main.nf

def pipelineVersion = params.pipeline_version ?: "0.3.0"

def helpMessage = """
================================================================================
  P i p e V a r   P i p e l i n e
  Version: ${pipelineVersion}
================================================================================
QUICK USAGE
  Single sample mode:
    nextflow run main.nf [--bam sample.bam | --vcf sample.vcf] [options]

  CSV batch mode:
    nextflow run main.nf --input_csv samples.csv --bam true [options]
    nextflow run main.nf --input_csv samples.csv --vcf true [options]

--------------------------------------------------------------------------------
EXECUTION PROFILES (nextflow.config)
--------------------------------------------------------------------------------
  -profile standard
      Default. SLURM executor + Singularity backend.

  -profile slurm_singularity
      Explicit SLURM + Singularity.

  -profile local_singularity
      Local executor + Singularity.

  -profile local_docker
      Local executor + Docker.

  Container mount paths (used by Singularity/Docker profiles):
    --annovar_host_path <DIR>   Host path mounted to /annovar
    --phenosv_host_path <DIR>   Host path mounted to /PhenoSV/train_data

--------------------------------------------------------------------------------
INPUT MODES
--------------------------------------------------------------------------------
  1) Single sample BAM/CRAM mode
     Required:
       --bam <FILE>
       --ref_fa <FILE>
       One phenotype source:
         --note <FILE>   (clinical note; pipeline runs PhenoTagger)
         --hpo  <FILE>   (HPO IDs; pipeline skips PhenoTagger)

  2) Single sample VCF mode
     Required:
       --vcf <FILE>
       --ref_fa <FILE>
       --mode <snp|sv>
       One phenotype source:
         --note <FILE> or --hpo <FILE>

  3) CSV batch BAM mode
     Required:
       --input_csv <FILE> --bam true --ref_fa <FILE>
     CSV columns:
       sample,file_path,note_path
       Optional age field for CSV prioritization flows:
         sample,file_path,note_path,age_of_onset
         sample,file_path,note_path,age
       Notes:
         age_of_onset is preferred if both columns exist
         age is interpreted per CSV row (per sample), not globally
         empty age is allowed (treated as not provided)
         non-empty age must be xd/xm/xy or integer years
         examples: 10d, 9m, 7y, 7 (normalized to 7y)
     Note handling:
       default            -> note_path treated as clinical note (PhenoTagger ON)
       --note no          -> note_path treated as HPO file (PhenoTagger OFF)

  4) CSV batch VCF mode
     Required:
       --input_csv <FILE> --vcf true --ref_fa <FILE> --mode <snp|sv>
     CSV columns:
       sample,file_path,note_path
       Optional age field for CSV prioritization flows:
         sample,file_path,note_path,age_of_onset
         sample,file_path,note_path,age
       Notes:
         age_of_onset is preferred if both columns exist
         age is interpreted per CSV row (per sample), not globally
         empty age is allowed (treated as not provided)
         non-empty age must be xd/xm/xy or integer years
         examples: 10d, 9m, 7y, 7 (normalized to 7y)

--------------------------------------------------------------------------------
CORE OPTIONS
--------------------------------------------------------------------------------
  --mode <snp|sv>          Run only SNP or SV branch. Omit to run both where applicable.
  --type <ont|pacbio|short>
                           Sequencing type for BAM/CRAM flows. Default: ont
  --light <yes|no>         Use lightweight SNP/SV models where supported. Default: no
  --genome <hg38|grch38>   Genome build for ExpansionHunter catalog. Default: hg38

--------------------------------------------------------------------------------
FILTERING OPTIONS
--------------------------------------------------------------------------------
  --gnomad <FLOAT>         Max gnomAD AF filter for SNP prioritization. Default: 0.0001
  --inheritance_mode <ml|omim|gnomad>
                           Dominant/recessive assignment mode. Default: ml (ML-first, OMIM fallback)
                           Note: gnomad uses gnomAD constraint (LOEUF) fallback lists.
  --include_clinvar_report <yes|no>
                           Include ClinVar-only calls in final prioritized report outputs. Default: yes
  --allow_unphased_comphet <yes|no>
                           Treat unphased 0/1 or 1/0 AR het pairs as compound het in final prioritization. Default: no
  --rankscore <FLOAT>      Minimum RankScore cutoff. Default: 0.50
  --rankscore_softwares <CSV> Comma-separated RankScore software list. Default: all
  --rankvar <FLOAT>        Minimum RankVar score cutoff. Default: 0.05
  --gq <INT>               Minimum genotype quality. Default: 20
  --ad <INT>               Minimum allele depth. Default: 15
  --phen2gene_filter <INT> Number of top Phen2Gene genes used for targeted mode. Default: 500
  --target <yes|no>        If yes, run targeted calling in phenotype-derived gene regions.

