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Minimetagenomics and Comparative Genomics for 2019Fall Raman-Sorted Single Cell Data EBPR

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ComparativeMetaGenome

This is the repository for Raman-Sorting Single-Cell Analysis of Minimetagenomics and Comparative Genomics


🦠 💦 Minimetagenomics Ananlysis

Code is stored in ./minimetagenomics/ with scripts and Snakemake-enabled workflow to achieve

  • Assembly

    • trim and QC by trimmomatic
    • contigs assembly by SPAdes
    • statistics of assembled contigs by quast
    • filtration by anvio
  • Mapping

    • mapping trimmed reads to filtered contigs by bowtie2
    • processing mapping results by samtools
    • duplicates removal by picard
  • Binning

    • contigs binning by anvio and CONCOCT
    • QC for bins by manual refinement and checkM
  • Functional Analysis

    • metagenome-assembled genome (MAG) recognized by gtdb-tk
    • Gene Calling: ORFs identified by metagenemark and prodigal
    • Gene Annotation: coding DNA sequences (CDSs) annotated by kofamscan
    • Metabolic Pathway: KEGG
  • Taxonomy for MAG and others

    • MAG 16S rRNA extracted by PROKKA
    • MAG taxonomy identified by BLAST
    • Other contigs taxnomy analyzed by Diamond
    • Phylogenetic tree established by NCBI taxonomy tree

🧬 🔬 Comparative Genomic Analysis

Code is stored in ./compgenome/ with workflow in JupyterNoteBook for comparative analysis,

with file names labeled by figure number to achieve data mining, analysis, and visualization.

Figure 1 for pipeline and Figure 4 for PAO metabolic models are designed by Microsoft Powerpoint.

  • Genome-scale metabolic modeling (GEMs) establishement and curation

    • Establish from .faa file using CarveMe
    • Improve and check quality of the GEM models using MEMOTE
    • Optimize and incorporate the ppk and ppx reactions into the GEMs if the genes are detected within the genome using COBRApy
  • Microbial metabolic & ecological interaction analysis

    • Microbial metabolic interaction based on GEMs using SMETANA
  • Comparative phylogenetic analysis

    • Stochastic OU process was applied to guide evolutionary pathways to improve the phylogram tree
    • Blomberg K statistic was calculated based on the R code with demonstrations in R Markdown

🧰 💻 Operon Analysis

Online web server was applied based on Operon Mapper published in Bioinformatics.

This is applied with the aim to identify operon for ppk and ppx

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