When RNA-Seq is the right tool

data2biology carries out complete RNA-Seq analyses: from raw FASTQ files to a list of differentially expressed genes with GO and KEGG enrichment. A project covering 10 to 20 samples usually takes about four weeks and costs approximately PLN 4,500 net (as of 2026). RNA-Seq answers the question of which genes change activity between conditions: treatment versus control, tissue versus tissue, timepoint versus timepoint, mutant versus wild type. Beyond the gene list itself, the analysis provides functional context — which biological processes and pathways are over-represented among those changes.

We work both with model organisms that have a well-annotated reference genome and with non-model species, where the starting point is often a de novo assembled transcriptome. We handle single- and paired-end data, stranded and unstranded libraries, and designs with several factors or time series.

How the data flows

Below we describe how we typically run this type of analysis. It is not a fixed procedure: the choice of tools depends on the organism, library type, sequencing depth and research question, and standards in bioinformatics change quickly. We agree the methodology with you before the project starts and update it as tools and reference databases evolve.

Input

FASTQ files (R1/R2) · reference genome and annotation (FASTA + GTF/GFF3) · sample metadata table

Read quality control

Base quality distribution, adapter content, duplication levels and contamination. Per-sample results are aggregated into a single report.

FastQCMultiQC

Trimming and filtering

Removal of adapters and low-quality bases, optionally filtering out rRNA reads or contamination from other organisms.

fastpTrimmomaticcutadaptSortMeRNA

Mapping or pseudo-alignment

With a genome available we use splice-aware alignment. When only expression levels matter, quantification without full alignment is a faster alternative.

STARHISAT2Salmonkallisto

Counting reads per gene and transcript

A genes × samples count matrix, plus transcript-level estimates when isoforms are of interest.

featureCountsHTSeq-countStringTietximport

Experiment-level QC

PCA, correlation between replicates and sample clustering. This step catches swapped labels, outlying replicates and batch effects before they distort the results.

DESeq2 (vst/rlog)PCAComplexHeatmap

Differential expression

Negative binomial modelling, Benjamini–Hochberg correction for multiple testing and, where needed, confounding factors included in the design.

DESeq2edgeRlimma-voom

Functional enrichment

Over-representation of GO terms and KEGG pathways among differential genes, complemented by GSEA on the full ranked gene list.

clusterProfilergProfiler2fgseatopGO

Visualisation and reporting

Publication-ready figures in raster and vector formats, plus a report describing the assumptions, the parameters used and a cautious summary of the results.

ggplot2ComplexHeatmapR Markdown

Output

Expression and differential gene tables · enrichment results · figures · BAM files and bigWig tracks · report and methods description

A typical data flow in an RNA-Seq project. Steps 3 and 4 are chosen depending on whether a reference genome is available.

What we pay attention to

Design and replication

The number of biological replicates affects the outcome more than the choice of software. With three replicates per group we detect clear changes, but weaker effects stay out of statistical reach. If the experiment is still being planned, we are glad to discuss group sizes and sequencing depth before any data exists — that is the cheapest moment for that conversation.

Significance thresholds

By default we treat genes with an adjusted p-value below 0.05 and an absolute log2 fold change of at least 1 as differentially expressed. These thresholds are a convention rather than a law of nature — if your field uses different ones we apply those, and we always report which values were used.

Non-model organisms

Without a reference genome we start from a de novo transcriptome assembly and functional annotation by similarity to protein databases. Interpretation is then more cautious, because some transcripts remain without a reliable functional assignment; we note this in the report.

What you receive

We deliver results in formats you can open without any bioinformatics background, and in parallel in formats suitable for further programmatic analysis. Below is an example file set for a project with a single comparison — the actual list depends on the scope and is agreed in the quote.

To see what such a package looks like in practice, have a look at our worked example: an RNA-Seq analysis of the Arabidopsis drought response — built on simulated data, but in exactly the format and scope a real project delivers.

File	Format	What it contains
multiqc_report.html	HTML	Aggregated quality report for all samples — opens in a browser. Read counts before and after trimming, per sample Percentage of mapped, unique and multi-mapping reads Quality distributions, GC content, duplication levels
counts_raw.tsv	TSV	Raw count matrix: rows are genes, columns are samples. The starting point for any re-analysis. Column 1: gene identifier (e.g. AT1G01010 or ENSG00000141510) Remaining columns: number of reads assigned to that gene in each sample
expression_TPM.xlsx	XLSX	Normalised expression levels, ready to browse in Excel. Sheet "TPM": values normalised for gene length and library depth Sheet "FPKM": alternative normalisation, if your publication uses it Descriptive columns: gene symbol, name, functional description
DE_results_<contrast>.xlsx	XLSX	The main result file: the full differential expression table, one sheet per comparison. gene_id, symbol, description — gene identity and description baseMean — mean normalised expression across all samples log2FoldChange and lfcSE — direction and magnitude of change with standard error pvalue, padj — raw significance and after Benjamini–Hochberg correction regulation — up / down / ns, using the agreed thresholds A separate "parameters" sheet with tool versions and thresholds used
enrichment_GO_KEGG.xlsx	XLSX	Enrichment results, separately for up- and down-regulated genes. GO term or KEGG pathway identifier and name Number of genes from your list in the term and in the background p-value, adjusted value and q-value Full list of genes driving the enrichment
figures/	PNG + PDF	Figures at publication resolution (PNG) and as vectors (PDF/SVG) for further editing. PCA and sample correlation map Volcano plot and MA plot Heatmap of differential genes Enrichment bar and dot plots
alignments/.bam + .bai	BAM	Aligned reads with indexes — you can inspect any gene in a genome browser such as IGV.
tracks/*.bw	bigWig	Library-size normalised coverage tracks for IGV or JBrowse.
methods.docx	DOCX	A ready draft of the Materials and Methods section with all tool versions, parameters and citations — for use in your manuscript.

What we need from you

Raw FASTQ files (gzip-compressed), ideally straight from the sequencer with no prior trimming
A sample table: file name, experimental group, replicate, and any batch information
The species and reference genome version if you have a preference — otherwise we select a current one
A description of your research question and the comparisons you care about
Library details: single-end or paired-end, stranded or not, poly(A) or rRNA-depleted

We agree the transfer method individually — most often we receive data on physical media or set up a dedicated SFTP account. Data is processed on our own servers and on servers rented from established providers.

Quotation and how we work

We quote every project individually. The cost depends mainly on the number of samples, the availability of a reference genome, the number of comparisons and the scope of analyses you ask for.

You tell us what you want to find out and what data you have.
If anything needs clarifying we arrange a short call — free of charge and without obligation.
Within 3–5 working days you receive a quote with the scope of work, the list of result files and a delivery date.
You decide. The quote carries no obligation.

What to expect

For orientation: a standard RNA-Seq analysis of 10–20 samples, from quality control to a differential gene list with GO and KEGG enrichment, typically costs around PLN 4,500 net (approx. EUR 1,100). That assumes an organism with an available reference genome and annotation, one or two main comparisons, and the result file set described above. De novo transcriptome assembly, isoform and splicing analyses, multifactorial designs and time series are quoted separately.

Any figures given are indicative and net of tax; VAT is added according to the applicable regulations. They do not constitute a binding offer. More about how we work in the FAQ.