Man pages for philliplab/MotifBinner
Bin reads by Motif

add_snpsAdds n snps to each sequence in seq_dat
align_sequencesA wrapper for a number of alignment tools
bin_by_nameGroups sequences together into bins based on their names
bin_fileBins a given FASTA file and outputs each bin as a seperate...
check_classificationChecks that the output from a classifier satisfies basic...
classify_absoluteRemoves the most outlying sequences in a bin until the...
classify_binClassify a bin's sequences into 'src' and 'out'
classify_bin_infovar_balanceFinds the outliers in a bin by balancing the amount of...
classify_bin_most_frequentClassifies the sequences in a set by only choosing the most...
classify_bin_randomRandomly classify a number of unique sequences to the 'out'...
clean_matchesInternal Function: Clean matches to the motifs
clean_seq_dataInternal Function: Clean sequence data for motif extraction
compare_distsCompare two distance matrices.
construct_consensusConstructs a consensus string using the specified technique
create_scenario_dataCompute the scenarios that are not already in the scenario...
dput_classified_binned_folderReads in all files from a folder, assuming that they are...
extract_motifsExtracts motifs from a set of reads
extract_motifs_iterativeA wrapper for extract motifs parallel that will iteratively...
extract_motifs_parA wrapper for extract motifs that will execute it in a...
fast_stringDistComputes a distance matrix between sequences by using...
file_to_consensusBins and constructs consensus sequences for an entire fastq...
gen_and_contaminate_readsGenerate contaminated reads
gen_error_profileGenerates a read error profile
gen_error_profile_uniformGenerates a read error profile by assigning a constant...
gen_pid_search_scenarioSimulate a scenario that can be used to test PID detection
gen_readsGenerates a specified number of reads from a given sequence...
gen_seqSimulates a random sequence of a given length
get_mislabel_test_dataReturns that dataset used for testing the mislabel detection
get_test_settingsProvides some settings useful for testing
list_unique_scenariosGiven all the test cases list the unique scenarios
load_or_initialize_cacheGiven the location of a cache file, intialize the cache
mostConsensusStringA custom consensus string constructor that just uses the...
muscle_parCopy of muscle function from ape package with tweaks to allow...
mutate_baseMutates a single base to another base
process_binGiven a groups of sequences that were binned together, throw...
process_fileProcesses a file into consensus bins
randomize_ambigRemoves ambiguous letters from a sequence by replacing them...
randomize_listRandomizes the order of the items of a list
read_classified_binned_fileReads a classified binned file and splits it into bins
read_sequence_fileReads in a sequence file given a file name.
remove_motifsInternal Function: Removes motifs from sequences given match...
run_all_testsRuns all the tests
run_testRun a test case
save_bin_reportGiven a report_dat data list, generate and save the...
save_bin_resultsGiven a report_dat data list, save the important results into...
score_all_classificationsGiven a list of datasets and a classification strategy, apply...
score_classificationGiven a test dataset and a classification strategy, apply the...
score_consensusGiven a test bin of sequences, benchmark how well the input...
twoStepConsensusStringA custom consensus string constructor that uses a two step...
philliplab/MotifBinner documentation built on May 25, 2019, 5:05 a.m.