sequenceanalysis

Sequence analysis refers to computational methods used to examine ordered series of symbols or characters, such as biological sequences (DNA, RNA, proteins), time series data, or text. In bioinformatics, it encompasses techniques to identify homology, conserved regions, and functional elements, infer evolutionary relationships, and interpret genomic data. Common tasks include sequence alignment, where sequences are arranged to maximize similarity; multiple sequence alignment; and scoring using substitution matrices. Motif discovery seeks recurring patterns that indicate structural or functional roles. Sequence similarity measures, such as identity metrics and scoring schemes like BLOSUM or PAM matrices, support clustering and database searching. In phylogenetics, aligned sequences underpin tree construction to reveal evolutionary relationships.

Data formats such as FASTA and FASTQ, and alignment formats like SAM/BAM, underpin workflows. Widely used tools and methods include BLAST for local similarity search; Clustal and MUSCLE for multiple alignment; Bowtie, BWA for read mapping; and HMMER for profile hidden Markov models. For protein sequences, structure- and function-predictive analyses complement sequence-based approaches.

Applications span comparative genomics, functional annotation, metagenomics, transcriptomics, and proteomics. Challenges include handling large-scale datasets, repetitive or highly divergent regions, sequencing errors, and the interpretation of noncoding regions. Ongoing developments include scalable algorithms, improved probabilistic models, and integration with experimental data, enabling more accurate annotation and understanding of biological sequences.