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1. Differential splicing across immune system lineages

Author

Ergun, Ayla, Doran, Graeme, Costello, James C, Paik, Henry H, Collins, James J, Mathis, Diane, Benoist, Christophe, Blair, David A, Dustin, Michael L, Shinton, Susan A, Hardy, Richard R, Shay, Tal, Regevc, Aviv, Cohen, Nadia, Brennan, Patrick, Brenner, Michael, Kim, Francis, Rao, Tata Nageswara, Wagers, Amy, Heng, Tracy, Ericson, Jeffrey, Rothamel, Katherine, Ortiz-Lopez, Adriana, Kreslavsky, Taras, Fletcher, Anne, Elpek, Kutlu, Bellemare-Pelletier, Angelique, Malhotra, Deepali, Turley, Shannon, Miller, Jennifer, Brown, Brian, Merad, Miriam, Gautier, Emmanuel L, Jakubzick, Claudia, Randolph, Gwendalyn J, Monach, Paul, Best, Adam J, Knell, Jamie, Goldrath, Ananda, Jojic, Vladimir, Koller, Daphne, Laidlaw, David, Collins, Jim, Gazit, Roi, Rossi, Derrick J, Malhotra, Nidhi, Sylvia, Katelyn, Kang, Joonsoo, Bezman, Natalie A, Sun, Joseph C, Min-Oo, Gundula, Kim, Charlie C, and Lanier, Lewis L

Subjects
Genetics, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Inflammatory and immune system, Alternative Splicing, B-Lymphocytes, Cell Lineage, Humans, T-Lymphocytes, ImmGen Consortium
Abstract

Alternative splicing (AS) allows increased diversity and orthogonal regulation of the transcriptional products of mammalian genomes. To assess the distribution and variation of alternative splicing across cell lineages of the immune system, we comprehensively analyzed RNA sequencing and microarray data generated by the Immunological Genome Project Consortium. AS is pervasive: 60% of genes showed frequent AS isoforms in T or B lymphocytes, with 7,599 previously unreported isoforms. Distinct cell specificity was observed, with differential exon skipping in 5% of genes otherwise coexpressed in both B and T cells. The distribution of isoforms was mostly all or none, suggesting on/off switching as a frequent mode of AS regulation in lymphocytes. From the identification of differential exon use in the microarray data, clustering of exon inclusion/exclusion patterns across all Immunological Genome Project cell types showed that ∼70% of AS exons are distributed along a common pattern linked to lineage differentiation and cell cycling. Other AS events distinguished myeloid from lymphoid cells or affected only a small set of exons without clear lineage specificity (e.g., Ptprc). Computational analysis predicted specific associations between AS exons and splicing regulators, which were verified by detection of the hnRPLL/Ptprc connection.

Published
2013

2. Optimization of Advanced Molecular Genetic Testing Utilization in Hematopathology: A Goldilocks Approach to Bone Marrow Testing.

Author

AlJabban, Ali, Paik, Henry, Aster, Jon C., Berliner, Nancy, Brouillard, Jennifer, Brown, Jennifer R., Burns, Kathleen H., Castillo, Jorge J., Card, James, Dal Cin, Paola, DeAngelo, Daniel J., Dorfman, David M., Ebert, Benjamin L., Garcia, Jacqueline S., Jacobson, Caron A., Lakhani, Hakim, Laubach, Jacob P., Ligon, Azra H., Lindeman, Neal I., and Lindsley, R. Coleman

Subjects
CLINICAL pathology, MOLECULAR diagnosis, PREDICTIVE tests, GENETIC testing, RETROSPECTIVE studies, WORKFLOW, COST effectiveness, DECISION making, DESCRIPTIVE statistics, CHI-squared test, RESEARCH funding, DATA analysis software, BONE marrow examination, ALGORITHMS, LONGITUDINAL method
Abstract

