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Variant Analysis
6
6.1 VARIANTS AND AUTOIMMUNE DISEASES
Mutations and genetic variants play a crucial role in the development and progression of autoimmune diseases, which arise when the immune system mistakenly targets the body’s own tissues. A
mutation is a permanent alteration in the nucleotide sequence of the genome of an organism. It can
occur spontaneously during DNA replication or be induced by external factors such as radiation,
chemicals, or viruses. They may be neutral, benecial, or deleterious, depending on their impact
on gene function and organismal tness. Deleterious mutations can affect immune signaling pathways, antigen processing, or tolerance mechanisms, thereby increasing an individual’s susceptibility
to various autoimmune conditions. Broadly, the types of mutations and variants involved include
single nucleotide polymorphisms (SNPs), insertions and deletions (indels), copy number variations
(CNVs), and larger structural variations. Each of these can have diverse effects on gene expression
or protein function, contributing in complex ways to disease phenotypes. Advances in genome-wide
association studies (GWAS), next-generation sequencing, and integrative omics have uncovered
numerous genetic variants implicated in autoimmune diseases, expanding our understanding of
genetic predisposition.
SNPs are the most extensively studied class of variants. They represent a single base change at
a specic locus and are cataloged in databases such as dbSNP. For example, the SNP rs2476601 in
the PTPN22 gene is one of the most well-established variants associated with multiple autoimmune
disorders, including rheumatoid arthritis (RA), type 1 diabetes, and systemic lupus erythematosus
(SLE). This variant results in arginine (R) at position 620, in PTPN22 protein, is replaced by tryptophan (W) (this can be abbreviated as R620W amino acid substitution), which alters T-cell receptor
signaling and contributes to autoreactivity. Another notable SNP is rs689 in the INS gene, linked
to type 1 diabetes, inuencing insulin expression in the thymus and affecting central tolerance. In
the context of multiple sclerosis (MS), SNPs in the HLA-DRB1 gene, such as rs3135388, have been
strongly associated with disease risk, underscoring the central role of the human leukocyte antigen
(HLA) system in autoimmune pathogenesis.
Beyond SNPs, structural variations like CNVs, recorded in databases such as dbVar, have
emerged as important contributors to autoimmune disease susceptibility. Copy number gains or
losses can lead to altered gene dosage, dysregulation of immune-related genes, or disruption of
gene architecture. For instance, CNVs in the FCGR3B gene, which encodes a receptor involved
in the clearance of immune complexes, have been implicated in SLE. Decreased copy number of
FCGR3B reduces the efciency of immune complex clearance, promoting chronic inammation
and autoantibody formation. Similarly, a CNV involving the C4 gene in the complement system is
associated with SLE and schizophrenia, demonstrating pleiotropic effects. Individuals with fewer
copies of C4A tend to have increased SLE risk, highlighting the role of complement in immune
regulation and disease susceptibility.
Recent studies also emphasize the signicance of non-coding and regulatory variants, including
those affecting enhancers, promoters, or non-coding RNAs. These variants may not alter protein
sequence but can modulate gene expression in a tissue-specic manner. For example, enhancer variants affecting the expression of IL2RA, the gene encoding the interleukin-2 receptor alpha chain,
are linked to type 1 diabetes and MS. Such regulatory variants are often identied through integrative analyses of GWAS signals and epigenomic data, including chromatin accessibility and histone
modication patterns. In RA, variants near the STAT4 and TR AF1-C5 loci have been shown to inuence gene expression in immune cells, correlating with disease susceptibility and clinical severity.
204 DO I: 10.1201/ 97810 03685 432- 6
205 Variant Analysis
In addition to germline variants, somatic mutations in immune cells have also been implicated in
certain autoimmune disorders, particularly those with late-onset or clonal features, such as autoimmune lymphoproliferative syndromes. Moreover, polygenic risk scores (PRS), which aggregate the
effects of multiple SNPs, are increasingly used to estimate an individual’s genetic predisposition
to autoimmune diseases. While the genetic architecture of these diseases is complex and involves
gene–environment interactions, the integration of variant databases like d bS N P, dbVa r, C li nVa r,
and gnomAD with functional genomics is shedding light on pathogenic mechanisms. This ongoing
research not only deepens our understanding of autoimmune diseases but also opens avenues for
personalized diagnostics and therapeutics based on genetic risk proling.
