Incorporating evolution of transcription factor binding sites into annotated alignments

Area of Science:

• Genomics
• Bioinformatics
• Evolutionary Biology

Background:

• Identifying transcription factor binding sites (TFBSs) is crucial for understanding gene regulation.
• Current methods often treat sequence alignment and TFBS annotation as separate steps.
• The distinct evolutionary dynamics of TFBSs compared to flanking sequences are not adequately addressed by most tools.

Purpose of the Study:

• To develop a novel approach that simultaneously aligns sequences and annotates conserved TFBSs.
• To explicitly incorporate the evolutionary properties of TFBSs into the prediction framework.
• To improve the identification of conserved TFBSs by accounting for their unique evolutionary trajectories.

Main Methods:

• Extension of the SimAnn framework, which builds upon the Smith-Waterman algorithm.
• Introduction of additional states for profiles to generate annotated alignments, including gaplessly aligned TFBSs (pair-profile hits).
• Incorporation of two position-specific evolutionary models to explicitly model TFBS evolution within the framework.

Main Results:

• Demonstrated proof of concept in a simulated setting, validating the extended approach (eSimAnn).
• Compared eSimAnn to an existing multi-step tool using experimentally verified human-mouse binding sites.
• Analyzed the interplay between alignment and TFBS prediction across varying evolutionary distances and profile qualities.

Conclusions:

• The extended SimAnn framework (eSimAnn) effectively incorporates TFBS evolutionary relationships for improved conserved TFBS identification.
• Simultaneous alignment and annotation, coupled with explicit modeling of TFBS evolution, offers advantages over traditional multi-step methods.
• This approach enhances the understanding of regulatory element evolution and conservation across species.