--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/varscan.py	Tue Dec 04 05:15:15 2018 -0500
@@ -0,0 +1,1178 @@
+#!/usr/bin/env python3
+from __future__ import print_function
+
+import argparse
+import io
+import os
+import subprocess
+import sys
+import tempfile
+import time
+from contextlib import ExitStack
+from functools import partial
+from threading import Thread
+
+import pysam
+
+
+class VariantCallingError (RuntimeError):
+    """Exception class for issues with samtools and varscan subprocesses."""
+
+    def __init__(self, message=None, call='', error=''):
+        self.message = message
+        self.call = call.strip()
+        self.error = error.strip()
+
+    def __str__(self):
+        if self.message is None:
+            return ''
+        if self.error:
+            msg_header = '"{0}" failed with:\n{1}\n\n'.format(
+                self.call, self.error
+            )
+        else:
+            msg_header = '{0} failed.\n'
+            'No further information about this error is available.\n\n'.format(
+                self.call
+            )
+        return msg_header + self.message
+
+
+class VarScanCaller (object):
+    def __init__(self, ref_genome, bam_input_files,
+                 max_depth=None,
+                 min_mapqual=None, min_basequal=None,
+                 threads=1, verbose=False, quiet=True
+                 ):
+        self.ref_genome = ref_genome
+        self.bam_input_files = bam_input_files
+        self.max_depth = max_depth
+        self.min_mapqual = min_mapqual
+        self.min_basequal = min_basequal
+        self.threads = threads
+        self.verbose = verbose
+        self.quiet = quiet
+
+        with pysam.FastaFile(ref_genome) as ref_fa:
+            self.ref_contigs = ref_fa.references
+            self.ref_lengths = ref_fa.lengths
+
+        self.pileup_engine = ['samtools', 'mpileup']
+        self.varcall_engine = ['varscan', 'somatic']
+        self.requires_stdout_redirect = False
+        self.TemporaryContigVCF = partial(
+            tempfile.NamedTemporaryFile,
+            mode='wb', suffix='', delete=False, dir=os.getcwd()
+        )
+        self.tmpfiles = []
+
+    def _get_pysam_pileup_args(self):
+        param_dict = {}
+        if self.max_depth is not None:
+            param_dict['max_depth'] = self.max_depth
+        if self.min_mapqual is not None:
+            param_dict['min_mapping_quality'] = self.min_mapqual
+        if self.min_basequal is not None:
+            param_dict['min_base_quality'] = self.min_basequal
+        param_dict['compute_baq'] = False
+        param_dict['stepper'] = 'samtools'
+        return param_dict
+
+    def varcall_parallel(self, normal_purity=None, tumor_purity=None,
+                         min_coverage=None,
+                         min_var_count=None,
+                         min_var_freq=None, min_hom_freq=None,
+                         p_value=None, somatic_p_value=None,
+                         threads=None, verbose=None, quiet=None
+                         ):
+        if not threads:
+            threads = self.threads
+        if verbose is None:
+            verbose = self.verbose
+        if quiet is None:
+            quiet = self.quiet
+        # mapping of method parameters to varcall engine command line options
+        varcall_engine_option_mapping = [
+            ('--normal-purity', normal_purity),
+            ('--tumor-purity', tumor_purity),
+            ('--min-coverage', min_coverage),
+            ('--min-reads2', min_var_count),
+            ('--min-var-freq', min_var_freq),
+            ('--min-freq-for-hom', min_hom_freq),
+            ('--p-value', p_value),
+            ('--somatic-p-value', somatic_p_value),
+            ('--min-avg-qual', self.min_basequal)
+        ]
+        varcall_engine_options = []
+        for option, value in varcall_engine_option_mapping:
+            if value is not None:
+                varcall_engine_options += [option, str(value)]
+        pileup_engine_options = ['-B']
+        if self.max_depth is not None:
+            pileup_engine_options += ['-d', str(self.max_depth)]
+        if self.min_mapqual is not None:
+            pileup_engine_options += ['-q', str(self.min_mapqual)]
+        if self.min_basequal is not None:
+            pileup_engine_options += ['-Q', str(self.min_basequal)]
+
+        # Create a tuple of calls to samtools mpileup and varscan for
+        # each contig. The contig name is stored as the third element of
+        # that tuple.
+        # The calls are stored in the reverse order of the contig list so
+        # that they can be popped off later in the original order
+        calls = [(
+            self.pileup_engine + pileup_engine_options + [
+                '-r', contig + ':',
+                '-f', self.ref_genome
+            ] + self.bam_input_files,
+            self.varcall_engine + [
+                '-', '{out}', '--output-vcf', '1', '--mpileup', '1'
+            ] + varcall_engine_options,
+            contig
+        ) for contig in self.ref_contigs[::-1]]
+
+        if verbose:
+            print('Starting variant calling ..')
+
+        # launch subprocesses and monitor their status
+        subprocesses = []
+        error_table = {}
+        tmp_io_started = []
+        tmp_io_finished = []
+        self.tmpfiles = []
+
+        def enqueue_stderr_output(out, stderr_buffer):
+            for line in iter(out.readline, b''):
+                # Eventually we are going to print the contents of
+                # the stderr_buffer to sys.stderr so we can
+                # decode things here using its encoding.
+                # We do a 'backslashreplace' just to be on the safe side.
