May 3, 2025
The genome is no longer an unreadable scroll, but a digital code waiting to be decrypted. DNA sequencing can make this possible as one of the most transformative revolutions in modern biology.
Did you know? In 2001, sequencing a full human genome was estimated to cost more than $95 million. In just 20 years, that sequencing cost had fallen more than 200,000-fold, to approximately $450. This seismic shift has propelled sequencing from specialized labs to routine research, fueling breakthroughs across precision medicine, cancer genomics, and drug discovery.
Today, the global sequencing market is projected to reach $97.8 billion by 2035. Mastering sequencing analysis has become an essential skill for biologists. If you’re looking to develop an in-depth understanding of DNA sequencing or refine your analysis pipeline, this guide will unpack:
DNA sequencing analysis involves determining the precise order of nucleotides within a DNA molecule. It’s a critical process for understanding genetic variations, gene function, and the molecular underpinnings of diseases. The introduction of high-throughput sequencing technologies has made DNA sequencing more accessible, faster, and affordable.
Quantum leaps in technology have driven the sequencing revolution:
Relatable: Basics and Applications of Next Generation Sequencing Technology
NGS generates vast amounts of raw data, but before any meaningful biological insights can be extracted, researchers must ensure the data is clean and reliable. This process begins with assessing sequencing quality, filtering out poor-quality reads, and trimming adapter sequences. Let’s break down the key steps in this crucial preprocessing phase:
FASTQ is the standard file format for storing sequencing reads along with their corresponding quality scores. Before proceeding to data analysis, it’s critical for researchers to assess the quality of the sequencing reads coming from high-throughput screening pipelines. Here are the key tools to examine:
A widely used tool that generates visual reports on key metrics:
MultiQC is an open-source Bioinformatics standard, providing built-in support for 150+ tools. It enables researchers to:
Example of a critical QC failure:
A sudden drop in quality scores at the 3’ end of reads suggests sequencing chemistry degradation—requiring trimming.
Sequencing reads often contain adapter sequences or low-quality bases at the ends, which can interfere with downstream analyses. Trimmomatic and Cutadapt are widely used tools to trim these unwanted sequences and improve data quality.
Let’s see how these trimming tools help remove these artifacts:
A flexible tool for trimming Illumina adapters and filtering poor-quality reads. Its common parameters are:
The tool specializes in adapter removal (useful for small RNA-seq or custom library preps). It can also handle barcode demultiplexing.
Demultiplexing is the process of recognizing barcodes and separating a pooled sample mixture into individual sample sequences, a crucial step in sequencing protocols that involve pooled samples.
Why trim?
After trimming, it’s important to reassess read quality to ensure that the data is clean and ready for alignment. This step significantly reduces errors in downstream applications like variant calling and gene expression analysis.
Researchers must re-run FastQC to confirm:
NGS generates millions of short DNA or RNA fragments (reads). To extract biological meaning, these reads must be accurately mapped to a reference genome—a process called read alignment.
Why Is It Important?
Selecting an appropriate reference genome is essential for accurate alignment. Researchers typically use GRCh38 for human genomes and mm10 for mice. This ensures that the reads align correctly and that variants are detected with high confidence.
The quality of alignment directly impacts the reliability of subsequent analysis. Popular alignment tools like Bowtie2, BWA, and HISAT2 are widely used due to their speed and accuracy.
Let’s break it down:
The alignment tools map the reads to the reference genome and generate SAM/BAM files, which store the aligned sequences.
These formats store the mapping positions of reads and include metadata like read quality, making it easier to manipulate and analyze large sequencing datasets.
Once reads are aligned, the next step is variant calling, where differences between the sample and reference genome are identified. Variants can be single nucleotide polymorphisms (SNPs) or insertions/deletions (indels).
Listed are the commonly used tools for calling variants and identifying potentially pathogenic mutations in the genome.
RNA sequencing (RNA-seq) is another critical application of sequencing analysis, providing insights into gene expression. It measures transcript abundance using aligned RNA-seq reads.
Tools like Kallisto use RNA-seq data to quantify gene expression levels across different conditions. Ideal for large-scale studies, it’s faster and can reveal gene activity, regulatory networks, and gene-environment interactions.
Sequencing analysis isn’t limited to DNA and RNA, providing critical insights into regulatory processes and genome organization. Here are two powerful techniques that enable researchers to explore complex genomic interactions:
Before starting sequencing analysis, researchers must follow these criteria:
Sequencing generates vast data, often in terabytes, so sufficient storage and computational power are crucial, for large projects like Whole-Genome Sequencing (WGS), cloud computing solutions are increasingly used to manage and process these datasets.
Accurate documentation of analysis steps, tool versions, and parameters is essential for reproducibility, especially in clinical settings. This ensures transparency, validation, and reliability of results, which is vital for research and diagnostic applications.
