Sequencing wgs for over 2000 subjects with Alzheimer’s is a true challenge. We are seeking a highly skilled bioinformatician who would relish the reality.
Please apply to whide [at] bidmc [dot] harvard [dot] edu with your cv
As part of the Cure Alzheimer’s Genome Project, the Precision RNA Medicine Core at Beth Israel Deaconess Medical Center seeks a highly motivated person to join our team. As a Computational Genetics Scientist, you will be technically responsible for benchmarking and continuous improvement of our genome analysis pipeline for whole genome sequencing (WGS) of subjects afflicted with Alzheimer’s. You will manage apprehension, storage, security and delivery of whole genome sequencing data into a cloud environment such as Terra where analysis will take place. You will oversee daily operation of the pipeline and handling of custom analysis, annotation and reporting for BIDMC staff and our collaborators. You should have significant experience in DNA sequencing analysis, including evidence of familiarity with best practice approaches to alignment, variant calling, and structural variant calling. It is important that you have previous experience in cloud-based analyses, preferably at scale. Expertise in Alzheimer’s disease genetics would be ideal. You will be comfortable with statistical methods for data QC and evaluation and presentation of both production metrics and scientific results. Your successful collaboration with other members of the computational biology team as well as external collaborators and clients is really important to us.
The Precision RNA Medicine Core (link here) is part of the Harvard Medical School Initiative for RNA Medicine and the Department of Pathology, BIDMC. It offers a unique environment with leading computational, experimental, and translational scientists working together to tackle open challenges in Precision Medicine.
Job duties will include, but are not limited to:
- Primary technical responsibility for maintenance, improvement, and operation of the WGS analysis pipeline
- Remaining current with and performing analysis of sequencing data using best practice workflows
- Development of novel sequence analysis methods where off-the-shelf methods are inadequate
- Outwards community facing interaction to support / drive best practice
- Generate and track appropriate quality metrics for high throughput sequencing operations
- Work with internal staff and collaborators to implement methods or prototype and benchmark customized analysis and annotation pipelines
- Work with the clinical team to manage transfer of research pipelines to the clinical lab as needed
- Work with the Broad Institute Terra management team to ensure optimal and efficient use of cloud facilities.
- Assist, collaborate, and consult with internal/external researchers on analysis of genomic data
- Interpret and present analysis results to co-workers and collaborators
- Publish results in scientific journals and give presentations at conferences when possible.
- Ph.D. in bioinformatics, computational biology, genetics, computer science or similar
- Experience in DNA sequencing analysis, including alignment, variant calling, structural variant calling, and interactive exploration of variant information and annotation.
- Experience in population genetics and genetics of complex traits.
- Proficiency in utilizing data from public resources such as Genome in a Bottle, 1000 Genomes, ExAC, and GnomAD
- Proficiency in R, and one or more programming languages such as Python, Perl, Java, or C/C++, willingness to learn new programming languages
- Experience working in Linux and running tasks in a cluster environment and experience in cloud environments
- Experience at minimum with sufficient statistical knowledge to develop and interpret standard QC metrics for sequencing
- Cloud data manipulation/analysis/processing
- Experience working in teams centered around a biological question and with external collaborators
- Excellent written and verbal communication skills.
Seeking the next leaders in computational systems medicine – Alzheimer’s Disease
Alzheimer’s resilience and pathogenesis
A postdoctoral traineeship is offered in Alzheimer’s resilience and pathogenesis. The candidate will lead integrative study to devise and apply computational methods that expose molecular events relating to protection against cognitive loss in Alzheimer’s subjects. The study will exploit single-cell, spatial, and tissue level transcriptomic data coupled with genomic regulatory circuitry and in-depth noncoding RNA data.
We actively encourage diversity: members of under represented minorities are particularly encouraged to apply.
The traineeship is part of our career development program to rapidly develop molecular models from signatures of resilience to apply to drug repurposing.
We will explore powerful neurodegeneration organoid assays of Alzheimer’s models where we can closely monitor pathway activity produced by Alzheimer’s pathology (see our NIH R01). The models will be compared to ongoing collected and existing human post-mortem sample data.
Developing integrative models, the candidate will elucidate the mechanism of action of and prioritize drug combinations for therapeutic intervention.
This exciting opportunity requires a candidate who is well versed in computational techniques with broad and deep experience in systems approaches to translational bioinformatics.
The candidate will apprehend and curate Alzheimer’s disease datasets to synthesize molecular signatures and build integrative models of disease and resilience. mRNAs, miRNAs, long ncRNAs, single-cell and tissue-level transcriptomes, methylation, acetylation and genome variant data will be available. Models will be incorporated into our pathway-disease-drug network. A major role will be to predict, test, and provide prioritized intervention strategies, such as drugs, miRNAs and potential diagnostics.
- A Ph.D. in a quantitative/computational field, including, but not limited to: bioinformatics, biostatistics, genetics, computational biology, or computer science.
- Strong background in biostatistics and computational biology, particularly in the areas related to next generation sequencing, biological networks, and pathway analysis.
- Solid knowledge and background in analysis and integration of genomic data. RNA-Seq, Small RNA-Seq, single-cell RNA-seq are of particular interest. Experience with genomic data of the human brain, mouse models, or 3D models is a big plus.
- Strong scripting/programming skills in R and/or Python. Experience with bash scripting, and high-performance computing is a plus.
- Strong interest in translational science and real-world therapeutics/diagnostics applications.
- Strong verbal and written communication skills, evidenced by peer-reviewed publications.
- Background and prior work in any of the following is a strong plus: neurodegenerative diseases, neuroscience, and drug-repurposing.
- Solid Communication skills,
- Ability to work independently and as part of a team.
- Ability to drive a research project from design stages to data analysis.
About the Hide Lab:
The Hide lab is based in Beth Israel Deaconess Medical Center/Harvard Medical School and is part of the Harvard Initiative for RNA Medicine and BIDMC Cancer Center. Our projects are funded by NIH, Harvard Medical School, and the Cure Alzheimer’s fund. We are a part of CIRCUITS consortium within CureAD foundation. Our collaboration extends to several US institutions –such as HMS, Mass General Hospital, and MIT—and international institutions.
As a member of Hide Lab you will:
- Work on cutting-edge research in computational and systems biology with access to cutting-edge Alzheimer’s disease omics data.
- Collaborate with world-class researchers in neurodegenerative diseases and non-coding RNA.
- Receive comprehensive, focused, career directed hands-on training to pursue your goals in research and academia including: scientific communication, collaboration, and grant writing.
- Benefit from training opportunities offered by Harvard Medical School, the Harvard Catalyst, and BIDMC.
- Work in a vibrant and dynamic lab environment with supportive colleagues.
The position is available immediately and can be renewed annually
How to Apply
Email applications including curriculum vitae, a summary statement of personal objectives and research interests, PDFs of your best two papers, and the names and email addresses of three references directly to: whide [at] bidmc [dot] harvard [dot] edu