Hidden Markov Models in Sequence Analysis
Hidden Markov models are probabilistic frameworks for modeling sequence patterns in gene finding, alignment, and protein family classification.
K-mer Analysis: Sequence Composition and Frequency
K-mer analysis counts short substrings of length k in sequences for genome characterization, error correction, and metagenomic binning.
Motif Discovery: Finding Regulatory Patterns in Sequences
Motif discovery identifies short, conserved sequence patterns that represent binding sites or functional elements.
Multiple Sequence Alignment: Comparing Three or More Sequences
Multiple sequence alignment extends pairwise alignment to compare multiple homologous sequences for phylogenetic and functional analysis.
Primer Design for PCR and Sequencing
Primer design creates short oligonucleotides with optimal thermodynamics and specificity for PCR amplification and sequencing reactions.
Promoter Analysis: Identifying Regulatory Regions
Promoter analysis identifies transcription start sites and regulatory elements that control gene expression.
Sequence Alignment: Pairwise Comparison of Biological Sequences
Sequence alignment arranges DNA, RNA, or protein sequences to identify regions of similarity and evolutionary relationships.
Sequence Assembly: Reconstructing DNA from Fragments
Sequence assembly algorithms merge overlapping DNA fragments into contiguous sequences for genome and transcriptome reconstruction.
Sequence Database Search with BLAST
BLAST finds regions of local similarity between query sequences and database entries for functional and evolutionary inference.
