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Genomic Data Science Community Network. Diversifying the genomic data science research community. Genome Res 2022; 32:1231-41. [PMID: 35858750 DOI: 10.1101/gr.276496.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/02/2022] [Indexed: 11/25/2022]
Abstract
Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.
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Brody Y, Kimmerling RJ, Maruvka YE, Benjamin D, Elacqua JJ, Haradhvala NJ, Kim J, Mouw KW, Frangaj K, Koren A, Getz G, Manalis SR, Blainey PC. Quantification of somatic mutation flow across individual cell division events by lineage sequencing. Genome Res 2018; 28:1901-1918. [PMID: 30459213 PMCID: PMC6280753 DOI: 10.1101/gr.238543.118] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 10/27/2018] [Indexed: 02/06/2023]
Abstract
Mutation data reveal the dynamic equilibrium between DNA damage and repair processes in cells and are indispensable to the understanding of age-related diseases, tumor evolution, and the acquisition of drug resistance. However, available genome-wide methods have a limited ability to resolve rare somatic variants and the relationships between these variants. Here, we present lineage sequencing, a new genome sequencing approach that enables somatic event reconstruction by providing quality somatic mutation call sets with resolution as high as the single-cell level in subject lineages. Lineage sequencing entails sampling single cells from a population and sequencing subclonal sample sets derived from these cells such that knowledge of relationships among the cells can be used to jointly call variants across the sample set. This approach integrates data from multiple sequence libraries to support each variant and precisely assigns mutations to lineage segments. We applied lineage sequencing to a human colon cancer cell line with a DNA polymerase epsilon (POLE) proofreading deficiency (HT115) and a human retinal epithelial cell line immortalized by constitutive telomerase expression (RPE1). Cells were cultured under continuous observation to link observed single-cell phenotypes with single-cell mutation data. The high sensitivity, specificity, and resolution of the data provide a unique opportunity for quantitative analysis of variation in mutation rate, spectrum, and correlations among variants. Our data show that mutations arrive with nonuniform probability across sublineages and that DNA lesion dynamics may cause strong correlations between certain mutations.
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Affiliation(s)
- Yehuda Brody
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Robert J Kimmerling
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139, USA
| | - Yosef E Maruvka
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MGH Cancer Center and Department of Pathology, Boston, Massachusetts 02114, USA
| | - David Benjamin
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Joshua J Elacqua
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
| | - Nicholas J Haradhvala
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MGH Cancer Center and Department of Pathology, Boston, Massachusetts 02114, USA
| | - Jaegil Kim
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Kent W Mouw
- Harvard Medical School, Boston, Massachusetts 02115, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Kristjana Frangaj
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Amnon Koren
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Gad Getz
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MGH Cancer Center and Department of Pathology, Boston, Massachusetts 02114, USA
| | - Scott R Manalis
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139, USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- MIT Department of Biological Engineering, Cambridge, Massachusetts 02139, USA
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