Terrance P. Snutch

GIRAFI improves sparse single-cell proteomics by imputing missing values and revealing cell states using graph-informed statistical learning, outperforming existing methods and recovering biological structure.

The paper introduces a cost-effective single-cell proteomics workflow and a data evaluation framework to improve data quality and biological interpretability.

The paper uses long-read sequencing to catalog Cacna1e splice variants in rat thalamus and computationally identifies four functionally distinct isoforms.

V3 primer pools with 22 extra alternates improve coverage; revised library preps raise double-barcode demultiplexing from 42.15% to 74.5%, cut misassignment 35x and bring reagent cost near £10 per sample.

The paper uses nanopore sequencing to analyze poly(A) RNA from human cells, identifying long transcripts and modifications not captured by traditional methods.

The paper assembles a complex C. elegans genome using MinION long-read sequencing and identifies genomic rearrangements not detectable with Illumina data.

Nanopore-only sequencing of GM12878 across 39 MinION flowcells (91.2 Gb) yielded an assembly with NG50 ∼3 Mb; adding 5× ultra-long reads raised NG50 to 6.4 Mbp.