.. _longreads-mode: Long Reads Analysis Mode ========================= The long reads analysis mode (``--type longreads``) is designed for processing PacBio or Nanopore long-read sequencing data. This mode uses Flye for metagenomic assembly, which is optimized for long-read technologies. Overview -------- The long reads mode is optimized for third-generation sequencing technologies (PacBio and Nanopore) that produce longer reads compared to Illumina short-read sequencing. Longer reads enable better assembly of complex metagenomic communities and improved detection of complete gene clusters. Workflow -------- The long reads workflow consists of the following main steps: 1. **Quality Control** (FastQC, optional for DNA) - FastQC quality assessment (primarily for RNA-seq data) - DNA long reads typically skip QC/trimming steps as they are less affected by adapters 2. **Taxonomic Filtering** (Kraken2, optional) - Taxonomic classification using Kraken2 (applied to RNA-seq data) - Extraction of reads matching specified taxonomy - Can be skipped with ``--skip_kraken_extraction`` 3. **Assembly** (Flye) - Long-read metagenomic assembly using Flye - Supports multiple Flye modes for different sequencing technologies - Configurable via ``--flye_mode`` parameter 4. **Gene Prediction** (Pyrodigal) - Prodigal-based gene finding optimized for metagenomic data - Generates protein sequences (FAA) and gene annotations (GFF) 5. **CAZyme Annotation** (run_dbCAN) - CAZyme identification using dbCAN database - CGC (CAZyme Gene Cluster) detection - Substrate prediction 6. **Read Mapping** (Minimap2 for DNA, BWA-MEM for RNA) - Mapping of long DNA reads back to assembled contigs using Minimap2 - Mapping of RNA reads (if provided) using BWA-MEM for expression analysis - Coverage calculation for genes and CGCs 7. **Abundance Calculation** - Gene-level abundance calculation based on read coverage - CGC abundance and visualization - Generation of bar plots and heatmaps 8. **Report Generation** (MultiQC) - Aggregated quality control and analysis reports Usage ----- .. note:: Before running the pipeline, make sure you have cloned the repository. See :ref:`nextflow-usage` for installation instructions. Basic Usage ~~~~~~~~~~~ The simplest command to run long reads analysis (assuming you are in the ``dbcan-nf`` directory): .. code-block:: bash nextflow run main.nf \ --input samplesheet.csv \ --outdir results \ --type longreads \ -profile docker \ --skip_kraken_extraction # based on the database size of kraken2, you can skip this step if the database is too large. Flye Mode Selection ~~~~~~~~~~~~~~~~~~~ The ``--flye_mode`` parameter allows you to specify the appropriate Flye mode for your sequencing technology: .. code-block:: bash # PacBio HiFi reads (default) nextflow run main.nf \ --input samplesheet.csv \ --outdir results \ --type longreads \ --flye_mode --pacbio-hifi \ -profile docker # PacBio raw reads nextflow run main.nf \ --input samplesheet.csv \ --outdir results \ --type longreads \ --flye_mode --pacbio-raw \ -profile docker # Nanopore raw reads nextflow run main.nf \ --input samplesheet.csv \ --outdir results \ --type longreads \ --flye_mode --nano-raw \ -profile docker # Nanopore high-quality reads nextflow run main.nf \ --input samplesheet.csv \ --outdir results \ --type longreads \ --flye_mode --nano-hq \ -profile docker With RNA-seq Data ~~~~~~~~~~~~~~~~~ When RNA-seq transcriptome data is provided in the samplesheet, the pipeline will: - Process RNA reads through quality control (FastQC + TrimGalore) - Apply Kraken2 taxonomic filtering (if enabled) - Map RNA reads to assembled contigs using BWA-MEM - Calculate RNA-based abundance for expression analysis - Generate separate DNA and RNA abundance plots Example with RNA-seq: .. code-block:: bash nextflow run main.nf \ --input samplesheet_with_rna.csv \ --outdir results \ --type longreads \ --flye_mode --pacbio-hifi \ -profile docker Skipping Steps ~~~~~~~~~~~~~~ You can skip certain steps if needed: .. code-block:: bash nextflow run main.nf \ --input samplesheet.csv \ --outdir results \ --type longreads \ --skip_kraken_extraction \ -profile docker Flye Mode Options ----------------- The ``--flye_mode`` parameter accepts the following Flye assembly modes: .. list-table:: Flye Mode Options :widths: 20 80 :header-rows: 1 * - Mode - Description * - ``--pacbio-hifi`` - PacBio HiFi (high-fidelity) reads. Default mode. Best for high-quality PacBio data. * - ``--pacbio-raw`` - PacBio raw (CLR) reads. Use for standard PacBio sequencing data. * - ``--nano-raw`` - Nanopore raw reads. Use for standard Nanopore sequencing data. * - ``--nano-hq`` - Nanopore