RNA-seq.snakefile

"""
Snakefile to analyse RNA-seq data.
"""
import sys
major, minor = sys.version_info[:2]
if major < 3 or (major == 3 and minor < 6):
    sys.exit("Need at least python 3.6\n")

# TODO: plot spike-in vs spike-in between libraries to detect anormal spike-ins: should be a straight line

# TODO: Add rules to take into account spike-ins.
# The size factor should be determined within the dynamic range of the spike-ins
# Use 1 RPKM for LDD
# See /Genomes/spike-ins/ERCC_RNA_Spike-In_Control_Mixes.pdf pp. 13-14
# Then use size factor to compute normalized RPKM and then fold changes like for small RNA IP

#TODO: same boxplots as small_RNA-seq
#TODO: for each contrast,  scatterplots with highlight of differentially expressed genes (with p-value colour code)
# and same scale for different biotypes
# boxplots of categories of genes (only protein-coding)
# Prepare scripts to compare with small RNA (fold vs fold)
#TODO: try to use bedtools intersect to do the counting (only reads strictly included in feature)
# bli@naples:/extra2/Documents/Mhe/bli/RNA_Seq_analyses$ mkfifo /tmp/gene_id
# bli@naples:/extra2/Documents/Mhe/bli/RNA_Seq_analyses$ mkfifo /tmp/count
# bli@naples:/extra2/Documents/Mhe/bli/RNA_Seq_analyses$ head -6 /tmp/WT_RT_1_protein_coding_counts_F1S.txt | tee >(cut -f9 | gtf2attr gene_id > /tmp/gene_id) | cut -f7,10 > /tmp/count & paste /tmp/gene_id /tmp/count
# "WBGene00022277"	-	170
# "WBGene00022276"	+	551
# "WBGene00022276"	+	522
# "WBGene00022276"	+	550
# "WBGene00022276"	+	550
# "WBGene00022276"	+	550


import os
OPJ = os.path.join
from glob import glob
from pickle import load
from collections import defaultdict
from itertools import combinations
from functools import partial
from gzip import open as gzopen

import warnings


def formatwarning(message, category, filename, lineno, line):
    """Used to format warning messages."""
    return "%s:%s: %s: %s\n" % (filename, lineno, category.__name__, message)


warnings.formatwarning = formatwarning


def debug_wildcards(wildcards):
    print(wildcards)
    return []


import matplotlib as mpl
# To be able to run the script without a defined $DISPLAY
mpl.use("PDF")
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
# https://stackoverflow.com/a/42768093/1878788
# from matplotlib.backends.backend_pgf import FigureCanvasPgf
# mpl.backend_bases.register_backend('pdf', FigureCanvasPgf)
# TEX_PARAMS = {
#     "text.usetex": True,            # use LaTeX to write all text
#     "pgf.rcfonts": False,           # Ignore Matplotlibrc
#     "pgf.texsystem": "lualatex",  # hoping to avoid memory issues
#     "pgf.preamble": [
#         r'\usepackage{color}'     # xcolor for colours
#     ]
# }
# mpl.rcParams.update(TEX_PARAMS)
mpl.rcParams["font.sans-serif"] = [
    "Arial", "Liberation Sans", "Bitstream Vera Sans"]
mpl.rcParams["font.family"] = "sans-serif"

import numpy as np
import pandas as pd
# To catch errors when plotting KDE
from scipy.linalg import LinAlgError
from scipy.stats import pearsonr, shapiro
from sklearn.linear_model import LinearRegression
import seaborn as sns
# To merge bigwig files
import pyBigWig
from mappy import fastx_read

from rpy2.robjects import Formula, StrVector
from libdeseq import do_deseq2
from libhts import aligner2min_mapq
from libhts import status_setter, plot_lfc_distribution, plot_MA
from libhts import median_ratio_to_pseudo_ref_size_factors, size_factor_correlations
from libhts import (
    plot_paired_scatters,
    plot_norm_correlations,
    plot_counts_distribution,
    plot_boxplots)
from libworkflows import wc_applied, ensure_relative, cleanup_and_backup
from libworkflows import get_chrom_sizes, column_converter, make_id_list_getter
from libworkflows import strip_split, save_plot, test_na_file, SHELL_FUNCTIONS, warn_context
from libworkflows import feature_orientation2stranded
from libworkflows import sum_by_family
from libworkflows import read_htseq_counts, sum_htseq_counts
from libworkflows import read_intersect_counts, sum_intersect_counts
from libworkflows import read_feature_counts, sum_feature_counts
from smincludes import rules as irules
from smwrappers import wrappers_dir

alignment_settings = {"bowtie2": "", "hisat2": "", "crac": "-k 20 --stranded --use-x-in-cigar"}

# Possible feature ID conversions
ID_TYPES = ["name", "cosmid"]

# TODO: make this part of the configuration
LFC_RANGE = {
    "spike_ins" : (-10, 10),
    "protein_coding" : (-10, 10),
    "DNA_transposons_rmsk" : (-10, 10),
    "RNA_transposons_rmsk" : (-10, 10),
    "satellites_rmsk" : (-10, 10),
    "simple_repeats_rmsk" : (-10, 10),
    "DNA_transposons_rmsk_families" : (-10, 10),
    "RNA_transposons_rmsk_families" : (-10, 10),
    "satellites_rmsk_families" : (-10, 10),
    "simple_repeats_rmsk_families" : (-10, 10),
    "pseudogene" : (-10, 10),
    "all_rmsk" : (-10, 10),
    "all_rmsk_families" : (-10, 10),
    "alltypes" : (-10, 10)}
# Cutoffs in log fold change
LFC_CUTOFFS = [0.5, 1, 2]
UP_STATUSES = [f"up{cutoff}" for cutoff in LFC_CUTOFFS]
DOWN_STATUSES = [f"down{cutoff}" for cutoff in LFC_CUTOFFS]
#status2colour = make_status2colour(DOWN_STATUSES, UP_STATUSES)
#STATUSES = list(reversed(DOWN_STATUSES)) + ["down", "NS", "up"] + UP_STATUSES
#STATUS2COLOUR = dict(zip(STATUSES, sns.color_palette("coolwarm", len(STATUSES))))