--------------------------------------------------------------------------------
OUTPUT / GENERAL
--------------------------------------------------------------------------------
  --out_prefix <STRING>    Output prefix. Default: PipeVar
  --output_directory <DIR> Output directory. Default: launch directory
  --help                   Print this help text and exit

--------------------------------------------------------------------------------
EXAMPLES
--------------------------------------------------------------------------------
  1) SLURM + Singularity (default), single long-read BAM, full path:
     nextflow run main.nf \\
       -profile standard \\
       --bam /data/sample.bam \\
       --ref_fa /refs/hg38.fa \\
       --note /data/note.txt \\
       --out_prefix patient1 \\
       --type ont

  2) Local + Docker, single VCF SNP re-annotation:
     nextflow run main.nf \\
       -profile local_docker \\
       --vcf /data/sample.vcf \\
       --ref_fa /refs/hg38.fa \\
       --mode snp \\
       --hpo /data/hpo.txt \\
       --out_prefix patient_vcf_snp

  3) CSV batch BAM with HPO inputs in note_path (PhenoTagger off):
     nextflow run main.nf \\
       -profile slurm_singularity \\
       --input_csv /data/samples.csv \\
       --bam true \\
       --ref_fa /refs/hg38.fa \\
       --note no

  4) Local + Singularity with custom mount sources:
     nextflow run main.nf \\
       -profile local_singularity \\
       --annovar_host_path /project/annovar \\
       --phenosv_host_path /project/train_data \\
       --bam /data/sample.bam \\
       --ref_fa /refs/hg38.fa \\
       --note /data/note.txt

NOTES
  - BAM mode requires index files (.bai for BAM, .crai for CRAM).
  - Reference FASTA index (.fai) must exist.
  - For VCF single-file mode, provide --mode snp or --mode sv.
  - For single-file mode, at least one of --note <FILE> or --hpo <FILE> is required.
  
================================================================================
"""


if (params.help) {
    println(helpMessage)
    exit(0)
}

// ------------------------------------------------------------------
// 0. PARAMETER NORMALIZATION (Fixing Capitalization/Spaces)
// ------------------------------------------------------------------

// We use the Elvis operator (?:) to handle nulls safely, defaulting to empty string if null
// We apply .trim() to remove spaces " ont " -> "ont"
// We apply .toLowerCase() to fix caps "ONT" -> "ont"

def clean_mode   = params.mode   ? params.mode.trim().toLowerCase()   : null
def clean_type   = params.type   ? params.type.trim().toLowerCase()   : 'ont' // Default from params
def clean_light  = params.light  ? params.light.trim().toLowerCase()  : 'no'
def clean_genome = params.genome ? params.genome.trim().toLowerCase() : 'hg38'
def raw_target_param = params.target ?: params.targeted
def clean_target = raw_target_param ? raw_target_param.trim().toLowerCase() : 'no'
def clean_note   = params.note   ? params.note.trim().toLowerCase()   : 'no'
def clean_inheritance_mode = params.inheritance_mode ? params.inheritance_mode.trim().toLowerCase() : 'ml'
def clean_include_clinvar_report = params.include_clinvar_report ? params.include_clinvar_report.trim().toLowerCase() : 'yes'
def clean_allow_unphased_comphet = params.allow_unphased_comphet ? params.allow_unphased_comphet.trim().toLowerCase() : 'no'
def clean_rankscore_softwares = params.rankscore_softwares ? params.rankscore_softwares.toString().trim() : ""

// ------------------------------------------------------------------
// 1. INPUT VALIDATION (Catching Typos)
// ------------------------------------------------------------------

