A flexible framework for rapid genome analysis and interpretation
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README.md

SpeedSeq

A flexible framework for rapid genome analysis and interpretation

C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth (2015). doi:10.1038/nmeth.3505.

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html

SpeedSeq workflow

Table of Contents

  1. Quick start
  2. Installation
  3. Reference genome and annotations
  4. Usage
  5. Example workflows
  6. SpeedSeq AMI (Amazon Machine Image)
  7. Troubleshooting

Quick start

  1. Install

    git clone --recursive https://github.com/hall-lab/speedseq
    cd speedseq
    make
    
  2. Run the example script

    cd example
    ./run_speedseq
    

    This should produce the following files:

    • example.bam
    • example.discordants.bam
    • example.splitters.bam
    • example.vcf.gz
    • example.sv.vcf.gz

Installation

Example installation commands

As a template for installation on other systems, we have provided the exact commands for a full installation of SpeedSeq and GEMINI on a blank Amazon Linux box. These commands encompass all of the installation steps outlined below.

Prerequisites

Configuration

System paths to SpeedSeq's component software are specified in the speedseq.config file, which should reside in the same directory as the SpeedSeq executable (for alternate locations use the -K flag). Upon installation, SpeedSeq attempts to automatically generate this file, but manual editing may be necessary.

Install core components

The core components enable standard functionality outlined in Quick start.

Compilation requires g++ and the standard C and C++ development libraries. Additionally, cmake is required for building the BamTools API within FreeBayes and LUMPY.

git clone --recursive https://github.com/hall-lab/speedseq
cd speedseq
make

Essential SpeedSeq components can be installed with make, which produces a log file (install.log) that details the compilation status.

The installation is modular, and its units can be built separately with make align, make var, make somatic, make sv, and make realign. This allows installation of only the desired components, eliminating extraneous dependencies. It further allows rebuilding of previously failed components.

If any components already exist on the system or fail to install, their paths can be manually specified by editing speedseq.config.

Install optional components

Optional components enable advanced features such as variant annotation and read-depth analysis.

Variant Effect Predictor
curl -OL https://github.com/Ensembl/ensembl-tools/archive/release/76.zip
unzip 76.zip
perl ensembl-tools-release-76/scripts/variant_effect_predictor/INSTALL.pl \
    -c $SPEEDSEQ_DIR/annotations/vep_cache \
    -a ac -s homo_sapiens -y GRCh37

cp ensembl-tools-release-76/scripts/variant_effect_predictor/variant_effect_predictor.pl $SPEEDSEQ_DIR/bin
cp -r Bio $SPEEDSEQ_DIR/bin

# Update the VEP and VEP_CACHE_DIR variables in speedseq.config to point to
# $SPEEDSEQ_DIR/bin/variant_effect_predictor.pl and $SPEEDSEQ_DIR/annotations/vep_cache
CNVnator

CNVnator requires the ROOT package as a prerequiste (https://root.cern.ch/drupal/)

  1. Install the ROOT package

    curl -OL ftp://root.cern.ch/root/root_v5.34.20.source.tar.gz
    tar -zxvf root_v5.34.20.source.tar.gz
    cd root
    ./configure --prefix=$PWD
    make
    
  2. Source thisroot.sh

    source /pathto/root/bin/thisroot.sh
    
  3. Compile CNVnator from the SpeedSeq directory

    cd $SPEEDSEQ_DIR
    make cnvnator-multi
    
  4. Before running SpeedSeq, you'll need to add the following line to speedseq.config or your .bashrc file. (Substitute the actual path to thisroot.sh on your system)

    source /pathto/root/bin/thisroot.sh
    

Reference genome and annotations

Reference genome

We recommend using the GRCh37 human genome for SpeedSeq, available here:
ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.gz
ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/human_g1k_v37.fasta.fai

The genome FASTA file should be unzipped and indexed with BWA before running SpeedSeq.

