Solution Brief

Lenovo Genomics Optimizationand Scalability Tool (GOAST)
Bioinformatics Solution
GOAST v4.0 optimized hardware accelerates execution speeds to boost lab productivity
Highlights
Lenovo GOAST v4.0 poweredby 5th Gen Intel® Xeon® Scalable
processors offers:• a leap in performance overprevious generations, speeding
sequential workflow processing
for faster time to insight

Introduction
Bioinformatics powers genomics research from basic biology to precision
medicine, drug discovery, agriculture, and more. While the field has existed forover 50 years, the past two decades have seen an explosion of bioinformatics
data. Much of this data comes from large national genomics initiatives such as
the “Genomics England/UK Biobank,” the “All of Us program” in the US, andSingapore’s “GenomeAsia.”

5M• GPU-like performance at CPUlevel prices, up to 50 percent lessthan boutique solutions relying
on GPUs or FPGAs without
additional licensing fees
• flexibility to deploy as a singlenode appliance or a cluster andgrow linearly with ease
• Snakemake as the workflow
manager

5K

England• preconfigured, optimized
workflows to free researchers’
time for research

30K

500K

CanadaEstonia

UK Biobank

1M

USA

Russia

150K

1M

150K

S Korea

Turkey45K

100M

Ireland

1M

100

235K

USA

China

Bangladesh

France100K

Global Programs

100K

Nigeria

1.5M Earth Biogenome

Japan

50K

Hong Kong

3M

100K

Saudi Arabia

100K

Singapore

UAE

Israel2M Astra Zeneca

100K

50K

120K
India

Indonesia

100K

Malaysia

1K

Regional Programs

1M “MEGA” European

20-Country Alliance

1K

Argentina

Chile

100K

Australia

Source: Frost & Sullivan, “Global Precision Market Growth Opportunities, Forecast to 2025,” Originally published in January 2017 and updated by Intel with internal market data

Figure 1. Large Scale Sequencing is already underway!
Propelled by the increasing affordability of Next-Generation Sequencing(NGS) technologies and advancements in high-performance computing (HPC)
storage and computing technologies, these initiatives aim to capture the genetic
variation of each nation’s people to spur advances in prevention, diagnosis, andtreatment for individuals and populations. They’re making significant progress.
At the UK Biobank, the whole genomes of 500,000 people recently went live tohelp researchers search their genetic code for links to disease. Analysis of thisdata has led to a variety of insights, including the realization that 4.8 percent of
the UK population has elevated liver iron levels, a key risk factor for significant
liver disease.1 Such discoveries point the way to more accurate diagnosis, lessinvasive testing, and quicker treatment.

Solutions Brief | Lenovo Genomics Optimization and Scalability Tool (GOAST) Bioinformatics Solution

The greatest challenges facing population genomics
efforts—and therefore downstream bioinformatics—havelong been scale and time. Population genomics requires
scaling up input data from exomes to whole genomes,
scaling up production levels (from a handful to tens of
thousands of samples), and having to do so under very shorttime frames. Two of three stages in population sequencing
take place in the HPC environment of a cluster orsupercomputer, including variant analyses (comparing how
a gene is “spelled” in different people), and downstream
bioinformatics (for example, measuring the effect ofvariations on function or disease). Therefore, scaling out
population genomics productions in a timely fashion largely
depends on the HPC technologies and the underlying
acceleration they can offer.
To address these challenges, in 2017, Intel began an
ongoing partnership with the Broad Institute to search fortechnological innovations that would reduce genomics
analytics times while retaining the scientific rigor andaccuracy scientists had come to know and expect of the
Broad’s Genome Analysis Toolkit (GATK).² Key outcomesof the Broad-Intel partnership include open-sourcing
GATK, the Genomics Kernel Library (including AVX-512
optimizations of the Smith-Waterman and Pair-HMM
algorithms), the Genomics DB variant store for population
analytics, and Intel-recommended hardware and workflow
configurations. Since its release in 2017, Intel’s referencearchitecture has afforded users of GATK’s Germline Variant
Calling Workflow accelerated performance by reducing
runtimes for whole genomes and exomes respectively.
Intel continues to work with the Broad Institute, helping to
optimize the Broad Institute's pipelines and GATK with Intellibraries, including the Intel Genomics Kernel Library.
Despite these advances, today—even at cluster andsupercomputer speeds—large-scale bioinformatics still
faces long execution times on massive volumes of data,
which delays “time to answer.” Currently, the only highperformance solutions to mitigate these challenges are
single-purpose boutique solutions that require expensive
specialty hardware and substantial licensing fees. These
single-purpose solutions require organizations to purchasemultiple architectures to support other types of researchin their data centers. Even organizations working in asingle subfield (for example, in genomics) find that thesingle-purpose solutions frequently do not offer sufficientcomputational resources for them to accomplish theirgoals. For example, those performing secondary genomics
analytics find that they also need computational resources
to gather, select, manage, transform, and describe
their data, in both primary and tertiary downstream
analyses. General-purpose data centers worldwide feel
this need even more acutely since the omics (genomics,

