示例#1
0
def main():
    parser = argparse.ArgumentParser(description="Extract mutations in VCF and save as simple tabulated file")

    parser.add_argument("vcf_paths", metavar="PATH", nargs="+", help="The VCF files")

    parser.add_argument("-o", dest="out_path", metavar="PATH", help="Output file. Use - for standard output.")

    bglogging.add_logging_arguments(self._parser)

    args = parser.parse_args()

    bglogging.initialize(self.args)

    log = bglogging.get_logger("vcf-to-snvs")

    if args.out_path is None:
        names = []
        for path in args.vcf_paths:
            if path != "-":
                base_path, name, ext = tsv.split_path(path)
                names += [name]

        prefix = os.path.commonprefix(*names) if len(names) > 0 else ""
        prefix = prefix.rstrip(".")
        if len(prefix) == 0:
            prefix = "genome"
        args.out_path = "{}.tsv.gz".format(prefix)

    with tsv.open(args.out_path, "w") as outf:
        tsv.write_line(outf, "CHR", "POS", "REF", "ALT")

        for path in args.vcf_paths:
            log.info("Reading {} ...".format(path))

            with tsv.open(path) as inf:
                types = (str, str, str, str)
                columns = [0, 1, 3, 4]
                for fields in tsv.lines(inf, types, columns=columns):
                    chrom, pos, ref, alt = fields

                    # ref = ref.upper().strip("N")
                    # alt = alt.upper().strip("N")

                    ref_len = len(ref)
                    alt_len = len(alt)

                    if ref_len != alt_len or ref_len == 0 or alt_len == 0:
                        continue

                    try:
                        pos = int(pos)
                    except:
                        continue

                    if ref_len == 1:
                        tsv.write_line(outf, chrom, pos, ref, alt)
                    else:
                        for i in range(ref_len):
                            tsv.write_line(outf, chrom, pos + i, ref[i], alt[i])
示例#2
0
	def parse_args(self, logger_name):
		bglogging.add_logging_arguments(self._parser)

		self.args = self._parser.parse_args()

		bglogging.initialize(self.args)

		self.logger = bglogging.get_logger(logger_name)

		return self.args, self.logger
示例#3
0
def main():
	parser = argparse.ArgumentParser(
		description="Plot training sets statistics")

	parser.add_argument("path", metavar="PATH",
						help="The statistics json file")

	parser.add_argument("-o", dest="out_path", metavar="PATH",
						help="The path to save the plot image.")

	parser.add_argument("-W", "--width", dest="fig_width", metavar="WIDTH", type=int,
						help="The image width.")

	parser.add_argument("-H", "--height", dest="fig_height", metavar="HEIGHT", type=int,
						help="The image height.")

	parser.add_argument("--dpi", dest="fig_dpi", metavar="DPI", type=int, default=100,
						help="The image dpi.")

	parser.add_argument("-p", "--predictors", dest="predictor_names", metavar="NAMES",
						help="The names of the predictors to represent (seppareted by commas).")

	parser.add_argument("-i", "--interactive", dest="interactive", action="store_true", default=False,
						help="Show the plot in interactive mode.")

	bglogging.add_logging_arguments(parser)

	args = parser.parse_args()

	bglogging.initialize(args)

	log = bglogging.get_logger("plot-stats")

	log.info("Loading state from {} ...".format(os.path.basename(args.path)))

	state = load_weights(args.path)

	predictor_names, stats = [state[k] for k in ["predictor_names", "stats"]]

	if args.predictor_names is not None:
		valid_names = set(predictor_names)
		args.predictor_names = [s.strip() for s in args.predictor_names.split(",")]
		predictor_names = [name for name in args.predictor_names if name in valid_names]

		if len(predictor_names) == 0:
			log.error("No scores selected. Please choose between: {}".format(", ".join(valid_names)))
			exit(-1)

	#log.info("Plotting ...")

	fig = plt.figure(figsize=(args.fig_width or 12, args.fig_height or 10.4), dpi=args.fig_dpi or 100)

	alpha = 0.7

	num_predictors = len(predictor_names)
	for i in range(num_predictors):
		predictor_name = predictor_names[i]

		predictor_stats = stats[predictor_name]
		(intervals, dp, dn, cump, cumn, cdp, cdn, tp, tn, fp, fn, mcc, accuracy, cutoff) = [
			predictor_stats[k] for k in [
				"values", "dp", "dn", "cump", "cumn", "cdp", "cdn", "tp", "tn", "fp", "fn", "mcc", "acc", "cutoff"]]

		dax = fig.add_subplot(4, num_predictors, i + 1, title="{}".format(predictor_name))
		cdax = fig.add_subplot(4, num_predictors, 1 * num_predictors + i + 1)
		tfax = fig.add_subplot(4, num_predictors, 2 * num_predictors + i + 1)
		aax = fig.add_subplot(4, num_predictors, 3 * num_predictors + i + 1)

