Presence/absence of an organism

Verify the presence of an organism in a sample

In certain scientific or technical contexts, it is necessary to determine whether a particular organism is present or not in a sample.

Genetic analysis makes it possible to identify this organism by searching for a specific DNA sequence unique to it.

Thanks to molecular biology techniques, it becomes possible to detect a target species even when it is present in very small quantities in a given environment.

This approach is used to analyze environmental, biological, or food samples and verify the presence of a specific organism.

When should you test for the presence of an organism?

Targeted detection of an organism can be useful in different situations:

  • verify the presence of a particular species in an environment,
  • detect a specific microorganism in a sample,
  • check for the presence of a pathogenic or undesirable organism,
  • confirm the existence of a species in the environment being studied.

These analyses provide a clear answer: the organism sought is present or absent in the sample.

An analysis tailored to each environment studied

The choice of analysis strategy depends in particular on:

  • the type of organism sought,
  • the nature of the sample (water, soil, biological sample, etc.),
  • the sensitivity required for detection.

Depending on these parameters, different molecular biology techniques can be used to detect the presence of an organism accurately.

How can a specific organism be detected?

Detection generally relies on identifying a genetic sequence characteristic of the organism being studied.

Targeted amplification by PCR

PCR (Polymerase Chain Reaction) is used to amplify a specific DNA region unique to the organism sought.
If this sequence is detected after amplification, this confirms the presence of the organism in the sample.

Sensitive detection by quantitative PCR

Quantitative PCR (qPCR) not only detects a target genetic sequence, but also identifies organisms present in very small quantities in a sample.

This approach offers high sensitivity and provides reliable results even in complex environments.

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