GotCloud Tutorial

In this tutorial, we illustrate some of the essential steps in the analysis of next generation sequence data.

For a background on GotCloud and Sequence Analysis Pipelines, see GotCloud

While GotCloud can run on a cluster of machines or instances, this tutorial is just a small test that just runs on the machine the commands are run on.

GotCloud and this basic tutorial were presented at the 2013 IBG Workshop. It was presented in two sessions. On Wednesday an overview was presented with steps for running the tutorial data: IBG2013GotCloud.pdf. On Friday more detail on the input files and what goes into generating the input files was presented: GotCloudIBGWorkshop2013Friday.pdf.

This tutorial is in the process of being updated for gotcloud version 1.08 (July 30, 2013).

STEP 1 : Setup GotCloud

GotCloud has been developed and tested on Linux Ubuntu 12.10 and 12.04.2 LTS but has not been tested on other Linux operating systems. It is not available for Windows. If you do not have your own set of machines to run on, GotCloud is also available for Ubuntu running on the Amazon Elastic Compute Cloud, see Amazon_Snapshot for more information.

We will use 3 different directories for this tutorial:

path to the directory where gotcloud is installed, default ~/gotcloud/
path to the directory where the example data is installed, default ~/gotcloudExample
path to your output directory, default ~/gotcloudTutorialOut/

If the directories specified above do not reflect the directories you would like to use, replace their occurrances in the instructions below with the appropriate paths.

Step 1a: Install GotCloud

In order to run this tutorial, you need to make sure you have GotCloud installed on your system.

If you have root and would like to install gotcloud on your system, follow: root access installation instructions

Otherwise, you can install it in your own directory:

Change to the directory where you want gotcloud/ installed
Download the gotcloud tar from the ftp site.
Extract the tar
Build (compile) the source
- Note: as the source builds, many messages will scroll through your terminal. You may even see some warnings. These messages are normal and expected. As long as the build does not end with an error, you have successfully built the source.

cd ~
wget ftp://share.sph.umich.edu/gotcloud/gotcloud_latest.tgz  # Download
tar xf gotcloud_latest.tgz     # Extracts into gotcloud/
cd gotcloud/src; make         # Build source

Step 1b: Install Example Dataset

Our dataset consists of individuals from Great Britain (GBR) sequenced by the 1000 Genomes Project. These individuals have been sequenced to an average depth of about 4x.

To conserve time and disk-space, our analysis will focus on a small region on chromosome 20, 42900000 - 43200000.

The alignment pipeline in this tutorial will be run on 2 of the individuals (HG00096, HG00100). The fastqs used for this step are reduced to reads that fall into our target region.

The snpcall and ldrefine pipelines will use previously aligned/mapped reads for 60 individuals to generate a list of polymorphic sites and estimate accurate genotypes at each of these sites.

The example dataset we'll be using is available at: ftp://share.sph.umich.edu/gotcloud/gotcloudExample.tgz

Change directory to where you want to install the Tutorial data
Download the dataset tar from the ftp site
Extract the tar

cd ~
wget ftp://share.sph.umich.edu/gotcloud/gotcloudExample_latest.tgz  # Download 
tar xf gotcloudExample_latest.tgz    # Extracts into gotcloudExample/

STEP 2 : Run GotCloud Alignment Pipeline

The first step in processing next generation sequence data is mapping the reads to the reference genome, generating per sample BAM files.

The alignment pipeline has multiple built-in steps to generate BAMs:

Align the fastqs to the reference genome
- handles both single & paired end
Merge the results from multiple fastqs into 1 file per sample
Mark Duplicate Reads are marked
Recalibrate Base Qualities

This processing results in 1 BAM file per sample.