		# distribution
		dax.grid()
		dax.plot(intervals, arrdiv(dp, max(dp)), "r-", alpha=alpha)
		dax.plot(intervals, arrdiv(dn, max(dn)), "b-", alpha=alpha)
		dax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
		dax.legend(('POS', 'NEG'), 'upper center', ncol=2, frameon=False, prop={'size':10})

		# cummulative distribution
		cdax.grid()
		cdax.plot(intervals, arrdiv(cdp, cump), "r-", alpha=alpha)
		cdax.plot(intervals, arrdiv(cdn, cumn), "b-", alpha=alpha)
		cdax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
		cdax.legend(('POS', 'NEG'), 'upper center', ncol=2, frameon=False, prop={'size':10})

		# TP/FN/FP/TN
		tfax.grid()
		tfax.plot(intervals, arrdiv(tp, cump), "r-", alpha=alpha)
		tfax.plot(intervals, arrdiv(fn, cump), "c--", alpha=alpha)
		tfax.plot(intervals, arrdiv(fp, cumn), "b-", alpha=alpha)
		tfax.plot(intervals, arrdiv(tn, cumn), "m--", alpha=alpha)
		tfax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
		tfax.legend(('TP', 'FN', 'FP', 'TN'), 'upper center', ncol=4, frameon=False, prop={'size':8})

		# MCC/Accuracy
		aax.grid()
		aax.plot(intervals, mcc, "g-", alpha=alpha)
		aax.plot(intervals, accuracy, "y-", alpha=alpha)
		aax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
		aax.legend(('MCC', 'Accuracy'), 'upper center', ncol=2, frameon=False, prop={'size':10})

	if args.out_path is not None:
		from matplotlib import pylab
		log.info("Saving image into {} ...".format(os.path.basename(args.out_path)))
		pylab.savefig(args.out_path, bbox_inches=0)

	if args.interactive:
		plt.show()
示例#4
0
def main():
	parser = argparse.ArgumentParser(
		description="Calculate weights")

	parser.add_argument("ranges_path", metavar="RANGES_PATH",
						help="JSON file generated with pred-list containing predictors stats. Only min and max are used.")

	parser.add_argument("training_path", metavar="TRAINING_PATH",
						help="The training set scores. ID column should be POS/NEG for positive/negative sets.")

	parser.add_argument("-o", dest="out_path", metavar="OUT_PATH",
						help="The file where weights will be saved. Use - for standard output.")

	parser.add_argument("-p", "--predictors", dest="predictors", metavar="PREDICTORS",
						help="Comma separated list of predictors to fetch")

	parser.add_argument("-P", "--precision", dest="precision", metavar="PRECISSION", type=int, default=3,
						help="Distribution precision")

	parser.add_argument("-f", "--full-state", dest="full_state", action="store_true", default=False,
						help="Save intermediate calculations to allow further exploration and plotting")

	bglogging.add_logging_arguments(parser)

	args = parser.parse_args()

	bglogging.initialize(args)

	logger = bglogging.get_logger("weights")
	
	if args.out_path is None:
		prefix = os.path.splitext(os.path.basename(args.training_path))[0]
		if prefix.endswith("-scores"):
			prefix = prefix[:-7]
		args.out_path = os.path.join(os.getcwd(), "{}-weights.json".format(prefix))

	if args.predictors is not None:
		args.predictors = [p.strip() for p in args.predictors.split(",")]

	logger.info("Loading ranges from {} ...".format(os.path.basename(args.ranges_path)))

	with open(args.ranges_path) as f:
		pred_stats = json.load(f)

	predictor_range = {}
	for pid, pstats in pred_stats.items():
		predictor_range[pid] = (pstats["min"], pstats["max"])

	logger.info("Reading training set {} ...".format(args.training_path if args.training_path != "-" else "from standard input"))

	with tsv.open(args.training_path) as f:

		# Select predictors from the available predictors in the dataset or user selection

		column_names, column_indices = tsv.header(f)
		available_predictors = [c for c in column_names if c not in set(COORD_COLUMNS)]
		if args.predictors is None:
			predictors = available_predictors
		else:
			missing_predictors = [p for p in args.predictors if p not in set(available_predictors)]
			if len(missing_predictors) > 0:
				logger.error("Missing predictors: {}".format(", ".join(missing_predictors)))
				exit(-1)
			predictors = args.predictors

	data = pd.read_csv(args.training_path, sep="\t", index_col=False,
					   usecols=["ID"] + predictors,
					   true_values=["POS"], false_values=["NEG"])

	data.rename(columns={"ID" : "EVT"}, inplace=True)

	# Initialize statistics

	logger.info("Initializing metrics ...")

	step = 1.0 / 10**args.precision

	stats = dict()

	state = dict(
		predictor_names = predictors,
		precision = args.precision,
		step = step,
		stats = stats)

	for predictor in predictors:
		d = data[["EVT", predictor]]
		d = d[np.isfinite(d.iloc[:, 1])]

		nump = d.iloc[:, 0].sum()
		numn = d.shape[0] - nump

		rmin, rmax = d.iloc[:, 1].min(), d.iloc[:, 1].max()

		dim = rmax - rmin
		size = int(dim / step) + 1
		values = [(x * step) + rmin for x in xrange(size)]

		logger.info("  {:10}: p={}, n={}, min={}, max={}, bins={}".format(predictor, nump, numn, rmin, rmax, size))

		stats[predictor] = dict(
			rmin = rmin,
			rmax = rmax,
			dim = dim,
			values = values,
			size = size,
			vmin = rmin,
			vmax = rmax,
			dp = [0] * size,
			dn = [0] * size,
			cdp = [0] * size,
			cdn = [0] * size,
			cump = 0,
			cumn = 0,
			tp = [0] * size,
			tn = [0] * size,
			fp = [0] * size,
			fn = [0] * size,
			mcc = [0] * size,
			acc = [0] * size,
			auc = [0] * size,
			cutoff = None,
			cutoff_index = None,
			cutoff_mcc = None,
			cutoff_acc = None,
			cutoff_auc = None)

	positive_count = data.iloc[:, 0].sum()
	negative_count = data.shape[0] - positive_count

	logger.info("  TOTAL     : positive={}, negative={}".format(positive_count, negative_count))

	logger.info("Calculating scores distribution and confusion matrices ...")



	logger.info("Calculating cumulative distribution ...")

	for predictor in predictors:
		predictor_stats = stats[predictor]
		dp, dn, cdp, cdn = [predictor_stats[k] for k in ["dp", "dn", "cdp", "cdn"]]
		cump = 0
		cumn = 0
		i = len(dp) - 1
		while i >= 0:
			cdp[i] = dp[i] + cump
			cump += dp[i]

			cdn[i] = dn[i] + cumn
			cumn += dn[i]

			i -= 1

		predictor_stats["cump"] = cump
		predictor_stats["cumn"] = cumn

		logger.info("  {}: cump={}, cumn={}".format(predictor, cump, cumn))

	logger.info("Calculating accuracy and cutoff ...")

	for predictor in predictors:
		predictor_stats = stats[predictor]
		values, size, tp, tn, fp, fn, mcc, acc = [predictor_stats[k] for k in [
													"values", "size", "tp", "tn", "fp", "fn", "mcc", "acc"]]

		cutoff = -1
		cutoff_index = -1
		best_mcc = -1e6
		for i in xrange(size):
			try:
				#http://en.wikipedia.org/wiki/Matthews_correlation_coefficient
				mcc[i] = (tp[i] * tn[i] - fp[i] * fn[i]) / sqrt((tp[i] + fp[i]) * (tp[i] + fn[i]) * (tn[i] + fp[i]) * (tn[i] + fn[i]))

				#http://en.wikipedia.org/wiki/Accuracy
				acc[i] = (tp[i] + tn[i]) / float(tp[i] + fp[i] + fn[i] + tn[i])
			except ZeroDivisionError:
				mcc[i] = 0
				acc[i] = 0

			if mcc[i] > best_mcc:
				cutoff = values[i]
				cutoff_index = i
				best_mcc = mcc[i]

		best_acc = max(acc)

		predictor_stats["cutoff"] = cutoff
		predictor_stats["cutoff_index"] = cutoff_index
		predictor_stats["cutoff_mcc"] = best_mcc
		predictor_stats["cutoff_acc"] = best_acc

		logger.info("  {}: cutoff={:.3f}, mcc={:.2f}, accuracy={:.2f}".format(
			predictor, cutoff, best_mcc * 100.0, best_acc * 100.0))

	if args.full_state:
		logger.info("Saving weights with full state ...")

