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Hennig, A.

Publications and source records attributed to Hennig, A..

2 recordsLinked to original sources

PneumoWiki: A pan-genome-based database for the pathogen Streptococcus pneumoniae

The Gram-positive bacterium Streptococcus pneumoniae is a major human pathogen that exhibits more than 100 different capsular serotypes and considerable genomic variation. S. pneumoniae is also an important model organism for basic and biomedical research, with a number of strains with different characteristics being used. To address this, we have created the manually curated pan-genome database PneumoWiki (https://pneumowiki.med.uni-greifswald.de) that integrates genomic data from 43 S. pneumoniae strains with various aspects of functional annotation. All data relating to a specific gene and gene product are compiled on a Gene page. In addition to the gene and protein sequences and annotation from NCBI RefSeq, the Gene pages contain data on, for example, gene essentiality, protein function and localization, and transcriptional regulation. The information is supplemented with links to the data sources as well as to other databases and relevant literature. The basic concept of PneumoWiki is the interlinked presentation of pan-genome-based and strain-specific information. Therefore, register tabs with strain names at the top of the Gene pages allow switching between orthologous genes and the corresponding pan-genome page. The pan-genome gene pages contain a summary with functional information and the occurrence of the gene, an orthologue table for the 43 S. pneumoniae strains, a multiple-strain genome viewer as well as a protein sequence alignment. PneumoWiki was developed as a user-friendly tool and is available free of charge. The data collected in PneumoWiki are accessible via various download options to support bioinformatic applications. Overall, PneumoWiki is a resource for the pneumococcal research community that supports the analysis and interpretation of research data and, in particular, enables the integration of the knowledge available for different S. pneumoniae strains.

microbiology↗

Efficient Merging of Genome Profile Alignments

MotivationWhole-genome alignment methods show insufficient scalability towards the generation of large-scale whole-genome alignments (WGAs). Profile alignment-based approaches revolutionized the fields of multiple sequence alignment construction methods by significantly reducing computational complexity and runtime. However, WGAs need to consider genomic rearrangements between genomes, which makes the profile-based extension of several whole-genomes challenging. Currently, none of the available methods offer the possibility to align or extend WGA profiles. ResultsHere, we present GPA, an approach that aligns the profiles of WGAs and is capable of producing large-scale WGAs many times faster than conventional methods. Our concept relies on already available whole-genome aligners, which are used to compute several smaller sets of aligned genomes that are combined to a full WGA with a divide and conquer approach. To align or extend WGA profiles, we make use of the SuperGenome data structure, which features a bidirectional mapping between individual sequence and alignment coordinates. This data structure is used to efficiently transfer different coordinate systems into a common one based on the principles of profiles alignments. The approach allows the computation of a WGA where alignments are subsequently merged along a guide tree. The current implementation uses progressiveMauve (Darling et al., 2010) and offers the possibility for parallel computation of independent genome alignments. Our results based on various bacterial data sets up to several hundred genomes show that we can reduce the runtime from months to hours with a quality that is negligibly worse than the WGA computed with the conventional progressiveMauve tool. AvailabilityGPA is freely available at https://lambda.informatik.uni-tuebingen.de/gitlab/ahennig/GPA. GPA is implemented in Java, uses progressiveMauve and offers a parallel computation of WGAs. Contactandre.hennig@uni-tuebingen.de

bioinformatics↗