MATLAB Examples

Generate bootstrap replicates of DNA sequences. The data generated by bootstrapping is used to estimate the confidence of the branches in a phylogenetic tree.

An analysis of the origin and diffusion of the SARS epidemic. It is based on the discussion of viral phylogeny presented in Chapter 7 of "Introduction to Computational Genomics. A Case

Construct phylogenetic trees from multiple strains of the HIV and SIV viruses.

How the analysis of synonymous and nonsynonymous mutations at the nucleotide level can suggest patterns of molecular adaptation in the genome of HIV-1. This example is based on the

Construct phylogenetic trees from mtDNA sequences for the Hominidae taxa (also known as pongidae). This family embraces the gorillas, chimpanzees, orangutans and humans.

Calculate Ka/Ks ratios for eight genes in the H5N1 and H2N3 virus genomes, and perform a phylogenetic analysis on the HA gene from H5N1 virus isolated from chickens across Africa and Asia. For

Illustrates a simple metagenomic analysis on a sample data set from the Sargasso Sea. It requires the taxonomy information included in the files gi_taxid_prot.dmp, names.dmp and

Illustrates how to use the rnafold and rnaplot functions to predict and plot the secondary structure of an RNA sequence.

How HMM profiles are used to characterize protein families. Profile analysis is a key tool in bioinformatics. The common pairwise comparison methods are usually not sensitive and specific

Use the Bioinformatics Toolbox™ to find potential primers that can be used for automated DNA sequencing.

Illustrates a simple approach to searching for potential regulatory motifs in a set of co-expressed genomic sequences by identifying significantly over-represented ungapped words of

Several ways of visualizing the results of functional metagenomic analyses. The discussion is based on two studies focusing on the metagenomic analysis of the human distal gut microbiome.

Perform a genome-wide analysis of a transcription factor in the Arabidopsis Thaliana (Thale Cress) model organism.

Test RNA-Seq data for differentially expressed genes using a negative binomial model.

Perform a genome-wide analysis of DNA methylation in the human by using genome sequencing.

Use the BIOGRAPH object to visually represent interconnected data.

How Bioinformatics Toolbox™ can be used to work with and visualize graphs.

Display, inspect and annotate the three-dimensional structure of molecules. This example performs a three-dimensional superposition of the structures of two related proteins.

Work with the clustergram function.

Enrich microarray gene expression data using the Gene Ontology relationships.

A secondary structure prediction method that uses a feed-forward neural network and the functionality available with the Neural Network Toolbox™.

Workflows for the analysis of gene expression data with the attractor metagene algorithm. Gene expression data is available for many model organisms and disease conditions. This example

Analyze Illumina BeadChip gene expression summary data using MATLAB® and Bioinformatics Toolbox™ functions.

In this example, you will use the parameter estimation capabilities of SimBiology™ to calculate F, the bioavailability, of the drug ondansetron. You will calculate F by fitting a model of

Construct a simple model with two species (A and B) and a reaction. The reaction is A -> B , which follows the mass action kinetics with the forward rate parameter k . Hence the rate of change is $

Perform a Monte Carlo simulation of a pharmacokinetic/pharmacodynamic (PK/PD) model for an antibacterial agent. This example is adapted from Katsube et al. [1] This example also shows how

Build a simple nonlinear mixed-effects model from clinical pharmacokinetic data.

Simulate and analyze a model in SimBiology® using a physiologically based model of the glucose-insulin system in normal and diabetic humans.

Use the sbioconsmoiety function to find conserved quantities in a SimBiology® model.

Build, simulate and analyze a model in SimBiology® using a pathway taken from the literature.

Make ensemble runs and how to analyze the generated data in SimBiology®.

Deploy a graphical application that simulates a SimBiology model. The example model is the Lotka-Volterra reaction system as described by Gillespie [1], which can be interpreted as a

Perform a parameter scan by simulating a model multiple times, each time varying the value of a parameter.

Correctly build a SimBiology® model that contains discontinuities.

Build and simulate a model using the SSA stochastic solver.

Build and simulate a model using the SSA stochastic solver and the Explicit Tau-Leaping solver.

Configure sbiofit to perform a hybrid optimization by first running the global solver particleswarm , followed by another minimization function, fmincon .

Increase the amount or concentration of a species by a constant value using the zero-order rate rule. For example, suppose species x increases by a constant rate k . The rate of change is:

Change the amount of a species similar to a first-order reaction using the first-order rate rule. For example, suppose the species x decays exponentially. The rate of change of species x is:

Create a rate rule where a species from one reaction can determine the rate of another reaction if it is in the second reaction rate equation. Similarly, a species from a reaction can determine

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