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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Jul 5, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Challenges and best practices in omics benchmarking.

Thomas G Brooks¹, Nicholas F Lahens¹, Antonijo Mrčela¹

¹Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.

Nature Reviews. Genetics

|January 12, 2024

Summary

This summary is machine-generated.

Omics data analysis requires robust benchmarking to ensure reliable results. This review addresses common pitfalls in omics benchmarking and provides guidelines for transparent and meaningful performance evaluation.

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Last Updated: Jul 5, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

The omics era, driven by technologies like microarrays and high-throughput sequencing, generates vast biological datasets.
Omics benchmarking, the evaluation of analytical methods for transcriptomics, proteomics, and metabolomics, has a 25-year history.
Despite maturity, challenges persist in generating meaningful benchmarking data and evaluating performance.

Purpose of the Study:

To identify and highlight common oversights and pitfalls in omics benchmarking.
To propose a methodology for addressing and transparently reporting benchmarking challenges.
To provide guidance on recent developments and common difficulties in omics data analysis.

Main Methods:

Review of existing omics benchmarking studies and methodologies.
Development of a comprehensive reporting guideline template (spreadsheet).
Survey of recent advancements and common challenges in the field.

Main Results:

Identification of recurring issues and potential biases in omics benchmarking practices.
Establishment of a structured approach for transparent reporting of benchmarking results.
Provision of practical guidance for researchers facing common omics data analysis challenges.

Conclusions:

Standardized and transparent omics benchmarking is crucial for reliable biological data analysis.
The proposed guidelines aim to improve the quality and interpretability of omics benchmarking studies.
Addressing common pitfalls will enhance the utility of omics data in scientific discovery.