+                stderr_buffer.write(line.decode(sys.stderr.encoding,
+                                                'backslashreplace'))
+            out.close()
+
+        try:
+            while subprocesses or calls:
+                while calls and len(subprocesses) < threads:
+                    # There are still calls waiting for execution and we
+                    # have unoccupied threads so we launch a new combined
+                    # call to samtools mpileup and the variant caller.
+
+                    # pop the call arguments from our call stack
+                    call = calls.pop()
+                    # get the name of the contig that this call is going
+                    # to work on
+                    contig = call[2]
+                    # Based on the contig name, generate a readable and
+                    # file system-compatible prefix and use it to create
+                    # a named temporary file, to which the call output
+                    # will be redirected.
+                    # At the moment we create the output file we add it to
+                    # the list of all temporary output files so that we can
+                    # remove it eventually during cleanup.
+                    call_out = self.TemporaryContigVCF(
+                        prefix=''.join(
+                            c if c.isalnum() else '_' for c in contig
+                        ) + '_',
+                    )
+                    # maintain a list of variant call outputs
+                    # in the order the subprocesses got launched
+                    tmp_io_started.append(call_out.name)
+                    if self.requires_stdout_redirect:
+                        # redirect stdout to the temporary file just created
+                        stdout_p2 = call_out
+                        stderr_p2 = subprocess.PIPE
+                    else:
+                        # variant caller wants to write output to file directly
+                        stdout_p2 = subprocess.PIPE
+                        stderr_p2 = subprocess.STDOUT
+                        call[1][call[1].index('{out}')] = call_out.name
+                        call_out.close()
+                    # for reporting purposes, join the arguments for the
+                    # samtools and the variant caller calls into readable
+                    # strings
+                    c_str = (' '.join(call[0]), ' '.join(call[1]))
+                    error_table[c_str] = [io.StringIO(), io.StringIO()]
+                    # start the subprocesses
+                    p1 = subprocess.Popen(
+                        call[0],
+                        stdout=subprocess.PIPE, stderr=subprocess.PIPE
+                    )
+                    p2 = subprocess.Popen(
+                        call[1],
+                        stdin=p1.stdout,
+                        stdout=stdout_p2,
+                        stderr=stderr_p2
+                    )
+                    # subprocess bookkeeping
+                    subprocesses.append((c_str, p1, p2, call_out, contig))
+                    # make sure our newly launched call does not block
+                    # because its buffers fill up
+                    p1.stdout.close()
+                    t1 = Thread(
+                        target=enqueue_stderr_output,
+                        args=(p1.stderr, error_table[c_str][0])
+                    )
+                    t2 = Thread(
+                        target=enqueue_stderr_output,
+                        args=(
+                            p2.stderr
+                            if self.requires_stdout_redirect else
+                            p2.stdout,
+                            error_table[c_str][1]
+                        )
+                    )
+                    t1.daemon = t2.daemon = True
+                    t1.start()
+                    t2.start()
+
+                    if verbose:
+                        print(
+                            'Calling variants for contig: {0}'
+                            .format(call[2])
+                        )
+
+                # monitor all running calls to see if any of them are done
+                for call, p1, p2, ofo, contig in subprocesses:
+                    p1_stat = p1.poll()
+                    p2_stat = p2.poll()
+                    if p1_stat is not None or p2_stat is not None:
+                        # There is an outcome for this process!
+                        # Lets see what it is
+                        if p1_stat or p2_stat:
+                            print()
+                            print(
+                                error_table[call][0].getvalue(),
+                                error_table[call][1].getvalue(),
+                                file=sys.stderr
+                            )
+                            raise VariantCallingError(
+                                'Variant Calling for contig {0} failed.'
+                                .format(contig),
+                                call='{0} | {1}'.format(call[0], call[1])
+                            )
+                        if p1_stat == 0 and p2_stat is None:
+                            # VarScan is not handling the no output from
+                            # samtools mpileup situation correctly so maybe
+                            # that's the issue here
+                            last_words = error_table[call][1].getvalue(
+                            ).splitlines()[-4:]
+                            if len(last_words) < 4 or any(
+                                not msg.startswith('Input stream not ready')
+                                for msg in last_words
+                            ):
+                                break
+                                # lets give this process a bit more time
+                            # VarScan is waiting for input it will never
+                            # get, stop it.
+                            p2.terminate()
+                            subprocesses.remove((call, p1, p2, ofo, contig))
+                            ofo.close()
+                            break
+                        if p2_stat == 0:
+                            # Things went fine.
+                            # maintain a list of variant call outputs
+                            # that finished successfully (in the order
+                            # they finished)
+                            tmp_io_finished.append(ofo.name)
+                            if verbose:
+                                print()
+                                print('Contig {0} finished.'.format(contig))
+                            if not quiet:
+                                print()
+                                print(
+                                    'stderr output from samtools mpileup/'
+                                    'bcftools:'.upper(),
+                                    file=sys.stderr
+                                )
+                                print(
+                                    error_table[call][0].getvalue(),
+                                    error_table[call][1].getvalue(),
+                                    file=sys.stderr
+                                )
+                            # Discard the collected stderr output from
+                            # the call, remove the call from the list of
+                            # running calls and close its output file.
+                            del error_table[call]
+                            subprocesses.remove((call, p1, p2, ofo, contig))
+                            # Closing the output file is important or we
+                            # may hit the file system limit for open files
+                            # if there are lots of contigs.
