This is a small library and a bunch of clients to perform various operations on FASTQ files (such as demultiplexing raw Illumina files, merging partial or complete overlaps, and/or performing quality filtering). It works with paired-end FASTQ files and has been tested with Illumina runs processed with CASAVA version 1.8.0 or higher.
Send your questions to meren at uchicago dot edu.
Feel free to cite this article (in which the codebase was first introduced) if you are using this codebase (and if you are happy with it).
- Installing
- Demultiplexing
- Config File Format
- Merging Partially Overlapping Illumina Pairs
- Merging Completely Overlapping Illumina Pairs
- Quality Filtering
- Questions?
# Installing
The easiest way to install illumina-utils is to do it through pip. To install the latest version, you can simply run this command on your terminal:
sudo pip install illumina-utils
Alternatively, you can download the source code from here, unpack it, and install running the following command from within the illumina-utils directory:
sudo python setup.py install
If you would like to play with the development version, you can create a copy of the codebase by simply installing git and running this command in your terminal window:
git clone git://github.com/meren/illumina-utils.git
Note: Once you have installed illumina-utils, you can type 'iu-' and press your TAB key twice to see all available scripts that are installed on your system. If you would like to take a look at the examples, you should download the latest release of the codebase, and examine/run BASH scripts in the examples directory. If you are confused, don't hesitate to send me an e-mail (meren at mbl edu).
## Requirements
In order to use this software package fully, you need following items available on your system:
- matplotlib (required for visualizations)
- python-Levenshtein (to merge partially overlapping reads)
- R (required for visualizations today and will be used for statistical analyses)
- ggplot2 (the R module that needs to be installed for R requirement)
Note: matplotlib and python-Levenshtein will be installed automatically if you install illumina-utils using pip or setup.py.
# Demultiplexing
If you have raw FASTQ files, you can demultiplex them into samples using iu-demultiplex script. In order to demultiplex a run, you will need an extra FASTQ file for indexes, and a TAB-delimited file for barcode-sample associations. The directory examples/demultiplexing contains sample files for demultiplexing. You can start the process by running the following command:
iu-demultiplex -s barcode_to_sample.txt --r1 r1.fastq --r2 r2.fastq --index index.fastq -o output/
If you have demultiplexed your raw files using this library, you can save yourself from generating config files (properties of which explained in the next section) by hand. The script iu-gen-configs takes the report file generated by iu-demultiplex, and automatically generates config files for each sample. For instance, the following command would have been an appropriate to run after the previous iu-demultiplex example:
iu-gen-configs output/00_DEMULTIPLEXING_REPORT
Most scripts that come with illumina-utils require a config file as an input to learn where are the input files, and where the output should go. A config file looks like this (there is also a sample file in the codebase):
[general]
project_name = project name
researcher_email = [email protected]
input_directory = /full/path/test_input
output_directory = /full/path/test_output
[files]
pair_1 = pair_1_aaa, pair_1_aab, pair_1_aac, pair_1_aad, pair_1_aae, pair_1_aaf
pair_2 = pair_2_aaa, pair_2_aab, pair_2_aac, pair_2_aad, pair_2_aae, pair_2_aaf
[prefixes]
pair_1_prefix = ^....TACGCCCAGCAGC[C,T]GCGGTAA.
pair_2_prefix = ^CCGTC[A,T]ATT[C,T].TTT[G,A]A.T
Before describing the purpose of each section, here is a useful note: in most cases you don't need to generate your config file manually. If you have your FASTQ files for R1 and R2, you can always generate a simple TAB-delimited file that associates FASTQ files with sample names, and use iu-gen-configs to generate your configs. Here is an example input for iu-gen-configs (the first line is mandatory, and the rest should describe each of your FASTQ files):
sample r1 r2
e_coli ecoli-R1.fastq ecoli-R2.fastq
(...) (...) (...)
## [general] section
This is a mandatory section that contains project_name, researcher_email, input_directory and output_directory directives.
Two critical declerations in [general] section are input_directory and output_directory:
input_directory: Full path to the directory where FASTQ files reside.output_directory: Full path to the directory where the output of the operation you will perform on this config to be stored. Since when it is Illumina we are dealing with huge files, the codebase is pretty conservative to protect users from making simple mistakes which may result in huge losses. So, if you don't create theoutput_directory, you will get an error (it will not be automatically generated). If there is already a file in theoutput_directorywith the same name with one of the outputs, you will get an error (it will not be overwritten).project_namewill be used as a prefix for the naming convention of output files, so it would be wise to choose something descriptive and UNIX-compatible.