// Define allowed vocabularies
def valid_modes   = ['snp', 'sv']
def valid_types   = ['ont', 'pacbio', 'short']
def valid_genomes = ['hg38', 'grch38']
def valid_inheritance_modes = ['ml', 'omim', 'gnomad']
def valid_yes_no = ['yes', 'no']

// CHECK 1: Validate Mode
if (clean_mode && !valid_modes.contains(clean_mode)) {
    error """
    ================================================================
    ERROR: Invalid Mode Specified
    ================================================================
    You provided: --mode "${params.mode}"
    
    We detected a typo. The valid options are:
      --mode snp
      --mode sv
      
    (Case insensitive, but spelling must match)
    ================================================================
    """
}

// CHECK 2: Validate Data Type
if (!valid_types.contains(clean_type)) {
    error """
    ================================================================
    ERROR: Invalid Data Type
    ================================================================
    You provided: --type "${params.type}"
    
    Valid options are:
      --type ont
      --type pacbio
      --type short
    ================================================================
    """
}

// CHECK 3: Validate Genome
if (!valid_genomes.contains(clean_genome)) {
    error """
    ================================================================
    ERROR: Unsupported Genome Build
    ================================================================
    You provided: --genome "${params.genome}"
    
    This pipeline currently supports:
      --genome hg38
      --genome grch38
    ================================================================
    """
}

// CHECK 4: Validate Inheritance Assignment Mode
if (!valid_inheritance_modes.contains(clean_inheritance_mode)) {
    error """
    ================================================================
    ERROR: Invalid Inheritance Assignment Mode
    ================================================================
    You provided: --inheritance_mode "${params.inheritance_mode}"

    Valid options are:
      --inheritance_mode ml
      --inheritance_mode omim
      --inheritance_mode gnomad

    Notes:
      - ml    = ML-first with OMIM fallback (current script behavior)
      - gnomad maps to LOEUF fallback lists
    ================================================================
    """
}

if (!valid_yes_no.contains(clean_include_clinvar_report)) {
    error """
    ================================================================
    ERROR: Invalid ClinVar Report Toggle
    ================================================================
    You provided: --include_clinvar_report "${params.include_clinvar_report}"

    Valid options are:
      --include_clinvar_report yes
      --include_clinvar_report no
    ================================================================
    """
}

if (!valid_yes_no.contains(clean_allow_unphased_comphet)) {
    error """
    ================================================================
    ERROR: Invalid Unphased CompHet Toggle
    ================================================================
    You provided: --allow_unphased_comphet "${params.allow_unphased_comphet}"

    Valid options are:
      --allow_unphased_comphet yes
      --allow_unphased_comphet no
    ================================================================
    """
}

if (clean_rankscore_softwares && clean_rankscore_softwares.split(",").every { it.trim().isEmpty() }) {
    error """
    ================================================================
    ERROR: Invalid RankScore Software List
    ================================================================
    You provided: --rankscore_softwares "${params.rankscore_softwares}"

    Provide a comma-separated software list, e.g.:
      --rankscore_softwares "REVEL,AlphaMissense,CADD_raw"
    ================================================================
    """
}

// ------------------------------------------------------------------
// INPUT VALIDATION AND ERROR MESSAGES
// ------------------------------------------------------------------

// 1. Check for Missing Inputs
if (!params.input_csv && !params.bam && !params.vcf) {
    error """
    ERROR: No input data specified.
    You must provide one of the following:
      --input_csv <path>  : Path to CSV manifest
      --bam <path>        : Path to single BAM/CRAM file
      --vcf <path>        : Path to single VCF file
    """
}

// 2. Check for Ambiguous CSV Logic (Silent Failure Prevention)
// Your current logic requires params.bam or params.vcf to be truthy to process a CSV.
if (params.input_csv && !params.bam && !params.vcf) {
    error """
    ERROR: Input CSV specified, but data type is undefined.
    When using --input_csv, you must also specify the data type flag to trigger the correct logic:
      --bam true   (if CSV contains BAM/CRAM paths)
      --vcf true   (if CSV contains VCF paths)
    """
}

// 3. Check for Missing Reference (Mandatory for BAM/CRAM)
// Logic: If BAM is being processed (either single or via CSV), Ref is required.
def is_bam_mode = params.bam || (params.input_csv && params.bam)
if (is_bam_mode && !params.ref_fa) {
    error """
    ERROR: Reference FASTA missing.
    Processing BAM/CRAM files requires a reference genome.
    Please specify: --ref_fa <path/to/reference.fasta>
    """
}