		out_path = args.out_path
		save_weights(out_path, state)

	else:
		logger.info("Saving weights ...")

		stats = {}

		reduced_state = dict(
			predictor_names=state["predictor_names"],
			precision=state["precision"],
			step=state["step"],
			stats=stats)

		for predictor in state["predictor_names"]:
			predictor_stats = state["stats"][predictor]
			stats[predictor] = dict(
				rmin=predictor_stats["rmin"],
				rmax=predictor_stats["rmax"],
				dim=predictor_stats["dim"],
				values=predictor_stats["values"],
				size=predictor_stats["size"],
				cdp=predictor_stats["cdp"],
				cdn=predictor_stats["cdn"],
				cutoff=predictor_stats["cutoff"],
				cutoff_index=predictor_stats["cutoff_index"])

		save_weights(args.out_path, reduced_state)

	return 0
示例#5
0
def main():
	parser = argparse.ArgumentParser(
		description="Calculate Condel label")

	parser.add_argument("db_path", metavar="DB_PATH",
						help="Functional scores database")

	parser.add_argument("weights_path", metavar="WEIGHTS",
						help="File containing the scores weights and cutoffs")

	parser.add_argument("-p", "--predictors", dest="predictors", metavar="PREDICTORS",
						help="Comma separated list of predictors")

	parser.add_argument("-u", "--updated-predictors", dest="updated_predictors", metavar="NAMES",
						help="Updated predictor names")

	bglogging.add_logging_arguments(parser)

	args = parser.parse_args()

	bglogging.initialize(args)

	log = bglogging.get_logger("calculate-label")

	log.info("Opening functional scores database ...")

	db = FannsSQLiteDb(args.db_path)
	db.open()

	log.info("Loading state ...")

	state = load_weights(args.weights_path)

	avail_predictors, precision, step, stats = [state[k] for k in ["predictor_names", "precision", "step", "stats"]]
	if args.predictors is not None:
		predictors = [p for p in [p.strip() for p in args.predictors.split(",")] if p in avail_predictors]
		if len(predictors) == 0:
			log.error("Unknown predictors: {}".format(args.predictors))
			log.error("Available predictor names are: {}".format(", ".join(avail_predictors)))
			exit(-1)
	else:
		predictors = avail_predictors

	if args.updated_predictors is not None:
		updated_predictors = [p.strip() for p in args.updated_predictors.split(",")]
		if len(predictors) != len(updated_predictors):
			log.error("Number of updated predictors does not match with the list of number of predictors")
			exit(-1)
	else:
		updated_predictors = ["{}_CLASS".format(p.upper()) for p in predictors]

	log.info("Available predictors: {}".format(", ".join(avail_predictors)))
	log.info("Selected predictors: {}".format(", ".join(predictors)))

	for predictor, updated_predictor in zip(predictors, updated_predictors):
		log.info("Creating predictor {} ...".format(updated_predictor))
		db.add_predictor(updated_predictor, FannsDb.CALCULATED_PREDICTOR_TYPE, source=[predictor])

	cutoffs = []
	for predictor in predictors:
		cutoff, mcc, acc = [stats[predictor][v] for v in ["cutoff", "cutoff_mcc", "cutoff_acc"]]
		log.info("{}: cutoff={}, MCC={}, accuracy={}".format(predictor, cutoff, mcc, acc))
		cutoffs += [cutoff]

	log.info("Calculating ...")

	start_time = partial_start_time = time.time()
	try:
		for num_rows, row in enumerate(db.query_scores(predictors=predictors), start=1):
			scores = row["scores"]
			d = {}
			for i, predictor in enumerate(predictors):
				score = scores[predictor]
				if score is None:
					continue

				cutoff = cutoffs[i]
				updated_predictor = updated_predictors[i]

				d[updated_predictor] = 0.0 if score < cutoff else 1.0

			db.update_scores(row["id"], d)

			partial_time = time.time() - partial_start_time
			if partial_time > 5.0:
				partial_start_time = time.time()
				elapsed_time = time.time() - start_time
				log.debug("  {} rows, {:.1f} rows/second".format(hsize(num_rows), num_rows / elapsed_time))

		db.commit()
	except KeyboardInterrupt:
		log.warn("Interrupted by Ctrl-C")
		db.rollback()
	except:
		db.rollback()
		raise
	finally:
		db.close()
示例#6
0
def main():
	parser = argparse.ArgumentParser(
		description="Plot training sets statistics")

	parser.add_argument("path", metavar="PATH",
						help="The statistics json file")

	parser.add_argument("-o", dest="out_path", metavar="PATH",
						help="The path to save the plot image.")