2

transcriptomics, proteomics) are only a fraction of the users
they must serve.
To meet the diverse needs of these organizations, Lenovo
partnered with Intel in 2019 to develop the GOAST system,a multi-purpose Genomics and Bioinformatics Optimized
platform, and has continued to evolve GOAST ever since.
Now, Lenovo has released new GOAST v4.0 with major
updates to accelerate bioinformatics performance further.

GOAST Capabilities
Extremely Fast Bioinformatics Analytics
Lenovo GOAST is a multi-purpose system specificallyengineered to meet the demands of bioinformatics
workloads. GOAST leverages an architecture of carefully
selected hardware tuned to accelerate bioinformatics
performance. Lenovo GOAST’s high-core, fast I/O, andhigh-memory specs (Table 1) excel at running the massively
parallel applications and sequential workflows common
in bioinformatics, including multi-omics (genomics,
transcriptomics, proteomics) applications. GOAST
accelerates mapping and whole-genome sequencing
variant calling analytics from days to hours—a process thattoday in many data centers around the world takes morethan 24 hours runs at a throughput rate of 1.77 samples perhour or 16 samples in 9 hours.

GOAST v4.0 Capabilities
Lenovo GOAST v4.0 is a recipe that comes with several
preconfigured and optimized workflows:• Germline variant calling•

Whole-Exome Sequencing (WES)•

Whole-Genome Sequencing (WGS)• Somatic variant calling• Tumor + Normal Pair
• Tumor Only

All needed dependencies come pre-installed in a Conda
environment that is easily replicated for additional users.
Workflows are submitted using the command line GOAST
Util Tool, which enables users to submit complex GATK
workflows with a single command. This tool automaticallyallocates resources to be used most efficiently basedon the number of samples submitted and the available
computational resources. Users may also monitor progress,
abort, and restart jobs, and manage temporary files.
GOAST v4.0 comes with several major updates including
additional workflows, Snakemake as the workflow manager,
using Conda to manage software installations, and acomplete rewrite of the backend GOAST Util Tool.

3

Solutions Brief | Lenovo Genomics Optimization and Scalability Tool (GOAST) Bioinformatics Solution

Table 1. GOAST v4.0 reference architectures for bioinformatics (fully customizable)
GOAST v4.0 Intel

GOAST v4.0 Intel

Processor• 2x Intel 8480+ CPUs (56 cores, 2.0 GHz, 350W),
Lenovo ThinkSystem SR650 V3 Server• 2x Intel 8592+ CPUs (64 cores, 1.9 GHz, 350W),
Lenovo ThinkSystem SR650 V3 Server

Memory• 1 TB RAM, 16x 64GB 4800 MT/s RDIMM• 1 TB RAM, 16x 64GB 5600 MT/s RDIMM

Storage• Min. 7 TB local SSD or NVMe

• Min. 7 TB local SSD or NVMe

GOAST Benefits
Increases Lab Productivity

Multi-purpose Bioinformatics Use

Accelerated execution speeds mean you get to process
more samples, find answers faster, and generate
breakthroughs that much sooner. GOAST outperforms
any other competing CPU-based (and even the FPGAand GPU-based) systems because Lenovo tunes GOAST
systems to meet the requirements of bioinformatics
pipelines running in-node workloads rather than thoseassumed in traditional HPC workloads. The result is theability to run software pipelines in higher throughputs.
Higher throughput capacity means batches of samples
analyzed in less time. (Table 2). Lenovo has performed
the heavy lifting of optimizing workflows as well as
ensuring software updates work seamlessly together.
This gives researchers the opportunity to focus on
research questions, instead of spending valuable timetuning hardware, tweaking software versions, andoptimizing workflows.