Var ia nt calling from high-throughput sequencing data is a critical computational process
in genomics that involves identifying differences between a sequenced genome and a reference
genome. The process begins with the generation of raw sequencing reads, typically in the form of
paired-end short reads from platforms such as Illumina. These reads undergo quality control steps
to remove low-quality bases, adapter contamination, and other sequencing artifacts. The cleaned
reads are then aligned or mapped to a reference genome using efcient alignment algorithms like
BWA or Bowtie2. This step generates alignment les in SAM or BAM format, where each read is
placed at its most likely origin in the reference genome.
Once reads are aligned, the next step involves preprocessing to ensure accurate variant detection. This includes sorting the alignments, marking duplicate reads (which may arise during PCR
amplication), and performing base quality score recalibration to correct systematic sequencing
errors. Tools like Picard and GATK are commonly used in this phase. After preprocessing, the
variant calling algorithms scan the alignments to identify sites where the sequenced data differs
from the reference. These differences can include single nucleotide variants (SNVs), insertions and
deletions (indels), and, in some workows, larger structural variants. Variant callers such as GATK
HaplotypeCaller, FreeBayes, or SAMtools mpileup use probabilistic models to assess the likelihood
of a variant being present, taking into account read depth, base quality, and allele balance.
The output of variant calling is typically a Variant Call Format (VCF) le, which contains
detailed information about each candidate variant, including its position, reference and alternative alleles, genotype likelihoods, and quality metrics. To distinguish true biological variants from
sequencing artifacts, additional ltering is applied based on quality thresholds or machine learning models trained on known variant datasets. For example, the GATK framework uses a process
called variant quality score recalibration (VQSR) to assign condence scores and classify variants
into high- and low-quality categories. In cases where multiple samples are analyzed simultaneously,
joint genotyping can be performed to increase accuracy and consistency across individuals.
The accuracy of variant calling can be inuenced by several factors, including sequencing depth,
genome complexity, the presence of repetitive regions, and the quality of the reference genome.
Therefore, variant callers often include specic strategies to handle complex regions or lowfrequency variants, particularly important in cancer genomics or studies of rare genetic disorders.
Following variant calling, annotation tools such as ANNOVAR or SnpEff are used to interpret the
biological signicance of each variant, linking them to genes, known polymorphisms in databases
like dbSNP or ClinVar, or predicted functional consequences. Overall, variant calling is a cornerstone of modern genomics, enabling discoveries in disease research, population genetics, and personalized medicine by translating raw sequence data into meaningful genetic insights.
In the context of autoimmune diseases, variant calling from high-throughput sequencing data
serves as a foundational step in autoimmune disease research, enabling the detection of genetic
variations that may underlie disease susceptibility, progression, and therapeutic response. One of
the primary goals is the identication of both common and rare variants that contribute to disease
predisposition. While GWAS have historically focused on common variants with modest effect
sizes, variant calling from whole-genome and whole-exome sequencing (WES) allows researchers
to uncover rare variants with potentially stronger, more penetrant effects in coding or regulatory
206 Bioinformatics of Autoimmune Diseases
regions. These variants can provide critical insights into disease mechanisms that are not detectable
through traditional genotyping arrays.
Another key objective is the comprehensive proling of variants across diverse genomic contexts, including coding regions, untranslated regions (UTRs), introns, promoters, enhancers, and
other regulatory elements. In autoimmune diseases, non-coding variants often play a substantial
role by modulating gene expression in immune cells. Accurate variant calling enables researchers to
map these functional non-coding variants and integrate them with expression quantitative trait loci
(eQTL) analyses, epigenomic annotations, and chromatin interaction data to determine their regulatory impact. This integration can reveal how genetic variants inuence gene expression networks
and immune pathways that drive autoimmunity.
