Three dimensional structures provide a wealth of information on the biological function and the evolutionary history of macromolecules. They can be used to examine sequence-structure-function relationships, interactions, active sites, and more.

An interactive Web application that enables users to visualize multiple alignments created by database search results or other software applications. The MSA Viewer allows users to upload an alignment and set a master sequence and to explore the data using features such as zooming and changing of coloration.

The Protein database is a collection of sequences from several sources, including translations from annotated coding regions in GenBank, RefSeq and TPA, as well as records from SwissProt, PIR, PRF, and PDB. Protein sequences are the fundamental determinants of biological structure and function.

CDTree is a stand-alone application for classifying protein sequences and investigating their evolutionary relationships. CDTree can import, analyze and update existing Conserved Domain (CDD) records and hierarchies, and also allows users to create their own.

Data file for "Vliet SMF, Hazemi M, Blatz D, Jensen M, Mayasich S, Transue TR, Simmons C, Wilkinson A, LaLone CA. Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation. J Vis Exp. 2023 Feb 10;(192). doi: 10.3791/63970. PMID: 36847398.". This dataset is associated with the following publication: Vliet, S., M. Hazemi, D. Blatz, M. Jensen, S. Mayasich, T. Transue, C. Simmons, A. Wilkinson, and C. Lalone. Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation. Journal of Visualized Experiments. JoVE, Somerville, MA, USA, 192, (2023).

Compute cDNA-to-Genomic sequence alignments.

A graphical analysis tool that finds all open reading frames in a user's sequence or in a sequence already in the database.

In this research, we propose a variant of the classical Matching Pursuit Decomposition (MPD) algorithm with significantly improved scalability and computational performance. MPD is a powerful iterative algorithm that decomposes a signal into linear combinations of its dictionary elements or “atoms”. A best fit atom from an arbitrarily defined dictionary is determined through cross-correlation. The selected atom is subtracted from the signal and this procedure is repeated on the residual in the subsequent iterations until a stopping criteria is met. A sufficiently large dictionary is required for an accurate reconstruction; this in return increases the computational burden of the algorithm, thus limiting its applicability and level of adoption. Our main contribution lies in improving the computational efficiency of the algorithm to allow faster decomposition while maintaining a similar level of accuracy. The Correlation Thresholding and Multiple Atom Extractions techniques were proposed to decrease the computational burden of the algorithm. Correlation thresholds prune insignificant atoms from the dictionary. The ability to extract multiple atoms within a single iteration enhanced the effectiveness and efficiency of each iteration. The proposed algorithm, entitled MPD++, was demonstrated using real world data set.