The high-performance GOAST system comes preloaded withomics tools to get you up and running on day one, or it can be
fully customized with the bioinformatics tools of your choice.

Table 2. Lab productivity for omics expected on a single
GOAST system*

Table 3. Genomics analytics software and otherdependencies preinstalled by GOAST v4.0

ExpectedLab
Productivity

Samples/
Node/Day
Samples/
Node/Year

50x WES samples**GOAST Intel

30x WGS samples**GOAST Intel

4th GenIntel®
Xeon®
Processor

5th GenIntel®
Xeon®
Processor

4th GenIntel®
Xeon®
Processor

5th GenIntel®
Xeon®
Processor

826

1011

36

42.4

~301K

~369K

~13.1K~15.5K* Performance is based on the processing of this NA12878 sample
which was sequenced on the NovaSeq 6000. All processing was
performed on local NVMe drives. Performance may vary basedon hardware setup and coverage of sample.
** WES - 64 samples were used and processed. WGS - 16 samples
were used and processed.

• For multi-omics analytics: Lenovo pre-installs the tools
and other dependencies in Table 3 that are necessary
to run the Broad Institute’s GATK Best Practices forGermline and Somatic SNP and Indel discovery. Lenovo
GOAST also provides preconfigured scripts to allow you
to run (submit, monitor, manage) samples on the Germline
workflow and Somatic workflow optimally on Lenovo
hardware with the GOAST Util Tool.
• For other bioinformatics: Install any tools of your choiceon GOAST systems or talk to the Lenovo team about
preinstalling your software pipeline of choice. GOAST
nodes are configured to support a wide range ofbioinformatics workflows.

SoftwareVersion

GATK

4.4.0.0

BWA-MEM2

2.2.1

Samtools

1.17

Picard Tools

3.0.0

OpenJDK

17.0.3

Snakemake*7.32.3

SLURM (Optional)*

23.02.4

OS (Recommended)

Rocky Linux 9.2*GOAST systems currently use Snakemake as the workflow
manager and job scheduler on a single-node setup and Slurm as
the job scheduling system on a multi-node setup.

4

Solutions Brief | Lenovo Genomics Optimization and Scalability Tool (GOAST) Bioinformatics Solution

Cost Effective
GOAST leverages an optimized CPU-based architecture;
thus, it requires no FPGAs or GPUs of any kind foracceleration. Users should expect GPU-like performancefor the optimized workflows – at CPU-level prices, or 50
percent lower than boutique solutions relying on FPGAs orGPUs, and no licensing fees. The Lenovo Bioinformatics
R&D group continually tests new bioinformatics pipelines
and releases to its customers hardware-tuned versions ofstandardized workflows such as the Broad Institute’s GATKBest Practices at no cost. In addition, GOAST solutions can
reduce investments needed to support large-scale projects
because a single server can replace up to 40 standard
nodes, reducing hardware, maintenance costs, and otherexpenses, including power consumption and cooling.

Scalable
The performance of Lenovo GOAST scales linearly fromsingle-node appliance to cluster implementation to serve
the needs of labs of all sizes, from small research groups tocommercial labs, and to national population-level projects.
This includes transitioning from WES to WGS, undertaking a
new project with greater scope and complexity, and expanding
both data and users. Organizations can scale linearly simply byadding compute and storage building blocks as needed.

Methods
Lenovo’s GOAST contains pre-tuned GATK scripts tomaximize the performance of variant calling on speciallytuned Lenovo hardware. The workflow manager used

Sequencer

The pipelines for Germline Variant Calling and Somatic
Variant Calling support both whole genome and wholeexome samples and follow the GATK Best Practices
pipeline. The preprocessing phase performs alignment
with BWAmem, continues with MarkDuplicates to remove
various duplication artifacts, and finishes with Base Quality
Score Recalibration (BQSR) to ensure that accurate quality
scores are being passed to the variant caller. GOAST
v4.0 provides the option to use BWA mem2 for up to a 30
percent increase in performance and to visualize FASTQC
reports with MultiQC (optional).
The Germline Variant Calling pipeline uses the standard
GATK workflow utilizing Haplotypecaller with an option
to emit VCF or GVCF files as output. Joint Calling uses
the option to choose between the GenomicsDB or
CombineVariants methods, depending on data types andcohort sizes. Variants from an individual sample or froma jointly called cohort of samples may be filtered with the
hard-filtering workflow.
The Somatic Variant Calling pipeline call variants withGATK’s Mutect2 calculates and filters contaminants.
This supports matched Tumor and Normal sample pairs(recommended) in addition to Tumor Only samples.
Optional annotation step of Funcotator.

