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4.3 - Interpreting BLAST results

time

  • Teaching: 15 minutes
  • Exercises: 15 minutes

Objectives and Key points

Objectives

  • Learn how to interpret BLAST results
  • Understand the meaning of identity, coverage, e-value, and bitscore metrics

Keypoints

  • Interpreting the results of BLAST alignments requires thought and attention

Interpretting the results of BLAST queries

It is important to remember, like most bioinformatics tools, BLAST has a specific job. In this case sequence alignment to a set of references. BLAST is really good at this job, but it does not offer interpretation of its alignments.

Interpretation is completely up to the user on a case by case basis. It is therefore important to know your data and to understand the output metrics given by BLAST to help you make a biologically usefull interpretation of the results.

There are typically four main metrics that we need ot check when reviewing a BLAST result:

Key BLAST metrics

Coverage

  • This value tells us as a percentage how much of our query aligns with the database match.
  • A small coverage value means only a small part of our sequence has matched. A perfect match would have a coverage of 100.

Identity

  • This represents the percent of bases which are identical between the query and the database hit, over the aligned region.

E-value

  • This is the number of hits equivalent to this hit that we would expect to see by chance alone.
  • Smaller E-values represent better hits, but an exact E-value cut off needs to be decided on a case by case basis.
  • E-values take into account the coverage and identity scores for each hit, and also the size of the database queried.

Bit score

  • Similarly to E-values, bit scores summarise the sequence similarity between the query and database hit.
  • Bit scores are calculated independently from the query sequence length and the database size, as databases are constantly evolving this makes bit scores a constant statistical indicator.
  • A higher bit score indicates a better hit.

Examining our file

With this in mind lets look at the results from our BLAST job.

Return to your blast_annotation/ folder, if you left it, and examine the new output file. Take a look at your results using the less or head command.

code

head output.txt
Output 
seq1    gi|1607238104|dbj|AP019558.1|   91.750  1794    148     0       1       1794    366818  3650250.0      2494    Mycoplasma bovis KG4397 DNA, complete genome    28903
seq1    gi|1441442372|gb|CP022588.1|    91.695  1794    149     0       1       1794    690905  6926980.0      2488    Mycoplasmopsis bovis strain MJ4 chromosome, complete genome     28903
seq1    gi|1315670167|emb|LT578453.1|   91.695  1794    149     0       1       1794    655989  6577820.0      2488    Mycoplasma bovis isolate JF4278 genome assembly, chromosome: I  28903
seq1    gi|2507795645|gb|CP058524.2|    91.695  1794    149     0       1       1794    401349  4031420.0      2488    Mycoplasmopsis bovis strain Mb49 chromosome     28903
seq1    gi|2507793515|gb|CP058496.2|    91.695  1794    149     0       1       1794    320995  3192020.0      2488    Mycoplasmopsis bovis strain Mb222 chromosome    28903
seq1    gi|2507792460|gb|CP058473.2|    91.695  1794    149     0       1       1794    1055619 10574120.0     2488    Mycoplasmopsis bovis strain VK22 chromosome     28903
seq1    gi|2507791648|gb|CP058453.2|    91.695  1794    149     0       1       1794    9966    11759 0.0      2488    Mycoplasmopsis bovis strain Mb287 chromosome    28903
seq1    gi|2507791648|gb|CP058453.2|    91.695  1794    149     0       1       1794    1120377 11221700.0     2488    Mycoplasmopsis bovis strain Mb287 chromosome    28903
seq1    gi|2506302979|gb|CP058448.2|    91.695  1794    149     0       1       1794    763992  7657850.0      2488    Mycoplasmopsis bovis strain Mb1 chromosome      28903
seq1    gi|2506302187|gb|CP058432.2|    91.695  1794    149     0       1       1794    354705  3564980.0      2488    Mycoplasmopsis bovis strain Mb216 chromosome    28903

It looks like a table, but awkwardly there are no column names. However, the names of the columns correspond to the values we provided in our slurm script at the start of this session.

Column header Meaning
qseqid Sequence ID of the query sequence (input file)
sseqid Sequence ID of the target sequence (reference database)
pident Percentage of identical positions between query and target
length Alignment length (sequence overlap) of the common region between query and target
mismatch Number of mismatches between query and target
gapopen Number of gap openings in the alignment
qstart Position in the query sequence where alignment begins
qend Position in the query sequence where alignment ends
sstart Position in the target sequence where alignment begins
send Position in the target sequence where alignment ends
evalue The E-value for the query/target match, as described above
bitscore The bit score for the query/target match, as described above
salltitles Display All Subject Title(s) for the target sequence
staxids Display the NCBI taxid value(s) for the target sequence

Applying these to the layout, we get something more sensible:

Table layout
qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore salltitles staxids
seq1 gi|1607238104|dbj|AP019558.1| 91.750 1794 148 0 1 1794 366818 365025 0.0 2494 Mycoplasma bovis KG4397 DNA, complete genome 28903
seq1 gi|1441442372|gb|CP022588.1| 91.695 1794 149 0 1 1794 690905 692698 0.0 2488 Mycoplasmopsis bovis strain MJ4 chromosome, complete genome 28903
seq1 gi|1315670167|emb|LT578453.1| 91.695 1794 149 0 1 1794 655989 657782 0.0 2488 Mycoplasma bovis isolate JF4278 genome assembly, chromosome: I 28903
seq1 gi|2507795645|gb|CP058524.2| 91.695 1794 149 0 1 1794 401349 403142 0.0 2488 Mycoplasmopsis bovis strain Mb49 chromosome 28903
seq1 gi|2507793515|gb|CP058496.2| 91.695 1794 149 0 1 1794 320995 319202 0.0 2488 Mycoplasmopsis bovis strain Mb222 chromosome 28903
seq1 gi|2507792460|gb|CP058473.2| 91.695 1794 149 0 1 1794 1055619 1057412 0.0 2488 Mycoplasmopsis bovis strain VK22 chromosome 28903
seq1 gi|2507791648|gb|CP058453.2| 91.695 1794 149 0 1 1794 9966 11759 0.0 2488 Mycoplasmopsis bovis strain Mb287 chromosome 28903
seq1 gi|2507791648|gb|CP058453.2| 91.695 1794 149 0 1 1794 1120377 1122170 0.0 2488 Mycoplasmopsis bovis strain Mb287 chromosome 28903
seq1 gi|2506302979|gb|CP058448.2| 91.695 1794 149 0 1 1794 763992 765785 0.0 2488 Mycoplasmopsis bovis strain Mb1 chromosome 28903
seq1 gi|2506302187|gb|CP058432.2| 91.695 1794 149 0 1 1794 354705 356498 0.0 2488 Mycoplasmopsis bovis strain Mb216 chromosome 28903

Exercise

Download the output.txt file do your computer and open it in Excel. Look through the sequence annotations, and determine the most likely organism that each sequence was obtained from.

Are there are values in the results table that you are skeptical about? If so, raise them as a discussion with the group.

Solution
  • seq1 = Mycoplasma bovis
  • seq2 = Bactrocera ritsemai
  • seq3 = Pepino mosaic virus
  • seq4 = No hit
  • seq5 = Fusarium oxysporum (reversed)