+                            ofo.close()
+                            break
+                # wait a bit in between monitoring cycles
+                time.sleep(2)
+        finally:
+            for call, p1, p2, ofo, contig in subprocesses:
+                # make sure we do not leave running subprocesses behind
+                for proc in (p1, p2):
+                    try:
+                        proc.terminate()
+                    except Exception:
+                        pass
+                # close currently open files
+                ofo.close()
+            # store the files with finished content in the order that
+            # the corresponding jobs were launched
+            self.tmpfiles = [f for f in tmp_io_started if f in tmp_io_finished]
+            # Make sure remaining buffered stderr output of
+            # subprocesses does not get lost.
+            # Currently, we don't screen this output for real errors,
+            # but simply rewrite everything.
+            if not quiet and error_table:
+                print()
+                print(
+                    'stderr output from samtools mpileup/bcftools:'.upper(),
+                    file=sys.stderr
+                )
+                for call, errors in error_table.items():
+                    print(' | '.join(call), ':', file=sys.stderr)
+                    print('-' * 20, file=sys.stderr)
+                    print('samtools mpileup output:', file=sys.stderr)
+                    print(errors[0].getvalue(), file=sys.stderr)
+                    print('varscan somatic output:', file=sys.stderr)
+                    print(errors[1].getvalue(), file=sys.stderr)
+
+    def _add_ref_contigs_to_header(self, header):
+        for chrom, length in zip(self.ref_contigs, self.ref_lengths):
+            header.add_meta(
+                'contig',
+                items=[('ID', chrom), ('length', length)]
+            )
+
+    def _add_filters_to_header(self, header):
+        varscan_fpfilters = {
+            'VarCount': 'Fewer than {min_var_count2} variant-supporting reads',
+            'VarFreq': 'Variant allele frequency below {min_var_freq2}',
+            'VarAvgRL':
+                'Average clipped length of variant-supporting reads < '
+                '{min_var_len}',
+            'VarReadPos': 'Relative average read position < {min_var_readpos}',
+            'VarDist3':
+                'Average distance to effective 3\' end < {min_var_dist3}',
+            'VarMMQS':
+                'Average mismatch quality sum for variant reads > '
+                '{max_var_mmqs}',
+            'VarMapQual':
+                'Average mapping quality of variant reads < {min_var_mapqual}',
+            'VarBaseQual':
+                'Average base quality of variant reads < {min_var_basequal}',
+            'Strand':
+                'Strand representation of variant reads < {min_strandedness}',
+            'RefAvgRL':
+                'Average clipped length of ref-supporting reads < '
+                '{min_ref_len}',
+            'RefReadPos':
+                'Relative average read position < {min_ref_readpos}',
+            'RefDist3':
+                'Average distance to effective 3\' end < {min_ref_dist3}',
+            'RefMapQual':
+                'Average mapping quality of reference reads < '
+                '{min_ref_mapqual}',
+            'RefBaseQual':
+                'Average base quality of ref-supporting reads < '
+                '{min_ref_basequal}',
+            'RefMMQS':
+                'Average mismatch quality sum for ref-supporting reads > '
+                '{max_ref_mmqs}',
+            'MMQSdiff':
+                'Mismatch quality sum difference (var - ref) > '
+                '{max_mmqs_diff}',
+            'MinMMQSdiff':
+                'Mismatch quality sum difference (var - ref) < '
+                '{max_mmqs_diff}',
+            'MapQualDiff':
+                'Mapping quality difference (ref - var) > {max_mapqual_diff}',
+            'MaxBAQdiff':
+                'Average base quality difference (ref - var) > '
+                '{max_basequal_diff}',
+            'ReadLenDiff':
+                'Average supporting read length difference (ref - var) > '
+                '{max_relative_len_diff}',
+        }
+        for filter_id, description in varscan_fpfilters.items():
+            header.filters.add(filter_id, None, None, description)
+
+    def _add_indel_info_flag_to_header(self, header):
+        header.info.add(
+            'INDEL', 0, 'Flag', 'Indicates that the variant is an INDEL'
+        )
+
+    def _compile_common_header(self, varcall_template, no_filters=False):
+        # fix the header generated by VarScan
+        # by adding reference and contig information
+        common_header = pysam.VariantHeader()
+        common_header.add_meta('reference', value=self.ref_genome)
+        self._add_ref_contigs_to_header(common_header)
+        if not no_filters:
+            # add filter info
+            self._add_filters_to_header(common_header)
+        # change the source information
+        common_header.add_meta('source', value='varscan.py')
+        # declare an INDEL flag for record INFO fields
+        self._add_indel_info_flag_to_header(common_header)
+        # take the remaining metadata from the template header produced by
+        # VarScan
+        with pysam.VariantFile(varcall_template, 'r') as original_data:
+            varscan_header = original_data.header
+        for sample in varscan_header.samples:
+            common_header.samples.add(sample)
+        common_header.merge(varscan_header)
+        return common_header
+
+    def pileup_masker(self, mask):
+        def apply_mask_on_pileup(piled_items):
+            for item, status in zip(piled_items, mask):
+                if status:
+                    yield item
+        return apply_mask_on_pileup
+
+    def get_allele_specific_pileup_column_stats(
+        self, allele, pile_column, ref_fetch
+    ):
+        # number of reads supporting the given allele on
+        # forward and reverse strand, and in total
+        var_reads_plus = var_reads_minus = 0
+        var_supp_read_mask = []
+        for base in pile_column.get_query_sequences():
+            if base == allele:
+                # allele supporting read on + strand
+                var_reads_plus += 1
+                var_supp_read_mask.append(True)
+            elif base.upper() == allele:
+                # allele supporting read on - strand
+                var_reads_minus += 1
+                var_supp_read_mask.append(True)
+            else:
+                var_supp_read_mask.append(False)
+        var_reads_total = var_reads_plus + var_reads_minus
+
+        if var_reads_total == 0:
+            # No stats without reads!