Var ia nt calling also aims to characterize somatic mutations in immune cells, particularly in
disorders where clonal expansion or mosaicism is suspected. This is especially relevant for lateonset autoimmune diseases or conditions with atypical features, where somatic variants may affect
immune signaling or tolerance mechanisms. Detecting these low-frequency variants requires highdepth sequencing and sensitive bioinformatic pipelines capable of distinguishing true somatic mutations from sequencing artifacts. These ndings help elucidate how acquired mutations contribute to
immune dysregulation and may inform future precision medicine strategies.
Furthermore, a critical goal is the detection of structural variants such as insertions, deletions, inversions, and CNVs. These larger genomic alterations can disrupt gene function or dosage, and variant
calling pipelines are increasingly incorporating specialized tools and algorithms designed to capture
such complex events. In autoimmune disease research, structural variants in genes related to the complement system, immunoglobulin loci, or HLA regions have been shown to signicantly affect disease
risk and phenotype. High-throughput variant calling facilitates a more holistic view of the genome,
allowing researchers to assess both sequence-level and structural changes contributing to disease.
Last, variant calling supports the construction of personalized genomic proles and PRS for
autoimmune diseases. By aggregating variant data from large cohorts and integrating it with clinical phenotypes, researchers can stratify individuals based on their genetic risk. This not only aids in
early detection and diagnosis but also guides the development of targeted therapies and individualized treatment plans. As sequencing technologies continue to advance, the goals of variant calling
are expanding beyond discovery toward clinical application, offering the potential for precision
medicine in autoimmune disease management.
Variant detection relies on several types of high-throughput sequencing data, each tailored to
specic research or clinical objectives. Whole-genome sequencing (WGS) provides the most comprehensive view of the genome, covering both coding and non-coding regions. It allows for the
detection of a wide range of variants, including SNVs, insertions and deletions (indels), structural
variants, and CNVs across the entire genome. This depth of information makes WGS particularly
valuable for discovering novel variants and studying complex traits or diseases with heterogeneous
genetic bases. However, the high cost and data storage requirements make it less common in routine
clinical use compared to more targeted approaches.
WES, in contrast, focuses only on the protein-coding regions of the genome, which comprise
about 1–2% of the genome but account for a signicant proportion of known disease-causing mutations. Because of its lower cost and data volume, WES is widely used in both research and diagnostics to identify coding variants linked to Mendelian disorders and other phenotypes. While WES
misses regulatory and intergenic variants, it provides high coverage of exons, allowing for accurate
variant detection in coding regions. RNA sequencing (RNA-Seq), while primarily used for transcriptome proling, can also be employed for variant calling. Variants detected from RNA-Seq data
reect only the transcribed portion of the genome, which adds a layer of functional context, especially in studies of gene expression, splicing, and allele-specic expression in disease states such as
cancer and autoimmune conditions.
Long-read sequencing technologies, such as those from Oxford Nanopore and Pacic Biosciences,
have transformed variant analysis by enabling the detection of large structural variants and complex
207 Variant Analysis
genomic rearrangements that are difcult to resolve with short-read platforms. Long reads span
repetitive or Guanine-Cytosine (GC)-rich regions more effectively and can provide phased haplotypes, revealing the cis or trans conguration of variants. These capabilities are particularly useful
in resolving ambiguities in immune loci, such as the HLA region, which is highly polymorphic and
relevant to many autoimmune diseases. While long-read sequencing has historically had higher
error rates, improvements in base calling and consensus algorithms are making it increasingly reliable for both research and clinical applications.
After variant calling, the resulting data is stored in standardized le formats, with the VCF being
the most widely used. A VCF le encodes each variant with details, including its genomic position,
reference and alternate alleles, quality scores, read depth, and genotype information for each sample.
