Computational graph pangenomics: a tutorial on data structures and their applications

Abstract

Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

Compressed Data Structures for Population-Scale Positional Burrows–Wheeler Transforms

“…We denote a matrix of h rows and w columns as M[1.. h ][1.. w ]. We let col(M) j denote the j -th column of M, i.e., the string drawn as col(M) j = M[1.. h ][ j ] = M[1][ j ]M[2][ j ] … M[ h ][ j ].…”

“…In the framework of computational pangenomics, the positional BWT (PBWT), which is a method of permuting the elements of each column of a h × w binary matrix M[1.. h ][1.. w ], is a key instrument in the compact representation of large haplotypes data sets [9]. Indeed, due to the intrinsic capability of the PBWT of saving space in memorizing haplotype data and even in analyzing large haplotypes panels, it is becoming a relevant data structure for pangenomics (see the recent tutorial on data structures [2]). It is used in relevant computational steps related to haplotype phasing and analysis, such as the matching procedure in reference panels of haplotypes.…”

“…It is used in relevant computational steps related to haplotype phasing and analysis, such as the matching procedure in reference panels of haplotypes. Moreover, the notion of PBWT has been extended to graph pangenomes representations of haplotypes with the name of Graph positional BWT or GBWT, and it is currently the building block of sequence to graph aligners in the VG toolkit [2].…”

“…It has been used for genotype imputation [26], and to create a genotype database search method that is privacy-preserving (PBWT-sec) [28]. Novak et al [22] and Sirén et al [29] used the PBWT to encode a graph for haplotype matching (g-PBWT) and graph pangenome indexing [2]. Sanaullah et al [27] replaced all arrays with linked lists to define a dynamic version of the PBWT (d-PBWT).…”

Sequence to graph alignment using gap-sensitive co-linear chaining

“…The latest version of industry-standard DRAGEN software by Illumina now uses a pangenome graph for mapping reads in highly polymorphic regions of a human genome [13]. For surveys of the recent algorithmic developments in this area, see [2,6,10,35]. Among the many computational tasks associated with pangenome graphs, sequence-to-graph alignment remains a core computational problem.…”