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.

Simplitigs as an efficient and scalable representation of de Bruijn graphs

“…Computational pan-genomics has recently emerged as an important sub-branch of bioinformatics [35] . One of the motivations is the analysis of sequencing data in the context of whole species.…”

Distance Indexing and Seed Clustering in Sequence Graphs

“…The resulting lack of diversity introduces a systematic bias that makes samples look more like the reference genome [20]. This reference bias can be reduced by using pangenomic models, which incorporate the genomic content of populations of individuals [1]. Sequence graphs are a popular representation of pangenomes that can express all of the variation in a pangenome [13].…”

Graphtyper: Population-scale genotyping using pangenome graphs

“…17 However, some reads cannot be aligned to a reference genome, particularly those 18 originating from highly polymorphic regions and regions absent from the reference genome. 19 Reference genome alignments are also generally done without awareness of variation, 20 causing mapping bias towards the reference allele and misalignments around indels 10,11 .…”

“…Although approaches 2 that find polymorphisms in reference-free assemblies have been developed to avoid these 3 limitations 16,17 , de novo assembly algorithms remain computationally expensive, have less 4 sensitivity 17 , and use data structures that have a complex coordinate system. 5 Pangenomes 12,18,19 have recently been proposed to counter weaknesses of both reference 6 alignments and de novo assemblies by extending the linear reference alignments with 7 variation-aware alignments 20 . Pangenomes incorporate prior information about variation, 8 allowing read aligners to better distinguish between sequencing errors in reads and true 9 sequence variation.…”

“…18 Our results show that GATK UG, Graphtyper and Samtools all had comparable compute 19 times and completed the genotyping in between 576 and 594 hours ( Table 1). The other five 20 genotypers required considerably greater compute times (1,030-12,964 hours). 21 We assessed the raw output of all eight genotyping pipelines to compare them independent 22 of filtering technique and to include analysis of all germline variation, somatic variation, and 23 not peer-reviewed) is the author/funder.…”