Annotations

For human genome alignment using the GRCh37 build, we recommend using the annotations/ceph18.b37.include.2014-01-15.bed windows to parallelize variant calling (speedseq var and speedseq somatic). This BED file excludes 15.6 Mb of the non-gapped genome where the coverage in the CEPH1463 pedigree was greater than twice the mode coverage plus 3 standard deviations. We believe these extremely high depth regions that we excluded are areas of misassembly in the GRCh37 human reference genome in which variant calling is time-consuming and error-prone.

Additionally, the regions in annotations/ceph18.b37.include.2014-01-15.bed are variable-width windows which each contain approximately the same coverage depth in the CEPH1463 pedigree, and sorted from highest to lowest depth. This ensures that the parallelization of Freebayes uses approximately the same amount of time per region.

The regions in annotations/ceph18.b37.exclude.2014-01-15.bed represent the complement of the regions in annotations/ceph18.b37.include.2014-01-15.bed.

In the speedseq sv module, we recommend excluding the genomic regions in the annotations/ceph18.b37.lumpy.exclude.2014-01-15.bed BED file. These regions represent the complement of those in annotations/ceph18.b37.include.2014-01-15.bed as well as the mitochondrial chromosome.

Usage

SpeedSeq is a modular framework with four components:

  • speedseq align - Process paired-end FASTQ sequences to produce a duplicate-marked, sorted, indexed BAM file that can be processed with other SpeedSeq modules.
  • speedseq var - Run FreeBayes one or more BAM files
  • speedseq somatic - Run FreeBayes on a tumor/normal pair of BAM files
  • speedseq sv - Run LUMPY on one or more BAM files, with optional breakend genotyping and read-depth calculation.
  • speedseq realign - Realign from a BAM file.

These modules operate independently of each other and produce universal output formats that are compatible with external tools. SpeedSeq modules can also run on BAM alignments that were produced outside of the SpeedSeq framework. . However, structural variant detection on BAM files generated outside of SpeedSeq will be slower due to two unique features of speedseq align. First, our alignment uses SAMBLASTER to automatically extract split and discordant reads for SV detection. While the speedseq sv module will internally extract split and discordant reads from regular BAM files, it takes much longer due to obligate name-sorting of the BAM file. Secondly, structural variant genotyping is much faster on BAM files processed by SAMBLASTER due to the addition of mate CIGAR and mate mapping quality tags. In the absence of these tags, SVTyper must jump to each read’s mate position in the BAM file, which greatly increases run time.

speedseq align

speedseq align converts paired-end FASTQ sequences to a duplicate-marked, sorted, indexed BAM file that can be processed with other SpeedSeq modules.

Internally, speedseq align runs the following steps to produce three output BAM files:

  1. Alignment with BWA-MEM
  2. Duplicate marking with SAMBLASTER
  3. Discordant-read and split-read extraction with SAMBLASTER
  4. Position sorting with Sambamba
  5. BAM indexing with Sambamba
usage:   speedseq align [options] <reference.fa> <in1.fq> [in2.fq]
Positional arguments
reference.fa    genome reference fasta file (required)
in1.fq          paired-end fastq file. if -p flag is used then expected to be
                  an interleaved paired-end fastq file, and in2.fq may be omitted.
                  (may be gzipped) (required)
in2.fq          paired-end fastq file. (may be gzipped) (required)
Alignment options
-o STR          output prefix [default: in1.fq]
-R              read group header line such as "@RG\tID:id\tSM:samplename\tLB:lib" (required)
-p              first fastq file consists of interleaved paired-end sequences
-t INT          number of threads to use [default: 1]
-T DIR          temp directory [./outprefix.XXXXXXXXXXXX]
-I FLOAT[,FLOAT[,INT[,INT]]]
                specify the mean, standard deviation (10% of the mean if absent), max
                  (4 sigma from the mean if absent) and min of the insert size distribution.
                  FR orientation only. [inferred]
Samblaster options
-i              include duplicates in splitters and discordants
                  (default: exclude duplicates)
-c INT          maximum number of split alignments for a read to be
                  included in splitter file [default: 2]
-m INT          minimum non-overlapping base pairs between two alignments
                for a read to be included in splitter file [default: 20]
Sambamba options
-M              amount of memory in GB to be used for sorting [default: 20]
Global options
-K FILE         path to speedseq.config file (default: same directory as speedseq)
-v              verbose
-h              show help message

