Unveiling Bioinformatics Algorithms: The Backbone of Modern Biology

Introduction

Bioinformatics has dramatically brought a change in the biological field because it makes it possible for the researchers to manage and analyze huge amount of biological information. Notably, in the middle of bioinformatics, there is a myriad of algorithms that enable data analysis to unravel time-pace life’s intricacies. This blog post will provide you with a taster of bioinformatics algorithms so that you are ready for our thorough rundown on the most important algorithms in this burgeoning field.

What are Bioinformatics Algorithms?

Bioinformatics algorithms have been described as computation approaches that are used in handling biological problems. They can be categorized from the basic easy to implement method to the complex difficult to adopt method. These algorithms are useful in string matching, comparative genomics, molecular biology, and bioinformatics among others. They make a raw biological data understandable which can be used to discover new things and develop new ideas in different areas like genomics, proteomics, and transcriptomics.

Why Is Algorithm Necessary in Bioinformatics?

• Data Analysis: In recent years due to emergence of high through put technologies, biological data has sharply increased. As will be seen, the use of algorithmic perspective makes it easier to understand this data. For example, sequencing technologies result in huge data sets that must be handled inexpensively and efficiently by powerful algorithms.

   

• Accuracy and Precision: Mathematical computations are something for which the algorithms offer exact and very accurate degree of solution which is very vital in biological sciences and medicine. They aid in diagnosing genetic disorders, and determining the likely structures of proteins, and the general makeup and function of genes, useful to pharmacologists in formulating precision medicine.
   

• Automation: They take a lot of time and energy to do and they can efficiently be handled by the software while the researcher can work on other important segments of their research. For instance, automating sequence alignment is far likely to be efficient than manually aligning the DNA molecules.
   

• Discovery: Biological data analysis with the help of algorithms reveals new patterns and connections that weren’t previously noted. For instance, clustering can help identify distinct types of the disease based on the sample clustering by gene expression.

Types of Bioinformatics Algorithms

Sequence Alignment Algorithms:

• Dynamic Programming: It is employed in algorithms such as Needleman-Wunsch and Smith- Waterman which are used in global and local alignments. These algorithms will apply a scoring matrix to identify the proper alignment for matches, mismatches, and gaps. It should be noted that while the Needleman-Wunsch algorithm takes an operation and aligns sequences from the beginning to the end like in a human readable document, the Smith-Waterman algorithm also aligns sequences, although it focuses more on regions of the sequences seen as blocks.

   

• Heuristic Methods: Used in the tools like BLAST (Basic Local Alignment Search Tool) and FASTA in order to get faster alignment of large sets of data. Heuristic methods work by providing approximations or short cuts that allow a solution to be returned quickly with high score for sequence alignments without having to necessarily get the best solution. BLAST, for instance, starts with the search for the shared areas of similarity through the use of small sequences known as “words” and only then it tries to expand the match’s area.

Machine Learning Algorithms:

• Supervised Learning: Models like SVM and the Random Forest models used in the classification and regression analysis. Using training data which is usually labeled, these algorithms are trained to predict on new data. SVMs detect the right hyperplane which keeps different classes of data in different sides while Random Forests, which are a type of decision trees, improve the accuracy of mathematical predictions.

   

• Unsupervised Learning: K-means and hierarchical clustering to cluster data points that have no prior knowledge of classes; it aids in identifying potential structures within data sets. K-means clustering is a method of splitting the data into ‘K’ clusters depending on the similarity whereas hierarchical clustering construct a tree of clusters depending on the similarity of the data.

Graph Algorithms:

• Shortest Path Algorithms: Applied in social network to determine the simplest route between the nodes. Dijkstra’s and A* are some of the most recognized algorithms out there. These algorithms are very important in deciphering of the relations and connections in biological networks.

   

• Traversal Algorithms: Breadth-First Search (BFS) and Depth-First Search (DFS) used for searching graphs as important for any analysis such as pathway and motif identification. While BFS visits all nodes at the present depth before moving to the next level of depth, a similar thing cannot be said for DFS because it tries to go as deep as possible before it goes back.

Phylogenetic Tree Construction Algorithms:

• Distance-Based Methods: Such categories as Neighbor Joining dedicated for construction the phylogenetic trees based on distances matrices. These methods employ certain linkages of sequences according to the evolutionary distances, and, therefore, can give an idea of the relation of the given species.
   

• Character-Based Methods: Cliadistics and cladistic analysis in terms of Maximum Parsimony and Maximum Likelihood methods for creating trees from character data subject to certain conditions regarding the tree’s structure. Maximum Parsimony aims at finding the most rooted tree with the least number of evolutionary events, while Maximum Likelihood is statistical in nature and finds the tree that has the highest likelihood of giving the data observed.

