diff --git a/README.md b/README.md index 469e0de..5b07457 100644 --- a/README.md +++ b/README.md @@ -58,11 +58,13 @@ Issue `make help` to get a list of `make` targets. ``` usage: cdna_classifier.py [-h] [-b primers] [-g phmm_file] [-c config_file] - [-q cutoff] [-r report_pdf] [-u unclass_output] - [-w rescue_output] [-S stats_output] + [-k kit] [-q cutoff] [-Q min_qual] [-z min_len] + [-r report_pdf] [-u unclass_output] + [-l len_fail_output] [-w rescue_output] + [-S stats_output] [-K qc_fail_output] [-Y autotune_nr] [-L autotune_samples] [-A scores_output] [-m method] [-x rescue] [-p] - [-t threads] [-B batch_size] + [-t threads] [-B batch_size] [-D read stats] input_fastx output_fastx Tool to identify, orient and rescue full-length cDNA reads. @@ -77,11 +79,16 @@ optional arguments: -g phmm_file File with custom profile HMMs (None). -c config_file File to specify primer configurations for each direction (None). + -k kit Use primer sequences from this kit (PCS109). -q cutoff Cutoff parameter (autotuned). + -Q min_qual Minimum mean base quality (7.0). + -z min_len Minimum segment length (50). -r report_pdf Report PDF (cdna_classifier_report.pdf). -u unclass_output Write unclassified reads to this file. + -l len_fail_output Write fragments failing the length filter in this file. -w rescue_output Write rescued reads to this file. -S stats_output Write statistics to this file. + -K qc_fail_output Write reads failing mean quality filter to this file. -Y autotune_nr Approximate number of reads used for tuning the cutoff parameter (10000). -L autotune_samples Number of samples taken when tuning cutoff parameter diff --git a/pychopper/__init__.py b/pychopper/__init__.py index 7b520fa..32c8dc1 100644 --- a/pychopper/__init__.py +++ b/pychopper/__init__.py @@ -2,4 +2,4 @@ __author__ = 'ONT Applications Group' __email__ = 'Apps@nanoporetech.com' -__version__ = '2.4.0' +__version__ = '2.5.0' diff --git a/pychopper/phmm_data/PCS110_primers.hmm b/pychopper/phmm_data/PCS110_primers.hmm new file mode 100644 index 0000000..4632302 --- /dev/null +++ b/pychopper/phmm_data/PCS110_primers.hmm @@ -0,0 +1,1022 @@ +HMMER3/f [3.3 | Nov 2019] +NAME VNP +LENG 76 +MAXL 146 +ALPH DNA +RF no +MM no +CONS yes +CS no +MAP yes +DATE Wed Jun 24 11:28:32 2020 +NSEQ 424519 +EFFN 10.478731 +CKSUM 3349094354 +STATS LOCAL MSV -8.6004 0.71863 +STATS LOCAL VITERBI -10.9200 0.71863 +STATS LOCAL FORWARD -3.1820 0.71863 +HMM A C G T + m->m m->i m->d i->m i->i d->m d->d + COMPO 1.72639 1.42168 1.36475 1.12294 + 1.38629 1.38629 1.38629 1.38629 + 0.01589 4.84307 4.84307 1.46634 0.26236 0.00000 * + 1 2.57846 3.02484 0.17323 3.36383 1 g - 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- - + 1.38629 1.38629 1.38629 1.38629 + 0.00670 5.00956 * 1.46634 0.26236 0.00000 * +// +HMMER3/f [3.3 | Nov 2019] +NAME -SSP +LENG 75 +MAXL 143 +ALPH DNA +RF no +MM no +CONS yes +CS no +MAP yes +DATE Wed Jun 24 11:28:56 2020 +NSEQ 430975 +EFFN 11.209650 +CKSUM 4049756316 +STATS LOCAL MSV -8.4366 0.71865 +STATS LOCAL VITERBI -10.0288 0.71865 +STATS LOCAL FORWARD -3.2265 0.71865 +HMM A C G T + m->m m->i m->d i->m i->i d->m d->d + COMPO 0.98441 1.21923 1.90285 1.70516 + 1.38629 1.38629 1.38629 1.38629 + 0.01505 4.89848 4.89527 1.46634 0.26236 0.00000 * + 1 2.99615 0.20191 2.68605 2.73790 1 c - - - + 1.38629 1.38629 1.38629 1.38629 + 0.43372 4.89846 1.06581 1.46634 0.26236 1.09862 0.40546 + 2 2.92324 0.21755 3.01303 2.37934 2 c - - - + 1.40359 1.33401 1.41286 1.39665 + 