Sequence Analysis

Sequence analysis in molecular biology involves identifying the sequence of nucleotides in a nucleic acid, or amino acids in a peptide or protein. By analyzing these sequences, the scientists can extract knowledge about their properties, biological function, structure and evolution. To carry out sequence analysis efficiently, it is important to first understand the source of the data, i.e., the different experimental methods used for determining the biological sequence. We then need to follow analytical strategies, depending on whether the sequence is genomic, transcriptomic or proteomic. Information from sequence analysis is used in many fields of biology. It gives information on the relationship between individual organisms, or between groups of organisms.

Types

Analytical Method

• Dynamic Programming
  Dynamic programming is both a mathematical optimization method and a computer programming method. It is widely used in bioinformatics for tasks such as sequence alignment, protein folding, RNA structure prediction and protein-DNA binding. Recently these algorithms have become very popular in bioinformatics and computational biology, particularly in the studies of nucleosome positioning and transcription factor binding.
• Artificial Neural Network
  Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
• Hidden Markov Model
  A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process. It describes an important class of stochastic processes, also known as stochastic functions, which are processes that vary randomly over time.
• Support Vector Machine
  Support vector machines (SVMs, also known as support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis.
• Bayesian Network
  A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor.
• Sequence Mining
  Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence.

Analytical Tools

• BLAST
  BLAST is a sequence similarity search program developed by NCBI. The online BLAST comparison tool is commonly used for sequence comparison analysis and primer design. Blast finds the most similar sequence, or the most similar sequence fragment, to the input sequence in the database through pairwise comparison. It has four functional modules: Nucleotide BLAST, Protein BLAST, BLASTX, and TBLASTN.
• DANMAN
  DANMAN is a simple and commonly used nucleic acid sequence analysis software. It supports multiple sequence alignment, sequence homology analysis, restriction site analysis, PCR primer design, plasmid mapping and other functions.
• Clustal
  Clustal is a multiple sequence alignment tool based on progressive alignment. Clustal W can be used not only for multiple sequence alignments, which is the most commonly used, but also for profile-profile alignments and the construction of phylogenetic trees based on the neighbor-joining method.
• Muscle
  Muscle uses an iterative method for comparison operations. Every optimization process is an iterative process, and this error is corrected through continuous rearrangement using dynamic programming algorithms. The subgroups are compared simultaneously to obtain a global alignment of all sequences.

STEMart provides you with a variety of sequence analysis equipment or consumables to meet your various R&D and application needs. If you have any questions or requirements for sequence analysis, please feel free to contact us.