1. Bioinformatics for Research
5/20/2018
Bioinformatics for Research
Module 1 Sequence Alignment
September 1, 2015
Mainlab Bioinformatics, Washington State University

2. Learning Outcomes
Understanding of what is sequence alignment and how is it useful
Understanding of the different types of sequence alignment and when you might use them
Understanding of the importance of homology
Understanding scoring matrices and when to use them.
Understanding that there are different alignment algorithms
Basic understanding of BLAST and it’s different flavors

3. What is Sequence Analysis ?
A sequence is ___________________________________
A biological sequence is __________________________ ______________________________________________
Sequence analysis in bioinformatics refers to__________ ______________________________________________ ______________________________________________ ______________________________________________ ______________________________________________ ______________________________________________

4. Concept of Sequence Alignment
An alignment is a mutual arrangement of two sequences
Shows where two sequences are similar, and where they differ
An ‘optimal’ alignment – most correspondences and the least differences
Sequences that are similar probably have the same function (descent from a common ancestor)
Sequence alignment involves the identification of the correct location of deletions and insertions that have occurred in either of the two lineages since divergence from a common ancestor.

5. Sequence Alignment
Sequence alignment is the procedure of comparing (at the residue level) two (pairwise alignment) or more sequences (multiple sequence alignment) by searching for a series of common features (characters or patterns) that occur in the same order in the sequences.
Sequence alignment is useful for discovering functional, structural and evolutionary information in biological sequences. =

6. Importance of Homology!
Homology strongly suggests that molecules have similar structure and function
Significantly similar molecular sequences are very unlikely to occur by chance.
Significant similarity between sequences infers that the sequences/structures are homologous i.e. at some point in the shared a common ancestor and therefore share structure and function.
Differences between families of species resulted from mutations during the course of evolution. Most of these changes are due to local mutations between nucleotide sequences

7. Orthologs
Sharing a common ancestor Orthologs : occur in separate species, common ancestor A1
Descendent 1
Ancestor A = A2
Descendent 2
Time

8. Paralogs
Sharing a Common Ancestor Paralogs : gene duplication independent of speciation (genome duplication)
Ancestor A1 A
Descendent A2 =
Time

9. Homology
Homology designates a qualitative relationship of common descent between entities
Two genes are either homologs or they are not !
It doesn’t make sense to say “two genes are 43% homologous” It doesn’t make sense to say “Jane is 43% diabetic”

10. Sequence Alignment
Changes in sequence may be found in alignments due to divergence from the common ancestor.
These changes are categorized as substitutions, insertions and deletions.
A primary use of sequence alignment is to determine if two sequences are sufficiently similar to declare them homologous and therefore likely to share similar structure and function
What else can we use sequence alignment for? ______________________________________ ______________________________________ ______________________________________ =

11. Sequence Alignment
What does sequence identity mean? ___________________ _________________________________________________
What does sequence similarity mean? __________________ _________________________________________________
What is sequence homology? _________________________ _________________________________________________ _________________________________________________ _________________________________________________ _________________________________________________ =

12. Sequence Alignment Methods
Method Use
Dot Matrix Plot General exploration of your sequence: Discovering Repeats Finding Rearrangements
- Predicting regions of self complimentary RNA
- Extracting portions of sequence to make a multiple alignment
Global Alignments Comparing two sequences over their entire length: Identifying long insertion/deletions
- Checking the quality of your data
- Identifying every mutation in your sequence
Local Alignments Comparing Sequences with partial homology:
- Making high quality alignments
- Making residue-per-residue analysis 12

13. Dot Matrix Plots
Also known as dot plots, they represent the simplest method of evaluating similarity between two sequences
Identifies all possible matches of residues between the two sequences.
One sequence (A) is listed horizontally (top of page) and the other sequence (B) vertically (left side of page).
Starting with the first character in B, the comparison moves across the row and places a dot in the plot space where both of the sequence elements are the same.
Adjacent regions of identity between the two sequences produce diagonal lines of dots in the plot.

14. Dot Matrix Plots
The diagonal line always appears when a sequence is compared to itself.
Can filter out random matches by using by increasing the window size
In a dot matrix, detection of matching regions may be improved by filtering out random matches.
This is done using a sliding window to compare the two sequences
Sliding windows
Window size: Number of characters to compare
Stringency: Number of characters that have to match exactly

15. Dot Matrix Plots Window size GAACTCATACGAATTCACATTAGAC
A larger window size is used for DNA sequences than for proteins because the number of random matches is much larger due to the use of only 4 DNA characters compared to 20 amino acid characters
For DNA sequences comparisons use long windows and high stringencies, e.g. 15 and 10.
For protein comparisons, use short windows and low stringencies except when looking for a short domain in a partially similar sequence. 15
GAACTCATACGAATTCACATTAGAC

