Bioinformatics Toolbox
Read, analyze, and visualize genomic and proteomic data
Have questions? Contact Sales.
Have questions? Contact Sales.
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 analysis.
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.
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.
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.
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.
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).
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.
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.
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.
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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