GET ACCESS TO AN IMPRESSIVE SET OF ANALYSIS TOOLS

BASED ON WGS DATA

USING BOTH

LONG AND
SHORT READ

SEQUENCES

   WHOLE GENOME MLST (wgMLST)  

  • A fully automated pipeline for high-resolution typing based on wgMLST

  • Synchronization of wgMLST nomenclature and sub-typing schemes from organism-specific public reference databases (e.g. BIGDSdb)

  • Flexible selection of typing loci possible (e.g. cgMLST, MLST) or any other user-defined selection

  • wgMLST allele assignments both based on the raw sequencing data (assembly-free) and on de novo assembled contigs (assembly-based)

  • In-depth quality assessment of the different steps in the pipeline

  • Integrated with the BIONUMERICS Calculation Engine for high-throughput analyses

  • 42 in-house developed bacteria specific schemas:

Acinetobacter baumannii
Bacillus cereus
Bacillus subtilis
Burkholderia cepacia
complex
Brucella spp.
Campylobacter coli - C. jejuni
Citrobacter
spp.
Clostridioides difficile
Cronobacter
spp.

Enterobacter cloacae
Enterococcus faecalis
Enterococcus faecium
Enterococcus raffinosus
Escherichia coli / Shigella
Francisella tularensis
Klebsiella aerogenes
Klebsiella oxytoca
Klebsiella pneumoniae

Lactobacillus sanfranciscensis
Legionella pneumophila
Leuconostoc
spp.
Listeria monocytogenes
Micrococcus
spp.
Mycobacterium bovis
Mycobacterium kansasii
Mycobacterium leprae
Mycobacterium tuberculosis

Neisseria gonorrhoeae
Neisseria meningitidis
Pasteurella multocida
Proteus vulgaris
Pseudomonas aeruginosa
Salmonella enterica
Serratia marcescens
Staphylococcus aureus
Staphylococcus epidermidis

Staphylococcus pseudointermedius
Stenotrophomonas maltophilia
Streptococcus agalactiae
Streptococcus mitis/oralis
Streptococcus pyogenes
Weissella
spp. 

   WHOLE GENOME SNP (wgSNP)  

  • In-house developed wgSNP analysis pipeline, starting from raw reads 

  • Flexible genome mapping onto multiple reference genomes

  • Perform read mapping by using one of the reference algorithms such as Bowtie2* or SNAP** 

  • Various SNP filtering templates available

  • Flexibility to create your own SNP filtering templates based on coverage or quality criteria, position, mutation types and much more 

  • In-depth quality assessment of retained SNPs

  • Integrated with the BIONUMERICS Calculation Engine for high-throughput analyses

  • Integrated CFSAN SNP pipeline*** (United States Food and Drug Administration, Center for Food Safety and Applied Nutrition),  
    producing SNP matrices from NGS data to be used in phylogenetic analysis of pathogenic organisms typically linked to food safety.

* Langdon, William B. "Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks." BioData mining 8.1 (2015): 1.
** Zaharia, Matei, et al. "Faster and more accurate sequence alignment with SNAP." arXiv preprint arXiv:1111.5572 (2011).
*** Davis, Steve, et al. "CFSAN SNP Pipeline: an automated method for constructing SNP matrices from next-generation sequence data." PeerJ Computer Science 1 (2015): e20.

  GENOME ALIGNMENT  

  • Comparative genome mapping and dot plot representation representing homologous sequences in direct or reverse orientation

  • Multiple genome alignment functionality 

  • Genome clustering and phylogeny analysis

  • Genome alignment-based SNP analysis and dN/dS calculation

   ANNOTATION  

  • Identification of coding regions in prokaryotic genomes

  • Annotation of sequences against one or multiple reference sequences based on feature identity and chromosome synteny

  • Annotation of sequences using the integrated Prokka* pipeline 

  • Integrated with the BIONUMERICS Calculation Engine for high-throughput analyses

* Seemann, Torsten. "Prokka: rapid prokaryotic genome annotation." Bioinformatics 30.14 (2014): 2068-2069.

   ASSEMBLERS  

  • De novo assembly, based on one of the integrated short read de novo assemblers, including Velvet*, SPAdes**, SKESA*** and Unicycler****

  • Generate more accurate “hybrid” assemblies by using Unicycler****, leveraging the benefits of both data types, namely the accuracy of short reads and the structural resolving power of long reads

  • Integrated with the BIONUMERICS Calculation Engine for high-throughput analyses

*Zerbino, Daniel R., and Ewan Birney. "Velvet: algorithms for de novo short read assembly using de Bruijn graphs." Genome research 18.5 (2008): 821-829.

** Bankevich, Anton, et al. "SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing." Journal of computational biology 19.5 (2012): 455-477.
*** Souvorov, Alexandre, Richa Agarwala, and David J. Lipman. "SKESA: strategic k-mer extension for scrupulous assemblies." Genome biology 19.1 (2018): 153.
**** Wick, Ryan R., et al. "Unicycler: resolving bacterial genome assemblies from short and long sequencing reads." PLoS computational biology 13.6 (2017): e1005595.

 

   PHYLOGENETIC ANALYSIS  

  • Estimating evolutionary relationships based on maximum parsimony and maximum likelihood methods, with phylogenetic distance scaling correction e.g. Jukes & Cantor or Kimura2

  • Inferring phylogenies of varying complexity based on maximum likelihood by using the embedded standard tools RAxML* or FastTree**

  • Integrated with the BIONUMERICS Calculation Engine for high-throughput analyses

*Stamatakis, Alexandros. "RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models." Bioinformatics 22.21 (2006): 2688-2690.

** Price, Morgan N., Paramvir S. Dehal, and Adam P. Arkin. "FastTree 2–approximately maximum-likelihood trees for large alignments." PloS one 5.3 (2010).

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