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Core Lectures > Sequence Similarity Searching > Flashcards

Sequence Similarity Searching Flashcards

1
Q

What is database structure determined by?

A

The requirements of designers/users

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2
Q

Complete this statement, databases can be local or…?

A

Remote

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3
Q

Complete this statement, querying can be manual or…?

A

Automated

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4
Q

What must providers such as NCBI/EBI balance across users?

A

Demand on computation resources

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5
Q

What does sequence similarity in DNA/proteins suggest?

A

Common ancestry

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6
Q

What might common ancestry imply?

A

Common function

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7
Q

What is the name given to homologs separated by a speciation event?

A

Orthologs

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8
Q

What is the name given to homologs separated by a duplication event?

A

Paralogs

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9
Q

Paralogs and orthologs are two types of homologous sequence, true or false?

A

True

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10
Q

What does the alignment or equivalencing of bases enable?

A

Maximisation of similarity

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11
Q

What could a database query look like?

A

Could simply be a sequence (DNA/protein)

Could be a logical structure, e.g. human + mitochondrial + HVS2

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12
Q

Why do sequence databases require specialised search tools?

A

Due to size and similarity

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13
Q

Is quantification of biological similarity easy or difficult?

A

Can be difficult

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14
Q

What can searching sequence databases for similar sequences predict about novel sequences?

A

Possible functions

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15
Q

What can alignments of sequences contain?

A

Mismatches and gaps

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16
Q

How are mismatches and gaps interpreted in sequence alignments?

A

As substitutions and indels respectively

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17
Q

What do alignment algorithms ideally try to identify about sequences?

A

The most likely evolutionary ‘path’ between sequences

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18
Q

What are databases?

A

Searchable collections of information

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19
Q

What does how we search databases depend on?

A

Database access, design and location

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20
Q

What does the quantification of sequence similarity require?

A

Alignment

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21
Q

What is the constant gap penalty?

A

Opening a gap of any size attracts a constant (a) negative score
= -a

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22
Q

What is the proportional gap penalty?

A

Opening a gap attracts a penalty proportional to its length (L)
= -(aL)

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23
Q

What is the affine gap penalty?

A

Opening a gap attracts a constant (a), extending it attracts a penalty (b) proportional to the gap’s length (L)
= -(a+bL) where a»b

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24
Q

What type of gap penalty is generally the most relevant biologically?

A

Affine

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25
Q

What does the choice of gap penalty depend on?

A

Software

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26
Q

What do amino acid side chains share?

A

Chemical properties (acidic/basic etc.)

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27
Q

What is the accepted theory about amino acid substitutions?

A

Chemically similar amino acids substitute more readily than chemically dissimilar amino acids

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28
Q

What is ‘built into’ amino acid substitution matrices?

A

Physico-chemical classification of amino acids

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29
Q

In the PAM250 (accepted point mutation) substitution matrix, what do similar amino acids score?

A

+ve score

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30
Q

In the PAM250 (accepted point mutation) substitution matrix, what do dissimilar amino acids score?

A

-ve score

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31
Q

What is the PAM250 (accepted point mutation) substitution matrix based on alignments of?

A

Closely related proteins

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32
Q

What are accepted point mutation substitution matrices extrapolated to?

A

Large (PAM120) and very large (PAM250) evolutionary distances

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33
Q

What is BLOSUM62 (blocks substitution matrix) based on alignments of?

A

Gap free alignments of short protein motifs (blocks)

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34
Q

What do the numbers represent in BLOSUM62 (blocks substitution matrix)?

A

Level of identity in alignments (BLOSUM62 = 62%)

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35
Q

BLOSUM is continually updated, true or false?

A

False, it is no longer updated but is still widely used

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36
Q

BLOSUM62 (blocks substitution matrix) has no extrapolation. What does this mean for distant relationships?

A

More reliable

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37
Q

What is the default amino acid substitution matrix of BLAST?

A

BLOSUM62 (blocks substitution matrix)

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38
Q

What are the types of BLAST searches and their uses?

