Analysis method and algorithm design of biological sequence problem based on generalized k-mer vector

K-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics.We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence.Problems,such as similarity calculations or sequence assembly,can be described in the k-mer vector space.It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory.In this study,we defined the k-mer vector space for the generalized biological sequences.The meaning of corresponding vector operations is explained in the biological context.We presented the vector/matrix form of several widely seen sequence-based problems,including read quantification,sequence assembly,and pattern detection problem.Its advantages and disadvantages are discussed.Also,we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods.It shows the practicability and convenience of this algorithm design strategy.

A Method for Bio-Sequence Analysis Algorithm Development Based on the PAR Platform

The problems of biological sequence analysis have great theoretical and practical value in modern bioinformatics.Numerous solving algorithms are used for these problems,and complex similarities and differences exist among these algorithms for the same problem,causing difficulty for researchers to select the appropriate one.To address this situation,combined with the formal partition-and-recur method,component technology,domain engineering,and generic programming,the paper presents a method for the development of a family of biological sequence analysis algorithms.It designs highly trustworthy reusable domain algorithm components and further assembles them to generate specifific biological sequence analysis algorithms.The experiment of the development of a dynamic programming based LCS algorithm family shows the proposed method enables the improvement of the reliability,understandability,and development efficiency of particular algorithms.

String kernels construction and fusion:a survey with bioinformatics application

The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of sequences.Therefore,the kernel method is significant in the solution of bioinformatics problems.Various kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish tasks.Mass biological sequence data occur in practical applications.Research of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in bioinformatics.The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction.This review focuses on the requirements of classification tasks of biological sequence data.It studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.