This takes a string which it matches using glob expansion to filenames, directory names and entire paths:. Some modules get sample names from the contents of the file and not the filename for example, stdout logs can contain multiple samples. In this case, you can skip samples by name instead:. All of these settings can be saved in a MultiQC config file so that you don't have to type them on the command line for every run. Finally, you can supply a file containing a list of file paths, one per row.
MultiQC only search the listed files. It's quite common to repeatedly create new reports as new analysis results are generated. Instead of manually deleting old reports, you can just specify the -f parameter and MultiQC will overwrite any conflicting report filenames. Sometimes, the same samples may be processed in different ways.
This will prefix every sample name with the directory path for that log file. As such, sample names should now be unique, and not overwrite one-another. By default, --dirs will prepend the entire path to each sample name. Set to a positive integer to use that many directories at the end of the path. A negative integer takes directories from the start of the path.
MultiQC is built around a templating system. The available templates are listed with multiqc --help. If you're interested in creating your own custom template, see the writing new templates section.
You must have Pandoc already installed for this to work. If you don't have Pandoc installed, you will get an error message that looks like this:. Also note that not all plots have flat image equivalents, so some will be missing at time of writing: FastQC sequence content plot, beeswarm dot plots, heatmaps. If you would like to generate MultiQC reports on the fly, you can print the output to standard out by specifying -n stdout.
Note that the data directory will not be generated and the template used must create stand-alone HTML reports. By default, MultiQC creates a directory alongside the report containing tab-delimited files with the parsed data. This is useful for downstream processing, especially if you're running MultiQC with very large numbers of samples.
Typically, these files are tab-delimited tables. Note that the data directory is never produced when printing the MultiQC report to stdout. Raw data for the plots are also saved to files. You can always save static image versions of plots from within MultiQC reports, using the Export toolbox in the side bar. Sometimes, it's desirable to choose which MultiQC modules run.
This could be because you're only interested in one type of output and want to keep the reports small. Or perhaps the output from one module is misleading in your situation.
MultiQC reports should work in any modern browser. If you find any report bugs, please report them as a GitHub issue. This shows an overview of key values, taken from all modules.
The aim of the table is to bring together stats for each sample from across the analysis so that you can see it in one place. Hovering over column headers will show a longer description, including which module produced the data.
Clicking a header will sort the table by that value. Clicking it again will change the sort direction. You can shift-click multiple headers to sort by multiple columns. Above the table there is a button called 'Configure Columns'. MultiQC modules can take plot more extensive data in the sections below the general statistics table. You can hover the mouse over data to see a tooltip with more information about that dataset.
Clicking and dragging on line graphs will zoom into that area. Plots have a grey bar along their base; clicking and dragging this will resize the plot's height:.
You can force reports to use interactive plots instead of flat by specifying the --interactive command line option see below.
Reports with large numbers of samples may contain flat plots. These are rendered when the MultiQC report is generated using MatPlotLib and are non-interactive flat images within the report.
The reason for generating these is that large sample numbers can make MultiQC reports very data-intensive and unresponsive crashing people's browsers in extreme cases. Plotting data in flat images is scalable to any number of samples, however. Flat plots in MultiQC have been designed to look as similar to their interactive versions as possible.
If you want to use the plot elsewhere eg. Just click the menu button in the top right of the plot:. You have a range of export options here.
When deciding on output format bear in mind that SVG is a vector format, so can be edited in tools such as Adobe Illustrator or the free tool Inkscape. The Plot scaling option changes how large the labels are relative to the plot. Some plots have buttons above them which allow you to change the data that they show or their axis. For example, many bar plots have the option to show the data as percentages instead of counts:.
MultiQC reports come with a 'toolbox', accessible by clicking the buttons on the right hand side of the report:. Active toolbox panels have their button highlighted with a blue outline.
You can hide the toolbox by clicking the open panel button a second time, or pressing Escape on your keyboard. If you run MultiQC plots with a lot of samples, plots can become very data-heavy. This makes it difficult to find specific samples, or subsets of samples. To help with this, you can use the Highlight Samples tool to colour datasets of interest. Simply enter some text which will match the samples you want to highlight and press enter or click the add button.
If you like, you can also customise the highlight colour. To make it easier to match groups of samples, you can use a regular expressions by turning on 'Regex mode'. You can test regexes using a nice tool at regex See a nice introduction to regexes here.
Note that a new button appears above the General Statistics table when samples are highlighted, allowing you to sort the table according to highlights. Search patterns can be changed after creation, just click to edit. To remove, click the grey cross on the right hand side. Sample names are typically generated based on processed file names. These file names are not always informative. To help with this, you can do a search and replace within sample names.
Again, regular expressions can be used. See above for details. Often, you may have a spreadsheet with filenames and informative sample names. To avoid having to manually enter each name, you can paste from a spreadsheet using the 'bulk import' tool:. Sometimes, you want to focus on a subset of samples. To temporarily hide samples from the report, enter a search string as described above into the 'Hide Samples' toolbox panel. Note that plots will tell you how many samples have been hidden.
This panel allows you to download MultiQC plots as images or as raw data. You can configure the size and characteristics of exported plot images: Width and Height set the output size of the images, scale sets how "zoomed-in" they should look typically you want the plot to be more zoomed for printing.
