Descriptions for Grant Writers
Investigators who are writing grants can find below a description of the Hutch Data Core and its services for their grant applications. Descriptions of the overall Fred Hutchinson Cancer Center Shared Resources program are available on the main Shared Resources grant information page.
Examples of publications made possible by Hutch Data Core staff are listed below.
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Hutch Data Core Grant Descriptions
The Hutch Data Core provides services to support the efficient and reproducible analysis of large-scale datasets for biomedical research, including next-generation genome sequencing, high-throughput microscopy, mass spectrometry and single-cell analysis. Staff within the Data Core include computational biologists, bioinformaticians, software engineers and cloud architects with extensive experience transforming raw data into scientific insight. Services include: project-based scientific analysis by expert bioinformaticians, automation of analytical pipelines using modern workflow management platforms, and publication of complex datasets with web-accelerated graphics. Additional expertise is available to help support the use of cloud computing technologies for novel research approaches, as well as training and documentation to help support the use of reproducible, open science approaches across the domain of bioinformatics.
Data Analysis & Consulting
Data analysis and consulting services are offered by an experienced team of bioinformatics specialists who provide support in experimental design, data analysis and programming for center investigators and members of the Fred Hutch/University of Washington Cancer Consortium. Staff have experience with a broad range of assays, with particular emphasis on massively parallel next-generation and third-generation sequencing. Assays routinely supported by core staff include bulk and single-cell expression profiling, single-cell immune receptor sequencing, CUT&RUN, ChIP-seq, ATAC-seq, whole-exome variant calling and targeted amplicon sequencing. Staff also conduct analysis of high-throughput CRISPR screens and data generated in the center’s unique and recently introduced AutoCUT&RUN facility. The level of support provided is customized to each project following initial consultation with the investigator and may include programming advice, assistance with troubleshooting R or Python code, guidance using commercial or academic analytical tools, and implementation of a full range of data analysis strategies that encompass data quality assessment, various statistical analyses, and generation of summary reports and figures.
Publications Made Possible by the Hutch Data Core
Following is a sample of the high-impact publications our work has made possible. For more publications, contact us.
- Alexandre A Germanos, Sonali Arora, Ye Zheng, Erica T Goddard, Ilsa M Coleman, Anson T Ku, Scott Wilkinson, Hanbing Song, Nicholas J Brady, Robert A Amezquita, Michael Zager, Annalysa Long, Yu Chi Yang, Jason H Bielas, Raphael Gottardo, David S Rickman, Franklin W Huang, Cyrus M Ghajar, Peter S Nelson, Adam G Sowalsky, Manu Setty, Andrew C Hsieh. (2022). Defining cellular population dynamics at single-cell resolution during prostate cancer progression. eLife, 11:e79076.
https://elifesciences.org/articles/79076
doi: https://doi.org/10.7554/eLife.79076 - Galeano Niño, J. L., Wu, H., LaCourse, K. D., Kempchinsky, A. G., Baryiames, A., Barber, B., Futran, N., Houlton, J., Sather, C., Sicinska, E., Taylor, A., Minot, S. S., Johnston, C. D., & Bullman, S. (2022). Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature, 171.
https://www.nature.com/articles/s41586-022-05435-0
doi: https://doi.org/10.1038/s41586-022-05435-0 - Galeano Niño, J.L., Wu, H., LaCourse, K.D. et al. INVADEseq to identify cell-adherent or invasive bacteria and the associated host transcriptome at single-cell-level resolution. Nature Protocols (2023).
https://www.nature.com/articles/s41596-023-00888-7
doi: https://doi.org/10.1038/s41596-023-00888-7 - Golob JL, Oskotsky TT, Tang AS, et al. (2023) Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research. Cell Rep Med. 2023 Dec 21:101350. doi: 10.1016/j.xcrm.2023.101350
- Hannah L. Itell, Daryl Humes, Julie Overbaugh. (2023). Several cell-intrinsic effectors drive type I interferon-mediated restriction of HIV-1 in primary CD4+ T cells. Cell Reports, Volume 42, Issue 6, 2023, 112556, ISSN 2211-1247.
https://www.sciencedirect.com/science/article/pii/S2211124723005673
doi: https://doi.org/10.1016/j.celrep.2023.112556 - Joseph K. Bedree, Kristopher Kerns, Tsute Chen, Bruno P. Lima, Guo Liu, Pin Ha, Jiayu Shi, Hsin Chuan Pan, Jong Kil Kim, Luan Tran, Samuel S. Minot, Erik L. Hendrickson, Eleanor I. Lamont, Fabian Schulte, Markus Hardt, Danielle Stephens, Michele Patel, Alexis Kokaras, Louis Stodieck, Yasaman Shirazi-Fard, Benjamin Wu, Jin Hee Kwak, Kang Ting, Chia Soo, Jeffrey S. McLean, Xuesong He, Wenyuan Shi. (2023). Specific host metabolite and gut microbiome alterations are associated with bone loss during spaceflight. Cell Reports, Volume 42, Issue 5, 2023, 112299, ISSN 2211-1247.
