ExpansionHunter: A sequence-graph based tool to analyze variation in short tandem repeat regions

Dolzhenko, Egor; Deshpande, Viraj; Schlesinger, Felix; Krusche, Peter; Petrovski, Roman; Chen, Sai; Emig-Agius, Dorothea; Gross, Andrew M.; Narzisi, Giuseppe; Bowman, Brett; Scheffler, Konrad; Vugt, Joke J.F.A. van; French, Courtney; Sanchis‐Juan, Alba; Ibáñez, Kristina; Tucci, Arianna; Lajoie, Bryan R.; Veldink, Jan H.; Raymond, Lucy; Taft, Ryan J.; Eberle, Michael A.

doi:10.1101/572545

Cited by 46 publications

(68 citation statements)

References 21 publications

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“…Graphs provide a natural way of expressing variation or uncertainty in a genome [ 1 , 2 ]. They have been used for diverse applications such as genome assembly [ 3 – 5 ], error correction [ 6 – 8 ], short tandem repeat genotyping [ 9 ], structural variation genotyping [ 10 ], and reference-free haplotype reconstruction [ 11 ]. With the growing usage of graphs, methods for handling graphs efficiently are becoming a crucial requirement for many applications.…”

Section: Introductionmentioning

confidence: 99%

“…In 2002, partial order alignment [ 19 ] (POA), a special case of Navarro’s algorithm for acyclic graphs, was published for multiple sequence alignment. Although POA is defined only for acyclic graphs, it can be extended to cyclic graphs by unfolding cyclic components, which is the approach taken by the VG toolkit [ 16 ] and ExpansionHunter [ 9 ]. The practical efficiency of this unfolding depends on the read length, and the graph topology and complex cyclic areas can lead to very large unfolded graphs [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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GraphAligner: rapid and versatile sequence-to-graph alignment

2020

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Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pangenome graph. Yet, so far, this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to the state-of-the-art tools, GraphAligner is 13x faster and uses 3x less memory. When employing GraphAligner for error correction, we find it to be more than twice as accurate and over 12x faster than extant tools.Availability: Package manager: https://anaconda.org/bioconda/graphalignerand source code: https://github.com/maickrau/GraphAligner

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GraphAligner: rapid and versatile sequence-to-graph alignment

2020

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“…VNTRs are associated with a wide spectrum of complex traits and diseases including attention-deficit disorder, Type 1 Diabetes and schizophrenia (Hannan 2018) . While STR variation has been profiled in human populations (Mallick et al 2016) and to find expression quantitative trait loci (eQTL) (Fotsing et al 2019;Gymrek et al 2016) , and variation at VNTR sequences may be detected for targeted loci (Bakhtiari et al 2018;Dolzhenko et al 2019) , the landscape of VNTR variation in populations and effects on human phenotypes are not yet examined genome-wide. Large scale sequencing studies including the 1000 Genomes Project (1000Genomes Project Consortium et al 2015 , TOPMed (Taliun et al 2019) and DNA sequencing by the Genotype-Tissue Expression (GTEx) project (G. Consortium and GTEx Consortium 2017) rely on high-throughput sequencing (SRS) characterized by SRS reads up to 150 bases.…”

Section: Introductionmentioning

confidence: 99%

Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs

Chaisson

2020

Preprint

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Variable number tandem repeats (VNTR) are genetic loci composed of consecutive repeats of short segments of DNA, with hypervariable repeat count and composition across individuals. Many genes have coding VNTR sequences, and noncoding VNTR variants are associated with a wide spectrum of clinical disorders such as Alzheimer's disease, bipolar disorder and colorectal cancer. The identification of VNTR length and composition provides the basis for downstream analysis such as expression quantitative trait loci discovery and genome wide association studies. Disease studies that use high-throughput short read sequencing do not resolve the repeat structures of many VNTR loci because the VNTR sequence is missing from the reference or is too repetitive to map. We solve the VNTR mapping problem for short reads by representing a collection of genomes with a repeat-pangenome graph, a data structure that encodes both the population diversity and repeat structure of VNTR loci. We developed software to build a repeat-pangenome using haplotype-resolved single-molecule sequencing assemblies, and to estimate VNTR length and sequence composition based on the alignment of short read sequences to the graph. Using long-read assemblies as ground truth, we are able to determine which VNTR loci may be accurately profiled using repeat-pangenome graph analysis with short reads. This enabled measuring the global diversity of VNTR sequences in the 1000-Genomes Project, and the discovery of expression quantitative trait loci in the Genotype-Tissue Expression Project. This analysis reveals loci that have significant differences in length and repeat composition between continental populations. Furthermore, the repeat pangenome graph analysis establishes an association between previously inaccessible variation and gene expression. Taken together, these indicate that measuring VNTR sequence diversity with repeat-pangenome graphs will be a critical component of future studies on human diversity and disease.

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“…Another alternative, less described, could be the use of dedicated tools for each type of insertion, instead of using only general-purpose SV callers. Among them, Expansion Hunter has been designed to detect tandem repeats, Pamir and Popins for novel insertions and TARDIS for large duplications [ 28 – 31 ].…”

Section: Discussionmentioning

confidence: 99%

Towards a better understanding of the low recall of insertion variants with short-read based variant callers

2020

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Background Since 2009, numerous tools have been developed to detect structural variants using short read technologies. Insertions >50 bp are one of the hardest type to discover and are drastically underrepresented in gold standard variant callsets. The advent of long read technologies has completely changed the situation. In 2019, two independent cross technologies studies have published the most complete variant callsets with sequence resolved insertions in human individuals. Among the reported insertions, only 17 to 28% could be discovered with short-read based tools. Results In this work, we performed an in-depth analysis of these unprecedented insertion callsets in order to investigate the causes of such failures. We have first established a precise classification of insertion variants according to four layers of characterization: the nature and size of the inserted sequence, the genomic context of the insertion site and the breakpoint junction complexity. Because these levels are intertwined, we then used simulations to characterize the impact of each complexity factor on the recall of several structural variant callers. We showed that most reported insertions exhibited characteristics that may interfere with their discovery: 63% were tandem repeat expansions, 38% contained homology larger than 10 bp within their breakpoint junctions and 70% were located in simple repeats. Consequently, the recall of short-read based variant callers was significantly lower for such insertions (6% for tandem repeats vs 56% for mobile element insertions). Simulations showed that the most impacting factor was the insertion type rather than the genomic context, with various difficulties being handled differently among the tested structural variant callers, and they highlighted the lack of sequence resolution for most insertion calls. Conclusions Our results explain the low recall by pointing out several difficulty factors among the observed insertion features and provide avenues for improving SV caller algorithms and their combinations.

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ExpansionHunter: A sequence-graph based tool to analyze variation in short tandem repeat regions

Cited by 46 publications

References 21 publications

GraphAligner: rapid and versatile sequence-to-graph alignment

GraphAligner: rapid and versatile sequence-to-graph alignment

Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs

Towards a better understanding of the low recall of insertion variants with short-read based variant callers

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