Prof. Yatish Turakhia

I am an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at University of California San Diego (UCSD) with a joint affiliation with the Department of Computer Science and Engineering (CSE), the Department of Bioengineering, and the Bioinformatics and Systems Biology(BISB) graduate program at UCSD.

My lab is also affiliated with the Center of Machine-Integrated Computing and Security (MICS), the Center of Microbiome Innovation (CMI), and the Institute for Genomic Medicine (IGM) at UCSD.

My recent CV.

Email: yturakhia [at] ucsd [dot] edu

Contact: +1 (858) 534-4496

Office: Room 0104, Franklin Antonio Hall, UC San Diego

Latest News

Sep 2024: Sunreet Khanna joins lab co-advised with Prof. Bill Lin. Welcome, Sunreet!
Aug 2024: Manu, Haochen, and Arnav present their summer research projects at the UCSD ECE SRIP conference.
July 2024: Anshu attends ISMB 2024 to present ROADIES.
June 2024: Sumit begins summer internship at AMD.
June 2024: Sumit and Harsh et al. release a preprint on PANMAN
June 2024: Pranav passes his PhD prelims exam. Congratulations, Pranav!
June 2024: Anshu passes her PhD prelims exam. Congratulations, Anshu!
May 2024: Yingqi wins ECE best undergraduate research award 2024! Congratulations, Yingqi!
May 2024: Anshu et al. release a preprint on ROADIES.
April 2024: Turakhia Lab organizes BioSys Workshop in Conjuction with ASPLOS 2024.
March 2024: Harsh completes his masters thesis on Compressive Microbial Pangenomics. Congratulations Harsh!
March 2024: Sumit attends HPCA 2024 to present TALCO and receives best paper nomination.
Jan 2024: Turakhia Lab welcomes new members Zexing, Manu, Haochen, and Arnav to the lab.

Affiliations

Sponsors and Collaborators

Our work is generously funded by the Centers for Disease Control and Prevention (CDC), California Department of Public Health (CDPH), Hellman Fellowship, Amazon Research Award (ARA), and the AMD AI & HPC Research Fund.

Research Mission

We are an interdisciplinary group of researchers working on a broad range of problems at the cutting-edge intersection of computer engineering and bioinformatics.

We work on problems that have enormous potential in biological and medical applications but where computational costs and speed impose a barrier.

Our research mission is to develop parallel algorithms, software, and domain-specific hardware accelerators that enable faster and cheaper progress in biology and medicine.

Ongoing Research

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Pangenomics | study genetic variation across genomes

Low-cost and high-throughput sequencing have enabled extensive genome sequencing, leading to the emergence of Pangenomics, an area that studies genetic variation within a species using multiple reference genomes. This research is set to impact numerous fields but requires a shift from traditional single-reference genome approaches. 

Our team is developing new data structures, algorithms, and hardware acceleration to facilitate compressive, comprehensive and faster pangenomic analysis.

Related Publications

  • Sumit Walia, Harsh Motwani, Kyle Smith, Russell Corbett-Detig, Yatish Turakhia, "Compressive Pangenomics Using Mutation-Annotated Networks" bioRxiv (2024) | DOI | Talk | PDF

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Phylogenetics | study evolutionary relationships of species

Phylogenetics studies the evolutionary relationships among biological sequences, organisms, or species, with applications in evolutionary biology, outbreak analysis, and comparative genomics. Large-scale consortiums like VGP and Earth Biogenome Project generate vast genomic data, offering deeper insights into species evolution. However, constructing and analyzing large-scale phylogenies presents computational challenges.

In collaboration with Prof. Siavash Mirarab's Lab at UCSD, our team focuses on developing new automated, scalable, hardware-software co-designed solutions to address these challenges.

Related Publications

  • Gupta A, Mirarab S, Turakhia Y. "Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES.", bioRxiv (2024) | DOI | Talk | PDF

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Hardware Acceleration | make computational genomics faster

Genomic data is one of the fastest-growing data types on the planet and is far outpacing Moore’s law. From personalized medicine to species conservation, genomic data has far-reaching applications, but computational costs are posing ever greater challenges to exploit the full potential of this data. 

We are developing novel algorithms and hardware (GPU/FPGA/ASIC) acceleration approaches to speed computational genomics tasks, such as genome assembly, read alignment, multiple sequence alignment, and whole-genome alignments, by orders of magnitude.

Related Publications

  • Walia S., Ye C., Bera A., Lodhavia D., Turakhia Y. "TALCO: Tiling Genome Sequence Alignment using Convergence of Traceback Pointers", HPCA (2024) | DOI | Talk | PDF

  • Goenka S.D., Turakhia Y., Paten B., Horowitz M. "SegAlign: A scalable GPU-based whole genome aligner", SC20 (2020) | DOI | Talk | PDF

  • Turakhia Y., Goenka S.D., Bejerano G., Dally W.J. "Darwin-WGA: A co-processor provides increased sensitivity in whole genome alignments with high speedup", HPCA (2019) | DOI | Talk | PDF

  • Turakhia Y., Bejerano G., Dally W.J. "Darwin: A Genomic Co-processor Provides 15,000× Speedup on long read assembly", ASPLOS (2018) | DOI | Talk | PDF

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Outbreak Analysis | real-time phylogenetics for pandemic analysis

With over 16 million (and counting) whole SARS-CoV-2 genomes sequences already, the COVID-19 pathogen is the most sequenced pathogen in history. Phylogenetic analysis using these genomes has played a vital role in tracking the virus evolution and transmission, but is posing major computational challenges.

Our lab is working closely with Prof. Russell Corbett-Detig’s lab at UC Santa Cruz in maintaining and refining a comprehensive phylogenetic tree consisting of all available SARS-CoV-2 genomes.

Related Publications

  • Kyle Smith, Cheng Ye, Yatish Turakhia. "Tracking and curating putative SARS-CoV-2 recombinants with RIVET", Bioinformatics (2023) | DOI | Talk

  • Yatish Turakhia, Bryan Thornlow, Angie Hinrichs, Jakob McBroome, Nicolas Ayala, Cheng Ye, Kyle Smith, Nicola De Maio, David Haussler, Robert Lanfear, and Russell Corbett-Detig. "Pandemic-scale phylogenomics reveals the SARS-CoV-2 recombination landscape", Nature (2022) | DOI | Talk

  • Cheng Ye, Bryan Thornlow, Angie Hinrichs, Alexander Kramer, Cade Mirchandani, Devika Torvi, Robert Lanfear, Russell Corbett-Detig, and Yatish Turakhia. "matOptimize: a parallel tree optimization method enables online phylogenetics for SARS-CoV-2", Bioinformatics (2022) | DOI | Talk

  • Yatish Turakhia, Bryan Thornlow, Angie S. Hinrichs, Nicola De Maio, Landen Gozashti, Robert Lanfear, David Haussler, and Russell Corbett-Detig. "Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic", Nature Genetics (2021) | DOI | Talk

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Wastewater-based Epidemiology | tracking pathogen evolution and transmission

Wastewater-based epidemiology (WBE) involves analyzing sewage data to identify the genome sequences of disease-causing pathogens. A UCSD team demonstrated that WBE can even detect emerging variants up to two weeks earlier than traditional clinical genomic surveillance.

Today, WBE is being rapidly adopted worldwide to monitor a broad spectrum of pathogens, including SARS-CoV-2, RSV, Monkeypox, influenza, and Polio. However, the vast amount of WBE data presents significant computational and algorithmic challenges.

We are developing novel algorithms and software tools that aim to enhance the accuracy, resolution, and timeliness of current WBE tools.

Related Publications

  • In preparation