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Table 5 Prune results for twelve alignment problems from the Rfam database.

From: Meta-Alignment with Crumble and Prune: Partitioning very large alignment problems for performance and parallelization

  

Time

Agree.

Pecan1

 

_a

_a

Prune w/Pecan

60%

_a

_a

 

30%

14.6

0.651

 

15%

5.35

0.649

 

7%

2.57

0.643

FSA2

 

13.6

0.792

Prune w/FSA

60%

10.3

0.669

 

30%

4.30

0.615

 

15%

2.39

0.636

 

7%

2.17

0.636

MUSCLE3

 

3.67

0.709

Prune w/MUSCLE

60%

3.03

0.704

 

30%

1.23

0.649

 

15%

1.03

0.672

 

7%

1.42

0.659

MAFFT4

 

0.04

0.693

SATé5

 

93.9

0.753

  1. 1 Pecan was run with default parameters.
  2. 2 FSA was run with the --exonerate, --anchored, --softmasked, and --fast flags.
  3. 3 MUSCLE was run with default parameters.
  4. 4 MAFFT was run with --treein option.
  5. 5 SATé was run with the -t option but limited to two iterations. We found that more iterations did almost nothing for accuracy.
  6. aThe majority of problems were unable to be aligned due to running out of memory.
  7. The run-time and agreement score of Prune alignments of twelve RNA alignment problems from the Rfam database. The average time and agreement over all twelve problems are shown. Pecan, FSA, and MUSCLE were used as the underlying alignment method of Prune. MAFFT and SATé were also tested to provide comparison. We were unable to apply Pecan without using Prune because of memory issues. Using Prune, we were able to use Pecan to solve these alignment problems. Prune achieved a very large speedup with little loss of accuracy. Other alignment methods achieved a large speedup but more accuracy was lost.