The alignment pipeline also includes Quality Control (QC) steps:

Visualization of various quality measures (QPLOT)
Screen for sample contamination & swap (VerifyBamID)

Run the alignment pipeline (the example aligns 2 samples) :

cd ~
gotcloud/gotcloud align --conf gotcloudExample/GBR2align.conf --outdir gotcloudTutorialOut --baseprefix gotcloudExample

Upon successful completion of the alignment pipeline (about 1-3 minutes), you will see the following message:

Processing finished in n secs with no errors reported

The final BAM files produced by the alignment pipeline are:

ls gotcloudTutorialOut/bams

In this directory you will see:

BAM (.bam) files - 1 per sample
- HG00096.recal.bam
- HG00100.recal.bam
BAM index files (.bai) – 1 per sample
- HG00096.recal.bam.bai
- HG00100.recal.bam.bai
Indicator files that the step completed successfully:
- HG00096.recal.bam.done
- HG00100.recal.bam.done

The Quality Control (QC) files are:

ls gotcloudTutorialOut/QCFiles

In this directory you will see:

VerifyBamID output files:
- HG00096.genoCheck.depthRG
- HG00096.genoCheck.depthSM
- HG00096.genoCheck.selfRG
- HG00096.genoCheck.selfSM
- HG00100.genoCheck.depthRG
- HG00100.genoCheck.depthSM
- HG00100.genoCheck.selfRG
- HG00100.genoCheck.selfSM

VerifyBamID step completion files – 1 per sample
- HG00096.genoCheck.done
- HG00100.genoCheck.done

QPLOT output files
- HG00096.qplot.R
- HG00096.qplot.stats
- HG00100.qplot.R
- HG00100.qplot.stats

QPLOT step completion files – 1 per sample
- HG00096.qplot.done
- HG00100.qplot.done

For information on the VerifyBamID output, see: Understanding VerifyBamID output

For information on the QPLOT output, see: Understanding QPLOT output

STEP 3 : Run GotCloud Variant Calling Pipeline

The next step is to analyze BAM files by calling SNPs and generating a VCF file containing the variant calls.

The variant calling pipeline has multiple built-in steps to generate BAMs:

Filter out reads with low mapping quality
Per Base Alignment Quality Adjustment (BAQ)
Resolve overlapping paired end reads
Generate genotype likelihood files
Perform variant calling
Extract features from variant sites
Perform variant filtering

To speed variant calling, each chromosome is broken up into smaller regions which are processed separately. While initially split by sample, the per sample data gets merged and is processed together for each region. These regions are later merged to result in a single Variant Call File (VCF) per chromosome. For the tutorial all of the data falls within a single region.

Run the variant calling pipeline:

cd ~
gotcloud/gotcloud snpcall --conf gotcloudExample/GBR60vc.conf --outdir gotcloudTutorialOut --numjobs 2 --region 20:42900000-43200000 --baseprefix gotcloudExample

Upon successful completion of the variant calling pipeline (about 3-4 minutes), you will see the following message:

 Commands finished in nnn secs with no errors reported

On SNP Call success, the VCF files of interest are:

ls gotcloudTutorialOut/vcfs/chr20/chr20.filtered*

This gives you the following files:

chr20.filtered.vcf.gz - vcf for whole chromosome after it has been run through hardfilters and SVM filters and marked with PASS/FAIL including per sample genotypes
chr20.filtered.sites.vcf - vcf for whole chromosome after it has been run through filters and marked with PASS/FAIL without the per sample genotypes
chr20.filtered.sites.vcf.norm.log - log file
chr20.filtered.sites.vcf.summary - summary of filters applied
chr20.filtered.vcf.gz.OK - indicator that the filtering completed successfully
chr20.filtered.vcf.gz.tbi - index file for the vcf file

Also in the ~/gotcloudTutorialOut/vcfs/chr20 directory are intermediate files:

the whole chromosome variant calls prior to any filtering:
- chr20.merged.sites.vcf - without per sample genotypes
- chr20.merged.stats.vcf
- chr20.merged.vcf - including per sample genotypes
- chr20.merged.vcf.OK - indicator that the step completed successfully
the hardfiltered (pre-svm filtered) variant calls:
- chr20.filtered.vcf.gz - vcf for whole chromosome after it has been run through hard filters
- chr20.hardfiltered.sites.vcf - vcf for whole chromosome after it has been run through filters and marked with PASS/FAIL without the per sample genotypes
- chr20.hardfiltered.sites.vcf.log - log file
- chr20.hardfiltered.sites.vcf.summary - summary of filters applied
- chr20.hardfiltered.vcf.gz.OK - indicator that the filtering completed successfully
- chr20.hardfiltered.vcf.gz.tbi - index file for the vcf file
40000001.45000000 subdirectory contains the data for just that region.

