BIOLOGICAL SEQUENCE ANALYSIS PDF
Many of the most powerful sequence analysis methods are now based on Biological sequence analysis: probabilistic models of proteins and nucleic. Computational sequence analysis has been around since the rst protein sequences To a rst approximation, deciding that two biological sequences are sim-. Bioinformatics and Systems Biology - Biological Sequence Analysis - by Richard Durbin. PDF; Export citation 6 - Multiple sequence alignment methods.
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Request PDF on ResearchGate | Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids | Probablistic models are becoming. Biological Sequence Analysis 1. Martin Tompa. Technical Report # Winter Department of Computer Science and Engineering. University of. Biological sequence analysisProbabilistic models of proteins and nucleic acids (ppti.info, ppti.info, ppti.info and ppti.infoson, Cambridge. University Press.
Main article: Multiple sequence alignment Example multiple sequence alignment There are millions of protein and nucleotide sequences known. These sequences fall into many groups of related sequences known as protein families or gene families. Relationships between these sequences are usually discovered by aligning them together and assigning this alignment a score.
There are two main types of sequence alignment. Pair-wise sequence alignment only compares two sequences at a time and multiple sequence alignment compares many sequences. Two important algorithms for aligning pairs of sequences are the Needleman-Wunsch algorithm and the Smith-Waterman algorithm.
A common use for pairwise sequence alignment is to take a sequence of interest and compare it to all known sequences in a database to identify homologous sequences. In general, the matches in the database are ordered to show the most closely related sequences first, followed by sequences with diminishing similarity. These matches are usually reported with a measure of statistical significance such as an Expectation value.
Problems and Solutions in Biological Sequence Analysis
Profile comparison[ edit ] In , Michael Gribskov, Andrew McLachlan, and David Eisenberg introduced the method of profile comparison for identifying distant similarities between proteins.
These profiles can then be used to search collections of sequences to find sequences that are related. In , a probabilistic interpretation of profiles was introduced by David Haussler and colleagues using hidden Markov models.
In recent years,[ when? These are known as profile-profile comparison methods. It is an integral part of modern DNA sequencing. Since presently-available DNA sequencing technologies are ill-suited for reading long sequences, large pieces of DNA such as genomes are often sequenced by 1 cutting the DNA into small pieces, 2 reading the small fragments, and 3 reconstituting the original DNA by merging the information on various fragments.
Still, the situation is hopeful. The models of molecular evolution proposed by Dayhoff and co-authors, Jukes and Cantor, and Kimura, are classical examples of fundamental advances in modeling of the complex processes of DNA and protein evolution. Notably these models focus on only a single site of a molecular sequence and require the further simplifying assumption that evolution of sequence sites occurs independently from each other.
Nevertheless, such models are useful starting points for understanding the Preface xiii function and evolution of biological sequences as well as for designing algorithms elucidating these functional and evolutionary connections.
For instance, amino acid substitution scores are critically important parameters of the optimal global Needleman and Wunsch and local Smith and Waterman sequence alignment algorithms. Biologically sensible derivation of the substitution scores is impossible without models of protein evolution. In the mid s the notion of the hidden Markov model HMM , having been of great practical use in speech recognition, was introduced to bioinformatics and quickly entered the mainstream of the modeling techniques in biological sequence analysis.
Theoretical advances that have occurred since the mid s have shown that the sequence alignment problem has a natural probabilistic interpretation in terms of hidden Markov models.
Biological Sequence Analysis (guided self study)
In particular, the dynamic programming DP algorithm for pairwise and multiple sequence alignment has the HMM-based algorithmic equivalent, the Viterbi algorithm. If the type of probabilistic model for a biological sequence has been chosen, parameters of the model could be inferred by statistical machine learning methods. Two competitive models could be compared to identify the one with the best fit. The events and selective forces of the past, moving the evolution of biological species, have to be reconstructed from the current biological sequence data containing significant noise caused by all the changes that have occurred in the lifetime of disappeared generations.
This difficulty can be overcome to some extent by the use of the general concept of self-consistent models with parameters adjusted iteratively to fit the growing collection of sequence data. Subsequently, implementation of this concept requires the expectation—maximization type algorithms able to estimate the model parameters simultaneously with rearranging data to produce the data structure such as a multiple alignment that fits the model better.
BSA describes several algorithms of expectation—maximization type, including the self-training algorithm for a profile HMM and the self-training algorithm for a phylogenetic HMM.
Given that the practice with many algorithms described in BSA requires significant computer programming, one may expect that describing the solutions would lead us into heavy computer codes, thus moving far away from the initial concepts and ideas. However, the majority of the BSA exercises have analytical solutions. Finally, we should mention that the references in the text to the pages in the BSA book cite the edition. We cordially thank our editor Katrina Halliday for tremendous patience and constant support, without which this book would never have come to fruition.
A Parallel Algorithm for Multiple Biological Sequence Alignment
Eddy, and Graeme Mitchison, for encouragement, helpful criticism and suggestions. Finally, we wish to express our particular gratitude to our families for great patience and constant understanding. The first chapter of BSA contains an introduction to the fundamental notions of biological sequence analysis: sequence similarity, homology, sequence alignment, and the basic concepts of probabilistic modeling.
Finding these distinct concepts described back-to-back is surprising at first glance. However, let us recall several important bioinformatics questions.
How could we construct a pairwise sequence alignment? How could we build an alignment of multiple sequences?
How could we create a phylogenetic tree for several biological sequences? How could we predict an RNA secondary structure?
None of these questions can be consistently addressed without use of probabilistic methods. The mathematical complexity of these methods ranges from basic theorems and formulas to sophisticated architectures of hidden Markov models and stochastic grammars able to grasp fine compositional characteristics of empirical biological sequences. The explosive growth of biological sequence data created an excellent opportunity for the meaningful application of discrete probabilistic models.
Perhaps, without much exaggeration, the implications of this new development could be compared with implications of the revolutionary use of calculus and differential equations for solving problems of classic mechanics in the eighteenth century.
The problems considered in this introductory chapter are concerned with the fundamental concepts that play an important role in biological sequence analysis: the maximum likelihood and the maximum a posteriori Bayesian estimation of the model parameters.
These concepts are crucial for understanding statistical inference from experimental data and are impossible to introduce without notions of conditional, joint, and marginal probabilities.Any savings in commu- The first-order Markov model and the new model of nication costs or disc space would therefore be small, approximate repeats described later also find some the exception being if we had multiple similar sequences insignificant, chance patterns. Chengpeng, B. For example, Fig.
High-throughput sequencing HTS overview, variant calling, Burrows-Wheeler transform and indexes, search space pruning. Krogh, and G.
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