Output

speedseq align produces three sorted, indexed BAM files (plus their corresponding .bai index files):

  • outprefix.bam
  • outprefix.splitters.bam
    • This BAM file contains split reads called by the BWA-MEM alignment of the library. It may be used as the -S flag input to speedseq sv. This file excludes duplicate reads by default, but they will be included if the -i flag is specified as a speedseq align command line parameter.
  • outprefix.discordants.bam
    • This BAM file contains discordant read-pairs called by the BWA-MEM alignment of the library. These reads may be discordant by strand orientation, intrachromosomal distance, or interchromosomal mapping. This BAM file may be used as the -D flag input to speedseq sv. This file excludes duplicate reads by default, but they will be included if the -i flag is specified as a speedseq align command line parameter.

speedseq var

speedseq var runs FreeBayes on one or more BAM files.

usage:   speedseq var [options] <reference.fa> <input1.bam> [input2.bam [...]]
Positional arguments
reference.fa    genome reference fasta file
input.bam       BAM file(s) to call variants on. Must have readgroup information,
                  and the SM readgroup tags will be the VCF column headers
Options
-o STR          output prefix [default: input1.bam]
-w FILE         BED file of windowed genomic intervals. For human genomes,
                  we recommend using the annotations/ceph18.b37.include.2014-01-15.bed
                  (see Annotations)
-q FLOAT        minimum variant QUAL score to output [1]
-t INT          number of threads to use [default: 1]
-T DIR          temp directory [./outprefix.XXXXXXXXXXXX]
-A              annotate the vcf with VEP
-K FILE         path to speedseq.config file [default: same directory as speedseq]
-v              verbose
-h              show help message

Output

speedseq var produces a single indexed VCF file that is optionally annotated with VEP.

  • outprefix.vcf.gz

speedseq somatic

speedseq somatic runs FreeBayes on a tumor/normal pair of BAM files

usage:   speedseq somatic [options] <reference.fa> <normal.bam> <tumor.bam>
Positional arguments
reference.fa      genome reference fasta file
normal.bam        germline BAM file(s) (comma separated BAMs from multiple libraries).
                    Must have readgroup information, and the SM readgroup tag will
                    be the VCF column header
tumor.bam         tumor BAM file(s) (comma separated BAMs for multiple libraries).
                    Must have readgroup information, and the SM readgroup tag will
                    be the VCF column header
Options
-o STR           output prefix [default: tumor.bam]
-w FILE          BED file of windowed genomic intervals. For human genomes,
                   we recommend using the annotations/ceph18.b37.include.2014-01-15.bed
                   (see Annotations)
-t INT           number of threads to use [default: 1]
-F FLOAT         require at least this fraction of observations supporting
                   an alternate allele within a single individual in order
                   to evaluate the position [0.05]
-C INT           require at least this count of observations supporting
                   an alternate allele within a single individual in order
                   to evaluate the position [2]
-S FLOAT         minimum somatic score (SSC) for PASS [18]
-q FLOAT         minimum QUAL score to output non-passing somatic variants [1e-5]
-T DIR           temp directory [./outprefix.XXXXXXXXXXXX]
-A               annotate the vcf with VEP
-K FILE          path to speedseq.config file (default: same directory as speedseq)
-v               verbose
-h               show help message

Output

speedseq somatic produces a single indexed VCF file that is optionally annotated with VEP.

  • outprefix.vcf.gz

speedseq sv

speedseq sv runs LUMPY on one or more BAM files, with optional breakend genotyping by SVTyper, and optional read-depth analysis by CNVnator.

