Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (27 results)

  • Thesis
  • Peer Reviewed
UC Santa Cruz Electronic Theses and Dissertations (2017)

The scale of the problems which human genomics is asked to solve necessitates that the field develop an ability to integrate and synthesize information across the entire human population. The abstraction of a single-copy human reference genome assembly, and the linear coordinate space that it induces, are more of a hindrance than a help at these scales. They can only ever represent one sample at any given place, and they make combining information about human variation across multiple studies and modalities difficult. To rectify these problems, I propose the construction and adoption of a graph-based alternative to the human reference genome assembly: a Human Genome Variation Map. I present here four research projects. The first is a theory of mapping to references that is extensible to graphs. The second describes a novel data structure for embedding individual haplotype sequences into a graph reference. The third surveys graph construction techniques to discover methods that produce graphs yielding read mapping and variant calling results superior to those obtained with linear, variation-free references. The fourth extends these improvement results to chromosome-scale graphs constructed from multiple sources and modalities of variation data. These four projects describe a research program aimed towards the eventual release of an official Human Genome Variation Map build, providing a piece of vital infrastructure for the analysis of human genomic variation at population scale.

    Cover page: Infrastructure for Scalable Analysis of Genomic Variation
    • Article
    • Peer Reviewed
    UC Santa Cruz Previously Published Works (2017)
    The human reference genome is part of the foundation of modern human biology and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to draw on representative collections of human genomes, brought together into reference cohorts. There are a number of techniques to represent and organize data gleaned from these cohorts, many using ideas implicitly or explicitly borrowed from graph-based models. Here, we survey various projects underway to build and apply these graph-based structures-which we collectively refer to as genome graphs-and discuss the improvements in read mapping, variant calling, and haplotype determination that genome graphs are expected to produce.
      Cover page: Genome graphs and the evolution of genome inference
      • Article
      • Peer Reviewed
      UC Santa Cruz Previously Published Works (2015)

      Motivation

      Sequence mapping is the cornerstone of modern genomics. However, most existing sequence mapping algorithms are insufficiently general.

      Results

      We introduce context schemes: a method that allows the unambiguous recognition of a reference base in a query sequence by testing the query for substrings from an algorithmically defined set. Context schemes only map when there is a unique best mapping, and define this criterion uniformly for all reference bases. Mappings under context schemes can also be made stable, so that extension of the query string (e.g. by increasing read length) will not alter the mapping of previously mapped positions. Context schemes are general in several senses. They natively support the detection of arbitrary complex, novel rearrangements relative to the reference. They can scale over orders of magnitude in query sequence length. Finally, they are trivially extensible to more complex reference structures, such as graphs, that incorporate additional variation. We demonstrate empirically the existence of high-performance context schemes, and present efficient context scheme mapping algorithms.

      Availability and implementation

      The software test framework created for this study is available from https://registry.hub.docker.com/u/adamnovak/sequence-graphs/.

      Contact

      [email protected]

      Supplementary information

      Supplementary data are available at Bioinformatics online.
        Cover page: Canonical, stable, general mapping using context schemes
        • Article
        • Peer Reviewed
        UC Santa Cruz Previously Published Works (2018)
        A superbubble is a type of directed acyclic subgraph with single distinct source and sink vertices. In genome assembly and genetics, the possible paths through a superbubble can be considered to represent the set of possible sequences at a location in a genome. Bidirected and biedged graphs are a generalization of digraphs that are increasingly being used to more fully represent genome assembly and variation problems. In this study, we define snarls and ultrabubbles, generalizations of superbubbles for bidirected and biedged graphs, and give an efficient algorithm for the detection of these more general structures. Key to this algorithm is the cactus graph, which, we show, encodes the nested decomposition of a graph into snarls and ultrabubbles within its structure. We propose and demonstrate empirically that this decomposition on bidirected and biedged graphs solves a fundamental problem by defining genetic sites for any collection of genomic variations, including complex structural variations, without need for any single reference genome coordinate system. Further, the nesting of the decomposition gives a natural way to describe and model variations contained within large variations, a case not currently dealt with by existing formats [e.g., variant cell format (VCF)].
          Cover page: Superbubbles, Ultrabubbles, and Cacti
          • Article
          UC Santa Cruz Previously Published Works (2019)

          Motivation

          The variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are nonbiological, unlikely recombinations of true haplotypes.

