ANDES

ANDES analyzes deep sequencing multiple sequence alignments (MSAs) to quantify nucleotide variation, detect polymorphisms, compute base conversion frequencies including transition/transversion rates, generate threshold-driven consensus sequences, and support inter-sample comparisons for applications such as 16S rRNA gene microbial genomics.

Key Features:

• Position Profile Data Structure: Captures nucleotide distributions across each genomic position from an MSA to represent sequence variability and conservation.
• Root Mean Square Deviation (RMSD) Plot: Produces RMSD plots to compare multiple samples on a position-by-position basis and identify deviations.
• Base Conversion Frequencies: Computes base conversion frequencies, including transition/transversion rates, to characterize mutation patterns.
• Variation Analysis (Shannon Entropy): Calculates Shannon entropy per position to quantify nucleotide variability and sequence diversity.
• Inter-Sample Clustering and Visualization: Performs clustering and visualization using dendrograms and multidimensional scaling (MDS) to reveal sample relationships.
• Consensus Sequence Generation and Polymorphism Detection: Generates threshold-driven consensus sequences and detects polymorphisms to identify predominant and variant alleles.
• Sequencing Quality Estimation: Estimates empirically determined sequencing quality values to provide measures of data reliability.

Scientific Applications:

• Microbial Genomics (16S rRNA gene): Analysis of deep sequencing biomarkers such as the 16S rRNA gene to detect subtle genetic variants in microbial studies.
• Detection of Intra- and Inter-Population Variation: Identification of subtle genetic variations within species and in mixed-species samples.
• Biodiversity, Evolution and Ecological Interactions: Supporting studies of biodiversity, evolutionary patterns, and ecological relationships through variant and diversity analysis.
• Comparative Analysis of Homogeneous Sequences: Comparative studies of high-depth homogeneous sequence datasets to assess sequence dynamics and conservation.

Methodology:

Implemented in Perl and R, ANDES constructs position profile data structures from MSAs and computes position-wise RMSD (with RMSD plots), base conversion frequencies including transition/transversion rates, Shannon entropy per position, inter-sample clustering via dendrograms and MDS, threshold-driven consensus sequence generation and polymorphism detection, and estimates empirically determined sequencing quality values.