The format is extensible and includes metadata lines for annotations such as gene names, predicted
functional effects, and population frequencies. For multi-sample projects, the VCF format supports
the storage of genotype calls across many individuals in a compact and structured way. Binary
versions of VCF les, known as BCF, are also used to save space and allow for faster computation.
In addition to VCF and BCF, structural variants and complex rearrangements are often stored
in formats such as BED, which represents genomic intervals, or specialized formats like GVF
(Genome Variation Format) and GFF3 for annotated features. Annotation tools may output variant
data with added functional or clinical information in custom formats or as augmented VCFs. These
les can be indexed and visualized in genome browsers or integrated into databases for downstream
analysis. As sequencing technologies and variant detection algorithms evolve, maintaining interoperability and efciency in le formats remains essential for the scalable analysis and sharing of
genomic data.
6.2 EXPERIMENTAL DESIGN FOR VARIANT CALLING
The accuracy and interpretability of variant calling heavily depend on careful experimental design
and rigorous sample preparation protocols. At the earliest stages, choosing the appropriate sequencing platform, coverage depth, and sample type are critical decisions shaped by the research question. For instance, high-coverage WGS is often necessary for detecting rare variants or structural
rearrangements, whereas whole-exome or targeted sequencing might sufce for identifying known
disease-associated mutations. DNA is the most common starting material for variant calling, and
its extraction must ensure high integrity and purity, free from contaminants such as proteins, RNA,
or chemical residues. The quality of extracted DNA is typically assessed using spectrophotometric
methods and fragment analysis, as degraded or low-concentration samples can introduce artifacts
in downstream variant analysis.
Different study designs offer distinct advantages depending on the hypothesis being tested.
Case-control studies are among the most widely used for identifying variants associated with autoimmune diseases. They compare allele frequencies between individuals with the disease (cases)
and without it (controls) to identify statistically signicant associations. However, such studies must
carefully match controls based on population background to avoid confounding due to population
stratication. Family-based designs, such as trio or pedigree studies, are powerful for identifying
inherited variants and de novo mutations, particularly in early-onset or rare autoimmune conditions.
Longitudinal studies, while more resource-intensive, provide temporal data and allow researchers
to assess how genetic variants interact with environmental factors over time or inuence disease
progression and response to treatment. The choice of design signicantly inuences the statistical
power, variant interpretation, and generalizability of the results.
While DNA remains the primary source material for variant calling, RNA-Seq is increasingly
used to detect variants in the transcribed portion of the genome. RNA-based variant calling provides insights into the functional impact of variants by linking them directly to gene expression. It is
especially useful in cancer and autoimmune research where gene expression patterns are often dysregulated. For example, RNA-Seq can identify allele-specic expression or splicing defects, which
208 Bioinformatics of Autoimmune Diseases
might not be apparent in DNA sequencing. However, RNA-Seq variant calling presents unique
challenges, such as variable transcript abundance, RNA editing, and intron–exon boundaries, which
can complicate accurate alignment and variant calling. Therefore, RNA-Seq variant calls are often
used in combination with DNA-based calls for validation and interpretation.
Sample quality control is essential to ensure reliable results across sequencing runs. This includes
conrming DNA/RNA purity, integrity, and concentration before library preparation. In large-scale
studies, consistency in sample handling, storage conditions, and sequencing protocols reduces technical variability. In addition to biological quality control, robust metadata collection is critical.
Metadata includes demographic information, clinical diagnosis, treatment history, environmental
exposures, and comorbid conditions, all of which can inuence variant interpretation. In autoimmune disease studies, where heterogeneity is common, richly annotated metadata enables stratication of samples and improves the precision of genotype–phenotype correlations.
