DNA sequencing produces an enormous volume of raw data by fragmenting DNA or RNA into millions of small pieces. These fragments, known as reads, are typically short, ranging from 50 to 300 base pairs in length. Modern technologies do not read the entire genetic sequence in one continuous strand.
The raw output is a collection of short, random sequences representing small snapshots of the original molecule. This raw data is unusable until a rigorous bioinformatics pipeline transforms it into a coherent, organized structure. This process allows for biological interpretation.
Initial Data Quality Control and Preprocessing
The first step in any sequencing analysis pipeline involves assessing and improving the quality of the raw reads. The sequencing process can introduce errors, especially towards the ends of the reads. Raw data is typically stored in a FASTQ file format, which contains the sequence of the bases and a corresponding quality score for each base.
Quality assessment relies on the Phred Quality Score, or Q-score, which is a logarithmic measure indicating the probability that a base call is incorrect. A Q-score of 20 means there is a 1 in 100 chance the base is wrong, corresponding to 99% accuracy. Reads or bases with scores below an acceptable threshold, such as Q20, are filtered out or trimmed from the ends of the sequence. This ensures only highly accurate data remains for analysis.
Preprocessing also involves removing adapter sequences that were added to the DNA fragments during library preparation. These adapters are purely technical and must be computationally clipped off before downstream analysis. This rigorous cleaning step ensures that subsequent analytical stages are performed on reliable data.
Alignment, Mapping, and Read Organization
Once the reads are cleaned, the next challenge is organizing the short fragments back into their original genomic context. For organisms with a known genetic blueprint, this is achieved through mapping or alignment. Each short read is compared against a complete reference genome, which serves as a template to determine the exact location and orientation of the read.
Specialized algorithms quickly compare the reads to the reference genome, often using indexing techniques to speed up the search. These tools tolerate small differences between the read and the reference. This tolerance accounts for natural genetic variation and remaining sequencing errors. For RNA studies, reads must be aligned in a “splice-aware” manner to correctly identify junctions where RNA sequences skip non-coding DNA.
The result of mapping is an organized file indicating precisely where each short read belongs on the reference genome. This file is typically a BAM (Binary Alignment Map) file, which is a compressed, indexed version of the text-based SAM file. Organizing the data transforms the random fragments into a continuous, coverage-based view of the genome. This view is the foundational step for all subsequent biological discovery.
Extracting Biological Meaning
With the reads aligned, the final stage moves from raw data coordinates to biological conclusions. For DNA sequencing, this involves variant calling, which identifies positions where aligned reads consistently differ from the reference genome. These differences can be small, such as a Single Nucleotide Polymorphism (SNP) or a small insertion or deletion (indel). They can also be larger structural variations.
Variant calling software analyzes stacked reads using statistical models and Phred quality scores. This analysis distinguishes between true genetic variation and random sequencing error. After identification, the next step is annotation, which assigns functional context to the finding. Annotation tools determine if the variant occurs within a gene, changes the resulting protein sequence, or has been associated with a disease in public databases.
For RNA studies, the primary goal is quantification, which measures the level of gene activity or expression. This is achieved by counting the number of reads that map to a specific gene or transcript. A higher read count correlates to higher expression. The resulting expression levels, often normalized as Fragments Per Kilobase of transcript per Million mapped reads (FPKM), are then compared between different biological conditions.
The final output is a set of interpretable results, such as a list of genetic mutations or a table of differentially active genes. These results directly address the initial scientific question. This transformation from short fragments to a concise, annotated set of biological findings represents the ultimate goal of sequencing data analysis.