+            return None
+
+        var_supp_only = self.pileup_masker(var_supp_read_mask)
+
+        # average mapping quality of the reads supporting the
+        # given allele
+        avg_mapping_quality = sum(
+            mq for mq in var_supp_only(
+                pile_column.get_mapping_qualities()
+            )
+        ) / var_reads_total
+
+        # for the remaining stats we need access to complete
+        # read information
+        piled_reads = [
+            p for p in var_supp_only(pile_column.pileups)
+        ]
+        assert len(piled_reads) == var_reads_total
+        sum_avg_base_qualities = 0
+        sum_dist_from_center = 0
+        sum_dist_from_3prime = 0
+        sum_clipped_length = 0
+        sum_unclipped_length = 0
+        sum_num_mismatches_as_fraction = 0
+        sum_mismatch_qualities = 0
+
+        for p in piled_reads:
+            sum_avg_base_qualities += sum(
+                p.alignment.query_qualities
+            ) / p.alignment.infer_query_length()
+            sum_clipped_length += p.alignment.query_alignment_length
+            unclipped_length = p.alignment.infer_read_length()
+            sum_unclipped_length += unclipped_length
+            read_center = p.alignment.query_alignment_length / 2
+            sum_dist_from_center += 1 - abs(
+                p.query_position - read_center
+            ) / read_center
+            if p.alignment.is_reverse:
+                sum_dist_from_3prime += p.query_position / unclipped_length
+            else:
+                sum_dist_from_3prime += 1 - p.query_position / unclipped_length
+
+            sum_num_mismatches = 0
+            for qpos, rpos in p.alignment.get_aligned_pairs():
+                if qpos is not None and rpos is not None:
+                    if p.alignment.query_sequence[qpos] != ref_fetch(
+                        rpos, rpos + 1
+                    ).upper():  # ref bases can be lowercase!
+                        sum_num_mismatches += 1
+                        sum_mismatch_qualities += p.alignment.query_qualities[
+                            qpos
+                        ]
+            sum_num_mismatches_as_fraction += (
+                sum_num_mismatches / p.alignment.query_alignment_length
+            )
+        avg_basequality = sum_avg_base_qualities / var_reads_total
+        avg_pos_as_fraction = sum_dist_from_center / var_reads_total
+        avg_num_mismatches_as_fraction = (
+            sum_num_mismatches_as_fraction / var_reads_total
+        )
+        avg_sum_mismatch_qualities = sum_mismatch_qualities / var_reads_total
+        avg_clipped_length = sum_clipped_length / var_reads_total
+        avg_distance_to_effective_3p_end = (
+            sum_dist_from_3prime / var_reads_total
+        )
+
+        return (
+            avg_mapping_quality,
+            avg_basequality,
+            var_reads_plus,
+            var_reads_minus,
+            avg_pos_as_fraction,
+            avg_num_mismatches_as_fraction,
+            avg_sum_mismatch_qualities,
+            avg_clipped_length,
+            avg_distance_to_effective_3p_end
+        )
+
+    def _postprocess_variant_records(self, invcf,
+                                     min_var_count2, min_var_count2_lc,
+                                     min_var_freq2, max_somatic_p,
+                                     max_somatic_p_depth,
+                                     min_ref_readpos, min_var_readpos,
+                                     min_ref_dist3, min_var_dist3,
+                                     min_ref_len, min_var_len,
+                                     max_relative_len_diff,
+                                     min_strandedness, min_strand_reads,
+                                     min_ref_basequal, min_var_basequal,
+                                     max_basequal_diff,
+                                     min_ref_mapqual, min_var_mapqual,
+                                     max_mapqual_diff,
+                                     max_ref_mmqs, max_var_mmqs,
+                                     min_mmqs_diff, max_mmqs_diff,
+                                     **args):
+        # set FILTER field according to Varscan criteria
+        # multiple FILTER entries must be separated by semicolons
+        # No filters applied should be indicated with MISSING
+
+        # since posterior filters are always applied to just one sample,
+        # a better place to store the info is in the FT genotype field:
+        # can be PASS, '.' to indicate that filters have not been applied,
+        # or a semicolon-separated list of filters that failed
+        # unfortunately, gemini does not support this field
+
+        with ExitStack() as io_stack:
+            normal_reads, tumor_reads = (
+                io_stack.enter_context(
+                    pysam.Samfile(fn, 'rb')) for fn in self.bam_input_files
+            )
+            refseq = io_stack.enter_context(pysam.FastaFile(self.ref_genome))
+            pileup_args = self._get_pysam_pileup_args()
+            for record in invcf:
+                if any(len(allele) > 1 for allele in record.alleles):
+                    # skip indel postprocessing for the moment
+                    yield record
+                    continue
+                # get pileup for genomic region affected by this variant
+                if record.info['SS'] == '2':
+                    # a somatic variant => generate pileup from tumor data
+                    pile = tumor_reads.pileup(
+                        record.chrom, record.start, record.stop,
+                        **pileup_args
+                    )
+                    sample_of_interest = 'TUMOR'
+                elif record.info['SS'] in ['1', '3']:
+                    # a germline or LOH variant => pileup from normal data
+                    pile = normal_reads.pileup(
