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https://github.com/lh3/bwa

Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)
https://github.com/lh3/bwa

bioinformatics fm-index genomics sequence-alignment

Last synced: 25 days ago
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Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)

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README 

        
## For the Impatient



```sh

# Download bwakit (or from  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][kithelp] 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:



![](http://lh3lh3.users.sourceforge.net/images/alt-demo.png)



#### 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] (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][novel], a 10kb contig assembled from

CHM1 short reads and present also in NA12878. You can try [BLAT][blat] or

[BLAST][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][sgdp] samples.

The sequences are included in `hs38DH-extra.fa` from the [BWA binary

package][res].



In addition to decoy, we also put multiple alleles of HLA genes in

`hs38DH-extra.fa`. These genomic sequences were acquired from [IMGT/HLA][hladb],

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][res], 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)][hla4], [Liu et al

(2013)][hla2], [Bai et al (2014)][hla3] and [Dilthey et al (2014)][hla1], 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.



[res]: https://sourceforge.net/projects/bio-bwa/files/bwakit

[sb]: https://github.com/GregoryFaust/samblaster

[grc]: http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/

[novel]: https://gist.github.com/lh3/9935148b71f04ba1a8cc

[blat]: https://genome.ucsc.edu/cgi-bin/hgBlat

[blast]: http://blast.st-va.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome

[sgdp]: http://www.simonsfoundation.org/life-sciences/simons-genome-diversity-project/

[hladb]: http://www.ebi.ac.uk/ipd/imgt/hla/

[grcdef]: http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/info/definitions.shtml

[hla1]: http://biorxiv.org/content/early/2014/07/08/006973

[hlalink]: http://www.hladiseaseassociations.com

[hlatools]: https://www.biostars.org/p/93245/

[hla2]: http://nar.oxfordjournals.org/content/41/14/e142.full.pdf+html

[hla3]: http://www.biomedcentral.com/1471-2164/15/325

[hla4]: http://genomemedicine.com/content/4/12/95

[kithelp]: https://github.com/lh3/bwa/tree/master/bwakit