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README-alt.md

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For the Impatient

# Download bwakit (or from <http://sourceforge.net/projects/bio-bwa/files/bwakit/> manually)
wget -O- http://sourceforge.net/projects/bio-bwa/files/bwakit/bwakit-0.7.12_x64-linux.tar.bz2/download \
  | gzip -dc | tar xf -
# Generate the GRCh38+ALT+decoy+HLA and create the BWA index
bwa.kit/run-gen-ref hs38DH   # download GRCh38 and write hs38DH.fa
bwa.kit/bwa index hs38DH.fa  # create BWA index
# mapping
bwa.kit/run-bwamem -o out -H hs38DH.fa read1.fq read2.fq | sh  # skip "|sh" to show command lines

This generates out.aln.bam as the final alignment, out.hla.top for best HLA genotypes on each gene and out.hla.all for other possible HLA genotypes. Please check out bwa/bwakit/README.md for details.

Background

GRCh38 consists of several components: chromosomal assembly, unlocalized contigs (chromosome known but location unknown), unplaced contigs (chromosome unknown) and ALT contigs (long clustered variations). The combination of the first three components is called the primary assembly. It is recommended to use the complete primary assembly for all analyses. Using ALT contigs in read mapping is tricky.

GRCh38 ALT contigs are totaled 109Mb in length, spanning 60Mbp of the primary assembly. However, sequences that are highly diverged from the primary assembly only contribute a few million bp. Most subsequences of ALT contigs are nearly identical to the primary assembly. If we align sequence reads to GRCh38+ALT blindly, we will get many additional reads with zero mapping quality and miss variants on them. It is crucial to make mappers aware of ALTs.

BWA-MEM is ALT-aware. It essentially computes mapping quality across the non-redundant content of the primary assembly plus the ALT contigs and is free of the problem above.

Methods

Sequence alignment

As of now, ALT mapping is done in two separate steps: BWA-MEM mapping and postprocessing. The bwa.kit/run-bwamem script performs the two steps when ALT contigs are present. The following picture shows an example about how BWA-MEM infers mapping quality and reports alignment after step 2:

Step 1: BWA-MEM mapping

At this step, BWA-MEM reads the ALT contig names from "idxbase.alt", ignoring the ALT-to-ref alignment, and labels a potential hit as ALT or non-ALT, depending on whether the hit lands on an ALT contig or not. BWA-MEM then reports alignments and assigns mapQ following these two rules:

  1. The mapQ of a non-ALT hit is computed across non-ALT hits only. The mapQ of an ALT hit is computed across all hits.

  2. If there are no non-ALT hits, the best ALT hit is outputted as the primary alignment. If there are both ALT and non-ALT hits, non-ALT hits will be primary and ALT hits be supplementary (SAM flag 0x800).

In theory, non-ALT alignments from step 1 should be identical to alignments against the reference genome with ALT contigs. In practice, the two types of alignments may differ in rare cases due to seeding heuristics. When an ALT hit is significantly better than non-ALT hits, BWA-MEM may miss seeds on the non-ALT hits.

If we don't care about ALT hits, we may skip postprocessing (step 2). Nonetheless, postprocessing is recommended as it improves mapQ and gives more information about ALT hits.

Step 2: Postprocessing

Postprocessing is done with a separate script bwa-postalt.js. It reads all potential hits reported in the XA tag, lifts ALT hits to the chromosomal positions using the ALT-to-ref alignment, groups them based on overlaps between their lifted positions, and then re-estimates mapQ across the best scoring hit in each group. Being aware of the ALT-to-ref alignment, this script can greatly improve mapQ of ALT hits and occasionally improve mapQ of non-ALT hits. It also writes each hit overlapping the reported hit into a separate SAM line. This enables variant calling on each ALT contig independent of others.

On the completeness of GRCh38+ALT

While GRCh38 is much more complete than GRCh37, it is still missing some true human sequences. To make sure every piece of sequence in the reference assembly is correct, the Genome Reference Consortium (GRC) require each ALT contig to have enough support from multiple sources before considering to add it to the reference assembly. This careful and sophisticated procedure has left out some sequences, one of which is this example, a 10kb contig assembled from CHM1 short reads and present also in NA12878. You can try BLAT or BLAST to see where it maps.

For a more complete reference genome, we compiled a new set of decoy sequences from GenBank clones and the de novo assembly of 254 public SGDP samples. The sequences are included in hs38DH-extra.fa from the BWA binary package.

In addition to decoy, we also put multiple alleles of HLA genes in hs38DH-extra.fa. These genomic sequences were acquired from IMGT/HLA, version 3.18.0 and are used to collect reads sequenced from these genes.

HLA typing

HLA genes are known to be associated with many autoimmune diseases, infectious diseases and drug responses. They are among the most important genes but are rarely studied by WGS projects due to the high sequence divergence between HLA genes and the reference genome in these regions.

By including the HLA gene regions in the reference assembly as ALT contigs, we are able to effectively identify reads coming from these genes. We also provide a pipeline, which is included in the BWA binary package, to type the several classic HLA genes. The pipeline is conceptually simple. It de novo assembles sequence reads mapped to each gene, aligns exon sequences of each allele to the assembled contigs and then finds the pairs of alleles that best explain the contigs. In practice, however, the completeness of IMGT/HLA and copy-number changes related to these genes are not so straightforward to resolve. HLA typing may not always be successful. Users may also consider to use other programs for typing such as Warren et al (2012), Liu et al (2013), Bai et al (2014) and Dilthey et al (2014), though most of them are distributed under restrictive licenses.

Preliminary Evaluation

To check whether GRCh38 is better than GRCh37, we mapped the CHM1 and NA12878 unitigs to GRCh37 primary (hs37), GRCh38 primary (hs38) and GRCh38+ALT+decoy (hs38DH), and called small variants from the alignment. CHM1 is haploid. Ideally, heterozygous calls are false positives (FP). NA12878 is diploid. The true positive (TP) heterozygous calls from NA12878 are approximately equal to the difference between NA12878 and CHM1 heterozygous calls. A better assembly should yield higher TP and lower FP. The following table shows the numbers for these assemblies:

Assembly hs37 hs38 hs38DH CHM1_1.1 huref
FP 255706 168068 142516 307172 575634
TP 2142260 2163113 2150844 2167235 2137053

With this measurement, hs38 is clearly better than hs37. Genome hs38DH reduces FP by ~25k but also reduces TP by ~12k. We manually inspected variants called from hs38 only and found the majority of them are associated with excessive read depth, clustered variants or weak alignment. We believe most hs38-only calls are problematic. In addition, if we compare two NA12878 replicates from HiSeq X10 with nearly identical library construction, the difference is ~140k, an order of magnitude higher than the difference between hs38 and hs38DH. ALT contigs, decoy and HLA genes in hs38DH improve variant calling and enable the analyses of ALT contigs and HLA typing at little cost.

Problems and Future Development

There are some uncertainties about ALT mappings - we are not sure whether they help biological discovery and don't know the best way to analyze them. Without clear demand from downstream analyses, it is very difficult to design the optimal mapping strategy. The current BWA-MEM method is just a start. If it turns out to be useful in research, we will probably rewrite bwa-postalt.js in C for performance; if not, we may make changes. It is also possible that we might make breakthrough on the representation of multiple genomes, in which case, we can even get rid of ALT contigs for good.