annotate file_operations.py @ 1:112751823323 draft

planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c

def fasta_from_SAR_dict(sar_dict, fa_file):
    """makes a multi fasta with candidates from SAR dictionary"""
    with fa_file as f:
        for data in sar_dict.values():
            f.writelines(">{}\n".format(data["description"]))
            f.writelines("{}\n".format(data["sequence"]))


def gff3_from_SAR_dict(sar_dict, gff3_file):
    """make a multi gff3 with candidates from SAR dictionary"""
    gff3_cols = [
        "Seqid",
        "Source",
        "Type",
        "Start",
        "End",
        "Score",
        "Strand",
        "Phase",
        "Attributes",
    ]
    with gff3_file as f:
        f.writelines(
            f"{gff3_cols[0]}\t{gff3_cols[1]}\t{gff3_cols[2]}\t{gff3_cols[3]}\t{gff3_cols[4]}\t{gff3_cols[5]}\t{gff3_cols[6]}\t{gff3_cols[7]}\t{gff3_cols[8]}\n"
        )
        if sar_dict:
            # print(sar_dict)
            for name, data in sar_dict.items():
                min_idx = 0
                f.writelines("##gff-version 3\n")
                f.writelines(f"##sequence-region {name}\n")
                n_start, n_end = split_seq_string(
                    data["TMD_" + str(data["biggest_sar"])][min_idx][4]
                )
                sar_start, sar_end = split_seq_string(
                    data["TMD_" + str(data["biggest_sar"])][min_idx][5]
                )
                c_start, c_end = split_seq_string(
                    data["TMD_" + str(data["biggest_sar"])][min_idx][6]
                )
                f.writelines(
                    f'{name}\tSAR_finder\tTopological domain\t{n_start}\t{n_end}\t.\t.\t.\tNote=N-terminal net charge is {data["TMD_"+str(data["biggest_sar"])][min_idx][2]}\n'
                )
                f.writelines(
                    f'{name}\tSAR_finder\tSAR domain\t{sar_start}\t{sar_end}\t.\t.\t.\tNote=residue % in SAR {[perc for perc in data["TMD_"+str(data["biggest_sar"])][min_idx][3]]},Total % is {round(sum(j for i,j in data["TMD_"+str(data["biggest_sar"])][min_idx][3]),2)}\n'
                )
                f.writelines(
                    f"{name}\tSAR_finder\tTopological domain\t{c_start}\t{c_end}\t.\t.\t.\tNote=C-terminus\n"
                )
        else:
            f.writelines("##gff-version 3\n")
            f.writelines(f"##sequence-region\n")


def tab_from_SAR_dict(sar_dict, stat_file, hydrophillic_res, sar_min, sar_max):
    """convert SAR dict to a dataframe"""
    columns = [
        "Name",
        "Protein Sequence",
        "Protein Length",
        "SAR Length",
        "SAR Start",
        "Putative SAR Sequence",
        "SAR End",
        [f"{res}%" for res in hydrophillic_res],
        "% Total",
        "N-term Sequence",
        "N-term net Charge",
    ]  # using different residues for percent calc: [f"{res}%" for res in hydrophillic_res]
    with stat_file as f:
        f.writelines(
            f"{columns[0]}\t{columns[1]}\t{columns[2]}\t{columns[3]}\t{columns[4]}\t{columns[5]}\t{columns[6]}\t{columns[7]}\t{columns[8]}\t{columns[9]}\t{columns[10]}\n"
        )
        if sar_dict:
            # print(sar_dict)
            for name, data in sar_dict.items():
                for tmd_size in range(sar_max, sar_min - 1, -1):
                    if "TMD_" + str(tmd_size) in data:
                        for each_match in data["TMD_" + str(tmd_size)]:
                            if each_match != [""]:
                                # print(f"{name} - {data}")
                                # print(each_match)
                                # for perc in each_match[3]:
                                #    print(perc)
                                try:
                                    f.writelines(
                                        f'{name}\t{data["sequence"]}\t{data["size"]}\t{tmd_size}\t{int(each_match[7])+1}\t{each_match[0]}\t{int(each_match[8])+1}\t{[perc for perc in each_match[3]]}\t{round(sum(j for i,j in each_match[3]),2)}\t{each_match[1]}\t{each_match[2]}\n'
                                    )
                                except IndexError:
                                    f.writelines(
                                        f"ERROR\tERROR\tERROR\tERROR\tERROR\tERROR\tERROR\tERROR\tERROR\tERROR\tERROR\n"
                                    )
                            else:
                                continue


def stat_file_from_SAR_dict(sar_dict, stat_file, sar_min, sar_max):
    """summary statistics from SAR finder function"""
    with stat_file as f:
        f.writelines("..........:::::: Candidate SAR Proteins ::::::..........\n\n")
        if sar_dict:
            for data in sar_dict.values():
                f.writelines(
                    "Protein Description and Name: {}\n".format(data["description"])
                )
                f.writelines("Protein Sequence: {}\n".format(data["sequence"]))
                f.writelines("Protein Length: {}\n".format(data["size"]))
                f.writelines("SAR Criteria matching region(s)\n")
                for tmd_size in range(sar_max, sar_min - 1, -1):
                    if "TMD_" + str(tmd_size) in data:
                        f.writelines("\nSAR length of {}:\n".format(tmd_size))
                        for each_match in data["TMD_" + str(tmd_size)]:
                            if each_match != [""]:
                                f.writelines(
                                    "\nPotential SAR domain sequence: {}\n".format(
                                        each_match[0]
                                    )
                                )
                                f.writelines(
                                    "N-term sequence: {}\n".format(each_match[1])
                                )
                                f.writelines(
                                    "N-term net charge: {}\n".format(each_match[2])
                                )
                                for each_perc_calc in each_match[3]:
                                    f.writelines(
                                        "Percent {} content: {}%\n".format(
                                            each_perc_calc[0], each_perc_calc[1]
                                        )
                                    )
                                f.writelines(
                                    "N-term coords: {}\n".format(each_match[4])
                                )
                                f.writelines("SAR coords: {}\n".format(each_match[5]))
                                f.writelines(
                                    "C-term coords: {}\n".format(each_match[6])
                                )
                                f.writelines("SAR start: {}\n".format(each_match[7]))
                            else:
                                continue
                f.writelines(
                    "========================================================\n\n"
                )
        else:
            f.writelines("No candidate SAR Proteins found")


def split_seq_string(input_range, python_indexing=True):
    """splits a #..# sequence into the two respective starts and ends, if python indexing, adds 1, otherwise keeps"""
    if python_indexing:
        values = input_range.split("..")
        start = int(values[0]) + 1
        end = int(values[1]) + 1
    else:
        values = input_range.split("..")
        start = values[0]
        end = values[1]

    return start, end


if __name__ == "__main__":
    pass