Running GOAST showing 50X coverage for WES,
30X coverage for WGS:

Methodology used for testing:

Biological
Sampleshere is Snakemake, and Slurm is the recommended job
scheduler for a cluster setup.

The data provided here are based on single-node testing.
These results are the average of three iterations. See
Figure 1 for an overview of a typical NGS analysis fromsample to variant output.

WGS/WES
Sequencing Reads

Variants

Variant-Calling Workﬂow

CreateSequenceGroupingTSV

ScatterIntervalList

MultiQC(optional)

BWAMemSort:
Fastq
BWA MEM 2

Mark
Duplicates

Sort
SampleBam
Sort

Base
Recalibrator

GatherBQSR
Reports

ApplyBQSR

Haplotype
CallerMergeVCFs

Index

2024 Lenovo Internal. All rights reserved.

Figure 2. A typical Next-Generation Sequencing (NGS) workflow illustrating the sequential software calls required toperform WGS/WES variant-calling analyses. Biological samples (i.e., blood, saliva, etc.) processed experimentally are input
into a sequencer, which in turn generates sequencing reads (fragments of DNA strings). The sequencing reads become theinput for the variant calling workflows. The GATK Single-Sample Germline Variant Calling workflow shown here consists of
12 main software calls using 7 different software suites that in turn execute 30+ tools. The output of the genomics workflow
feeds into variant analyses (comparing how a gene is “spelled” in different people), and downstream tertiary bioinformatics
work (e.g., measuring effect of variations on function or disease).

5

Solutions Brief | Lenovo Genomics Optimization and Scalability Tool (GOAST) Bioinformatics Solution

The number of samples (exome) processed per day on theIntel platform, starting from a 2nd Gen Intel CPU to a 5th Gen
Intel CPU with the updated GOAST platform, is shown in
Figure 2.
a. Measuring performance. The present study measured
performance as execution time for the entire workflow,
starting from the input file, called a FASTQ file, to the *.vcf
output file. Lenovo's search for a “hardware + software +system recipe” prioritized identifying the best solution for
performance, cost, and usability. Lenovo also provided athroughput measure in the form of genomes (or exomes)
per compute node per day.
b.Hardware evaluations. Two different Intel Xeon Scalable
processors were used for all full workflow analyses: Intel
Xeon Platinum 8480+ and Intel Xeon Platinum 8592+
processors. Solutions incorporating the latest Intel Xeon
Platinum 8480+ processors and Intel Xeon Platinum
8592+ processors deliver significant performance gains
as compared to similarly configured solutions based on
previous-generation Intel Xeon Scalable processors.

Results
GOAST: A Two-pronged HPC-Architecture Plus
HPC-Scaler Solution
Determining the factors affecting the performance ofgenomics workflows is a complex problem. Genomics
workflows string together a combination of 28 java
applications, 24 of which are single-threaded, and 4 ofwhich are multi-threaded. Given the heterogeneity of
the genomics tools and their corresponding profilingcharacteristics, the path to an optimal hardware recipe

Number of samples

1200

1011

1000

826

800
600
400

375

439

224

200
0
Intel 6248R Intel 8380 Intel 8380 Intel 8480+ Intel 8592+
GOAST
GOAST
GOAST
GOAST
GOAST
v1.0
v2.1
v3.1
v4.0
v4.0

Given the lack of consensus among genomics developers
and the scientific computing staff that supports them,
Lenovo set out to systematically evaluate as many of thesedependencies as possible and the effect of permutationsof hardware, software versions, tool parameters, execution
modes, system tunings, and data types on the performanceof GATK workflows. Lenovo’s search for a “hardware+ software + system” recipe prioritized identifying thebest solution for performance, cost, and usability. Such acomprehensive evaluation led to hundreds of simulations
on many different hardware configurations spanning overa year. The study yielded two resources for deploying andscaling HPC for Genomics: GOAST Architecture andGOAST Scaler.
As Figure 2 illustrates, GOAST architecture has delivered
up to a 4.5X speed-up for whole exomes (WES) and up to a
6X increase for all genomes (WGS) since the introduction of
GOAST v1.0.
As a result of permutation tests of the hardware, software,and system factors affecting the performance of genomics
workflows, Lenovo identified an optimized architecture thatcan process over 1,000 WES samples per node per day and
42 WGS samples per node per day. GOAST architecture
leverages an optimized variant-calling workflow and aconcise, simple, non-specialty hardware recipe to deliver anaffordable solution with peak performance.

