Features

• FMAP provides a more sensible reference protein sequence database based on UniRef90.
• Identification of differentially-abundant genes (KEGG Orthology)
• Mapping differentially-abundant genes to pathways (KEGG Pathway)
• Mapping differentially-abundant genes to operons (ODB3)

Downloads

• GitHub page
• Example script
  
  • The example data was designed from Case study 4.
  • The whole process needs 1.2 GB disk space and < 4GB RAM. It will take about 1 hour with 4 cpus (Intel 2.60GHz).
• Example script (custom)
  
  • A custom database of "Fusobacterium nucleatum" and "UniRef100" will be built using "FMAP_database.pl" and "FMAP_prepare.pl" instead of "FMAP_download.pl".
  • It will take about 6 hours with 4 cpus (Intel 2.60GHz).
• FMAP_ubuntu.vmdk (virtual machine disk, login: fmapuser, password: fmap)

Requirements

• Perl - Scripting language
• R - Statistical computing
• Statistics::R - Perl interface with the R statistical program
  
  • Use CPAN to install the module
    

    perl -MCPAN -e 'install Statistics::R'

  • or download the source and compile manually
    

    wget 'http://search.cpan.org/CPAN/authors/id/F/FA/FANGLY/Statistics-R-0.33.tar.gz'
    tar zxf Statistics-R-0.33.tar.gz
    cd Statistics-R-0.33
    perl Makefile.PL
    make
    make test
    make install

• Mapping program providing BLASTX search of sequencing reads: DIAMOND or USEARCH
• Linux commands: wget, cat, sort
• Bio::DB::Taxonomy - Access to a taxonomy database (which is required only if you want to build a custom database.)

FMAP workflow

• You can generate a count table for metagenomeSeq, DESeq, or edgeR using the option "-c" of "FMAP_table.pl".

Case study 1 - SRP002423: Crohn's disease (8) vs. Healthy (4)

• Data type: metagenomic sequencing (454, Single-end)
• Data preprocessing: human sequence removal
• Sample selection: 12 samples used in the publication of PMID:23209564
• Result tables (mapping, orthology abundance, operon, pathway): MS Excel, Text zip
• Heatmap of differentially-abundant genes (log2 RPKM)

• Differentially-abundant operons
  

• Pathways enriched with differentially-abundant genes
  

Case study 2 - SRP000109: ocean microbiome 25m (20) vs. 500m (20)

• Data type: metagenomic sequencing (454, Single-end)
• Result tables (mapping, orthology abundance, operon, pathway): MS Excel, Text zip
• Photosynthesis

• Caprolactam degradation

Case study 3 - SRP050543: root caries (9) vs. sound root surfaces (10)

• Data type: metatranscriptomic sequencing (Illumina, Single-end, Read length: 101)
• Data preprocessing: NGS QC Toolkit and human sequence removal
• Result tables (mapping, orthology abundance, operon, pathway): MS Excel, Text zip
• Heatmap of differentially-abundant genes (log2 RPKM)

• Example pathways
  
  • Flagellar assembly
  • Bacterial chemotaxis
  • Phosphotransferase system (PTS)

Case study 4 - SRP044400: schizophrenia (5) vs. none (9)

• Data type: metagenomic sequencing (Illumina, Single-end, Read length: 101)
• Data preprocessing: NGS QC Toolkit and human sequence removal
• Sample selection: 14 SRA samples used in the analysis were selected based on QC-passed read counts.
• Result tables (mapping, orthology abundance, operon, pathway): MS Excel, Text zip
• Nitrotoluene degradation pathway including pyruvate ferredoxin oxidoreductase operon

References

• UniProt: a hub for protein information. Nucleic Acids Res. 2015 Jan;43(Database issue):D204-12.
• Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014 Jan;42(Database issue):D199-205.
• KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 Jan 1;28(1):27-30.
• ODB: a database for operon organizations, 2011 update. Nucleic Acids Res. 2011 Jan;39(Database issue):D552-5.
• ODB: a database of operons accumulating known operons across multiple genomes. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D358-62.

Citation

• Kim J, Kim MS, Koh AY, Xie Y, Zhan X.
  "FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies"
  BMC Bioinformatics. 2016 Oct 10;17(1):420.
  PMID: 27724866

Contacts

• Jiwoong Kim (Jiwoong.Kim@UTSouthwestern.edu)
• Min Soo Kim (MinS.Kim@UTSouthwestern.edu)
• Xiaowei Zhan (Xiaowei.Zhan@UTSouthwestern.edu)