• • Sequence QC
  • Mapping onto reference genome; statistics of sequencing depth and coverage
  • Variant (SNP & InDel) calling, annotation and statistics

• • Sequence QC
  • De novo assembly for genome size <10Mb
  • Gene Prediction

• • Sequence QC
  • Tabulation of variants in vcf table
  • Tabulation of Phylogeny

• • Sequence QC
  • Mapping onto reference genome; statistics of sequencing depth and coverage
  • Variant (SNP & InDel) calling, annotation and statistics
  • Somatic variant (SNP, InDel or CNV) calling, annotation and statistics (paired tumor samples)

• • Sequence QC
  • Mapping Reads to Reference Genome (provided by customer)
  • Gene Expression Quantification
  • Correlation analysis (For biological replicates only)
  • Differential Expression Analysis (two or more groups of samples)
  • GO Enrichment Analysis of Differentially Expressed Genes (DEGs) (two or more groups of samples)
  • KEGG Pathway Enrichment Analysis of Differentially Expressed Genes (DEGs) (two or more groups of samples)

• • Sequence QC
  • De novo Transcriptome Assembly
  • Gene Functional Annotation Using different databases (e.g. NR, NT, KO, Swiss-Prot, GO and/or PFAM)
  • GO, COG, KEGG Classification
  • CDS Prediction
  • Gene Expression Analysis
  • Correlation analysis (For biological replicates only)
  • Differential Expression Analysis (two or more groups of samples)
  • GO Enrichment Analysis of Differentially Expressed Genes (DEGs) (two or more groups of samples)
  • KEGG Pathway Enrichment Analysis of Differentially Expressed Genes (DEGs) (two or more groups of samples)

• • Sequence QC
  • Filtering host genome sequences
  • Assembly
  • Species annotation (microorganic sequences extracted from NCBI NR database)
  • Gene prediction
  • Gene annotation (KEGG, eggNOG, CAZy database)
  • Species/Gene/Function abundance statistics and cluster analysis
  • Comparative analysis (among samples)

Note1: Underlined = popular region.
Note2: If you have any target taxonomy in the standard analysis, the selection of the specific region must refer to the existing literature review.

• • Sequence QC
  • OTUs (Operational Taxonomic Units) Clustering and Filtering
  • OTUs Analysis and Species Annotation, including KRONA results, OTUs Heatmap, Taxonomic Abundance and up to 5 Venn diagrams
  • Alpha-diversity Analysis, including Alpha Indices table with ANOVA, Rarefaction curve, Rank abundance curve
  • Beta-diversity Analysis, including Ordination (CCA, RDA, DPCoA, NMDS, MDS, PCoA), Unifrac distance analysis, UPGMA (Unweighted Pair-group Method with Arithmetic Means)
  • Phylogenetic Trees, including OTU co-occurrence networks & Metacoder
  • Statistics Between Groups: DESeq2, ANOSIM, LEfSE
  • Metagenome Functional Prediction using Picrust2

• • Data QC and trimming
  • Amplicon Sequence Variants (ASV) table
  • Taxonomic characterization
  • Alpha diversity analysis
  • Beta diversity analysis
  • Phylogenetic tree

• • Sequence QC
  • OTUs (Operational Taxonomic Units) Clustering and Filtering
  • Species Annotation by comparing unique sequences against a curated reference database from NCBI
  • OTU Analysis, including KRONA results, OTUs Heatmap, Taxonomic Abundance and up to 5 Venn diagrams*.
  • Alpha-diversity Analysis, including Alpha Indices table with ANOVA, Rarefaction curve, Rank abundance curve.
  • Beta-diversity Analysis, including Ordination (CCA, RDA, PCoA, NMDS, MDS) and PCoA Plots.

• • Sequence QC
  • mCs detection, methylation level calculation
  • Methylation level and frequency distribution
  • Differentially Methylated Site (DMS) detection
  • Differentially Methylated Regions (DMRs), Differentially Methylated Promoter (DMPs) detection and annotation
  • Function enrichment of DMR-associated genes and DMP-associated genes
  • Visualization of BS seq data
  • Comparative analysis (among samples)

• • Sequence QC
  • Mapping onto reference genome
  • Peak calling
  • Motif prediction
  • Peak annotation and functional analysis of peak-associated genes
  • Summary of differential peaks and functional analysis of differential peak related genes
  • Visualization of ChIP-seq data

• • Data trimming to pick out the best reads comprising 40X coverage
  • Assemble the reads into long contigs, polish the assembly using the PacBio reads
  • Error correction with Illumina clean reads
  • Annotate the assembled genome with open reading frames
  • Assembly of Plasmids

• • Data trimming to pick out the best reads comprising 40X coverage
  • Assemble the reads into long contigs, polish the assembly using the PacBio reads
  • Error correction with Illumina clean reads
  • Annotate the assembled genome with open reading frames 

• • Data intergration from whole genome sequencing (hybrid assembly of Pacbio long reads and Illumina short reads) and (no.) sets of Transcriptomic data (6Gb/12Gb each)
  • Assemble the reads into long contigs with RNA evidences
  • Annotate the assembled genome with open reading frames
  • Deliverables in fna and gtf format