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Wasabi is an open-source, web-based graphical environment for evolutionary sequence analysis and visualization, designed to work with multiple sequence alignments within their phylogenetic context. Its interactive user interface provides convenient access to external data sources and computational tools and is easily extendable with custom tools and pipelines using a plugin system. Wasabi stores intermediate editing and analysis steps as workflow histories and provides direct-access web links to datasets, allowing for reproducible, collaborative research, and easy dissemination of the results. In addition to shared analyses and installation-free usage, the web-based design allows Wasabi to be run as a cross-platform, stand-alone application and makes its integration to other web services straightforward.This chapter gives a detailed description and guidelines for the use of Wasabi's analysis environment. Example use cases will give step-by-step instructions for practical application of the public Wasabi, from quick data visualization to branched analysis pipelines and publishing of results. We end with a brief discussion of advanced usage of Wasabi, including command-line communication, interface extension, offline usage, and integration to local and public web services. The public Wasabi application, its source code, documentation, and other materials are available at http//wasabiapp.org.In this chapter, we introduce core functionality of the Jalview interactive platform for the creation, analysis, and publication of multiple sequence alignments. A workflow is described based on Jalview's core functions from data import to figure generation, including import of alignment reliability scores from T-Coffee and use of Jalview from the command line. The accompanying notes provide background information on the underlying methods and discuss additional options for working with Jalview to perform multiple sequence alignment, functional site analysis, and publication of alignments on the web.Bioinformatic analysis of functionally diverse superfamilies can help to study the structure-function relationship in proteins, but represents a methodological challenge. The Mustguseal web-server can build large structure-guided sequence alignments of thousands of homologs that cover all currently available sequence variants within a common structural fold. The input to the method is a PDB code of the query protein, which represents the protein superfamily of interest. The collection and subsequent alignment of protein sequences and structures is fully automated and driven by the particular choice of parameters. Four integrated sister web-methods-the Zebra, pocketZebra, visualCMAT, and Yosshi-are available to further analyze the resulting superimposition and identify conserved, subfamily-specific, and co-evolving residues, as well as to classify and study disulfide bonds in protein superfamilies. The integration of these web-based bioinformatic tools provides an out-of-the-box easy-to-use solution, first of its kind, to study protein function and regulation and design improved enzyme variants for practical applications and selective ligands to modulate their functional properties. In this chapter, we provide a step-by-step protocol for a comprehensive bioinformatic analysis of a protein superfamily using a web-browser as the main tool and notes on selecting the appropriate values for the key algorithm parameters depending on your research objective. The web-servers are freely available to all users at https//biokinet.belozersky.msu.ru/m-platform with no login requirement.The Database of Aligned Structural Homologs (DASH) is a tool for efficiently navigating the Protein Data Bank (PDB) by means of pre-computed pairwise structural alignments. We recently showed that, by integrating DASH structural alignments with the multiple sequence alignment (MSA) software MAFFT, we were able to significantly improve MSA accuracy without dramatically increasing manual or computational complexity. In the latest DASH update, such queries are not limited to PDB entries but can also be launched from user-provided protein coordinates. Here, we describe a further extension of DASH that retrieves intermolecular interactions of all structurally similar domains in the PDB to a query domain of interest. We illustrate these new features using a model of the NYN domain of the ribonuclease N4BP1 as an example. We show that the protein-nucleotide interactions returned are distributed on the surface of the NYN domain in an asymmetric manner, roughly centered on the known nuclease active site.Large-scale multigene datasets used in phylogenomics and comparative genomics often contain sequence errors inherited from source genomes and transcriptomes. These errors typically manifest as stretches of non-homologous characters and derive from sequencing, assembly, and/or annotation errors. The lack of automatic tools to detect and remove sequence errors leads to the propagation of these errors in large-scale datasets. PREQUAL is a command line tool that identifies and masks regions with non-homologous adjacent characters in sets of unaligned homologous sequences. PREQUAL uses a full probabilistic approach based on pair hidden Markov models. On the front end, PREQUAL is user-friendly and simple to use while also allowing full customization to adjust filtering sensitivity. It is primarily aimed at amino acid sequences but can handle protein-coding nucleotide sequences. PREQUAL is computationally efficient and shows high sensitivity and accuracy. In this chapter, we briefly introduce the motivation for PREQUAL and its underlying methodology, followed by a description of basic and advanced usage, and conclude with some notes and recommendations. GSK2110183 supplier PREQUAL fills an important gap in the current bioinformatics tool kit for phylogenomics, contributing toward increased accuracy and reproducibility in future studies.Long DNA and RNA reads from nanopore and PacBio technologies have many applications, but the raw reads have a substantial error rate. More accurate sequences can be obtained by merging multiple reads from overlapping parts of the same sequence. lamassemble aligns up to ∼1000 reads to each other, and makes a consensus sequence, which is often much more accurate than the raw reads. It is useful for studying a region of interest such as an expanded tandem repeat or other disease-causing mutation.