Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

BASIC LOCAL ALIGNMENT SEARCH TOOL EXPERIMENT

A major part of the role of the Bioinformatician is to make sense of the massive amount of

genetics data accumulated in data warehouse such as NCBI, EMBL-ENA and DDBJ.

To address these important issues, bioinformaticians normally explore the relationships between

different species that are descended from a common ancestor. And then use any sequence

similarity they find to infer function and evolutionary relationships. A common method for

examining and comparing genes involves searching for similarities between newly sequenced

DNA and databases of gene sequences that have already been described.

• identify orthologs and paralogs

• discover new genes or proteins

• discover variants of genes or proteins

• investigate expressed sequence tags (ESTs)

• explore and examine protein structure and function

Activity 1: BLAST search

1. Download sequence from Blackboard=> BFG =>Learning Materials =>Week 2

2. Go to NCBI Blast at https://blast.ncbi.nlm.nih.gov/ -> click "nucleotide blast"

3. Copy and paste or upload the sequence into the ‘Enter Query Sequence’ box.

4. Set Parameters as follows:

a. Choose "Nucleotide collection (nr/nt)" as the search database. NR is the "non-

Redundant" database, which contains all non-redundant (non-identical)

sequences from GenBank and the full genome databases.

b. In "Program Selection" select Optimise for "Somewhat similar sequences

(blastn)"

5. Click the BLAST button to launch the search.

RESULTS:

1. What is the identifier (Accession)?

AK092514.1

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

2. What is the alignment score ("max score")?

832

3. What is the percent identity and query coverage?

The Percent identity is 96.68% and the query coverage is 58%.

4. What is the E-value?

0.0

5. Are there any gaps in the alignment?

Yes, 5/512 (0%)

Repeat steps 1 – 5. This time set step 6a to Human G+T:

1. What is the identifier (Accession)?

NM_001091.4

2. What is the alignment score ("max score")?

823

3. What is the percent identity and query coverage?

The Percent identity is 96.68% and the query coverage is 57%

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

4. What is the E-value?

0.0

5. Are there any gaps in the alignment?

5/512(0%)

Repeat steps 1 – 8. This time set step 6a to Mouse G+T:

1. What is the identifier (Accession)?

XM_030255662.1

2. What is the alignment score ("max score")?

515

3. What is the percent identity and query coverage?

The Percent identity is 84.23% and the query coverage is 54%

4. What is the E-value?

2e-143

5. Are there any gaps in the alignment?

4/482(0%)

6. What are the sizes (in basepairs) of the databases we used for the last two

BLAST searches? (Tip: Expand the "Search summary" section near the top by

clicking it).

For the Human G+T search, the size of the database is 3,864,692,881bps

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

For the Mouse G+T search, the size of the database is 3,237,895,141.

7. What is the ratio between the database sizes in the two BLAST searches?

3,864,692,881/3,237,895,141 = 1.194 ratio

The database size for the human BLAST search is About 19% bit bigger than the mouse one.

8. What is the ratio between the E-values (for the best human hits) in the two

BLAST searches?

For the human search the e-value is 0.0 while for the mouse the e-value is 2e-143.

The human e-value is more significant.

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.

9. What is the relationship between database size and E-value for hits with

identical alignment score?

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. So, if the database increases there is also a higher

probability of finding a match than in a smaller database

10. If the database size is doubled, what will happen to the E-value?

The e-value should be higher in larger databases compared to smaller ones.

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

Activity 2: Assessing the statistical significance of BLAST hits

As observed from preceding experiments, there is a risk of getting false positive results (hits

to sequences that are not related to our input sequence) by purely stochastic means. In this

section let’s investigate this further by examining what happens when we submit randomly

generated sequence to BLAST searches.