	parser.add_argument("-p", "--predictors", dest="predictor_names", metavar="NAMES",
						help="The names of the predictors to represent (seppareted by commas).")

	parser.add_argument("-i", "--interactive", dest="interactive", action="store_true", default=False,
						help="Show the plot in interactive mode.")

	bglogging.add_logging_arguments(parser)

	args = parser.parse_args()

	bglogging.initialize(args)

	log = bglogging.get_logger("plot-stats")

	log.info("Loading state ...")

	state = load_weights(args.path)

	predictor_names, stats = [state[k] for k in ["predictor_names", "stats"]]

	if args.predictor_names is not None:
		valid_names = set(predictor_names)
		args.predictor_names = [s.strip() for s in args.predictor_names.split(",")]
		predictor_names = [name for name in args.predictor_names if name in valid_names]

		if len(predictor_names) == 0:
			log.error("No scores selected. Please choose between: {}".format(", ".join(valid_names)))
			exit(-1)

	log.info("Plotting ...")

	fig = plt.figure()
	ax = fig.add_subplot(111)
	ax.grid()
	ax.set_xlabel("False Positive Rate (1 - especificity)")
	ax.set_ylabel("True Positive Rate (sensitivity)")
	
	num_predictors = len(predictor_names)
	for predictor_name in predictor_names:
		predictor_stats = stats[predictor_name]
		(size, tp, tn, fp, fn) = [predictor_stats[k] for k in ["size", "tp", "tn", "fp", "fn"]]
		
		tpr = [1.0] * (size + 1)
		fpr = [1.0] * (size + 1)
		for i in range(size):
			tpr[i + 1] = (float(tp[i]) / (tp[i] + fn[i]))
			fpr[i + 1] = (float(fp[i]) / (fp[i] + tn[i]))

		ax.plot(fpr, tpr, "-")
	
	ax.legend(tuple(predictor_names), "lower right", shadow=False)

	ax.plot([0.0, 1.0], [0.0, 1.0], "--", color="0.75")

	if args.out_path is not None:
		from matplotlib import pylab
		pylab.savefig(args.out_path, bbox_inches=0)

	if args.interactive:
		plt.show()
示例#7
0
def main():
	parser = argparse.ArgumentParser(
		description="Calculate Condel score")

	parser.add_argument("db_path", metavar="DB_PATH",
						help="Functional scores database")

	parser.add_argument("weights_path", metavar="WEIGHTS",
						help="File containing the scores weights and cutoffs")

	parser.add_argument("-p", "--predictors", dest="predictors", metavar="PREDICTORS",
						help="Comma separated list of predictors")

	parser.add_argument("-u", "--updated-predictor", dest="updated_predictor", metavar="NAME",
						help="Updated predictor name")

	bglogging.add_logging_arguments(parser)

	args = parser.parse_args()

	bglogging.initialize(args)

	log = bglogging.get_logger("calculate")

	log.info("Opening functional scores database ...")

	db = FannsSQLiteDb(args.db_path)
	db.open()

	updated_predictor = args.updated_predictor or "CONDEL"

	predictors = set([p["id"] for p in db.predictors()])
	if updated_predictor not in predictors:
		log.info("  Creating predictor {} ...".format(updated_predictor))
		db.add_predictor(updated_predictor, FannsDb.CALCULATED_PREDICTOR_TYPE, source=predictors)

	log.info("Loading state ...")

	state = load_weights(args.weights_path)

	avail_predictors, precision, step, stats = [state[k] for k in ["predictor_names", "precision", "step", "stats"]]
	if args.predictors is not None:
		predictors = [p for p in [p.strip() for p in args.predictors.split(",")] if p in avail_predictors]
		if len(predictors) == 0:
			log.error("Unknown predictors: {}".format(args.predictors))
			log.error("Available predictor names are: {}".format(", ".join(avail_predictors)))
			exit(-1)
	else:
		predictors = avail_predictors

	log.info("Available predictors: {}".format(", ".join(avail_predictors)))
	log.info("Selected predictors: {}".format(", ".join(predictors)))

	log.info("Calculating ...")

