Inferred from High Throughput Experiment (HTP): Difference between revisions

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*A published reference should always be cited in the reference column, and no value should be entered into the with/from column of HTP annotations.
*A published reference should always be cited in the reference column, and no value should be entered into the with/from column of HTP annotations.
== General guidance for HTP experiments ==
=== What qualifies as HTP data? ===
The term high-throughput data is often used to describe data that has been generated by automatic or semi-automatic methodology without validation of the results for individual gene products. The experiments can be viewed as screens: experiments performed in parallel without explicit target selection; they are generally not hypothesis-driven.
As HTP datasets are generated from the scaling of experimental techniques used for hypothesis-driven approaches, the type of experiment itself cannot be used to define a HTP experiment. Characteristics that are often associated with HTP experiments should be used to guide the curator’s decision as to whether it should be classed as HTP for the purposes of annotation. These include:
*Applying the same workflow to a large number genes/gene products.
*Generating data in an automated or semi-automated fashion.
*Addressing open-ended rather than hypothesis-driven questions.
*Generating dataset(s) (usually presented in tabular form).
*Datasets containing ‘false positives’.
*Ascribing the same property to all gene products that fall within a given measurement range.
HTP evidence codes can be used to annotate high-quality high-throughput studies. In cases where this can be determined, the dataset should contain less than 1% of false positives. The HTP evidence codes map directly to their low-throughput counterparts and to ensure consistency, the same rules applied for annotation regardless of throughput, ensuring consistency.


== Examples of Usage ==
== Examples of Usage ==

Revision as of 07:11, 23 February 2018

HTP: High Throughput Experiment

Overview

HTP: Inferred from High Throughput Experiment

  • This code is used in an annotation to indicate that an high throughput experimental assay has been located in the cited reference, whose results indicate a gene product's function, process involvement, or subcellular location (indicated by the GO term). The HTP code is equivalent to the conventional EXP code.
  • The HTP code is the parent code for the HDA, HMP, HGI and HEP high throughput experimental codes.
  • The HTP evidence code can be used where any of the high throughput assays described for the HDA, HMP, HGI, or HEP evidence codes is reported. However it is highly encouraged that groups should annotate to one of the more specific experimental codes (HDA, HMP, HGI, or HEP) instead of HTP.
  • A published reference should always be cited in the reference column, and no value should be entered into the with/from column of HTP annotations.

General guidance for HTP experiments

What qualifies as HTP data?

The term high-throughput data is often used to describe data that has been generated by automatic or semi-automatic methodology without validation of the results for individual gene products. The experiments can be viewed as screens: experiments performed in parallel without explicit target selection; they are generally not hypothesis-driven. As HTP datasets are generated from the scaling of experimental techniques used for hypothesis-driven approaches, the type of experiment itself cannot be used to define a HTP experiment. Characteristics that are often associated with HTP experiments should be used to guide the curator’s decision as to whether it should be classed as HTP for the purposes of annotation. These include:

  • Applying the same workflow to a large number genes/gene products.
  • Generating data in an automated or semi-automated fashion.
  • Addressing open-ended rather than hypothesis-driven questions.
  • Generating dataset(s) (usually presented in tabular form).
  • Datasets containing ‘false positives’.
  • Ascribing the same property to all gene products that fall within a given measurement range.

HTP evidence codes can be used to annotate high-quality high-throughput studies. In cases where this can be determined, the dataset should contain less than 1% of false positives. The HTP evidence codes map directly to their low-throughput counterparts and to ensure consistency, the same rules applied for annotation regardless of throughput, ensuring consistency.


Examples of Usage

Quality Control Checks

Evidence and Conclusion Ontology

ECO:0006056 high throughput evidence used in manual assertion

Review Status

Back to: Guide to GO Evidence Codes