The ~/gotcloudTutorialOut/split/chr20 folder contains a VCF with just the sites that pass the filters.

ls ~/gotcloudTutorialOut/split/chr20/

chr20.filtered.PASS.vcf.gz – vcf of just sites that pass all filters
chr20.filtered.PASS.split.1.vcf.gz - intermediate file
chr20.filtered.PASS.split.err - log file
chr20.filtered.PASS.split.vcflist - list of intermediate files
subset.OK

In addition to the vcfs subdirectory, there are additional intermediate files/directories:

glfs – holds genotype likelihood format GLF files split by chromosome, region, and sample
pvcfs – holds intermediate vcf files split by chromosome and region

Note: the tutorial does not produce a target directory, but if you run with targeted data, you may see that.

STEP 4 : Run GotCloud Genotype Refinement Pipeline

The next step is to perform genotype refinement using linkage disequilibrium information using Beagle & ThunderVCF.

Run the LD-aware genotype refinement pipeline:

~/gotcloud/gotcloud ldrefine --conf ~/gotcloudExample/GBR60vc.conf --outdir ~/gotcloudTutorialOut --numjobs 2 --baseprefix ~/gotcloudExample

Upon successful completion of this pipeline (about 10 minutes), you will see the following message:

Commands finished in nnn secs with no errors reported

The output from the beagle step of the genotype refinement pipeline is found in:

ls ~/gotcloudTutorialOut/beagle/chr20/chr20.filtered.PASS.beagled.vcf.gz ~/gotcloudTutorialOut/beagle/chr20/chr20.filtered.PASS.beagled.vcf.gz.tbi

The output from the thunderVcf (final step) of the genotype refinement pipeline is found in:

ls ~/gotcloudTutorialOut/thunder/chr20/GBR/chr20.filtered.PASS.beagled.GBR.thunder.vcf.gz ~/gotcloudTutorialOut/thunder/chr20/GBR/chr20.filtered.PASS.beagled.GBR.thunder.vcf.gz.tbi

STEP 5 : Run GotCloud Association Analysis Pipeline (EPACTS)

We will assume that the EPACTS are installed in the following directory

setenv EPACTS /path/to/epacts

(If you need to install EPACTS, please refer to the documentation at EPACTS#Installation_Details)

$EPACTS/epacts single --vcf ~/gotcloudTutorialOut/vcfs/chr20/chr20.filtered.vcf.gz --ped ~/gotcloudExample/test.GBR60.ped \\
   --out ~/gotcloudTutorialOut/epacts --test q.linear --run 1 --top 1 --chr 20

Upon successful run, you will see files starting with ~/gotcloudTutorialOut/epacts

ls ~/gotcloudTutorialOut/epacts*

To see the top associated variants, you can run

less ~/gotcloudTutorialOut/epacts.epacts.top5000

To see the locus-zoom like plot, you can type the following command (assuming GNU gnuplot 4.2 or higher version was installed)

xpdf ~/gotcloudTutorialOut/epacs.zoom.20.42987877.pdf

Click Exampe LocusZoom PDF to see the expected output pdf

Frequently Asked Questions (FAQs)

I ran the tutorial example successfully, how can I run it with my real sequence data?

Congratulations for your successful run of your GotCloud Tutorial. Please see #Tutorial Inputs section to prepare your own input files for your sequence data. You will need to specify the FASTQ files associated with its sample names as explained. In addition, you will need to download the full reference and resource file across whole genome (the Tutorial contains only chr20 portion to make it compact) See #Alignment Configuration File section for the detailed information. Also, please refer to the original documentation of GotCloud for more detailed guide on installation beyond the scope of the tutorial.

