WELCOME TO THE MAQC SOCIETY
New MAQC Society Project Proposal
Inflammation as class of biological mechanisms underlies a spectrum of diseases and poses a unique opportunity to understand both its fundamental (core) and disease-state specific features. With the emergence of single-cell sequencing (e.g., scRNA-seq), there are now competing methods to traditional immunohistochemstry (IHC) tests, single- to multiplexed ELISAs, and flow cytometry methods for identifying and classifying individual cell types of interest. Therefore, we would like to form a MAQC Society project and Working Group(s) devoted to assays associated with inflammatory diseases. For more information go to the
New MAQC Society Projects page.
Due to the international COVID19 pandemic, the MAQC Society rescheduled the MAQC Society 2020 Meeting for April 28-30, 2021. The new meeting in 2021 will be virtual. The MAQC Society 2021 meeting will be a joint virtual meeting with a sister society, MCBIOS, that will hold its meeting from April 26-28, 2021. Click on the button below to submit abstracts, register and get additional info about the joint meeting.
The objective of the MAQC Society is to communicate, promote, and advance reproducible science principles and quality control for analysis of the massive data generated from the existing and emerging technologies in solving biological, health, and medical problems. Thus, the goals of the Society are to (1) advocate and facilitate the development and application of quality control practice and standard analysis protocols of bioinformatics and biostatistics for enhanced reproducibility across multiple experiments, laboratories, and data analysis methods, and (2) advance our understanding and best practices in the analysis of massive data from emerging technologies applied in drug development, clinical application, and safety/risk assessment.
MAQC SOCIETY AT A GLANCE
A Bit of Background
Historically, the MAQC/SEQC has been a US FDA‐led community-wide consortium effort to address issues relating to the application of constantly evolving high‐throughput genomics technologies to either assess safety and efficacy of FDA regulated products or their safe and effective use in clinical applications as in vitro diagnostic devices. The MAQC consortium completed three projects between 2005 ‐2014 (namely MAQC I, II and III/SEQC), resulting in ~30 publications, one third of which were published in Nature Biotechnology. Furthermore, two of these papers were among the most cited in Nature Biotechnology in the last 20 years. The MAQC Society is currently completing work on SEQC2 resulting in additional publications in Nature Biotechnology and other journals regarding best practices in DNA analysis. Additional information about MAQC Consortium projects is available here. The success enjoyed by consortium members led us to create a new society whose mission is to promote reproducible scientific research, especially research that involves analysis of massive data sets but which include additional areas benefiting human health: medical and pathology imaging, proteomics and other -omics, large health-related studies and databases, etc.
Recent Activities and Meetings
MAQC Workshops, Key Publications, and Society Meetings
Inaugural Meeting of Little Rock Chapter of MAQC Society
Sept 7-8, 2018
Comprehensive Assessments of NGS Onco-panel Technologies
Univ of Arkansas for Medical Sciences (UAMS), Little Rock, AR
Inaugural Meeting of the MAQC Society
April 12, 2017
SAS Institute, Cary, NC
SEQC Nature Biotechnology and other Publications
Su Z, Łabaj PP, Li S, Thierry-Mieg J, Thierry-Mieg D, Shi W, Wang C, Schroth GP, Setterquist RA, Thompson JF, Jones WD. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature biotechnology. 2014 Sep;32(9):903.
Li S, Łabaj PP, Zumbo P, Sykacek P, Shi W, Shi L, Phan J, Wu PY, Wang M, Wang C, Thierry-Mieg D. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nature biotechnology. 2014 Sep;32(9):888.
Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nature biotechnology. 2014 Sep;32(9):926.
Gong B, Wang C, Su Z, Hong H, Thierry-Mieg J, Thierry-Mieg D, Shi L, Auerbach SS, Tong W, Xu J. Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq. Scientific data. 2014 Aug 26;1:140021.
Munro SA, Lund SP, Pine PS, Binder H, Clevert DA, Conesa A, Dopazo J, Fasold M, Hochreiter S, Hong H, Jafari N. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nature communications. 2014 Sep 25;5:5125.
MAQC-II Nature Biotechnology and Pharmacogenomics Publications
Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy Jr JD, et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature biotechnology. 2010 Aug;28(8):827.
Tillinghast GW. Microarrays in the clinic. Nature biotechnology. 2010 Aug;28(8):810.
Fan X, Lobenhofer EK, Chen M, Shi W, Huang J, Luo J, Zhang J, Walker SJ, Chu TM, Li L, Wolfinger R. Consistency of predictive signature genes and classifiers generated using different microarray platforms. The pharmacogenomics journal. 2010 Aug;10(4):247.
Parry, R.M., Jones, W. D., Stokes, T. H., et al. (2010) k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction, Pharmacogenomics, J 10: 292-309; doi:10.1038/ tpj.2010.56.
