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

Abstract

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.

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Fig. 1: Graph representation with its tabular form and HISAT2 indexes and alignment output.
Fig. 2: Construction of the graph human reference, that is a Genotype genome.
Fig. 3: HISAT-genotype assembly of two HLA-A alleles through a guided k-mer assembly graph.
Fig. 4: Comparisons of HISAT2, Bowtie2, BWA-mem, and vg using 10 million simulated read pairs that include SNPs.

Data availability

The CAAPA genome data are available from dbGaP (accession phs001123.v1.p1).

Code availability

HISAT2 and HISAT-genotype are open-source software freely available at https://github.com/DaehwanKimLab/hisat2. The HISAT2 package includes programs and application programming interfaces for C++, Python and JAVA that rapidly retrieve genomic locations from repeat alignments for use in downstream analyses such as variant calling, peak calling and differential gene expression analysis.

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Acknowledgements

We would like to express our thanks to K. Barnes and M. Daya for sharing Omixon’s HLA results with us. We would like to thank B. Langmead and J. Pritt for their invaluable contributions to our discussions on HISAT2. We also greatly appreciate the generosity of G. Danuser and D. Reed in providing wet-lab bench space and equipment for us. This work was supported in part by the National Human Genome Research Institute under grants R01-HG006102 and R01-HG006677 to S.L.S. and by the Cancer Prevention Research Institute of Texas under grant RR170068 to D.K. All authors read and approved the final manuscript.

Author information

D.K. and S.L.S. performed the analysis and discussed the results of HISAT2 and HISAT-genotype. D.K. designed and implemented HISAT2 and HISAT-genotype. J.M.P. optimized the index-building algorithm of HISAT2. D.K. and C.P. implemented the repeat-indexing algorithm of HISAT2. D.K., C.P. and C.B. performed the evaluations of the various programs. D.K. performed the wet-lab experiments. D.K., C.B. and S.L.S. wrote the manuscript.

Correspondence to Daehwan Kim.

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Competing interests

The authors declare no competing interests.

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Integrated supplementary information

Figure Supplementary 1 Space efficient representation of the table in Figure 1a.

In the two ‘Node rank’ columns on the left, since node ranks are given in consecutive and increasing order, one bit (0 or 1) can be used to represent a node rank instead of 4 bytes (any number between 0 and 4,294,967,295) to manage offsets for a human genome. 1 and 0 are used to indicate a new node rank and to indicate an additional outgoing or incoming edge that a node has, respectively. To retrieve a node rank, simply summing up the 1s gives rise to that node’s rank. Since the labels in the ‘First’ column are already sorted, five numbers are enough to represent the column, two for As, three for Cs, four for Gs, three for Ts, and two for Zs. In the ‘Last’ column, two bits are used to represent each label: 00 for A, 01 for C, 10 for G, and 11 for T. 00 is also used to represent Z. HISAT2 internally resolves whether 00 represents A or Z. The right table is the space efficient representation of the left table after these transformations.

Supplementary Figure 2 Alternative approaches to graph representation.

In a and b, a reference consisting of only alleles of genes of interest can introduce significant mapping bias by mapping reads from regions not included in the restricted reference, as illustrated in more detail in Supplementary Fig. 3. In c, an aligner using the current human reference may not be able to map many reads if they originated from alleles that are substantially different from the human reference allele. In d, a reference consisting of the human reference plus numerous alleles of HLA genes enables mapping of reads from even substantially different alleles. Most HLA-typing methods, such as HLA-VBSeq, HLA*PRG, Kourami, and Graphtyper, are based on c, d, or a combination thereof to initially identify HLA reads, after which HLA-VBSeq uses approach a, and HLA*PRG, Kourami, and Graphtyper use a small-scale graph representation as described in b to perform typing. Kourami assembles only exons of HLA genes, while HISAT-genotype is able to assemble full-length sequences of HLA genes including exons and introns.

Supplementary Figure 3 Incorrect reference leading to mis-alignment and bias.

An illustration of the benefits of using the right reference/index when working with sequencing reads. The figure shows the alignment of reads to the whole genome (upper right) and to one particular genomic region denoted as Region 3 (lower right). When using the whole genome for aligning the six example reads, reads are perfectly aligned to the correct regions (regions 1, 2, 3, 4, 5, and 6). However, if the example reads are aligned using only one particular region (e.g. Region 3), five out of the six reads are incorrectly aligned to that region because alignment programs allow for a few mismatches. For example, in order to identify and extract reads that belong to the HLA-DRB1 gene from whole-genome sequencing reads, one may attempt to align them only to the HLA-DRB1 gene region. In one experiment, we found that this strategy produced 1100 times more reads mapped to HLA-DRB1 than a whole-genome alignment produced, because of the numerous pseudogene copies of DRB1 in the genome.