The tick boxes below these settings allow you to download multiple plots in one go. Plots with multiple tabs will all be exported as files when using the Data tab.
For plots with multiple tags, the currently visible plot will be exported. You can also save static plot images when you run MultiQC. See Exporting Plots for more information. To avoid having to re-enter the same toolbox setup repeatedly, you can save your settings using the 'Save Settings' panel. Just pick a name and click save. To load, choose your set of settings and press load or delete. Loaded settings are applied on top of current settings.
All configs are saved in browser local storage - they do not travel with the report and may not work in older browsers. Whilst most MultiQC settings can be specified on the command line, MultiQC is also able to parse system-wide and personal config files.
At run time, it collects the configuration settings from the following places in this order overwriting at each step if a conflicting config variable is found:.
If you installed MultiQC with pip or conda you won't have this file locally, but you can find it on GitHub: MultiQC typically generates sample names by taking the input or log file name, and 'cleaning' it. If it finds any matches, everything to the right is removed.
For example, consider the following config:. Usually you don't want to overwrite the defaults though you can. File name cleaning can also take strings to remove instead of removing with truncation. Also regex strings can be supplied to match patterns and remove or keep matching substrings. If you just supply a string, the default behavior is similar to "trim".
The filename will be truncated beginning with the matching string. You can also remove a substring with a regular expression. Here's a good resource to interactively try it out. This simplifies things if you can e.
This process of cleaning sample names can sometimes result in exact duplicates. A duplicate sample name will overwrite previous results. Problems caused by this will typically be discovered be fewer results than expected. One scenario where clashing names can occur is when the same file is processed in different directories.
Only the last will be shown. Many bioinformatics tools have standard output formats, filenames and other signatures. This works well most of the time, until someone has an automated processing pipeline that renames things. For this reason, as of version v0. Copy the section for the program that you want to modify and paste this into your config file. Make sure you make it part of a dictionary called sp as follows:. Search patterns can specify a filename match fn or a file contents match contents.
MultiQC begins by indexing all of the files that you specified and building a list of the ones it will use. You can skip samples by their resolved sample names after cleaning with two config options: The first takes a list of strings to be used for glob pattern matching same behaviour as the command line option --ignore-samples , the latter takes a list of regex patterns. MultiQC has been written with the intention of being used for any number of samples. This means that it should work well with 6 samples or Very large sample numbers are becoming increasingly common, for example with single cell data.
Producing reports with data from many hundreds or thousands of samples provides some challenges, both technically and also in terms of data visualisation and report usability. One problem with large reports is that the browser can hang when the report is first loaded.
This is because it loading and processing the data for all plots at once. To mitigate this, large reports may show plots as grey boxes with a "Show Plot" button. Clicking this will render the plot as normal and prevents the browser from trying to do everything at once. By default this behaviour kicks in when a plot has 50 samples or more. Reports with many samples start to need a lot of data for plots.
This results in inconvenient report file sizes can be s of megabytes and worse, web browser crashes. To allow MultiQC to scale to these sample numbers, most plot types have two plotting functions in the code base - interactive using HighCharts and flat rendered with MatPlotLib. Flat plots take up the same disk space irrespective of sample number and do not consume excessive resources to display. By default, MultiQC generates flat plots when there are or more samples.
Report tables with thousands of samples table rows can quickly become impossible to use. To avoid this, tables with large numbers of rows are instead plotted as a Beeswarm plot aka.
These plots have fixed dimensions with any number of samples. Hovering on a dot will highlight the same sample in other rows. By default, MultiQC starts using beeswarm plots when a table has rows or more.
Sometimes it's useful to specify a single small config option just once, where creating a config file for the occasion may be overkill. Config variables should be given as a YAML string. You will usually need to enclose this in quotes. If MultiQC is unable to understand your config you will get an error message saying Could not parse command line config. As an example, the following command configures the coverage levels to use for the Qualimap module: MultiQC offers a few ways to customise reports to easily add your own branding and some additional report-level information.
These features are primarily designed for core genomics facilities. Note that much more extensive customisation of reports is possible using custom templates. You can also specify the title and comment, as well as a subtitle and the introductory text in your config file:. Set this to False to hide this, or set it to a string to use your own text.
To add your own custom logo to reports, you can add the following three lines to your MultiQC configuration file:. The URL will make the logo open up a new web browser tab with your address and the title sets the mouse hover title text. You can add custom information at the top of reports by adding key: For example, if you have the following saved:.
Although it is possible to rename samples manually and in bulk using the report toolbox , it's often desirable to embed such renaming patterns into the report so that they can be shared with others. For example, a typical case could be for a sequencing centre that has internal sample IDs and also user-supplied sample names.
Or public sample identifiers such as SRA numbers as well as more meaningful names. It's possible to supply a file with one or more sets of sample names using the --sample-names command line option. This file should be a tab-delimited file with a header row used for the report button labels and then any number of renamed sample identifiers. If supplied, buttons will be generated at the top of the report with your labels.
Clicking these will populate and apply the Toolbox renaming panel. It's also possible to supply such renaming patterns within a config file useful if you're already generating a config file for a run. Sometimes you may want to add a custom comment above specific sections in the report.
