SeqArray Overview

Introduction

Whole-genome sequencing (WGS) data is being generated at an unprecedented rate

Methods

CoreArray (C++ library)

Two R packages

Methods -- Advantages

Methods -- File Contents

File: SeqArray/extdata/CEU_Exon.gds (387.3K)
|--+ description   [  ] *
|--+ sample.id   { Str8 90 ZIP_ra(30.8%), 222B }
|--+ variant.id   { Int32 1348 ZIP_ra(35.7%), 1.9K }
|--+ position   { Int32 1348 ZIP_ra(86.4%), 4.6K }
|--+ chromosome   { Str8 1348 ZIP_ra(2.66%), 91B }
|--+ allele   { Str8 1348 ZIP_ra(17.2%), 928B }
|--+ genotype   [  ] *
|  \--+ data   { Bit2 2x90x1348 ZIP_ra(28.4%), 16.8K } *
|--+ phase   [  ]
|  \--+ data   { Bit1 90x1348 ZIP_ra(0.36%), 55B } *
|--+ annotation   [  ]
|  |--+ id   { Str8 1348 ZIP_ra(41.0%), 5.8K }
|  |--+ qual   { Float32 1348 ZIP_ra(0.91%), 49B }
|  |--+ filter   { Int32,factor 1348 ZIP_ra(0.89%), 48B } *
|  |--+ info   [  ]
|  |  |--+ AA   { Str8 1348 ZIP_ra(24.2%), 653B } *
|  |  \--+ HM2   { Bit1 1348 ZIP_ra(117.2%), 198B } *
|  \--+ format   [  ]
|     \--+ DP   [  ] *
|        \--+ data   { Int32 90x1348 ZIP_ra(33.8%), 160.3K }
\--+ sample.annotation   [  ]
   \--+ family   { Str8 90 ZIP_ra(34.7%), 135B }

Methods -- Key Functions

Table 1: The key functions in the SeqArray package.

| Function | Description | |:-------------|:-------------------------------------------| | seqVCF2GDS | Reformats VCF files | | seqSetFilter | Defines a data subset of samples or variants | | seqGetData | Gets data from a SeqArray file with a defined filter | | seqApply | Applies a user-defined function over array margins | | seqParallel | Applies functions in a computing cluster |

Benchmark

Benchmark -- Test 1 (sequentially)

# load the R package
library(SeqArray)

# open the file
genofile <- seqOpen("1KG_chr1.gds")

# apply a user-defined function over variants
system.time(afreq <- seqApply(genofile, "genotype",
    FUN = function(x) { mean(x==0L, na.rm=TRUE) },
    as.is="double", margin="by.variant")
)

10.8 minutes on Linux with Intel Xeon CPU @2GHz and 128GB RAM function(x) { mean(x==0L, na.rm=TRUE) } is a user-defined function, where x is an integer matrix:

                           sample
  allele [,1] [,2] [,3] [,4] [,5]
    [1,]    0    1    0   NA    1
    [2,]    0    0    0    1    0

0 -- reference allele, 1 -- the first alternative allele

Benchmark -- Test 2 (in parallel)

seqParallel() splits genotypes into 4 non-overlapping parts according to different cores.

# load the R package
library(parallel)

# create a computing cluster with 4 cores
seqParallelSetup(4)

# run in parallel
system.time(afreq <- seqParallel(gdsfile=genofile,
    FUN = function(f) {
        seqApply(f, "genotype", as.is="double", margin="by.variant",
            FUN = function(x) mean(x==0L, na.rm=TRUE))
    }, split = "by.variant")
)

3.1 minutes (vs. 10.8m in Test 1)

Benchmark -- Test 3 (C++ Integration)

library(Rcpp)

# dynamically define an inline C/C++ function in R
cppFunction('double RefAlleleFreq(IntegerMatrix x) {
    int nrow = x.nrow(), ncol = x.ncol();
    int cnt=0, zero_cnt=0, g;
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            if ((g = x(i, j)) != NA_INTEGER) {
                cnt ++;
                if (g == 0) zero_cnt ++;
            }
    }}
    return double(zero_cnt) / cnt;
}')

system.time(
    afreq <- seqApply(genofile, "genotype", RefAlleleFreq,
        as.is="double", margin="by.variant")
)

1.5 minutes (significantly faster! vs. 10.8m in Test 1)

Conclusion

SeqArray is of great interest to

SeqVarTools (Bioconductor)

SNPRelate (Bioconductor)

Resource

1000 Genomes Project: http://bochet.gcc.biostat.washington.edu/seqarray/1000genomes

  1. Phase 1:

  2. Phase 3:

Acknowledgements

Department of Biostatistics at University of Washington -- Seattle

Genetic Analysis Center:



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SeqArray documentation built on Nov. 17, 2018, 6 p.m.