aphid

aphid implements hidden Markov models (HMMs) and profile HMMs in R for creating, training, visualizing, importing, and exporting statistical models for DNA, RNA, and protein sequence analysis.

Key Features:

• Model development and training: Derives and trains standard HMMs and profile HMMs to model probabilistic patterns in DNA, RNA, and protein sequences.
• Profile HMMs: Supports position-specific emission and transition parameters in profile HMMs for modeling sequence families.
• Visualization: Plots HMMs to display model states and transitions.
• Import/Export: Imports and exports models in multiple formats for interoperability with other bioinformatics tools.
• Efficient computation: Implements Viterbi, forward, and backward dynamic programming algorithms in C++ with parallelization for improved performance.

Scientific Applications:

• Sequence alignment: Uses profile HMMs to align sequences and identify homologous regions.
• Gene prediction: Models exon-intron boundaries and other genomic features for gene structure prediction.
• Protein family analysis: Classifies and analyzes protein families using profile HMMs.
• Phylogenetic studies: Supports analyses to infer evolutionary relationships among species or genes.

Methodology:

Derivation and training of standard HMMs and profile HMMs; plotting of HMMs; import and export of models; dynamic programming algorithms including Viterbi, forward, and backward implemented in C++ with parallelization; profile HMMs incorporate position-specific emission and transition parameters, and HMM states correspond to biological features such as nucleotides or amino acids.