# Or use --configfile instead
#configfile:
#    "RNA-seq.config.json"


spikein_dict = config["spikein_dict"]
spikein_microliters = spikein_dict["microliters"]
spikein_dilution_factor = spikein_dict["dilution_factor"]
# To get the expected number of attomoles of a given spike-in,
# multiply this by the value found in column 4 of Genomes/spike-ins/ERCC92_concentrations.txt
spikein_microliter_equivalent = spikein_microliters / spikein_dilution_factor
if spikein_microliter_equivalent:
    spikein_concentrations = pd.read_table(
        spikein_dict["concentrations"],
        index_col="ERCC ID",
        usecols=["ERCC ID", "concentration in Mix %s (attomoles/ul)" % spikein_dict["mix"]])
    from scipy.constants import Avogadro
    # attomole: 10^{-18} mole
    molecules_per_attomole = Avogadro / 10**18
    spikein_expected_counts = spikein_concentrations * spikein_microliter_equivalent * molecules_per_attomole
    spikein_expected_counts.columns = ["expected_counts"]

# http://sailfish.readthedocs.io/en/master/library_type.html
LIB_TYPE = config["lib_type"]
ORIENTATIONS = config["orientations"]
# key: library name
# value: dictionary
#   key: replicate number
#   value: path to raw data
lib2raw = config["lib2raw"]
LIBS = list(lib2raw.keys())
REPS = config["replicates"]
#COND_PAIRS = config["cond_pairs"]
#CONTRASTS = [f"{cond1}_vs_{cond2}" for [cond1, cond2] in COND_PAIRS]
DE_COND_PAIRS = config["de_cond_pairs"]
msg = "\n".join([
    "Some contrats do not use known library names.",
    "Contrasts:"
    ", ".join([f"({cond}, {ref})" for (cond, ref) in DE_COND_PAIRS])])
assert all([cond in LIBS and ref in LIBS for (cond, ref) in DE_COND_PAIRS]), msg
IP_COND_PAIRS = config["ip_cond_pairs"]
msg = "\n".join([
    "Some contrats do not use known library names.",
    "Contrasts:"
    ", ".join([f"({cond}, {ref})" for (cond, ref) in IP_COND_PAIRS])])
assert all([cond in LIBS and ref in LIBS for (cond, ref) in IP_COND_PAIRS]), ""
COND_PAIRS = DE_COND_PAIRS + IP_COND_PAIRS
DE_CONTRASTS = [f"{cond1}_vs_{cond2}" for [cond1, cond2] in DE_COND_PAIRS]
IP_CONTRASTS = [f"{cond1}_vs_{cond2}" for [cond1, cond2] in IP_COND_PAIRS]
contrasts_dict = {"de" : DE_CONTRASTS, "ip" : IP_CONTRASTS}
CONTRASTS = DE_CONTRASTS + IP_CONTRASTS
CONTRAST2PAIR = dict(zip(CONTRASTS, COND_PAIRS))
CONDITIONS = [{
    "lib" : lib,
    "rep" : rep} for rep in REPS for lib in LIBS]
# We use this for various things in order to have always the same library order:
COND_NAMES = ["_".join((
    cond["lib"],
    cond["rep"])) for cond in CONDITIONS]
# COND_COLUMNS = pd.DataFrame(CONDITIONS).assign(
#     cond_name=pd.Series(COND_NAMES).values).set_index("cond_name")


BIOTYPES = config["biotypes"]

RMSK_BIOTYPES = [
    "DNA_transposons_rmsk",
    "RNA_transposons_rmsk",
    "satellites_rmsk",
    "simple_repeats_rmsk"]
RMSK_FAMILIES_BIOTYPES = [
    "DNA_transposons_rmsk_families",
    "RNA_transposons_rmsk_families",
    "satellites_rmsk_families",
    "simple_repeats_rmsk_families"]

BIOTYPES_TO_JOIN = {
    "all_rmsk": [biotype for biotype in BIOTYPES if biotype in RMSK_BIOTYPES],
    "all_rmsk_families": [biotype for biotype in BIOTYPES if biotype in RMSK_FAMILIES_BIOTYPES],
    # We only count "protein_coding", not "protein_codin_{5UTR,CDS,3UTR}"
    "alltypes": [biotype for biotype in BIOTYPES if not biotype.startswith("protein_coding_")]}
JOINED_BIOTYPES = list(BIOTYPES_TO_JOIN.keys())
DE_BIOTYPES = [biotype for biotype in LFC_RANGE.keys() if biotype in BIOTYPES + JOINED_BIOTYPES]
if "spike_ins" in BIOTYPES:
    PAIR_SCATTER_BIOTYPES = ["spike_ins"]
else:
    PAIR_SCATTER_BIOTYPES = []
ID_LISTS = config.get("boxplot_gene_lists", [])
#ID_LISTS = [
#    "germline_specific", "histone",
#    "csr1_prot_si_supertargets_48hph",
#    "spermatogenic_Ortiz_2014", "oogenic_Ortiz_2014"]
aligner = config["aligner"]
########################
# Genome configuration #
########################
genome_dict = config["genome_dict"]
genome = genome_dict["name"]
chrom_sizes = get_chrom_sizes(genome_dict["size"])
genomelen = sum(chrom_sizes.values())
genome_db = genome_dict["db"][aligner]
# bed file binning the genome in 10nt bins
genome_binned = genome_dict["binned"]
annot_dir = genome_dict["annot_dir"]
# TODO: figure out the difference between OPJ(convert_dir, "wormid2name.pickle") and genome_dict["converter"]
convert_dir = genome_dict["convert_dir"]
gene_lists_dir = genome_dict["gene_lists_dir"]
avail_id_lists = set(glob(OPJ(gene_lists_dir, "*_ids.txt")))
#COUNTERS = config["counters"]
# htseq_count is very slow
# bedtools intersect eats all the RAM when there are a lot of rRNAs:
# https://github.com/arq5x/bedtools2/issues/565
COUNTERS = ["feature_count"]
#output_dir = config["output_dir"]
#workdir: config["output_dir"]
output_dir = os.path.abspath(".")
transcriptomes_dir = OPJ("merged_transcriptomes")
log_dir = OPJ(f"logs_{genome}")
data_dir = OPJ("data")

