Bioinformatics helps us understand complex biological problems by investigating similarities and differences that exist at sequence. The first section provides an overview of biological sequences (nucleic acids and proteins). A complete NGS workflow to generate a consensus sequence and haplotypes is discussed. Deep sequencing is highly important due to its ability to address an increasingly diverse range of biological problems such as the ones encountered in therapeutics. Mol Biol Evol 17:189–197īaum LE, Petrie T (1966) Statistical inference for probabilistic functions of finite state Markov chains. This chapter focuses on several biological sequence analysis techniques used in computational biology and bioinformatics. Sequence analysis is incomplete without discussing next-generation sequencing (NGS) data. Bioinformatics 15: 122–130īruno WJ, Socci ND, Halpern AL (2000) Weighted neighbor joining: a likelihood-based approach to distance-based phylogeny reconstruction. J Mol Evol 25:351–360īucka-Lassen K, Caprani O, Hein J (1999) Combining many multiple alignments in one improved alignment. J Comput Biol 7:203–214įeng DF, Doolittle RF (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modem molecular genetics.

Smith TF, Waterman MS (1981) Identification of common molecular subsequences. Biological sequences generally refer to sequences of nucleotides or amino acids. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. Artificial Intelligence (AI) has recently had. This process is experimental and the keywords may be updated as the learning algorithm improves. The rapid increase in biological data dimension is a challenge for traditional computational analysis methods. These keywords were added by machine and not by the authors. The examples are used and elaborated in such a way that the algorithms can be easily understood by students with nonmathematical and nonstatistical background. Bioinformatics is now a necessary part of the tools and training of all. The chapter is organized in the form of basic questions that arise in the researchers’ mind and their step-by-step solution using important algorithms and statistical methods. Course Description: Biological sequence analysis (bioinformatics) is the study of the relationships between biological sequences and the implication of these relationships for macromolecular structure, function, and evolution. This chapter is an attempt to highlight some of the commonly used algorithms for the biological sequence analysis ranging from pairwise sequence analysis, multiple sequence analysis, phylogenetic analysis, and prediction of the probability of a desired motif in the sequence. With the increase in huge amount of biological sequence data from large genome and proteome sequencing projects, efforts have been made to develop computational algorithms and databases to manage the information.