Bioinformatics

GenDisCal is intended to systematically compare large numbers of bacterial genomes against each other. This can be used to cluster unknown genomes into groups belonging to the same species, or as a way to rapidly identify the species to which individual strains belong. GenDisCal is primarily designed around using short (3~8bp) oligonucleotide frequencies, but can also be used to compute average nucleotide identity using NCBI-Blast (must be installed separately) or an approximation thereof. GenDisCal also comes with a graphical user interface, making it easy to use on both Windows and Linux.

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BacPipe is a comprehensive, rapid, and computationally low-resource bioinformatics pipeline for the analysis of bacterial whole genome sequences obtained from second and third-generation sequencing technologies. Users can choose to directly analyse raw sequencing reads or contigs or scaffolds in BacPipe. The pipeline is an ensemble of state-of-the-art, open-access bioinformatics tools for quality verification, genome assembly and Annotation, and identification of the bacterial genotype (MLST and emm typing), resistance genes, plasmid(s), virulence genes, and single nucleotide polymorphisms (SNPs). The outbreak module in BacPipe can be used, along with the SNPs and patient metadata, to simultaneously analyse many strains to understand evolutionary relationships and rapidly construct bacterial transmission routes. Importantly, BacPipe is designed to run multiple tools simultaneously which considerably reduces the time-to-result. BacPipe offer an automated pipeline that will contribute to overcoming one of the primary hurdles faced by microbiologists for analysing and interpreting WGS data, facilitating its direct application for routine patient care in hospitals and public health and infection control monitoring.

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For 16S rRNA metagenomics studies, the 454 pyrosequencing technology was one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation).  NoDe (Noise Detector) is an error correction algorithm designed for 454 Pyrosequencing data. It is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units. NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.

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DynamiC is taxonomically driven region-specific clustering algorithm. It replaces the use of a standard cut-off (commonly 97%) for the clustering of 16S amplicons into operational taxonomic units (OTUs). The use of  generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. DynamiC -on the other hands-  takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. This is implemented by taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species.

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