Options
-B FILE          full BAM file(s) (comma separated) (required)
                   example: -B in1.bam,in2.bam,in3.bam
-S FILE          split reads BAM file(s) (comma separated, order same as in -B)
                   (auto-generated if absent)
                   example: -S in1.splitters.bam,in2.splitters.bam,in3.splitters.bam
-D FILE          discordant reads BAM file(s) (comma separated, order same as in -B)
                   (auto-generated if absent)
                   example: -D in1.discordants.bam,in2.discordants.bam,in3.discordants.bam
-R FILE          indexed reference genome fasta file (required)
-o STR           output prefix [in1.bam]
-t INT           threads [1]
-x FILE          BED file to exclude
-g               genotype SV breakends with svtyper
-d               calculate read-depth with CNVnator
-A               annotate the vcf with VEP
-P               output LUMPY probability curves in VCF
-m INT           minimum sample weight for a call [default: 4]
-r FLOAT         trim threshold [0]
-T DIR           temp directory [./outprefix.XXXXXXXXXXXX]
-k               keep temporary files
Global options
-K FILE          path to speedseq.config file (default: same directory as speedseq)
-v               verbose
-h               show help message

Output

speedseq sv produces a bgzipped, indexed VCF file.

  • outprefix.sv.vcf.gz

speedseq realign

speedseq realign allows alignment from one or more BAM files, rather than FASTQ inputs. It automatically parses read group information from the BAM header to mark duplicates by library.

usage:   speedseq realign [options] <reference.fa> <in1.bam> [in2.bam [...]]
Positional arguments
reference.fa    genome reference fasta file (indexed with bwa)
in.bam          BAM file(s) (must contain read group tags)
Alignment options
-o STR          output prefix [in.realign]
-I FLOAT[,FLOAT[,INT[,INT]]]
                specify the mean, standard deviation (10% of the mean if absent), max
                  (4 sigma from the mean if absent) and min of the insert size distribution.
                  FR orientation only. [inferred]
-n              rename reads for smaller file size
-t INT          threads [1]
-T DIR          temp directory [./output_prefix.XXXXXXXXXXXX]
Samblaster options
-i              include duplicates in splitters and discordants
                  (default: exclude duplicates)
-c INT          maximum number of split alignments for a read to be
                  included in splitter file [default: 2]
-m INT          minimum non-overlapping base pairs between two alignments
                for a read to be included in splitter file [default: 20]
Sambamba options
-M              amount of memory in GB to be used for sorting [default: 20]
Global options
-K FILE         path to speedseq.config file (default: same directory as speedseq)
-v              verbose
-h              show help message

Output

speedseq realign output is identical to that produced by speedseq align.

Example workflows

Call variants on a single sample

  1. Use speedseq align to produce a sorted, duplicate-marked, BAM alignment from paired-end fastq data.

    speedseq align \
        -o NA12878 \
        -R "@RG\tID:NA12878.S1\tSM:NA12878\tLB:lib1" \
        human_g1k_v37.fasta \
        NA12878.1.fq.gz \
        NA12878.2.fq.gz
    

    Note: if using an interleaved paired-end fastq file, use the -p flag

    speedseq align \
        -o NA12878 \
        -p \
        -R "@RG\tID:NA12878.S1\tSM:NA12878\tLB:lib1" \
        human_g1k_v37.fasta \
        NA12878.interleaved.fq.gz
    
  2. Use speedseq var to call SNVs and indels on a single sample.

    speedseq var \
        -o NA12878 \
        -w annotations/ceph18.b37.include.2014-01-15.bed \
        human_g1k_v37.fasta \
        NA12878.bam
    
  3. Use speedseq sv to call structural variants. The optional -g and -d flags perform breakend genotyping and read-depth calculation respectively

    speedseq sv \
        -o NA12878 \
        -x annotations/ceph18.b37.lumpy.exclude.2014-01-15.bed \
        -g \
        -d \
        -B NA12878.bam \
        -D NA12878.discordants.bam \
        -S NA12878.splitters.bam
    

Call variants on a single sample sequenced with multiple libraries

  1. Use speedseq align to produce a sorted, duplicate-marked, BAM alignment of each library.

    speedseq align -o NA12878_S1 -R "@RG\tID:NA12878.S1\tSM:NA12878\tLB:lib1" \
        human_g1k_v37.fasta \
        NA12878.S1.1.fq.gz \
        NA12878.S1.2.fq.gz
    
    speedseq align -o NA12878_S2 -R "@RG\tID:NA12878.S2\tSM:NA12878\tLB:lib2" \
        human_g1k_v37.fasta \
        NA12878.S2.1.fq.gz \
        NA12878.S2.2.fq.gz
    
    speedseq align -o NA12878_S3 -R "@RG\tID:NA12878.S3\tSM:NA12878\tLB:lib3" \
        human_g1k_v37.fasta \
        NA12878.S3.1.fq.gz \
        NA12878.S3.2.fq.gz
    