// 5. Check for Ref Fasta Index (Optional but recommended)
// If ref is provided, ensure .fai exists to prevent downstream crashes
if (params.ref_fa) {
    def ref_fai = file("${params.ref_fa}.fai")
    if (!ref_fai.exists()) {
        error """
        ERROR: Reference index (.fai) not found.
        Expected at: ${ref_fai}
        Please index your reference: samtools faidx ${params.ref_fa}
        """
    }
}

include { SINGLE_ALIGNMENT_ALL_LONGPHASE } from './subworkflows/single_alignment_all_longphase' 
include { SINGLE_ALIGNMENT_ALL_NGS } from './subworkflows/single_alignment_all_ngs'
include { SINGLE_ALIGNMENT_LONG_SNP } from './subworkflows/single_alignment_long_snp'
include { SINGLE_ALIGNMENT_LONG_SV } from './subworkflows/single_alignment_long_sv'
include { SINGLE_ALIGNMENT_NGS_SNP } from './subworkflows/single_alignment_ngs_snp'
include { SINGLE_ALIGNMENT_NGS_SV } from './subworkflows/single_alignment_ngs_sv'
include { SINGLE_ALIGNMENT_VCF_SNP } from './subworkflows/single_alignment_vcf_snp'
include { SINGLE_ALIGNMENT_VCF_SV } from './subworkflows/single_alignment_vcf_sv'

include { INPUT_CSV_ALIGNMENT_ALL_LONGPHASE } from './subworkflows/input_csv_alignment_all_longphase'
include { INPUT_CSV_ALIGNMENT_ALL_NGS } from './subworkflows/input_csv_alignment_all_ngs'
include { INPUT_CSV_ALIGNMENT_LONG_SNP } from './subworkflows/input_csv_alignment_long_snp'
include { INPUT_CSV_ALIGNMENT_LONG_SV } from './subworkflows/input_csv_alignment_long_sv'
include { INPUT_CSV_ALIGNMENT_NGS_SV } from './subworkflows/input_csv_alignment_ngs_sv'
include { INPUT_CSV_NGS_SNP } from './subworkflows/input_csv_alignment_ngs_snp'
include { INPUT_CSV_ALIGNMENT_VCF_SNP } from './subworkflows/input_csv_alignment_vcf_snp'
include { INPUT_CSV_ALIGNMENT_VCF_SV } from './subworkflows/input_csv_alignment_vcf_sv'




workflow {
	def input_age = null
	if ( params.input_csv ) {
		input_age = Channel
    .fromPath( params.input_csv )
    .splitCsv( header:true )
    .map { row ->
        def out_prefix = row.sample
        def has_age_of_onset = row.containsKey('age_of_onset')
        def has_age = row.containsKey('age')
        def age_source = has_age_of_onset ? 'age_of_onset' : (has_age ? 'age' : null)
        def age_value = ''

        if (age_source != null) {
            def raw_age = row[age_source]
            age_value = raw_age == null ? '' : raw_age.toString().trim().toLowerCase()
            def age_match = (age_value =~ /^(\d+)([dmy])?$/)
            if (age_value && !age_match.matches()) {
                error """
                ERROR: Invalid age value in input CSV for sample '${out_prefix}'.
                Column '${age_source}' must be empty or match one of:
                  - <integer>
                  - <integer><unit> where unit is d/m/y
                Received: '${age_value}'
                """
            }
            if (age_value && !age_match.group(2)) {
                age_value = "${age_match.group(1)}y"
            }
        }

        return tuple(out_prefix, age_value)
    }
		if ( params.vcf ) {
		input_vcf = Channel
    .fromPath( params.input_csv )
    .splitCsv( header:true )
    .map { row ->
        def vcf_file = file(row.file_path, checkIfExists: true)
        def note_file = file(row.note_path, checkIfExists: true)
	def out_prefix = row.sample

        // Return a List/tuple in a specific order
        return tuple(
            row.sample,
            vcf_file,
            note_file,
        )
    			}
	input_bam=null
		}
		else if ( params.bam ) { 
input_bam = Channel
    .fromPath( params.input_csv )
    .splitCsv( header:true )
    .map { row ->
        def bam_file = file(row.file_path, checkIfExists: true)
        
