// publications / [4] inferring phenotype from genome sequence

Mutational phenotypes:

Density of somatic mutations across chromosomal domains is a mutational phenotype that can differentiate human tissues // Driver mutations are poor classifiers of cancer (sub)type, while passenger mutation-based phenotypes are highly predictive // Trinucleotide signatures and regional mutation density phenotypes are complementary in classifying tumors.

Transcriptional and epigenomic phenotypes:

A joint analysis of genomes, transcriptomes and epigenomes matches human cancer cell lines to tumor (sub)types in TCGA // ~20% of the 614 examined cell lines were not a good match to the cancer type they were meant to represent // ~7% of cell lines are a close match a different cancer type, sometimes representing instances of metastatic skin cancer
Human genetic diseases differ in whether NMD typically aggravates or alleviates the phenotypic effects of nonsense variants // Failure to trigger NMD is a cause of ineffective gene inactivation by CRISPR–Cas9 gene editing // NMD strongly determines the efficacy of cancer immunotherapy, with only transcripts that escape NMD predicting a response

Morphological and metabolic phenotypes:

We have systematically annotated >3,000 prokaryotic taxa with >400 phenotypes, while drawing on comparative genomics and text mining techniques. This reveals thousands of gene families causally involved in various microbial traits, as well as pervasive epistasis that has shaped gene repertoires of these organisms. 


"All we know about the world teaches us that the effects of A and B are always different - in some decimal place - for any A and B. Thus asking 'are the effects different?' is foolish." -- John Tukey