Welcome to SqueezeMeta’s documentation!

SqueezeMeta is a fully automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support allowing the co-assembly of related metagenomes and the retrieval of individual metagenome-assembled genomes (MAGs) via binning procedures. Thus, SqueezeMeta features several characteristics:

1. Several assembly and co-assembly algorithms and strategies for short and long reads

2. Several binning algorithms for the recovery of metagenome-assembled genomes (MAGs)

3. Taxonomic annotation, functional annotation and quantification of genes, contigs, and bins

4. Support for the annotation and quantification of pre-existing assemblies or collections of genomes

5. Support for de-novo metatranscriptome assembly and hybrid metagenomics/metatranscriptomics projects

6. Support for the annotation of unassembled shotgun metagenomic reads

7. An R package to easily explore your results, including bindings for microeco and phyloseq

Note

Check out the Use cases section for more information.

SqueezeMeta uses a combination of custom scripts and external software packages for the different steps of the analysis:

1. Assembly

2. RNA prediction and classification

3. ORF (CDS) prediction

4. Homology searching against taxonomic and functional databases

5. Hmmer searching against Pfam database

6. Taxonomic assignment of genes

7. Functional assignment of genes (OPTIONAL)

8. Blastx on parts of the contigs with no gene prediction or no hits

9. Taxonomic assignment of contigs, and check for taxonomic disparities

10. Coverage and abundance estimation for genes and contigs

11. Estimation of taxa abundances

12. Estimation of function abundances

13. Merging of previous results to obtain the ORF table

14. Binning with different methods

15. Binning integration with DAS tool

16. Taxonomic assignment of bins, and check for taxonomic disparities

17. Checking of bins with CheckM2 (and optionally classify them with GTDB-Tk)

18. Merging of previous results to obtain the bin table

19. Merging of previous results to obtain the contig table

20. Prediction of kegg and metacyc patwhays for each bin

21. Final statistics for the run

22. Generation of tables with aggregated taxonomic and functional profiles

Detailed information about the different steps of the pipeline can be found in the Scripts, output files and file format section.

Contents

• Use cases
  • Choosing an assembly strategy
  • Analyzing metatranscriptomes
  • Combined analysis of metagenomes and metatranscriptomes
  • Alternative analysis modes
  • Working with Oxford Nanopore MinION and PacBio reads
  • Working in a low-memory environment
  • Tips for working in a computing cluster
  • Downstream analysis of SqueezeMeta results
  • Analyzing SqueezeMeta results in your desktop computer
• Installation and testing
  • Downloading or building databases
  • Updating SqueezeMeta
  • Vendored tools
• Execution, restart and running scripts
  • Scripts location
  • Execution
  • Arguments
  • Example SqueezeMeta call
  • The samples file
  • Restart
  • Running scripts
• Advanced annotation
  • Using a user-supplied database for taxonomic annotation
  • Using external databases for functional annotation
  • Extra-sensitive detection of ORFs
• Scripts, output files and file format
  • Step 1: Assembly
  • Step 2: RNA finding
  • Step 3: Gene prediction
  • Step 4: Homology searching against taxonomic (nr) and functional (COG, KEGG) databases
  • Step 5: HMM search for Pfam database
  • Step 6: Taxonomic assignment
  • Step 7: Functional assignment
  • Step 8: Blastx on parts of the contigs without gene prediction or without hits
  • Step 9: Taxonomic assignment of contigs
  • Step 10: Mapping of reads to contigs and calculation of abundance measures
  • Step 11: Calculation of the abundance of all taxa
  • Step 12: Calculation of the abundance of all functions
  • Step 13: Creation of the ORF table
  • Step 14: Binning
  • Step 15: Merging bins with DAS Tool
  • Step 16: Taxonomic assignment of bins
  • Step 17: Running CheckM2 and optionally GTDB-Tk on bins
  • Step 18: Creation of the bin table
  • Step 19: Creation of the contig table
  • Step 20: Prediction of pathway presence in bins using MinPath
  • Step 21: Final statistics for the run
  • Step 22: Calculation of summary tables for the project
• Alternative analysis modes
  • Taxonomic and functional annotation of unassembled reads
  • Fast screening of unassembled short reads for a particular function
  • Mapping reads to a reference
  • Functional and taxonomic annotation of genes and genomes
• The SQMtools R package
  • Design philosophy
  • Loading data into SQMtools
  • Creating subsets of your data
  • Combining SQM and SQMlite objects
  • Creating plots and exporting data
  • Interacting with other R packages
  • Working with bins
  • Data normalization strategies
  • List of functions and detailed documentation
• Adding new binners and assemblers
• Utility scripts
  • Compressing a SqueezeMeta project into a zip file
  • Generating summary tables
  • Estimation of the sequencing depth needed for a project
  • Adding new databases to an existing project
  • Integration with external tools
• Explanation of SqueezeMeta algorithms
  • The LCA algorithm
  • Consensus taxonomic annotation for contigs and bins
  • Limitations of taxonomic assignments from shotgun metagenomics data
  • The fun3 algorithm
  • Doublepass: blastx on contig gaps
  • The COVER algorithm