        // FIX: Manipulate the full string path, avoiding 'parent' nullability issues
        // and handle both .bam.bai and .bai conventions automatically.
        def bai_path = bam_file.toString().replaceFirst(/\.bam$/, ".bai")
        def bai = file(bai_path)
        
        // If the standard .bai doesn't exist, try the .bam.bai convention
        if (!bai.exists()) {
             bai = file("${bam_file}.bai")
        }

        // CRAM override
        if (bam_file.name.endsWith('.cram')) {
            bai = file("${bam_file}.crai")
        }
        
        // Ensure index actually exists before returning tuple to avoid downstream staging errors
        if (!bai.exists()) {
            error "Index file not found for ${bam_file}. Looked for: ${bai_path} or ${bam_file}.bai"
        }

        def note_file = file(row.note_path, checkIfExists: true)
	def out_prefix = row.sample
	def bam_file_parent = bam_file.parent
	def note_file_parent = note_file.parent

        // Return the tuple
        return tuple(
            out_prefix,
            bam_file,
            bai,
            note_file,
        )
                        }
	input_vcf=null

		}
	}
        else if ( params.bam != null ) {
Channel
    .fromPath(params.bam ) 
    .map { file ->
        def meta = [ id: file.simpleName ]
        
        // Dynamic Logic: If it ends in cram, look for .crai, else .bai
        // Note: Check if your index naming is file.cram.crai or file.crai
        def index = file.name.endsWith('.cram') 
                    ? file.parent / "${file.name}.crai" 
                    : file.parent / "${file.name}.bai"
        
