Siavash Mirarab

Postdoctoral Position in Molecular Ecology

Tue, 16 Aug 2022 00:00:00 -0700

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

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.
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.
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.
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.
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.
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.
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.
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.

Corrigenda

Wed, 06 Oct 2021 00:00:00 -0700

Here, we gather the list of small corrections we have had to make to our papers through the years. May the list remain short:

Zhang, Chao, Celine Scornavacca, Erin K Molloy, and Siavash Mirarab. “ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy.” Molecular Biology and Evolution 37, no. 11 (2020): 3292–3307. doi:10.1093/molbev/msaa139.
- Published correction here
Sayyari, Erfan and Siavash Mirarab. “Fast coalescent-based computation of local branch support from quartet frequencies”. Molecular Biology and Evolution 33, no. 7 (2016): 1654-1668. doi:10.1093/molbev/msw079
- In the supplementary material, Proof of Lemma 1, Page 17, we had a small (if embarrasing) error where we wrote something akin to P(X)=P(X|Y)+P(X|¬Y) instead of P(X)=P(X,Y)+P(X,¬Y). The error did not impact anything because we actually needed the P(X)=P(X,Y)+P(X,¬Y) in the proof. The fixed version of the supplementary material is provided here.

Awards won by students in the lab

Tue, 10 Aug 2021 00:00:00 -0700

2023:

Yueyu Jiang: SSB Publisher’s Award for Excellence in Systematic Research
Ali Osman Berk Şapcı: RECOMB-CG best paper award

2021:

Eleonora Rachtmana: ARCS Foundation Fellowship Award
Uyen Mai: Marye Anne Fox Endowed Fellowship
Chao Zhang: Runnerup for best student paper in MBE, 2020

2020:

Mein Balaban: UC San Diego Center for Microbiome Innovation (CMI) Grand Challenges Award

2019:

Uyen Mai
Maryam Rabiee

Researchethics

Fri, 16 Apr 2021 00:00:00 -0700

A note to self and the lab. The following link provides useful guidelines from ECE Jacobs school on ethics in research: https://jacobsschool.ucsd.edu/ethics

Blurb

Sat, 04 Jul 2020 00:00:00 -0700

Siavash Mirarab is a Professor in the ECE Department at the University of California, San Diego, and is also affiliated with the CSE department and the Center for Microbiome Innovation. He obtained his Ph.D. from the Computer Science department at UT-Austin and was advised by Prof. Tandy Warnow. His Ph.D. research was supported by an NSERC PGS award and Howard Hughes Medical Institute international student fellowship, and his dissertation won the honorable mention for the 2015 ACM Doctoral Dissertation Award. He has been a recipient of the 2017 Sloan Research Fellowship in Computational & Evolutionary Molecular Biology, the NSF CAREER award, and the MIRA (R35) awrd from NIGMS (NIH). He has been named on the Clarivate Highly Cited Researcher lists in 2023, 2024, and 2025. He has contributed to many international projects and is part of the Vertebrate Genome Project Council. His interest is in computational biology. His lab develops methods that target large-scale analyses in various areas, including phylogenomics, microbiome and metagenomic analyses using phylogenetic approaches, large-scale multiple sequence alignment, and HIV transmission network reconstruction.

Best paper award

Thu, 18 May 2017 00:00:00 -0700

Our paper has won the best paper award in GLBIO 2017.

Mai, Uyen, Erfan Sayyari, and Siavash Mirarab. 2017. “Minimum Variance Rooting of Phylogenetic Trees and Implications for Species Tree Reconstruction.”

An extended version of the paper is currently under revision in a journal.

Resources for programming

Sun, 26 Mar 2017 00:00:00 -0700

Prospective students, especially those from ECE, typically need to improve their programming skills before they can do useful work in my lab. Niema Moshiri has shared with me these suggestions for online material that students can use to brush up their coding and algorithmic skills.

Python: Codecademy Python Track
Data Structures and algorithms:
- Niema has his own online textbook
- There is an online course developed by several of our CSE faculty (including Sasha Kulikov and Pavel Pevzner). We especially recommend the first course of the series
- The appendix B from this textbook from my former advisor, Tandy Warnow is also very useful (while its publically available)
- Once at UCSD, students can take CSE 101 (undergrad algorithms) or CSE 202 (grad algorithms).
Bioinformatics: Pavel Pevezner has several useful MOOCs. The programming assignments might be a bit intense for new students (there’s a lot of them, and they’re fairly difficult).
- You can simply watch the lecture videos to learn about the biological problems and the bioinformatics algorithmic solutions.
- If you want to tackle the full-on course, you can take the full online course.
- If you are at UCSD, instead of taking the course online, you can take the in-person course (CSE 282), which would count for ECE students as a technical elective.
R: DataCamp
Unix:
- Linux Journey
- Codecademy Unix Track
Git: GitHub tutorial and Codecademy Git Track

Postdoc position for the CNIHR project (HIV transmission network)

Tue, 14 Jun 2016 00:00:00 -0700

We are now hiring a Postdoc for an exciting new project funded by the CNIHR program. See the call for applications below.