+                        record.chrom, record.start, record.stop,
+                        **pileup_args
+                    )
+                    sample_of_interest = 'NORMAL'
+                else:
+                    # TO DO: figure out if there is anything interesting to do
+                    # for SS status codes 0 (reference) and 5 (unknown)
+                    yield record
+                    continue
+
+                # apply false-positive filtering a la varscan fpfilter
+                # find the variant site in the pileup columns
+                for pile_column in pile:
+                    if pile_column.reference_pos == record.start:
+                        break
+                # extract required information
+                # overall read depth at the site
+                read_depth = pile_column.get_num_aligned()
+                assert read_depth > 0
+                # no multiallelic sites in varscan
+                assert len(record.alleles) == 2
+                if record.samples[sample_of_interest]['RD'] > 0:
+                    ref_stats, alt_stats = [
+                        self.get_allele_specific_pileup_column_stats(
+                            allele,
+                            pile_column,
+                            partial(
+                                pysam.FastaFile.fetch, refseq, record.chrom
+                            )
+                        )
+                        for allele in record.alleles
+                    ]
+                else:
+                    ref_stats = None
+                    alt_stats = self.get_allele_specific_pileup_column_stats(
+                        record.alleles[1],
+                        pile_column,
+                        partial(pysam.FastaFile.fetch, refseq, record.chrom)
+                    )
+                ref_count = 0
+                if ref_stats:
+                    ref_count = ref_stats[2] + ref_stats[3]
+                    if ref_stats[1] < min_ref_basequal:
+                        record.filter.add('RefBaseQual')
+                    if ref_count >= 2:
+                        if ref_stats[0] < min_ref_mapqual:
+                            record.filter.add('RefMapQual')
+                        if ref_stats[4] < min_ref_readpos:
+                            record.filter.add('RefReadPos')
+                        # ref_stats[5] (avg_num_mismatches_as_fraction
+                        # is not a filter criterion in VarScan fpfilter
+                        if ref_stats[6] > max_ref_mmqs:
+                            record.filter.add('RefMMQS')
+                        if ref_stats[7] < min_ref_len:
+                            # VarScan fpfilter does not apply this filter
+                            # for indels, but there is no reason
+                            # not to do it.
+                            record.filter.add('RefAvgRL')
+                        if ref_stats[8] < min_ref_dist3:
+                            record.filter.add('RefDist3')
+                if alt_stats:
+                    alt_count = alt_stats[2] + alt_stats[3]
+                    if (
+                        alt_count < min_var_count2_lc
+                    ) or (
+                        read_depth >= max_somatic_p_depth and
+                        alt_count < min_var_count2
+                    ):
+                        record.filter.add('VarCount')
+                    if alt_count / read_depth < min_var_freq2:
+                        record.filter.add('VarFreq')
+                    if alt_stats[1] < min_var_basequal:
+                        record.filter.add('VarBaseQual')
+                    if alt_count > min_strand_reads:
+                        if (
+                            alt_stats[2] / alt_count < min_strandedness
+                        ) or (
+                            alt_stats[3] / alt_count < min_strandedness
+                        ):
+                            record.filter.add('Strand')
+                    if alt_stats[2] + alt_stats[3] >= 2:
+                        if alt_stats[0] < min_var_mapqual:
+                            record.filter.add('VarMapQual')
+                        if alt_stats[4] < min_var_readpos:
+                            record.filter.add('VarReadPos')
+                        # alt_stats[5] (avg_num_mismatches_as_fraction
+                        # is not a filter criterion in VarScan fpfilter
+                        if alt_stats[6] > max_var_mmqs:
+                            record.filter.add('VarMMQS')
+                        if alt_stats[7] < min_var_len:
+                            # VarScan fpfilter does not apply this filter
+                            # for indels, but there is no reason
+                            # not to do it.
+                            record.filter.add('VarAvgRL')
+                        if alt_stats[8] < min_var_dist3:
+                            record.filter.add('VarDist3')
+                    if ref_count >= 2 and alt_count >= 2:
+                        if (ref_stats[0] - alt_stats[0]) > max_mapqual_diff:
+                            record.filter.add('MapQualDiff')
+                        if (ref_stats[1] - alt_stats[1]) > max_basequal_diff:
+                            record.filter.add('MaxBAQdiff')
+                        mmqs_diff = alt_stats[6] - ref_stats[6]
+                        if mmqs_diff < min_mmqs_diff:
+                            record.filter.add('MinMMQSdiff')
+                        if mmqs_diff > max_mmqs_diff:
+                            record.filter.add('MMQSdiff')
+                        if (
+                            1 - alt_stats[7] / ref_stats[7]
+                        ) > max_relative_len_diff:
+                            record.filter.add('ReadLenDiff')
+                else:
+                    # No variant-supporting reads for this record!
