What is BLAST Software

Introduction to Blast Software



1. BLAST (Basic Local Alignment Search Tool) was developed in 1989 at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH).
   BLAST uses statistical methods to compare a DNA or protein input sequence (a.k.a. “query sequence”) to a database of sequences (“subject sequences”) and return those sequences that have a significant level of similarity to the query sequence.
   Although BLAST is a complex set of programs with many functions and applications and a sophisticated statistical underpinning, its most popular use – using a DNA or protein sequence to query NCBI’s GenBank database – is fairly straightforward, thanks to NCBI’s BLAST web portal.
   


2. Imagine that you have obtained a DNA sequence as part of your research. For example, you may have obtained the DNA as a means of identifying a specimen, or in the process of attempting to clone a gene of interest (e.g., in order to learn more about its function, to produce a protein of interest in large quantities, or to produce a transgenic organism).
   In order to identify the specimen or confirm that you have obtained the correct DNA, you wish to compare your DNA sequence to a database of known sequences.
   GenBank is the NIH database of publicly-available nucleotide sequence data (daily exchange of data with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures global coverage and consensus across the major databases), and currently includes over 108 million sequences from over 260,000 named organisms – a search of GenBank is therefore a great place to look for the identity of your unidentified DNA sequence.
   


3. The BLAST algorithm calculates similarity scores for local alignments (i.e., the most similar regions between 2 sequences) between the query sequence and subject sequences using specific scoring matrices, and returns a table of the best matches (“hits”) from the database.
   The hit table includes several useful pieces of information, including the similarity score, query coverage (percent of the query sequence that overlaps the subject sequence), E-value (see below), and max identity (percent similarity between the query and subject sequences over the length of the coverage area).
   


4. The Expect value (e-value) of the hit is defined by NCBI as follows: “The Expect value (E) is a parameter that describes the number of hits one can ‘expect’ to see by chance when searching a database of a particular size. It decreases exponentially as the Score (S) of the match increases. Essentially, the E value describes the random background noise. For example, an E value of 1 assigned to a hit can be interpreted as meaning that in a database of the current size one might expect to see 1 match with a similar score simply by chance. The lower the E-value, or the closer it is to zero, the more ‘significant’ the match is.”


5. 
   Exercise
   
   Imagine that you are a researcher involved in the assessment of the spread of Phytophthora ramorum from imported plant material.
   Customs officials have stopped a shipment of plants that show signs of P. ramorum infection, though the plant is not a previously-known host.
   Customs has asked you to determine whether the plants are infected with P. ramorum. From the infected leaf material you have obtained a culture of an organism that appears to be a Phytophthora, but the culture is not sporulating so you cannot identify the organism through visual inspection.
   You therefore decided to extract DNA from the culture, use PCR to amplify the DNA encoding the nuclear ribosomal internal transcribed spacer region (ITS; a region of DNA commonly used for ‘DNA barcoding’ in fungi and fungal-like organisms), and obtain the sequence of the ITS DNA.
   The content of the DNA sequence is stored in the file “unknown_DNA_for BLAST.txt”
   
   

Further Reading

1. NCBI Handbook: BLAST.
http://www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=handbook&part=ch16#A614
2. NCBI: The Statistics of Sequence Similarity Scores.
http://www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-1.html
3. Korf I, Yandell M, Bedell J. 2003. BLAST. O'Reilly Media. 368 pp.

Instructions
1. Examine the file “unknown_DNA_for BLAST.txt,” in the desktop folder containing the course files. The file is in FASTA format. The first line (starting with a greater-than symbol) includes a description of the sequence, and the following lines include the sequence itself.
2. Navigate to the NCBI BLAST website:
   http://blast.ncbi.nlm.nih.gov.
3. Under “Basic BLAST,” choose the “nucleotide blast” option, which will take you to the nucleotide BLAST data entry form.
4. Under “Enter Query Sequence,” use the “Browse” button (below the large white data entry window) to upload the file “unknown_DNA_for BLAST.txt” from the desktop folder containing the course files.


5. Under “Choose Search Set,” select the radio button corresponding to “Others (nr, etc.).
6. Select the blue “BLAST” button in the lower left-hand corner.


7. With the assistance of your instructor, examine the 4 parts of the BLAST output – the header, Graphic Summary, Descriptions, and Alignments.
   What is the closest match to your “unknown” sequence, and how good is the match? Is it likely that true sequence homology exists (i.e., that the hit didn’t occur simply by chance)?



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