## [files] section
files section is where you list your file names to be found under input_directory. Each file name has to be comma separated. The index of each file name in the comma seperated list, must match with its pair in the second list (see the example config file above).
prefixes section is optional. If you have barcodes and primers in your reads, and you want them to be trimmed, you can use regular expressions to specify them. If prefixes are defined, results would contain only pairs that matched them.
Pairs generated by paritally overlapping library preperation can be merged using iu-merge-pairs. Once you create your config file, you simply call it with the config file as a parameter.
By default, merging program uses Levenshtein distance to find the best merging strategy for two reads in a pair, starting from the minimum expected overlap (15 nt is the default, and can be changed through the appropriate command line parameter).
The merging strategy requires Levenshtein module to be installed.
iu-merge-pairs will create FASTA files for reads that were merged successfuly, or failed to merge. In the FASTA file for merged reads, the length of the overlapped region and the number of mismatches found in the overlapped part will be reported in the header line for each entry. The place of mismatch will be shown with capital letters in the sequences.
An example header line from the FASTA file for merged reads is shown below:
>M01028:4:000000000-A1Y0P:1:1101:18829:1947 1:N:0:1|o:83|m/o:0.048193|MR:n=0;r1=2;r2=2|Q30:p,77;p,76|mismatches:4
Each field is separated from each other by a "|" character. Field one is the original defline from the FASTQ file of read 1. Following items explain details of these fields and command line options that affect them:
o: Length of the overlap.m/o: The P value. P value is the ratio of the number of mismatches and the length of the overlap. Merged sequences can be discarded based on this ratio. The default is 0.3. This value should be changed through the command line parameter-Pdepending on the expected overlap size (if the expected length of overlap is 100 nts and if you choose to eliminate any pairs with more than 5 mismatches at the overlapping region, you can set the-Pparameter to 0.05). A more intuitive way to eliminate pairs with more than a certain amount mismatches at the overlapped region is to use the parameter--max-num-mismatches.MR: "Mismatches Recovered". When there is a mismatch in the overlapped region, the base to be used in the final merged sequence is picked from whichever read possesses the higher Q-score (and shown as a capital letter in the merged sequence). If a mismatch is recovered from read 1, it increases the number next to r1 in this field, and so forth. However, if there is a disagreement between two reads, and neither of the reads have a Q-score higher than a minimum acceptable value, the corresponding base denoted with anNin the merged read, and the number next tononeis increased by one. By default, the minimum Q-score value is 10. This value can be changed via the command line parameter--min-qual-score. Note that if--ignore-Nsflag is not declared, all merged sequences that had at least one disagreement which can't be recovered from either read due to--min-qual-scorevalue will be discarded.Q30: By default, quality filtering is being done based only on the mismatches found in the overlapped region, and the beginning and the end of merged reads are not being checked. However a final control can be enforced using the command line flag--enforce-Q30-check. This flag turns on the Q30 check, as it was explained by Minoche et al. Briefly, Q30-check eliminates pairs if the 66% of bases in the first half of each read do not have Q-scores over Q30. Note that Q30 is applied only to the parts of reads that did not overlap. If either of reads fail Q30 check, merged sequence is discarded.p,77;p,76in the example header reads as "read 1 passed Q30 check (threforep, failed case denoted by anf), and 77 bases in the first half of it had a better Q-score than 30; read 2 passed Q30 check, and 76 bases in the first half of it had a better Q-score than 30". If Q30-check was not enforcedn/aappears next to it.mismatches: Number of mismatches at the overlapped region for quick filtering of resulting reads. Using--max-num-mismatchesparameter, you can remove any pair with more than a certain number of mismatches at the overlapped region.
Here is a snippet from the merged sequences file (reads are trimmed from both ends for readability):
>M01028:4:000000000-A1Y0P:1:1101:15704:1943 1:N:0:1|o:87|m/o:0.022989|MR:n=0;r1=2;r2=0|Q30:p,77;p,72|mismatches:2
[...]ggtagatggaatataacatgtagcggtgaaatGctTagatatgttatggaacaccgattgcgaaggcagtctactaagtcgatattgacgctgaggcacgaaagcgtgggtagcgaacag[...]
