def centrifuge_invocation(config, **kwargs): wc_str = '{fs_prefix}/{df}/taxa/centr__{params}__v1.0.4_beta/{database}/{df_sample}/{preproc}/{df_sample}_report.tsv' sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs) config['requested_results'] += [{ 'result': 'centrifuge', 'sample_set': sample_set }]
def map_bowtie2(config, **kwargs): wc_str = '{fs_prefix}/{df}/mapped/bowtie2__{params}__{version}/{reference}/{df_sample}/{preproc}/{df_sample}.sam' sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs) config['requested_results'] += [{ 'result': 'map-bowtie2', 'sample_set': sample_set }]
def mp2_invocation(config, **kwargs): wc_str = '{fs_prefix}/{df}/taxa/mp2__{params}__v3.0/{database}/{df_sample}/{preproc}/{df_sample}.mp2' sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs) config['requested_results'] += [{ 'result': 'metaphlan2', 'sample_set': sample_set }]
def mp2_rare_invocation(config, **kwargs): sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) props = ['0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9'] for p in props: wc_str = '{{fs_prefix}}/{{df}}/taxa/mp2__{{params}}__v2.96.1/{{database}}/{{df_sample}}/{{preproc}}/{{df_sample}}_sbsmpl{prop}.mp2'.format( prop=p) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs) config['requested_results'] += [{ 'result': 'metaphlan2', 'sample_set': sample_set }]
def bbtools_tadpole_invocation(config, params, **kwargs): wc_str = '{fs_prefix}/{df}/reads/{preproc}__bbtdpl_{params}/{df_sample}_R1.fastq.gz' kwargs.update({'params': params}) if (kwargs['df'] is None): previous_requested_result = config['requested_results'][-1] if previous_requested_result['preprocessing']: sample_set = previous_requested_result['sample_set'] sample_set['preproc'] = sample_set['preproc']+'__'+previous_requested_result['preprocessing_addition'] else: sample_set, sample_set_name = generic_command_individual_samples(config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs) config['requested_results'] += [{'result': 'bbtools-tadpole', 'sample_set': sample_set, 'preprocessing': True}]
def result_invocation(config, **kwargs): # kwargs.update({'params': params}) if 'preset' in kwargs.keys( ) and self.preset_manager is not None: preset = self.preset_manager.find_preset_by_name( kwargs['preset']) if preset is not None: kwargs['preset'] = preset['full_name'] else: click.secho('NO SUCH PRESET', fg='red') exit() sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list( sample_set, self.result_wc, **kwargs)
def bbtools_tadpole_invocation(config, params, **kwargs): wc_str = '{fs_prefix}/{df}/reads/{preproc}__bbtsbsmpl_{params}/{df_sample}_R1.fastq.gz' kwargs.update({'params': params}) sample_set, sample_set_name = generic_command_individual_samples(config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs)
def result_invocation(config, strand, **kwargs): sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list(sample_set, self.result_wc, strand=strand)
def seqtk_subsample_invocation(config, **kwargs): # print(config['wc_config']) wc_str = '{fs_prefix}/{df}/reads/{preproc}__seqtk_sbsmpl/{df_sample}_R1.fastq.gz' sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs)
def rmhumbbmap_invocation(config, **kwargs): wc_str = '{fs_prefix}/{df}/reads/{preproc}__rmhum_bbmap/{df_sample}_R1.fastq.gz' sample_set, sample_set_name = generic_command_individual_samples( config, **kwargs) config['requests'] += generate_result_list(sample_set, wc_str, **kwargs)