Workflow configuration

HRIBO allows different customization to be able to handle different types of input data and customize the analysis. On this page we explain the different options that can be set to easily customize the workflow.

Default workflow

We provide a default config.yaml with generally well-defined default values in the template folder of the HRIBO GitHub repository.

Biology Settings

This section contains general settings for the workflow.

adapter sequence

adapter: ""

The adapter sequence that will be removed using cutadapt. It is important to add it if you know that your data was not trimmed before.

genome file

genome: "genome.fa"

The path to the genome file in fasta format. Only fasta format is supported. Make sure that the genome file has the same identifiers as the reference annotation file.

annotation file

annotation: "annotation.gff"

The path to the annotation file in gff format. Only gff format is supported. Make sure that the annotation file has the same identifiers as the reference genome file.

sample sheet

samples: "HRIBO/samples.tsv"

The path to the sample sheet (example provided in the templates folder). The sample sheet describes the relation between the different samples used for the experiment.

alternative start codons

alternativestartcodons: ["GTG","TTG"]

A list of alternative start codons that will be used to predict ORFs. The default is ["GTG","TTG"].

Differential Expression Settings

differentialexpression

differentialexpression: "on"

This option allows you to turn on or off differential expression analysis. If you do not have multiple conditions defined in the sample sheet and differential expression is activated, you will receive an error message. Options are “on / off”.

features

features: ["CDS", "sRNA"]

This option allows you to specify which features should be used for differential expression analysis. Any feature that appears in your reference annotation is allowed. We suggest using CDS and sRNA features.

contrasts

contrasts: ["treated1-untreated1", "treated2-untreated2"]

This option allows you to specify which contrasts should be used for differential expression analysis. The order will affect the directionality of the log2FC values in the output files.

adjusted pvalue cutoff

padjCutoff: 0.05

This option allows you to specify the adjusted pvalue cutoff for differential expression analysis. The default is 0.05. All results will be present in the output, this will is soley used to add additional pre-filtered list in the output excel tables.

log2 fold change cutoff

log2fcCutoff: 1.0

This option allows you to specify the log2 fold change cutoff for differential expression analysis. The default is 1.0. All results will be present in the output, this will is soley used to add additional pre-filtered list in the output excel tables. Only positive values are allowed. Respective negative values to determine down-regulation will be generated automatically by multiplying by -1.

ORF predictions

deepribo: "on"

Activating DeepRibo predictions will give you a different file with ORF predictions. By experience, the top DeepRibo results tend to be better than those of reparation. For archea, where reparation performs very poorly, DeepRibo is the preferred option.

Warning

DeepRibo cannot cope with genomes containing special IUPAC symbols, ensure that your genome file contains only A, G, C, T, N symbols.

Read statistics Settings

readLengths: "10-80"

This option allows you to specify the read lengths that should be used for read statistics analysis. The default is 10-80. We allow combinations of intervals and single values, e.g. 10-40,55,70.

Metagene Profiling Settings

There exist multiple options to customize the metagene profiling analysis.

Positions outside of the ORF

positionsOutsideORF: 50

This option allows you to specify the number of positions outside of the ORF that should be considered for metagene profiling analysis. The default is 50.

Positions inside of the ORF

positionsInsideORF: 150

This option allows you to specify the number of positions inside of the ORF that should be considered for metagene profiling analysis. The default is 150. Genes that are shorter than the specified number of positions inside of the ORF will be ignored.

Filtering Methods

filteringMethods: ["overlap", "length", "rpkm"]

This option allows you to specify which filtering methods should be used for metagene profiling analysis. The default is ["overlap", "length", "rpkm"].

These methods are used to filter out genes that would cause artifacts in the metagene profiling analysis.

  • The overlap method filters out genes that overlap with other genes.

  • The length method filters out genes that are shorter than the threshold.

  • The rpkm method filters out genes below the rpkm threshold.

