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Lead-Computational Research Scientist
St. Jude Children's Research HospitalUnited Statesfull_timeVerifiedPosted 13 Feb 2025
💰 $238,160/yr($125,840/yr – $238,160/yr)
About the role
The Lead-Computational Research Scientist is responsible for leading computational research projects, executes research strategies, and develops new research methods under little supervision.
With vast knowledge accumulated from patient tumor data, we are now characterizing cell models (cell lines and PDX models) for childhood cancers to discover novel vulnerabilities that can lead to new therapeutic targets. In this role you will analyze the genomics data from >1,000 cancer cell models to identify their driver alterations and you will investigate the representativeness of cell models against the mutational diversity observed in patient populations. You will also study the similarities and differences of driver alterations between pediatric and adult cancers. Published studies from external and internal cohorts will be compiled for the comparison. In addition to the extensive internal tools we have developed, you will develop novel tools during the investigation to generate novel analyses and to integrate new data types. You are expected to present findings in regular internal/external forums and to draft figures/manuscripts with the help of the PI and the team.
Recognized for state-of-the-art computational infrastructure, well-established analytical pipelines, and deep genomic analysis expertise, St. Jude offers a work environment where you will directly impact the care of pediatric cancer patients. As a Senior Computational Research Scientist, your responsibilities include analyzing data generated from a variety of second- and third-generation sequencing applications that interrogate a broad range of human gene regulatory biology.
The Ma Lab has focused on studying childhood cancer genomics in the past decade and has led or participated numerous projects on various childhood cancers including B-ALL, T-ALL, AML, Wilms Tumor, Neuroblastoma, Osteosarcoma, and various Brain Tumors, in the setting of diagnosis, relapse, and metastatic samples. These works not only provided valuable insights into the precision diagnosis and classification of childhood cancers but also led to the development of powerful tools and computational methods that enabled precise and efficient characterization of cancer relevant mutations, as well as early detection of cancer.
The Department of Computational Biology provides access to high-performance computing clusters, a cloud computing environment, innovative visualization tools, highly automated analytical pipelines, and mentorship from faculty scientists with experience in data analysis, data management, and delivery of high-quality results for competitive projects. We encourage first-author, high-profile publications to share our novel discoveries. Take the first step to joining our team by applying now!
Relevant Papers: (#: corresponding author)
Ma X, …, Zhang J. Rise and fall of subclones from diagnosis to relapse in pediatric B-acute lymphoblastic leukaemia. Nat Commun. 2015 Mar 19;6:6604. doi: 10.1038/ncomms7604. PubMed PMID: 25790293; PubMed Central PMCID: PMC4377644.
Ma X, …, Zhang J. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature. 2018 Mar 15;555(7696):371-376. doi: 10.1038/nature25795. Epub 2018 Feb 28. PubMed PMID: 29489755; PubMed Central PMCID: PMC5854542.
Ma X, …, Zhang J. Analysis of error profiles in deep next-generation sequencing data. Genome Biol. 2019 Mar 14;20(1):50. doi: 10.1186/s13059-019-1659-6. PubMed PMID: 30867008; PubMed Central PMCID: PMC6417284.
Brady SW, Ma X*, …, Zhang J. The Clonal Evolution of Metastatic Osteosarcoma as Shaped by Cisplatin Treatment. Mol Cancer Res. 2019 Apr;17(4):895-906. doi: 10.1158/1541-7786.MCR-18-0620. Epub 2019 Jan 16. PubMed PMID: 30651371.
Li B, Brady SW, Ma X*, …, Zhang J. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood. 2020 Jan 2;135(1):41-55. doi: 10.1182/blood.2019002220. PubMed PMID: 31697823; PubMed Central PMCID: PMC6940198.
Davis EM, …, Ma X#. SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data. Genome Biol. 2021 Jan 25;22(1):37. doi: 10.1186/s13059-020-02254-2. PubMed PMID: 33487172
Schwartz JR, …, Ma X#, Klco JM#. The acquisition of molecular drivers in pediatric therapy-related myeloid neoplasms. Nat Commun. 2021 Feb 12;12(1):985. doi: 10.1038/s41467-021-21255-8. PubMed PMID: 33579957
Huang BJ, …, Ma X#, Meshinchi S#. CBFB-MYH11 fusion transcripts distinguish acute myeloid leukemias with distinct molecular landscapes and outcomes. Blood Adv. 2021 Dec 14;5(23):4963-4968. doi: 10.1182/bloodadvances.2021004965. PMID: 34547772.
