Get Started with Bioinformatics Toolbox

Bioinformatics Toolbox™ provides algorithms and apps for building bioinformatics pipelines, Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. You can read genomic and proteomic data and explore this data with the Genomics Viewer app and visualize with spatial heatmaps and clustergrams. The toolbox (with Statistics and Machine Learning Toolbox™) provides statistical, regression, classification, and (with Deep Learning Toolbox™) deep learning techniques to build complete analysis pipelines.

The Biopipeline Designer app lets you interactively build bioinformatics pipelines for genomic data analysis, locally or in the cloud. You can use a combination of built-in bioinformatics pipeline blocks that integrate proven NGS libraries and custom blocks to extend analyses with community tools. You can create a pipeline to preprocess reads, map them to a reference genome, and perform statistical analysis, like RNA-Seq differential expression analysis or ChIP-Seq genome-wide analysis.

Tutorials

• Exploring a Nucleotide Sequence Using the Sequence Viewer App
  

  Use a graphical interface for the sequence functions.

• Explore a Protein Sequence Using the Sequence Viewer App
  

  Use the Biological Sequence Viewer to investigate protein sequences.

• Compare Sequences Using Sequence Alignment Algorithms
  

  Determining the similarity between two sequences is a common task in computational biology.

• View and Align Multiple Sequences
  

  Use the Sequence Alignment app to visually inspect a multiple alignment and make manual adjustments.

• Exchange Bioinformatics Data Between Excel and MATLAB
  

  Example of using Spreadsheet Link™ with software to view bioinformatic data.

Featured Examples

Comparing Whole Genomes

Compare whole genomes for organisms, which allows you to compare the organisms at a very different resolution relative to single gene comparisons. Instead of just focusing on the differences between homologous genes you can gain insight into the large-scale features of genomic evolution.

• Open Script

Working with Whole Genome Data

Create a memory mapped file for sequence data and work with it without loading all the genomic sequence into memory. Whole genomes are available for human, mouse, rat, fugu, and several other model organisms. For many of these organisms one chromosome can be several hundred million base pairs long. Working with such large data sets can be challenging as you may run into limitations of the hardware and software that you are using. This example shows one way to work around these limitations in MATLAB®.

• Open Script

Identifying Differentially Expressed Genes from RNA-Seq Data

Use a negative binomial model to test RNA-Seq data for differentially expressed genes.

• Open Script