Bioinformatics Toolbox™ provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank®. You can explore and visualize this data with sequence browsers, spatial heatmaps, and clustergrams. The toolbox also provides statistical techniques for detecting peaks, imputing values for missing data, and selecting features.
You can combine toolbox functions to support common bioinformatics workflows. You can use ChIP-Seq data to identify transcription factors; analyze RNA-Seq data to identify differentially expressed genes; identify copy number variants and SNPs in microarray data; and classify protein profiles using mass spectrometry data.
Gain deeper understanding of sequence features, functions, and evolution by performing analyses on nucleotide or amino acid sequences. Compare sequences using pairwise or multiple sequence alignment methods. Calculate sequence properties and statistics to gain more insight on physical, chemical, and biological characteristics of your data. Perform BLAST searches against known sequences in online or local databases. Determine the evolutionary relationships between organisms by building phylogenetic trees from pairwise distances of sequences.
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Mathworks’ Bioinformatics toolbox documentation: sequence analysis
Gene expression and genetic variant analysis of microarray data Microarrays contain oligonucleotide or cDNA probes to measure the expression levels of genes on a genomic scale. Bioinformatics Toolbox™ lets you preprocess expression data from microarrays using various normalization and filtering methods. Use the normalized data to identify differentially expressed genes and perform enrichment analysis of expression results using Gene Ontology. You can also detect genetic variants such as copy number variations (CNVs) and single nucleotide polymorphism (SNPs) from comparative genomic hybridization (CGH) data. Visualize gene and protein-protein interaction networks using graph theory algorithms.
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Mathworks’ Bioinformatics toolbox documentation: Microarray analysis
High-throughput sequencing methods generate large amounts of sequence data and require robust computational tools for further analysis. Bioinformatics Toolbox™ provides algorithms to support common analysis workflows for Next-Generation Sequencing (NGS) data, such as filtering and trimming reads, mapping reads to references, counting the number of reads mapped to genomic features, and performing statistical analyses.
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Mathworks’ bioinformatics toolbox documentation: High-Throughput sequencing
3-D structures of proteins and molecules are often necessary to understand their functions at a molecular level. Bioinformatics Toolbox™ lets you import such structural information stored in protein data bank (PDB) files and visualize them interactively. Superpose the structures and analyze them using Ramachandran plots. You can also predict and draw the secondary structure of an RNA sequence.
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Mathworks’ bioinformatics toolbox documentation: Structural analysis
Data from separation techniques that produce traces with peaks, including MS, LC/MS, NMR, chromatography, and electrophoresis Mass spectrometry and other bioanalytical techniques are essential in biological research to identify and quantify various biomolecules, such as proteins. The toolbox lets you import raw mass spectrometry data from various instruments. You can preprocess such data and improve its quality by normalizing, correcting the baseline of peak signals, and resampling high-resolution data. Characterize the data by detecting peaks and aligning them with references. Detect potential biomarkers by using statistical and machine learning algorithms.
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Mathworks’ bioinformatics toolbox documentation: Mass Spectrometry and Bioanalytics The bioinformatics toolkit documentation from Mathwork thoroughly explains the methods required to execute the aforementioned analysis. Each study includes feature examples that are quite helpful in acquiring a thorough knowledge of the process.
Videos
Watch mathworks Bioinformatics toolbox’s examples videos at here