Ethical considerations are paramount in studies involving genetic data, especially in diseases
like autoimmunity where lifelong health information is implicated. Informed consent must clearly
communicate the scope of data usage, potential ndings, data sharing policies, and the rights of participants to withdraw. Researchers must also address the possibility of incidental ndings, genetic
variants unrelated to the study but potentially relevant to a participant’s health. Biobanking, which
involves the long-term storage of biological samples and associated data, has become a key component of autoimmune research. Modern biobanks incorporate secure data systems, standardized
protocols for sample preservation, and governance models that enable responsible data access while
protecting privacy. With increasing international collaboration, harmonization of ethical frameworks and data-sharing agreements is essential to maximize the impact of variant calling in understanding and treating autoimmune diseases.
6.3 VARIANT CALLING BEST PRACTICES FOR AUTOIMMUNE DISEASES
The variant calling workow (Figure 6.1) begins with the acquisition of high-quality sequencing
data, which forms the foundation for accurate detection of genetic variants. For studies on autoimmune diseases, WGS or WES is typically employed, depending on the research objectives and available resources. WGS is preferred when non-coding regulatory regions are of interest, while WES
offers a cost-effective alternative focused on coding regions where many disease-associated variants are known to reside. Once sequencing is complete, raw data in FASTQ format undergo quality
FIGURE 6.1 The general variant calling owchart.
209 Variant Analysis
control to assess read quality, GC content, and potential adapter contamination. Tools like FastQC
and Trimmomatic are commonly used in this stage to remove low-quality reads and artifacts, ensuring cleaner data for alignment.
Following quality control, the reads are aligned to a reference genome, usually the latest GRCh38
build, using a highly accurate aligner such as BWA-MEM. The output is a SAM/BAM le, which
records the position of each read on the reference genome. This alignment step is crucial, especially
for autoimmune disease-associated loci, many of which reside in complex genomic regions like the
HLA cluster, where high sequence similarity and polymorphism can complicate mapping. To address
this, best practices include using specialized aligners or reference graphs that better handle variability in immune loci. Once aligned, post-processing steps such as sorting, marking duplicates, and base
quality score recalibration are conducted to improve data reliability. These steps are vital for reducing false positives and are standardized in frameworks such as the GATK best practices pipeline.
Var ia nt calling itself is carried out using tools like GATK HaplotypeCaller, FreeBayes, or
DeepVariant, which apply probabilistic models or machine learning to identify potential variants.
These tools evaluate the evidence for differences at each genomic position, taking into account base
quality, allele balance, and depth of coverage. For autoimmune studies, joint genotyping across all
samples is often recommended to improve variant detection sensitivity and consistency, especially
for identifying shared variants among cases. The resulting output is a VCF le, containing the position, type, and genotype of each variant. The variant calls are then subjected to quality ltering
using hard thresholds or more advanced techniques like VQSR, which uses training sets of known
variants to distinguish true positives from sequencing artifacts.
Annotation of the ltered variants is the next critical step. Tools like A NNOVA R, SnpEff, or VEP
(Variant Effect Predictor) are used to assign biological meaning to each variant by predicting functional effects, mapping variants to genes, and cross-referencing databases such as dbS NP, ClinVa r,
gnomAD, and autoimmune-specic resources. For autoimmune disease research, this step is particularly informative, as it helps highlight variants affecting immune-related genes, such as PTPN22,
STAT4, IL23R, and HLA-DRB1. Annotation can also include predictions of pathogenicity—using
algorithms like CADD or REVEL, and integration with expression data can help determine whether
variants have regulatory impacts.
Best practices for autoimmune disease variant calling also emphasize the importance of integrating genetic data with phenotypic and environmental metadata. Because autoimmune disorders often
involve gene–environment interactions and present heterogeneously across individuals, statistical
models that account for covariates such as age, sex, ancestry, and exposure history enhance the
power of association analyses. Furthermore, combining DNA- and RNA-based variant calls can
provide a functional context for interpreting disease-associated variants, especially those affecting
gene expression or splicing. In particular, RNA-Seq can be used to identify allele-specic expression patterns or aberrant transcripts linked to variants detected in DNA data.