#NORM_TYPES = config["norm_types"]
if spikein_microliter_equivalent:
    SIZE_FACTORS = ["protein_coding", "miRNA", "median_ratio_to_pseudo_ref", "spike_ins"]
    NORM_TYPES = ["spike_ins", "protein_coding", "median_ratio_to_pseudo_ref"]
else:
    SIZE_FACTORS = ["protein_coding", "miRNA", "median_ratio_to_pseudo_ref"]
    NORM_TYPES = ["protein_coding", "median_ratio_to_pseudo_ref"]
    assert "spike_ins" not in BIOTYPES
assert set(SIZE_FACTORS).issubset(set(BIOTYPES) | {"median_ratio_to_pseudo_ref"})
assert set(NORM_TYPES).issubset(set(SIZE_FACTORS))

# PolyU tail min and max length
minU, maxU = 1, 5

# Seems to interfere with temp files deletion
wildcard_constraints:
    rep="\d+",
    orientation="|".join(ORIENTATIONS),
    biotype="|".join(BIOTYPES + JOINED_BIOTYPES),
    fold_type="|".join(["mean_log2_RPM_fold", "log2FoldChange", "lfcMLE"]),

#ruleorder: join_all_counts > gather_counts

# Define functions to be used in shell portions
shell.prefix(SHELL_FUNCTIONS)


def specific_input(aligner):
    if aligner == "hisat2":
        files = []
        # files = [
        #     expand(
        #         OPJ(aligner, f"mapped_{genome}", "ballgown",
        #             "{lib}_{rep}_on_%s.gtf" % genome),
        #         lib=LIBS, rep=REPS),
        #     OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "compare.stats"),
        #     OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "merged.gtf"),]
    elif aligner == "bowtie2":
        files = []
    elif aligner == "crac":
        files = []
    else:
        raise NotImplementedError("%s is not yet handeled." % aligner)
    return files

counts_files = [
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "{lib}_mean_{mapped_type}", "{lib}_mean_{biotype}_{orientation}_TPM.txt"),
        counter=COUNTERS, lib=LIBS, mapped_type=[f"on_{genome}"],
        biotype=["alltypes"], orientation=ORIENTATIONS),
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "all_{mapped_type}", "{biotype}_{orientation}_TPM.txt"),
        counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
        biotype=["alltypes"], orientation=ORIENTATIONS),
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "all_{mapped_type}", "{biotype}_{orientation}_RPK.txt"),
        counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
        biotype=BIOTYPES + JOINED_BIOTYPES, orientation=ORIENTATIONS),
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "all_{mapped_type}", "{biotype}_{orientation}_counts.txt"),
        counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
        biotype=BIOTYPES + JOINED_BIOTYPES, orientation=ORIENTATIONS),
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{counter}",
    #         "summaries", f"{{lib}}_{{rep}}_on_{genome}_{{orientation}}_counts.txt"),
    #     counter=COUNTERS, lib=LIBS, rep=REPS, orientation=ORIENTATIONS),
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{counter}",
    #         f"{{lib}}_{{rep}}_on_{genome}", "{biotype}_{orientation}_counts.txt"),
    #     counter=COUNTERS, lib=LIBS, rep=REPS, biotype=BIOTYPES, orientation=ORIENTATIONS),
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{counter}",
    #         f"{{lib}}_{{rep}}_on_{genome}", "{biotype}_{orientation}_counts_gene_names.txt"),
    #     counter=COUNTERS, lib=LIBS, rep=REPS, biotype=BIOTYPES, orientation=ORIENTATIONS),]
    # Just a test:
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{counter}",
    #         "summaries", "{lib}_{rep}_{mapped_type}_{orientation}_counts.txt"),
    #     counter=COUNTERS, lib=LIBS, rep=REPS,
    #     mapped_type=[f"on_{genome}", f"polyU_on_{genome}"], orientation=ORIENTATIONS),
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{counter}",
    #         "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts.txt"),
    #     counter=COUNTERS, lib=LIBS, rep=REPS,
    #     mapped_type=[f"on_{genome}", f"polyU_on_{genome}"], biotype=BIOTYPES, orientation=ORIENTATIONS),
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{counter}",
    #         "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts_gene_names.txt"),
    #     counter=COUNTERS, lib=LIBS, rep=REPS,
    #     mapped_type=[f"on_{genome}", f"polyU_on_{genome}"], biotype=BIOTYPES, orientation=ORIENTATIONS),]
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "summaries", "{lib}_{rep}_{mapped_type}_{orientation}_counts.txt"),
        counter=COUNTERS, lib=LIBS, rep=REPS,
        mapped_type=[f"on_{genome}", f"unique_on_{genome}"], orientation=ORIENTATIONS),
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts.txt"),
        counter=COUNTERS, lib=LIBS, rep=REPS,
        mapped_type=[f"on_{genome}", f"unique_on_{genome}"], biotype=BIOTYPES, orientation=ORIENTATIONS),
    expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts_gene_names.txt"),
        counter=COUNTERS, lib=LIBS, rep=REPS,
        mapped_type=[f"on_{genome}", f"unique_on_{genome}"], biotype=BIOTYPES, orientation=ORIENTATIONS),]

