Kraken Hub Script Fix

On NCBI's website, GFF3 files only contain annotation and not the nucleotide sequence so cannot be used. You need to download the GenBank files plus nucleotide sequence and convert them. When downloading, click on the show sequence option, Update View then Send to a File of type GenBank. You can then use the Bio::Perl script bp_genbank2gff3.pl to convert to GFF3. Just be aware that mixing different gene prediction methods and annotation pipelines can give noisier results.

Kraken Hub Script

Ola Brynildsrud has created a python script called scoary which takes in a csv file of traits, and the gene presence and absense spreadsheet, then performs a pan genome wide association analysis.It highlights genes which appear to be linked to the traits and gives lots of stats.

This contributed script by Marco Galardini is not installed by default but can be very useful. Additional details can be found here in the repository.It provides 3 figures, showing the tree compared to a matrix with the presence and absence of core and accessory genes. The next is an pie chart of the breakdown of genes and the number of isolate they are present in. And finally there is a graph with the frequency of genes versus the number of genomes.

David Powell has produced the FriPan website which allows for interactive visualisation of the output of Roary. Jason Kwong has created a converter script to transform the output of Roary into a suitable format for FriPan.

There is an additional script called create_pan_genome_plots.R which requires R and the ggplot2 library. It takes in the *.Rtab files and produces graphs on how the pan genome varies as genomes are added (in random orders).

If you see a warning like about the 'Use of uninitialized value' in Encode.pm, don't worry, it's just a warning and has no impact the script. If you want to get rid of this warning, just update the Encode perl module to the latest version.

Ahead of aligning the sequencing reads, most aligners/mappers require the generation of index of the genome. This step may take quite a while, depending on the size of the genome. Bovine genome is 2.7Gb in size, and bowtie2 indexing step takes 50 minutes using 12 CPUs. In the interest of time, I am providing the indexing slurm script, but we are not going to go through this step in the workshop. Instead, we are going to link the index files that I have generated.

Once Kraken2 successfully finishes, we should have two files in each sample subfolder inside 03-Kraken: samplename.kraken.out and samplename.kraken_report.out. Please take a look at the two files and see what they contain.

This step runs very fast, a few seconds. It generates two files for each sample in its corresponding subdirectory inside 03-Kraken: samplename_report_species.txt and samplename.kraken_report_bracken.out. Please take a look at both files to understand what they contain.

Start RStudio and go to the folder that you have downloaded the three files. Then open the R markdown file (Differential.kraken.Rmd). If RStudio has not prompted you to install packages, then please follow the instructions below for installing the packages we need.

It takes a long time to run MetaBAT2, so I am providing the script for those of you who are interested in learning the process. It involves mapping the sequencing reads to the assembled contigs, calculate coverage information and run binning algorith. Many other methods exist to improve metagenomic binning: GraphBin2. The binning results are usually checked for quality using CheckM.

This tool allows us to gain information at function level. However, please keep in mind that this analysis provides the potential functions the community members possess. They should be interpreted differently from the same analysis using metatranscriptomics data.

In addition to the commit-msg hook, you can use server-side hooks to apply policies for your project. The server runs these scripts before and after the push. The server-side hook, like commit-msg hook, requires Python to be installed.

When the server handles the push from a client, the pre-receive script is run first. When commits does not have proper Jira issue tagging, an error message from client to server is raised.

To create a new database, an HLA reference fasta, transcriptome-wide transcript fa and gtf, an exclusion bed, and a hla CWD allele file are required. The transcript files and exclusion bed are used to create the distractome, which helps control for homology between HLA genes and other transcripts. The exclusion bed denotes genomic regions to exclude from the distractome. Any reads assigned to the distractome will be excluded from analysis. While we recommend the IPD/IMGT HLA database and GENCODE as the source of these references, any files can be used as long as they adhere to the following naming and format conventions.

To run HLAProfiler you will need to know the location of the HLAProfiler script as well as the classify executable from Kraken.The HLAProfiler script should be located in the miniconda bin directory (/path/to/minconda/bin/). The classify executable will be found in the kraken-ea directory under the share directory (/path/to/miniconda/share/kraken-ea-version/).

Hovering over column headers will show a longer description, including whichmodule 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 multipleheaders to sort by multiple columns.

Of course, using conda is optional, but it greatly increases reproducibility.Snakemake is not limited to wrappers (although its wrapper repository provides many in the field of bioinformatics), but also supports direct execution of shell commands and integration of custom scripts (e.g., for plotting).

It is possible to plot a dashed line showing the theoretical GC content for areference genome. MultiQC comes with genome and transcriptome guides for Humanand Mouse. You can use these in your reports by adding the following MultiQCconfig keys (see Configuring MultiQC):

Flexbar preprocesses high-throughput sequencing dataefficiently. 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.

Currently only two stats are displayed in MultiQC. Two bargraphs are created for the read classication and the strand orientation of the identified full length transcripts. Additional stats could be included on further request.

The general stats table contains a value that displays the percentage of full length transcripts. This value is calculated from the cumulative length of reads where Pychopper found primers at both ends.

SortMeRNA is a program tool for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data.

The Kallisto module parses logs generated byKallisto,a program for quantifying abundances of transcripts from RNA-Seq data, or more generallyof target sequences using high-throughput sequencing reads.

BUSCO v2 provides quantitative measures for the assessment of genomeassembly, gene set, and transcriptome completeness, based onevolutionarily-informed expectations of gene content from near-universalsingle-copy orthologs selected from OrthoDB v9.

This module takes the JSON output of the HOPS postprocessing R script (Version>= 0.34). to recreate the possible positives heatmap, with the heat intensityrepresenting the number of 'ancient DNA characteristics' categories (smalledit distance, damage, both edit distance and aDNA damage) that a particulartaxon has.

MACS2 (Model-based Analysis of ChIP-Seq) is a tool for identifying transcriptfactor binding sites. MACS captures the influence of genome complexity toevaluate the significance of enriched ChIP regions.

Note that some scripts (for example, junction_annotation.py) produce the results used by MultiQC as standard-error.To use with MultiQC, make sure that you redirect this to a file using 2> mysample.log.

Bioinformatics projects often include non-standardised analyses, with results from customscripts or in-house packages. It can be frustrating to have a MultiQC report describingresults from 90% of your pipeline but missing the final key plot. To help with this,MultiQC has a special "custom content" module.

This kind of customisation should work with most Custom Content types.For example, using an image called some_science_mqc.jpeg gives us a report section some_science,which we can then add a nicer name and description to:

Note that some things, such as parent_name are taken from the first file that MultiQC findswith this parent_id. So it's a good idea to specify this in every file.parent_description and extra is taken from the first file where it is set.

Much like the counts / percentages buttons above, you can add a button whichswitches the data displayed in a single plot. Make sure that both datasetsare stored in named javascript variables, then add the following markup:

Note the CSS class active which specifies which button is 'pressed' on page load.data-ylab and data-xlab can be used to specify the new axes labels.data-newdata should be the name of the javascript object with the new datato be plotted and data-target should be the CSS selector of the plot to change.

Some of the events that take place in the general javascriptcode trigger jQuery events which you can hook into from within yourmodule's code. This allows you to take advantage of events generatedby the global theme whilst keeping your code modular.

Secondly, you can copy additional files with your report when it is generated.This is usually used to copy required images or scripts with the report. Theseshould be a list of file or directory paths, relative to the __init__.py file.Directory contents will be copied recursively. 041b061a72