+                    # This can happen in rare cases because of
+                    # samtools mpileup issues, but indicates a
+                    # rather unreliable variant call.
+                    record.filter.add('VarCount')
+                    record.filter.add('VarFreq')
+                yield record
+
+    def _indel_flagged_records(self, vcf):
+        for record in vcf:
+            record.info['INDEL'] = True
+            yield record
+
+    def _merge_generator(self, vcf1, vcf2):
+        try:
+            record1 = next(vcf1)
+        except StopIteration:
+            for record2 in vcf2:
+                yield record2
+            return
+        try:
+            record2 = next(vcf2)
+        except StopIteration:
+            yield record1
+            for record1 in vcf1:
+                yield record1
+            return
+        while True:
+            if (record1.start, record1.stop) < (record2.start, record2.stop):
+                yield record1
+                try:
+                    record1 = next(vcf1)
+                except StopIteration:
+                    yield record2
+                    for record2 in vcf2:
+                        yield record2
+                    return
+            else:
+                yield record2
+                try:
+                    record2 = next(vcf2)
+                except StopIteration:
+                    yield record1
+                    for record1 in vcf1:
+                        yield record1
+                    return
+
+    def merge_and_postprocess(self, snps_out, indels_out=None,
+                              no_filters=False, **filter_args):
+        temporary_data = self.tmpfiles
+        self.tmpfiles = []
+        temporary_snp_files = [f + '.snp.vcf' for f in temporary_data]
+        temporary_indel_files = [f + '.indel.vcf' for f in temporary_data]
+
+        for f in temporary_data:
+            try:
+                os.remove(f)
+            except Exception:
+                pass
+
+        def noop_gen(data, **kwargs):
+            for d in data:
+                yield d
+
+        if no_filters:
+            apply_filters = noop_gen
+        else:
+            apply_filters = self._postprocess_variant_records
+
+        output_header = self._compile_common_header(
+            temporary_snp_files[0],
+            no_filters
+        )
+        if indels_out is None:
+            with open(snps_out, 'w') as o:
+                o.write(str(output_header).format(**filter_args))
+                for snp_f, indel_f in zip(
+                    temporary_snp_files, temporary_indel_files
+                ):
+                    with pysam.VariantFile(snp_f, 'r') as snp_invcf:
+                        # fix the input header on the fly
+                        # to avoid Warnings from htslib about missing contig
+                        # info
+                        self._add_ref_contigs_to_header(snp_invcf.header)
+                        self._add_filters_to_header(snp_invcf.header)
+                        self._add_indel_info_flag_to_header(snp_invcf.header)
+                        with pysam.VariantFile(indel_f, 'r') as indel_invcf:
+                            # fix the input header on the fly
+                            # to avoid Warnings from htslib about missing
+                            # contig info
+                            self._add_ref_contigs_to_header(indel_invcf.header)
+                            self._add_filters_to_header(indel_invcf.header)
+                            self._add_indel_info_flag_to_header(
+                                indel_invcf.header
+                            )
+                            for record in apply_filters(
+                                self._merge_generator(
+                                    snp_invcf,
+                                    self._indel_flagged_records(indel_invcf)
+                                ),
+                                **filter_args
+                            ):
+                                o.write(str(record))
+                    try:
+                        os.remove(snp_f)
+                    except Exception:
+                        pass
+                    try:
+                        os.remove(indel_f)
+                    except Exception:
+                        pass
+
+        else:
+            with open(snps_out, 'w') as o:
+                o.write(str(output_header).format(**filter_args))
+                for f in temporary_snp_files:
+                    with pysam.VariantFile(f, 'r') as invcf:
+                        # fix the input header on the fly
+                        # to avoid Warnings from htslib about missing
+                        # contig info and errors because of undeclared
+                        # filters
+                        self._add_ref_contigs_to_header(invcf.header)
+                        self._add_filters_to_header(invcf.header)
+                        for record in apply_filters(
+                            invcf, **filter_args
+                        ):
+                            o.write(str(record))
+                    try:
+                        os.remove(f)
+                    except Exception:
+                        pass
+            with open(indels_out, 'w') as o:
+                o.write(str(output_header))
+                for f in temporary_indel_files:
+                    with pysam.VariantFile(f, 'r') as invcf:
+                        # fix the input header on the fly
+                        # to avoid Warnings from htslib about missing
+                        # contig info and errors because of undeclared
+                        # filters
+                        self._add_ref_contigs_to_header(invcf.header)
+                        self._add_filters_to_header(invcf.header)
+                        self._add_indel_info_flag_to_header(invcf.header)
+                        for record in apply_filters(
+                            self._indel_flagged_records(invcf), **filter_args
+                        ):
+                            o.write(str(record))
+                    try:
+                        os.remove(f)
+                    except Exception:
+                        pass
+
+
+def varscan_call(ref_genome, normal, tumor, output_path, **args):
+    """Preparse arguments and orchestrate calling and postprocessing."""
