Bioinformatics Toolbox



Bioinformatics Toolbox

Read, analyze, and visualize genomic and proteomic data

Build Bioinformatics Pipelines

With the Biopipeline Designer app you can interactively build and run end-to-end bioinformatics pipelines, locally or in the cloud. Build a pipeline with built-in blocks that integrate proven NGS libraries or custom blocks to extend analyses with community tools for each step in the process. Run pipelines in parallel (using Parallel Computing Toolbox) and in batch mode.

Next Generation Sequencing (NGS)

The toolbox provides algorithms and visualization techniques for NGS. For example, you can preprocess reads, map them to a reference genome, and perform statistical analyses, such as differential expression analysis from RNA-Seq data or ChIP-Seq data analysis.

Sequence Analysis

Apply sequence analysis methods, including pairwise sequence, sequence profile, and multiple sequence alignment. Manipulate and evaluate sequences to gain a deeper understanding of your data. Perform BLAST searches against known sequences in online or local databases.

Mass Spectrometry Data Analysis

Bioinformatics Toolbox enables the analysis of SELDI, MALDI, LC/MS, and GC/MS data. You can smooth, align, and normalize spectra and use classification, statistical, and machine learning techniques to create classifiers and identify potential biomarkers.

Phylogenetic Tree Analysis

Construct phylogenetic trees using hierarchical linkage with a variety of techniques, including neighbor joining, single and complete linkage, and Unweighted Pair Group Method Average (UPGMA).

Reading Genomic and Proteomic Data

You can read data from common file formats such as SAM, BAM, FASTA, FASTQ, GTF, and GFF and from online databases such as NCBI Gene Expression Omnibus, GenBank®, and the Sequence Read Archive. You can use specialized data containers for data too large to fit in memory.

Statistical and Machine Learning Algorithms

Bioinformatics Toolbox provides functions that build on the Statistics and Machine Learning Toolbox, which offers interactive tools for feature selection, classification, regression, mapping, and displaying hierarchy plots and pathways.

Microarray Data Analysis

­Normalize microarray data using a variety of methods. Identify differentially expressed genes and perform enrichment analysis of expression results using gene ontology. Visualize gene and protein-protein interaction networks using graph theory algorithms.

Deploying and Sharing Apps

Turn your data analysis program into a customized software application. Build custom user interfaces; integrate with existing C, C++, and Java™ applications; and deploy standalone apps.

"MATLAB enables young biologists to learn enough programming and math without being afraid of the code. They can write in MATLAB as if it were English."

Dr. Jonas Almeida, Medical University of South Carolina

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