LACA: Long Amplicon Consensus Analysis

LACA is a reproducible and scalable workflow for Long Amplicon Consensus Analysis, e.g., 16S rRNA gene. Using snakemake as the job controller, LACA is wrapped into a python package for development and mainteniance. LACA provides an end-to-end solution from bascecalled reads to the final count matrix.

Important: LACA is only tested in Linux systems, i.e., Ubuntu.

preprint on bioRxiv

Docker image

The easiest way to use LACA is to pull the docker image from Docker Hub for cross-platform support.

docker pull yanhui09/laca

To use the docker image, you need to mount your data directory, e.g., pwd, to the /home in the container.

docker run -it -v `pwd`:/home --privileged yanhui09/laca

Installation from GitHub repository

Conda is the only required dependency prior to installation. Miniconda is enough for the whole pipeline.

1. Clone the Github repository and create an isolated conda environment

   git clone https://github.com/yanhui09/laca.git
   cd laca
   conda env create -n laca -f env.yaml 

   You can speed up the whole process if mamba is installed.

   mamba env create -n laca -f env.yaml 

2. Install LACA with pip

To avoid inconsistency, we suggest installing LACA in the above conda environment

conda activate laca
pip install --editable .

At this moment, LACA uses a compiled but tailored guppy for barcode demultiplexing (in our lab).
Remember to prepare the barcoding files in guppy if new barcodes are introduced. Click me

Example

laca init -b /path/to/basecalled_fastqs -d /path/to/database    # init config file and check
laca run all                                         # start analysis

Usage

Usage: laca [OPTIONS] COMMAND [ARGS]...

  LACA: a reproducible and scaleable workflow for Long Amplicon Consensus
  Analysis. To follow updates and report issues, see:
  https://github.com/yanhui09/laca.

Options:
  -v, --version  Show the version and exit.
  -h, --help     Show this message and exit.

Commands:
  init  Prepare the config file.
  run   Run LACA workflow.

LACA is easy to use. You can start a new analysis in two steps using laca init and laca run .

Remember to activate the conda environment if LACA is installed in a conda environment.

conda activate laca

1. Intialize a config file with laca init

laca init will generate a config file in the working directory, which contains the necessary parameters to run LACA.

Usage: laca init [OPTIONS]

  Prepare config file for LACA.

Options:
  -b, --bascdir PATH              Path to a directory of the basecalled fastq
                                  files. Option is mutually exclusive with
                                  'merge', 'merge_parent', 'demuxdir'.
  -x, --demuxdir PATH             Path to a directory of demultiplexed fastq
                                  files. Option is mutually exclusive with
                                  'merge', 'merge_parent', 'bascdir'.
  --merge PATH                    Path to the working directory of a completed
                                  LACA run  [Mutiple]. Runs will be combined
                                  if --merge_parent applied. Option is
                                  mutually exclusive with 'bascdir',
                                  'demuxdir'.
  --merge-parent PATH             Path to the parent of the working
                                  directories of completed LACA runs. Runs
                                  will be combined if --merge applied. Option
                                  is mutually exclusive with 'bascdir',
                                  'demuxdir'.
  -d, --dbdir PATH                Path to the taxonomy databases.  [required]
  -w, --workdir PATH              Output directory for LACA.  [default: .]
  --demuxer [guppy|minibar]       Demultiplexer.  [default: guppy]
  --fqs-min INTEGER               Minimum number of reads for the
                                  demultiplexed fastqs.  [default: 1000]
  --no-pool                       Do not pool the reads for denoising.
  --subsample                     Subsample the reads.
  --no-chimera-filt               Do not filter chimeric reads.
  --no-primer-check               Do not check primer pattern.
  --cluster [isONclust|umapclust|meshclust]
                                  Cluster approaches.  [Mutiple]  [default:
                                  isONclust, meshclust]
  --consensus [kmerCon|miniCon|isoCon|umiCon]
                                  Consensus methods.  [Mutiple]  [default:
                                  kmerCon]
  --quant [seqid|minimap2]        Create abundance matrix by sequence id or
                                  minimap2.  [Mutiple]  [default: seqid]
  --uchime                        Filter chimeras by uchime-denovo in vsearch.
  --jobs-min INTEGER              Number of jobs for common tasks.  [default:
                                  2]
  --jobs-max INTEGER              Number of jobs for threads-dependent tasks.
                                  [default: 6]
  --ont                           Use config template for ONT reads. Option is
                                  mutually exclusive with 'isoseq'.
  --isoseq                        Use config template for PacBio CCS reads.
                                  Option is mutually exclusive with 'ont'.
  --longumi                       Use primer design from longumi paper (https:
                                  //doi.org/10.1038/s41592-020-01041-y).
                                  Option is mutually exclusive with
                                  'simulate'.
  --simulate                      Use config template for in silicon test.
                                  Option is mutually exclusive with 'longumi'.
  --clean-flags                   Clean flag files.
  -h, --help                      Show this message and exit.

2. Start analysis with laca run

laca run will trigger the full workflow or a specfic module under defined resource accordingly. Get a dry-run overview with -n. Snakemake arguments can be appened to laca run as well.

Usage: laca run [OPTIONS] {demux|qc|clust|kmerCon|miniCon|isoCon|umiCon|quant|
                taxa|tree|all|merge|initDB|simulate} [SNAKE_ARGS]...

  Run LACA workflow.

Options:
  -w, --workdir PATH     Working directory for LACA.  [default: .]
  -c, --configfile FILE  Config file for LACA. Use config.yaml in working
                         directory if not specified.
  -j, --jobs INTEGER     Maximum jobs to run in parallel.  [default: 6]
  -m, --maxmem FLOAT     Specify maximum memory (GB) to use. Memory is
                         controlled by profile in cluster execution.
  --profile TEXT         Snakemake profile for cluster execution.
  -n, --dryrun           Dry run.
  -h, --help             Show this message and exit.