  2. Merge the samples

    sambamba merge NA12878_merged.bam NA12878_S1.bam NA12878_S2.bam NA12878_S3.bam
    sambamba index NA12878_merged.bam
    
  3. Use speedseq var to call SNVs and indels.

    speedseq var \
        -o NA12878 \
        -w annotations/ceph18.b37.include.2014-01-15.bed \
        human_g1k_v37.fasta \
        NA12878_merged.bam
    
  4. Use speedseq sv to call structural variants.

    speedseq -sv \
        -o NA12878 \
        -x annotations/ceph18.b37.lumpy.exclude.2014-01-15.bed \
        -B NA12878_merged.bam \
        -S NA12878_merged.splitters.bam \
        -D NA12878_merged.discordants.bam \
        -R human_g1k_v37.fasta
    

Call variants on multiple samples

  1. Use speedseq align to produce sorted, duplicate-marked, BAM alignments for each sample.

    speedseq align \
        -o NA12877 \
        -R "@RG\tID:NA12877.S1\tSM:NA12877\tLB:lib1" \
        human_g1k_v37.fasta \
        NA12877.1.fq.gz \
        NA12877.2.fq.gz
    
    speedseq align \
        -o NA12878 \
        -R "@RG\tID:NA12878.S1\tSM:NA12878\tLB:lib2" \
        human_g1k_v37.fasta \
        NA12878.1.fq.gz \
        NA12878.2.fq.gz
    
    speedseq align \
        -o NA12879 \
        -R "@RG\tID:NA12879.S1\tSM:NA12879\tLB:lib3" \
        human_g1k_v37.fasta \
        NA12879.1.fq.gz \
        NA12879.2.fq.gz
    
  2. Use speedseq var to call SNVs and indels on multiple samples.

    speedseq var \
        -o cephtrio \
        -w annotations/ceph18.b37.include.2014-01-15.bed \
        human_g1k_v37.fasta \
        NA12877.bam \
        NA12878.bam \
        NA12879.bam
    
  3. Use speedseq sv to call structural variants on multiple samples.

    speedseq sv \
        -o cephtrio \
        -x annotations/ceph18.b37.lumpy.exclude.2014-01-15.bed \
        -B NA12877.bam,NA12878.bam,NA12879.bam \
        -D NA12877.discordants.bam,NA12878.discordants.bam,NA12879.discordants.bam \
        -S NA12877.splitters.bam,NA12878.splitters.bam,NA12879.splitters.bam
    

Call variants on a tumor/normal pair

  1. Use speedseq align to produce sorted, duplicate-marked, BAM alignments for the tumor/normal pair

    speedseq align \
        -o TCGA-B6-A0I6.normal \
        -p \
        -R "@RG\tID:TCGA-B6-A0I6-10A-01D-A128-09\tSM:TCGA-B6-A0I6-10A-01D-A128-09\tLB:lib1" \
        human_g1k_v37.fasta \
        TCGA-B6-A0I6-10A-01D-A128-09.interleaved.fq.gz
    
    speedseq align \
        -o TCGA-B6-A0I6.tumor \
        -p \
        -R "@RG\tID:TCGA-B6-A0I6-10A-01D-A128-09\tSM:TCGA-B6-A0I6-10A-01D-A128-09\tLB:lib1" \
        human_g1k_v37.fasta \
        TCGA-B6-A0I6-01A-11D-A128-09.interleaved.fq.gz
    
  2. Use speedseq somatic to call SNVs and indels on the tumor/normal pair.

    speedseq somatic \
        -o TCGA-B6-A0I6 \
        -w annotations/ceph18.b37.include.2014-01-15.bed \
        -F 0.05 \
        -q 1 \
        human_g1k_v37.fasta \
        TCGA-B6-A0I6.normal.bam \
        TCGA-B6-A0I6.tumor.bam
    