A

Nucleotide query versus nucleotide database, i.e. what gene is this?
Protein query versus protein database, i.e. what protein is this?
Translated nucleotide query versus protein database, i.e. does this DNA sequence code for a known protein?
Protein query versus translated nucleotide database, i.e. can we identify a DNA sequence that might encode this protein?

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39
Q

What are the 4 sections in the results page of a BLAST search?

A 
Search information (including RID)
Graphical summary (conserved domain search)
Results table (hyperlinked to alignments)
Alignments (download links)
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40
Q

Databases contain more information than can be searched practically by observation, true or false?

A

True

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41
Q

Most databases are relational. What does this mean?

A

The data are organised into table with defined inter-relationships

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42
Q

Does the manual querying of remote databases require specialist knowledge?

A

Little

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43
Q

What might automated querying of local databases enable?

A

Greater throughput and flexibility

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44
Q

Cheaper hardware for databases can increase locally available resources but what does it also make quite costly?

A

Administration

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45
Q

What do highly similar (>80%) sequences probably share?

A

A common ancestor and thus probably are homologous

46
Q

Why is it necessary to quantify similarity of sequences?

A

To distinguish between ‘chance’ and ‘real’ similarity (common ancestry)

47
Q

What are alignments generally interpreted in terms of?

A

An explicit model of molecular evolution (substitutions and indels)

48
Q

Sequences are either homologous or not, true or false?

A

True, i.e. they either share a common ancestor or they do not

49
Q

Any two sequences can show similarity simply by ‘chance’, true or false?

A

True

50
Q

Is the alignment of pairs of long sequences computationally easy or intensive?

A

Intensive due to a large number of equivalences

51
Q

What do dynamic programming algorithms (DPAs) theoretically allow?

A

Exhaustive identification of optimal alignments

52
Q

Are DPA methods slow or fast for searching large databases?

A

Often considered too slow

53
Q

What can DPA methods find?

A

Optimal global or local alignments

54
Q

Give an example of a global algorithm

A

Needleman-Wunsch

55
Q

What do global algorithms try to find?

A

Similarity over the whole sequence

56
Q

Give an example of a local algorithm

A

Smith-Waterman

57
Q

What do local alignments try to find?

A

Just local regions

They do not try to align the whole sequence

58
Q

Which is the most biologically relevant, global or local alignment?

A

Local alignment

59
Q

Different algorithms will give similar alignments of the same sequences, true or false?

A

False, different algorithms can give very different alignments of the same sequences

60
Q

What are the pros and cons of exhaustive algorithms to find optimal alignments?

A

Accurate but slow

61
Q

Genes with shared common ancestors are homologous and so may be what?

A

Very similar in sequence

62
Q

Do exhausative mathematical algorithms find the alignment with the maximum or minimum similarity?

A

Maximum

63
Q

What does distinguishing between ‘real’ and ‘chance’ alignments require?

A

The comparison of some measure of similarity

64
Q

What do scoring procedures need to take into account?

A

Biology

65
Q

How can we measure similarity of sequences?

A

By scoring the alignment

66
Q

What do E-values enable?

A

Comparison between searches and provide an objective threshold for significance

67
Q

What can protein sequences identify?

A

More distant evolutionary relationships (depending on scoring matrix)

68
Q

What can DNA searches find?

A

Evolutionary close relationships

69
Q

BLAST is fast, but what might it miss?

A

Optimal alignments

70
Q

DNA and protein databases can only be searched effectively using what algorithms?

A

Heuristic algorithms (speed)

71
Q

How can we compare results when using different methods for alignment?

A

Using p values and E values

72
Q

What does E value stand for?

A

The expect value

73
Q

What do E values indicate?

A

How often a match at a given p value would be expected to occur in the database, by chance (i.e. when the sequences are unrelated)

74
Q

Should ‘real’ alignments between sequences that do share a common ancestor show more or less similarity than ‘chance’ alignments of sequences that do not?