# Actually not necessarily IP data
# TODO: generalize contrast types
if contrasts_dict["ip"]:
    ip_fold_boxplots = [
        expand(
            OPJ(aligner, f"mapped_{genome}", "{counter}", "fold_boxplots_{mapped_type}",
                "{contrast_type}_{orientation}_{biotype}_{fold_type}_{id_list}_boxplots.pdf"),
            #counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
            counter=COUNTERS, mapped_type=[f"on_{genome}"],
            contrast_type=["ip"],
            orientation=ORIENTATIONS, biotype=DE_BIOTYPES,
            fold_type=["mean_log2_RPM_fold"], id_list=ID_LISTS),]
else:
    ip_fold_boxplots = []

if contrasts_dict["de"]:
    de_fold_boxplots = [
        expand(
            OPJ(aligner, f"mapped_{genome}", "{counter}", "fold_boxplots_{mapped_type}",
                "{contrast_type}_{orientation}_{biotype}_{fold_type}_{id_list}_boxplots.pdf"),
            counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
            contrast_type=["de"],
            orientation=ORIENTATIONS, biotype=DE_BIOTYPES,
            fold_type=["log2FoldChange"], id_list=ID_LISTS),]
else:
    de_fold_boxplots = []

de_files = []
if len(REPS) > 1:
    de_files += expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "deseq2_{mapped_type}", "{contrast}", "{orientation}_{biotype}", "{contrast}_counts_and_res.txt"),
        counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
        contrast=DE_CONTRASTS, orientation=ORIENTATIONS, biotype=DE_BIOTYPES)
    de_files += expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "deseq2_{mapped_type}", "{contrast}", "{orientation}_{biotype}", "{contrast}_{fold_type}_distribution.pdf"),
        counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
        contrast=DE_CONTRASTS, orientation=ORIENTATIONS, biotype=DE_BIOTYPES,
        fold_type=["log2FoldChange"])
    de_files += expand(
        OPJ(aligner, f"mapped_{genome}", "{counter}",
            "deseq2_{mapped_type}", "{contrast}", "{orientation}_{biotype}", "{contrast}_MA_with_{id_list}.pdf"),
        counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
        contrast=DE_CONTRASTS, orientation=ORIENTATIONS, biotype=DE_BIOTYPES,
        id_list=ID_LISTS + ["lfc_statuses"])
    de_files += de_fold_boxplots
else:
    warnings.warn("DESeq2 analyses will not be run because there are not enough replicates.\n")

bigwig_files = [
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}",
    #         "{lib}_{rep}_{orientation}_{mapped_type}_by_{norm_type}.bw"),
    #     lib=LIBS, rep=REPS, orientation=ORIENTATIONS,
    #     mapped_type=[f"on_{genome}", f"polyU_on_{genome}"], norm_type=NORM_TYPES),
    # expand(
    #     OPJ(aligner, f"mapped_{genome}", "{lib}_mean",
    #         "{lib}_mean_{orientation}_{mapped_type}_by_{norm_type}.bw"),
    #     lib=LIBS, orientation=ORIENTATIONS,
    #     mapped_type=[f"on_{genome}", f"polyU_on_{genome}"], norm_type=NORM_TYPES),]
    expand(
        OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}",
            "{lib}_{rep}_{orientation}_{mapped_type}_by_{norm_type}.bw"),
        lib=LIBS, rep=REPS, orientation=ORIENTATIONS,
        #mapped_type=[f"on_{genome}", f"unique_on_{genome}"], norm_type=NORM_TYPES),
        mapped_type=[f"on_{genome}"], norm_type=NORM_TYPES),
    expand(
        OPJ(aligner, f"mapped_{genome}", "{lib}_mean",
            "{lib}_mean_{orientation}_{mapped_type}_by_{norm_type}.bw"),
        lib=LIBS, orientation=ORIENTATIONS,
        #mapped_type=[f"on_{genome}", f"unique_on_{genome}"], norm_type=NORM_TYPES),]
        mapped_type=[f"on_{genome}"], norm_type=NORM_TYPES),]

if spikein_microliter_equivalent:
    spikein_files = [
        expand(OPJ(aligner, f"mapped_{genome}", "{counter}",
            f"all_on_{genome}", "{lib}_{rep}_spike_ins_{orientation}_TPM_response.pdf"),
            counter=COUNTERS, orientation=ORIENTATIONS, lib=LIBS, rep=REPS),]
else:
    spikein_files = []

replicates_comparisons = []
if len(REPS) > 1:
    ## TODO: debug:
    # Something to do with partial expand on quantification type?
    # Missing input files for rule compare_replicates:
    # results_44hph/hisat2/mapped_C_elegans/{counter}/all_{mapped_type}/{biotype}_{orientation}_RPM.txt
    # MissingInputException in line 1646 of /home/bli/src/bioinfo_utils/RNA-seq/RNA-seq.snakefile:
    # Missing input files for rule differential_expression:
    # results_44hph_vs_38hph/hisat2/mapped_C_elegans/{counter}/all_{mapped_type}/{biotype}_{orientation}_counts.txt
    # see https://bitbucket.org/snakemake/snakemake/issues/956/placeholders-not-properly-matched-with
    replicates_comparisons.extend(
        expand(
            OPJ(aligner, f"mapped_{genome}", "{counter}",
                "all_{mapped_type}", "replicates_comparison",
                "{lib}", "{biotype}_{orientation}_{quantif_type}_correlations.txt"),
            counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
            lib=LIBS, biotype=["alltypes"],
            orientation=ORIENTATIONS, quantif_type=["TPM"]))
    replicates_comparisons.extend(
        expand(
            OPJ(aligner, f"mapped_{genome}", "{counter}",
                "all_{mapped_type}", "replicates_comparison",
                "{lib}", "{biotype}_{orientation}_{quantif_type}_correlations.txt"),
            counter=COUNTERS, mapped_type=[f"on_{genome}", f"unique_on_{genome}"],
            lib=LIBS, biotype=BIOTYPES + ["alltypes"],
            orientation=ORIENTATIONS, quantif_type=["counts", "RPM"]))
else:
    warnings.warn("Replicates comparisons will not be run because there are not enough replicates.\n")

