# Programming with fastachar¶

The graphical user interface is intended to provide easy access to the functionality offered by the fastachar module. Rather than using the graphical interface, the user can also create her/his own python scripts.

## Example script¶

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 # Example script how to do an analysis of a fasta file accessing the # fasta module directly, and not using a graphical interface. The # example script reads a fasta file, and divides all the species in a # two sets, one that with species names that match a regular # expression, and a set with sequences that does not match the regular # expression. Then, for set A, the differences within this set as well # as its unique characters are computed. Finally, the results are # reported and dumped on the terminal. import sys sys.path.insert(0, '..') import fastachar filename = "../data/COI_sequences_MUSCLE.fas" alignment = fastachar.fasta_io.Alignment() # The sequences in this alignemnt typically look like this: # >WBET001_Nototeredo_norvagica_Ms_TK # that is, an ID, followed by an underscore and a species name. In # order to parse this sequence header correctly, we must tell the # alignment reader how this header is constructed. # See http://www.rexegg.com/regex-quickstart.html for a reference table. alignment.set_fasta_hdr_fmt(header_format='{ID}_{SPECIES}', IDregex = '[A-Z0-9]+', SPECIESregex = '[A-Z][a-z_]+') errno, errmesg = alignment.load(filename) if errno: # we have a non-zero error, so something went wrong. Print # the corresponding message to give us a clue print(errmesg) else: # all well. species = alignment.get_species_list() print("Species in this file:") for s in species: print("{:30s}".format(s)) print() # Divide all the species in two groups, set A that matches the regex, # and set B that does not. Notice we can use regular expressions here too. lst_A, lst_B = alignment.select_two_sequence_sets("Lyrodus.pedicellatus.*[mM][Ss]") # We could also use other methods to extract specific sequences. # Let's investigate Lyrodus pedicellatus. We suspect that the # sequences found in Turkey, they end with TK might be different # from those found in France (ending in Fr). So we select all # Lyrodus species, but exclude those ending in TK, for lst_A, and # do the same for lst_B, but invert the selection. lst_A = alignment.select_sequences(regex='Lyrodus[_ ]pedicellatus.*', invert=False, exclude='.*[Tt][Kk]') lst_B = alignment.select_sequences(regex='Lyrodus[_ ]pedicellatus.*', invert=True, exclude='.*[Tt][Kk]') S = fastachar.fasta_logic.SequenceLogic() # Compute the unique characters in A with respect to B method = "MDC" mcds = S.compute_mdcs(lst_A, lst_B, method) # Report the results to the terminal. reportxls = fastachar.fasta_io.ReportXLS() report = fastachar.fasta_io.Report(filename, reportxls = reportxls) report.report_header(lst_A, lst_B, method) report.report_mdcs("List A", lst_A, lst_B, mcds, method) # compute non unique charachters in B nucs = S.list_non_unique_characters_in_set(lst_B) report.report_header(lst_A, lst_B, method='nucs') report.report_nucs("List B", lst_B, nucs) #reportxls.save('test.xls') 

The advantage of using scripts such as the one above, is that it is easier to redo an analysis, modify an existing one, or batch analyses a number of fasta files.

The API for the class SequenceData can be consulted Module Index.