16. Dot Matrix Plots
Try this with the example THEFATCAT as sequence 1 and THEFASTCAT as sequence 2. What does the result tell you? T H E F A T C A T T H E F A S T C A T

17. Sequence Analysis Dot Plot Programs http://emboss.bioinformatics.nl/
Dottup - Displays a wordmatch dotplot of two sequences
Dotmatcher - Draw a threshold dotplot of two sequences
Dotpath - Draw a non-overlapping wordmatch dotplot of two sequences
Polyplot - Draw dotplots for all-against-all comparison of a sequence set 17

18. Types of Sequence Alignment
Global: Alignments that stretch over the entire sequence length include as many matching residues as possible G G S D N W S A - T I P G G N – R A W A A M N P A
Used to align two closely related sequences over similar length
Useful for checking minor differences between two sequences, analyze polymorphism between closely related species, comparing two sequences that partially overlap
Needleman-Wunsch Algorithm

19. Types of Sequence Alignment
Local: Higher priority given to aligning local regions of high similarity rather than extending the alignment to neighboring residues with lower scores D T G A D T G A
Dynamic programming - Smith Waterman Algorithm (provides the best possible alignment, but slow!)
Heuristic methods - BLAST and FASTA use fast approximate methods to align two sequences.
Smith-Waterman Algorithm

20. Types of Sequence Alignment
Local Cont.
Heuristic algorithms are empirical (use rules of thumb to align)
Much faster than dynamic programming algorithms so better suited for database searches
Does not guarantee an optimal alignment like dynamic algorithms
Question – You have found a homolog to your unknown gene of interest using BLAST, what might you do to optimize the alignment?

21. Alignment Algorithms
Require a scoring system for evaluating match or mismatch of 2 characters (aa or nt)

22. Substitution Matrices
Likelihood of One Amino Acid Mutated into Another Over Evolutionary Time
Negative Score: Unlikely to Happen (e.g., Gly/Trp, -7)
Positive Score: Conservative Substitution (e.g., Lys/Arg, +3)
High Score for Identical Matches: Rare Amino Acids (e.g., Trp, Cys) 10

23. Scoring Matrices
There are several of them and the choice can affect the outcome
Values proportional to the probability one aa mutates into another
Can be based on chemical similarity, functional similarity, structure, evolutionary similarity etc
Common matrices for protein comparisons
PAM (Point Accepted Mutation) – based on global alignments of closely related proteins that are at least 85% identical and are based on an implicit model of evolution
PAM250 matrix is widely used
PAM is not necessarily good for identifying relationships in highly divergent species. Does not account for conserved blocks or motifs

24. Scoring Matrices Blossum Matrices Blossum62
Look only for differences in conserved, ungapped regions of a protein family
Directly calculated, using no extrapolations
More sensitive to structural or functional substitutions
Generally perform better than PAM matrices for local similarity searches (Henikoff and Henikoff, 1993)
Blossum62
Every possible identity and substitution is assigned a score based on the observed frequencies of such occurrences in alignments of related proteins
BLOSUM 62 is a matrix calculated from comparisons of sequences with no less than 62% divergence. Also Blossum 32 or 80
Default for BLAST

25. Scoring Matrices

26. Alignment Algorithms Require
A penalty function for gaps in sequences
A method for finding an optimal pairing of sequences (may introduce gaps to optimize the score)
A gap is a space introduced into one alignment to compensate for insertions and deletions in the sequences being compared
For each gap introduced there is a penalty and extending the gap further increases the penalty
AGGVLIIQVG
llllllxxxx
AGGVLIQVG-
lllllxllll
AGGVL-IQVG
llllllxlll
AGGVLI-QVG
Score the residue matches and score the residue gaps

27. Heuristic Algorithms FASTA: Based on K-Tuples (2-Amino Acid)
BLAST: Triples of Conserved Amino Acids
Gapped-BLAST: Allow Gaps in Segment Pairs
PHI-BLAST: Pattern-Hit Initiated Search
PSI-BLAST: Position-Specific Iterated Search What is a heuristic algorithm?

28. FASTA Algorithm at EBI

29. BLAST Algorithms BLAST (Basic Local Alignment Search Tool)
To search a sequence against the database
Extremely fast
Robust
Most widely used
It finds very short segment pairs between the query and sequence in the database
These segments are then extended in both directions until the maximum possible score of this particular segment is reached
Available at NCBI, EBI and many other community database sites

30. BLAST
A BLAST search has five components: query, database, program, search purpose/goal and results interpretation
Query: a sequence that you want to find out more information about
Database: need to know what databases are available (NCBI, EBI etc)
Program: what program to select to meet your specific purpose
Interpreting your results, what does it mean?