rule all:
    input:
        specific_input(aligner),
        expand(
            OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_samtools_stats.txt"),
            lib=LIBS, rep=REPS),
        expand(
            OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_coverage.txt"),
            lib=LIBS, rep=REPS),
        bigwig_files,
        spikein_files,
        # Uses too much memory when there are many libraries
        # expand(OPJ(aligner, f"mapped_{genome}", "{counter}",
        #     f"all_on_{genome}", "scatter_pairs/{biotype}_{orientation}_{quantif_type}_scatter_pairs.pdf"),
        #     counter=COUNTERS, biotype=["alltypes"], orientation=ORIENTATIONS, quantif_type=["TPM"]),
        # expand(OPJ(aligner, f"mapped_{genome}", "{counter}",
        #     f"all_on_{genome}", "scatter_pairs/{biotype}_{orientation}_{quantif_type}_scatter_pairs.pdf"),
        #     counter=COUNTERS, biotype=PAIR_SCATTER_BIOTYPES, orientation=ORIENTATIONS, quantif_type=["counts", "RPK"]),
        expand(
            OPJ(aligner, f"mapped_{genome}", "{counter}",
                "summaries", "all_{mapped_type}_{orientation}_{biotype}_norm_correlations.pdf"),
            counter=COUNTERS, mapped_type=[f"on_{genome}"], orientation=ORIENTATIONS, biotype=BIOTYPES + ["alltypes"]),
        expand(
            OPJ(aligner, f"mapped_{genome}", "{counter}",
                "summaries", "all_{mapped_type}_{orientation}_{biotype}_norm_counts_distrib.pdf"),
            counter=COUNTERS, mapped_type=[f"on_{genome}"], orientation=ORIENTATIONS, biotype=BIOTYPES + ["alltypes"]),
        de_files,
        ip_fold_boxplots,
        counts_files,
        replicates_comparisons,
        ##


include: ensure_relative(irules["link_raw_data"], workflow.basedir)
#include: "../snakemake_wrappers/includes/link_raw_data.rules"


# def mapping_command(aligner):
#     """This function returns the shell commands to run given the *aligner*."""
#     if aligner == "hisat2":
#         shell_commands = """
# cmd="hisat2 -p {threads} --dta --seed 123 -t --mm -x %s -U {input.fastq} --no-unal --un-gz {output.nomap_fastq} -S {output.sam}"
# echo ${{cmd}} 1> {log.log}
# eval ${{cmd}} 1>> {log.log} 2> {log.err}
# """ % genome_db
#     elif aligner == "bowtie2":
#         shell_commands = """
# cmd="bowtie2 -p {threads} --seed 123 -t --mm -x %s -U {input.fastq} --no-unal --un-gz {output.nomap_fastq} -S {output.sam}"
# echo ${{cmd}} 1> {log.log}
# eval ${{cmd}} 1>> {log.log} 2> {log.err}
# """ % genome_db
#     else:
#         raise NotImplementedError("%s is not yet handled." % aligner)
#     return shell_commands

# Could this be useful?
# https://stackoverflow.com/a/5328139/1878788
#def debug_output():
#    exc_type, exc_value, tb = sys.exc_info()
#    if tb is not None:
#        print(tb.tb_frame.f_locals)
#    return ""

rule map_on_genome:
    input:
        fastq = OPJ(data_dir, "{lib}_{rep}.fastq.gz"),
    output:
        # temp because it uses a lot of space
        sam = temp(OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_on_%s.sam" % genome)),
        #sam = temp(OPJ(aligner, "mapped_%s" % genome, "{lib}_{rep}_on_%s.sam" % genome)),
        nomap_fastq = OPJ(aligner, f"not_mapped_{genome}", "{lib}_{rep}_unmapped_on_%s.fastq.gz" % genome),
    params:
        aligner = aligner,
        index = genome_db,
        settings = alignment_settings[aligner],
    message:
        "Mapping {wildcards.lib}_{wildcards.rep} on %s." % genome
    log:
        log = OPJ(log_dir, "map_on_genome_{lib}_{rep}.log"),
        err = OPJ(log_dir, "map_on_genome_{lib}_{rep}.err")
    resources:
        io=5
    threads:
        4
    #shell:
    #    mapping_command(aligner)
    wrapper:
        f"file://{wrappers_dir[0]}/map_on_genome"


rule extract_nomap_polyU:
    f"""
    Extract from the non-mappers those that end with a polyU tail
    of length from {minU} to {maxU}."""
    input:
        nomap_fastq = OPJ(aligner, f"not_mapped_{genome}", "{lib}_{rep}_unmapped_on_%s.fastq.gz" % genome),
    output:
        nomap_polyU = OPJ(aligner, f"not_mapped_{genome}", "{lib}_{rep}_unmapped_on_%s_polyU.fastq.gz" % genome),
        nomap_polyU_stats = OPJ(aligner, f"not_mapped_{genome}", "{lib}_{rep}_unmapped_on_%s_polyU_stats.txt" % genome),
    run:
        stats = defaultdict(int)
        with gzopen(output.nomap_polyU, "wb") as fq_out:
            for (name, seq, qual) in fastx_read(input.nomap_fastq):
                trimmed = seq.rstrip("T")
                nb_T = len(seq) - len(trimmed)
                stats[nb_T] += 1
                if minU <= nb_T <= maxU:
                    # Find number of ending Ts
                    fq_out.write(f"@{name}\n{trimmed}\n+\n{qual:.{len(trimmed)}}\n".encode("utf-8"))
        with open(output.nomap_polyU_stats, "w") as stats_file:
            tot = 0
            for (nb_T, stat) in sorted(stats.items()):
                stats_file.write(f"{nb_T}\t{stat}\n")
                tot += stat
            stats_file.write(f"tot\t{tot}\n")