+
+    if args.pop('split_output'):
+        if '%T' in output_path:
+            out = (
+                output_path.replace('%T', 'snp'),
+                output_path.replace('%T', 'indel')
+            )
+        else:
+            out = (
+                output_path + '.snp',
+                output_path + '.indel'
+            )
+    else:
+        out = (output_path, None)
+
+    instance_args = {
+        k: args.pop(k) for k in [
+            'max_depth',
+            'min_mapqual',
+            'min_basequal',
+            'threads',
+            'verbose',
+            'quiet'
+        ]
+    }
+    varscan_somatic_args = {
+        k: args.pop(k) for k in [
+            'normal_purity',
+            'tumor_purity',
+            'min_coverage',
+            'min_var_count',
+            'min_var_freq',
+            'min_hom_freq',
+            'somatic_p_value',
+            'p_value'
+        ]
+    }
+
+    v = VarScanCaller(ref_genome, [normal, tumor], **instance_args)
+    v.varcall_parallel(**varscan_somatic_args)
+    v.merge_and_postprocess(*out, **args)
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser()
+    p.add_argument(
+        'ref_genome',
+        metavar='reference_genome',
+        help='the reference genome (in fasta format)'
+    )
+    p.add_argument(
+        '--normal',
+        metavar='BAM_file', required=True,
+        help='the BAM input file of aligned reads from the normal sample'
+    )
+    p.add_argument(
+        '--tumor',
+        metavar='BAM_file', required=True,
+        help='the BAM input file of aligned reads from the tumor sample'
+    )
+    p.add_argument(
+        '-o', '--ofile', required=True,
+        metavar='OFILE', dest='output_path',
+        help='Name of the variant output file. With --split-output, the name '
+             'may use the %%T replacement token or will be used as the '
+             'basename for the two output files to be generated (see '
+             '-s|--split-output below).'
+    )
+    p.add_argument(
+        '-s', '--split-output',
+        dest='split_output', action='store_true', default=False,
+        help='indicate that separate output files for SNPs and indels '
+             'should be generated (original VarScan behavior). If specified, '
+             '%%T in the --ofile file name will be replaced with "snp" and '
+             '"indel" to generate the names of the SNP and indel output '
+             'files, respectively. If %%T is not found in the file name, it '
+             'will get interpreted as a basename to which ".snp"/".indel" '
+             'will be appended.'
+    )
+    p.add_argument(
+        '-t', '--threads',
+        type=int, default=1,
+        help='level of parallelism'
+    )
+    p.add_argument(
+        '-v', '--verbose',
+        action='store_true',
+        help='be verbose about progress'
+    )
+    p.add_argument(
+        '-q', '--quiet',
+        action='store_true',
+        help='suppress output from wrapped tools'
+    )
+    call_group = p.add_argument_group('Variant calling parameters')
+    call_group.add_argument(
+        '--normal-purity',
+        dest='normal_purity', type=float,
+        default=1.0,
+        help='Estimated purity of the normal sample (default: 1.0)'
+    )
+    call_group.add_argument(
+        '--tumor-purity',
+        dest='tumor_purity', type=float,
+        default=1.0,
+        help='Estimated purity of the tumor sample (default: 1.0)'
+    )
+    call_group.add_argument(
+        '--max-pileup-depth',
+        dest='max_depth', type=int, default=8000,
+        help='Maximum depth of generated pileups (samtools mpileup -d option; '
+             'default: 8000)'
+    )
+    call_group.add_argument(
+        '--min-basequal',
+        dest='min_basequal', type=int,
+        default=13,
+        help='Minimum base quality at the variant position to use a read '
+             '(default: 13)'
+    )
+    call_group.add_argument(
+        '--min-mapqual',
+        dest='min_mapqual', type=int,
+        default=0,
+        help='Minimum mapping quality required to use a read '
+             '(default: 0)'
+    )
+    call_group.add_argument(
+        '--min-coverage',
+        dest='min_coverage', type=int,
+        default=8,
+        help='Minimum site coverage required in the normal and in the tumor '
+             'sample to call a variant (default: 8)'
+    )
+    call_group.add_argument(
+        '--min-var-count',
+        dest='min_var_count', type=int,
+        default=2,
+        help='Minimum number of variant-supporting reads required to call a '
+             'variant (default: 2)'
+    )
+    call_group.add_argument(
+        '--min-var-freq',
+        dest='min_var_freq', type=float,
+        default=0.1,
+        help='Minimum variant allele frequency for calling (default: 0.1)'
+    )
+    call_group.add_argument(
+        '--min-hom-freq',
+        dest='min_hom_freq', type=float,
+        default=0.75,
+        help='Minimum variant allele frequency for homozygous call '
+             '(default: 0.75)'
+    )
+    call_group.add_argument(
+        '--p-value',
+        dest='p_value', type=float,
+        default=0.99,
+        help='P-value threshold for heterozygous call (default: 0.99)'
+    )
+    call_group.add_argument(
+        '--somatic-p-value',
+        dest='somatic_p_value', type=float,
+        default=0.05,
+        help='P-value threshold for somatic call (default: 0.05)'
+    )
+    filter_group = p.add_argument_group('Posterior variant filter parameters')
+    filter_group.add_argument(
+        '--no-filters',
+        dest='no_filters', action='store_true',
+        help='Disable all posterior variant filters. '
