Biology subjects

Schaffer, D. E.

Publications and source records attributed to Schaffer, D. E..

3 recordsLinked to original sources

bioRxiv2023

kMermaid: Ultrafast functional classification of microbial reads

Shotgun metagenomic sequencing can determine both taxonomic and functional content of microbiomes. However, current functional classification methods for metagenomic reads require substantial computational resources and yield ambiguous classifications, limiting downstream quantitative analyses. Existing k-mer based methods to classify microbial sequences into species-level groups have immensely improved taxonomic classification, but this concept has not been extended to functional classification. Here we introduce kMermaid, for classifying metagenomic reads into functional clusters of proteins. Using protein k-mers, kMermaid allows for highly accurate and ultrafast functional classification, with a fixed memory usage, and can easily be employed on a typical computer.

microbiology↗

bioRxiv2022

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

Protein-coding differences between mammals often fail to explain phenotypic diversity, suggesting involvement of enhancers, often rapidly evolving regions that regulate gene expression. Identifying associations between enhancers and phenotypes is challenging because enhancer activity is context-dependent and may be conserved without much sequence conservation. We developed TACIT (Tissue-Aware Conservation Inference Toolkit) to associate open chromatin regions (OCRs) with phenotypes using predictions in hundreds of mammalian genomes from machine learning models trained to learn tissue-specific regulatory codes. Applying TACIT for motor cortex and parvalbumin-positive interneurons to neurological phenotypes revealed dozens of new OCR-phenotype associations. Many associated OCRs were near relevant genes, including brain size-associated OCRs near genes mutated in microcephaly or macrocephaly. Our work creates a forward genomics foundation for identifying candidate enhancers associated with phenotype evolution. One Sentence SummaryA new machine learning-based approach associates enhancers with the evolution of brain size and behavior across mammals.

genomics↗

bioRxiv2022

Characterizing the landscape of viral expression in cancer by deep learning

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

bioinformatics↗