rule remap_on_genome:
    input:
        fastq = rules.extract_nomap_polyU.output.nomap_polyU,
    output:
        # temp because it might use a lot of space
        sam = temp(OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_polyU_on_%s.sam" % genome)),
        nomap_fastq = OPJ(aligner, f"not_mapped_{genome}", "{lib}_{rep}_polyU_unmapped_on_%s.fastq.gz" % genome),
    params:
        aligner = aligner,
        index = genome_db,
        settings = alignment_settings[aligner],
    message:
        "Remapping {wildcards.lib}_{wildcards.rep} polyU-trimmed unmapped reads on %s." % genome
    log:
        log = OPJ(log_dir, "remap_on_genome_{lib}_{rep}.log"),
        err = OPJ(log_dir, "remap_on_genome_{lib}_{rep}.err")
    resources:
        io=5
    threads:
        4
    #shell:
    #    mapping_command(aligner)
    wrapper:
        f"file://{wrappers_dir[0]}/map_on_genome"


def source_sam(wildcards):
    mapped_type = wildcards.mapped_type
    if mapped_type == f"on_{genome}":
        ## debug
        # Why rules.map_on_genome.output.sam doesn't get properly expanded?
        #return rules.map_on_genome.output.sam
        return OPJ(aligner, f"mapped_{genome}", f"{wildcards.lib}_{wildcards.rep}_on_%s.sam" % genome)
        ##
    elif mapped_type == f"polyU_on_{genome}":
        return rules.remap_on_genome.output.sam
    elif mapped_type == f"unique_on_{genome}":
        raise NotImplementedError(f"{mapped_type} only handled from count level on.")
    else:
        raise NotImplementedError(f"{mapped_type} not handled (yet?)")


rule sam2indexedbam:
    input:
        #debug_wildcards,
        # sam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}.sam"),
        sam = source_sam,
    output:
        # sorted_bam = protected(OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam")),
        # index = protected(OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam.bai")),
        sorted_bam = protected(OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_{mapped_type}_sorted.bam")),
        index = protected(OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_{mapped_type}_sorted.bam.bai")),
    message:
        "Sorting and indexing sam file for {wildcards.lib}_{wildcards.rep}_{wildcards.mapped_type}."
    log:
        log = OPJ(log_dir, "sam2indexedbam_{lib}_{rep}_{mapped_type}.log"),
        err = OPJ(log_dir, "sam2indexedbam_{lib}_{rep}_{mapped_type}.err"),
    resources:
        mem_mb=4100,
        io=45
    threads:
        8
    wrapper:
        f"file://{wrappers_dir[0]}/sam2indexedbam"


rule compute_mapping_stats:
    input:
        sorted_bam = rules.sam2indexedbam.output.sorted_bam,
        #sorted_bam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam"),
    output:
        stats = OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_{mapped_type}_samtools_stats.txt"),
    shell:
        """samtools stats {input.sorted_bam} > {output.stats}"""


rule get_nb_mapped:
    """Extracts the number of mapped reads from samtools stats results."""
    input:
        stats = rules.compute_mapping_stats.output.stats,
        #stats = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_samtools_stats.txt"),
    output:
        nb_mapped = OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_{mapped_type}_nb_mapped.txt"),
    shell:
        """samstats2mapped {input.stats} {output.nb_mapped}"""


rule compute_coverage:
    input:
        sorted_bam = rules.sam2indexedbam.output.sorted_bam,
        #sorted_bam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam"),
    output:
        coverage = OPJ(aligner, f"mapped_{genome}", "{lib}_{rep}_{mapped_type}_coverage.txt"),
    params:
        genomelen = genomelen,
    shell:
        """
        bases=$(samtools depth {input.sorted_bam} | awk '{{sum += $3}} END {{print sum}}') || error_exit "samtools depth failed"
        python3 -c "print(${{bases}} / {params.genomelen})" > {output.coverage}
        """


rule assemble_transcripts:
    input:
        sorted_bam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam"),
    output:
        gtf = OPJ(aligner, f"mapped_{genome}", "stringtie", f"{{lib}}_{{rep}}_on_{genome}.gtf"),
    params:
        annot = OPJ(annot_dir, "genes.gtf"),
    message:
        "Assembling transcripts for {wildcards.lib}_{wildcards.rep} with stringtie."
    log:
        log = OPJ(log_dir, "assemble_transcripts_{lib}_{rep}.log"),
        err = OPJ(log_dir, "assemble_transcripts_{lib}_{rep}.err")
    resources:
        io=5
    threads:
        4
    shell:
        """
        cmd="stringtie -p {threads} -G {params.annot} -o {output} -l {wildcards.lib}_{wildcards.rep} {input}"
        echo ${{cmd}} 1> {log.log}
        eval ${{cmd}} 1>> {log.log} 2> {log.err}
        """


rule merge_transcripts:
    input:
        expand(OPJ(aligner, f"mapped_{genome}", "stringtie", f"{{lib}}_{{rep}}_on_{genome}.gtf"), lib=LIBS, rep=REPS),
    output:
        merged = OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "merged.gtf"),
    params:
        annot = OPJ(annot_dir, "genes.gtf"),
    message:
        "Merging transcripts with stringtie."
    log:
        log = OPJ(log_dir, "merge_transcripts.log"),
        err = OPJ(log_dir, "merge_transcripts.err")
    resources:
        io=5
    shell:
        """
        cmd="stringtie --merge -G {params.annot} -o {output} {input}"
        echo ${{cmd}} 1> {log.log}
        eval ${{cmd}} 1>> {log.log} 2> {log.err}
        """


rule gffcompare:
    input:
        merged = OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "merged.gtf"),
    output:
        stats = OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "compare.stats"),
    params:
        compare = OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "compare"),
        annot = OPJ(annot_dir, "genes.gtf"),
    message:
        "Merging transcripts with stringtie."
    log:
        log = OPJ(log_dir, "gffcompare.log"),
        err = OPJ(log_dir, "gffcompare.err")
    resources:
        io=5
    shell:
        """
        cmd="gffcompare -r {params.annot} -o {params.compare} {input.merged}"
        echo ${{cmd}} 1> {log.log}
        eval ${{cmd}} 1>> {log.log} 2> {log.err}
        """


rule quantify_transcripts:
    input:
        merged = OPJ(transcriptomes_dir, aligner, f"mapped_{genome}", "stringtie", "merged.gtf"),
        sorted_bam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam"),
    output:
        gtf = OPJ(aligner, f"mapped_{genome}", "ballgown", f"{{lib}}_{{rep}}_on_{genome}.gtf"),
    message:
        "Quantifying transcripts for {wildcards.lib}_{wildcards.rep} with stringtie."
    log:
        OPJ(log_dir, "quantify_transcripts_{lib}_{rep}.log")
    threads:
        4
    resources:
        io=5
    shell:
        """
        cmd="stringtie -e -B -p {threads} -G {input.merged} -o {output} {input.sorted_bam}"
        echo ${{cmd}} 1> {log}
        eval ${{cmd}} 1>> {log}
        """