+             'If specified, all following options will be ignored'
+    )
+    filter_group.add_argument(
+        '--min-var-count2',
+        dest='min_var_count2', type=int,
+        default=4,
+        help='Minimum number of variant-supporting reads (default: 4)'
+    )
+    filter_group.add_argument(
+        '--min-var-count2-lc',
+        dest='min_var_count2_lc', type=int,
+        default=2,
+        help='Minimum number of variant-supporting reads when depth below '
+             '--somatic-p-depth (default: 2)'
+    )
+    filter_group.add_argument(
+        '--min-var-freq2',
+        dest='min_var_freq2', type=float,
+        default=0.05,
+        help='Minimum variant allele frequency (default: 0.05)'
+    )
+    filter_group.add_argument(
+        '--max-somatic-p',
+        dest='max_somatic_p', type=float,
+        default=0.05,
+        help='Maximum somatic p-value (default: 0.05)'
+    )
+    filter_group.add_argument(
+        '--max-somatic-p-depth',
+        dest='max_somatic_p_depth', type=int,
+        default=10,
+        help='Depth required to run --max-somatic-p filter (default: 10)'
+    )
+    filter_group.add_argument(
+        '--min-ref-readpos',
+        dest='min_ref_readpos', type=float,
+        default=0.1,
+        help='Minimum average relative distance of site from the ends of '
+             'ref-supporting reads (default: 0.1)'
+    )
+    filter_group.add_argument(
+        '--min-var-readpos',
+        dest='min_var_readpos', type=float,
+        default=0.1,
+        help='Minimum average relative distance of site from the ends of '
+             'variant-supporting reads (default: 0.1)'
+    )
+    filter_group.add_argument(
+        '--min-ref-dist3',
+        dest='min_ref_dist3', type=float,
+        default=0.1,
+        help='Minimum average relative distance of site from the effective '
+             '3\'end of ref-supporting reads (default: 0.1)'
+    )
+    filter_group.add_argument(
+        '--min-var-dist3',
+        dest='min_var_dist3', type=float,
+        default=0.1,
+        help='Minimum average relative distance of site from the effective '
+             '3\'end of variant-supporting reads (default: 0.1)'
+    )
+    filter_group.add_argument(
+        '--min-ref-len',
+        dest='min_ref_len', type=int,
+        default=90,
+        help='Minimum average trimmed length of reads supporting the ref '
+             'allele (default: 90)'
+    )
+    filter_group.add_argument(
+        '--min-var-len',
+        dest='min_var_len', type=int,
+        default=90,
+        help='Minimum average trimmed length of reads supporting the variant '
+             'allele (default: 90)'
+    )
+    filter_group.add_argument(
+        '--max-len-diff',
+        dest='max_relative_len_diff', type=float,
+        default=0.25,
+        help='Maximum average relative read length difference (ref - var; '
+             'default: 0.25)'
+    )
+    filter_group.add_argument(
+        '--min-strandedness',
+        dest='min_strandedness', type=float,
+        default=0.01,
+        help='Minimum fraction of variant reads from each strand '
+             '(default: 0.01)'
+    )
+    filter_group.add_argument(
+        '--min-strand-reads',
+        dest='min_strand_reads', type=int,
+        default=5,
+        help='Minimum allele depth required to run --min-strandedness filter '
+             '(default: 5)'
+    )
+    filter_group.add_argument(
+        '--min-ref-basequal',
+        dest='min_ref_basequal', type=int,
+        default=15,
+        help='Minimum average base quality for the ref allele (default: 15)'
+    )
+    filter_group.add_argument(
+        '--min-var-basequal',
+        dest='min_var_basequal', type=int,
+        default=15,
+        help='Minimum average base quality for the variant allele '
+             '(default: 15)'
+    )
+    filter_group.add_argument(
+        '--max-basequal-diff',
+        dest='max_basequal_diff', type=int,
+        default=50,
+        help='Maximum average base quality diff (ref - var; default: 50)'
+    )
+    filter_group.add_argument(
+        '--min-ref-mapqual',
+        dest='min_ref_mapqual', type=int,
+        default=15,
+        help='Minimum average mapping quality of reads supporting the ref '
+             'allele (default: 15)'
+    )
+    filter_group.add_argument(
+        '--min-var-mapqual',
+        dest='min_var_mapqual', type=int,
+        default=15,
+        help='Minimum average mapping quality of reads supporting the variant '
+             'allele (default: 15)'
+    )
+    filter_group.add_argument(
+        '--max-mapqual-diff',
+        dest='max_mapqual_diff', type=int,
+        default=50,
+        help='Maximum average mapping quality difference (ref - var; '
+             'default: 50)'
+    )
+    filter_group.add_argument(
+        '--max-ref-mmqs',
+        dest='max_ref_mmqs', type=int,
+        default=100,
+        help='Maximum mismatch quality sum of reads supporting the ref '
+             'allele (default: 100)'
+    )
+    filter_group.add_argument(
+        '--max-var-mmqs',
+        dest='max_var_mmqs', type=int,
+        default=100,
+        help='Maximum mismatch quality sum of reads supporting the variant '
+             'allele (default: 100)'
+    )
+    filter_group.add_argument(
+        '--min-mmqs-diff',
+        dest='min_mmqs_diff', type=int,
+        default=0,
+        help='Minimum mismatch quality sum difference (var - ref; default: 0)'
+    )
+    filter_group.add_argument(
+        '--max-mmqs-diff',
+        dest='max_mmqs_diff', type=int,
+        default=50,
+        help='Maximum mismatch quality sum difference (var - ref; default: 50)'
+    )
+    args = vars(p.parse_args())
+    varscan_call(**args)