Comments can be written in Markdown. When clicking that in the report's left hand side navigation, the web browser URL has gatk-compare-overlap appended. By default, modules are included in the report as in the order specified in config. Any modules found which aren't in this list are appended at the top of the report. To specify certain modules that should always come at the top of the report, you can configure config. A module can be specified multiple times in either config.
By itself you'll just get two identical report sections. However, you can also supply configuration options to the modules as follows:. These overwrite the defaults that are hardcoded in the module code. These filter the file searches for a given list of glob filename patterns:. For example, to run the FastQC module twice, before and after adapter trimming, you could use the following config:.
Note that if you change the name then you will get multiples of columns in the General Statistics table. If unchanged, the topmost module may overwrite output from the first iteration. Let me know if this is a problem.. Sometimes it's desirable to customise the order of specific sections in a report, independent of module execution. To do this, follow a link in a report navigation to skip to the section you want to move must be a major section header, not a subheading.
You can change this number eg. Report tables such as the General Statistics table can get quite wide. To help with this, columns in the report can be hidden.
Some MultiQC modules include columns which are hidden by default, others may be uninteresting to some users. To allow customisation of this behaviour, the defaults can be changed by adding to your MultiQC config file.
Make a note of the Group and ID for the column that you'd like to alter. These are then added to the config as follows:. Note that you can set these to True to show columns that would otherwise be hidden by default.
High values push columns to the right hand side of the table and low to the left. The default value is It's possible to highlight values in tables based on their value. Rules can be applied to every table column, or to specific columns only, using that column's unique ID. These make any table cells that match the string pass or true have text with a green background, orange for warn , red for fail and so on.
There can be multiple tests for each style of formatting - if there is a match for any, it will be applied. The following comparison operators are available:. It's possible to highlight matches in any number of colours. MultiQC comes with the following defaults:. You can generate hex colour codes with lots of tools, for example http: Note that the different sets of rules are formatted in order. So if a value matches both pass and fail then it will be formatted as a fail.
To make numbers in the General Statistics table easier to read and compare quickly, MultiQC sometimes divides them by one million typically read counts. If your samples have very low read counts then this can result in the table showing counts of 0.
To change this behaviour, you can customise three config variables in your MultiQC config. The defaults are as follows:. By default, the interactive HighCharts plots in MultiQC reports use spaces for thousand separators and points for decimal places e.
For example, the following config would result in the following alternative number formatting: This formatting currently only applies to the interactive charts. It may be extended to apply elsewhere in the future submit a new issue if you spot somewhere where you'd like it. One tricky bit that caught me out whilst writing this is the different type casting between Python, YAML and Jinja2 templates. This is especially true when using an empty variable:. Hopefully MultiQC will be easy to use and run without any hitches.
If you have any problems, please do get in touch with the developer Phil Ewels by e-mail or by submitting an issue on github. Before that, here are a few things previously encountered that may help In this scenario, MultiQC finds some logs for the bioinformatics tool in question, but not all of your samples appear in the report. This is the most common question I get regarding MultiQC operation.
Usually, this happens because sample names collide. This happens innocently a lot - MultiQC overwrites previous results of the same name and you get the last one seen in the report. To solve this, try running MultiQC with the -d and -s flags.
The Clashing sample names section of the docs explains this in more detail. Another reason that log files can be skipped is if the log filesize is very large. For example, this could happen with very long concatenated standard out files. In this case, you have run a bioinformatics tool and have some log files in a directory. When you run MultiQC with that directory, it finds nothing for the tool in question. If everything looks fine, then MultiQC probably needs extending to support your data.
Tools have different versions, different parameters and different output formats that can confuse the parsing code. Please open an issue with your log files and we can get it fixed. The mkl library provides optimisations for numpy , a requirement of MatPlotLib.
Recent versions of Conda have a bundled version which should come with a licence and remove the warning. See this page for more info. If you already have Conda installed you can get the updated version by running:. Another way around it is to uninstall mkl. It seems that numpy works without it fine:.
See more here and here. If you're not using Conda, try installing MultiQC with that instead. You can find instructions here. Two MultiQC dependencies have been known to throw errors due to problems with the Python locale settings, or rather the lack of those settings.
Running MultiQC gives the following error:. Click can have a similar problem if the locale isn't set when using Python 3. That generates an error that looks like this:. You can fix both of these problems by changing your system locale to something that will be recognised.
One way to do this is by adding these lines to your. This program searches for and removes remnant adapter sequences from High-Throughput Sequencing HTS data and optionally trims low quality bases from the 3' end of reads following adapter removal. AdapterRemoval can analyze both single end and paired end data, and can be used to merge overlapping paired-ended reads into longer consensus sequences.
Additionally, the AdapterRemoval may be used to recover a consensus adapter sequence for paired-ended data, for which this information is not available. AfterQC can simply go through all fastq files in a folder and then output three folders: There are two versions of this software: This module currently only covers output from the latter. BioBloom Tools BBT provides the means to create filters for a given reference and then to categorize sequences.
This methodology is faster than alignment but does not provide mapping locations. BBT was initially intended to be used for pre-processing and QC applications like contamination detection, but is flexible to accommodate other purposes. This tool is intended to be a pipeline component to replace costly alignment steps. Cluster Flow is a simple and flexible bioinformatics pipeline tool. It's designed to be quick and easy to install, with flexible configuration and simple customization.
