The human reference genome represents only a small number of individuals, which limits its usefulness for genotyping. We present a method named HISAT2 (hierarchical indexing for spliced alignment of transcripts 2) that can align both DNA and RNA sequences using a graph Ferragina Manzini index. We use HISAT2 to represent and search an expanded model of the human reference genome in which over 14.5 million genomic variants in combination with haplotypes are incorporated into the data structure used for searching and alignment. We benchmark HISAT2 using simulated and real datasets to demonstrate that our strategy of representing a population of genomes, together with a fast, memory-efficient search algorithm, provides more detailed and accurate variant analyses than other methods. We apply HISAT2 for HLA typing and DNA fingerprinting; both applications form part of the HISAT-genotype software that enables analysis of haplotype-resolved genes or genomic regions. HISAT-genotype outperforms other computational methods and matches or exceeds the performance of laboratory-based assays. A graph-based genome indexing scheme enables variant-aware alignment of sequences with very low memory requirements.

Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

Reference genomes guide our interpretation of DNA sequence data. However, conventional linear references represent only one version of each locus, ignoring variation in the population. Poor representation of an individual's genome sequence impacts read mapping and introduces bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation across a population, including large-scale structural variation such as inversions and duplications. Previous graph genome software implementations have been limited by scalability or topological constraints. Here we present vg, a toolkit of computational methods for creating, manipulating, and using these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays, with improved accuracy over alignment to a linear reference, and effectively removing reference bias. These capabilities make using variation graphs as references for DNA sequencing practical at a gigabase scale, or at the topological complexity of de novo assemblies.

/pdf/variation-graph-toolkit-improves-read-mapping-by-b8gf23n8qw.pdf

Variation graph toolkit improves read mapping by representing genetic variation in the reference.

Pan-Genome of Wild and Cultivated Soybeans.

The recent advances in sequencing technologies enable the assembly of individual genomes to the quality of the reference genome. How to integrate multiple genomes from the same species and make the integrated representation accessible to biologists remains an open challenge. Here, we propose a graph-based data model and associated formats to represent multiple genomes while preserving the coordinate of the linear reference genome. We implement our ideas in the minigraph toolkit and demonstrate that we can efficiently construct a pangenome graph and compactly encode tens of thousands of structural variants missing from the current reference genome.

/pdf/the-design-and-construction-of-reference-pangenome-graphs-996hc42t9j.pdf

The design and construction of reference pangenome graphs with minigraph

Transposable elements (TEs) are insertional mutagens that contribute greatly to the plasticity of eukaryotic genomes, influencing the evolution and adaptation of species as well as physiology or disease in individuals Measuring TE expression helps to understand not only when and where TE mobilization can occur but also how this process alters gene expression, chromatin accessibility or cellular signalling pathways Although genome-wide gene expression assays such as RNA sequencing include transposon-derived transcripts, most computational analytical tools discard or misinterpret TE-derived reads Emerging approaches are improving the identification of expressed TE loci and helping to discriminate TE transcripts that permit TE mobilization from chimeric gene–TE transcripts or pervasive transcription Here we review the main challenges associated with the detection of TE expression, including mappability, insertional and internal sequence polymorphisms, and the diversity of the TE transcriptional landscape, as well as the different experimental and computational strategies to solve them Computational tools to analyse RNA sequencing data often disregard or even misinterpret reads derived from transposable elements This Review highlights the main challenges associated with the detection of transposable element expression, including mappability, sequence polymorphisms and transcript diversity, and discusses the experimental and computational strategies to overcome them

https://www.hal.inserm.fr/inserm-03261002/document

Measuring and interpreting transposable element expression.

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5 h using a system with 36 CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses. Graph Genome Pipeline is a read-alignment and variant-calling pipeline based on graph genomes that offers improved read-mapping and variant-calling accuracy while achieving speed comparable to those of linear reference genome pipelines.

/pdf/fast-and-accurate-genomic-analyses-using-genome-graphs-5chawbv0f1.pdf

Fast and accurate genomic analyses using genome graphs.

The human reference genome serves as the foundation for genomics by providing a scaffold for sequencing read alignment, but currently only reflects a single consensus haplotype, impairing read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our graph genome aligner and variant calling pipeline consume around 5.5 and 2 hours per high coverage whole-genome-sequenced sample, respectively, comparable to those of state-of-the-art linear reference genome-based methods. Using orthogonal benchmarks based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5 percentage point increase in variant calling recall, which extrapolates into 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be directly genotyped from read alignments in a rapid and accurate fashion. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is the first practical step towards fulfilling the promise of graph genomes to radically enhance the scalability and precision of genomic analysis by incorporating prior knowledge of population characteristics.

/pdf/fast-and-accurate-genomic-analyses-using-genome-graphs-4i5th2q7t7.pdf

Gulfem Demir

Papers

Fast and accurate genomic analyses using genome graphs.

Fast and Accurate Genomic Analyses using Genome Graphs