A

Greater similarity

75
Q

What are the pros and cons of maximisation scores?

A

They ensure the best alignment but are not comparable

76
Q

Are default scores and comparing alignments good to use?

A

Generally good, but experimentation is possible

77
Q

What does the percentage of identically aligned residues allow?

A

Comparison of different alignments

78
Q

How is the percentage of identically aligned residues calculated?

A

The number of matches by the length of the alignment and multiplied by 100

79
Q

When scoring alignments, what is the assumption about nucleotides (G, A, T, C)?

A

They substitute equally for one another

80
Q

What type of selection are protein sequences under for structure and function?

A

Stabilising selection

81
Q

Are changes between chemically similar amino acids more or less likely to be deleterious?

A

Less likely

82
Q

When scoring protein alignments, particular substitutions can have different scores. What does this depend upon?

A

Their chemical similarity, e.g. LEU to ILE or PRO to TRP

83
Q

Almost any 2 sequences can be aligned by using what?

A

Gaps (indels)

84
Q

How is the quality of an alignment assessed?

A

Using a scoring matrix

Matches are +ve, mismatches are 0, gaps are -ve

85
Q

What do heavy gap penalties help the discovery of?

A

Biologically meaningful alignments

86
Q

What do gaps in a query represent?

A

Insertion/deletion events, relative to ancestor

87
Q

How are mismatches (substitutions) in an alignment scored?

A

Equivalent

G>T = G>C = -1

88
Q

Algorithms maximise the score of indels but what does biology indicate about indels?

A

That they should be rare

89
Q

What are the pros and cons of heuristic algorithms?

A

They are not guaranteed to find the best alignment but are much faster (by 5-20x)

90
Q

What is the main problem with DPAs?

A

They are too slow for large databases

91
Q

What does sequence database searching involve?

A

Aligning query to all database sequences and ranking ‘hits’ by similarity/quality

92
Q

What is the main advantage to using DPAs?

A

They are guaranteed to find the highest scoring alignment (but biological reality?)

93
Q

What does alignment allow?

A

Quantification of similarity between sequences

94
Q

What does the statistical theory of alignment enable?

A

P values to be calculated

95
Q

What is a p value?

A

The probability of observing as high scoring alignment between two unrelated sequences of similar length and composition

96
Q

What p value indicates a significant match?

A

p<0.05

97
Q

Why are E values greater than 0.01 unlikely to reflect real matches?

A

Since as good a match would be expected to occur at least 1% of the time, even for unrelated sequences

98
Q

E values are always calculated in the same way, true or false?

A

False

99
Q

How are E values calculated for BLAST?

A

E = pX

Where X is the total length of all the sequences in the database (treats the database as one large sequence)

100
Q

How are E values calculated for FASTA?

A

E = pN

Where N is the number of sequences

101
Q

What is the default word size (W) for BLAST?

A

3 for proteins

11 for DNA

102
Q

BLAST searches only for word matches above what?

A

A threshold, T

103
Q

What did early versions of BLAST only allow for?

A

Ungapped alignments (but T allows mismatches)

104
Q

What happens to high-scoring segment pairs (HSPs) along the BLAST query?

A

They are reported and ordered by score

105
Q

What do current versions of BLAST allow which improves its sensitivity?

A

Gapped alignments

106
Q

In BLAST searches, all matches above T are extended until what?

A

The introduction of gaps causes the alignment score to fall quickly

107
Q

Alignments are scored in order to quantify what?

A

Similarity

108
Q

What are the most common heuristic algorithms?

A

FASTA and BLAST

109
Q

Initial alignment hits are examined to see if they can be what?

A

Extended

110
Q

Generally speaking, what do heuristic methods do?

A

Break the query into short ‘words’ and look for matches above a threshold level in the subject database

111
Q

What do DPAs find?

A

A computationally optimal solution to the alignment problem

112
Q

What do heuristic methods assume?

A

That high scoring alignments contain short regions of exact matches

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