Input Files for GotCloud Tutorial

This section describes the input files needed for the GotCloud tutorial. You don't need to know this detail to run the tutorial, but if you're interested in understanding the structure of GotCloud pipeline and run with your own sample, this would be a good starting point

Alignment Pipeline

List of Input Files needed for Alignment

The command-line inputs to the tutorial alignment pipeline are:

Configuration File (--conf)
- Specifies the configuration file to use when running
Output Directory (--outdir)
- Directory where the output should be placed.

Additional information required to run the alignment pipeline:

Index file of FASTQs
Alignment Reference Files

For the tutorial, these values are specified in the configuration file.

Alignment Configuration File

The configuration file contains KEY = VALUE settings that override defaults and set specific values for the given run.

 
INDEX_FILE = GBR60fastq.index 
############ 
# References 
REF_DIR = chr20Ref 
AS = NCBI37 
FA_REF = $(REF_DIR)/human_g1k_v37_chr20.fa 
DBSNP_VCF =  $(REF_DIR)/dbsnp135_chr20.vcf.gz 
HM3_VCF = $(REF_DIR)/hapmap_3.3.b37.sites.chr20.vcf.gz

This configuration file sets:

INDEX_FILE - file containing the fastqs to be processed as well as the read group information for these fastqs.
Reference Information: see Alignment Reference Files for more information
- AS - assembly value to put in the BAM

The index file and chromosome 20 references used in this tutorial are included with the example data under the ~/gotcloudExample directory. The tutorial uses chromosome 20 only references in order to speed the processing time.

When running with your own data, you will need to update the:

Index File to contain the information for your own fastq files
- See Alignment FASTQ Index File for more information on the contents of the index file.
The reference files to be whole genome references
- If you are just running chromosome 20, you can use the tutorial references
- Whole genome reference files can be downloaded from GotCloudReference.
- See Alignment Reference Files for more information.

Note: It is recommended that you use absolute paths (full path names, like “/home/mktrost/gotcloudReference” rather than just “gotcloudReference”). This example does not use absolute paths in order to be flexible to where the data is installed, but using relative paths requires it to be run from the correct directory.

Reference Files

Reference files are required for running both the alignment and variant calling pipelines.

The configuration keys for setting these are:

FA_REF - Genome sequence reference files (needed for both pipelines)
DBSNP_VCF – DBSNP site VCF file (needed for both pipelines)
HM3_VCF - HAPMAP site VCF file (needed for both pipelines)
INDEL_PREFIX - Indel sites file (need for variant calling pipeline)

The tutorial configuration file is setup to point to the required chromosome 20 reference files which are included with the tutorial example data in ~/gotcloudExample/chr20Ref/.

If you are running more than just chromosome 20, you will need whole genome reference files which can be downloaded from GotCloudReference.

The configuration settings for these files are setup in the default configuration so do not need to be specified. You just need to set REF_DIR in your configuration file to the path where you installed your reference files.

To learn more about the reference files that are required, see GotCloud: Reference Files.

Alignment Output Directory

This setting tells the alignment pipeline where to write the output and intermediate files.

The output directory will be created if it doesn't already exist and will contain the following Directories/files:

bams - directory containing the final bams/bai files
- HG00096.OK - indicates that this sample completed alignment processing
- HG00100.OK - indicates that this sample completed alignment processing
failLogs - directory containing logs from steps that failed
- this directory is only created if an error is detected
Makefiles - directory containing the makefiles with commands for processing each sample
- biopipe_HG00096.Makefile – commands for processing sample HG00096
- biopipe_HG00100.Makefile – commands for processing sample HG00100
- biopipe_HG00096.Makefile.log – log file from running the associated Makefile
- biopipe_HG00100.Makefile.log – log file from running the associated Makefile
QCFiles - directory containing the QC Results
tmp - directory containing temporary alignment files
- bwa.sai.t – contains temporary files for the 1st step of bwa that generates sai files from fastq files
  - fastq
    - *.done – indicator files that the step to generate the file completed
    - HG0096
- alignment.bwa – contains temporary files for the 2nd step of bwa that generates BAM files
  - fastq
    - *.done – indicator files that the step to generate the file completed
- alignment.pol - contains temporary files for the polish bam step that cleans up BAM files
- alignment.dedup – contains temporary files for the deduping step
  - *.done – indicator files that the step to generate the file completed
  - *.metrics – metrics files for the deduping step
- alignment.recal – contains temporary files for the deduping step
  - *.log – contains information about the recalibration step
  - *.qemp – contains the recalibration tables used for recalibrating each BAM file

