Bioinformatics Questions and Answers – Comparison of FASTA and BLAST

This set of Bioinformatics Multiple Choice Questions & Answers (MCQs) focuses on “Comparison of FASTA and BLAST”.

1. In FASTA, For a Z-score > 15, the match can be considered extremely ______ with _____ of a homologous relationship.
a) insignificant, uncertainty
b) significant, uncertainty
c) significant, certainty
d) insignificant, certainty
View Answer

Answer: c
Explanation: If Z is in the range of 5 to 15, the sequence pair can be described as highly probable homologs. If Z<5, their relationship is described as less certain.

2. BLAST uses a _______ to find matching words, whereas FASTA identifies identical matching words using the _____
a) substitution matrix, hashing procedure
b) substitution matrix, blocks
c) hashing procedure, substitution matrix
d) ktups, substitution matrix
View Answer

Answer: a
Explanation: BLAST and FASTA have been shown to perform almost equally well in regular database searching; However, there are some notable differences between the two approaches. The major difference is in the seeding step– BLAST uses a substitution matrix to find matching words, whereas FASTA identifies identical matching words using the hashing procedure.

3. Which of the following is not a benefit or a factual of FASTA over BLAST?
a) FASTA scans smaller window sizes
b) It gives more sensitive results
c) It gives less sensitive results
d) It gives results with a better coverage rate for homologs
View Answer

Answer: c
Explanation: By default, FASTA scans smaller window sizes. Thus, it gives more sensitive results than BLAST, with a better coverage rate for homologs. However, it is usually slower than BLAST.
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4. The use of low-complexity masking in the BLAST procedure means that it may have higher specificity than FASTA because potential false positives are reduced.
a) True
b) False
View Answer

Answer: a
Explanation: In addition to the given statement, BLAST sometimes gives multiple best-scoring alignments from the same sequence. FASTA returns only one final alignment.

5. Which of the following is not a benefit of BLAST?
a) Handling of gaps
b) Speed
c) More sensitive
d) Statistical rigor
View Answer

Answer: a
Explanation: In addition to this, user friendly UI of BLAST is also one of its benefits. However, it does not handle gaps well. In that case gapped BLAST is better.
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6. BLAST might not find matches for very short sequences.
a) True
b) False
View Answer

Answer: a
Explanation: In BLAST, similarity matching of words is involved. If no words are found similar, then no alignment is detected and hence it might not find matches for very short sequences.

7. BLAST often produces several short HSPs rather than a single aligned region.
a) True
b) False
View Answer

Answer: a
Explanation: The results of the word matching and attempts to extend the alignment are segments. They are called as HSPs (High-Scoring Segment Pairs). BLAST often produces several short HSPs rather than a single aligned region.
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8. FASTA is derived from logic of the dot plot.
a) True
b) False
View Answer

Answer: a
Explanation: Because of this, it computes best diagonals from all frames of alignment. The method looks for exact matches between words in query and test sequence.

9. The gapped portion in the diagonals represents matches in FASTA.
a) True
b) False
View Answer

Answer: b
Explanation: The diagonal’s nature indicates the matching of the sequences. After all diagonals are found, it tries to join diagonals by adding gaps. Further, it Computes alignments in regions of best diagonals.
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10. The initiation of FASTA format has ____ symbol.
a) >
b) <
c) /
d) *
View Answer

Answer: a
Explanation: Its format is simple as used by almost all programs. Header line has > at the beginning. Also no specific requirements are there for line length, characters, etc.

Sanfoundry Global Education & Learning Series – Bioinformatics.

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Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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