||5U01CA235510-02 Interpret this number
||University Of Tx Md Anderson Can Ctr
||Computational Tools for Analysis and Visualization of Quality Control Issues in Metabolomic Data
* * * Abstract * * *
In omic studies of all types (e.g., genomic, transcriptomic, proteomic, metabolomic), technical batch effects
pose a fundamental challenge to quality control and reproducibility. The possibilities for serious error are
greatly magnified in metabolomics, however, due to a range of possible platform, operator, instrument, and
environmental factors that can cause batch (or trend) effects. Hence, there is a need for routine surveillance
and correction of batch effects within and across metabolomics laboratories and technological platforms.
Accordingly, we propose here to develop the MetaBatch algorithms, computational tool, and web portal.
For development of MetaBatch, we will leverage our experience in developing MBatch, a tool that became
indispensible for quality-control of data in all 33 projects of The Cancer Genome Atlas (TCGA) program. Our
first aim is to translate the successful quality control model from TCGA to metabolomics by customizing and
extending the MBatch pipeline for detection, quantitation, diagnosis, interpretation, and correction of batch and
trend effects. The second aim is to develop and incorporate innovative metabolomics-specific algorithms,
including major visualization resources such as our interactive Next-Generation Clustered Heat Maps. The
third aim is to distribute MetaBatch to the research community as open-source software and in cloud-based
and Galaxy versions. The fourth aim is to provide plug-in capability for integration of MetaBatch with other
metabolomic resources, prominently including Metabolomics Workbench (in collaboration with Dr. Shankar
Subramaniam) and others developed within the Common Fund Metabolomics Program. Our fifth aim is to
promote MetaBatch actively and interact extensively with other Consortium members and the metabolomics
research community. With active support from MD Anderson Faculty and Academic Development, we will
provide documentation, tutorials, videos, demonstrations, and training to accelerate use and to solicit feedback
on limitations, possible improvements, and additional modules that would be useful in real-world workflows.
We bring a variety of assets to the project, including: the MBatch resource as a starting point for software
development; multidisciplinary expertise in bioinformatics, biostatistics, software engineering, biology, and
clinical medicine; PIs with a combined 21 years of experience in molecular profiling studies of clinical disease
(in a consortial context); international leadership in batch effects analysis; a software engineering team with a
track record of producing high-end, highly visual bioinformatics packages and websites; a team of 20 Analysts
whose expertise can be called on; extensive computing resources, including one of the most powerful
academically based machines in the world; strong institutional support; and close working relationships with
first-class basic, translational, and clinical researchers throughout MD Anderson, one of the foremost cancer
centers in the country. Our bottom-line mission will be to aid the research community's effort to improve rigor
and reproducibility in metabolomics for scientific understanding and to alleviate disease.
Glutaminase Activity of L-Asparaginase Contributes to Durable Preclinical Activity against Acute Lymphoblastic Leukemia.
, Horvath T.D.
, Tan L.
, Link T.
, Harutyunyan K.G.
, Pontikos M.A.
, Anishkin A.
, Du D.
, Martin L.A.
, Yin E.
, et al.
Molecular cancer therapeutics, 2019 09; 18(9), p. 1587-1592.