Matteo Pellegrini
University of California-Los Angeles, CA·Computer Science
Sequencing machines now produce measurements of gene activity for whole tissues, for individual cells, and even mapped onto positions within a tissue slice. Turning those enormous tables of numbers into biological answers requires specialized computational work that most experimental groups cannot do alone.
- Fields
Computer Science
Genetics
Biology
- Topics
bioinformatics
transcriptomics
single-cell RNA sequencing
- Grants
- 2 known grants, through May 2029
What they work on
- Process bulk, single-cell and spatial transcriptomic data for consortium projects using established pipelines, and return interpretable results to collaborators without extensive computational expertise.
- Interpret gene-activity profiles in terms of cell type composition and inflammatory signatures, using tools the core developed such as GEDIT for bulk deconvolution, ACTINN for single-cell cell-type assignment and SAVANT for signature-database analysis.
- Integrate transcriptomic data with genetic and lipidomic measurements, and deliver interactive visualization through an R Shiny server that displays individual genes on two-dimensional single-cell maps.
- Process next-generation sequencing data to measure transcriptomes and epigenomes, including alignment of DNA methylation data with the core's BSBolt software, for the stem cell aging program.
- Build models of age-dependent genomic change, using machine learning to construct epigenetic clocks and the Epigenetic Pacemaker method to capture non-linear trajectories of DNA methylation with age.
- Estimate the cell types present across tissue sections from spatial genomic data to support analyses of age-dependent changes in stem cells and other cell types.
More about the research
Both of these awards fund a bioinformatics core: a shared analysis hub that processes sequencing data for the other projects in a larger program and returns results that biologists can read directly. The two cores serve different programs — one a consortium analyzing gene activity and inflammation, the other a program on how stem cells change with age — but use overlapping tools and expertise in genomics and the underlying biology.
Why it matters
Genomic experiments only pay off if their data can be interpreted, and these cores make that possible for collaborators without computational training. Understanding how gene activity and DNA methylation marks change with age in stem cells could clarify why tissues lose repair capacity over a lifetime, while tools that read inflammatory signatures and cell-type composition from tissue data may help other projects pinpoint which cells drive disease. The openly named software packages are themselves resources other labs can use.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jovanny Zabaleta
Louisiana State University Health Sciences Center-New Orleans, LA·Genetics
Provide next-generation sequencing services, including whole transcriptome and RNA sequencing, whole exome sequencing, and array-based DNA methylation and SNP analysis, to cancer research project leaders.
Willard Freeman
University of Oklahoma-Health Sciences Center, OK·Genetics
Perform quantitatively precise analyses of DNA modifications, methylation and hydroxymethylation, from genome-wide to gene-specific scale for aging studies, including whole-genome epigenomic profiling by nanopore sequencing.
Michael Whitfield
Dartmouth College, NH·Genetics
Enhance the research competitiveness of junior faculty in genomic data science and experimental genomics through mentoring and a supportive research environment.
The school
University of California-Los Angeles: 84 labs on PI Pages, $1.9B a year on research, 803 research doctorates a year.
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