MATLAB Examples

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.

Retrieve gene expression data series (GSE) from the NCBI Gene Expression Omnibus (GEO) and perform basic analysis on the expression profiles.

Programmatically search and retrieve data from NCBI's Entrez databases using NCBI's Entrez Utilities (E-Utilities).

A number of ways to look for patterns in gene expression profiles.

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

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

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

Use the Global Optimization Toolbox with the Bioinformatics Toolbox™ to optimize the search for features to classify mass spectrometry (SELDI) data.

Classify mass spectrometry data and shows some statistical tools that can be used to look for potential disease markers and proteomic pattern diagnostics.

Identify differentially expressed genes from microarray data and uses Gene Ontology to determine significant biological functions that are associated to the down- and up-regulated

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

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

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

Various ways to explore and visualize raw microarray data. The example uses microarray data from a study of gene expression in mouse brains [1].

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

Improve the quality of raw mass spectrometry data. In particular, this example illustrates the typical steps for preprocesssing protein surface-enhanced laser

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

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

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

Use MATLAB® and Bioinformatics Toolbox™ for preprocessing Affymetrix® oligonucleotide microarray probe-level data with two preprocessing techniques, Robust Multi-array Average

Manipulate, preprocess and visualize data from Liquid Chromatography coupled with Mass Spectrometry (LC/MS). These large and high dimensional data sets are extensively utilized in

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

Enrich microarray gene expression data using the Gene Ontology relationships.

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®.

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

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

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.

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:

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

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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