Finally, validation of key variants through independent technologies, such as Sanger sequencing or qPCR, is a recommended best practice, especially when variants are considered for clinical interpretation or therapeutic targeting. Data sharing through repositories like dbGaP or EGA,
adherence to FAIR data principles, and ethical management of participant information are also critical in ensuring the long-term impact and reproducibility of variant calling studies in autoimmune
diseases. As methods continue to evolve, incorporating multi-omic data and leveraging machine
learning will further rene variant interpretation and accelerate discoveries in autoimmune disease
genetics.
6.4 VARIANT CALLING PIPELINE
In this section, we will walk through the pipeline step by step. The workow has been modularized into Python functions for clarity and ease of management. The pipeline requires two primary
inputs: (1) paired-end FASTQ les stored in a designated directory, and (2) a CSV le containing
210 Bioinformatics of Autoimmune Diseases
TABLE 6.1
Study Metadata used for Variant Calling
runID RF
ERR9539328 Negative
ERR9539251 Negative
ERR9539353 Negative
ERR9539358 Positive
ERR9539313 Positive
ERR9539319 Positive
the study design metadata, including the sample SRA run IDs and rheumatoid factor (RF) status
(Table 6.1). For demonstration purposes, we will use FASTQ data from six individuals: three with
positive RF and three with negative RF. This analysis is intended to demonstrate the process of
conducting variant analysis, rather than to draw any scientic conclusions.
To begin the demonstration, we rst need to download the raw data from the NCBI SRA database. Start by creating a text le containing the run IDs, with one ID per line, and name this le
id s.t xt . For better organization, create a dedicated project directory. Within this directory, copy
the run IDs from Table 6.1 into a new le and save it as id s.t xt . Next, copy the download _
fa stq.p y script into the same directory and execute it. The script will automatically create a
subdirectory named d ata/raw and download the corresponding FASTQ les into it.
Please note that the download process may take some time depending on your internet speed and
system performance. Also, ensure that you have sufcient disk space to store both the raw data and
the les generated during subsequent analysis steps. Once the les have been downloaded, you can
use the gzip command to compress them and conserve storage space.
The second step is to create a directory named meta, and within that directory, create a le
called metadata.txt. This le should be in CSV format and contain the following information:
run ID,RF
ERR9539328,negative
ERR9539251,negative
ERR9539353,negative
ERR9539358,positive
ERR9539313,positive
ERR9539319,positive
The complete pipeline is implemented in the script variant _ calling _ pipeline.py. In
the following sections, we will provide a detailed discussion of the functions that constitute this pipeline.
6.4.1 DOWNLOADING AND INDEXING A REFERENCE GENOME
def run_command(command):
print(f"Running: {command}")
subprocess.run(command, shell=True, check=True)
def download_and_index_reference(reference_url, output_dir):
os.makedirs(output_dir, exist_ok=True)
compressed_path = os.path.join(output_dir, "reference.fasta.gz")
ref_fasta = os.path.join(output_dir, "reference.fasta")
ref_dict = ref_fasta.replace(".fasta", ".dict")
run_command(f"wget -O {compressed_path} {reference_url}")
run_command(f"gunzip -c {compressed_path} > {ref_fasta}")
211 Variant Analysis
run_command(f"samtools faidx {ref_fasta}")
# Delete. dict if it exists
if os.path.exists(ref_dict):
os.remove(ref_dict)
run_command(
f"gatk CreateSequenceDictionary "
f"-R {ref_fasta} "
f"-O {ref_dict}"
)
run_command(f"bwa index {ref_fasta}")
return ref_fasta
The download _ and _ index _ reference function initiates and prepares the reference
genome, a fundamental requirement for any read alignment and variant calling pipeline. It begins
by creating the output directory if it does not already exist, ensuring that the necessary workspace
is available. The function then downloads a compressed reference genome le from a given URL
using wget and decompresses it into a standard FASTA format using gunzip. This FASTA le
will serve as the primary reference sequence for mapping reads and identifying genetic variants.