Alignment FASTQ Index File

There are four fastq files in {ROOT_DIR}/test/align/fastq/Sample_1 and four fastq files in {ROOT_DIR}/test/align/fastq/Sample_2, both in paired-end format. Normally, we would need to build an index file for these files. Conveniently, an index file (indexFile.txt) already exists for the automatic test samples. It can be found in {ROOT_DIR}/test/align/, and contains the following information in tab-delimited format:

MERGE_NAME FASTQ1                           FASTQ2                           RGID   SAMPLE    LIBRARY CENTER PLATFORM 
Sample1    fastq/Sample_1/File1_R1.fastq.gz fastq/Sample_1/File1_R2.fastq.gz RGID1  SampleID1 Lib1    UM     ILLUMINA 
Sample1    fastq/Sample_1/File2_R1.fastq.gz fastq/Sample_1/File2_R2.fastq.gz RGID1a SampleID1 Lib1    UM     ILLUMINA 
Sample2    fastq/Sample_2/File1_R1.fastq.gz fastq/Sample_2/File1_R2.fastq.gz RGID2  SampleID2 Lib2    UM     ILLUMINA 
Sample2    fastq/Sample_2/File2_R1.fastq.gz fastq/Sample_2/File2_R2.fastq.gz RGID2  SampleID2 Lib2    UM     ILLUMINA

If you are in the {ROOT_DIR}/test/align directory, you can use this file as-is. If you prefer, you can create a new index file and change the MERGE_NAME, RGID, SAMPLE, LIBRARY, CENTER, or PLATFORM values. It is recommended that you do not modify existing files in {ROOT_DIR}/test/align.

If you want to run this example from a different directory, make sure the FASTQ1 and FASTQ2 paths are correct. That is, each of the FASTQ1 and FASTQ2 entry in the index file should look like the following:

{ROOT_DIR}/test/align/fastq/Sample_1/File1_R1.fastq.gz

Alternately, if you want to run this example from a different directory, but do not want to edit the index file, you can create a relative path to the test fastq files so their path agrees with that listed in the index file:

ln -s {ROOT_DIR}/test/align/fastq fastq

This will create a symbolic link to the test fastq directory from your current directory.

(More information about: the index file.)

Analyzing a Sample

Using UMAKE, you can analyze BAM files by calling SNPs, and generate a VCF file containing the results. Once again, we can analyze BAM files used in the automatic test. For this example, we have 60 BAM files, which can be found in {ROOT_DIR}/test/umake/bams. These contain sequence information for a targeted region in chromosome 20.

In addition to the BAM files, you will need three files to run UMAKE: an index file, a configuration file, and a bed file (needed to analyze BAM files from targeted/exome sequencing).

Index file

First, you need a list of all the BAM files to be analyzed. Conveniently, the a test index file (umake_test.index) already exists in {ROOT_DIR}/test/umake/. It contains the following information:

NA12272 ALL     bams/NA12272.mapped.ILLUMINA.bwa.CEU.low_coverage.20101123.chrom20.20000001.20300000.bam 
NA12004 ALL     bams/NA12004.mapped.ILLUMINA.bwa.CEU.low_coverage.20101123.chrom20.20000001.20300000.bam 
... 
NA12874 ALL     bams/NA12874.mapped.LS454.ssaha2.CEU.low_coverage.20101123.chrom20.20000001.20300000.bam

You can use this file directly if you change your current directory to {ROOT_DIR}/test/umake/.