        return [ file, index ]
    }
    .set { bam }
                out_prefix=Channel.value(params.out_prefix)
        }
        else if ( params.vcf != null ) {
                vcf=Channel.value(params.vcf)
                out_prefix=Channel.value(params.out_prefix)
        }
	//Need to add dict file in case there is we are running haplotypecaller!!!!
	if (params.ref_fa != null && params.light == 'yes' && params.type == 'short' ) {
ref_fa = Channel
    .fromPath(params.ref_fa)
    .map { fa_file ->
        def fai_file = file("${fa_file}.fai")
        def dict_file = file("${fa_file.parent}/${fa_file.baseName}.dict")
        return [ fa_file, fai_file, dict_file ]
    }
    .first()
	}
        else if ( params.ref_fa != null ) {
ref_fa = Channel
    .fromPath(params.ref_fa)
    .map { fa_file ->
        def fai_file = file("${fa_file}.fai")
        return [ fa_file, fai_file ]
    }
    .first()
        }
	// `is_note` controls whether subworkflows run phenotagger ("yes") or treat the input as HPO IDs ("no").
	def is_note = "no"
	if ( params.input_csv ) {
		// CSV mode default: note_path is treated as clinical notes unless user explicitly sets --note no.
		is_note = (clean_note == 'no') ? "no" : "yes"
	}
	else {
		// Single-file mode: `note` must be a file path; `hpo` is used directly as HPO input.
		if ( params.note != null && clean_note != 'yes' && clean_note != 'no' ) {
			note=Channel.value(params.note)
			is_note = "yes"
		}
		else if ( params.hpo != null ) {
			note=Channel.value(params.hpo)
			is_note = "no"
		}
		else {
			error """
			ERROR: Missing phenotype input for single-file mode.
			Provide one of:
			  --note <clinical_note_file>
			  --hpo  <hpo_id_file>
			"""
		}
	}
	output_directory_check=file(params.output_directory)
        if ( !output_directory_check.exists() ) {
	output_directory_check.mkdirs()
        }
	type=Channel.value(params.type)
	def inheritance_mode_script = (clean_inheritance_mode == 'gnomad') ? 'LOEUF' : 'OMIM'
	inheritance_mode=Channel.value(inheritance_mode_script)
	include_clinvar_report=Channel.value(clean_include_clinvar_report)
	allow_unphased_comphet=Channel.value(clean_allow_unphased_comphet)
	gnomad=Channel.value(params.gnomad)
        rankscore_filter=Channel.value(params.rankscore)
	rankscore_softwares=Channel.value(clean_rankscore_softwares)
	rankvar_filter=Channel.value(params.rankvar)
	phen2gene_top_n=Channel.value(params.phen2gene_filter)
	gq=Channel.value(params.gq)
	ad=Channel.value(params.ad)
	short_snp_caller = Channel.value(clean_light == 'yes' ? 'haplotypecaller' : 'deepvariant')
	long_snp_caller = Channel.value(clean_light == 'yes' ? 'nanocaller' : 'clair3')
	def target="null"
	if ( clean_target == 'yes' ) {
		target = "yes"
	}
	if ( params.input_csv ) {
        if ( input_vcf != null ) {
            if ( params.mode == 'sv' ) {
                INPUT_CSV_ALIGNMENT_VCF_SV(input_vcf, input_age, ref_fa, phen2gene_top_n, is_note, target, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
            }
            else if ( params.mode == 'snp' ) {
                INPUT_CSV_ALIGNMENT_VCF_SNP(input_vcf, input_age, ref_fa, rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
            }
        }
        else if ( input_bam != null ) {
	            if ( params.type == 'short' ) {
	                    if ( params.mode == 'sv' ) {
	                        INPUT_CSV_ALIGNMENT_NGS_SV(input_bam, input_age, ref_fa,  is_note, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	                    else if ( params.mode == 'snp' ) {
                        INPUT_CSV_NGS_SNP(input_bam, input_age, ref_fa,  rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, short_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
				}
				else {
                                INPUT_CSV_ALIGNMENT_ALL_NGS(input_bam, input_age, ref_fa,  rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, short_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                        }
	            }
	            else { // Long reads
	                    if ( params.mode == 'sv' ) {
	                        INPUT_CSV_ALIGNMENT_LONG_SV(input_bam, input_age, ref_fa,  is_note, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	                    else if ( params.mode == 'snp' ) {
                        INPUT_CSV_ALIGNMENT_LONG_SNP(input_bam, input_age, ref_fa,  rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, long_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	                    else {
                        INPUT_CSV_ALIGNMENT_ALL_LONGPHASE(input_bam, input_age, ref_fa,  rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, long_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	            }
	        }
	    }
    else { // Single File Mode
        if ( params.vcf ) {
            if ( params.mode == 'sv' ) {
                SINGLE_ALIGNMENT_VCF_SV(vcf, out_prefix, ref_fa,  note, phen2gene_top_n, is_note, target, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
            }
            else if ( params.mode == 'snp' ) {
                SINGLE_ALIGNMENT_VCF_SNP(vcf, out_prefix, ref_fa,  note, rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
            }
        }
        else if ( params.bam != null ) {
	            if ( params.type == 'short' ) {
	                    if ( params.mode == 'sv' ) {
	                        SINGLE_ALIGNMENT_NGS_SV(bam, out_prefix, ref_fa,  note, is_note, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	                    else if ( params.mode == 'snp' ) {
                        SINGLE_ALIGNMENT_NGS_SNP(bam, out_prefix, ref_fa,  note, rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, short_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
			    else {
			                SINGLE_ALIGNMENT_ALL_NGS(bam, out_prefix, ref_fa,  note, rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, short_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
			    }
	            }
	            else { // Long reads
	                    if ( params.mode == 'sv' ) {
	                        SINGLE_ALIGNMENT_LONG_SV(bam, out_prefix, ref_fa,  note, is_note, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	                    else if ( params.mode == 'snp' ) {
                        SINGLE_ALIGNMENT_LONG_SNP(bam, out_prefix, ref_fa,  note, rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, long_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	                    else {
                        SINGLE_ALIGNMENT_ALL_LONGPHASE(bam, out_prefix, ref_fa,  note, rankscore_filter, rankscore_softwares, phen2gene_top_n, gnomad, gq, ad, rankvar_filter, is_note, target, long_snp_caller, inheritance_mode, include_clinvar_report, allow_unphased_comphet)
	                    }
	            }
	        }
	    }
}



workflow.onComplete {
        println "Pipeline completed at: $workflow.complete"
        println "Execution status: ${ workflow.success ? 'OK' : 'failed' }"
}

workflow.onError {
        println "Error: Pipeline execution stopped with the following message: ${workflow.errorMessage}"
}