PURPOSE This study investigated the effectiveness of algorithmic testing in hematopathology at the Brigham and Women's Hospital and Dana-Farber Cancer Institute (DFCI). The algorithm was predicated on test selection after an initial pathologic evaluation tomaximize cost-effective testing, especially for expensive molecular and cytogenetic assays. MATERIALS AND METHODS Standard ordering protocols (SOPs) for 17 disease categories were developed and encoded in a decision support application. Six months of retrospective data from application beta testing was obtained and compared with actual testing practices during that timeframe. In addition, 2 years of prospective data were also obtained from patients at one community satellite site. RESULTS A total of 460 retrospective cases (before introduction of algorithmic testing) and 109 prospective cases (following introduction)were analyzed. In the retrospective data, 61.7% of tests (509 of 825) were concordant with the SOPs while 38.3% (316 of 825) were overordered and 30.8%(227 of 736) of SOP-recommended tests were omitted. In the prospective data, 98.8% of testing was concordant (244 of 247 total tests) with only 1.2%overordered tests (3 of 247) and 7.6%omitted tests (20 of 264 SOP-recommended tests; overall P < .001). The cost of overordered tests before implementing SOP indicates a potential annualized saving of $1,347,520 in US dollars (USD) in overordered testing at Brigham and Women's Hospital/DFCI. Only two of 316 overordered tests (0.6%) returned any additional information, both for extremely rare clinical circumstances. CONCLUSION Implementation of SOPs dramatically improved test ordering practices, with a just right number of ancillary tests that minimizes cost and has no significant impact on acquiring key informative test results. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Supplementary Table S1 from Prognostic DNA Methylation Biomarkers in Ovarian Cancer

Author

Wei, Susan H., primary, Balch, Curtis, primary, Paik, Henry H., primary, Kim, Yoo-Sung, primary, Baldwin, Rae Lynn, primary, Liyanarachchi, Sandya, primary, Li, Lang, primary, Wang, Zailong, primary, Wan, Joseph C., primary, Davuluri, Ramana V., primary, Karlan, Beth Y., primary, Gifford, Gillian, primary, Brown, Robert, primary, Kim, Sun, primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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4. Supplementary Table 2 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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5. Supplementary Table 4 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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6. Supplementary Table 3 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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7. Supplementary Figure Legends 1-3 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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8. Supplementary Figures 1-3 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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9. Supplementary Table 5 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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10. Supplementary Table 6 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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11. Supplementary Table 1 from Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., primary, Hartman-Frey, Cori, primary, Chen, Lei, primary, Paik, Henry, primary, Oyer, Samuel L., primary, Salisbury, Jonathan D., primary, Cheng, Alfred S.L., primary, Li, Lang, primary, Abbosh, Phillip H., primary, Huang, Tim H-M., primary, and Nephew, Kenneth P., primary

Published
2023
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12. ImmGen at 15