          Results

          We augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows–Wheelertransform (GBWT). We demonstrate the scalability of the new implementation by building a whole-genome index of the 5,008 haplotypes of the 1000 Genomes Project, and an index of all 108,070 TOPMed Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.

          Availability

          Our software is available at https://github.com/vgteam/vg , https://github.com/jltsiren/gbwt , and https://github.com/jltsiren/gcsa2 .

          Contact

          [email protected]

          Supplementary information

          Supplementary data are available.
          • 1 supplemental PDF
          Cover page: Haplotype-aware graph indexesCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Santa Cruz Previously Published Works (2020)

          Motivation

          The variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are non-biological, unlikely recombinations of true haplotypes.

          Results

          We augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows-Wheeler transform. We demonstrate the scalability of the new implementation by building a whole-genome index of the 5008 haplotypes of the 1000 Genomes Project, and an index of all 108 070 Trans-Omics for Precision Medicine Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.

          Availability and implementation

          Our software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt and https://github.com/jltsiren/gcsa2.

          Supplementary information

          Supplementary data are available at Bioinformatics online.
            Cover page: Haplotype-aware graph indexes
            • Article
            • Peer Reviewed
            UC Santa Cruz Previously Published Works (2020)

            Motivation

            Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become much more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but complicated in a graph context. In read mapping algorithms such distance calculations are fundamental to determining if seed alignments could belong to the same mapping.

            Results

            We have developed an algorithm for quickly calculating the minimum distance between positions on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms are efficient and practical to use for a new generation of mapping algorithms based upon genome graphs.

            Availability and implementation

            Our algorithms have been implemented as part of the vg toolkit and are available at https://github.com/vgteam/vg.
              Cover page: Distance indexing and seed clustering in sequence graphs
              • Article
              • Peer Reviewed
              UC Santa Cruz Previously Published Works (2023)
              Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.
                Cover page: Haplotype-aware pantranscriptome analyses using spliced pangenome graphsCreative Commons 'BY' version 4.0 license
                • Article
                UC Santa Cruz Previously Published Works (2021)
                Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our novel toolchain can construct spliced pangenome graphs, map RNA-seq data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. This workflow improves accuracy over state-of-the-art RNA-seq mapping methods, and it can efficiently quantify haplotype-specific transcript expression without needing to characterize a sample’s haplotypes beforehand.
                  Creative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Santa Cruz Previously Published Works (2018)
                  Efforts to incorporate human genetic variation into the reference human genome have converged on the idea of a graph representation of genetic variation within a species, a genome sequence graph. A sequence graph represents a set of individual haploid reference genomes as paths in a single graph. When that set of reference genomes is sufficiently diverse, the sequence graph implicitly contains all frequent human genetic variations, including translocations, inversions, deletions, and insertions. In representing a set of genomes as a sequence graph, one encounters certain challenges. One of the most important is the problem of graph linearization, essential both for efficiency of storage and access, and for natural graph visualization and compatibility with other tools. The goal of graph linearization is to order nodes of the graph in such a way that operations such as access, traversal, and visualization are as efficient and effective as possible. A new algorithm for the linearization of sequence graphs, called the flow procedure (FP), is proposed in this article. Comparative experimental evaluation of the FP against other algorithms shows that it outperforms its rivals in the metrics most relevant to sequence graphs.
                    Cover page: A Flow Procedure for Linearization of Genome Sequence Graphs
                    Top