Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples. Next-generation sequencing (NGS) has greatly influenced metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly and annotation for known and novel microorganisms. This pipeline will solve these challenges.

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Pipeline

Data QC De novo assembly Reference based assembly Assembly QC Contigs annotation Contigs taxonomy classification Phylogenetic analysis Downstream analysis Reports

The main steps in this pipeline are - Quality Assessment of raw data and assembly , Assembly (with or without Reference genome) and Annotation.

Data quality control (QC)

The objective of performing Raw data QC is to filter the bad reads and trim the long reads in input data so as to remove poor quality nucleotides. This pre-processing is mandatory as it allows us to increase the proportion of good reads in input data. We use our proprietary QC engine – Thuli, that generates summary statistics of sequence and quality data along with graphs and plots. Thuli is extremely lightweight making raw data QC easy and swift.

With automated trim and filter options, the data used for downstream analysis is cleaned and made sure it is devoid of low-quality sequences or sequence artifacts that might lead to erroneous conclusions.

Reads coming from host organism are considered as contaminants for this particular pipeline and hence are filtered out along with bad reads.

De novo assembly

Assembly refers to the process of stitching up genomes from scratch. This means aligning and merging fragments from a longer DNA sequence in order to reconstruct the original sequence (or) Assembling short reads to create full-length (sometimes novel) sequences.

De novo assembly is performed without the aid of a reference genome, is necessary when sequencing a novel species or novel DNA sequence. Here paired-end reads are utilized to assemble low-depth regions and use progressive depth on contigs to reduce errors in high-depth regions. Even sequencing data with highly uneven depth are assembled with good coverage using de Bruijn graph based assembly approach.

Reference-based assembly

This process is pretty much similar to a denovo assembly but for a guided approach. A reference genome is used as a guide for placing the fragments of genome in their respective regions. This reference genome serves essentially as a backbone upon which the assembly is performed. This is a relatively quicker process compared to denovo assembly since this involves building a sequence that is similar but not necessarily identical to the reference sequence.

Assembly quality control (QC)

Next generation sequencing technologies cannot produce the complete sequence of a chromosome/organism. Instead, they generate large numbers of reads, ranging from dozens to thousands of consecutive bases, sampled from different parts of the genome. Genome assembly software combine the reads into larger regions called contigs and subsequently to scaffolds. However, current sequencing technologies and software face many complications that impede reconstruction of full chromosomes, including errors in reads and large repeats in the genome.

Different assembly programs use different heuristic approaches to tackle these challenges, resulting in many differences in the contigs they output. Here many assembly-related stats are evaluated for optimal values. These stats include percentage coverage, depth of coverage, mapped or unmapped read counts.

Contigs annotation

Once a genome is sequenced, it needs to be annotated to make sense of it. Annotation is the process of identifying the locations of genes and all of the coding regions in a genome/contig/scaffold and determining what those genes do. In other words, adding useful information(gene,protein,position etc.) to the data. An annotation(irrespective of the context) is a note added by way of explanation or commentary and inside Genome Explorer, rapid annotation of prokaryotic genomes is done with the help of inbuilt knowledgebase.

Contigs taxonomy classification

Taxonomical classification is the process of defining hierarchical groups of biological organisms on the basis of shared characteristics. The broadest classifications are by domain and kingdom; the most specific classification is by genus and species. The hierarchical groupings in between include phylum, class, family, and order.

Within Genome Explorer, this analysis is driven by our metagenome engine – Kirumi. Kirumi is an ultrafast microbial identification and analysis system that identies microbial flora with Taxonomic classification & relative abundance within the metagenomic/environmental samples. KIRUMI also Identifies whether the microbe is pathogenic or not along with related disease information. (PS: Kirumi is solely a research-based tool and must not considered as a means or replacement of medical test anyhow.)

Phylogenetic analysis

Phylogenetic analysis involves inferring or estimating how organisms are related to one another through evolutionary time. The results from phylogenetic analysis are usually depicted as branching, treelike diagrams that represent an estimated pedigree of the inherited relationships among molecules (''gene trees''), organisms, or both.

Downstream analysis

Downstream analysis includes functional analysis like Gene Ontology and KEGG pathway analyses. These analyses are customised according to the nature of samples collected and the purpose for which they were sequenced eg. Food-borne disease microbiome, Soil/environmental metagenome, tissue microbiome, Clinical/Fomite samples, etc.

Reports for these are customised according to the selected objective and analyses performed.

Supported Organisms

1

Homo sapiens

2

Mus musculus

Reports

Turn your complex genomic data into a meaningful report of actionable information and insightful visualizations.
Reports are highly customizable and exportable in industry-class formats.

Cumulative Plot

GC Content Plot

Phylogenetic Tree