Umeda M, …, Ma X#, Klco JM#.
With vast knowledge accumulated from patient tumor data, we are now characterizing cell models (cell lines and PDX models) for childhood cancers to discover novel vulnerabilities that can lead to new therapeutic targets. In this role you will analyze the genomics data from >1,000 cancer cell models to identify their driver alterations and you will investigate the representativeness of cell models against the mutational diversity observed in patient populations. You will also study the similarities and differences of driver alterations between pediatric and adult cancers. Published studies from external and internal cohorts will be compiled for the comparison. In addition to the extensive internal tools we have developed, you will develop novel tools during the investigation to generate novel analyses and to integrate new data types. You are expected to present findings in regular internal/external forums and to draft figures/manuscripts with the help of the PI and the team.
Recognized for state-of-the-art computational infrastructure, well-established analytical pipelines, and deep genomic analysis expertise, St. Jude offers a work environment where you will directly impact the care of pediatric cancer patients. As a Senior Computational Research Scientist, your responsibilities include analyzing data generated from a variety of second- and third-generation sequencing applications that interrogate a broad range of human gene regulatory biology.
The Ma Lab has focused on studying childhood cancer genomics in the past decade and has led or participated numerous projects on various childhood cancers including B-ALL, T-ALL, AML, Wilms Tumor, Neuroblastoma, Osteosarcoma, and various Brain Tumors, in the setting of diagnosis, relapse, and metastatic samples. These works not only provided valuable insights into the precision diagnosis and classification of childhood cancers but also led to the development of powerful tools and computational methods that enabled precise and efficient characterization of cancer relevant mutations, as well as early detection of cancer.
The Department of Computational Biology provides access to high-performance computing clusters, a cloud computing environment, innovative visualization tools, highly automated analytical pipelines, and mentorship from faculty scientists with experience in data analysis, data management, and delivery of high-quality results for competitive projects. We encourage first-author, high-profile publications to share our novel discoveries. Take the first step to joining our team by applying now!
Relevant Papers: (#: corresponding author)
Ma X, …, Zhang J. Rise and fall of subclones from diagnosis to relapse in pediatric B-acute lymphoblastic leukaemia. Nat Commun. 2015 Mar 19;6:6604. doi: 10.1038/ncomms7604. PubMed PMID: 25790293; PubMed Central PMCID: PMC4377644.
Ma X, …, Zhang J. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature. 2018 Mar 15;555(7696):371-376. doi: 10.1038/nature25795. Epub 2018 Feb 28. PubMed PMID: 29489755; PubMed Central PMCID: PMC5854542.
Ma X, …, Zhang J. Analysis of error profiles in deep next-generation sequencing data. Genome Biol. 2019 Mar 14;20(1):50. doi: 10.1186/s13059-019-1659-6. PubMed PMID: 30867008; PubMed Central PMCID: PMC6417284.
Brady SW, Ma X*, …, Zhang J. The Clonal Evolution of Metastatic Osteosarcoma as Shaped by Cisplatin Treatment. Mol Cancer Res. 2019 Apr;17(4):895-906. doi: 10.1158/1541-7786.MCR-18-0620. Epub 2019 Jan 16. PubMed PMID: 30651371.
Li B, Brady SW, Ma X*, …, Zhang J. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood. 2020 Jan 2;135(1):41-55. doi: 10.1182/blood.2019002220. PubMed PMID: 31697823; PubMed Central PMCID: PMC6940198.
Davis EM, …, Ma X#. SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data. Genome Biol. 2021 Jan 25;22(1):37. doi: 10.1186/s13059-020-02254-2. PubMed PMID: 33487172
Schwartz JR, …, Ma X#, Klco JM#. The acquisition of molecular drivers in pediatric therapy-related myeloid neoplasms. Nat Commun. 2021 Feb 12;12(1):985. doi: 10.1038/s41467-021-21255-8. PubMed PMID: 33579957
Huang BJ, …, Ma X#, Meshinchi S#. CBFB-MYH11 fusion transcripts distinguish acute myeloid leukemias with distinct molecular landscapes and outcomes. Blood Adv. 2021 Dec 14;5(23):4963-4968. doi: 10.1182/bloodadvances.2021004965. PMID: 34547772.
Umeda M, …, Ma X#, Klco JM#.
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