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Noble Trios: simulated pedigrees for benchmarking de novo variant discovery methods

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Synopsis

Motivation

Modeling genome sequences realistically is incredibly complicated, and most simulations are gross oversimplifications of many features observed in real data. Nevertheless, there is tremendous value, espcially while developing new methods, in frequently evaluating performance on a data set where the "correct" answers are known. While the true test of a method is its accuracy with real sequence data, experience demonstrates that even trivially small simulated data sets can reveal bugs in software and bring erroneous assumptions to the fore. Deliberately small data sets can also be processed in much less time with fewer computational resources, enabling continuous testing strategies not feasible with large data sets.

Availability

The Noble Trios is a collection of simulated pedigrees created for assessing the performance of de novo variant discovery methods. Each trio is derived from a randomly generated genome sequence with a septanucleotide composition equivalent to that of the human genome. From this "reference" sequence, shared (inherited) and unique (de novo) genomic variants are simulated for three hypothetical individuals: a mother, father, and child. For each trio this data set furnishes a Fastq file containing simulated 2x100bp paired-end Illumina reads providing 30x coverage of each sample, as well as a VCF file annotating the locations of the shared and unique variants.

Sequence data and variant annotations can be obtained anonymously from the Open Science Framework at https://osf.io/anr56/. Full disclosure of the pipeline used to produce the data set is available at https://github.com/standage/noble.

Meet the Nobles

Trio name Genome size # Shared variants # Unique (de novo in proband) variants
helium 2.5 Mbp 100 5
neon 25 Mbp 200 10
argon 250 Mbp 300 12
krypton 2.5 Gbp 750 60

Manifest

  • Snakefile: the simulation procedures were executed as a Snakemake workflow
  • noble.json: configuration for the Snakemake workflow
  • Dockerfile: detailed description of the software configuration environment used to execute the workflow
  • human.order6.mm: initial states and transition states of an 6th-order Markov model of nucleotide composition, used for simulating genome sequences

Workflow and Implementation

The figure below summarizes the workflow invoked for each trio.

Noble trio workflow

  • simulategenome: The nuclmm package is used to simulate a haploid "reference" genome sequence.
  • simulatetriovariants: The kevlar library is used to simulate shared and unique variants with respect to the reference for a hypothetical trio and to produce a diploid genome sequence for each individual.
  • sequencing: The wgsim tool is used to simulate Illumina whole genome shotgun sequencing of each individual's genome with an effective error rate of approximately 1%.
  • trustedkmers: As a preliminary error correction step, k-mers from all 3 samples are analyzed using Lighter to determine "trusted" k-mers in the data set.
  • errorcorrect: Using the "trusted" k-mers, Lighter is again used to correct sequencing errors in the read data sample-by-sample.
  • interleave_compress: Finally, standard shell tools are used to interleave and compress the paired read files.

The following guide provides commands to show and execute the workflow.

# Do a "dry-run": show the commands that will be executed to create the "helium" trio
snakemake --configfile noble.json -np helium-{mother,father,proband}-reads-cor.fq.gz

# Actually execute the commands to create the "helium" trio
snakemake --configfile noble.json -p helium-{mother,father,proband}-reads-cor.fq.gz

# Create the "neon" trio, allowing up to 4 commands to run simultaneously,
#   and skip the error correction steps.
snakemake --configfile noble.json --jobs 4 -p neon-{mother,father,proband}-reads.fq.gz

# Create all trios
snakemake --configfile noble.json --jobs 4 -p all

Generating the helium and neon trios requires only a few minutes of runtime. The argon and krypton trios requires a few hours.

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A collection of data sets for benchmarking de novo variant discovery tools

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