Algorithms for sequence analysis via mutagenesis

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Conventional DNA sequencing methods face limitations, failing to sequence certain DNA regions.
• These unsequenceable regions, present in various genomes including the human genome, can harbor critical genetic information.
• A novel technique, sequence analysis via mutagenesis (SAM), has been developed to address these sequencing challenges.

Purpose of the Study:

• To present algorithms for analyzing and interpreting data generated by the sequence analysis via mutagenesis (SAM) technique.
• To enable the sequencing of previously unsequenceable DNA regions.

Main Methods:

• The study introduces three core algorithms for sequence analysis via mutagenesis (SAM).
• Algorithm 1: Predicts the number of mutants needed for accurate target sequence inference.
• Algorithm 2: Infers the target DNA sequence from derived mutant sequences.
• Algorithm 3: Assigns quality values to each base in the inferred sequence.

Main Results:

• The developed algorithms effectively analyze and interpret data from the SAM technique.
• Demonstrated successful inference of target sequences using mutant sequences in laboratory experiments.
• Provided a method for assessing the accuracy and reliability of the inferred DNA sequences.

Conclusions:

• The presented algorithms significantly advance the capabilities of sequence analysis via mutagenesis (SAM).
• This approach offers a viable solution for sequencing challenging DNA regions, expanding genomic research possibilities.
• The algorithms provide a robust framework for interpreting SAM data, enhancing its utility in biological studies.