https://www.sciencedirect.com/science/article/pii/S2211124723003108
doi: https://doi.org/10.1016/j.celrep.2023.112299. - Lee EM, Srinivasan S, Purvine SO, et al. (2023). Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome. mSystems, 2023 Aug 31;8(4):e0067822. doi: 10.1128/msystems.00678-22
- Minot SS, Garb B, Roldan A, et al. (2023). MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies. Cell Rep Methods, 2023 Nov 20;3(11):100639. doi: 10.1016/j.crmeth.2023.100639
- Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., Spielmann, M., Palis, J., Doherty, D., Steemers, F. J., Glass, I. A., Trapnell, C., & Shendure, J. (2020). A human cell atlas of fetal gene expression. Science, 370(6518), eaba7721.
https://www.science.org/doi/10.1126/science.aba7721
doi: https://doi.org/10.1126/science.aba7721 - Domcke, S., Hill, A. J., Daza, R. M., Cao, J., O’Day, D. R., Pliner, H. A., Aldinger, K. A., Pokholok, D., Zhang, F., Milbank, J. H., Zager, M. A., Glass, I. A., Steemers, F., J., Doherty, D., Trapnell, C., Cusanovich, D. A., & Shendure, J. (2020). A human cell atlas of fetal chromatin accessibility. Science, 370(6518), eaba7612.
https://www.science.org/doi/10.1126/science.aba7612
doi: https://doi.org/10.1126/science.aba7612 - Tian, Y., Carpp, L. N., Miller, H. E. R., Zager, M., Newell, E. W., & Gottardo, R. (2021). Single-cell immunology of SARS-CoV-2 infection. Nature Biotechnology, 40(1), 30-41.
https://www.nature.com/articles/s41587-021-01131-y
doi: https://doi.org/10.1038/s41587-021-01131-y - Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen, William M. Mauck, Shiwei Zheng, Andrew Butler, Maddie J. Lee, Aaron J. Wilk, Charlotte Darby, Michael Zager, Paul Hoffman, Marlon Stoeckius, Efthymia Papalexi, Eleni P. Mimitou, Jaison Jain, Avi Srivastava, Tim Stuart, Lamar M. Fleming, Bertrand Yeung, Angela J. Rogers, Juliana M. McElrath, Catherine A. Blish, Raphael Gottardo, Peter Smibert, Rahul Satija. (2021). Integrated analysis of multimodal single-cell data. Cell, Volume 184, Issue 13, 2021, Pages 3573-3587.e29, ISSN 0092-8674.
https://www.sciencedirect.com/science/article/pii/S0092867421005833
doi: https://doi.org/10.1016/j.cell.2021.04.048. - Su, Y., Chen, D., Yuan, D., Lausted, C., Choi, J., Dai, C. L., Voillet, V., Duvvuri, V., R., Scherler, K., Troisch, P., Baloni, P., Qin, G., Smith, B., Kornilov, S. A., Rostomily, C., Xu, A., Li, J., Dong, S., Rothchild, A., Zhou, J., Murray, K., Edmark, R., Hong, S., Heath, J. E., Earls, J., Zhang, R., Xie, J., Li, S., Roper, R., Jones, L., Zhou, Y., Rowen, L., Liu, R., Mackay, S., O’Mahony, D. S., Dale, C. R., Wallick, J. A., Algren, H., A., Zager, M. A., the ISB-Swedish COVID19 Biobanking Unit, Wei, W., Price, N. D., Huang, S., Subramanian, N., Wang, K., Magis, A. T., Hadlock, J. J., Hood, L., Aderem, A., Bluestone, J. A., Lanier, L. L., Greenberg, P. D., Gottardo, R., Davis, M. M., Goldman, J. D., & Heath, J. R. (2020). Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell, 183(6), 1479-1495.e20.
https://www.cell.com/cell/fulltext/S0092-8674(20)31444-6
doi: https://doi.org/10.1016/j.cell.2020.10.037 - Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., Trapnell, C., & Shendure, J. (2019). The single-cell transcriptional landscape of mammalian organogenesis. Nature, 566(7745), 496-502.