Neighboring Genes Distance

neighboringGenesDistance: 50

This option allows you to specify the distance to neighboring genes that will be considered to filter out overlapping genes. The default is 50. This means that genes that are closer than 50nt to another gene will be filtered out.

RPKM Threshold

rpkmThreshold: 10.0

This option allows you to specify the RPKM threshold that will be used to filter out genes below the threshold. The default is 10.0.

Length Cutoff

lengthCutoff: 50

This option allows you to specify the length cutoff that will be used to filter out genes below the threshold. Be aware that genes that are smaller than the positionsInsideORF will be filtered out anyway.

Mapping Methods

mappingMethods: ["fiveprime", "threeprime", "centered", "global"]

This option allows you to specify which mapping methods should be used for metagene profiling analysis. The default is ["fiveprime", "threeprime"]. Multiple mappings can be used and result in multiple output files/folders.

  • The fiveprime method will use the 5’ end of the reads to count the number of reads per position.

  • The threeprime method will use the 3’ end of the reads to count the number of reads per position.

  • The centered method will use the center nucleotides of each read to count the number of reads per position.

  • The global method will use the entire read to count the number of reads per position.

Read Lengths:

readLengths: "25-34"

This option allows you to specify the read lengths that should be used for metagene profiling analysis. The default is 25-34. We allow combinations of intervals and single values, e.g. 25-34,40,50.

Normalization Methods

normalizationMethods: ["raw", "cpm"]

This option allows you to specify which normalization methods should be used for metagene profiling analysis. The default is ["raw", "cpm"].

Output Formats

outputFormats: ["svg", "pdf", "png", "jpg", "interactive"]

This option allows you to specify which output formats should be used for metagene profiling analysis. The default is ["svg", "interactive"]. The interactive option will generate an interactive html file that can be used to explore the metagene profiling results.

PlotlyJS

This option is only used when the interactive output format is selected.

includePlotlyJS: "integrated"

This option allows you to specify how the plotly.js library should be included in the interactive html file. The default is integrated.

  • The integrated option will include the plotly.js library in the html file. The file will be larger, but can be used offline. (+3.7mb/file)

  • The online option will include a link to the plotly.js library in the html file.

  • The local option will include a link to a local plotly.js library in the html file. This option requires the plotly.js library to be available in the same folder as the html file.

Colors

colorList: []

This option allows you to specify a list of colors that should be used for the metagene profiling analysis. The default is []. Per default a list of colorblind-friendly colors will be used. If you want to change the colors, make sure that the number of colors matches the number of samples. We also suggest to use hex colors, e.g. #ff0000.

Paired-end support

Until full paired-end support is added, we allow paired-end data by merging it into single-end data. Therefore, we use the tool flash2 to convert paired-end data to single-end data.

In order to use paired-end data, simply replace the Snakefile with the Snakefile_pairedend. This will now require a special samples_pairedend.tsv, which is also available in the HRIBO templates folder.

method

condition

replicate

fastqFile

fastqFile2

RIBO

A

1

fastq/RIBO-A-1_R1.fastq.gz

fastq/RIBO-A-1_R2.fastq.gz

RIBO

A

2

fastq/RIBO-A-2_R1.fastq.gz

fastq/RIBO-A-2_R2.fastq.gz

RIBO

B

1

fastq/RIBO-B-1_R1.fastq.gz

fastq/RIBO-B-1_R2.fastq.gz

RIBO

B

2

fastq/RIBO-B-2_R1.fastq.gz

fastq/RIBO-B-2_R2.fastq.gz

RNA

A

1

fastq/RNA-A-1_R1.fastq.gz

fastq/RNA-A-1_R2.fastq.gz

RNA

A

2

fastq/RNA-A-2_R1.fastq.gz

fastq/RNA-A-2_R2.fastq.gz

RNA

B

1

fastq/RNA-B-1_R1.fastq.gz

fastq/RNA-A-1_R2.fastq.gz

RNA

B

2

fastq/RNA-B-2_R1.fastq.gz

fastq/RNA-A-1_R2.fastq.gz