Discover published sets by community

Pairwise alignment concerns the positioning of two sequences to maximize their similarity. It is the first step towards any comparative analysis in bioinformatics.

Overlap alignment is designed for finding the best-matching substring between two sequences. It is often used in sequence assembly and read mapping.

Structural alignment aligns protein sequences based on their three-dimensional conformation. It is crucial for understanding the functional homology and evolution of proteins when primary sequence similarity is low.

Local alignment identifies regions of similarity within longer sequences that might be widely divergent overall. It is used to find conserved patterns within sequences.

Multiple Sequence Alignment (MSA) refers to the alignment of three or more biological sequences and is fundamental for various analyses. MSAs are useful for protein domain analysis, phylogenetic tree construction, and function prediction.

Global alignment is an alignment that spans the entire length of the sequences being compared. It is used when the sequences are believed to be of similar length and largely homologous.

Consensus alignment generates an alignment that represents all the sequences in a group, often derived from a multiple sequence alignment. It is a summarized representation of those sequences.

Profile alignment uses a profile of a multiple sequence alignment - a table scoring the likelihood of each possible residue at each position in the alignment - to align a new sequence. It allows for subtle patterns to be detected in highly variable sequences.

Semi-global alignment (also known as end-space free alignment) aligns sequences from end to end without penalizing gaps at the beginning and end. Suitable for sequences where prefixes or suffixes might be unaligned (e.g., gene sequences with different lengths).