Fast coalescent-based support values

Fri, 15 Apr 2016 00:00:00 -0700

Our new paper on coalescent-based branch support estimation just came out at MBE.

We introduce a fast and simple method for computing support for branches of an unrooted species tree according to the multi-species coalescent model. The main insight behind the method is simple. For any quartet of species, we can compute the frequencies of its three possible topologies in gene trees, and we can translate these frequencies to probabilities that each of the alternatives is correct. The topology with the highest frequency is the most likely species tree topology. But how much more likely is that dominant topology compared to the two alternative topologies? The answer depends on 1) the number of genes, and 2) the difference between the quartet frequency of the dominant topology and the alternatives. This relationship can be analytically derived easily by using properties of a multinomial distribution. The figure below shows an example of the relationship between a branch’s quartet support and its probability of correctness.

Starting with this principle, we show how all quartet frequencies can be quickly computed for each branch in a given species tree (the running time is linear in the number of species and the number of genes for each species tree branch). We further use some simplifying assumptions, which enable us to compute a branch support by summarizing the frequencies of all quartets around the branch. Perhaps our most important assumption is the “locality” assumption: for any branch, we assume all the four clusters around it are correct.

We test the new support calculation method on datasets that violate our assumptions and have high levels of gene tree estimation error. In simulation studies, we show that local posterior probabilities are remarkably accurate and give a more reliable measure of support than the standard multi-locus bootstrapping.

Our new approach fits very well within ASTRAL. ASTRAL now uses local posterior probabilities to quickly compute branch support for the species tree topology that it computes. ASTRAL can compute branch support for a dataset with 1000 genes and 1000 species in a matter of minutes on a normal laptop.

By the way, we also use computed quartet frequencies for computing branch lengths in coalescent units for internal branches of the species tree (e.g., ASTRAL’s output). This feature is also added to ASTRAL.

Project report on NSF award 1565862

Wed, 27 Jan 2016 00:00:00 -0800

In this page, we report outcomes of the project “Using genomic context to understand evolutionary histories of individual genes” supported by an NSF award, 1565862 (2016–2019).

Tools

ASTRAL-III: Polynomial time species tree estimation from unrooted gene trees using a quartet-based approach
ASTRAL-MP: A multi-threaded and randomized version of ASTRAL
INSTRAL: Extends ASTRAL for phylogenetic placement
Minimum Variance (MV) rooting: Fast rooting of trees, minimizing variance of root to tip distance
TreeShrink: Detecting suspiciously long branches on a (set of) tree(s)
DISTIQUE: Species tree inference using distance
DiscoVista: A visualization tool for showing discordance among gene trees and species trees
TADA: Data augmentation for learning from microbiome compositional data
Dual-birth: A set of scripts to work with the dual-birth model

Datasets

We keep a repository of all the datasets produced in our lab here: https://sites.google.com/eng.ucsd.edu/datasets/

Those produced as part of this project include:

ASTRAL-III data: https://gitlab.com/esayyari/ASTRALIII
ASTRAL-multi individual data: https://gitlab.com/mrabiee/ASTRAL-multiind/
ASTRAL-MP: https://sites.google.com/eng.ucsd.edu/datasets/astral/astral-mp
INSTRAL: https://doi.org/10.5061/dryad.cs59t13
Polytomy test data: https://gitlab.com/esayyari/polytomy and https://github.com/esayyari/polytomytest
LocalPP: https://esayyari.github.io/FastLocalBranchSupport.html
Fragmentary data: https://github.com/esayyari/Fragments
TreeShrink: https://uym2.github.io/TreeShrink/
MVRoot: https://uym2.github.io/MinVar-Rooting/
Dual-birth: https://doi.org/10.5061/dryad.13n52

Publications

Published Journal papers:

Sayyari, Erfan and Mirarab, Siavash. “Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction”,” BMC Genomics, v.17, 2016, p. 101–113. doi:10.1186/s12864-016-3098-z
Sayyari, Erfan and Mirarab, Siavash. “Fast Coalescent-Based Computation of Local Branch Support from Quartet Frequencies”,” Molecular Biology and Evolution, v.33, 2016, p. 1654–166. doi:10.1093/molbev/msw079
Mai, Uyen and Sayyari, Erfan and Mirarab, Siavash. “Minimum variance rooting of phylogenetic trees and implications for species tree reconstruction”,” PLOS ONE, v.12, 2017, p. e0182238. doi:10.1371/journal.pone.0182238
Sayyari, Erfan and Whitfield, James B and Mirarab, Siavash. “Fragmentary Gene Sequences Negatively Impact Gene Tree and Species Tree Reconstruction”,” Molecular Biology and Evolution, v.34, 2017, p. 3279–329. doi:10.1093/molbev/msx261
Shekhar, Shubhanshu and Roch, Sebastien and Mirarab, Siavash. “Species tree estimation using ASTRAL: how many genes are enough?”,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, v.PP, 2017, p. 1–1. doi:10.1109/TCBB.2017.2757930
Mai, Uyen and Mirarab, Siavash. “TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees”,” BMC Genomics, v.19, 2018, p. 272. doi:10.1186/s12864-018-4620-2
Janssen, Stefan and McDonald, Daniel and Gonzalez, Antonio and Navas-Molina, Jose A. and Jiang, Lingjing and Xu, Zhenjiang Zech and Winker, Kevin and Kado, Deborah M. and Orwoll, Eric and Manary, Mark and Mirarab, Siavash and Knight, Rob (2018). “Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information.”. mSystems. 3 (3), e00021–18. doi:10.1128/mSystems.00021-18
Moshiri, Niema and Mirarab, Siavash. “A Two-State Model of Tree Evolution and Its Applications to Alu Retrotransposition”,” Systematic Biology, v.67, 2018, p. 475–489. doi:10.1093/sysbio/syx088
Sayyari, Erfan and Mirarab, Siavash. “Testing for Polytomies in Phylogenetic Species Trees Using Quartet Frequencies”,” Genes, v.9, 2018, p. 132. doi:10.3390/genes9030132
Sayyari, Erfan and Whitfield, J.B. James B. and Mirarab, Siavash. “DiscoVista: Interpretable visualizations of gene tree discordance”,” Molecular Phylogenetics and Evolution, v.122, 2018, p. 110–115. doi:10.1016/j.ympev.2018.01.019
Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash. “ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees”,” BMC Bioinformatics, v.19, 2018, p. 153. doi:10.1186/s12859-018-2129-y
Yin, John and Zhang, Chao and Mirarab, Siavash and Schwartz, Russell. “ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization,” Bioinformatics, 2019. doi:10.1093/bioinformatics/btz211
Rabiee, Maryam and Mirarab, Siavash (2019). “INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores”. Systematic Biology. doi:10.1093/sysbio/syz045
Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash (2019). “Multi-allele species reconstruction using ASTRAL”. Molecular Phylogenetics and Evolution. 130 286–296. doi:10.1016/j.ympev.2018.10.033
Sayyari, Erfan and Kawas, Ban and Mirarab, Siavash (2019). “TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification”. Bioinformatics. 35 (14), i31–i40. doi:10.1093/bioinformatics/btz394

Published conference papers:

Mai, Uyen and Mirarab, Siavash (2017). “TreeShrink: Efficient Detection of Outlier Tree Leaves”. Comparative Genomics: 15th International Workshop, RECOMB CG 2017, Barcelona, Spain, October 4-6, 2017, Proceedings Meidanis, Joao and Nakhleh, Luay. Springer International Publishing. Cham. 116–140. doi:10.1007/978-3-319-67979-2_7.
Zhang, Chao and Sayyari, Erfan and Mirarab, Siavash (2017). “ASTRAL-III: Increased Scalability and Impacts of Contracting Low Support Branches”. Comparative Genomics: 15th International Workshop, RECOMB CG 2017, Barcelona, Spain, October 4-6, 2017, Proceedings 10562 LNBI. Meidanis, Joao and Nakhleh, Luay. Springer International Publishing. Cham. 53–75. doi:10.1007/978-3-319-67979-2_4.

Opinion article

Mirarab, Siavash. “Phylogenomics: Constrained gene tree inference”,” Nature Ecology “\&” Evolution, v.1, 2017, p. 0056. doi:10.1038/s41559-016-0056

Students:

Undergraduate students:

Wang, Yuan (she is now a PhD student at Princton)
Yin, John (he is now a PhD student at UWMadison)
Zhang, Chao (he is now a PhD student at UCSD, our lab)
Jia, Xingfan (she is now a MS student)

Graduate students:

See this page for details.

Erfan Sayyari and Niema Moshiri were both patially supported on this grant and have now graduated.
Chao Zhang, Uyen Mai, Metin Balaban, and Maryam Rabiee are currently (8/2019) in the middle of finishing their PhD