Computational pan-genomics: status, promises and challenges

Abstract

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Keywords: data structures; haplotypes; pan-genome; read mapping; sequence graph.

Figure 1.

A sequence graph assembled from the Homo sapiens reference genome and all transcripts of the EEF1A1 gene in Ensembl v80 [93]. Each node represents a sequence found in the reference genome or transcripts, edges (black solid lines) indicate adjacencies that have been observed in transcripts and colored horizontal bars correspond to paths of known transcripts (annotated with their Ensembl transcript ID), where each dotted line indicates that a node in the graph is part of this transcript. The path annotated with coordinates (6:73515750–73523797) corresponds to the reference genome.

Figure 2.

Illustration of operations to be supported by a pan-genome data structure.

Figure 3.

Selected examples of pan-genome representations: (A) three unaligned sequences, colors highlight similarities; (B) a MSA of the same three sequences; (C) the DBG of the first (red) sequence block; (D) acyclic sequence graph, paths representing the three haplotypes shown as solid/dashed/dotted lines; (E) cylic sequence graph; (F) Li–Stephens model of the first nine characters with states indicated by circles, emission distributions given in boxes and transitions given by arrows; dashed arrows indicate the (less likely) ‘recombination’ transitions.