Metagenomics

Technical Specifications

TECHNICAL SPECIFICATIONS

Item	Details
Sequencing Data Volume	6 G / 10G
Assembly	Assembly into contigs with MEGAHIT (succinct de Bruijn graph); N50, contig counts and length distributions reported
Gene Prediction	Prodigal predicts prokaryotic and metagenomic ORFs, outputting gene sequences, protein sequences and GFF files
Non-redundant Gene Set	MMseqs2 clustering at 95% identity and 90% alignment coverage for dereplication; gene abundance quantified by Bowtie2 mapping
Taxonomic & Functional Annotation	Taxonomic annotation by DIAMOND against NCBI-NR; functional annotation across five databases — GO, KEGG, COG, CAZy and CARD (including antibiotic resistance ontology ARO)

Analysis Workflow

ANALYSIS WORKFLOW

Data QC

Raw · cleaned data

Assembly

MEGAHIT · contigs

Gene Prediction

Prodigal · ORF

Gene-set construction

MMseqs2 · Bowtie2

Functional annotation

NR / KEGG / CAZy

Composition analysis

Venn · Bar · heatmap

Diversity analysis

Alpha · Beta

Differential Analysis

LEfSe · rank-sum tests

Bioinformatics Analyses

BIOINFORMATIC MODULES

Module	Core Methods & Metrics	Key Outputs
Sequencing Statistics	Raw fastq (R1/R2) statistics: data volume, read counts, base counts, Q20/Q30 and GC content; base retention rate after adapter trimming and quality filtering	Raw and cleaned data statistics tables, base-quality and content distribution plots
Assembly	MEGAHIT (succinct de Bruijn graph) assembles effective sequences per sample into contigs; N50, contig counts and length distributions reported	Assembly statistics tables, length distribution plots
Gene Prediction	Prodigal predicts prokaryotic and metagenomic ORFs and coding regions, outputting gene sequences, protein sequences and GFF files	Gene sequence files, protein sequence files, GFF
Non-redundant Gene Set	MMseqs2 clustering at 95% identity and 90% alignment coverage for dereplication; per-sample effective reads mapped to the gene set with Bowtie2 for abundance	Non-redundant gene set, gene abundance tables
Taxonomic & Functional Annotation	DIAMOND against NCBI-NR for taxonomic annotation with abundance computed from domain to species; functional annotation across GO, KEGG, COG, CAZy and CARD (including ARO)	Annotation summary tables, per-database level abundance tables
Composition analysis	Venn diagrams, community bar plots and abundance heatmaps at species (Species/Genus/Family, etc.) and function (KO/COG/CAZy/ARO/GO/Pathway) levels	Venn diagram, community bar plots, heatmap
Diversity analysis	Alpha-diversity indices with intergroup boxplots; beta-diversity distance matrices, hierarchical clustering, PCA / PCoA / NMDS ordination and ANOSIM tests	Index boxplots, PCA / PCoA / NMDS plots, ANOSIM plots
Differential Analysis	LEfSe (LDA) at species and functional levels; intergroup Wilcoxon rank-sum tests to screen differential taxa and functional units	LEfSe plots, LDA bar charts, differential test tables

Example Results

SELECTED FIGURES FROM ACTUAL REPORTS

Contig Length Distribution

Length-interval distribution of assembled contigs per sample, with N50 and other assembly metrics.

KEGG Functional Annotation

Gene counts at KEGG level-2 pathways, colored by major categories such as metabolism and genetic information processing.

CAZy Annotation

Gene counts across the six functional modules (GH / GT / PL / CE / AA / CBM).

Shared / Unique Species Venn Diagram

Shared and unique species between Treat and Con groups.

Species Composition Bar Plot (Genus)

Genus-level relative abundance per sample, visualizing intergroup community-structure differences.

Species Abundance Heatmap (Excerpt)

Abundance distribution and hierarchical clustering of top species across samples; selected species shown.

PCA (Species)

Principal-component score plot showing inter-sample compositional differences and within-group clustering.

PCoA (Species, Bray-Curtis)

Principal coordinates analysis on species distance matrices; confidence ellipses show group structure.

LEfSe LDA Bar Chart (Excerpt)

Significantly different species between groups with LDA scores; colors distinguish groups; top items shown.

Applications

APPLICATIONS

Microbiome Functional PotentialNovel Gene & Pathway DiscoveryResistance Genes & Environmental MonitoringHost–Microbe InteractionsMetagenome-Assembled Genomes

FAQ

How long does a Metagenomics project take?

From sample QC approval, standard timelines are 3-6 weeks (library prep, sequencing and bioinformatics included); large cohorts or deep custom analyses follow the confirmed study plan.

What if my samples fail QC?

Failed QC is reported immediately with reasons, and we discuss resampling or plan adjustments with you — sequencing never proceeds without qualification.

What bioinformatics is included? Can I add custom analyses?

Standard delivery covers QC, mapping/assembly, quantification, differential and functional enrichment analyses. Custom deep-dive and multi-omics integrative analyses are supported on request.

What samples do you need for Metagenomics?

Total RNA ≥1 μg with RIN ≥7 (snap-frozen, dry ice); microbial and faecal samples ≥200 mg (sterile, −80 °C); whole blood for WGS ≥500 μL (EDTA only, never heparin). RNA integrity matters more than concentration. See Technical Resources → Sample Guide.