Whole Genome Samples (WGS)**
Samples per day(Higher is Better)

From v2.1 to v3.1, Lenovo began using
fastq ﬁles as input instead of ubams.
From v3.1 to v4.0, they began using a
new input ﬁle with RTA3 quality
scores instead of RTA2.
These changes meant that the ﬁleswere smaller, and performance
improved due to improved I/O issues.

42.4

45
40
Number of samples

Whole Exome Samples (WES)**Samples per day(Higher is Better)is not obvious. To date, most of the guidance in the researchcommunity regarding improving genomics HPC hasboiled down to speculations of the role of high memory,high storage speeds, and/or high core counts as possible
indicators of performance.

36

35
30
25
20
15
10

7

11

14

5
0
Intel 6248R Intel 8380 Intel 8380 Intel 8480+ Intel 8592+
GOAST
GOAST v1.0 GOAST v2.1 GOAST v3.1 GOAST
v4.0
v4.0

Figure 3. Results for multigenerational comparison data.
Similarly, GOAST v4.0, running on 5th Gen Intel Xeon processors, enables processing of approximately 14 percent moretumor normal samples per day than was possible with 4th Gen Intel Xeon processors (Figure 3).
** WES - 64 samples were used and processed. WGS - 16 samples were used and processed.

Solutions Brief | Lenovo Genomics Optimization and Scalability Tool (GOAST) Bioinformatics Solution

6

Tumor Normal Samples (TNS)
Samples per day(Higher is Better)
3500

Number of samples

3000

3268
2844

2500
2000
1500
1000
500
0
TNS
Intel 8480+ | GOAST v4.0

Intel 8592+ | GOAST v4.0

Figure 4. Gen-over-gen increase in tumor normal samples processed daily. For WES, 64 samples were used and processed.
For WGS, 16 samples were used and processed.
Together, these results highlight how the optimized
performance of GOAST v4.0 can ease the data analysis
bottleneck and decrease time to discovery.

Summary
Through five iterations of GOAST, Lenovo and Intel haveworked closely together to meet the computational needsof customers’ bioinformatics workloads. This collaboration
has yielded steady improvements in performance with each
new iteration. With GOAST v4.0, the latest update from 4th
Gen to 5th Gen Intel Xeon processors is, again, accelerating
execution speeds, allowing for the processing of 22 percent
more WES samples, 17 percent more WGS samples, and 14
percent more tumor normal samples every day.

Improvements are ongoing, as GOAST remains in active
development with Lenovo adapting for changes ofprevalent datatypes, ensuring that software updates work
seamlessly together, and adding workflows as customers
request them. The most recent additions include Somatic
Variant Calling and Joint Calling.
Preconfigured, optimized hardware and workflows
streamline processing for reduced time to insight. Equallyconsequential, the performance of GOAST scales
linearly, which makes it easy for organizations of all sizes
to take on projects with greater scope and complexity.
Moving forward, Lenovo and Intel plan to continue theircollaboration on GOAST, keeping the focus on finding new
ways to enable research through increased efficiency
and usability.

Accelerated by IntelTo deliver the best experience possible, Lenovo and Intel have optimized this solution
to leverage Intel capabilities like processor accelerators not available in other systems.
Accelerated by Intel means enhanced performance to help you achieve new innovations
and insight that can give your company an edge.