Shi W, Bessarabova M, Dosymbekov D, Dezso Z, Nikolskaya T, Dudoladova M, Serebryiskaya T, Bugrim A, Guryanov A, Brennan RJ, Shah R. Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. The pharmacogenomics journal. 2010 Aug;10(4):310.
Oberthuer A, Juraeva D, Li L, Kahlert Y, Westermann F, Eils R, Berthold F, Shi L, Wolfinger RD, Fischer M, Brors B. Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients. The pharmacogenomics journal. 2010 Aug;10(4):258.
Luo J, Schumacher M, Scherer A, Sanoudou D, Megherbi D, Davison T, Shi T, Tong W, Shi L, Hong H, Zhao C. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. The pharmacogenomics journal. 2010 Aug;10(4):278-91.
Hong H, Shi L, Su Z, Ge W, Jones WD, Czika W, Miclaus K, Lambert CG, Vega SC, Zhang J, Ning B. Assessing sources of inconsistencies in genotypes and their effects on genome-wide association studies with HapMap samples. The pharmacogenomics journal. 2010 Aug;10(4):364.
10th MAQC Project Meeting - Inaugural SEQC Meeting
Dec 16-17, 2008
US FDA, Silver Spring, MD
9th MAQC Project Meeting
Sept 18-19, 2008
Best Practices for Developing and Validating Microarray-Based Predictive Models
US FDA, 10903 New Hampshire Ave, Silver Spring MD
Reproducibility, Sensitivity, and Specificity Publication from MAQC
Shi, L, Jones, WD, Jensen, RV, et al. (2008). The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies, BMC Bioinformatics, 9(Suppl 9):S10.
8th MAQC Project Meeting
March 24-26, 2008
Development and Validation of Predictive Models
US FDA, Rockville, MD
7th MAQC Project Meeting
May 24-25, 2007
Development and Validation of Predictive Models
SAS Institute, Cary, NC
6th MAQC Project Meeting
Nov 28-29, 2006
Washington, Marriott Hotel and the FDA White Oak Facility, Silver Spring, MD
MAQC Nature Biotechnology Publications
Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, De Longueville F, Kawasaki ES, Lee KY, Luo Y, et al. The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nature biotechnology. 2006 Sep;24(9):1151.
Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, Hurban P. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature biotechnology. 2006 Sep;24(9):1162.
Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, Boysen C, Hunkapiller K, Jensen RV, Knight CR, Lee KY, Ma Y. Evaluation of DNA microarray results with quantitative gene expression platforms. Nature biotechnology. 2006 Sep;24(9):1115.
Shippy R, Fulmer-Smentek S, Jensen RV, Jones WD, Wolber PK, Johnson CD, Pine PS, Boysen C, Guo X, Chudin E, Sun YA. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nature biotechnology. 2006 Sep;24(9):1123.
Patterson TA, Lobenhofer EK, Fulmer-Smentek SB, Collins PJ, Chu TM, Bao W, Fang H, Kawasaki ES, Hager J, Tikhonova IR, Walker SJ. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nature biotechnology. 2006 Sep;24(9):1140.
Tong W, Lucas AB, Shippy R, Fan X, Fang H, Hong H, Orr MS, Chu TM, Guo X, Collins PJ, Sun YA. Evaluation of external RNA controls for the assessment of microarray performance. Nature biotechnology. 2006 Sep;24(9):1132.
5th Face-to-Face MAQC Consortium Meeting
Sept 21, 2006
National Center for Toxicological Research, FDA, Jefferson, AR
4th Face-to-Face MAQC Consortium Meeting
Feb 3-4, 2006
UMass, Boston, MA
3rd Face-to-Face MAQC Consortium Meeting
Dec 1-2, 2005
Palo Alto, CA
Initial MAQC Consortium Meeting
May 2-3, 2005
The MAQC Project: Calibrated RNA Samples, Reference Datasets, and QC Metrics/Thresholds
FDA Parklawn Building, 5600 Fisher's Lane, Rockville, MD 20857
Kohn's Second Law: An experiment is reproducible until another laboratory tries to repeat it
We should be asking, 'How much of an effect is there?', not 'Is there an effect?'”
MAQC Society Board of Directors
A Board of Directors is chartered to develop the Society and oversee the Society’s activities. Specifically, the Board is dedicated to building and strengthening the reproducible science practice by (1) engaging scientists in academia, industry, and government, (2) fostering collaboration and harmonization of education, research tools, and new technologies, and (3) building the global-scientific infrastructure needed to advance and promote reproducible science.
L-R Back Row: Dr. Weida Tong, Dr. Joaquin Dopazo, Dr. Cesare Furlanello, Dr. Leming Shi, Dr. Matthias Fischer, Dr. Christopher Mason, Dr. Benjamin Haibe-Kains, Dr. Russell Wolfinger, Dr. Wendell Jones
L-R Front Row: Dr. Wenjun Bao, Dr. Rebecca Kusko, Dr. Shraddha Thakkar, Dr. Susanna Sansone