Li Fang

US Patent:

6686174, Feb 3, 2004

Filed:

Apr 16, 1999

Appl. No.:

09/293427

Inventors:

Li Fang - Burlingame CA
Masanori Mitta - Kyoto, JP
Masayori Inouye - Piscataway NJ

Assignee:

The University of Medicine and Dentistry of New Jersey - New Brunswick NJ

International Classification:

C07H 2102

US Classification:

435 691, 4352523, 43525233, 4353201, 435471, 536 231, 536 241, 536 245

Abstract:

A method of inhibiting the translation of bacterial mRNA is disclosed. The method comprises overexpressing in a bacterium an mRNA which contains a sequence which is complementary to the anti-downstream box region of the 16S rRNA. RNA and DNA constructs for the overexpression of the mRNA of the invention are disclosed. Further, there are disclosed isolated DNA constructs that direct the prolonged expression of a heterologous gene in a cold-shocked bacterium at reduced temperature. The construct can comprise a promoter region of a cold-shocked inducible gene. The replication vehicle comprising such DNA constructs and a method for overexpressing a heterologous gene in a bacterium transformed with such a replication vehicle are also disclosed.

US Patent:

20210222248, Jul 22, 2021

Filed:

Apr 14, 2017

Appl. No.:

16/093588

Inventors:

- Pleasanton CA, US
Aparna CHHIBBER - San Mateo CA, US
Marghoob MOHIYUDDIN - Foster City CA, US
Li Tai FANG - San Francisco CA, US
Hugo Y.K. LAM - Sunnyvale CA, US

Assignee:

Roche Sequencing Solutions, Inc. - Pleasanton CA

International Classification:

C12Q 1/6886
G16B 20/20

Abstract:

Described herein are methods, systems, and apparatuses for detecting significantly mutated genes/pathways in a cancer cohort. A driver gene detection technique taking into account the heterogeneous mutational context in a cancer cohort is disclosed. A statistical model of a gene-specific mutation rate distribution (e.g., using an optimized gene specific mean estimation and/or a gene-specific dispersion estimation) is used to model a sample/gene-specific background mutation rate. The statistical model may then be used to detect gene/pathway enrichment and distinguish tumor suppressors and oncogenes based on the spatial distribution of non-silent mutations, loss-of-function mutations, and/or gain-of-function mutations.