https://www.nature.com/articles/s41586-019-0969-x
doi: https://doi.org/10.1038/s41586-019-0969-x - McFerrin, L. G., Zager, M., Zhang, J., Krenn, G., McDermott, R., Horse-Grant, D., Silgard, E., Colevas, K., Shannon, P., Bolouri, H., & Holland, E. C. (2018) Analysis and visualization of linked molecular and clinical cancer data by using Oncoscape. Nature Genetics, 50(9), 1203-1204.
https://www.nature.com/articles/s41588-018-0208-7
doi: https://doi.org/10.1038/s41588-018-0208-7 - Cimino, P. J., Zager, M., McFerrin, L., Wirsching, H., Bolouri, H., Hentschel, B., von Deimling, A., Jones, D., Reifenberger, G., Weller, M., & Holland, E. C. (2017). Multidimensional scaling of diffuse gliomas: application to the 2016 World Health Organization classification system with prognostically relevant molecular subtype discovery
https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-017-0443-7
doi: https://doi.org/10.1186/s40478-017-0443-7 - Hao, Y., Hao, S., Andersen-Nissen, E., Mauck III, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zager, M., Hoffman, P., Stoeckius, M., Papalexi, E., Mimitou, E. P., Jain, J., Srivastava, A., Stuart, T., Fleming, L. M., Yeung, B., Rogers, A. J., McElrath, J. M., Blish, C. A., Gottardo, R., Smibert, P., & Satija, R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.e29. https://www.cell.com/cell/fulltext/S0092-8674(21)00583-3
doi: https://doi.org/10.1016/j.cell.2021.04.048 - Pattwell, S. S., Arora, S., Nuechterlein, N., Zager, M., Loeb, K. R., Cimino, P. J., Holland, N. C., Reche-Ley, N., Bolouri, H., Almiron-Bonnin, D. A., Szulzewsky, F., Phadnis, V. V., Ozawa, T., Wagner, M. J., Haffner, M. C., Cao, J., Shendure, J., & Holland, E. C. (2022). Oncogenic role of a developmentally regulated NTRK2 splice variant.
https://www.biorxiv.org/content/10.1101/2022.01.07.475392v2
doi: https://doi.org/10.1101/2022.01.07.475392 - Veatch, J. R., Jesernig, B. L., Kargl, J., Fitzgibbon, M., Lee, S. M., Baik, C., Martins, R., Houghton, M., & Riddell, S. (2019). Endogenous CD4+ T cells recognize neoantigens in lung cancer patients, including recurrent oncogenic KRAS and ERBB2 (Her2) driver mutations. Cancer Immunology Research, 7(6), 910-922.
https://aacrjournals.org/cancerimmunolres/article/7/6/910/469471/Endogenous-CD4-T-Cells-Recognize-Neoantigens-in
doi: https://doi.org/10.1158/2326-6066.CIR-18-0402 - Isabella, A. J., Barsh, G. R., Stonick, J. A., Dubrulle, J., & Moens, C. B. (2020). Retinoic acid organizes the zebrafish vagus motor topographic map via spatiotemporal coordination of Hgf/Met signaling. Developmental Cell, 53(3), 344-357.e5.
https://www.sciencedirect.com/science/article/pii/S153458072030229X?via%3Dihub
doi: https://doi.org/10.1016/j.devcel.2020.03.017 - Minot, S. S., Barry, K. C., Kasman, C., Golob, J. L., & Willis, A. D. (2021). geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response. Genome Biology, 22, 135.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02355-6
doi: https://doi.org/10.1186/s13059-021-02355-6 - Veatch J, Lesernig B, Kargl J et al. Endogenous CD4(+) T cells recognize neoantigens in lung cancer patients, including recurrent oncogenic KRAS and ERBB2 (Her2) driver mutations. Cancer Immunol Res. 2019;7(6):910-922. doi: 10.1158/2326-6066.CIR-18-0402
- Isabella A, Barsh G, Stonick J, et al. Retinoic acid organizes the zebrafish vagus motor topographic map via spatiotemporal coordination of Hgf/Met signaling. Dev Cell. 2020;53(3):344-357.e5. doi: 10.1016/j.devcel.2020.03.017
- Cao J, O’Day D, Pliner H, et al. A human cell atlas of fetal gene expression. Science. 2020;370(6518):eaba7721. doi: 10.1126/science.aba7721
- Su Y, Chen D, Yean D, et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell. 2020;183(6):1479-1495. doi: 10.1016/j.cell.2020.10.037
- Minot S, Barry K, Kasman C, et al. Geneshot: Gene-level metagenomics identifies genome islands associated with immunotherapy response. Genome Biol. 2021;22:135. doi: 10.1186/s13059-021-02355-6