Reduction, alignment and visualisation of large diverse sequence families

Area of Science:

• Bioinformatics
• Computational Biology

Background:

• Managing large volumes of sequence data from searches is challenging.
• Distinguishing relevant hits from unrelated sequences in raw search results is difficult.
• Existing clustering methods prioritize speed over comprehensive sequence alignment.

Purpose of the Study:

• To develop an efficient method for clustering and aligning large sequence datasets.
• To bridge the gap between rapid clustering and detailed multiple sequence alignment.
• To provide a clear visualization of sequence family relationships and identify inconsistencies.

Main Methods:

• The MULSEL method combines fast peptide-based pre-sorting with a cascade of mini-alignments.
• Representative sequences are selected based on intrinsic and user-specified criteria for iterative alignment.
• The method utilizes robust profile/profile alignment techniques.

Main Results:

• MULSEL effectively clusters and aligns large sequence families.
• The method generates a reduced, representative sequence collection.
• Execution on moderate datasets (tens of thousands) is rapid, completing in seconds to minutes on a laptop.

Conclusions:

• MULSEL offers a seamless transition from clustering to multiple sequence alignment.
• The graphical output aids in identifying misalignments and inconsistent sequences.
• This approach enhances the interpretability and reliability of large-scale sequence data analysis.