Generate your own random sequences:

1. Go to Random Sequence Generator at

http://www.bioinformatics.org/sms2/random_dna.html

2. Generate three DNA sequences of length 25bp

3. Copy and paste your three new DNA sequences in FASTA format in BLASTN

("nucleotide blast")

4. Select the option "Somewhat similar sequences (blastn)" in the section "Program

Selection".

5. Choose "Nucleotide Collection (nr/nt)"

NOTE: For this small artificial sequences reset some parameters. Otherwise we'll not be able to see

the intended results:

1. Go to Algorithm Parameters

2. Deselect the "Automatically adjust parameters for short input sequences" option.

3. Set the E-value cut-off ("Expect threshold") to 50

4. Click BLAST,

5. Select which of your query sequences to inspect in the drop-down box near the top of

the page

1. Do you find any sequences that look like your input sequences ? (paste in a few

example alignments in your report)

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

2. What is the typical length of the hits (the alignment length)?

The length is 100 bp.

3. What is the typical % identity?

Percent Identity: 100%

4. In what range is the bit-scores ("max score")? Note: This is conceptually the

same as the "alignment score"

The alignment max score is 42.1.

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

5. What is the range of the E-values?

0.50

6. What is the biological significance of these hits / is there any biological

meaning?

While the hits are real, they mean that they represent sequences that actually exist in the

database. However, we know that our query sequences are completely random, which

means that they have no evolutionary relation to the hits. Due to the pool of data being so

vast, the only reason we came across our hits was due to pure chance. Our sequence is

very small, consisting of only 25bp and so its size allows for a higher probability of matching

with other sequences in the database.

Activity 3: Protein sequences and BLASTP

Sequence Manipulation Suite: Random Protein Sequence at

http://www.bioinformatics.org/sms2/random_protein.html

Generate three protein sequences of length 25 aa :

• The distribution of amino acids will be equal (5% prob) and this is different from true

biological sequences - however this is not important for this first part of the exercise.

• The process for which BLASTP selects candidate sequences for full Smith-Waterman

alignment is different from BLASTN. (BLASTN - a single short (11 bp +) perfect match hit is

needed. For BLASTP - a pair of "near match" hits of 3 aa within a 40 aa window is needed).

1. Go to "Protein BLAST" page at NCBI and choose blastp .

2. Paste in the sequences in FASTA format, and choose the "NR" database (this is the

protein version, consisting of translated CDS'es, UniProt etc).

3. Set the parameters as follows:

a. In the "Algorithm Parameters" section select BLOSUM62 as the

b. alignment matrix to use

c. Set the "Expect threshold" to 1000 (default: 10)

d. DISABLE the "Short queries" parameters (otherwise your selected parameters will be

ignored).

4. Perform the BLAST search.

My Random Sequence:

>random sequence 1 consisting of 25 residues.

RHLSFHAPQWYQDHSQKRGAANFVS

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

1. How big is the database this time?

2. What is the length of the alignment, and do they contain gaps?

The length is 72 and it contains 0 gaps.

3. What is the E-values?

The evalue is 3.2.

4. Try to inspect a few of the alignments in details ("+" means similar sequences) -

do you find any that look plausible, if we for a moment ignore the length/E-

value?

Yes, some could be plausible however they are very short to be significant.

5. If we had used the default E-value cut-off of 10 would any hits have been

found?

No. E-values were in the range 1-50. If we had run BLASTN with an E-value threshold of

1000, we would have had many pages of hits for each query sequence.

Bioinformatics and Functional Genomics Student ID: 21485094

Lecturer: Dr. Obed Brew Beatriz Manso

6. If we compare the result from BLAST'ing random DNA sequences to random

Peptide sequences - which of search has the higher risk of returning false

positives (results that appear plausible, maybe even significant, but are truly

unrelated)? Remember to take E-values into your consideration.

With DNA sequences, you run a higher risk of getting a false hit (an unrelated sequence

with an "acceptable" E-value).

BLASTN uses an E-value cut-off of 50, while BLASTP uses 1000 in order to find any hits.