high-quality reads. Use for Q20+ or similar high-quality Nanopore data. Output Files ------------ The pipeline generates output files organized in the following directory structure: Assembly Results ~~~~~~~~~~~~~~~~ - ``flye/`` - ``*_assembly.fasta.gz``: Assembled contigs in FASTA format (gzipped) - Assembly statistics and reports Gene Prediction ~~~~~~~~~~~~~~~ - ``pyrodigal/`` - ``*.faa.gz``: Predicted protein sequences (gzipped) - ``*.gff.gz``: Gene annotations in GFF format (gzipped) CAZyme Annotation ~~~~~~~~~~~~~~~~~ - ``rundbcan/`` - ``*_dbcan/``: Directory containing all run_dbCAN results Files in this directory: - ``overview.tsv``: CAZyme annotation overview - ``dbCAN_hmm_results.tsv``: HMM-based CAZyme predictions - ``dbCANsub_hmm_results.tsv``: Subfamily predictions - ``diamond.out``: DIAMOND search results - ``*_cgc.gff``: CGC annotations - ``*_cgc_standard_out.tsv``: CGC standard output - ``*_substrate_prediction.tsv``: Substrate predictions - ``*_synteny_pdf/``: Synteny plots for CGCs Read Mapping ~~~~~~~~~~~~ - ``minimap2/``: Minimap2 alignment files for long DNA reads - ``*.bam``: Aligned reads - ``*.bam.bai``: BAM index files - ``bwa_index_mem/``: BWA-MEM alignment files for RNA reads (if provided) - ``*.bam``: Aligned reads - ``*.bam.bai``: BAM index files Coverage and Abundance ~~~~~~~~~~~~~~~~~~~~~~~ - ``dbcan_utils_cal_coverage/`` - ``*_depth.txt``: Gene coverage depth files - ``dbcan_utils_cal_abund/`` - ``*_abund/``: Abundance calculation results Files in this directory: - ``*_abund.txt``: Gene abundance values - ``*_cgc_abund.txt``: CGC abundance values - ``dbcan_plot/`` - ``*_pdf/``: Visualization plots Files in this directory: - ``heatmap.pdf``: Abundance heatmap - ``ec.pdf``: EC number distribution - ``family.pdf``: CAZyme family distribution - ``subfamily.pdf``: Subfamily distribution - ``cgc_depth_plot/`` - ``*_cgc_depth.tsv``: CGC depth coverage data - ``*_cgc_depth.pdf``: CGC depth plots Quality Control ~~~~~~~~~~~~~~~ - ``fastqc/``: FastQC reports (for RNA-seq data) - ``trimgalore/``: TrimGalore reports (for RNA-seq data) - ``multiqc/``: MultiQC aggregated report Pipeline Information ~~~~~~~~~~~~~~~~~~~~ - ``pipeline_info/``: Execution reports, parameters, and software versions Key Features ------------ - **Long-Read Optimized**: Uses Flye assembler optimized for PacBio and Nanopore reads - **Flexible Flye Modes**: Supports multiple Flye modes for different sequencing technologies - **Dual Mapping**: Minimap2 for long DNA reads, BWA-MEM for RNA reads - **Complete Gene Clusters**: Longer reads enable better assembly of complete CGCs - **RNA-seq Integration**: Optional RNA-seq support for expression analysis Advantages of Long Reads ------------------------ - **Better Contiguity**: Longer contigs due to long-read sequencing - **Complete Genes**: More complete gene predictions, especially for large genes - **Reduced Fragmentation**: Fewer fragmented gene clusters - **Repeat Resolution**: Better resolution of repetitive regions - **Structural Variants**: Improved detection of structural variations Considerations -------------- - **Computational Requirements**: Long-read assembly typically requires more memory and time - **Error Rates**: Long reads may have higher error rates than short reads - **Coverage**: Lower coverage requirements compared to short reads - **Cost**: Long-read sequencing may be more expensive per base Best Practices -------------- 1. **Choose Correct Mode**: Select the appropriate ``--flye_mode`` for your sequencing technology 2. **Quality Assessment**: Review FastQC reports for RNA-seq data 3. **Coverage Planning**: Long reads require less coverage than short reads (typically 20-30x) 4. **Resource Allocation**: Ensure sufficient memory for Flye assembly When to Use Long Reads Mode ---------------------------- **Recommended**: - PacBio or Nanopore sequencing data - When complete gene clusters are important - When dealing with complex or repetitive regions - When structural variation detection is needed **Not Recommended**: - Illumina short-read data (use ``--type shortreads`` instead) - When computational resources are very limited - When only basic CAZyme annotation is needed Example Results --------------- For example visualizations from long reads mode, see :ref:`nextflow-results-examples`. See Also -------- - :ref:`shortreads-mode` - Short reads mode documentation - :ref:`assemfree-mode` - Assembly-free mode documentation - :ref:`nextflow-parameters` - Complete parameter reference - :ref:`nextflow-results-examples` - Example results and visualizations - `Flye Documentation `_ - Flye assembler documentation