General settings

To configure this workflow, modify the following files to reflect your dataset and differential expression analysis model:

• config/samples.tsv: samples sheet with covariates and conditions
• config/units.tsv: (sequencing) units sheet with raw data paths
• config/config.yaml: general workflow configuration and differential expression model setup

samples sheet

For each biological sample, add a line to the sample sheet in config/samples.tsv. The column sample is required and gives the sample name. Additional columns can specify covariates (including batch effects) and conditions. These columns can then be used in the diffexp: models: specification section in config/config.yaml (see below)

Missing values can be specified by empty columns or by writing NA.

units sheet

For each sample, add one or more sequencing unit lines (runs, lanes or replicates) to the unit sheet in config/units.tsv. For each unit, provide either of the following:

• The path to two pairead-end FASTQ files in the columns fq1, fq2.
• The path to a single-end FASTQ file in the column fq1. For single-end data, you also need to specify fragment_len_mean and fragment_len_sd, which should usually be available from your sequencing provider.
• The path to a single-end BAM file in the column bam_single
• The path to a paired-end bam BAM file in the column bam_paired

Missing values can be specified by empty columns or by writing NA.

config.yaml

This file contains the general workflow configuration and the setup for the differential expression analysis performed by sleuth. Configurable options should be explained in the comments above the respective entry or right here in this config/README.md section. If something is unclear, don't hesitate to file an issue in the rna-seq-kallisto-sleuth GitHub repository.

differential expression model setup

The core functionality of this workflow is provided by the software sleuth. You can use it to test for differential expression of genes or transcripts between two or more subgroups of samples.

main sleuth model

The main idea of sleuth's internal model, is to test a full: model (containing (a) variable(s) of interest AND batch effects) against a reduced: model (containing ONLY the batch effects). So these are the most important entries to set up under any model that you specify via diffexp: models:. If you don't know any batch effects, the reduced: model will have to be ~1. Otherwise it will be the tilde followed by an addition of the names of any columns that contain batch effects, for example: reduced: ~batch_effect_1 + batch_effect_2. The full model than additionally includes variables of interest, so fore example: full: ~variable_of_interest + batch_effect_1 + batch_effect_2.

sleuth effect sizes

Effect size estimates are calculated as so-called beta-values by sleuth. For binary comparisons (your variable of interest has two factor levels), they resemble a log2 fold change. To know which variable of interest to use for the effect size calculation, you need to provide its column name as the primary_variable:. And for sleuth to know what level of that variable of interest to use as the base level, specify the respective entry as the base_level:.

preprocessing params

For adapter trimming, cutadapt is used, with some defaults for standard Illumina data given in the config.yaml. For more details see the comments in the config.yaml or the cutadapt documentation. For parameter suggestions for Lexogen 3' QuantSeq data, see the section below.

For transcript quantification, kallisto is used. For details regarding its command line arguments, see the kallisto documentation.

Lexogen 3' QuantSeq data analysis

For Lexogen 3' QuantSeq data analysis, please set experiment: 3-prime-rna-seq: activate: true in the config/config.yaml file. For more information information on Lexogen QuantSeq 3' sequencing, see: https://www.lexogen.com/quantseq-3mrna-sequencing/ In addition, for Lexogen 3' FWD QuantSeq data, we recommend setting the params: cutadapt-se: with:

    adapters: "-a 'r1adapter=AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=7;max_error_rate=0.005'"
    extra: "--minimum-length 33 --nextseq-trim=20 --poly-a"

This is an adaptation of the Lexogen read preprocessing recommendations for 3' FWD QuantSeq data. Changes to the recommendations are motivated as follows:

• -m: We switched to the easier to read --minimum-length and apply this minimum length globally. In addition, we increase the minimum length to a default of 33 that makes more sense for kallisto quantification.
• -O: Instead of this option, minimum overlap is specified per expression.
• -a "polyA=A{20}": We replace this with cutudapts dedicated option for --poly-a tail removal (which is run after adapter trimming).
• -a "QUALITY=G{20}": We replace this with cutudapts dedicated option for the removal artifactual trailing Gs in NextSeq and NovaSeq data: --nextseq-trim=20.
• -n: With the dedicated cutadapt options getting applied in the right order, this option is not needed any more.
• -a "r1adapter=A{18}AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=3;max_error_rate=0.100000": We remove A{18}, as this is handled by --poly-a. We increase min_overlap to 7 and set the max_error_rate to the Illumina error rate of about 0.005, both to avoid spurious adapter matches being removed.
• -g "r1adapter=AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC;min_overlap=20": This is not needed any more, as -a option will lead to complete removal of read sequence if adapter is found at the start of the read, see: https://cutadapt.readthedocs.io/en/stable/guide.html#rightmost
• --discard-trimmed: We omit this, as the -a with the adapter sequence will lead to complete read sequence removal if adapter is found at start, and the --minimum-length will then discard such empty reads.

meta comparisons

Meta comparisons allow for comparing two full models against each other. The axes represent the log2-fold changes (beta-scores) for the two models, with each point representing a gene. Points on the diagonal indicate no difference between the comparisons, while deviations from the diagonal suggest differences in gene expression between the treatments. For more details see the comments in the config.yaml.