1. Large-Scale Multiple Sequence Alignment
Tandy Warnow
Founder Professor of Computer Science
The University of Illinois at Urbana-Champaign

2. 1kp: Thousand Transcriptome Project
G. Ka-Shu Wong
U Alberta
J. Leebens-Mack
U Georgia
N. Wickett
Northwestern
N. Matasci
iPlant
T. Warnow, S. Mirarab, N. Nguyen
UIUC UCSD UCSD
Plus many many other people…
Plant Tree of Life based on transcriptomes of ~1200 species
More than 13,000 gene families (most not single copy)
Gene Tree Incongruence
Challenge:
Alignments and trees on > 100,000 sequences

3. 1. 2 classes of MC: easy, moderate-to-difficult 2. true alignment
3. 2 classes: ClustalW, everything else
Alignment error, measured this way, isn't a perfect predictor of tree error, measured this way.
1000-taxon models, ordered by difficulty (Liu et al., Science 324(5934): , 2009) 3

4. Estimate ML tree on merged alignment
Re-aligning on a tree A B D C A B
Decompose dataset C D
Align subsets A B
Comment on subset size C D
Estimate ML tree on merged alignment
ABCD
Merge
sub-alignments

5. SATé and PASTA Algorithms
Tree
Obtain initial alignment and estimated ML tree
Estimate ML tree on new alignment
Use tree to compute new alignment
Alignment
Repeat until termination condition, and
return the alignment/tree pair with the best ML score

6. 1000 taxon models, ordered by difficulty, Liu et al
1000 taxon models, ordered by difficulty, Liu et al., Science 324(5934): , 2009
For moderate-to-difficult datasets, SATe gets better trees and alignments than all other estimated methods. Close to what you might get if you had access to true alignment.
Opens up a new realm of possibility: Datasets currently considered “unalignable” can in fact be aligned reasonably well. This opens up the feasibility of accurate estimations of deep evolutionary histories using a wider range of markers.
TRANSITION: can we do better? What about smaller simulated datasets? And what about biological datasets?
24 hour SATé-I analysis, on desktop machines
(Similar improvements for biological datasets)

7. 1000-taxon models ranked by difficulty
SATé-2 better than SATé-1
1000-taxon models ranked by difficulty
SATé-1 (Liu et al., Science 2009): can analyze up to 8K sequences
SATé-2 (Liu et al., Systematic Biology 2012): can analyze up to ~50K sequences

8. PASTA: even better than SATé-2
PASTA: Mirarab, Nguyen, and Warnow, J Comp. Biol. 2015
Simulated RNASim datasets from 10K to 200K taxa
Limited to 24 hours using 12 CPUs
Not all methods could run (missing bars could not finish)

9. 1kp: Thousand Transcriptome Project
G. Ka-Shu Wong
U Alberta
J. Leebens-Mack
U Georgia
N. Wickett
Northwestern
N. Matasci
iPlant
T. Warnow, S. Mirarab, N. Nguyen
UIUC UCSD UCSD
Plus many many other people…
Plant Tree of Life based on transcriptomes of ~1200 species
More than 13,000 gene families (most not single copy)
Gene Tree Incongruence
Challenge:
Alignments and trees on > 100,000 sequences

10. 1KP dataset: more than
100,000 p450 amino-acid
sequences, many fragmentary

11. datasets with fragments.
1KP dataset: more than
100,000 p450 amino-acid
sequences, many fragmentary
All standard multiple
sequence alignment
methods we tested
performed poorly on
datasets with fragments.

12. UPP
UPP = “Ultra-large multiple sequence alignment using Phylogeny-aware Profiles” Nguyen, Mirarab, and Warnow. Genome Biology, Purpose: highly accurate large-scale multiple sequence alignments, even in the presence of fragmentary sequences.

13. UPP Uses an ensemble of HMMs
UPP = “Ultra-large multiple sequence alignment using Phylogeny-aware Profiles” Nguyen, Mirarab, and Warnow. Genome Biology, Purpose: highly accurate large-scale multiple sequence alignments, even in the presence of fragmentary sequences.
Uses an ensemble of HMMs

14. Simple idea (not UPP)
Select random subset of sequences, and build “backbone alignment”
Construct a Hidden Markov Model (HMM) on the backbone alignment
Add all remaining sequences to the backbone alignment using the HMM

15. One Hidden Markov Model for the entire alignment?

16. One Hidden Markov Model for the backbone alignment?
HMM 1

17. Or 2 HMMs?
HMM 1
HMM 2

18. Or 4 HMMs?
HMM 1
HMM 2
the bit score doesn’t depend on the size of the sequence database, only on the profile HMM and the target sequence
HMM 3
HMM 4

19. Or all 7 HMMs? HMM 1 HMM 2 m HMM 4 HMM 7 HMM 3 HMM 5 HMM 6
the bit score doesn’t depend on the size of the sequence database, only on the profile HMM and the target sequence
HMM 3
HMM 5
HMM 6

20. UPP Algorithmic Approach
Select random subset of full-length sequences, and build “backbone alignment” with PASTA
Construct an “Ensemble of Hidden Markov Models” on the backbone alignment
Add all remaining sequences to the backbone alignment using the Ensemble of HMMs

