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Chou, Michael

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Chou

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Chou, Michael
Author's Publications: search.filters.isAuthorOfPublication.1151be97-0115-43e9-ad2d-032af6c2fac1

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Now showing 1 - 7 of 7
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    Harvard Personal Genome Project: lessons from participatory public research
    (BioMed Central, 2014) Ball, Madeleine; Bobe, Jason R; Chou, Michael; Clegg, Tom; Estep, Preston W; Lunshof, Jeantine; Vandewege, Ward; Zaranek, Alexander Wait; Church, George
    Background: Since its initiation in 2005, the Harvard Personal Genome Project has enrolled thousands of volunteers interested in publicly sharing their genome, health and trait data. Because these data are highly identifiable, we use an ‘open consent’ framework that purposefully excludes promises about privacy and requires participants to demonstrate comprehension prior to enrollment. Discussion Our model of non-anonymous, public genomes has led us to a highly participatory model of researcher-participant communication and interaction. The participants, who are highly committed volunteers, self-pursue and donate research-relevant datasets, and are actively engaged in conversations with both our staff and other Personal Genome Project participants. We have quantitatively assessed these communications and donations, and report our experiences with returning research-grade whole genome data to participants. We also observe some of the community growth and discussion that has occurred related to our project. Summary We find that public non-anonymous data is valuable and leads to a participatory research model, which we encourage others to consider. The implementation of this model is greatly facilitated by web-based tools and methods and participant education. Project results are long-term proactive participant involvement and the growth of a community that benefits both researchers and participants.
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    An unbiased index to quantify participant’s phenotypic contribution to an open-access cohort
    (Nature Publishing Group, 2017) Chan, Yingleong; Tung, Michael; Garruss, Alexander; Zaranek, Sarah W.; Chan, Ying Kai; Lunshof, Jeantine; Zaranek, Alexander W.; Ball, Madeleine P.; Chou, Michael; Lim, Elaine T.; Church, George
    The Personal Genome Project (PGP) is an effort to enroll many participants to create an open-access repository of genome, health and trait data for research. However, PGP participants are not enrolled for studying any specific traits and participants choose the phenotypes to disclose. To measure the extent and willingness and to encourage and guide participants to contribute phenotypes, we developed an algorithm to score and rank the phenotypes and participants of the PGP. The scoring algorithm calculates the participation index (P-index) for every participant, where 0 indicates no reported phenotypes and 100 indicate complete phenotype reporting. We calculated the P-index for all 5,015 participants in the PGP and they ranged from 0 to 96.7. We found that participants mainly have either high scores (P-index > 90, 29.5%) or low scores (P-index < 10, 57.8%). While, there are significantly more males than female participants (1,793 versus 1,271), females tend to have on average higher P-indexes (P = 0.015). We also reported the P-indexes of participants based on demographics and states like Missouri and Massachusetts have better P-indexes than states like Utah and Minnesota. The P-index can therefore be used as an unbiased way to measure and rank participant’s phenotypic contribution towards the PGP.
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    Using Bacteria to Determine Protein Kinase Specificity and Predict Target Substrates
    (Public Library of Science, 2012) Chou, Michael; Prisic, Sladjana; Lubner, Joshua M.; Church, George; Husson, Robert; Schwartz, Daniel
    The identification of protein kinase targets remains a significant bottleneck for our understanding of signal transduction in normal and diseased cellular states. Kinases recognize their substrates in part through sequence motifs on substrate proteins, which, to date, have most effectively been elucidated using combinatorial peptide library approaches. Here, we present and demonstrate the ProPeL method for easy and accurate discovery of kinase specificity motifs through the use of native bacterial proteomes that serve as in vivo libraries for thousands of simultaneous phosphorylation reactions. Using recombinant kinases expressed in E. coli followed by mass spectrometry, the approach accurately recapitulated the well-established motif preferences of human basophilic (Protein Kinase A) and acidophilic (Casein Kinase II) kinases. These motifs, derived for PKA and CK II using only bacterial sequence data, were then further validated by utilizing them in conjunction with the scan-x software program to computationally predict known human phosphorylation sites with high confidence.