Cluster Flow easy enough to set up and use for non-bioinformaticians given a basic knowledge of the command line , and it's simplicity makes it great for low to medium throughput analyses. The Cutadapt module parses results generated by Cutadapt , a tool to find and remove adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads. This module should be able to parse logs from a wide range of versions of Cutadapt.
It's been tested with log files from v1. Note that you will need to change the search pattern for very old log files such as v. See the module search patterns section of the MultiQC documentation for more information.
An application to clip adapter sequences and merge reads in ancient DNA analysis. The FastQ Screen module parses results generated by FastQ Screen , a tool that allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect. By default, the module creates a plot that emulates the FastQ Screen output with blue and red stacked bars showing unique and multimapping read counts.
This is also shown when generating flat-image plots. The directory and zip file are often both present. To speed up MultiQC execution, zip files will be skipped if the file name suggests that they will share a sample name with data that has already been parsed.
You can customise the patterns used for finding these files in your MultiQC config see Module search patterns. The below code shows the default file patterns:. It is possible to plot a dashed line showing the theoretical GC content for a reference genome. MultiQC comes with genome and transcriptome guides for Human and Mouse. Only one theoretical distribution can be plotted.
The following guides are available: Alternatively, a custom theoretical guide can be used in reports. Please see the package readme for more details.
Result files from this package are searched for with the following search pattern can be customised as described above:. If you want to always use a specific custom file for MultiQC reports without having to add it to the analysis directory, add the full file path to the same MultiQC config variable described above:. The Fastp module parses results generated by Fastp.
Fastp can simply go through all fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of paramaters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.
Flexbar preprocesses high-throughput sequencing data efficiently. It demultiplexes barcoded runs and removes adapter sequences. Moreover, trimming and filtering features are provided. Flexbar increases read mapping rates and improves genome as well as transcriptome assemblies.
This module parses the output from the InterOp Summary executable and creates a table view. The executable used can easily be installed from the BioConda channel using conda install -c bioconda illumina-interop. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence. JELLYFISH can count k-mers using an order of magnitude less memory and an order of magnitude faster than other k-mer counting packages by using an efficient encoding of a hash table and by exploiting the "compare-and-swap" CPU instruction to increase parallelism.
The general usage of jellyfish to be parsed by MultiQC module needs to be:. The KAT multiqc module interprets output from KAT distribution analysis json files, which typically contain information such as estimated genome size and heterozygosity rates from your k-mer spectra. The algorithm is mostly aimed at ancient DNA and Illumina data but can be used for any dataset.
The Skewer module parses results generated by Skewer , an adapter trimming tool specially designed for processing next-generation sequencing NGS paired-end sequences.
The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. Users can override this using the configuration option:. The Bismark module parses logs generated by Bismark , a tool to map bisulfite converted sequence reads and determine cytosine methylation states. The Bowtie 1 module parses results generated by Bowtie , an ultrafast, memory-efficient short read aligner.
The Bowtie 2 module parses results generated by Bowtie 2 , an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. Please note that the Bowtie 2 logs are difficult to parse as they don't contain much extra information such as what the input data was.
A typical log looks like this:. If not, it takes the filename as the sample name. Bowtie 2 is used by other tools too, so if your log file contains the word bisulfite , MultiQC will assume that this is actually Bismark and ignore the Bowtie 2 logs.
The module can summarise data from the following BBMap output files descriptions from command line help output:. These logs are indistinguishable and summary statistics will appear in MultiQC reports labelled as Bowtie2. Note that if you specify --summary-file when running HISAT2 the same summary output appears both there and in the stdout. So if you save both with different names you may end up with duplicate samples in your MultiQC report.
The Kallisto module parses logs generated by Kallisto , a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. Note - MultiQC parses the standard out from Kallisto, not any of its output files abundance. As such, you must capture the Kallisto stdout to a file when running to use the MultiQC module. The Salmon module parses results generated by Salmon , a tool for quantifying the expression of transcripts using RNA-seq data.
This MultiQC module parses summary statistics from the Log. Sample names are taken either from the filename prefix sampleNameLog. If there is no filename prefix, the sample name is set as the name of the directory containing the file.
In addition to this summary log file, the module parses ReadsPerGene. The Bcftools module parses results generated by Bcftools , a suite of programs for interacting with variant call data. To collapse such statistics in the substitutions plot, you can add the following section into your configuration:.
BUSCO v2 provides quantitative measures for the assessment of genome assembly, gene set, and transcriptome completeness, based on evolutionarily-informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB v9. Disambiguation algorithm for reads aligned to two species e. Finally, using such normalized and standardized files, deepTools can create many publication-ready visualizations to identify enrichments and for functional annotations of the genome.
In particular, the following are supported:. Please be aware that some tools namely, plotFingerprint --outRawCounts and plotCoverage --outRawCounts are only supported as of deepTools version 2. You can find details regarding the configuration file location here. Note that sample names are parsed from the text files themselves, they are not derived from file names.
The featureCounts module parses results generated by featureCounts , a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations. Developed by the Data Science and Data Engineering group at the Broad Institute , the GATK toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping.
Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. BaseRecalibrator is a tool for detecting systematic errors in read base quality scores of aligned high-throughput sequencing reads. It outputs a base quality score recalibration table that can be used in conjunction with the PrintReads tool to recalibrate base quality scores. VariantEval is a general-purpose tool for variant evaluation.
The goleft indexcov module parses results generated by goleft indexcov. It uses the PED and ROC data files to create diagnostic plots of coverage per sample, helping to identify sample gender and coverage issues.
By default, we attempt to only plot chromosomes using standard human-like naming chr1, chr X but you can specify chromosomes for detailed ROC plots for alternative naming schemes in your configuration with:.
The HOMER tag directory submodule parses output from files tag directory output files, generating a number of diagnostic plots. HTSeq is a general purpose Python package that provides infrastructure to process data from high-throughput sequencing assays. The methylQA module parses results generated by methylQA , a methylation sequencing data quality assessment tool.
With this information, miRTrace can detect exogenous miRNAs, which could be contamination derived, e. Used to generate three quality metrics: The NSC Normalized strand cross-correlation and RSC relative strand cross-correlation metrics use cross-correlation of stranded read density profiles to measure enrichment independently of peak calling.
It uses thousand genome samples as backgrounds to calibrate the relatedness calculation and to make ancestry predictions. It does this very quickly by sampling, by using C for computationally intensive parts, and by parallelization. The Picard module parses results generated by Picard , a set of Java command line tools for manipulating high-throughput sequencing data. Any numbers not found in the reports will be ignored.
The coverage levels available for HsMetrics are typically 1, 2, 10, 20, 30, 40, 50 and X. The coverage levels available for WgsMetrics are typically 1, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90 and X. Generally, Picard adds identifiable content to the output of function calls.
This is not the case for ValidateSamFile. The Preseq module parses results generated by Preseq , a tool that estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count.
It also includes a lot of data in the reports, which can unnecessarily inflate report file sizes. To disable this feature and show all of the data, add the following to your MultiQC configuration:. Preseq reports its numbers as "Molecule counts".
This isn't always very intuitive, and it's often easier to talk about sequencing depth in terms of coverage. You can plot the estimated coverage instead by specifying the reference genome or target size, and the read length in your MultiQC configuration:. MultiQC comes with effective genome size presets for Human and Mouse, so you can provide the genome build name instead, like this: The following values are supported: When the genome and read sizes are provided, MultiQC will plot the molecule counts on the X axis "total" data and coverages on the Y axis "unique" data.
However, you can customize what to plot on each axis counts or coverage , e. The Prokka module analyses summary results from the Prokka annotation pipeline for prokaryotic genomes. The Prokka module accepts two configuration options:. The module assumes that the first two words are the organism name and the third is the sample name. So the above will give a sample name of Sample1. If you prefer, you can set config. The QoRTs software package is a fast, efficient, and portable multifunction toolkit designed to assist in the analysis, quality control, and data management of RNA-Seq datasets.
Its primary function is to aid in the detection and identification of errors, biases, and artifacts produced by paired-end high-throughput RNA-Seq technology. The Qualimap module parses results generated by Qualimap , a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.
Note that Qualimap must be run with the -outdir option as well as -outformat HTML which is on by default. Qualimap adds lots of columns to the General Statistics table.
To avoid making the table too wide and bloated, some of these are hidden by default Error Rate , M Aligned , M Total reads. You can override these defaults in your MultiQC config file - for example, to show Error Rate by default and hide Ins. See the relevant section of the documentation for more detail. In addition to this, it's possible to customise which coverage thresholds calculated by the Qualimap BamQC module default: QUAST evaluates genome assemblies by computing various metrics, including.
By default, the QUAST module is configured to work with large de-novo genomes, showing thousands of contigs, mega-base pairs and other sensible defaults. The default module values are shown above. Note that you can pass as many file paths to MultiQC as you like and use glob expansion eg. This module shows the Spearman correlation heatmap if both Spearman and Pearson's are found. To plot Pearson's by default instead, add the following to your MultiQC config file:.
This module search for the file. You can choose to hide sections of RSeQC output and customise their order. To do this, add and customise the following to your MultiQC config file:. The Samblaster module parses results generated by Samblaster , a tool to mark duplicates and extract discordant and split reads from sam files. The Samtools module parses results generated by Samtools , a suite of programs for interacting with high-throughput sequencing data.
The samtools idxstats prints its results to standard out no consistent file name and has no header lines no way to recognise from content of file. As such, idxstats result files must have the string idxstat somewhere in the filename. There are a few MultiQC config options that you can add to customise how the idxstats module works. A typical configuration could look as follows:.
The sargasso module parses results generated by Sargasso , a tool for separating mixed-species RNA-seq reads according to their species of origin. The SnpEff module parses results generated by SnpEff , a genetic variant annotation and effect prediction toolbox.
It annotates and predicts the effects of variants on genes such as amino acid changes. MultiQC parses the summary. See the SnpEff documentation for more information. To be able to display these you will need to change the MultiQC configuration to allow for larger logfiles, see the MultiQC documentation. The Supernova module parses the reports from an assembly run. As a bare minimum it requires the file report. If you are anything like the author remiolsen , you might only have files often renamed to, e.