For More Information, Visit These Sites• HPC Solutions | High-Performance Computing | Lenovo US• Powering groundbreaking plant genomics research | Lenovo US• Bringing personalized medicine to citizens across Saudi, Arabia | Lenovo US• How Multi-Scaled HPC-Enabled Genomics Will Help Save Your Life | nextplatform.com• Unlocking new insights into serious diseases | Lenovo USSolutions Brief | Lenovo Genomics Optimization and Scalability Tool (GOAST) Bioinformatics Solution

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McKay A, Wilman HR, Dennis A, Kelly M, Gyngell ML, Neubauer S, et al. (2018) Measurement of liver iron by magnetic resonance imaging
in the UK Biobank population. PLoS ONE 13(12): e0209340. https://doi.org/10.1371/journal.pone.0209340
2
Van der Auwera GA et al. From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. 2013.
Curr Protoc Bioinformatics. 43:11.10.1-11.10.33.
1

Configurations
1-node, 2x Intel® Xeon® Platinum 8480+, 56 cores, HT On, Turbo On, NUMA 2, Integrated Accelerators Available [used]: DLB 2 [0],DSA 2 [0], IAA 2 [0], QAT 2 [0], Total Memory 1024GB (16x64GB DDR5 4800 MT/s [4800 MT/s]), BIOS ESE118F-2.22, microcode
0x2b0004b1, 2x Ethernet Controller E810-XXV for SFP, 4x I350 Gigabit Network Connection, 2x BCM57508 NetXtreme-E
10Gb/25Gb/40Gb/50Gb/100Gb/200Gb Ethernet, 2x 894.3G Micron_7450_MTFDKBA960TFR, 1x 7T SAMSUNG MZQL27T6HBLA00A07, Red Hat Enterprise Linux 9.2 (Plow), 5.14.0-284.30.1.el9_2.x86_64, WORKLOAD+VERSION, COMPILER, LIBRARIES, OTHER_SW,
score=?UNITS. Test by Lenovo as of 10/27/23.
1-node, 2x Intel® Xeon® Platinum 8592+, 64 cores, HT On, Turbo On, NUMA 2, Integrated Accelerators Available [used]: DLB 2 [0],DSA 2 [0], IAA 2 [0], QAT 2 [0], Total Memory 1024GB (16x64GB DDR5 5600 MT/s [5600 MT/s]), BIOS ESE122N-3.10, microcode
0x21000161, 2x Ethernet Controller E810-XXV for SFP, 4x I350 Gigabit Network Connection, 2x BCM57508 NetXtreme-E
10Gb/25Gb/40Gb/50Gb/100Gb/200Gb Ethernet, 2x 894.3G Micron_7450_MTFDKBA960TFR, 1x 7T SAMSUNG MZQL27T6HBLA00A07, Red Hat Enterprise Linux 9.2 (Plow), 5.14.0-284.30.1.el9_2.x86_64, WORKLOAD+VERSION, COMPILER, LIBRARIES, OTHER_SW,
score=?UNITS. Test by Lenovo as of 10/31/23.
1-node, 2x Intel® Xeon® 8380,40 cores (2.4GHz, 270W), SR630 V2, 512GB RAM, 16x 32GB/3200MHz DIMMs, 7TB SSD
1-node, 2x Intel® Xeon® 8380, 40 cores(2.30GHz, 270 W), 1024GB RAM, 16x 32GB/3200MHz DIMMs, 7TB SSD
1-node, 2x Intel® Xeon® 6248R CPUs, 24 cores (3.0GHz, 205W), SD650, 384GB RAM, 12x 32GB/2933MHz DIMMs, 1.92TB, 1x 2.5" SAS SSD
Notices & Disclaimers
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See
backup for configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
Intel technologies may require enabled hardware, software or service activation.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names andbrands may be claimed as the property of others.
*Other names and brands may be claimed as the property of others.
0424/MIM/JW/PDF
359770-001US

Genomics Glossary
Term/Acronym

Definition

Exome

An exome is the sequence of all the exons in a genome, reflecting the protein-coding portionof a genome. In humans, the exome is about 1.5 percent of the genome.

Genome

The genome is the entire set of DNA instructions found in a cell. In humans, the genome
consists of 23 pairs of chromosomes located in the cell’s nucleus, as well as a small
chromosome in the cell’s mitochondria. A genome contains all the information needed for anindividual to develop and function.

Variant

A DNA sequence observed at a site on a single chromosome. Note that the DNA sequence ina reference sequence at that site is also a variant. A ref-matching variant is the variant found in
the reference sequence. A ref-mismatching variant is any other variant.

Tumor only(Comparison Mode)

Compares a tumor-normal pair of sequences to find variants called in the tumor but not in thenormal.