21. Evaluation Simulated datasets (some have fragmentary sequences):
10K to 1,000,000 sequences in RNASim – complex RNA sequence evolution simulation
1000-sequence nucleotide datasets from SATé papers
5000-sequence AA datasets (from FastTree paper)
10,000-sequence Indelible nucleotide simulation
Biological datasets:
Proteins: largest BaliBASE and HomFam
RNA: 3 CRW datasets up to 28,000 sequences

22. RNASim Million Sequences: alignment error
Notes:
We show alignment error
using average of SP-FN and SP- FP.
 UPP variants have better alignment scores than PASTA.
 (Not shown: Total Column Scores – PASTA more accurate than UPP)
 No other methods tested could complete on these data

23. RNASim Million Sequences: tree error
Using 12 processors:
UPP(Fast,NoDecomp) took 2.2 days,
UPP(Fast) took days, and
PASTA took 10.3 days

24. UPP is very robust to fragmentary sequences
Under high rates of evolution,
PASTA is badly impacted
by fragmentary sequences (the same is true for other methods).
UPP continues to have good
accuracy even on datasets
with many fragments under
all rates of evolution.
Performance on fragmentary datasets of the 1000M2 model condition

25. UPP Running Time
Wall-clock time used (in hours) given 12 processors

26. What about BAli-Phy?
 BAli-Phy (Redelings and Suchard): leading method for statistical co-estimation of alignments and trees:
Like Bayesian phylogeny estimation, it is expected to be the most rigorous and accurate technique for estimating trees and alignments!

27. BAli-Phy: Better than PASTA!
Alignment Accuracy (TC score)
MAFFT
PASTA
BAli-Phy
40%
30%
20%
10% 0%
# Taxa:
Total-Column Score
100
Indelible (DNA)
200
100
RNAsim (RNA)
200
Simulator
Simulated datasets with 100 or 200 sequences.
*Averages over 10 replicates

28. But: BAli-Phy is limited to small datasets
From Too many taxa?
“BAli-Phy is quite CPU intensive, and so we recommend using 50 or fewer taxa in order to limit the *me required to accumulate enough MCMC samples. (Despite this recommendation, data sets with more than 100 taxa have occasionally been known to converge.) We recommend initially pruning as many taxa as possible from your data set, then adding some back if the MCMC is not too slow.”

29. Estimate ML tree on merged alignment
Re-aligning on a tree A B D C A B
Decompose dataset C D
Align subsets: MAFFT A B
Comment on subset size C D
Estimate ML tree on merged alignment
ABCD
Merge
sub-alignments

30. Re-aligning on a tree Decompose dataset Align subsets: BAli-Phy??
Comment on subset size C D
Estimate ML tree on merged alignment
ABCD
Merge
sub-alignments

31. Comparing default PASTA to PASTA+BAli-Phy on simulated datasets with 1000 sequences
Decomposition to 100-sequence subsets, one iteration of PASTA+BAli-Phy

32. Results on 10,000-sequence datasets, backbone size 1000
Comparing UPP variants where the backbone alignment is computed using either default PASTA or PASTA+BAli-Phy
Total Column Score
0.100
PAST
A+BAli−Phy Better
0.075
PASTA+BAli−Phy ●
data
0.050 ● ●
Indelible M2 RNAsim ● ● ● ●● ● ●
PASTA Better
0.025 ●
0.000
0.000
0.025
0.050
PASTA
0.075
0.100
Recall (SP−Score)
Tree Error: Delta RF (FastTree−2)
1.000
PAST
A+BAli−Phy Better
0.015
PASTA+BAli−Phy Bette ●r
0.975 ●
PASTA+BAli−Phy ●●
0.010 ●
● ● ● ●●
PASTA
0.950 ● ● ● ● ● ● ●
PASTA Better
0.005
● ● ●
PASTA Better
0.925
0.900
0.000
0.900
0.925
0.950
PASTA
0.975
1.000
0.000
PASTA+BAli−Phy
0.015
Results on 10,000-sequence datasets, backbone size 1000

33. Summary
Large-scale multiple sequence alignment (MSA) is achievable with good accuracy using divide-and-conquer plus iteration, and enable preferred MSA methods to be used on very large datasets
PASTA and UPP can provide good accuracy on large (million- sequence) datasets with high heterogeneity
Fragmentary sequences present additional challenges that UPP can address
PASTA and UPP at
PASTA+BAli-Phy at

34. Acknowledgments
PASTA and UPP: Nam Nguyen (now postdoc at UIUC) and Siavash Mirarab (now faculty at UCSD), undergrad: Keerthana Kumar (at UT-Austin)
PASTA+BAli-Phy: Mike Nute (PhD student at UIUC)
Current NSF grants: ABI (multiple sequence alignment)
Grainger Foundation (at UIUC), and UIUC
TACC, UTCS, Blue Waters, and UIUC campus cluster
PASTA, UPP, SEPP, and TIPP are available on github at see also
PASTA+BAli-Phy at