In the same folder, this module will search for the following plots and render them:. Also note that if there are more than 5 tumour subclones, their percentages are summed. Please check GitHub if you'd like these added or better still , would like to contribute!
A key step in any genetic analysis is to verify whether data being generated matches expectations. In addition, it detects possible sample mixture from population allele frequency only, which can be particularly useful when the genotype data is not available. Using a mathematical model that relates observed sequence reads to an hypothetical true genotype, verifyBamID tries to decide whether sequence reads match a particular individual or are more likely to be contaminated including a small proportion of foreign DNA , derived from a closely related individual, or derived from a completely different individual.
This module currently only imports data from the. The chipmix and freemix columns are imported into the general statistics table. It is expected to be further developed in future releases, which may break backwards compatibility. There are also probably quite a few bugs.
Use at your own risk! Please report bugs or missing functionality as a new GitHub issue. Bioinformatics projects often include non-standardised analyses, with results from custom scripts or in-house packages. To help with this, MultiQC has a special "custom content" module. All plot types can be generated using custom content - see the test files for examples of how data should be structured.
If your data comes from a released bioinformatics tool, you shouldn't be using this feature of MultiQC! Sure, you can probably get it to work, but it's better if a fully-fledged core MultiQC module is written instead. That way, other users of MultiQC can also benefit from results parsing. Note that proper MultiQC modules are more robust and powerful than this custom-content feature.
You can also write modules in MultiQC plugins if they're not suitable for general release. If you can choose exactly how your data output looks, then the easiest way to parse it is to use a MultiQC-specific format. These files contain configuration information specifying how the data should be parsed, alongside the data.
If you want to use YAML, this is an example of how it should look:. For maximum compatibility with other tools, you can also use comma-separated or tab-separated files. Include commented header lines with plot configuration in YAML format:. If no configuration is given, MultiQC will do its best to guess how to visualise your data appropriately. To see examples of typical file structures which are understood, see the test data used to develop this code.
Something will be probably be shown, but it may produce unexpected results. This is useful as you can keep everything contained within a single file including stuff unrelated to this specific custom content feature of MultiQC.
This must contain a section with a unique id, specific to your new report section. Finally, the contents of this second dictionary will look the same as the above stand-alone YAML files. Use a list of headers in pconfig keys prepended with - to specify the order of columns in the General Statistics table. See the general statistics docs for more information about configuring data for the General Statistics table.
It's not always possible or desirable to include MultiQC configuration within a data file. If this is the case, you can add to the MultiQC configuration to specify how input files should be parsed. The only difference is that no data subsection is given and a search pattern for the given id must be supplied.
Search patterns are added as with any other module. As mentioned above - if no configuration is given, MultiQC will do its best to guess how to visualise your data appropriately.
If you have multiple different Custom Content sections, their order will be random and may vary between runs. To avoid this, you can specify an order in your MultiQC config as follows:.
Each section name should be the ID assigned to that section. You can explicitly set this see below , or the Custom Content module will automatically assign an ID. To find out what your custom content section ID is, generate a report and click the side navigation to your section.
The browser URL should update and show something that looks like this:. Note that any Custom Content sections found that are not specified in the config will be placed at the top of the report. See below for how these config options can be specified either within the data file or in a MultiQC config file.
All of these configuration parameters are optional, and MultiQC will do its best to guess sensible defaults if they are not specified. The other section configuration keys are merged for each file, with identical keys overwriting what was previously parsed.
This approach means that it's possible to have a single file containing data for multiple samples, but it's also possible to have one file per sample and still have all of them summarised. Data types generalstats and beeswarm are only possible by setting the above configuration keys these can't be guessed by data format.
Configuration of specific plots follows the same syntax as used when writing modules. To find out more, please see the later docs. Specifically, the plot config docs for bar graphs , line graphs , scatter plots , tables , beeswarm plots and heatmaps. Because of the way this module works, there are a few specifics that can trip you up.
Most of these should probably be fixed one day. Feel free to complain on gitter or submit a pull request! I'll try to keep a list here to help the wary Although they're both tables, note that general stats configures columns with a list in the pconfig scope see above example.
Files that are just tables use headers instead. The first column in every table is reserved for the sample name. As such, it shouldn't contain data. All header configuration will be ignored for the first column.
The only exception is name: MultiQC has been developed to be as forgiving as possible and will handle lots of invalid or ignored configurations.
This is useful for most users but can make life difficult when getting MultiQC to work with a new custom content format. To help with this, you can run with the --lint flag, which will give explicit warnings about anything that is not optimally configured. Probably the best way to get to grips with Custom Content is to see some examples. The MultiQC automated testing runs with a bunch of different files, and I try to add to these all the time. You can see these examples here: Writing a new module can at first seem a daunting task.
However, MultiQC has been written and refactored to provide a lot of functionality as common functions. Provided that you are familiar with writing Python and you have a read through the guide below, you should be on your way in no time!
If you have any problems, feel free to contact the author - details here: New modules can either be written as part of MultiQC or in a stand-alone plugin.
If your module is for a publicly available tool, please add it to the main program and contribute your code back when complete via a pull request. If your module is for something very niche, which no-one else can use, you can write it as part of a custom plugin.