# def htseq_orientation2stranded(wildcards):
#     orientation = wildcards.orientation
#     if orientation == "fwd":
#         if LIB_TYPE[-2:] == "SF":
#             return "yes"
#         elif LIB_TYPE[-2:] == "SR":
#             return "reverse"
#         else:
#             raise ValueError(f"{LIB_TYPE} library type not compatible with strand-aware read counting.")
#     elif orientation == "rev":
#         if LIB_TYPE[-2:] == "SF":
#             return "reverse"
#         elif LIB_TYPE[-2:] == "SR":
#             return "yes"
#         else:
#             raise ValueError(f"{LIB_TYPE} library type not compatible with strand-aware read counting.")
#     elif orientation == "all":
#         return "no"
#     else:
#         exit("Orientation is to be among \"fwd\", \"rev\" and \"all\".")


def biotype2annot(wildcards):
    if wildcards.biotype.endswith("_rmsk_families"):
        biotype = wildcards.biotype[:-9]
    else:
        biotype = wildcards.biotype
    return OPJ(annot_dir, f"{biotype}.gtf")


# rule htseq_count_reads:
#     input:
#         sorted_bam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam"),
#         bai = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam.bai"),
#     output:
#         counts = OPJ(aligner, f"mapped_{genome}", "htseq_count", f"{{lib}}_{{rep}}_on_{genome}", "{biotype}_{orientation}_counts.txt"),
#         counts_converted = OPJ(aligner, f"mapped_{genome}", "htseq_count", f"{{lib}}_{{rep}}_on_{genome}", "{biotype}_{orientation}_counts_gene_names.txt"),
#     params:
#         stranded = htseq_orientation2stranded,
#         mode = "union",
#         annot = biotype2annot,
#     message:
#         "Counting {wildcards.orientation} {wildcards.biotype} reads for {wildcards.lib}_{wildcards.rep} with htseq-count."
#     benchmark:
#         OPJ(log_dir, "htseq_count_reads", "{lib}_{rep}_{biotype}_{orientation}_benchmark.txt")
#     log:
#         log = OPJ(log_dir, "htseq_count_reads", "{lib}_{rep}_{biotype}_{orientation}.log"),
#         err = OPJ(log_dir, "htseq_count_reads", "{lib}_{rep}_{biotype}_{orientation}.err"),
#     wrapper:
#         f"file://{wrappers_dir[0]}/htseq_count_reads"


def source_sorted_bam(wildcards):
    mapped_type = wildcards.mapped_type
    if mapped_type.startswith("unique_"):
        real_mapped_type = mapped_type[7:]
    else:
        real_mapped_type = mapped_type
    return OPJ(
        aligner, f"mapped_{genome}",
        f"{wildcards.lib}_{wildcards.rep}_{real_mapped_type}_sorted.bam")


def source_index(wildcards):
    mapped_type = wildcards.mapped_type
    if mapped_type.startswith("unique_"):
        real_mapped_type = mapped_type[7:]
    else:
        real_mapped_type = mapped_type
    return OPJ(
        aligner, f"mapped_{genome}",
        f"{wildcards.lib}_{wildcards.rep}_{real_mapped_type}_sorted.bam.bai")


rule feature_count_reads:
    input:
        sorted_bam = source_sorted_bam,
        index = source_index,
        #sorted_bam = rules.sam2indexedbam.output.sorted_bam,
        #index = rules.sam2indexedbam.output.index,
    output:
        counts = OPJ(aligner, f"mapped_{genome}", "feature_count", "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts.txt"),
        counts_converted = OPJ(aligner, f"mapped_{genome}", "feature_count", "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts_gene_names.txt"),
    #wildcard_constraints:
    #    mapped_type = "|".join([f"on_{genome}", f"polyU_on_{genome}"])
    params:
        min_mapq = partial(aligner2min_mapq, aligner),
        stranded = feature_orientation2stranded(LIB_TYPE),
        annot = biotype2annot,
        # pickled dictionary that associates gene ids to gene names
        converter = genome_dict["converter"]
    message:
        "Counting {wildcards.orientation} {wildcards.biotype} reads for {wildcards.lib}_{wildcards.rep} {wildcards.mapped_type} with featureCounts."
    benchmark:
        OPJ(log_dir, "feature_count_reads", "{lib}_{rep}_{mapped_type}_{biotype}_{orientation}_benchmark.txt")
    log:
        log = OPJ(log_dir, "feature_count_reads", "{lib}_{rep}_{mapped_type}_{biotype}_{orientation}.log"),
        err = OPJ(log_dir, "feature_count_reads", "{lib}_{rep}_{mapped_type}_{biotype}_{orientation}.err"),
    shell:
        """
        tmpdir=$(TMPDIR=/var/tmp mktemp -dt "feature_{wildcards.lib}_{wildcards.rep}_{wildcards.mapped_type}_{wildcards.biotype}_{wildcards.orientation}.XXXXXXXXXX")
        cmd="featureCounts {params.min_mapq} -a {params.annot} -o {output.counts} -t transcript -g "gene_id" -O -M --primary -s {params.stranded} --fracOverlap 0 --tmpDir ${{tmpdir}} {input.sorted_bam}"
        featureCounts -v 2> {log.log}
        echo ${{cmd}} 1>> {log.log}
        eval ${{cmd}} 1>> {log.log} 2> {log.err} || error_exit "featureCounts failed"
        rm -rf ${{tmpdir}}
        cat {output.counts} | id2name.py {params.converter} > {output.counts_converted}
        """


