The labs of Siavash Mirarab and Vineet Bafna are jointly looking for a postdoctoral researcher working in the area of molecular ecology.

Project Description

The project aims to develop new computational tools and evaluate existing methods for using environmentally sampled genome-wide data to help ecologists and conservation biologists quantify the biodiversity in an environment. Our focus will be on non-microbial species (marine vertebrates in particular) and very precise detections of what is present in a set of samples (ideally at the species or population levels). Simply put, the project is on metagenomics of non-microbial marine species. The rationale is that the reduced cost of obtaining genome-wide data opens a path to very precise detection (beyond what meta-barcoding enables) with costs that are not dramatically higher. However, realizing this goal will require new methods and better testing of existing methods. We will tackle both challenges.

The project will build on the prior work in our labs on genome skimming [1-8] with more focus on environmental sampling. Some of the biological motivations are further described in [8]. Other papers are examples of methodological work we have done in this area. The specific focus of this project will be on mixture deconvolution, correction of biases in various types of data, and population level characterization. Topics such as metagenomics, evolution, and sequencing technologies will be relevant. The work will be in close collaboration with the Australian non-profit organization Minderoo that funds the position. This organization will generate and provide much of the data used in the project and will work closely with our labs.

Required Skills

The project will involve method development, benchmarking, and biological data analysis. As such, a wide range of expertise will be needed. The best candidates will be computer scientists or bioinformaticians with an interest in working with real biological data or biologists with an interest and skills in developing and testing new computational methods. While the ability to think analytically and design new mathematical models and inference algorithms will be a big asset, the project will need someone with a hands-on approach (rather than a person interested in pure mathematics or programming). Thus, we believe two types of candidates will fit well: biologists with good computational skills or computer scientists with a keen interest in biology.

Logistics

  • If interested, please write directly to smiararb@ucsd.edu with Postdoc interest in the title.
  • The start date: as soon as possible. Preferably no later than January 2023.
  • Salary: negotiable and dependent on the candidate’s experience; we plan for level 2 of UCSD salary scale unless when higher scales are warranted.
  • Requirements: a Ph.D. in computer science, computer engineering, electrical engineering, bioinformatics, or biology.
  • The location: primary location is San Diego (UCSD), but more creative arrangements will be considered; in particular, there may be room to accommodate long stays in Australia.
  • Duration: 2 years, with the second year contingent on progress in the first year.

Relevant work from previous projects in the lab

  1. Sarmashghi, Shahab, Kristine Bohmann, M. Thomas P. Gilbert, Vineet Bafna, and Siavash Mirarab. “Skmer: Assembly-Free and Alignment-Free Sample Identification Using Genome Skims.” Genome Biology 20, no. 1 (December 13, 2019): 34. https://doi.org/10.1186/s13059-019-1632-4.

  2. Balaban, Metin, Shahab Sarmashghi, and Siavash Mirarab. “APPLES: Scalable Distance-Based Phylogenetic Placement with or without Alignments.” Edited by David Posada. Systematic Biology 69, no. 3 (May 1, 2020): 566–78. https://doi.org/10.1093/sysbio/syz063.

  3. Sarmashghi, Shahab, Metin Balaban, Eleonora Rachtman, Behrouz Touri, Siavash Mirarab, and Vineet Bafna. “Estimating Repeat Spectra and Genome Length from Low-Coverage Genome Skims with RESPECT.” Edited by Nicola Segata. PLOS Computational Biology 17, no. 11 (November 15, 2021): e1009449. https://doi.org/10.1371/journal.pcbi.1009449.

  4. Rachtman, Eleonora, Metin Balaban, Vineet Bafna, and Siavash Mirarab. “The Impact of Contaminants on the Accuracy of Genome Skimming and the Effectiveness of Exclusion Read Filters.” Molecular Ecology Resources 20, no. 3 (May 4, 2020): 1755-0998.13135. https://doi.org/10.1111/1755-0998.13135.

  5. Bohmann, Kristine, Siavash Mirarab, Vineet Bafna, and M. Thomas P. Gilbert. “Beyond DNA Barcoding: The Unrealized Potential of Genome Skim Data in Sample Identification.” Molecular Ecology 29, no. 14 (July 29, 2020): 2521–34. https://doi.org/10.1111/mec.15507.

  6. Rachtman, Eleonora, Shahab Sarmashghi, Vineet Bafna, and Siavash Mirarab. “Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships.” Cell Systems, no. In Press (2022). https://doi.org/10.2139/ssrn.3986497.

  7. Rachtman, Eleonora, Vineet Bafna, and Siavash Mirarab. “CONSULT: Accurate Contamination Removal Using Locality-Sensitive Hashing.” NAR Genomics and Bioinformatics 3, no. 3 (June 23, 2021): 10.1101/2021.03.18.436035. https://doi.org/10.1093/nargab/lqab071.

  8. Bohmann, Kristine, Siavash Mirarab, Vineet Bafna, and M. Thomas P. Gilbert. “Beyond DNA Barcoding: The Unrealized Potential of Genome Skim Data in Sample Identification.” Molecular Ecology 29, no. 14 (July 29, 2020): 2521–34. https://doi.org/10.1111/mec.15507.