Realignment Methods

Realigners based on vertical and horizontal partitioning

1. RASCAL (2003)

The alignment is initially divided horizontally and vertically to identify well-aligned, reliable regions. Potential alignment errors are detected by comparing statistical models of the reliable regions. RASCAL then performs a single realignment of each badly aligned region using an algorithm similar to that implemented in ClustalW.

Partitioning steps:

1. The alignment is divided horizontally into sequence subfamilies using Secator

2. Then the alignment is divided vertically into 'core block’ regions that are reliably aligned in the majority of the sequences.

These global core blocks are determined using the mean distance column scores implemented in the NorMD OF. Local core block regions are also determined for each subfamily individually, using the same method as for the complete family.

• Test data: Protein (BALIBASE, ProDom)

• The source code link is unavailable.

2. Crumble and Prune (2011)

[vertical] Crumble deals with problems that are large in sequence length. It breaks up long alignment problems into shorter problems.

[horizontal] Prune handles problems with a large number of sequences. It cuts up deep alignment problems into sub-problems with fewer sequences.

Job-tree combine Crumble and Prune to align long and deep sequences.

• Test data: human genome, Rfam

• The source code link is unavailable.

Realigners based on vertical partitioning

Realigners based on horizontal partitioning

Realignment in MSA tools

Vertical-oriented realignment in MSA tools

Horizontal-oriented realignment in MSA tools