Courtesy of Lawrence Livermore National Laboratory
The COVID-19 pandemic has sparked a wave of new research and development at the Lab, and Nisha Mulakken is very busy.
The biostatistician has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated novel species identification for human health, animal health, biodefense, and environmental sampling scenarios.
The LLMDA relies on high performance computing to compare DNA sequences. Nisha added 60-base pair probes to the LLMDA to target the SARS-CoV-2 genome, then ran multiple algorithms in parallel over the 41,450 SARS-CoV-2 reference sequences and compared those regions against genomes from all other known viruses.
“After optimizing for various parameters, I settled on a set of 78 probes out of over 77,000 candidates that should be able to uniquely identify any SARS-CoV-2 genome, as well as distinguish some strains of SARS-CoV-2 from others,” said Mulakken.
Given the pandemic’s urgency, she accomplished this optimization in about a month using LLNL’s Corona supercomputing cluster and funding from the CARES Act.
Mulakken’s lab career has spanned 2000 to 2008 and 2017 to the present. She first heard about the lab’s internships when she was an undergrad studying genetics at UC Davis. LLNL computer scientist Tom Slezak invited her to attend a day-long bioinformatics course he taught at the university.
“I was the only undergraduate and only woman in the room,” she recalls.
With a recommendation from a teaching assistant and encouragement from Tom, Mulakken became an LLNL intern after graduation in 2000. As a UC Berkeley graduate student in biostatistics, she returned to the lab for three more summers.
“The internships solidified my vision for my future career,” she said. “At the start of my first summer, I had taken only one Java programming class. I learned about databases, biostatistics for gene expression analyses, building a system for finding microbial DNA signatures, and various other topics each summer. The internships taught me that I needed to strengthen my computer science and statistics skills in order to make the most of a career in the multidisciplinary field of bioinformatics.”
Mulakken has mentored several students in bioinformatics projects over the years.
“The opportunities we are exposed to early in our careers can shape the limits we place on ourselves and our approaches to challenges we encounter throughout our careers,” she said, emphasizing the importance of normalizing women’s presence in technical field. “Women can contribute their unique talents to solving scientific problems instead of being intimidated before giving themselves a fair chance.”
In 2020, she mentored Duke University graduate student Emilia Grzesiak in LLNL’s Data Science Summer Institute (DSSI). Their project applied machine learning to trace CRISPR technology vectors to the source lab.
“The CRISPR process creates small signatures within the vectors that are not easy to detect without the aid of targeted algorithms,” said Mulakken. “Emilia used a convolutional neural network to identify the source lab from the patterns in the vector sequences. She replicated the results from literature and dramatically improved the accuracy using different model optimizations.”
Mulakken was also recently named the DSSI’s co-director for 2021, alongside director Goran Konjevod. “I hope the students will experience the Lab’s collaborative culture, learn about academic topics and practical applications they may not have been exposed to yet, and genuinely enjoy getting to know each other and their mentors,” she said.