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ResiDB: An automated database manager for sequence data.

ABSTRACT: The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/.

SUBMITTER: Hendling M

PROVIDER: S-EPMC7816582 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPhylogenetic analysis of large, multiple-gene datasets, assembled from public sequence databases, is rapidly becoming a popular way to approach difficult phylogenetic problems. Supermatrices (concatenated multiple sequence alignments of multiple genes) can yield more phylogenetic signal than individual genes. However, manually assembling such datasets for a large taxonomic group is time-consuming and error-prone. Additionally, sequence curation, alignment and assessment of the results of phylogenetic analysis are made particularly difficult by the potential for a given gene in a given species to be unrepresented, or to be represented by multiple or partial sequences. We have developed a software package, TaxMan, that largely automates the processes of sequence acquisition, consensus building, alignment and taxon selection to facilitate this type of phylogenetic study.ResultsTaxMan uses freely available tools to allow rapid assembly, storage and analysis of large, aligned DNA and protein sequence datasets for user-defined sets of species and genes. The user provides GenBank format files and a list of gene names and synonyms for the loci to analyse. Sequences are extracted from the GenBank files on the basis of annotation and sequence similarity. Consensus sequences are built automatically. Alignment is carried out (where possible, at the protein level) and aligned sequences are stored in a database. TaxMan can automatically determine the best subset of taxa to examine phylogeny at a given taxonomic level. By using the stored aligned sequences, large concatenated multiple sequence alignments can be generated rapidly for a subset and output in analysis-ready file formats. Trees resulting from phylogenetic analysis can be stored and compared with a reference taxonomy.ConclusionTaxMan allows rapid automated assembly of a multigene datasets of aligned sequences for large taxonomic groups. By extracting sequences on the basis of both annotation and BLAST similarity, it ensures that all available sequence data can be brought to bear on a phylogenetic problem, but remains fast enough to cope with many thousands of records. By automatically assisting in the selection of the best subset of taxa to address a particular phylogenetic problem, TaxMan greatly speeds up the process of generating multiple sequence alignments for phylogenetic analysis. Our results indicate that an automated phylogenetic workbench can be a useful tool when correctly guided by user knowledge.

Project description:Shotgun and positional proteomics study of a mouse embryonic stem cell line. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both SwissProt and RIBO-seq derived translation products, applicable for LC-MSMS spectrum identification. To record the impact of using the constructed deep proteome database we performed two alternative MS-based proteomic strategies: (I) a regular shotgun proteomic and (II) an N-terminal COFRADIC approach. The obtained fragmentation spectra were searched against the custom database (combination of UniProtKB-SwissProt and RIBO-seq derived translation sequences) using three different search engines: OMSSA (version 2.1.9), X!Tandem (TORNADO, version 2010.01.01.04) and Mascot (version 2.3). The first two were run from the SearchGUI graphical user interface (version 1.10.4). A combination of X!Tandem and Mascot was used for the N-terminal COFRADIC analysis, a combination of all three search engines for the shotgun proteome analysis. Note that OMMSA cannot cope with the protease setting semi-ArgC/P needed to analyze N-terminal COFRADIC data.For the shotgun proteome data, trypsin was set as cleavage enzyme allowing for one missed cleavage, and singly to triply charged precursors or singly to quadruple charged precursors were taken into account respectively for the Mascot or X!Tandem/OMSSA search engines, and the precursor and fragment mass tolerance were set to respectively 10 ppm and 0.5 Da. Methionine oxidation to methionine-sulfoxide, pyroglutamate formation of N-terminal glutamine and acetylation (protein N-terminus) were set as variable modifications. For the N-terminal COFRADIC analysis the protease setting semi-ArgC/P (Arg-C specificity with arginine-proline cleavage allowed) was used. No missed cleavages were allowed and the precursor and fragment mass tolerance were also set to respectively 10 ppm and 0.5 Da. Carbamidomethylation of cysteine and methionine oxidation to methionine-sulfoxide and 13C3D2-acetylation of lysines were set as fixed modifications. Peptide N-terminal acetylation or 13C3D2-acetylation and pyroglutamate formation of N-terminal glutamine were set as variable modifications and instrument setting was put on ESI-TRAP. Protein and peptide identification in addition to data interpretation was done using the PeptideShaker algorithm (http://code.google.com/p/peptide-shaker, version 0.18.3), setting the false discovery rate to 1% at all levels (protein, peptide, and peptide to spectrum matching). Aforementioned tools and algorithms (SearchGui, X!Tandem, OMSSA, and PeptideShaker) are freely available as open source.

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ResiDB: An automated database manager for sequence data.

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