7 Best Computational Biology Books To Read in [2024]

The development of Next Generation Sequencing and microarray has led to massive data production in the field of biology. The enormous data requires thorough analysis and speculation. For these tasks, the field of computational biology came into existence.

This study field has lots of unsolved mysteries. The study of human genes and genomes is wide. Lots of undiscovered domains can be identified with the help of the best Computational Biology textbooks. Reading can help computational biologists to decipher the code of life.

We have prepared a list of the Best Computational Biology books for learners of every grade. These guides are unique and well-written and cover almost every potential topic for good biological research. Keep on reading to find out more about them.

Best Computational Biology Books Recommended For All Level Learners

There are countless textbooks on computational biology on the market. Among so many available books to learn Computational Biology from, we recommend our 3 best picks for you. The classification is as follows:

These are some of the best Computational Biology books for beginners, intermediates, and advanced computational biology learners depending on their choice. You can read about each of the above textbooks in the upcoming section.

Besides these three, more books on Computational biology are described below.


1. Bioinformatics Algorithms

Bioinformatics Algorithms are a collection of the most applied computational and mathematical algorithms in computational biology. The beginning includes a description of DNA and the hidden messages in the genomes. The replication origins in bacterial genomes, yeast, archaea, and the computing probabilities of patterns in a string are described.

The molecular clocks and their role is described with the reference to DNA patterns. Finding motifs, algorithms, motif scoring and search, randomised algorithm are written descriptively. The assembly of genome, reconstruction of strings, overlap graphs, Euler’s theorem, and algorithm to find Eulerian cycles are mentioned in the mid-section.

The DNA sequencing technologies, human genome, and the associated problems are described in the best possible way. The fragile regions, breakpoints, SARS, its evolution, phylogenetics, and tree construction are written down lucidly. The disease-causing mutations, pattern matching, HIV vaccine, and the problems due to vaccine development are given.

Why You Should Buy This Book

  • Comprehensively written with real-life examples for easy understanding
  • Simple language is used to describe algorithms
  • A dynamic approach for learning computational biology
  • A separate chapter is dedicated for antibiotics, its discovery, its production in organisms
  • The application of yeast in wine making, clustering genes, hierarchal clustering
  • The peptide sequencing, identification, monkey theorem included

Level (best suitable for): Beginner to Intermediate


2. Introduction to Computational Biology

Introduction to Computational Biology begins with an introductory section on molecular biology, mathematics, statistics and computer science. The initial chapters are a brief on the biological concepts and the biological chemistry.  The methods for cloning and cloned libraries, partial digestion, and genomes per microgram are elucidated separately.

Chapter is dedicated for mapping by fingerprinting, by anchoring, clone overlap and more. The sequence assembly by shotgun, sequence accuracy, hybridisation are described. Databases for DNA and protein sequence retrieval, rapid sequence analysis, repeats in a sequence, and statistical comparisons are briefly explained.

The dynamic programming alignment of two sequences, global distance alignment, local alignment and clamps, tracebacks, inversions, map alignments are principle topics. Multiple sequence alignment, cystic fibrosis gene, profile analysis, probability and statistics for sequence alignment, sequence patterns, are discussed in a separate chapter.

Why You Should Buy This Book

  • An overview on all the topics is provided at the end of every chapter
  • Phylogenetic tree construction by different methods
  • secondary structure of RNA and minimum free energy structures are well-written
  • Restriction maps, graph theory, and classification of multiple solutions provided
  • Algorithms for DDP, approaches to DDP simulated annealing, mapping with the real data described

Level (best suitable for): Intermediate to Advance


3. Algorithms in Bioinformatics: A Practical Introduction

Algorithms in Bioinformatics: A Practical Introduction begins by describing the fundamental units of molecular biology- macromolecules, genomes, genes, chromosomes, mutations, central dogma, PTM, population genetics, biotechnological tools, and the history of Bioinformatics. The concept of sequence similarity and global alignment problems are included.

The Needleman-Wunsch algorithm, local alignment, gap banality, and scoring function for DNA and protein are briefly mentioned. The suffix tree and FM index are essential topics. The methods of genome alignment by maximum unique match, dynamic programming algorithm, Heuristic algorithm, dot plot for visualization, and more are mentioned.

A chapter for database searches is provided such as-biological database, Smith-Waterman algorithm, FASTA algorithm, BLAST, variations of BLAST algorithm, QUASAR, locality sensitive hashing, BWT-SW, and more. Multiple sequence alignment and the methods involved in solving the problem are given.

Why You Should Buy This Book

  • A detailed guide for intermediate computational biologists
  • Written in simple-to-understand language with a dynamic approach
  • Comprehensively written and distinctively divided chapters
  • The phylogenetic comparison using MAST and consensus tree problem elaborated
  • Dynamic programming method, ClustalW, MUSCLE, Log expectations score defined

Level (best suitable for): Intermediate


4. An Introduction to Bioinformatics Algorithms

An Introduction to Bioinformatics Algorithms is a compilation of algorithms and complexities. The introductory chapter defines biological and computer algorithms. The Big -O notation, design techniques, greedy algorithms, dynamic programming, machine learning, randomized algorithms, tractable and intractable problems are discussed.

A chapter is given on molecular biology for a description of genes, genetic material, codes for genes, structure of DNA, protein, DNA copying, DNA length measurement, probing DNA, difference in species on genetic level, and why bioinformatics is necessary. Topics of restriction mapping, algorithms, regulatory motives, profiles, median string, greedy algorithms, breakpoints, and much more are included as separate chapters.