Recent innovations in sequencing technologies have pushed the boundaries of what’s possible.
In fact, the dominant player in NGS alone has been responsible for sequencing over 90% of the world’s genomes.
Source: Illumina
While Sanger sequencing was the gold standard for decades, its low throughput (max ~1,000 bases per run) makes it impractical for large-scale studies.
In contrast, NGS can process billions of reads simultaneously, reducing costs from 3 billion per genome to under $600 today, according to National Human Genome Research Institute (NHGRI).
However, Sanger remains crucial for clinical validation and small-target confirmation due to its unmatched precision.
Before we wrap up, let’s find out the mainstream applications of sequencing analysis.
The integration of sequencing analysis with multiomics technologies promises to push the boundaries of what's possible in understanding disease at a granular level. By combining genomic, transcriptomic, and epigenomic data, researchers are better equipped with deeper insights into disease biology and enabling more precise medical interventions.
WGS provides a complete genetic blueprint of an organism, covering both coding and non-coding regions. It’s used in rare disease diagnosis, population genetics, and evolutionary studies.
Limitations:
In fact, WGS can dramatically reduce the time spent and overcome the large costs associated with missed or delayed diagnosis.
Targeted sequencing, such as exome or panel sequencing, focuses on specific genes or regions, like exons, cancer hotspots, or pharmacogenomic markers. It is more cost-effective and efficient, making it ideal for clinical diagnostics.
For example, BRCA testing in breast cancer enables the identification of genetic mutations associated with increased cancer risk.
Applications:
Both these methods are integral to sequencing analysis, with applications ranging from genetic disease research to cancer diagnostics.
Biomarker Identification:
Sequencing enables the identification of genetic variants, such as mutations and deletions, that impact drug responses and disease development. Take, for example, it can predict responses to EGFR-targeted therapies based on mutations in the EGFR gene.
Drug Resistance Mechanisms:
Sequencing allows researchers to track the emergence of drug resistance mutations in diseases like HIV, TB, and cancer.
Besides, it also guides the development of next-generation therapeutics and improves patient outcomes.
Tumor Profiling:
Tumor profiling identifies driver mutations, which can lead to actionable therapeutic targets like PARP inhibitors or immunotherapy, guiding personalized cancer treatment strategies.
Examples of driver mutations:
Liquid biopsies, using circulating tumor DNA (ctDNA) sequencing, offer a non-invasive approach for cancer detection and minimal residual disease (MRD) monitoring by analyzing tumor-derived DNA fragments circulating in the blood.
Genetic Disorders & Rare Diseases: DNA sequencing plays a pivotal role in diagnosing genetic disorders, especially rare diseases that have eluded traditional diagnostic methods.
Epigenetics & Gene Regulation: DNA sequencing technologies are crucial in the study of epigenetics, particularly when combined with methylation sequencing (Whole Genome Bisulfite Sequencing - WGBS) and ChIP-seq. These technologies allow researchers to examine gene silencing mechanisms that occur due to DNA methylation and histone modification, which play key roles in diseases like imprinting disorders and Alzheimer’s.
Microbiome & Infectious Disease: Tracks pathogen evolution (e.g., SARS-CoV-2 variants) and gut microbiome dysbiosis in metabolic diseases.
Note: Huntington's disease (HD) is an inherited disorder that causes nerve cells (neurons) in parts of the brain to gradually break down and die.
Suggested Read: How Artificial Intelligence is Changing Healthcare and Its Uses
Sequencing analysis has already had a profound impact on biological research. As it continues to advance, the potential applications of NGS technologies will only expand, allowing for deeper and more precise exploration of genetic variations, disease pathways, and therapeutic targets.
At Biostate AI, we are committed to supporting the future of sequencing analysis by providing affordable, high-quality RNA sequencing services. Our platform is optimized for multiomics research, enabling you to gain actionable insights from complex biological data with precision and ease.
Whether you're conducting gene expression studies, exploring genomic variants, or working with multiomics data, our services ensure you can overcome the complexities of DNA sequencing analysis and bring your research to the next level.
Get a quote for your sequencing analysis today and partner with Biostate AI for accurate, reliable, and efficient results.
Sequencing analysis is the process of determining the sequence of nucleotides in a DNA molecule and analyzing this data to identify genetic variations, gene expression levels, and other biological insights.
Sequencing analysis allows researchers to identify specific genetic variations that influence how individuals respond to treatments, paving the way for personalized therapies tailored to each patient's genetic profile.
Common tools include:
Sequencing analysis helps identify mutations driving tumor progression, track clonal evolution, and develop targeted therapies for specific cancer subtypes.
Interpreting sequencing analysis results involves aligning reads to a reference genome, identifying variants, and analyzing gene expression or regulatory features using specialized software tools.
