blastn vs blastp: Which One You Should Use?
The BLAST (Basic Local Alignment Search Tool) is a highly used bioinformatics application in genomic analysis to find the region of similarities (to infer homology) between the biological sequences.
blastn and blastp
are a part of BLAST family tools, and are used for sequence similarity searching against a reference database.
The main difference between blastn and blastp is that blastn performs nucleotide search against a nucleotide
database, whereas blastp performs protein search against a protein database.
| Tool | Query sequence | Target database sequence | |
|---|---|---|---|
blastn |
Nucleotide | Nucleotide | |
blastp |
protein | Protein |
The blastn algorithm is commonly used when you want to find similarities between DNA or RNA sequences.
For example, you can use blastn to find similar genes or identify conserved regions in genomic DNA, mapping sequences to the genomes, etc.
blastn is less preferred if your goal is to identify the protein coding sequences for functional annotation. This is
due to multiple nucleotide sequences encoding the same amino acid sequence (degeneracy of the genetic code).
Further, in comparison to protein alignments, nucleotide alignments generally have high number of gaps.
blastp, on the other hand, can retrieve matches with fewer nucleotide similarities, making it more suitable for
finding identical amino acid sequences encoded by different nucleotide sequences.
Furthermore, when assessing homology, nucleotide alignments (blastn) typically require a higher percentage similarity
(generally >70%), while protein alignments (blastp) consider sequences as homologous with a percentage similarity of >30%-40%.
But, you should also assess the E-value and bit score (in addition to percentage similarity) for inferring the homology as sequences with < 30% percentage similarity are also homologous (when they have significant E-value).
References
- Pearson, W. R. (2013). An Introduction to Sequence Similarity (“Homology”) Searching. Current Protocols in Bioinformatics / Editoral Board, Andreas D. Baxevanis … [et al.], 0 3.
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