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1 # MIRUReader
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2
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3 ## Description
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4
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5 Identify 24-locus MIRU-VNTR for *Mycobacterium tuberculosis* complex (MTBC) directly from long reads generated by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio). Also work on assembled genome.
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6
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7 ## Requirements
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8
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9 * Linux
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10 * primersearch from [EMBOSS](http://emboss.sourceforge.net/download/)
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11 * install from the official website or
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12 * install via conda `conda install -c bioconda emboss`
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13 * Ensure the primersearch command is in your device's environment path, where primersearch program can be executed directly by typing `primersearch` on the commandline
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14 * [*pandas*](https://pandas.pydata.org/)
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15 * can be installed via conda `conda install pandas` or via PyPI `pip install pandas`
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16 * [*statistics*](https://pypi.org/project/statistics/)
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17 * can be installed via PyPI `pip install statistics`
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18
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19 ## Installation
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20
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21 `git clone https://github.com/phglab/MIRUReader.git`
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22
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23 ## Change log
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24 #### 13/09/2019
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25 - Added a check to ensure primersearch is executable prior to MIRUReader program execution
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26 - Updated documentation to the README
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27
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28 #### 04/07/2019
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29 - Update output format for option '--details'.
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30
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31 #### 14/06/2019
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32 - Auto convert fastq to fasta.
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33
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34 ## Usage example
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35
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36 For one sample analysis:
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37 ```
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38 python /your/path/to/MIRUReader.py -r sample.fasta -p sampleID > miru.txt
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39 ```
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40
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41 For multiple samples analysis:
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42 1. Create a mapping file (mappingFile.txt) that looks like:
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43
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44 sample_001.fasta sample_001 \
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45 sample_002.fasta sample_002 \
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46 ...
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47
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48 2. Then run the program:
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49 ```
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50 cat mappingFile.txt | while read -a line; do python /your/path/to/MIRUReader.py -r ${line[0]} -p ${line[1]}; done > miru.multiple.txt
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51 ```
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52
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53 ## Output example
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54
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55 ```
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56 sample_prefix 0154 0424 0577 0580 0802 0960 1644 1955 2059 2163b 2165 2347 2401 2461 2531 2687 2996 3007 3171 3192 3690 4052 4156 4348
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57 sample_001 2 4 4 2 3 3 3 2 2 5 4 4 4 2 5 1 6 3 3 5 3 7 2 3
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58 ```
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59
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60 Notes:
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61 * The program is compatible to Python 2 and Python 3.
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62 * Accepted reads file format includes '.fastq', '.fastq.gz', '.fasta', and '.fasta.gz'.
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63 * The program output is a tab-delimited plain text which can be copied to or opened in Excel spreadsheet.
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64
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65 ## Full usage
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66
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67 | Main options | Description |
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68 | ------------ | ----------- |
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69 | -r READS | Input reads file in fastq/fasta format, can be gzipped or not gzipped |
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70 | -p PREFIX | Sample ID required for naming output file. |
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71 | --table TABLE | Allele calling table, default is MIRU_table. Can be user-defined in fixed format. However, providing custom allele calling table for other VNTR is not tested. |
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72 | --primers PRIMERS | Primers sequences, default is MIRU_primers. Can be user-defined in fixed format. |
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73
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74
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75 | Optional options | Description |
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76 | ---------------- | ----------- |
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77 | --amplicons | Use output from primersearch ("prefix.18.primersearch.out") and summarize MIRU profile directly. |
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78 | --details | This option is for further inspection. It displays details of repeat count for each loci with total mismatch error in the primer sequences alignment. |
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79 | --nofasta | Delete fasta file generated if your input read is in fastq format. |
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80
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81 ## FAQ
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82 1. **Why are there two MIRU allele calling tables (MIRU_table and MIRU_table_0580)?**
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83
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84 MIRU loci 0580 (MIRU_table_0580) consist of a different numbering system for determination of repeat numbers as compared to the other 23 MIRU locus (MIRU_table) for MTBC isolates.
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85
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86
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87 ## Troubleshooting
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88 1. If an error message `OSError: primersearch is not found.` appears, please ensure your `primersearch` executable file is in your environment path (`echo $PATH`) and can be called directly.
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