11]. Similar to the split read, split molecules refer to large segments of DNA that span an SV breakpoint. Therefore, when mapped to the reference genome, the mapping of these segments would be discontinuous. We redesign our earlier algorithm, VALOR, to specifically leverage Linked-Read sequencing data to discover large inversions and characterize interspersed segmental duplications. We implement our new algorithms in a new software package, called VALOR 2 .1 Alterations of DNA content and organization larger than 50 bp, commonly referred to as genomic structural variations (SVs) [2], are among the major drivers of evolution [24,29], and diseases of genomic origin [38]. Despite decades of research they remain difficult to accurately characterize contributing to our lack of full understanding of the etiology of complex diseases, termed missing heritability [9].High-throughput sequencing (HTS) technologies are widely employed to discover and genotype various classes of SVs since their inception [18,13,26,34,12,19,36]. However, effectiveness has been limited by either very short read lengths (e.g., Illumina), or high error rates and prohibiting cost (e.g., PacBio and Oxford Nanopore). The human genome complexity further contributes to our lack of full characterization of structural variants, especially large-scale duplications and balanced rearrangements due to the repetitive and duplicated sequence at the SV breakpoints [17]. Despite high error rates, long reads offer improvement in complex SV discovery, either used alone [10,16], or when integrated with standard short-read sequencing data [32].Recently Linked-Read sequencing methods such as the 10x Genomics system (10xG) was introduced as an alternative method to generate highly accurate Illumina short reads data with additional long-range information [27]. In the 10xG system, large DNA molecules (typically 10-100 Kbp) are barcoded an...