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BLAST ANALYSIS

Basic Local Alignment Search Tool (BLASTn & BLASTp)

The Beginner's Guide: The Google Search of Biology

Imagine you find a torn page from a book with a single sentence on it, and you want to know which book in the world it came from. Reading every book in existence letter-by-letter would take millions of years. This is the exact problem scientists face when they sequence an unknown piece of DNA.

BLAST is the ultimate search engine for biology. Instead of reading the entire database base-by-base, it uses a brilliant shortcut (a heuristic algorithm). It breaks your unknown DNA into tiny 11-letter "words." It rapidly scans the global database for an exact match to just one of those tiny words. Once it finds a match, it drops an anchor and tries to extend the match left and right to see how big the matching region is. This allows BLAST to search billions of DNA bases in mere seconds!

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1. Aim & Algorithmic Principle

To perform heuristic local sequence alignment using the BLAST algorithm to establish homology, infer evolutionary phylogeny, and predict the functional annotation of unknown query sequences.

The Mathematics of the E-Value

When you run a BLAST search, the most important number is the E-value (Expectation Value). It is not a p-value! The E-value tells you how many times you would expect to find a match of this exact quality entirely by random chance, given the current size of the database.

• An E-value of 10 means you'd expect to find this match 10 times by pure luck. (This is garbage data).
• An E-value of 0.0 means it is mathematically impossible for this match to be random. (This is a verified biological homolog).
• Note: Because the E-value depends on database size, the exact same sequence search will yield a slightly worse E-value next year, because the NCBI database will have grown larger, increasing the chance of random matches!

Algorithmic View: Seeding & Extension

Fig 1: The Heuristic Shortcut. BLAST extracts a tiny "Word" from your query and drops it like an anchor onto a matching spot in the global database. From that anchor, it scans left and right to see how far the local alignment extends, scoring matches and penalizing gaps!

2. The Interactive BLAST Terminal

To perform this lab experiment, you need to access the correct alignment algorithms. Click the live portals below to open the specific NCBI BLAST suite you need in a new tab!

BLASTn

Nucleotide → Nucleotide

BLASTp

Protein → Protein

BLASTx

Translated DNA → Protein

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3. The Protocol: Running the Algorithm

1. Input Data: Obtain your unknown sequence and format it strictly in FASTA format. Paste it into the large "Enter Query Sequence" box.
2. Database Selection: By default, it searches the nr/nt (Non-redundant) database, which contains all organisms. If you know your DNA came from a human, restrict the "Organism" box to Homo sapiens (taxid:9606) to drastically speed up the computation.
3. Algorithm Parameters:
   • Megablast: Use this if you are looking for highly similar, nearly identical matches (e.g., identifying a species from a barcode). Word size = 28.
   • Blastn: Use this if you are looking for distant evolutionary relatives across different species. Word size = 11.
4. Execution: Click the "BLAST" button. The NCBI supercomputers will queue your request and process it.
5. Analysis: Scroll down to the Descriptions Table. Sort the table by E-value (lowest is best).

Digital View: Base-by-Base Alignment

Fig 2: Scoring the Alignment. The algorithm rewards positive points for exact matches (vertical lines). It subtracts points (penalties) when there is a mutation/mismatch (Red X) or when a nucleotide was deleted causing a gap in the sequence (Yellow Dash). The final calculated sum is the "Bit Score".

4. Interpretation & Data Matrix

Metric	Definition	Target Value
E-Value	Statistical probability that this alignment occurred by random chance.	≈ 0.0 (Must be < 0.05)
Identity (%)	The percentage of exact letter matches in the aligned region.	> 95% (For same species)
Query Cover	How much of your inputted sequence was actually used in the alignment.	> 90%

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🧠 Deep Biotech Viva Quiz!

Tap the questions below to reveal the advanced answers examiners love to ask.

1. What does it mean if my BLAST result says "Low Complexity Region Masked"?

✅ Answer: It hid repetitive DNA to stop the algorithm from crashing.

Many eukaryotic genomes contain massive stretches of junk DNA that just repeat the same letters over and over (e.g., AAAAAAAAAAAAAAAAA). This is called a "Low Complexity Region". If BLAST tries to search for a string of A's, it will find millions of meaningless matches, crashing the server and destroying your E-value. By default, BLAST applies a "Mask" to these regions, turning them into N's or X's so the algorithm ignores them entirely!

2. When running BLASTp (Proteins), why do we use a BLOSUM62 Matrix?

✅ Answer: To score evolutionary amino acid substitutions.

In DNA, a mismatch is just a mismatch. But in Proteins, some mismatches are biologically acceptable! If evolution swaps a positively charged Arginine for a positively charged Lysine, the protein still functions perfectly. The BLOSUM62 (Blocks Substitution Matrix) is a massive scoring grid. It gives you positive points for matching identical amino acids, but it ALSO gives you partial points for "safe" evolutionary mutations, while heavily penalizing deadly mutations that destroy protein folding!

3. Can I have an alignment with 100% Identity but a terrible E-value?

✅ Answer: Yes, if your query sequence is too short.

If you input a tiny sequence, like "A-T-G-C-T-A", it will easily find a 100% exact, perfect match in the database. However, statistically, a random 6-letter sequence appears millions of times across all known genomes purely by mathematical chance. Therefore, despite the 100% Identity score, your E-value will be extremely high (e.g., 50.0), telling you that this match is biologically meaningless.