>M01028:4:000000000-A1Y0P:1:1101:18231:1947 1:N:0:1|o:86|m/o:0.058140|MR:n=0;r1=5;r2=0|Q30:p,74;p,66|mismatches:5
[...]ggaaagtggaatttctaGTGTagaggtgaaattcgtagatattagaaagaacatcaaaggcGaaggcaactttctggatcattactgacactgaggaacgaaagcatgggtagcgaagag[...]
>M01028:4:000000000-A1Y0P:1:1101:18829:1947 1:N:0:1|o:83|m/o:0.048193|MR:n=0;r1=2;r2=2|Q30:p,77;p,76|mismatches:4
[...]ggggggtagaatTccacgtgtagcagtgaaatgcgtagagatgtggaGgaatAtcaatggcgaaggcagccccctgggataacactgacgCtcatgcacgaaagcgtggggagcgaacag[...]
If the program runs successfully, these files will appear in the output_directory:
project_name_MERGED(successfuly merged reads)project_name_FAILED(failed sequences due tom/o)project_name_FAILED_WITH_Ns(failed merged sequences for having ambiguous bases)project_name_FAILED_Q30(failed merged sequences for not passing Q30-check, if enforced)project_name_MISMATCHES_BREAKDOWN(number of mismatches breakdown)project_name_STATS(numbers regarding the run)
project_name_MISMATCHES_BREAKDOWN file can be visualized using the R script, iu-visualize-mismatch-distribution, included in the codebase (it will require ggplot2 to be available on the system). Here is an example:
When iu-merge-pairs is run with --compute-qual-dicts it will also generate visualization of quality scores for different number of mismatch levels. Please see command line options for more information.
The project_name_STATS file that is created in the output directory contains important information about the merging operation. It is a good practice to check the numbers and make sure there is no anomalies. Here is an example output:
Number of pairs analyzed 2500
Prefix failed in read 1 0
Prefix failed in read 2 0
Prefix failed in both 0
Passed prefix total 2500
Failed prefix total 0
Merged total 1479
Merge failed total 1021
Merge discarded due to P 598
Merge discarded due to Ns 348
Merge discarded due to Q30 75
Total number of mismatches 13101
Mismatches recovered from read 1 10360
Mismatches recovered from read 2 1413
Mismatches replaced with N 1328
Mismatches breakdown:
0 372
1 326
2 225
3 154
4 120
5 86
6 70
7 49
8 40
9 21
10 11
11 4
12 1
Command line iu-merge-pairs miseq_partial_overlap_config.ini z --enforce --compute
Work directory /path/to/the/working/directory
"p" value 0.300000
Min overlap size 15
Min Q-score for mismatches 10
Ns ignored? False
Q30 enforced? True
Slow merge? False
## Recovering high-quality reads from merged reads file
If iu-merge-pairs finishes successfuly, it will generate project_name_MERGED for successfuly merged reads. A successful merge depends on the o/r value, Q30-check and lack of ambiguous bases in the merged sequence. However, succesfully merged reads based on user-defined or default parameters may not be as accurate as needed depending on the project. If you haven't used --max-num-mismatches parameter, your merged file may contain sequences that are merged poorly.
Further elimination of reads can be done by filtering out reads based on the number of mismatches they present at the overlapped region. For instance, user can decide to use only merged sequences with 0 mismatches from the resulting FASTA file.
Program iu-filter-merged-reads can be used to retain high-quality reads from project_name_MERGED file. To retain reads with 0 mismatches at the overlapped region you can simply run this command on your project_name_MERGED to generate a file with filtered reads project_name_FILTERED:
iu-filter-merged-reads project_name_MERGED --max-mismatches 0 --output project_name_FILTERED
Resulting file would be the file to use in downstream analyses.
Please use iu-merge-pairs the same way explained in the Merging Partially Overlapping Illumina Pairs section, but include your command line these two flags:
(...) --marker-gene-stringent --retain-only-overlap
You can be extremely stringent with this approach by allowing 0 mismatches at the overlapped region:
(...) --marker-gene-stringent --retain-only-overlap --max-num-mismatches 0
An example complete overlap analysis is demonstrated in the examples directory of the codebase.