31. NCBI Protein Databases

32. NCBI Nucleotide Databases

33. NCBI Nucleotide Databases

34. BLAST Program Selection
Nucleotide queries
Protein queries

35. BLAST Program Selection
Specialized queries
Protein queries

36. Optional parameters in (blastn)

37. Basic Optional Parameters in BLASTP

38. Advanced Blast Parameters
The default parameters are not always the right parameters for your search, depends on your question
G Cost to open gap: default = 5 for nucleotides/11 for proteins
E Cost to extend gap: default =2 for nucleotides/1 for proteins
Q Penalty for nucleotide mismatch: default = -3
R Reward for nucelotide match: default = 1
E Expectation value: default = 10

40. BLAST Choice of Programs
MEGABLAST is specifically designed to efficiently find long alignments between very similar sequences and thus is the best tool to use to find the identical match to your query sequence
Discontiguous MEGABLAST is better at finding nucleotide sequences similar, but not identical, to your nucleotide query. This program uses non-contiguous word within a longer window of template. In coding mode, the third base wobbling is taken into consideration by focusing on finding matches at the first and second codon positions while ignoring the mismatches in the third position. Searching in discontiguous MEGABLAST using the same word size is more sensitive and efficient than standard blastn using the same word size

41. BLAST Choice of Programs
Search for short nearly exact matches" is useful for primer or short nucleotide searches. Short sequences (less than 20 bases) will often not find any significant matches to the database entries under the standard nucleotide-nucleotide BLAST settings. The usual reasons for this are that the significance threshold governed by the Expect value parameter is set too stringently and the default word size parameter is set too high. You can adjust both the word size and the expect value on the standard BLAST pages to work with short sequences
A common use of this page is to check the specificity of PCR or hybridization primers. A useful way to check a pair of PCR primers is to first concatenate them by inserting string of 20 or more N's in between the two primers, and then search the concatenated pair as one sequence. Since BLAST looks for local alignments and automatically searches both strands, there is no need to reverse complement the reverse primer before doing the concatenation or the search.

42. BLAST Choice of Programs
Use the Trace Archive BLAST page to search raw primary sequence trace files. The sequence data come from a variety of projects and sequencing strategies, including Whole Genome Shotgun (WGS), BAC end sequencing, and EST sequencing. The trace data are single pass sequencing reads not trimmed for quality or vector contamination. Their average lengths are between 500 to 700 bp.
Standard protein BLAST is designed for protein searches. Standard protein-protein BLAST (blastp) is used for both identifying a query amino acid sequence and for finding similar sequences in protein databases. Like other BLAST programs, blastp is designed to find local regions of similarity. When sequence similarity spans the whole sequence, blastp will also report a global alignment, which is the preferred result for protein identification purposes.
PSI-BLAST is designed for more sensitive protein-protein similarity searches. Position-Specific Iterated (PSI)-BLAST is the most sensitive BLAST program, making it useful for finding very distantly related proteins or new members of a protein family. Use PSI-BLAST when your standard protein-protein BLAST search either failed to find significant hits, or returned hits with descriptions such as "hypothetical protein" or "similar to...".

43. Database Search Questions
What database should I search?
What kind of sequences should I search with?
What E-value is significant?
What can I reliably infer about the function of my sequence based on homology?

44. Databases Bigger databases have more sequences.
Bigger databases are also more redundant, which can skew the statistics.
Bigger databases are also poorly annotated (homology with an "unidentified sequence" doesn't really tell you much)
Bigger databases take lots of time to search.
Smaller databases (like Swiss-Prot) are often better curated and annotated.

45. Databases Smaller databases are much less redundant.
Smaller databases can contain phylogenetically relevant sequences (all plant)
Smaller databases are much faster to search.

46. What is a significant E-value?
For a single search, an E-value of 10-3 is significant, though typically quite distant.
For multiple searches, the E-value cutoff varies according to the number of searches.

47. Multiple Sequence Searches
e.g. 15,000 EST query sequences
A 10-3 E-value cutoff means that you should expect one false positive in 1000 searches.
Thus with 15,000 searches, we should expect 15 false positives with a cutoff of 10-3.
To reduce the chances of identifying a false positive, set the E-value cutoff lower.
For 15,000 searches, an E-value cutoff of 10-5 will mean that you should expect 0.15 false positives.

48. Multiple Sequence Searches
In general:
DNA to DNA alignment
For nucleotide sequences at least 100 bp long, if 70% of your nucleotides are identical with your match sequence then they can be considered to be homologous
AA to AA alignment For amino acid sequences at least 100 aa long, if 25% of your aa are identical with your match sequence then they can be considered to be homologous.
Below these values, the alignments are considered to be in the twilight zone! 48