Lemur

Lemur is a tool for rapid and accurate taxonomic profiling on long-read metagenomic datasets

Installation

Obtaining the CLI

lemur can be installed via

conda install -c bioconda lemur

Alternative option

lemur can also be installed by copying the ./lemur file to anywhere on your system's path.

Obtaining the database

The current database (RefSeq v221 bacterial and archaeal genes, and RefSeq v222 fungal genes) is available at

Usage

Basic usage

For minimal example you will need to specify the following parameters: the input FASTQ file containing the reads (-i/--input flag), a directory to store the Lemur output (-o/--output flag), path to the database directory (-d/--db-prefix flag), path to the taxonomy file in the TSV format (--tax-path flag), and desired taxonomic aggregation rank (-r/--rank flag).

lemur -i examples/example-data/example.fastq \
      -o example-output \
      -d examples/example-db \
      --tax-path examples/example-db/taxnomy.tsv \
      -r species

The output in the example-output folder will consist of raw relative_abundance.tsv file with taxonomic IDs, lineage information, and inferred relative abundance (F column). There will also be a relative_abundance-[rank].tsv where the rank is specified by the -r/--rank flag (e.g. in the above example it will be species). The *P_rgs_df* files capture individual inferred probabilities of a given read comign from a particular taxon.

Parameter descriptions

Main arguments:

  -i INPUT, --input INPUT
                        Input FASTQ file for the analysis
  -o OUTPUT, --output OUTPUT
                        Folder where the Lemur output will be stored
  -d DB_PREFIX, --db-prefix DB_PREFIX
                        Path to the folder with marker gene DB for each marker gene
  --tax-path TAX_PATH   Path to the taxonomy.tsv file 
  -t NUM_THREADS, --num-threads NUM_THREADS
                        Number of threads you want to use
  --aln-score {AS,edit,markov}, --aln-score {AS,edit,markov}
                        AS: Use SAM AS tag for score, edit: Use edit-type distribution for score, markov: Score CIGAR as Markov chain
  -r RANK, --rank RANK  Taxonomic rank used for final aggregation

minimap2 arguments:

  --mm2-N MM2_N         minimap max number of secondary alignments per read [50]
  --mm2-K MM2_K         minibatch size for minimap2 mapping [500M]
  --mm2-type {map-ont,map-hifi,map-pb,sr}
                        ONT: map-ont [map-ont], PacBio (hifi): map-hifi, PacBio (CLR): map-pb, short-read: sr

Miscellaneous arguments:

  --keep-alignments     Keep SAM files after the mapping (might require a lot of disk space)
  -e LOG_FILE, --log-file LOG_FILE
                        File for logging [default: stdout]
  --sam-input SAM_INPUT Use a SAM file as input and skip read mapping step
  --verbose             Enable DEBUG level logging
  --save-intermediate-profile
                        Will save abundance profile at every EM step
  --width-filter        Apply uniform coverage filter

Additional flags:

  -h, --help            show usage help message and exit
  -v, --version         show program's version number and exit