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    Clinical Assessment Incorporating a Personal Genome
    (Elsevier BV, 2010-05) Ashley, Euan A; Butte, Atul J; Wheeler, Matthew T; Chen, Rong; Klein, Teri E; Dewey, Frederick E; Dudley, Joel T; Ormond, Kelly E; Pavlovic, Aleksandra; Morgan, Alexander A; Pushkarev, Dmitry; Neff, Norma F; Hudgins, Louanne; Gong, Li; Hodges, Laura M; Berlin, Dorit S; Thorn, Caroline F; Sangkuhl, Katrin; Hebert, Joan M; Woon, Mark; Sagreiya, Hersh; Whaley, Ryan; Knowles, Joshua W; Chou, Michael; Thakuria, Joseph V; Rosenbaum, Abraham M; Zaranek, Alexander Wait; Church, George; Greely, Henry T; Quake, Stephen R; Altman, Russ B
    Background The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context. Methods We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patient's full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre-test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks. Findings Analysis of 2·6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death—TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he might have a low initial dosing requirement for warfarin. Many variants of uncertain importance were reported. Interpretation Although challenges remain, our results suggest that whole-genome sequencing can yield useful and clinically relevant information for individual patients.
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    Predicting Protein Post-translational Modifications Using Meta-analysis of Proteome Scale Data Sets
    (Elsevier BV, 2009-02) Schwartz, Daniel; Chou, Michael; Church, George
    Protein post-translational modifications are an important biological regulatory mechanism, and the rate of their discovery using high throughput techniques is rapidly increasingly. To make use of this wealth of sequence data, we introduce a new general strategy designed to predict a variety of post-translational modifications in several organisms. We used the motif-x program to determine phosphorylation motifs in yeast, fly, mouse, and man and lysine acetylation motifs in man. These motifs were then scanned against proteomic sequence data using a newly developed tool called scan-x to globally predict other potential modification sites within these organisms. 10-fold cross-validation was used to determine the sensitivity and minimum specificity for each set of predictions, all of which showed improvement over other available tools for phosphoprediction. New motif discovery is a byproduct of this approach, and the phosphorylation motif analyses provide strong evidence of evolutionary conservation of both known and novel kinase motifs.
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    Extensive Phosphorylation With Overlapping Specificity by Mycobacterium Tuberculosis Serine/ Threonine Protein Kinases
    (National Academy of Sciences, 2010-04-20) Prisic, Sladjana; Dankwa, Selasi; Schwartz, Dana; Chou, Michael; Locasale, JW; Kang, CM; Bemis, G; Church, George; Steen, Hanno; Husson, RN
    The Mycobacterium tuberculosis genome encodes 11 serine/threonine protein kinases (STPKs) that are structurally related to eukaryotic kinases. To gain insight into the role of Ser/Thr phosphorylation in this major global pathogen, we used a phosphoproteomic approach to carry out an extensive analysis of protein phosphorylation in M. tuberculosis. We identified more than 500 phosphorylation events in 301 proteins that are involved in a broad range of functions. Bioinformatic analysis of quantitative in vitro kinase assays on peptides containing a subset of these phosphorylation sites revealed a dominant motif shared by six of the M. tuberculosis STPKs. Kinase assays on a second set of peptides incorporating targeted substitutions surrounding the phosphoacceptor validated this motif and identified additional residues preferred by individual kinases. Our data provide insight into processes regulated by STPKs in M. tuberculosis and create a resource for understanding how specific phosphorylation events modulate protein activity. The results further provide the potential to predict likely cognate STPKs for newly identified phosphoproteins.
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    A Public Resource Facilitating Clinical Use of Genomes
    (National Academy of Sciences, 2012-07-24) Ball, Madeleine P.; Thakuria, Joseph V.; Zaranek, Alexander Wait; Clegg, Tom; Rosenbaum, Abraham M.; Wu, Xiaodi; Angrist, Misha; Bhak, Jong; Bobe, Jason R; Callow, Matthew J.; Cano, Carlos; Chou, Michael; Chung, Wendy K.; Douglas, Shawn M.; Estep, Preston W.; Gore, Athurva; Hulick, Peter; Labarga, Alberto; Lee, Je-Hyuk; Lunshof, Jeantine E.; Kim, Byung Chul; Kim, Jong-Il; Li, Zhe; Murray, Michael F; Nilsen, Geoffrey B.; Peters, Brock A.; Raman, Anugraha M.; Rienhoff, Hugh Y.; Robasky, Kimberly; Wheeler, Matthew T.; Vandewege, Ward; Vorhaus, Daniel B.; Yang, Joyce L.; Yang, Luhan; Aach, John; Ashley, Euan A.; Drmanac, Radoje; Kim, Seong-Jin; Li, Jin Billy; Peshkin, Leonid; Seidman, Christine; Seo, Jeong-Sun; Zhang, Kun; Rehm, Heidi; Church, George
    Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved “open consent” process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain—we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.