The process is almost identical, though it keeps the code bases separate. For more information about this, see the docs about MultiQC Plugins below. MultiQC has been developed to be as forgiving as possible and will handle lots of invalid or ignored code. This is useful most of the time but can be difficult when writing new MultiQC modules especially during pull-request reviews.
Note that the automated MultiQC continuous integration testing runs in this mode, so you will need to pass all lint tests for those checks to pass. This is required for any pull-requests. The directory should share its name with the module.
Once your submodule files are in place, you need to tell MultiQC that they are available as an analysis module. This is done within setup. Copy one of the existing module lines and change it to use your module name.
The order is irrelevant, so stick to alphabetical if in doubt. Once this is done, you will need to update your installation of MultiQC:.
So that MultiQC knows what order modules should be run in, you need to add your module to the core config file. This contains the name of modules in order of precedence. Add your module here in an appropriate position. Next up, you need to create a documentation file for your module. The reason for this is twofold: Secondly, having the file there with the appropriate YAML front matter will make the module show up on the MultiQC homepage so that everyone knows it exists.
This process is automated once the file is added to the core repository. Feel free to add your name to the list of credits at the bottom of the readme. If you've copied one of the other entry point statements, it will have ended in: To use the helper functions bundled with MultiQC, you should extend this class from multiqc. This will give you access to a number of functions on the self namespace. Ok, that should be it! Try adding a print "Hello World!
Last thing - MultiQC modules have a standardised way of producing output, so you shouldn't really use print statements for your Hello World ;.
The first thing that your module will need to do is to find analysis log files. Available models for motif occurrences: However, it is also possible to estimate this model from a separate, usualy larger set of sequences, such a all promoters of a genome. The Illumina Pipeline is a set of software tools for the unix platform provided to users of the Illumina Genome Analyzer.
The pipeline reads the images produced by the sequencer, analyzes them, performs base calling, and optionally aligns sequences to a reference genome. The user is provided with a set of sequences corresponding to each read, along with quality scores representing the accuracy of each base call.
Table of Contents Quality Scores. A numeric Phred score represents the error probability of a given base call. When a nucleotide sequence is produced by sequencing, random error results in the possibility that any given base call may be incorrect. Thus, a quality score is provided for each base. A more detailed outline is given in this slide show from Illumina.
The phred score can be calculated from the error probability of a given base call: Get sample fastq file system "wget http: Import Reads From Fastq file system "wget http: The functions allow processing of reads with variable length, but most plots are only meaningful if the read positions in the FASTQ file are aligned with the sequencing cycles.
For instance, constant length clipping of the reads on either end or variable length clipping on the 3' end maintains this relationship, while variable length clipping on the 5' end without reversing the reads erases it. Related resources on this topic can be found here: R " Optional download of sample fastq files system "wget http: The argument 'klength' specifies the k-mer length and 'batchsize' the number of reads to random sample from each fastq file.
Output plot to PDF pdf "fastqReport. To plot only specific rows and columns, one can assign them to the '-a' and '-c' arguments using this syntax: The arguments '-s' and '-k' specify the random sampling size and the k-mer length, respectively. Option 1 of 2: Download test data from internet wget http: If logged into biocluster.
Rsamtools SNP Calling This step requires the sorted and indexed alignment and genome from previous steps unix command to extract pileup check samtools options first system "samtools pileup -vcf genome. Rnw" library SNPAnno chunk number 5: DNAStringSet genomefile chunk number Rnw" library SNPAnno chunk number R" biocLite " BSgenome. Data Import library chipseq ; library lattice data cstest ; cstest Loads a reduced test data set containing the start positions and orientation of the aligned reads for three mouse chromosomes.
Each of its components contains the data from one Illumina lane. Therefore, it can be useful in certain cases to extend the reads to cover the entire binding site of DNA binging proteins. See help file for details. The results are stored in a run-length encoded form as Rle object.
Plots the distribution of read numbers against the number of islands. Strand-Specific Peak Calling and Plotting peak. Differential Peaks Among Two Samples Simplest strategy is to combine the data from two samples and then compare the contribution of each sample to the peaks. The file needs to be downloaded from the BayesPeak site , uncompressed and stored in the current working directory of an R session. The incorporation of a control sample is beneficial but not required for this function.
The aligned read data can be read directly from a BED file or provided as a data frame or a RangedData object as in this example. For speed reasons the analysis is restricted in this example to a small subrange on chromosome For a complete analysis of an entire genome, one usually wants to omit the arguments: In addition, one can increase the number of CPU cores utilized for the computation under the 'mc.
Consult the BayesPeak manual for more details. R " Imports the rangeCoverage function for computing coverage information of peak calls. The function expects the peak ranges in an IRanges object for single chromosome data or as a list of IRanges for multiple chromosome data. In addition, the corresponding aligned read data need to be provided as a RangedData object.
The first column contains the total coverage and the other two the coverage for the positive and negative strands, respectively. To obtain mean coverage values, assign 'viewMeans' to the summaryFct argument. The files need to be downloaded from the BayesPeak site , uncompressed and stored in the current working directory of an R session.
Peaks Identified by both Methods: NCBI36 Loads gene locations for human genome. NCBI36 Combines the peak information with the genomic context. GO Term Enrichment Analysis library org. This includes introns which are rare in this example. It also accepts a ScanBamParam function under the param argument to filter the reads stored in a bam file, e. Alternatively, one can provide here the true library sizes with: For single factor experiments, edgeR uses the quantile-adjusted conditional maximum likelihood qCML method.