The dynamic programming algorithms, Manhattan tourist problem, edit distance and alignments, local and global sequence alignments, gap penalties, gene prediction similarity-based approaches, and spliced alignments are written lucidly. The clustering trees, gene expression analysis, reconstruction methods, and Hierarchical clustering are defined.

Why You Should Buy This Book

  • The use of bioinformatical tools written in concise text in simple language
  • Types of algorithms such as recursive, iterative, fast, slow, and more described
  • The hidden Markov models and parameter estimation
  • The combinatorial pattern matching, hash tables, BLAST comparisons
  • Randomised algorithms include random projections and Gibbs Sampling topics

Level (best suitable for): Beginner to Intermediate

Also Check:

5. Computational Biology: A Hypertextbook

Computational Biology: A Hypertextbook is a very interesting textbook for practical learners and hands-on for computational biologists. The book is divided into eight brief chapters. It is not a very detailed book but definitely an intriguing one. It begins with an introduction to biological databases and the storage of information.

The next chapter describes the BLAST algorithm and its application in sequence similarity searches. The protein analysis chapter describes hydrophobicity plotting and the prediction of proteins’ secondary structure. The sequence alignment methods, dynamic programming, the similar patterns in the data such as protein sequence motifs and positions-specific weight matrices are described in great detail.

Phylogenetics, the algorithms, the applications of the software, and the methods to construct trees are given. The probability of mutations, generation of PAM, and BLOSUM substitution matrices are described towards the end. The concluding chapter is on bioinformatics programming. It defines the codes and programming protocols for building computational biology software.

Why You Should Buy This Book

  • Short and crisp topics written in a simple and straightforward style
  • The structure prediction of RNA is a fundamental topic for RNA analysis
  • Phylogeny construction based on mitochondrial DNA, molecular clock, and its applications
  • Sequence comparison, backtracking, scoring alignments, global and local alignments explained
  • character-based phylogeny, distance-based phylogeny, UPGMA, NJ are briefly discussed

Level (best suitable for): Beginner


6. Introduction to Computational Biology: An Evolutionary Approach

Introduction to Computational Biology: An Evolutionary Approach focuses on evolutionary studies using computer sciences over years. Part one comprises the analysis based on sequences such as pairwise alignment, PAM, BLOSUM matrices, global and local alignments, and the application of methods of alignment. The testing of evolutionary hypothesis- HKA and McDonald-Kreitman tests, implementations.

The biological sequences and the exact string-matching problems are discussed in a separate chapter. The keyword trees, suffix tree construction, maximum repeats, K -mismatches, and more are discussed. The fast alignment such as heuristic and optimal alignment methods, statistics of local alignment, application, and more are defined in a separate chapter.

Multiple sequence alignment, scoring, dynamic programming, HMM, profile analysis, and similar topics for computational biology are defined. The second part of the book is on the analysis of evolutionary relations among sequences. The different tree construction methods and algorithms are defined.

Why You Should Buy This Book

  • Software demonstrations for each topic and exercises provided to solve a real-life biological problem
  • A chapter summary for a quick revision on brief topics
  • The molecular clock, applications of evolutionary studies defined
  • The testing of evolutionary hypothesis- HKA and McDonald-Kreitman tests, implementations
  • Gene prediction, computational gene finding methods, accuracies, ab initio methods, comparative methods, and associated problems
  • Population study for genes, mutation models, and drift balance focussed

Level (best suitable for): Intermediate to Advance


7. Bioinformatics with Python Cookbook

Bioinformatics with Python Cookbook is one of the worth reading textbooks on computational biology. The description of low-quality genome references, traversing genome annotations, and extracting genes from reference using annotations. A chapter is dedicated to population genetics, managing data sets, extracting data sets from different databases, computing F-statistics, performing principal components analysis, and investigating population structure with the admixture.

Emphasis is laid on population genetic simulation, simulating selection, stepping stone models, modeling complex demographic scenarios, and more. Interestingly written chapter on phylogenetics, preparation of data sets alignment, comparisons, reconstruction of trees, and visualizing the data.

A chapter on the usage of Protein Data Bank (PDB) is provided and finding protein sequences, extracting information, molecular distances computation, and animation using Pymol, and parsing files using Biopython is given.

Bioinformatics work pipeline, galaxy servers, accessing and developing galaxy tools, generic pipelines, and deploying variant analysis are necessary topics. The application of python in big genomics data sets, use of high-performance data, parallel computing, statistics, optimizing code, and more are lucidly written.

Why You Should Buy This Book

  • In-depth step-wise analysis protocols and Advance NGS processing
  • Working with the high-quality reference genomes
  • Finding orthologues and retrieving gene ontology information
  • studying genome accessibility and filtering SNP data, processing NGS data with HTSeq
  • Methods are explained for a computational biologist or python programmer

Level (best suitable for): Intermediate to Advance

In this article, we have discussed the best computational biology books available for learners from any level of study. Each book is divided into various sections starting from an introduction. It guides on accessing data from gene bank and NCBI databases. Details on performing the basic sequence analysis, alignment of data, analyzing data, and more are given.

Every computational biologist must be aware of the mathematical and computer algorithms involved in solving a complex biological problem. By reading the best books on computational biology, learners can know about the principles and fundamentals of the problem.

Each book mentioned by us is worth a read. The best three picks recommended by us are mentioned at the opening sections of the article. You can choose anyone depending on your needs and level of understanding. However, go for the books that challenge your abilities.