Author

Aguilar, Stephanie Vargas, Aguilar, Oscar, Allan, Rhys, Amir, El Ad David, Angeli, Veronique, Artyomov, Maxim, Asinovski, Natasha, Astarita, Jilian, Austen, K. Frank, Bajpai, Geetika, Barrett, Nora, Baysoy, Alev, Benoist, Christophe, Bellemare-Pelletier, Angelique, Berg, Brad, Best, Adam, Bezman, Natalie, Blair, David, Blander, Julie, Bogunovic, Milena, Brennan, Patrick, Brenner, Michael, Brown, Brian, Buechler, Matthew, Buenrostro, Jason, Casanova, Maria Acebes, Choi, Kyunghee, Chow, Andrew, Chudnovskiy, Aleksey, Cipoletta, Daniela, Cohen, Nadia, Collins, James, Colonna, Marco, Cook, Alison, Costello, James, Cremasco, Viviana, Crowl, Ty, Crozat, Karine, Cruse, Richard, D’angelo, June, Dalod, Marc, Davis, Scott, Demiralp, Cagatay, Deng, Tianda, Desai, Jigar, Desland, Fiona, Dhainaut, Maxime, Ding, Jiarui, Doedens, Andrew, Dominguez, Claudia, Doran, Graeme, Dress, Regine, Dustin, Michael, Dwyer, Daniel, Dzhagalov, Ivan, Elpek, Kutlu, Ergun, Ayla, Ericson, Jeff, Esomonu, Eunice, Fairfax, Keke, Fletcher, Anne, Frascoli, Michela, Fuchs, Anja, Gainullina, Anastasiia, Gal-Oz, Shani, Gallagher, Michael, Gautier, Emmanuel, Gazit, Roi, Gibbings, Sophie, Giraud, Matthieu, Ginhoux, Florent, Goldrath, Ananda, Gotthardt, Dagmar, Gray, Daniel, Greter, Melanie, Grieshaber-Bouyer, Ricardo, Guilliams, Martin, Haidermota, Sara, Hardy, Randy, Hashimoto, Daigo, Helft, Julie, Hendricks, Deborah, Heng, Tracy, Hill, Jonathan, Hyatt, Gordon, Idoyaga, Juliana, Jakubzick, Claudia, Jarjoura, Jessica, Jepson, Daniel, Jia, Baosen, Jianu, Radu, Johanson, Tim, Jordan, Stefan, Jojic, Vladimir, Kamimura, Yosuke, Kana, Veronica, Kang, Joonsoo, Kapoor, Varun, Kenigsberg, Ephriam, Kent, Andrew, Kim, Charles, Kim, Edy, Kim, Francis, Kim, Joel, Kim, Kiwook, Kiner, Evgeny, Knell, Jamie, Koller, Daphne, Kozinn, Larry, Krchma, Karen, Kreslavsky, Taras, Kronenberg, Mitchell, Kwan, Wing-Hong, Laidlaw, David, Lam, Viola, Lanier, Lewis, Laplace, Catherine, Lareau, Caleb, Lavin, Yonit, Lavine, Kory, Leader, Andrew, Leboeuf, Marylene, Lee, Jacob, Lee, Jisu, Li, Bo, Li, Hu, Li, Yuesheng, Lionakis, Michail, Luche, Herve, Lynch, Lydia, Magen, Assaf, Maier, Barbara, Malhotra, Deepali, Malhotra, Nidhi, Malissen, Marie, Maslova, Alexandra, Mathis, Diane, Mcfarland, Adelle, Merad, Miriam, Meunier, Etienne, Miller, Jennifer, Milner, Justin, Mingueneau, Michael, Min-Oo, Gundula, Monach, Paul, Moodley, Devapregasan, Mortha, Arthur, Morvan, Maelig, Mostafavi, Sara, Muller, Soren, Muus, Christoph, Nabekura, Tsukasa, Rao, Tata Nageswara, Narang, Vipin, Narayan, Kavitha, Ner-Gaon, Hadas, Nguyen, Quyhn, Nigrovic, Peter, Novakovsky, German, Nutt, Stephan, Omilusik, Kayla, Ortiz-Lopez, Adriana, Paidassi, Helena, Paik, Henry, Painter, Michio, Paynich, Mallory, Peng, Vincent, Potempa, Marc, Pradhan, Rachana, Price, Jeremy, Qi, Yilin, Qi, Yiqing, Quon, Sara, Ramirez, Ricardo, Ramanan, Deepshika, Randolph, Gwendalyn, Regev, Aviv, Rhoads, Andrew, Robinette, Michelle, Rose, Samuel, Rossi, Derrick, Rothamel, Katie, Sachidanandam, Ravi, Sathe, Priyanka, Scott, Charlotte, Seddu, Kumba, See, Peter, Sergushichev, Alexey, Shaw, Laura, Shay, Tal, Shemesh, Avishai, Shinton, Susan, Shyer, Justin, Sieweke, Michael, Smillie, Chris, Spel, Lotte, Spidale, Nick, Stifano, Giuseppina, Subramanian, Ayshwarya, Sun, Joseph, Sylvia, Katelyn, Tellier, Julie, This, Sébastien, Tomasello, Elena, Todorov, Helena, Turley, Shannon, Vijaykumar, Brinda, Wagers, Amy, Wakamatsu, Ei, Wang, Chendi, Wang, Peter, Wroblewska, Aleksandra, Wu, Jun, Yang, Edward, Yang, Liang, Yim, Aldrin, Yng, Lim Sheau, Yoshida, Hideyuki, Yu, Bingfei, Zhou, Yan, Zhu, Yanan, Ziemkiewicz, Caroline, Microenvironment, Cell Differentiation, Immunology and Cancer (MICMAC), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Centre d'Immunologie de Marseille - Luminy (CIML), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), The Walter and Eliza Hall Institute of Medical Research (WEHI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), National University of Singapore (NUS), Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), Harvard Medical School [Boston] (HMS), Genentech, Inc. [San Francisco], Brigham & Women’s Hospital [Boston] (BWH), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Brown University, Lymphocytes B effecteurs et à mémoire – Effector and memory B cells, Centre International de Recherche en Infectiologie - UMR (CIRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Centre International de Recherche en Infectiologie (CIRI), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Crozat, Karine, and Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
0301 basic medicine, Regulation of gene expression, [SDV.IMM] Life Sciences [q-bio]/Immunology, Test data generation, Computer science, [SDV]Life Sciences [q-bio], Immunology, Gene regulatory network, Genomics, Genome project, Data science, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, microRNA, Immunology and Allergy, DECIPHER, [SDV.IMM]Life Sciences [q-bio]/Immunology, Scientific publishing, ComputingMilieux_MISCELLANEOUS, 030215 immunology
Abstract