The 'pair' argument allows the user to determine which samples to compare. For more detailed examples, see sections in edgeR manual. It handles multiple factors by fitting generalized linear models GLM with a design matrix.
The CR method can be used to calculate a common dispersion for all the tags, trended dispersion depending on the tag abundance, or separate dispersions for individual tags using the different estimateGLMX function group. To estimate tagwise dispersions, the empirical Bayes method is applied to squeeze tagwise dispersions towards common dispersion or trended dispersions whichever exists.
Normalization Factors The use of normalization factors, such as the weighted trimmed mean of M-values TMM , can be considered for RNA samples with extreme composition bias, where a small number of genes accounts for most of the reads in a library. The following software suggestions provide this utility: This is particularly useful for identifying SNPs.
MAQ can also be used as a general purpose short read alignment tool, and contains some useful file format converters. Bowtie is an ultra-fast alignment tool. It does not currently support gapped alignment. Bowtie is generally an excellent choice if you don't need any of the extended features provided by lower performance tools.
Bowtie web page Running Bowtie wget http: Alternative- copy from biocluster folder bowtie inspect command line options bowtie-build genome.
Session Info If you receive errors or unexpected behavior when performing exercises from this manual, please double check that you are using the same software version s for which it was written: UTF-8 attached base packages: This site was accessed times detailed access stats.
The Bioconductor project fills this gap by providing a rapidly growing suite of well designed R packages for analyzing traditional and HT-Seq datasets. These 'BioC-Seq' packages allow to analyze these sequences with impressive speed performance. Their accelerations are achieved by using memory efficient string containers and performing the time consuming computations with calls to external programs that are implemented in compiled languages e.
Together these packages form a novel framework that allows researchers to develop efficient pipelines by performing complex data analysis in a high level data analysis and programming environment. These utilities are very useful for basic sequence analysis tasks. However, often it is much more effective to use for these tasks dedicated Bioconductor sequence analysis packages, such as Biostrings.
This manual primarily introduces functions and data objects from the Bioconductor project. Nevertheless, the following commands provide a brief summary of some very useful string handling functions available in R's base distribution. Table of Contents Bioconductor Packages Biostrings Documentation The Biostrings package from Bioconductor provides an advanced environment for efficient sequence management and analysis in R.
Handling Single Sequences with XString The XString class allows to represent the different types of biosequence strings in the subclasses: Table of Contents Masking Sequences Biostrings allows to mask XString objects in order to exclude undesired regions from consideration by various analysis tools, e.
The advantage of this masking approach is that it does not result in any information loss, because masks can be turned on and off any time.
In addition, the process is very memory efficient. The subclasses for the different types of biosequences are: The following examples introduce a subset of these basic sequence analysis functions.
Table of Contents Trimming of Flanking Sequences e. Adaptors Many cloning and barcoding approaches append flanking sequences e. These artificial sequence fragments need to be removed to allow a reliable matching of the sequences against their corresponding genomes or transcriptomes. The trimLRPatterns function is a very efficient and flexible utility for removing flanking patterns from both ends of sequences. Table of Contents Pattern Search and Alignment Tools for Genome Mapping Biostrings contains a wide spectrum of sequence search and pattern matching tools.
This section provides only a brief introduction into a subset of these tools. Table of Contents Computing Pairwise Sequence Alignments The pairwise sequence alignment function from Biostrings provides a flexible environment for computing optimal pairwise alignments using different dynamic programming algorithms.
Almost all MSA programs support this format e. The following examples introduce a variety of useful analysis routines on MSAs using this object class. Table of Contents Analysis Routines with Biostrings The following examples introduce a variety of useful analysis routines that can be accomplished with functions provided by the Biostrings package. The code retrieves the matching counts and mapping coordinates for all probes and probe sets. Table of Contents Analyzing Assembly Results With growing read lengths, de novo assemblies of genome or transcriptome data are nowadays a routine task in the NGS field.
Note, comparisons among N50 values from different assemblies are only meaningful when using for their calculation the same combined length value. Thus, a known target length value can often be a good solution for comparing assembly results.
The following contigStats function calculates the N50 values according to the more efficient cumulative sum method. In addition, it generates a distribution plot of the cumulative contig lengths, which can be a very informative visual representation for comparing assembly results.
Table of Contents Handling Sequence Ranges with IRanges and GenomicRanges Integer ranges are commonly used to represent coordinates of alignment positions, or various annotation features e. Its classes and methods provide support for many more high-level packages like GenomicRanges, ShortRead, Rsamtools, etc. Table of Contents GenomicRanges Overview Documentation The GenomicRanges package serves as the foundation for representing genomic locations within the Bioconductor project.
Both the input and output objects are of class GRanges. The following example is based on the very efficient range objects provided by the IRanges library. The algorithm was developed for detecting transcription factor TF binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be applied to any ranked list of DNA sequences.
Documentation Table of Contents ShortRead Documentation The ShortRead package provides input, quality control, filtering, parsing, and manipulation functionality for short read sequences produced by high throughput sequencing technologies.