  3. Use speedseq sv to call structural variants on the tumor/normal pair.

    speedseq sv \
        -o TCGA-B6-A0I6 \
        -x annotations/ceph18.b37.lumpy.exclude.2014-01-15.bed \
        -B TCGA-B6-A0I6.normal.bam,TCGA-B6-A0I6.tumor.bam \
        -D TCGA-B6-A0I6.normal.discordants.bam,TCGA-B6-A0I6.tumor.discordants.bam \
        -S TCGA-B6-A0I6.normal.splitters.bam,TCGA-B6-A0I6.tumor.splitters.bam \
        -R human_g1k_v37.fasta
    

Call de novo mutations in a trio

  1. Use speedseq align to produce sorted, duplicate-marked, BAM alignments (as shown above).

  2. Use speedseq var to call SNVs and indels on multiple samples with high sensitivity.

    speedseq var \
        -o trio \
        -w annotations/ceph18.b37.include.2014-01-15.bed \
        -q 0.01 \
        human_g1k_v37.fasta \
        mother.bam \
        father.bam \
        child.bam
    
  3. Filter variants for de novo status using GEMINI's built-in analysis tools.

SpeedSeq AMI

SpeedSeq is available as a public AMI (Amazon Machine Image) on the Amazon Elastic Compute Cloud (EC2).

  1. Log in to the Amazon AWS console

  2. From the EC2 Dashboard, set your region to N. Virginia and click "Launch Instance" EC2 dashboard

  3. Choose, "Community AMIs" in the sidebar and search for SpeedSeq in the dialog box. Select SpeedSeq

  4. Select hardware specifications. For deep whole genomes, we recommend c3.8xlarge (32 vCPUs, 60 GB RAM). For testing purposes any machine with 16 GB RAM is sufficient. Instance type

  5. Add storage for the data. Note that the SpeedSeq footprint is ~ 26 GB (including the reference genome and GEMINI). Add storage

  6. Launch the instance and log in.

    ssh -i mykey.pem ec2-user@ec2-54-173-62-218.compute-1.amazonaws.com
    
  7. Run the SpeedSeq test script.

    ./run_speedseq.sh
    

    This should produce the following files:

    • example.bam
    • example.discordants.bam
    • example.splitters.bam
    • example.vcf.gz
    • example.sv.vcf.gz

Frequently asked questions (FAQ)

  • Can I use SpeedSeq on exome data?
    • SpeedSeq can detect SNVs and indels from exome data using speedseq var or speedseq somatic. However, you should not use the excluded regions, as these were designed for WGS data. SpeedSeq cannot detect SVs from exome data.

Troubleshooting

If you encounter errors or strange behavior from SpeedSeq, please report them to the issues page with the following information:

  • Description of the problem
  • The exact command that you ran, using the "-v" option for verbose logging information
  • Any error or status information produced by SpeedSeq
  • Anything you've tried to resolve the issue

Common issues

  • Installation failure with error: "No targets specified and no makefile found."

    Ensure that SpeedSeq was cloned with the --recursive flag

  • Installation failure while compiling FreeBayes or LUMPY (in the var and sv modules respectively)

    These two components use BamTools, which requires CMake for compilation. Ensure that CMake is installed on your system

  • Installation reports, "WARNING: CNVnator not compiled because the ROOT package is not installed. Please see the README for instructions on manually installing ROOT."

    This indicates that the ROOT package has not been installed, or the $ROOTSYS variable has not been set. See the CNVnator install section for details.

  • Runtime error: "ImportError: No module named argparse"

    Ensure you are running Python 2.7 or later.

  • CNVnator fails to run during speedseq sv

    This may be due to a problem with the ROOT installation. Try configuring the ROOT package without the --prefix flag. Then run

    ./configure
    make
    

    Add /pathto/root/bin/thisroot.sh to the speedseq.config file

  • Python errors

    Python errors commonly result from incompatibilities with older versions of Pysam. SpeedSeq runs on Pysam versions 0.8.0 and newer.