0.06204 3.10362 4.18223 0.67015 0.71668 0.19472 1.73199 + 3 3.07746 0.19433 3.02246 2.50276 5 c - - - + 0.42168 2.16785 2.16785 2.16099 + 0.50079 1.01162 3.49592 0.07910 2.57632 0.99811 0.45978 + 4 0.78508 1.06680 2.50211 2.13795 8 a - 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- - + 1.62654 0.89337 1.62654 1.62198 + 0.11454 2.40654 4.01210 0.28639 1.39019 0.85411 0.55454 + 53 0.34494 2.54959 2.00688 2.53553 111 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.16802 4.88792 1.91646 1.46634 0.26236 0.94739 0.49062 + 54 3.97012 0.13338 3.86005 2.46595 112 c - - - + 1.39599 1.39224 1.39599 1.36138 + 0.06239 4.44778 3.02053 1.28129 0.32529 0.35437 1.20938 + 55 0.14936 3.79538 2.50194 3.37146 116 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.05637 4.86325 3.05570 1.46634 0.26236 0.84629 0.56038 + 56 2.43968 0.17911 4.03727 2.82768 117 c - - - + 0.80891 1.68634 1.74115 1.63917 + 0.29076 1.95896 2.19551 0.19947 1.71017 0.96419 0.48012 + 57 0.45227 2.07511 2.09866 2.15716 120 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.08545 4.76801 2.61180 1.46634 0.26236 0.59225 0.80538 + 58 0.10679 3.98891 2.94743 3.49679 121 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.05813 4.80745 3.03017 1.46634 0.26236 0.61416 0.77891 + 59 0.49095 1.96280 1.88388 2.34870 122 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.01949 4.84372 4.47224 1.46634 0.26236 0.67197 0.71479 + 60 1.94388 2.87403 0.28975 2.95783 123 g - - - + 1.21913 1.00194 1.75515 1.80506 + 0.20165 1.82854 3.81819 0.24589 1.52331 0.95226 0.48755 + 61 0.21263 2.93742 2.72766 2.61495 126 a - - - + 1.47422 1.16115 1.47422 1.47422 + 0.15486 3.33614 2.22665 0.59901 0.79707 0.97727 0.47214 + 62 4.10622 0.11783 4.34539 2.50454 128 c - - - + 1.38629 1.38629 1.38629 1.38629 + 0.07250 4.78444 2.78754 1.46634 0.26236 1.16075 0.37579 + 63 0.31081 2.72642 2.31520 2.27351 129 a - - - + 1.32594 1.37215 1.40896 1.44187 + 0.04894 3.80365 3.67022 0.97847 0.47142 1.00311 0.45687 + 64 2.99526 0.23430 3.16007 2.15046 133 c - - - + 1.33071 1.39144 1.40407 1.42132 + 0.06601 3.91527 3.12476 0.92557 0.50469 0.57378 0.82872 + 65 2.25261 0.22159 3.24497 2.90653 135 c - - - + 1.31188 1.54830 1.11802 1.65468 + 0.09010 2.61374 4.35086 0.61652 0.77614 1.01896 0.44780 + 66 1.89262 2.80560 0.30766 2.92458 139 g - - - + 1.38629 1.38629 1.38629 1.38629 + 0.05620 4.86221 3.05931 1.46634 0.26236 1.23398 0.34409 + 67 0.28044 2.51853 2.53590 2.46772 140 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.04867 4.82665 3.23168 1.46634 0.26236 0.82685 0.57523 + 68 4.18395 0.11259 4.42668 2.53466 141 c - - - + 1.38629 1.38629 1.38629 1.38629 + 0.10987 4.83769 2.34215 1.46634 0.26236 0.81263 0.58643 + 69 0.17238 3.20896 2.75339 2.91453 142 a - - - + 1.38629 1.38629 1.38629 1.38629 + 0.01888 4.78662 4.56946 1.46634 0.26236 0.82618 0.57575 + 70 0.11019 3.26011 3.45595 3.36993 143 a - - - + 1.46671 1.30495 1.18683 1.64535 + 0.14495 2.29817 3.36706 0.40691 1.09572 0.97310 0.47467 + 71 4.19340 0.09882 4.27032 2.73305 148 C - - - + 1.38629 1.38629 1.38629 1.38629 + 0.07726 4.84233 2.71115 1.46634 0.26236 0.87777 0.53736 + 72 3.52362 3.60640 3.73844 0.08385 149 T - - - + 1.38587 1.38857 1.38219 1.38857 + 0.01936 4.72768 4.57314 1.41058 0.27972 0.64947 0.73882 + 73 3.18192 2.90268 3.70235 0.12902 151 t - - - + 1.53244 1.41314 1.34830 1.26964 + 0.07014 2.88758 4.42071 0.42770 1.05557 1.15418 0.37880 + 74 3.60344 3.61026 3.26078 0.09721 153 T - - - + 1.38629 1.38629 1.38629 1.38629 + 0.01569 4.86940 4.84234 1.46634 0.26236 0.85674 0.55259 + 75 3.83254 0.11846 4.10277 2.61001 154 c - - - + 1.38629 1.38629 1.38629 1.38629 + 0.00763 4.87971 * 1.46634 0.26236 0.00000 * +// diff --git a/pychopper/phmm_data/PCS110_primers.hmm.h3f b/pychopper/phmm_data/PCS110_primers.hmm.h3f new file mode 100644 index 0000000..2f1fb31 Binary files /dev/null and b/pychopper/phmm_data/PCS110_primers.hmm.h3f differ diff --git a/pychopper/phmm_data/PCS110_primers.hmm.h3i b/pychopper/phmm_data/PCS110_primers.hmm.h3i new file mode 100644 index 0000000..d79c432 Binary files /dev/null and b/pychopper/phmm_data/PCS110_primers.hmm.h3i differ diff --git a/pychopper/phmm_data/PCS110_primers.hmm.h3m b/pychopper/phmm_data/PCS110_primers.hmm.h3m new file mode 100644 index 0000000..a16e728 Binary files /dev/null and b/pychopper/phmm_data/PCS110_primers.hmm.h3m differ diff --git a/pychopper/phmm_data/PCS110_primers.hmm.h3p b/pychopper/phmm_data/PCS110_primers.hmm.h3p new file mode 100644 index 0000000..775e014 Binary files /dev/null and b/pychopper/phmm_data/PCS110_primers.hmm.h3p differ diff --git a/pychopper/primer_data/PCS110_primers.fas b/pychopper/primer_data/PCS110_primers.fas new file mode 100644 index 0000000..3bfdeac --- /dev/null +++ b/pychopper/primer_data/PCS110_primers.fas @@ -0,0 +1,4 @@ +>VNP +ACTTGCCTGTCGCTCTATCTTCGAGGAGAGTCCGCCGCCCGCAAGTTT +>SSP +TTTCTGTTGGTGCTGATATTGCTTT diff --git a/scripts/cdna_classifier.py b/scripts/cdna_classifier.py index 0e8ed9f..ea4e715 100755 --- a/scripts/cdna_classifier.py +++ b/scripts/cdna_classifier.py @@ -28,6 +28,8 @@ '-g', metavar='phmm_file', type=str, default=None, help="File with custom profile HMMs (None).", required=False) parser.add_argument( '-c', metavar='config_file', type=str, default=None, help="File to specify primer configurations for each direction (None).") +parser.add_argument( + '-k', metavar='kit', type=str, default="PCS109", help="Use primer sequences from this kit (PCS109).") parser.add_argument( '-q', metavar='cutoff', type=float, default=None, help="Cutoff parameter (autotuned).") parser.add_argument( @@ -174,7 +176,7 @@ def _detect_anomalies(st, config): anom = [] for tmp in raw_anom: pc = tmp[1] * 100.0 / total - if pc >= 1: + if pc >= 3.0: anom.append((tmp[0], tmp[1], pc)) if len(anom) > 0: sys.stderr.write("Detected {} potential artefactual primer configurations:\n".format(len(anom))) @@ -219,14 +221,23 @@ def _plot_pd_line(df, title, report, alpha=0.7, xrot=0, vline=None): def _plot_stats(st, pdf): "Generate plots and save to report PDF" R = report.Report(pdf) - _plot_pd_bars(st.loc[st.Category == "Classification", ].copy(), "Classification of output reads", R, ann=True) + rs = st.loc[st.Category == "Classification", ] + _plot_pd_bars(rs.copy(), "Classification of output reads", R, ann=True) + found, rescue, unusable = float(rs.loc[rs.Name == "Primers_found", ].Value), float(rs.loc[rs.Name == "Rescue", ].Value), float(rs.loc[rs.Name == "Unusable", ].Value) + total = found + rescue + unusable + found = found / total * 100 + rescue = rescue / total * 100 + unusable = unusable / total * 100 + sys.stderr.write("-----------------------------------\n") + sys.stderr.write("Reads with two primers:\t{:.2f}%\nRescued reads:\t\t{:.2f}%\nUnusable reads:\t\t{:.2f}%\n".format(found, rescue, unusable)) + sys.stderr.write("-----------------------------------\n") _plot_pd_bars(st.loc[st.Category == "Strand", ].copy(), "Strand of oriented