3 Biological Sequence Data

What sequence data represent

Biological sequence data represent DNA, RNA, or proteins as ordered strings of letters. Before analysis, identify what each sequence represents, check that its file format and alphabet are valid, and preserve important information such as identifiers and sequencing quality.

Common sequence formats

Sequence-only records

FASTA stores nucleotide or protein sequence letters. Each record starts with a header line beginning with >; the sequence follows and is often wrapped across lines. The first word in the header is commonly used as the sequence identifier. FASTA does not store per-base sequencing quality scores.

FASTQ is commonly used for sequencing reads. Each record has four lines: an identifier beginning with @, the sequence, a + line, and a string encoding one quality score per base. The sequence and quality strings must have the same length. FASTQ quality scores are often Phred-scaled quality scores: higher scores indicate a lower estimated probability of an incorrect base call. Confirm which quality-score encoding the software expects, since historical FASTQ files may use different encodings.

Records with annotations

GenBank and EMBL/ENA records can include sequence alongside annotations such as organism, features, and references. Use these formats when the record context matters; FASTA is often more convenient when a tool needs sequence alone.

Sequence types and alphabets

DNA typically uses A, C, G, and T; RNA uses A, C, G, and U. A sequence may also contain ambiguity codes when a base cannot be identified precisely. For example, N means any nucleotide. Keep legitimate ambiguity symbols when appropriate, and check the target tool’s accepted alphabet rather than replacing them automatically.

Protein sequences use one-letter amino-acid codes. Some records use X for an unknown residue; other special symbols may indicate ambiguity or a stop. Validate protein input against the intended analysis’s requirements, because some tools accept only a restricted set of residues.

Sequence identity also depends on the biological region represented. Do not mistake a transcript for genomic DNA or a coding sequence for a full transcript: these represent different biological regions.

Prepare and verify sequence data

Use this workflow to prepare sequence data while retaining information needed for analysis.

1. Inspect the file and records. Confirm the format, number of sequences, identifiers, and whether records are complete. Check for blank records, malformed headers, unexpected symbols, and accidental text or formatting characters.

2. Verify the sequence type. Check that DNA, RNA, or protein data use the expected alphabet, and distinguish the biological region represented, such as a transcript, genomic DNA, or a coding sequence.

3. Review read quality. For FASTQ data, inspect per-base quality patterns and identify low-quality reads or read ends. Trimming or filtering can help, but document the method and avoid removing data without a reason. Do not discard FASTQ quality scores by converting to FASTA unless the downstream task no longer needs them.

4. Screen for technical and biological problems. Look for adapter, primer, or vector sequence, contamination, sample mix-ups, and unexpected chimeras. These can mislead similarity searches and other analyses; use suitable screening tools and investigate suspicious results.

5. Normalize carefully. Remove whitespace or line wrapping only as required by the software, standardize line endings, and preserve sequence identifiers. Convert U to T only when the intended analysis specifically requires DNA-style notation. Keep the original data and record transformations.

6. Save and verify the prepared output. Use a format accepted by the next tool, retain a copy of the unmodified input, and check that the output parses correctly and has the expected sequence count and lengths.

Choosing a format for the task

A DNA FASTA record may be ready for a sequence search after its header and alphabet have been checked and unwanted adapter sequence has been ruled out. Raw sequencing reads, by contrast, should generally remain in FASTQ while base-call quality is still relevant.

In general, FASTA stores sequence letters, FASTQ stores reads with per-base quality scores, and annotated record formats add biological context. Reliable preparation checks format, sequence type, alphabet, quality, contamination, and identifiers while preserving the original data and documenting changes.