Once the FASTA le is prepared, the function runs samtools faidx to create a FASTA index
le, which enables rapid random access to any part of the reference genome. This index is required
by many downstream tools that process genomic data. Before generating a new sequence dictionary
with GATK’s CreateSequenceDictionary, the function checks whether a .dict le already
exists and removes it to avoid conicts or using outdated metadata. The sequence dictionary is a
necessary input for GATK-based tools, ensuring that the reference genome is correctly interpreted
during analysis. Finally, the function runs bwa index to generate alignment indices specically
for BWA, enabling fast and accurate read alignment to the reference.
This function’s impact on the variant calling pipeline is foundational. It ensures that the reference
genome is correctly formatted and indexed for use across a variety of bioinformatics tools. Without
these indexing steps, the pipeline would fail or operate inefciently during alignment, quality recalibration, and variant calling. By automating the entire setup process, this function also reduces
user error and standardizes the environment, contributing to the accuracy and reproducibility of
the pipeline.
6.4.2 QUALITY CONTROL
def quality_control(fastq_files, output_dir):
os.makedirs(output_dir, exist_ok=True)
for fq in fastq_files:
run_command(f"fastqc {fq} -o {output_dir}")
def trim_reads(sample_name, r1, r2, output_dir):
os.makedirs(output_dir, exist_ok=True)
out_r1 = os.path.join(output_dir, f"{sample_name}_R1_trimmed.fastq.gz")
out_r2 = os.path.join(output_dir, f"{sample_name}_R2_trimmed.fastq.gz")
run_command(
f"trimmomatic PE {r1} {r2} {out_r1} /dev/null "
f"{out_r2} /dev/null SLIDINGWINDOW:4:20 MINLEN:50"
)
return out_r1, out_r2
The quality _ control function will assess the quality of raw sequencing reads before any
further processing takes place in the variant calling pipeline. It takes a list of FASTQ les and an
output directory as inputs. The function rst ensures that the specied output directory exists or
creates it if it doesn’t. Then, for each FASTQ le provided, it runs the fastqc command-line tool,
212 Bioinformatics of Autoimmune Diseases
which generates a comprehensive quality report. These reports include important metrics such as
per-base sequence quality, GC content, sequence duplication levels, and potential contamination.
The outputs are saved in the designated directory, allowing users to visually inspect and evaluate
the data quality before proceeding with trimming and alignment steps. This helps to identify problematic samples early in the analysis and guides decisions on how to preprocess the data.
The trim _ reads function focuses on improving read quality by trimming low-quality bases
and ltering out short reads using the trimmomatic tool. It is specically designed to handle pairedend reads, accepting a sample name and two FASTQ les representing the forward (R1) and reverse
(R2) reads, along with an output directory. The function constructs le paths for the trimmed outputs using the sample name, then invokes trimmomatic in paired-end mode. The trimming settings include a sliding window lter that removes low-quality regions (with a window size of 4 and
minimum average quality of 20) and a minimum length lter that discards reads shorter than 50
bp. Output les for the successfully paired, trimmed reads are retained, while unpaired reads are
discarded by redirecting them to /dev/null. The function returns the paths to the cleaned and
compressed FASTQ les, which are then ready for alignment to the reference genome. This step is
essential for improving mapping accuracy and reducing noise in downstream variant calling.
