Sequencebased

Sequencebased (often written as sequence-based or sequence based) is an adjective used to describe approaches that rely on information contained in the ordered sequence of elements, rather than on aggregate properties alone. In biology and bioinformatics, sequence-based methods analyze nucleic acid or amino acid sequences to infer identity, function, evolutionary relationships, or structural characteristics. This contrasts with structure-based or function-based approaches. Common sequence-based tasks include sequence alignment, motif discovery, homology detection, and phylogenetic analysis. Tools such as BLAST, FASTA, and various multiple sequence alignment algorithms enable comparisons across large data sets, supporting genome annotation and comparative genomics.

In data science and computer science, sequence-based analyses treat data as ordered sequences, such as time series, sentences, or event logs. Sequence-based models include Markov models, hidden Markov models, and neural networks designed for sequence data (such as recurrent neural networks and transformers). These methods are used for forecasting, language modeling, interpretation of biological sequences, and user behavior analysis, among others. The defining characteristic is the reliance on temporal or positional information encoded in the order of items.

Challenges include sequencing or data errors, uneven coverage, and biases that affect interpretation in biology, as well as overfitting and data sparsity in machine learning applications. The term is often written with a hyphen (sequence-based) but may appear as sequencebased in some texts. See also sequence analysis, sequence alignment, and time series analysis.