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CancerMine, a resource based on literature mining, offers a database of drivers, oncogenes and tumor suppressors for gene–cancer associations, updated monthly.
Fluorescence intensity fluctuation spectrometry provides a rapid and accurate measurement of the identity, abundance and stability of protein oligomers.
Epi-illumination SPIM enables fast, volumetric, high-resolution, subcellular imaging of any sample compatible with a standard inverted fluorescence microscope.
Iso-LFM enables rapid, instantaneous volumetric imaging of biological processes with isotropic and improved resolution by simultaneously capturing orthogonal light fields.
ECCITE-seq combines the single-cell analysis of multiple modalities, for example transcriptome, immune cell receptors, cell surface proteins and single-guide RNAs.
A mass-spectrometry-compatible surfactant called Azo effectively solubilizes proteins, is rapidly degraded by ultraviolet irradiation and enables top-down proteomic analysis of membrane proteins.
To address the issue of intra-tissue heterogeneity in cancer genomics, we developed Texomer, which enables joint analysis of bulk DNA and RNA sequencing data for allele-specific deconvolution and quantification of tumor heterogeneity.
High-throughput screening of fragment libraries of yeast genes yields dominant negative polypeptides on the basis of their decreased frequencies in cells after a growth selection.
Massively parallel Cpf1 array profiling (MCAP) targets genes and gene pairs that are candidate drivers of metastasis in cancer. In vivo profiling of single and double gene knockouts enables quantitative mapping of the genes’ contribution to metastatic phenotypes.
Selene is a deep learning library that enables the expansion of existing deep learning models to new data, the development of new model architectures, and the evaluation of these new models on biological sequence data.
Covalent linking of a histone-modification-specific antibody to MNase allows for the isolation of fragments with the desired histone mark, which can be amplified and sequenced. This approach is sensitive enough to profile histone modifications in single cells.
A computational approach facilitates molecular formula, metabolite class, and structure assignment for plant metabolites on the basis of LC–MS analysis of fully 13C-labeled and unlabeled plants.
DARTS first uses public domain data to train a deep neural network to predict differential alternative splicing; the predictions are then combined with observed RNA-seq data in a Bayesian framework to infer changes in alternative splicing between biological samples.
SNAC-tags allow for versatile sequence-specific cleavage of soluble and membrane proteins with Ni2+ under biocompatible conditions, bypassing enzymatic cleavage and enabling cleavage in situations where commonly used enzymes fail.
The solid media portable cell killing assay uses metabolism-sensitive staining to illuminate the killing of antibiotic-tolerant bacteria under resource-depleted conditions, thereby enabling multiplex, genome-scale analyses for the identification of target strains.
The field synthesis method for generating any scanned light sheet is based on a new mathematical theorem in Fourier analysis and has important practical implications for simpler, multicolor lattice light-sheet microscopy.
FIt-SNE, a sped-up version of t-SNE, enables visualization of rare cell types in large datasets by obviating the need for downsampling. One-dimensional t-SNE heatmaps allow simultaneous visualization of expression patterns from thousands of genes.