Biological sequence analysis

By Terence P. Speed. 2003

1,099 citations recorded by the source corpus.

This talk will review a little over a decade's research on applying certain stochastic models to biological sequence analysis. The models themselves have a longer history, going back over 30 years, although many novel variants have arisen since that time. The function of the models in biological sequence analysis is to summarize the information concerning what is known as a motif or a domain in bioinformatics, and to provide a tool for discovering instances of that motif or domain in a separate sequence segment. We will introduce the motif models in stages, beginning from very simple, non-stochastic versions, progressively becoming more complex, until we reach modern profile HMMs for motifs. A second example will come from gene finding using sequence data from one or two species, where generalized HMMs or generalized pair HMMs have proved to be very effective.

Subfields: Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/739aad5629eb4d4f9dccc4cab110921c56a12504

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Citations in the corpus

Learning Script Knowledge with Web Experiments (2010, methodology): “In bioinformatics, the elements of Σ could be nucleotides and a sequence could be a DNA sequence; in our case, Σ contains the individual event descriptions in our data, and the sequences are the ESDs.”
Clustering metagenomic sequences with interpolated Markov models (2010, methodology): “Markov models have proven to be an invaluable tool for sequence analysis [45], including capturing genome signatures [43].”
Bootstrapping Phylogenetic Trees: Theory and Methods (2003, methodology): “Various suggestions for more believable models have been included in different procedures: • Hidden Markov model for rate variation along the sequences as discussed by Yang (1994) and Felsenstein and Churchill (1996) (see Durbin, Eddy, Krogh and Mitchison, 1998, for a review).”
Diffusion Kernels on Graphs and Other Discrete Input Spaces (2002, result): “For the derivation of recursive formulæ such as this, and comparison to other measures of similarity between strings, see (Durbin et al., 1998).”
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011, background): “Detailed description of pseudocounts can be found in [42] and in [58].”
An HMM model for coiled-coil domains and a comparison with PSSM-based predictions (2002, methodology): “HMMs have become a standard technique in sequence analysis (Baldi et al., 1994; Durbin et al., 1998; Eddy, 1995; Hughey and Krogh, 1996; Krogh et al., 1994).”
A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data (2011, methodology): “Le and Durbin (2010) derived Equation (16). The conclusion is correct, but the derivation is not rigorous: the binomial coefficient in Equation (13) was left out. Yi et al. (2010) came to a similar set of equations to Equations (15) and (20), but the prior is taken from the estimated site allele fre”
Fast-Find: A novel computational approach to analyzing combinatorial motifs (2006, background): “Examples of the former type include regular expressions (reviewed in [5]) and weight matrices (reviewed in [6]); examples of the latter include suffix trees and suffix arrays (reviewed in [7]).”
Hidden Markov Models and their Applications in Biological Sequence Analysis (2009, methodology): “} in an efficient manner [3].”
Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment (2003, methodology): “Pairwise MSA can be extended efﬁciently to multiple sequences via the iterative pairwise alignment, a polynomial-time method commonly used in computational biology (Durbin et al., 1998).”
OLAV: Towards high‐throughput tandem mass spectrometry data identification (2003, methodology): “P ( F (cid:2) z,D,s,H 1 ) (cid:2) P ( B (cid:2) z,D,s,H 1 ) P ( Y (cid:2) z,D,s,H We introduce a HMM [29, 32] for estimating each factor in Eq.”
The Algorithm Design Manual (2020, background): “Multiple sequence alignment for computational biology is large field, with the books of Gusfield [Gus97] and Durbin [DEKM98] serving as excellent introductions.”
PhyloGibbs: A Gibbs Sampling Motif Finder That Incorporates Phylogeny (2005, background): “is a so-calledweightmatrix (WM) [1,2]w,whose componentswai give theprobabilitiesoffindingbasea2fA,C,G,Tgatposition i”
Multiple Sequence Alignment Based on Profile Alignment of Intermediate Sequences (2007, methodology): “:3) is the gap extension probability (Durbin et al. 1998).”