1. Independent scientist
Robert Edgar
Independent scientist
Multiple alingnment
and database search

2. Multiple alignment in 16S world
Curated 16S multiple alignments:
RDP
SILVA
Greengenes
MSA methods for 16s:
ARB
Infernal
NAST

3. Why multiple alignment for 16S?
Computational efficiency
Alignment is often most expensive step
Pre-computed alignment faster
Some claim more accurate than pair-wise
I disagree

4. Global alignment Most MSAs are global
Curated 16S alignments are global
CLUSTALW, MUSCLE, MAFFT, PROBCONS etc.
Approximate homology over full length
No rearrangements
Duplications, inversions, translocations
Models only these mutations:
Substitutions
Few short, non-overlapping indels

5. Issues with global MSA Rearrangements Tandem duplications
Churn (hyper-variable regions)
Scaling to large datasets

6. Domain rearrangements
Multiple alignment of different nicotin­amide nucleotide transhydrogenase sequences related to the bovine protein (NNTM). ENTHI, Entamoeba histolitica; EIMTE, Eimeria tenella; CAEEL, Caenorhabditis elegans; ACEAT, Acetabularia acetabulum; NEUCR, Neurospora crassa. The ancestor of the orthologs in the protozoan branch has undergone a circular permutation: note that the motif H-I-J appears on the left in the protozoans Entamoeba and Eimeria but on the right in the other species. Letters are used for brevity, the corresponding ProDom IDs are shown under the alignment.
Weiner J 3rd, Thomas G, Bornberg-Bauer E. (2005), Rapid motif-based prediction of circular permutations in multi-domain proteins. Bioinformatics Apr 1;21(7):932-7.

7. Tandem duplications
Short tandem duplications most common source of short insertions in human genome
No evidence for tandems in 16S
Common in proteins

8. Tandem duplications & arrays
Gamma crystallin, tandem duplication of greek key domain.
Ribonuclease inhibitor, tandem array of leucine-rich repeats.

9. Alignments with tandemsF T R E P R E P T E R N L E F T R E P R E P T E R N L E F T R E P T E R N L E F T R E P R E P T E R N
Tandems cause 1:n homology
Cannot be represented in conventional alignment format

10. What is a correct alignment?
Per-residue homology
Two residues aligned iff homologous
Homology can be ambiguous (churn)
Cannot be determined experimentally
Structural similarity
Two residues aligned iff same position in structure
Structural correspondence is fuzzy
Not well-defined...
...but can be useful if limitations are understood

11. Churn in hyper-variable regions
HISTORY
ALIGNMENT
Are B and E homologous?
A B C
Deletion
A B C
A - C
A E D
Substitution
A - D
A B - C
Insertion
A - E D
A E D

12. Churn in hyper-variable regions

13. Alignment by structure
Alignable Ambiguous Not alignable
Gradual transition from alignable to ambiguous.
Distantly related (low %id) structures are ambiguous
Methods disagree on alignments
Structure benchmarks cannot measure specificity
SABmark, FSA nonsense

14. Structure methods disagree
A Godzik (1996), The structural alignment between two proteins: is there a unique answer? Protein Sci Jul;5(7):

15. Homology but diverged structure
From SABmark benchmark [van Walle et al, 2004]
MUSCLE aligns conserved 10mer (red), allegedly incorrect

16. Protein MSA benchmarks
BALIBASE published in 1999
Started "benchmark war"
CLUSTALW has ~40k citations
PREFAB
Pair-wise structure alignments
SABmark
OXBENCH
Multiple structure alignments

17. Nucleotide MSA benchmark
BRALIBASE, based on solved RNA structures
Only credible nucleotide benchmark
Too "easy", hard to discriminate methods

