January 16, 2013 | Kazutaka Katoh*1,2 and Daron M. Standley1
This paper presents significant updates to the MAFFT multiple sequence alignment (MSA) program, including new features such as options for adding unaligned sequences, adjusting nucleotide alignment direction, constrained alignment, and parallel processing. The authors provide examples to illustrate how these features work individually and in combination, and discuss limitations and misalignments that can occur with certain methods. They also describe ongoing efforts to improve accuracy and address limitations. Key improvements include the ability to add sequences to existing alignments, which is particularly useful for large MSAs, and the --addfragments option for handling short reads. The paper highlights the trade-offs between accuracy and speed in different alignment strategies and provides detailed examples of how to use these new features effectively. Additionally, it discusses the integration of structural information into MSA calculations and the use of parallel processing to enhance performance.This paper presents significant updates to the MAFFT multiple sequence alignment (MSA) program, including new features such as options for adding unaligned sequences, adjusting nucleotide alignment direction, constrained alignment, and parallel processing. The authors provide examples to illustrate how these features work individually and in combination, and discuss limitations and misalignments that can occur with certain methods. They also describe ongoing efforts to improve accuracy and address limitations. Key improvements include the ability to add sequences to existing alignments, which is particularly useful for large MSAs, and the --addfragments option for handling short reads. The paper highlights the trade-offs between accuracy and speed in different alignment strategies and provides detailed examples of how to use these new features effectively. Additionally, it discusses the integration of structural information into MSA calculations and the use of parallel processing to enhance performance.