sequenceprocessing

Sequence processing refers to computational methods for analyzing, transforming, and extracting information from ordered data sequences. It spans disciplines such as computer science, bioinformatics, signal processing, and music information retrieval. Sequences may be textual data (strings), numerical time series, genetic sequences (DNA, RNA, proteins), or symbolic melodies.

Core operations include indexing, slicing, normalization, filtering, and transformation. Typical tasks are pattern matching and subsequence search, alignment and similarity scoring, motif discovery, segmentation, and annotation. In genetics, sequence processing often involves quality control of raw reads, adapter and error trimming, assembly, alignment to reference genomes, and variant calling. In time-series and signal domains, preprocessing may involve resampling, smoothing, detrending, and spectral analysis. In text processing, sequence processing underlies tokenization, n-gram extraction, and sequence modeling.

Common techniques range from exact algorithms such as dynamic programming-based sequence alignment and edit distance to probabilistic models like hidden Markov models, and to signal processing methods including Fourier and wavelet transforms for periodic patterns. Efficient data structures, including suffix trees and suffix arrays for fast subsequence queries, are also used. Typical tools and libraries include general-purpose data processing ecosystems (NumPy, SciPy, pandas), bioinformatics libraries (Biopython), and domain-specific software for audio, genomic workflows, or text processing. Performance considerations include handling very long sequences, memory management, parallelization, and the use of approximate methods to balance accuracy and speed.

Applications span genomics, transcriptomics, text mining, music information retrieval, and sensor data analysis. See also sequence alignment, string processing, and time-series analysis.