6.4.3 READ MAPPING
def align_reads(sample_name, ref_genome, r1_trimmed, r2_trimmed, output_dir):
os.makedirs(output_dir, exist_ok=True)
sam_file = os.path.join(output_dir, f"{sample_name}.sam")
bam_file = os.path.join(output_dir, f"{sample_name}.bam")
rg_bam_file = os.path.join(output_dir, f"{sample_name}_rg.bam")
# Step 1: Align without read group
run_command(
f"bwa mem {ref_genome} "
f"{r1_trimmed} {r2_trimmed} "
f"> {sam_file}"
)
# Step 2: Convert to BAM
run_command(f"samtools view -Sb {sam_file} > {bam_file}")
# Step 3: Add proper read group
run_command(
(
f"picard AddOrReplaceReadGroups "
f"I={bam_file} "
f"O={rg_bam_file} "
f"RGID={sample_name} "
f"RGLB=lib1 "
f"RGPL=ILLUMINA "
f"RGPU=unit1 "
f"RGSM={sample_name} "
f"VALIDATION_STRINGENCY=LENIENT"
)
)
return rg_bam_file
The align _ reads function carries out a critical step in the variant calling pipeline by align-
ing high-quality, trimmed paired-end reads to a reference genome and preparing them with standardized metadata for downstream processing. It takes a sample name, reference genome path, the
paths to the trimmed forward and reverse reads, and an output directory. The function rst ensures
that the output directory exists and then denes the paths for the intermediate SAM le, an initial
BAM le, and the nal BAM le that includes proper read group information.
213 Variant Analysis
The function proceeds in three stages. First, it uses BWA-MEM to align the paired reads to the reference genome, generating a SAM le that contains alignment information for each read. This le
is in human-readable format but not optimized for storage or processing, so in the second step, the
function converts the SAM le to a binary BAM format using samtools view. The BAM format
is more compact and efcient for analysis tools to handle. In the third and nal step, the function
uses Picard‘s AddOrReplaceReadGroups tool to attach standardized metadata to each read,
including sample ID, library, platform, platform unit, and sample name. These read group tags are
essential for tools such as GATK to correctly distinguish between different samples and sequencing
runs, and they enable important steps like duplicate marking and base quality score recalibration
to operate correctly.
By producing a BAM le that contains both accurate alignment information and complete read
group metadata, this function ensures that each sample is traceable and properly formatted for the
variant calling workow. This step signicantly contributes to the reliability of downstream analyses by enabling the pipeline to process multiple samples in a consistent and reproducible manner. It
ensures compatibility with industry-standard tools and provides the structural backbone for the rest
of the pipeline’s data processing.
6.4.4 MARKING DUPLICATE READS
def convert_sort_mark_duplicates(sample_name, bam_file, output_dir):
os.makedirs(output_dir, exist_ok=True)
sorted_bam = os.path.join(output_dir, f"{sample_name}_sorted.bam")
dedup_bam = os.path.join(output_dir, f"{sample_name}_dedup.bam")
metrics_file = os.path.join(output_dir, f"{sample_name}_metrics.txt")
run_command(f"samtools sort {bam_file} -o {sorted_bam}")
run_command(
f"gatk MarkDuplicates "
f"-I {sorted_bam} "
f"-O {dedup_bam} "
f"-M {metrics_file}"
)
return dedup_bam
The convert _ sort _ mark _ duplicates function is responsible for preparing raw
alignment data for accurate variant analysis by sorting the alignments and identifying duplicate
reads. It takes a sample name, a BAM le (typically produced from aligning sequencing reads to a
reference genome), and an output directory. The function ensures the output directory exists before
proceeding with further processing. It denes the le paths for the sorted BAM le, the deduplicated BAM le, and a metrics le that will contain statistics about the duplication process.
The function begins by sorting the input BAM le using samtools sort. Sorting the reads
by their genomic coordinates is an essential prerequisite for many downstream tools in the variant
calling pipeline, particularly those that rely on the positional context of reads to function correctly.
After sorting, the function runs GATK’s MarkDuplicates tool to identify and label duplicate
reads. These duplicates often arise during PCR amplication steps in library preparation and can
lead to articial ination of read depth and false variant calls if left unaddressed. The deduplicated
output le contains all reads, but those identied as duplicates are agged accordingly so that variant callers can ignore them during analysis.
This function is vital for ensuring the integrity of the data passed on to variant callers. By
sorting and marking duplicates, it minimizes technical artifacts and enhances the reliability of
variant detection. The deduplicated BAM le it produces becomes a clean and accurate representation of the sample’s aligned reads, suitable for base quality recalibration and condent variant
calling.
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