18. MSA methods CLUSTALW (1994) T-COFFEE (1999) PROBCONS
Still most widely used
Newer methods definitively better
MUSCLE, (2004) MAFFT (2004) faster and more accurate
T-COFFEE (1999)
Pioneered consistency
Now PROBCONS (2004) is faster and more accurate
PROBCONS
Most accurate, but MUSCLE & MAFFT better scaling

19. Diminishing returns Last few years Many new methods
Claims: ~2% better on benchmarks
Validation problems, especially BALIBASE
Method A better than B only on average
CLUSTALW ≥ PROBCONS on 1/3 of sets
Is 2% real or significant in practice?
IMO not proven, dubious

20. PRANK
Published in Science

21. PRANK
PRANK less accurate than CLUSTALW

22. Significant advances in MSA since 2004

23. BALIBASE v3 90% aligned by sequence, not structure!
Some sets have zero or one structure
Aligned by sequence only
Not independent of sequence methods
Comparing my sequence method against theirs
Many structures have unclear homology
So gold standard sequence alignment not possible
Many regions are definitively not homologous

24. BALIBASE v3 Structures unknown except for SH2
Same domain in many sets, not independent (p-values)
Grossly violates global alignment assumptions
Some published validations use full-length sequences
Complete nonsense!
Edgar RC (2010)., Quality measures for protein alignment benchmarks, NAR 2010 Apr;38(7):

25. BALIBASE v3
Edgar RC (2010)., Quality measures for protein alignment benchmarks, NAR 2010 Apr;38(7):

26. Pair-wise or multiple? Multiple pros Multiple cons
Can be more accurate
Pre-computed alignment saves expensive step
Multiple cons
Can be less accurate
Accuracy degrades with number of sequences
Each new sequence adds ~Nε errors for some ε, total N2ε
Exponentially more difficult to reconcile diverged regions
Does not scale to very large datasets

27. When / why is multiple better?
Accuracy decreases with distance A C
Pair-wise A B C
Multiple
Transitive alignment
Intermediate sequences
Only if highly variable rates B A C

28. USEARCH Pair-wise alignments on the fly Scales to very large databases
New paradigm in database search
Edgar (2010), Search and clustering orders of magnitude faster than BLAST, Bioinformatics.
Fast heuristics identify top candidate hits
Finds top hit, or top few hits
Often 10s s x faster than BLAST
Heuristic local or global alignments
BLAST-like algorithm for alignment

29. drive5.com/usearch

30. Pair-wise vs. multiple Conserved Hyper-variable Conserved NAST MUSCLE
ACATGCAAGTCGAACGCTGAAGC-CCAGCTTGCTGGGTGG-AT GAGTGGCGAACGGGTGAGTAA
|||||||||||||||| || ||||||||||||||||||||
ACATGCAAGTCGAACG AAG CATCTTCGGATGCTTAGTGGCGAACGGGTGAGTAA
4 gaps, 2/6 identities
MUSCLE
ACATGCAAGTCGAACGCTG-AAGCCCAGCTTGCTGGGTGGATGAGTGGCGAACGGGTGAGTAA
|||||||||||||||| | | | | | ||||||||||||||||||
ACATGCAAGTCGAACGAAGCAT---CTTCGGAT----GCTTAG--TGGCGAACGGGTGAGTAA
4 gaps, 4/17 identities
USEARCH
ACATGCAAGTCGAACG AAGCATCTTCGGATGCTTAGTGGCGAACGGGTGAGTAA
|||||||||||||||| ||| | | || | | ||||||||||||||||||||
ACATGCAAGTCGAACGCTGAAGCCCAGCTTGCTGGGTGGATGAGTGGCGAACGGGTGAGTAA
1 gap, 9/18 identities

31. USEARCH speed and accuracy
RFAM test
RFAM database has ~200,000 RNAs
Classified into ~1,400 families
Extract 1,000 to use as query
Remainder is search database
True positive if hit in same family
False positive if hit in different family
Families may in fact be distantly related

32. Benchmarks at drive5.com

33. RFAM results

34. RFAM results

35. RFAM results