reads", R, ann=True) _plot_pd_bars(st.loc[st.Category == "RescueStrand", ].copy(), "Strand of rescued reads", R, ann=True) _plot_pd_bars(st.loc[st.Category == "UnclassHitNr", ].copy(), "Number of hits in unclassified reads", R) _plot_pd_bars(st.loc[st.Category == "RescueHitNr", ].copy(), "Number of hits in rescued reads", R) _plot_pd_bars(st.loc[st.Category == "RescueSegmentNr", ].copy(), "Number of usable segments per rescued read", R) - if args.q is None: - _plot_pd_line(st.loc[st.Category == "AutotuneSample", ].copy(), "Usable bases as function of cutoff(q). Best q={:.4f}".format(args.q), R, vline=args.q) + if q_bak is None: + _plot_pd_line(st.loc[st.Category == "AutotuneSample", ].copy(), "Usable bases as function of cutoff(q). Best q={:.4g}".format(args.q), R, vline=args.q) udf = st.loc[st.Category == "Unusable", ].copy() udf.Name = np.log10(1.0 + np.array(udf.Name, dtype=float)) _plot_pd_line(udf, "Log10 length distribution of trimmed away sequences.", R) @@ -244,17 +255,21 @@ def _plot_stats(st, pdf): if args.c is not None: CONFIG = open(args.c, "r").readline().strip() + kits = {"PCS109": {"HMM": os.path.join(os.path.dirname(phmm_data.__file__), "cDNA_SSP_VNP.hmm"), "FAS": os.path.join(os.path.dirname(primer_data.__file__), "cDNA_SSP_VNP.fas"), }, "PCS110": { + "HMM": os.path.join(os.path.dirname(phmm_data.__file__), "PCS110_primers.hmm"), "FAS": os.path.join(os.path.dirname(primer_data.__file__), "PCS110_primers.fas")}} + if args.g is None: - args.g = os.path.join(os.path.dirname(phmm_data.__file__), "cDNA_SSP_VNP.hmm") + args.g = kits[args.k]["HMM"] if args.b is None: - args.b = os.path.join(os.path.dirname(primer_data.__file__), "cDNA_SSP_VNP.fas") + args.b = kits[args.k]["FAS"] if args.x is not None and args.x in ('DCS109'): if args.x == "DCS109": CONFIG = "-:VNP,-VNP" config = utils.parse_config_string(CONFIG) + sys.stderr.write("Using kit: {}\n".format(args.k)) sys.stderr.write("Configurations to consider: \"{}\"\n".format(CONFIG)) in_fh = sys.stdin @@ -306,9 +321,10 @@ def backend(x, pool, q=None, mb=None): else: raise Exception("Invalid backend!") - # Pick the -q maximizing the number of classified reads using frid search: + # Pick the -q maximizing the number of classified reads using grid search: nr_records = None tune_df = None + q_bak = args.q if args.q is None: nr_cutoffs = args.L cutoffs = np.linspace(0.0, 1.0, num=nr_cutoffs) @@ -356,7 +372,7 @@ def backend(x, pool, q=None, mb=None): tune_df["Value"] += [args.q] if best_qi == (len(class_reads) - 1): sys.stderr.write("Best cuttoff value is at the edge of the search interval! Using tuned value is not safe! Please pick a q value manually and QC your data!\n") - sys.stderr.write("Best cutoff (q) value is {:.4f} with {:.0f}% of the reads classified.\n".format(args.q, class_reads[best_qi] * 100 / len(read_sample))) + sys.stderr.write("Best cutoff (q) value is {:.4g} with {:.0f}% of the reads classified.\n".format(args.q, class_reads[best_qi] * 100 / len(read_sample))) if nr_records is not None: input_size = nr_records diff --git a/setup.cfg b/setup.cfg index 3c80947..7878ebf 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 2.4.0 +current_version = 2.5.0 commit = True tag = True diff --git a/setup.py b/setup.py index 8332e33..dc628b3 100644 --- a/setup.py +++ b/setup.py @@ -26,7 +26,7 @@ setup( name='pychopper', - version='2.4.0', + version='2.5.0', description="A tool to identify full length cDNA reads.", long_description=readme, author="ONT Applications Group",