International audience; 700 comment | SERIES ImmGen at 15 Nature Immunology's 20 th anniversary is a good opportunity to reminisce about the ImmGen collective endeavor-its goals, successes and horror stories-and the group's exploration of various modes of scientific publishing. The Immunological Genome Project T he Immunological Genome Project (ImmGen) is a collaborative group of immunology and computational biology laboratories that perform a thorough dissection of gene expression and its regulation in the immune system of the mouse. This activity first centered on mRNA expression and then expanded to microRNA (miRNA), chromatin structure, nuclear organization and protein-RNA relationships. Shared protocols, data generation and QC pipelines have yielded data that can be directly compared from >250 stem, lymphoid and myeloid cell types, at baseline or under challenge. The group develops and applies computational tools to decipher regulatory connections and transcriptional control. From its inception, data generated by ImmGen were meant to be a public resource, and they can be accessed through dedicated web and smartphone platforms that use interactive graphic displays that make the results intuitive to users.

Published
2020
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14. Diverse Gene Expression and DNA Methylation Profiles Correlate with Differential Adaptation of Breast Cancer Cells to the Antiestrogens Tamoxifen and Fulvestrant

Author

Fan, Meiyun, primary, Yan, Pearlly S., additional, Hartman-Frey, Cori, additional, Chen, Lei, additional, Paik, Henry, additional, Oyer, Samuel L., additional, Salisbury, Jonathan D., additional, Cheng, Alfred S.L., additional, Li, Lang, additional, Abbosh, Phillip H., additional, Huang, Tim H-M., additional, and Nephew, Kenneth P., additional

Published
2006
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15. Prognostic DNA Methylation Biomarkers in Ovarian Cancer

Author

Wei, Susan H., primary, Balch, Curtis, additional, Paik, Henry H., additional, Kim, Yoo-Sung, additional, Baldwin, Rae Lynn, additional, Liyanarachchi, Sandya, additional, Li, Lang, additional, Wang, Zailong, additional, Wan, Joseph C., additional, Davuluri, Ramana V., additional, Karlan, Beth Y., additional, Gifford, Gillian, additional, Brown, Robert, additional, Kim, Sun, additional, Huang, Tim H-M., additional, and Nephew, Kenneth P., additional

Published
2006
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16. A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-{alpha}

Author

Li, Lang, Cheng, Alfred S. L., Jin, Victor X., Paik, Henry H., Fan, Meiyun, Li, Xiaoman, Zhang, Wei, Robarge, Jason, Balch, Curtis, Davuluri, Ramana V., Kim, Sun, Huang, Tim H.-M., and Nephew, Kenneth P.

Abstract

Motivation: To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-α (ERα), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs. Results: Biologically, our proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERα target promoters (P-value < 0.001). The up-regulated targets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP, USF2) and (DBP, MYOGENIN); and down-regulated ERα target genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (DBP, MYOGENIN). Statistically, our proposed mixture model-based discriminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; such integrative power cannot be achieved by current methods. Availability: The software is available on request from the authors. Contact: lali@iupui.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2006
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