Illuminating the dark matter in metabolomics

By R. D. da Silva, Pieter C. Dorrestein, R. Quinn. 2015

484 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. David S. Wishart
2. Tobias Kind
3. Oliver Fiehn
4. I. Blaženović
5. Jian Ji
6. Anchi Guo
7. Craig Knox
8. Yifeng Liu

In Spectroscopy

1. David S. Wishart
2. Oliver Fiehn
3. Tobias Kind
4. R. Mandal
5. R. Greiner
6. Carin Li
7. I. Blaženović
8. Jian Ji

Citations in the corpus

• Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, background): “However, a large percentage of these signals remain structurally unknown [5], and therefore compound identification remains one of the large obstacles in metabolomics [6,7].”
• HMDB 5.0: the Human Metabolome Database for 2022 (2021, background): “In many cases, the number of m/z features in untargeted MS-based human metabolomics studies that can be confidently identified is typically <2% (11).”
• MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools (2019, background): “Although significant advances have been made in molecular mining workflows, chemical annotation as well as classification tools [1–4,7,8,10,14–16], chemical structure annotation remains the major and most challenging bottleneck in mass spectrometry-based metabolomics as most of our biological interp”
• HMDB 4.0: the human metabolome database for 2018 (2017, background): “of confidently identified MS peaks in most metabolomic studies are being identified, even via simple mass matching (16,17).”