Sample Annotation
Sample Annotation - Related Videos
08:09
Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Cited by 8 •
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Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
03:36
Genome Annotation and Assembly
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2021
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
06:16
mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Cited by 11 •
2021
Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.
07:55
An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering
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Cited by 4 •
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A novel sample holder for macromolecular X-ray crystallography along with a suitable handling protocol is presented. The system allows crystal growth, crystal soaking and in situ diffraction data collection at both, ambient and cryogenic temperature without the need of any crystal manipulation or mounting.
10:23
Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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Cited by 2 •
2023
Presented here is a detailed protocol for the conduction of three-dimensional cephalometric analysis with the use of human cone beam computed tomography scans.
FAQs
Reliable annotation depends on recording consistent fields such as specimen or patient identifier, collection time, tissue type, treatment status, and observed features. Using the same metadata categories across samples makes records easier to trace and compare. In medical datasets, this consistency supports quality control and more dependable downstream analysis.
Controlled vocabularies and structured databases address different parts of the same problem. Controlled vocabularies standardize the terms used for labels, while structured databases organize those labels with associated sample information. Together, they reduce variation between records and make comparisons across studies more meaningful, especially when clinical or biological datasets must be reviewed as a group.
Treatment status provides context about how a sample relates to an intervention, whereas observed features describe characteristics identified in the specimen or dataset. Keeping these categories distinct preserves the difference between sample history and sample findings. That separation can help clinical researchers compare specimens more accurately and develop better-supported diagnostic or predictive analyses.
A practical workflow records the sample or patient identifier, collection time, tissue type, treatment status, and observed features in a structured format. Standardized terms or controlled vocabularies should be applied consistently, and the completed record should remain linked to the sample. This approach improves traceability and prepares the material for quality-controlled analysis.
Medical applications include biobanking, pathology, and clinical research. In each setting, annotations connect specimens with relevant descriptive information so samples can be organized, compared, and analyzed systematically. The same principle also supports genomic and imaging datasets, where consistent metadata helps researchers evaluate results across collections or studies.
Well-organized annotations give researchers a consistent way to relate sample characteristics, treatment status, and observed features to study data. This organization supports the development of diagnostic methods and predictive models by improving comparison across samples. It also contributes to evidence-based treatment research by making clinical and biological datasets more interpretable and reliable.