## "Complete Overlap" analysis for V6
The workflow for complete overlap analysis for the V6 region has been described in Eren et al. With illumina-utils v1.4.6 we made slight changes to the workflow. Instead of iu-analyze-v6-complete-overlaps, we now use iu-merge-pairs program and then remove V6 primers from complete overlaps with zero mismatches using iu-trim-V6-primers. The script v6-complete-overlap.sh demonstrates the new workflow and contains commands to (1) generate a config file for a given sample, (2) merge reads in FASTQ files with complete overlap and zero mismatches, and (3) remove V6 primers from resulting reads. Please don't hesitate to get in touch if you have any questions.
Quality filtering suggestions made by Minoche et al is implemented in iu-filter-quality-minoche script. The output of the script includes these files:
project_name-STATS.txt(file that contains all the numbers about quality filtering process, an example output can be seen below)project_name-QUALITY_PASSED_R1.fa(pair 1's that passed quality filtering)project_name-QUALITY_PASSED_R2.fa(matching pair 2's)project_name-READ_IDs.cPickle.z(gzipped cPickle object for Python that keeps the fate of read IDs, this file may be required by other scripts in the library for purposes such as visualization, or extracting a particular group of reads from the original FASTQ files)
If the program is run with --visualize-quality-curves option, these files will also be generated in the output directory:
project_name-PASSED.png(visualization of mean quality scores per tile for pairs that passed the quality filtering)project_name-FAILED_REASON_C33.png(visualization of mean quality scores per tile for pairs that failed quality filtering due to C33 filtering (C33: less than 2/3 of bases were Q30 or higher in the first half of the read following the B-tail trimming))project_name-FAILED_REASON_N.png(same above, but for pairs that contained an ambiguous base after B-tail trimming)project_name-FAILED_REASON_P.png(same above, but for pairs that were too short after B-tail trimming)project_name-Q_DICT.cPickle.z(gzipped cPickle object for Python that holds mean quality scores for each group of reads)
$ cat 9022_B9-STATS.txt
number of pairs analyzed : 122929
total pairs passed : 109041 (%88.70 of all pairs)
total pair_1 trimmed : 6476 (%5.94 of all passed pairs)
total pair_2 trimmed : 9059 (%8.31 of all passed pairs)
total pairs failed : 13888 (%11.30 of all pairs)
pairs failed due to pair_1 : 815 (%5.87 of all failed pairs)
pairs failed due to pair_2 : 12193 (%87.80 of all failed pairs)
pairs failed due to both : 880 (%6.34 of all failed pairs)
FAILED_REASON_P : 12223 (%88.01 of all failed pairs)
FAILED_REASON_N : 38 (%0.27 of all failed pairs)
FAILED_REASON_C33 : 1627 (%11.72 of all failed pairs)
Quality filtering suggestions made by Bokulich et al is implemented in iu-filter-quality-bokulich script. The output of the script includes these files:
project_name-STATS.txtproject_name-QUALITY_PASSED_R1.faproject_name-QUALITY_PASSED_R2.faproject_name-READ_IDs.cPickle.z
If the program is run with --visualize-quality-curves option, these files will also be generated in the output directory:
project_name-PASSED.pngproject_name-FAILED_REASON_P.png(visualization of mean quality scores per tile for pairs that failed quality filtering for being too short after quality trimming)project_name-FAILED_REASON_N.png(same above, but having more ambiguous bases thannafter quality trimming)project_name-Q_DICT.cPickle.z
number of pairs analyzed : 122929
total pairs passed : 111598 (%90.78 of all pairs)
total pair_1 trimmed : 1994 (%1.79 of all passed pairs)
total pair_2 trimmed : 9227 (%8.27 of all passed pairs)
total pairs failed : 11331 (%9.22 of all pairs)
pairs failed due to pair_1 : 738 (%6.51 of all failed pairs)
pairs failed due to pair_2 : 10159 (%89.66 of all failed pairs)
pairs failed due to both : 434 (%3.83 of all failed pairs)
FAILED_REASON_P : 11299 (%99.72 of all failed pairs)
FAILED_REASON_N : 32 (%0.28 of all failed pairs)
Please don't hesitate to get in touch with me via meren at uchicago dot edu.