	start_time = partial_start_time = time.time()
	try:
		for num_rows, row in enumerate(db.query_scores(predictors=predictors), start=1):
			scores = row["scores"]
			condel = wsum = 0
			for predictor, score in scores.items():
				if score is None:
					continue

				predictor_stats = stats[predictor]
				rmin, rmax, dim, size, cdp, cdn, cutoff = [predictor_stats[k] for k in [
																"rmin", "rmax", "dim", "size", "cdp", "cdn", "cutoff"]]

				if predictor in PREDICTOR_TRANSFORM:
					score = PREDICTOR_TRANSFORM[predictor](score)

				r = (score - rmin) / dim
				index = int(r * size) if score < rmax else size - 1

				if score < cutoff:
					w = 1 - cdn[index]
				else:
					w = 1 - cdp[index]

				wsum += w
				condel += w * score

				#log.info("{}={}, w={} -> {}".format(predictor_name, score, w, score * w))

			if wsum != 0:
				condel /= wsum

				d = {updated_predictor : condel}
				db.update_scores(row["id"], d)

				#log.info(">>> CONDEL={}".format(condel))
			else:
				log.warn("wsum = 0, condel={}, scores={}".format(condel, repr(scores)))

			partial_time = time.time() - partial_start_time
			if partial_time > 5.0:
				partial_start_time = time.time()
				elapsed_time = time.time() - start_time
				log.debug("  {} rows, {:.1f} rows/second".format(hsize(num_rows), num_rows / elapsed_time))

		log.info("Commit ...")
		db.commit()
	except KeyboardInterrupt:
		log.warn("Interrupted by Ctrl-C")
		db.rollback()
	except:
		db.rollback()
		raise
	finally:
		db.close()
示例#8
0
def main():
	parser = argparse.ArgumentParser(
		description="Prepare SNV's dataset from individual training sets")

	parser.add_argument("pos_path", metavar="POS_SET",
						help="The positive training set file")

	parser.add_argument("neg_path", metavar="NEG_SET",
						help="The negative training set file")

	parser.add_argument("-m", "--map", dest="map_path", metavar="MAP",
						help="Optional mapping file for feature id's. Format: DST SRC")

	parser.add_argument("-o", dest="out_path", metavar="PATH",
						help="Output file. Use - for standard output.")

	bglogging.add_logging_arguments(parser)

	args = parser.parse_args()

	bglogging.initialize(args)

	logger = bglogging.get_logger("training-sets")

	if args.out_path is None:
		prefix = os.path.commonprefix([
							os.path.splitext(os.path.basename(args.pos_path))[0],
							os.path.splitext(os.path.basename(args.neg_path))[0]])

		prefix = prefix.rstrip(".")

		args.out_path = os.path.join(os.getcwd(), "{}-training.tsv".format(prefix))

	if args.map_path is not None:
		logger.info("Loading map ...")

		prot_map = {}
		with tsv.open(args.map_path) as f:
			for dst_feature, src_feature in tsv.lines(f, (str, str)):
					if len(src_feature) > 0:
						if src_feature not in prot_map:
							prot_map[src_feature] = set([dst_feature])
						else:
							prot_map[src_feature].add(dst_feature)
	else:
		prot_map = None
	
	logger.info("Processing ...")

	hits = dict(POS=0, NEG=0)
	fails = dict(POS=0, NEG=0)

	start_time = datetime.now()

	with tsv.open(args.out_path, "w") as wf:

		for event_type, path in (("POS", args.pos_path), ("NEG", args.neg_path)):

			logger.info("  [{}] Reading {} ...".format(event_type, path))

			with tsv.open(path) as f:
				types = (str, int, str, str)
				for protein, pos, aa1, aa2 in tsv.lines(f, types):
					protein = protein.strip()

					if prot_map is not None:
						if protein not in prot_map:
							logger.debug("[{}] Unmapped protein: {}".format(event_type, protein))
							fails[event_type] += 1
							continue
						proteins = prot_map[protein]
					else:
						proteins = [protein]

					hits[event_type] += 1

					for p in proteins:
						tsv.write_line(wf, p, pos, aa1.strip(), aa2.strip(), event_type)

	logger.info("               POS       NEG")
	logger.info("SNVs      {POS:>8}  {NEG:>8}".format(**hits))
	if args.map_path is not None:
		logger.info("unmapped  {POS:>8}  {NEG:>8}".format(**fails))

	logger.info("Finished. Elapsed time: {}".format(datetime.now() - start_time))