Alternately, if you want to copy and use this index file to a different directory, you can create a symbolic link to the bams folder as follows:

ln -s {ROOT_DIR}/test/umake/bams bams

(More information about: the index file.)

BED file

This file contains a single line:

chr20   20000050        20300000

You can copy this to the current directory and use it as-is.

(More information about: targeted/exome sequencing settings.)

Configuration file

A configuration file (umake_test.conf) already exists in {ROOT_DIR}/test/umake/. It contains the following information:

CHRS = 20 BAM_INDEX = GBR60bam.index

References

REF_ROOT = chr20Ref

REF = $(REF_ROOT)/human_g1k_v37_chr20.fa INDEL_PREFIX = $(REF_ROOT)/1kg.pilot_release.merged.indels.sites.hg19 DBSNP_VCF = $(REF_ROOT)/dbsnp135_chr20.vcf.gz HM3_VCF = $(REF_ROOT)/hapmap_3.3.b37.sites.chr20.vcf.gz

CHRS = 20 
TEST_ROOT = $(UMAKE_ROOT)/test/umake 
BAM_INDEX = $(TEST_ROOT)/umake_test.index 
OUT_PREFIX = umake_test 
REF_ROOT = $(TEST_ROOT)/ref 
# 
REF = $(REF_ROOT)/karma.ref/human.g1k.v37.chr20.fa 
INDEL_PREFIX = $(REF_ROOT)/indels/1kg.pilot_release.merged.indels.sites.hg19 
DBSNP_PREFIX =  $(REF_ROOT)/dbSNP/dbsnp_135_b37.rod 
HM3_PREFIX =  $(REF_ROOT)/HapMap3/hapmap3_r3_b37_fwd.consensus.qc.poly 
# 
RUN_INDEX = TRUE        # create BAM index file 
RUN_PILEUP = TRUE       # create GLF file from BAM 
RUN_GLFMULTIPLES = TRUE # create unfiltered SNP calls 
RUN_VCFPILEUP = TRUE    # create PVCF files using vcfPileup and run infoCollector 
RUN_FILTER = TRUE       # filter SNPs using vcfCooker 
RUN_SPLIT = TRUE        # split SNPs into chunks for genotype refinement 
RUN_BEAGLE = FALSE  # BEAGLE - MUST SET AFTER FINISHING PREVIOUS STEPS 
RUN_SUBSET = FALSE  # SUBSET FOR THUNDER - MAY BE SET WITH BEAGLE STEP TOGETHER 
RUN_THUNDER = FALSE # THUNDER - MUST SET AFTER FINISHING PREVIOUS STEPS 
############################################################################### 
WRITE_TARGET_LOCI = TRUE  # FOR TARGETED SEQUENCING ONLY -- Write loci file when performing pileup 
UNIFORM_TARGET_BED = $(TEST_ROOT)/umake_test.bed # Targeted sequencing : When all individuals has the same target. Otherwise, comment it out 
OFFSET_OFF_TARGET = 50 # Extend target by given # of bases 
MULTIPLE_TARGET_MAP =  # Target per individual : Each line contains [SM_ID] [TARGET_BED] 
TARGET_DIR = target    # Directory to store target information 
SAMTOOLS_VIEW_TARGET_ONLY = TRUE # When performing samtools view, exclude off-target regions (may make command line too long)

If you are running this from a different directory, you will want to change some of the lines as follows:

BAM_INDEX = {CURRENT_DIR}/umake_test.index 
UNIFORM_TARGET_BED = {CURRENT_DIR}/umake_test.bed

where {CURRENT_DIR} is the absolute path to the directory that contains the index and bed files.

An additional option can be added in the configuration file:

OUT_DIR = {OUT_DIR}

where {OUT_DIR} is the name of directory in which you want the output to be stored. If you do not specify this in the configuration file, you will need to add an extra parameter when you run UMAKE in the next step.

(More information about: the configuration file, reference files.)

Further Information

Mapping (Alignment) Pipeline

Variant Calling Pipeline (UMAKE)

Tutorial: GotCloud

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