From sequencing data to biological interpretation.
Data processing follows the study question. Data quality, analytical criteria and references must be considered together so that the results can be interpreted.
A step connected to sampling design.
The meaning of a set of sequences depends on how the samples were collected and analyzed. Bioinformatics must consider the objective, markers, controls and records accompanying the material.
The scope therefore goes beyond running a tool on files. It starts with an assessment of the available data and the information they can support.
Quality before interpretation.
- Organization: link files to samples, controls and study records.
- Quality control: assess data characteristics and apply criteria compatible with the approach.
- Processing: define steps and parameters consistent with the markers and objective.
- Interpretation: present results alongside the criteria used and their limitations.
Comparison is more than finding similarity.
In identification and metabarcoding studies, sequences are compared with reference databases. The coverage of these databases and the quality of their records influence the level of identification possible.
Assignment criteria must be defined and documented. When the data do not support species-level identification, interpretation must preserve the taxonomic resolution achieved, without filling gaps with assumptions.
Bioinformatics in metabarcoding ↗Information that can be examined.
The proposal defines the formats and level of interpretation of the deliverables. Results should be organized so that they can be linked to the samples and the choices guiding the analysis can be understood.
Comparisons between sampling campaigns or locations also depend on compatible sampling and molecular procedures. Processing does not automatically correct differences introduced at these stages.
We need to understand the data and the study.
To discuss the scope, gather the objective, data type, markers used, sampling records and information about controls and previous processing, when available.
Incomplete data or insufficient data quality can limit the analysis. A feasibility assessment guides what can be done; there is no advance guarantee of recovering all the desired information.
Explore the approaches integrated into the study ↗Shall we connect your data to the project question?
Tell us about the project context, environment and the question you need to answer. An initial conversation helps define the approach and scope.