# rule feature_count_unique_reads:
#     input:
#         sorted_bam = source_sorted_bam,
#         index = source_index,
#         #sorted_bam = rules.sam2indexedbam.output.sorted_bam,
#         #index = rules.sam2indexedbam.output.index,
#     output:
#         counts = OPJ(aligner, f"mapped_{genome}", "feature_count", "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts.txt"),
#         counts_converted = OPJ(aligner, f"mapped_{genome}", "feature_count", "{lib}_{rep}_{mapped_type}", "{lib}_{rep}_{biotype}_{orientation}_counts_gene_names.txt"),
#     #wildcard_constraints:
#     #    mapped_type = f"unique_on_{genome}"
#     params:
#         min_mapq = partial(aligner2min_mapq, aligner),
#         stranded = feature_orientation2stranded(LIB_TYPE),
#         annot = biotype2annot,
#         # pickled dictionary that associates gene ids to gene names
#         converter = genome_dict["converter"]
#     message:
#         "Counting {wildcards.orientation} {wildcards.biotype} reads for {wildcards.lib}_{wildcards.rep} {wildcards.mapped_type} with featureCounts."
#     benchmark:
#         OPJ(log_dir, "feature_count_reads", "{lib}_{rep}_{mapped_type}_{biotype}_{orientation}_benchmark.txt")
#     log:
#         log = OPJ(log_dir, "feature_count_reads", "{lib}_{rep}_{mapped_type}_{biotype}_{orientation}.log"),
#         err = OPJ(log_dir, "feature_count_reads", "{lib}_{rep}_{mapped_type}_{biotype}_{orientation}.err"),
#     shell:
#         """
#         tmpdir=$(mktemp -dt "feature_{wildcards.lib}_{wildcards.rep}_{wildcards.mapped_type}_{wildcards.biotype}_{wildcards.orientation}.XXXXXXXXXX")
#         cmd="featureCounts {params.min_mapq} -a {params.annot} -o {output.counts} -t transcript -g "gene_id" -O -M --primary -s {params.stranded} --fracOverlap 0 --tmpDir ${{tmpdir}} {input.sorted_bam}"
#         featureCounts -v 2> {log.log}
#         echo ${{cmd}} 1>> {log.log}
#         eval ${{cmd}} 1>> {log.log} 2> {log.err} || error_exit "featureCounts failed"
#         rm -rf ${{tmpdir}}
#         cat {output.counts} | id2name.py {params.converter} > {output.counts_converted}
#         """


# def biotype2merged(wildcards):
#     if wildcards.biotype.endswith("_rmsk_families"):
#         biotype = wildcards.biotype[:-9]
#     else:
#         biotype = wildcards.biotype
#     return OPJ(annot_dir, f"{biotype}_merged.bed")
#
#
# def intersect_orientation2stranded(wildcards):
#     orientation = wildcards.orientation
#     if orientation == "fwd":
#         if LIB_TYPE[-2:] == "SF":
#             return "-s"
#         elif LIB_TYPE[-2:] == "SR":
#             return "-S"
#         else:
#             raise ValueError(f"{LIB_TYPE} library type not compatible with strand-aware read counting.")
#     elif orientation == "rev":
#         if LIB_TYPE[-2:] == "SF":
#             return "-S"
#         elif LIB_TYPE[-2:] == "SR":
#             return "-s"
#         else:
#             raise ValueError(f"{LIB_TYPE} library type not compatible with strand-aware read counting.")
#     elif orientation == "all":
#         return ""
#     else:
#         exit("Orientation is to be among \"fwd\", \"rev\" and \"all\".")
#
#
# rule intersect_count_reads:
#     input:
#         sorted_bam = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam"),
#         bai = OPJ(aligner, f"mapped_{genome}", f"{{lib}}_{{rep}}_on_{genome}_sorted.bam.bai"),
#     output:
#         counts = OPJ(aligner, f"mapped_{genome}", "intersect_count", f"{{lib}}_{{rep}}_on_{genome}", "{lib}_{rep}_{biotype}_{orientation}_counts.txt"),
#         counts_converted = OPJ(aligner, f"mapped_{genome}", "intersect_count", f"{{lib}}_{{rep}}_on_{genome}", "{lib}_{rep}_{biotype}_{orientation}_counts_gene_names.txt"),
#     params:
#         stranded = intersect_orientation2stranded,
#         annot = biotype2merged,
#     message:
#         "Counting {wildcards.orientation} {wildcards.biotype} reads for {wildcards.lib}_{wildcards.rep} with bedtools intersect."
#     benchmark:
#         OPJ(log_dir, "intersect_count_reads", "{lib}_{rep}_{biotype}_{orientation}_benchmark.txt")
#     log:
#         log = OPJ(log_dir, "intersect_count_reads", "{lib}_{rep}_{biotype}_{orientation}.log"),
#         err = OPJ(log_dir, "intersect_count_reads", "{lib}_{rep}_{biotype}_{orientation}.err"),
#     threads: 4  # to limit memory usage, actually
#     wrapper:
#         f"file://{wrappers_dir[0]}/intersect_count_reads"


rule summarize_counts:
    """For a given library, write a summary of the read counts for the various biotypes."""
    input:
        biotype_counts_files = expand(OPJ(aligner, f"mapped_{genome}", "{{counter}}", "{{lib}}_{{rep}}_{{mapped_type}}", "{{lib}}_{{rep}}_{biotype}_{{orientation}}_counts.txt"), biotype=BIOTYPES),
    output:
        summary = OPJ(aligner, f"mapped_{genome}", "{counter}", "summaries", "{lib}_{rep}_{mapped_type}_{orientation}_counts.txt")
    run:
        if wildcards.counter == "htseq_count":
            sum_counter = sum_htseq_counts
        elif wildcards.counter == "intersect_count":
            sum_counter = sum_intersect_counts