Figure Supplementary 4 CEPH pedigree #1463 consisting of 17 members across three generations.

TBA.

Supplementary Figure 5 GFM index.

The size of each block is 128 bytes, consisting of 32 4-byte cells. Each block stores: (1) four 4-byte numbers for the accumulated numbers of occurrences of A, C, G, and T up to that block, (2) one 4-byte number for the accumulated number of 1s up to the block in the Node rank (Outgoing edges) of the right table in Fig. 1a, (3) one 4-byte number for the row number of the Node rank (incoming edge) corresponding to the accumulated number of 1s indicated in (2), (4) 208 labels (or nucleotides) corresponding to the Last column of the right table in Fig. 1a, and (5) 208 ‘OUT’ bits and 208 ‘IN’ bits corresponding to the Node rank columns of the table.

Supplementary Figure 6 Alignment of a small 3-bp query using a GFM index.

How to align a 3-bp query, TAG, whose TG corresponds to the last two nucleotides of the original reference sequence, GAGCTG, and where A is a 1-bp insertion in the query. Searching from the right end of the query to the left, the nodes labeled ‘G’ are first selected (node IDs ‘4’, ‘5’, ‘6’, and ‘7’). Then the incoming edges of those nodes are examined to identify which has a preceding base ‘A’. Nodes ‘5’ and ‘7’ qualify, with preceding nodes ‘1’ and ‘2’. These in turn are examined to determine which of these nodes is preceded by a base ‘T’. Only one of the two nodes, node ‘2’, has a preceding node, ‘8’, whose label corresponds to ‘T’. Node ‘8’ is chosen as a mapped location for the query. This is the final alignment of the query shown in the prefix-sorted graph, and additional algorithms convert it to the corresponding alignment in the original graph.

Supplementary Figure 7 Construction of repeat sequences specifically for alignment purposes.

Given a de Bruijn graph in which k = 3 and each k-mer is present at least C times (e.g. five times) in the genome, a k-mer is chosen (e.g. the most frequently occurring k-mer) and extended in the left and right directions. Note that a de Bruijn graph can be easily constructed from a k-mer table. The extended sequence consisting of k-mers is called a repeat sequence. If there is a branch during extension, one of the k-mers is chosen (e.g. the most frequently occurring k-mer at the branch). In a, for example, a k-mer, TTT, is chosen and shown in yellow, and then extended in both directions until there is no extension possible, resulting in a repeat sequence, CCGTTTAC. In order to find the next repeat sequence, the k-mers belonging to the previously identified repeat sequence are removed as shown in b. A k-mer, CTT, is chosen and not extended in b as it has no k-mers to extend. In c, TAT is initially chosen and extended into TATTGT in orange. Finally, in d, TGC is chosen and not extended. Each repeat sequence consists of sub-sequences that exist in the reference genome, and a sub-sequence consists of one or more consecutive k-mers. We only store one location per sub-sequence, instead of per k-mer.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Supplementary Tables 1–9 and Supplementary Note 1

Reporting Summary

Supplementary Dataset 1

HLA-A gene assembly of PG genome NA12878

Supplementary Dataset 2

HISAT-genotype’s HLA typing results for 17 PG genomes on HLA-A, HLA-B, HLA-C, HLA-DQA1, HLA-DQB1 and HLA-DRB1

Supplementary Dataset 3

Description: HLA-A gene assembly of PG genome NA12892

Supplementary Dataset 4

HISAT-genotype’s HLA typing results for 917 CAAPA genomes on HLA-A, HLA-B, HLA-C, HLA-DQA1, HLA-DQB1 and HLA-DRB1

Supplementary Dataset 5

Comparisons of HISAT-genotype and Kourami for HLA typing using simulated reads (see Supplementary Note 1 for description)

Supplementary Dataset 6

HISAT-genotype initial DNA fingerprinting results for 17 PG genomes

Supplementary Dataset 7

PowerPlex Fusion results for 17 PG genomes (raw signal image data)

Supplementary Dataset 8

List of alleles for 13 DNA fingerprinting loci and the amelogenin locus from the NIST short tandem repeat database

Supplementary Dataset 9

List of eight additionally incorporated alleles for four DNA fingerprinting loci D8S1179, D13S317, VWA and D21S11

Supplementary Dataset 10

HLA-A gene assembly of CAAPA genome LP6005093-DNA_E03

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