Optimization Algorithms:

• Genetic Algorithms: Similar to natural selection used in the optimization problems related to bioinformatics. Organisms develop solutions through generations to get the best result. They are applied in protein folding simulation and the synthesis of the best molecules for use in pharmaceuticals.
   

• Simulated Annealing: A stochastic method to estimate the global optimum of a given function in the problem such as protein folding and molecular docking. The given realization is similar to the method of slowly cooling the metal to eliminate defects, that is why it is called simulated annealing, with the aim to get through all the probable solutions and gradually move to the best configuration.

Statistical Algorithms:

• Expectation-Maximization (EM): Employed when estimating parameters restricted by dependencies between the observed indices in cases of latent variables, typical in the mixture models. The EM algorithm repeatedly computes the probability of the missing data (expectation step) and Adjust the parameters (Maximization step) until they reach an optimum.

   

• Bayesian Inference: Techniques of estimation for performing inference in the context of the Bayesian approach to statistical modeling. Bayesian analysis is the technique in which it uses prior belief with the data collected and integrates to enhance the credibility of an hypothesis, which finds application in bioinformatics and some of them include: genetic association analysis and gene expression.

Applications of Bioinformatics Algorithms:

• Genomics: Predicting the genes, regulatory elements, and SNP from the genome sequences for the organism. It facilitates sequence assembly, variant identification and labeling, as well as disease/phenotype associated gene identification.

   

• Transcriptomics: Using the gene expression profiles for deciphering the development and progression of diseases as well as cellular functions. Both the processes like sequence reads alignment to reference genome and gene expression quantification in RNA-Seq are dependent on the algorithms in bioinformatics.

   

• Proteomics: Thus, the identification and quantification of the proteins to understand their roles and relationships. There are applications in the mass spectrometry data analysis of proteins, identification of proteins and their relative quantification in order to determine the proteome differences between the various biological states.

   

• Structural Biology: Biomolecule shape prognosis and structure analysis on atomic and molecular level. Molecular modeling, docking, and dynamics simulations through algorithms facilitate the study of the relationship between the structure of proteins and other macromolecules and their function.

   

• Systems Biology: Implementing and simulating system models as a means of evaluating and predicting the systems’ behavior. There are algorithms that combine the data from different omics types to reconstruct and analyze the biological networks: network analysis and pathway modeling.

Conclusion

Bioinformatics algorithms are the cornerstone of contemporary molecular biology; they help researchers to manage and interpret huge datasets obtained with the help of omics technologies. Over the course of this blog series, we shall explore the fundamental algorithms in bioinformatics and give you all need-to-know information on their workings, uses, and relevance. Check our next post in which we will describe the dynamic programming algorithms for sequence alignment in more details.

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Key References for Further Reading

• Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of molecular biology, 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

   

• Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of molecular biology, 48(3), 443–453. https://doi.org/10.1016/0022-2836(70)90057-4

   

• Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of molecular biology, 147(1), 195–197. https://doi.org/10.1016/0022-2836(81)90087-5

   

• Pearson, W. R., & Lipman, D. J. (1988). Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences of the United States of America, 85(8), 2444–2448. https://doi.org/10.1073/pnas.85.8.2444

   

• Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297.http://dx.doi.org/10.1007/BF00994018

   

• A. K. Jain, M. N. Murty, and P. J. Flynn. 1999. Data clustering: a review. ACM Comput. Surv. 31, 3 (Sept. 1999), 264–323. https://doi.org/10.1145/331499.331504

   

• Dijkstra, E.W. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959). https://doi.org/10.1007/BF01386390

   

• Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms (3rd ed.). MIT Press.

   

• N Saitou, M Nei, The neighbor-joining method: a new method for reconstructing phylogenetic trees., Molecular Biology and Evolution, Volume 4, Issue 4, Jul 1987, Pages 406–425, https://doi.org/10.1093/oxfordjournals.molbev.a040454

   

• Felsenstein J. (1981). Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of molecular evolution, 17(6), 368–376. https://doi.org/10.1007/BF01734359

   

• Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press. \

   

• Kirkpatrick, S., Gelatt, C. D., Jr, & Vecchi, M. P. (1983). Optimization by simulated annealing. Science (New York, N.Y.), 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671

   

• Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977), Maximum Likelihood from Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39: 1-22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

   

• Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b16018

   

• Lander, E. S., & Waterman, M. S. (1988). Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics, 2(3), 231–239. https://doi.org/10.1016/0888-7543(88)90007-9

   

• Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics, 10(1), 57–63. https://doi.org/10.1038/nrg2484

   

• Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198–207. https://doi.org/10.1038/nature01511

   

• Levitt M. (2001). The birth of computational structural biology. Nature structural biology, 8(5), 392–393. https://doi.org/10.1038/87545

   

• Kitano H. (2002). Systems biology: a brief overview. Science (New York, N.Y.), 295(5560), 1662–1664. https://doi.org/10.1126/science.1069492