Microbial Genomics

The development of NGS technologies for assaying microbial genetic material in an environment has become a powerful new approach for rapidly characterizing microbial communities, given a significant percentage of the microbes are uncultivable.

These experimental approaches along with novel computational methods, are enabling us to understand the role of microbial shifts in human health and disease, track and monitor contamination of food sources, understand disease susceptibility in plants of agricultural importance, rapidly respond to infectious disease outbreaks, characterize diversity, biogeographic variability for environmental impact assessment and ecological remediation, etc. The Human Microbiome Project and the Earth Microbiome Project have only attested to the growing importance and relevance of microbiome research.

We support data analytics for (i) Meta-taxonomic approaches that profile a community by amplicon sequencing of marker genes (16S rRNA, 18S rRNA, ITS etc), (ii) Direct metagenomic taxonomic classification approaches for quantitative community profiling and identification of organisms with close relatives in the database, (iii) Characterization of uncultivated microbes towards a more qualitative understanding of metabolic capability through assembly and binning of the reads followed by analysis of the binned draft genomes to obtain a comprehensive catalog of genes and functions in all organisms, (iv) Profiling RNAs from a complex microbial environment through meta-transcriptomics, etc.

Whether you are working on a pipeline of therapeutics for various disease indications, characterizing a microbial collection for seed treatment or novel products to kill insects or fungal pathogens, or improving pathogen detection and traceback for infectious disease surveillance, we can help translate your microbiome research into practical applications.

Our integrated data solutions lets you manage and explore your processed data derived from analytical pipelines. Simply upload your BIOM file with taxonomic profiles from 16S or metagenome analysis tools and pipelines, search over your BIOM files, explore and visualize the data.

Tools

QIIME2Powerful, extensible, and decentralized end-to-end microbiome analysis package with a focus on data, visualization and analysis transparency.
MothurOpen-source, platform-Independent, community supported software for describing and comparing microbial communities.
MetaPhlAn2Tool for profiling the composition of microbial communities from metagenomic shotgun sequencing data based on clade-specific marker genes.
HUMAnN2The HMP Unified Metabolic Analysis Network is a method for efficiently and accurately determining the presence, absence, and abundance of metabolic pathways in a microbial community from metagenomic or metatranscriptomic sequencing data.
CentrifugeVery rapid and memory-efficient system for the classification of DNA sequences from microbial samples.
Kraken2System for assigning taxonomic labels to short DNA sequences from metagenomic sequencing data.
KaijuFast taxonomic classification based on sequence comparison to a reference database of microbial proteins.
MEGANComprehensive toolbox for analyzing large-scale microbiome sequencing data.
CLARKSupervised taxonomic classification of metagenomics reads using discriminative k-mers.
GOTTCHAGene-independent and signature-based metagenomic taxonomic profiling method.
Sourmashk-mer based taxonomic exploration and classification routines for genome and metagenome analysis.
Taxonomerk-mer based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from both clinical and environmental samples.
DIAMONDAlignment tool for aligning short DNA sequencing reads to a protein reference database.
MOCAT2Package for analyzing metagenomic datasets - generate taxonomic and functional profiles, assemble reads into scaftigs and predict genes in assembled sequences.
MetaSPAdesVersatile metagenomic assembler.
MetaVelvetMetagenomic assembly tool for mixed short reads of multiple species.
Ray MetaTools for scalable de novo metagenome assembly and profiling based on uniquely-colored k-mers.
MEGAHITSingle node assembler for NGS data from large and complex metagenomics samples, such as soil.
MaxBinSoftware for binning assembled metagenomic sequences based on an Expectation-Maximization algorithm.
CONCOCTAlgorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes.
MetaBATEfficient tool for accurately reconstructing single genomes from complex microbial communities.
VizBinReference-independent visualization and human-augmented binning of metagenomic data.

File types

Group 2983CSV
Group 2994TSV
Group 3005SAM
Group 3016BAM
Group 3027CRAM
Group 3049FASTA
Group 3038FASTQ
Group 3060BED
Group 3071Wig
Group 3082bigBED
Group 3093bigWig
Group 3137bedGraph
Group 3104GTF
Group 3126GFF3
Group 3071BIOM

Visual analytics

Bar chartGraphs depicting relative abundance of taxa or OTUs in each sample for various levels of taxonomic hierarchy.
Donut partition chartPie diagram depicting relative abundance of taxa or OTUs in each sample for various levels of taxonomic hierarchy.
Bubble chartSize of bubble depicts relative abundance of taxa or OTUs in each sample for various levels of taxonomic hierarchy.
Sankey diagramFlow diagrams depicting relative abundance of taxa or OTUs in each sample at various levels of taxonomic hierarchy.
HeatmapColor-coded matrices of community similarity that can be reordered to reveal patterns among samples.
DendrogramTree-like diagrams clustering samples by community similarity.
Principal Coordinate Analysis (PCoA)2-D and 3-D graphical representations of the relative similarity of samples and metadata.
Krona chartHierarchical pie diagrams that provide intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications.
Phylogenetic treeEvolutionary relationships of taxa or OTUs overlaid with relative abundance and sample metadata.
Pathway mapMaps of biochemical capability of bacterial taxa.
NetworkUnderstand microbiome-microbiome and microbiome-environment interactions.