An innovative mass spectrometry platform that utilizes sulfoxide-containing MS-cleavable heterobifunctional photoactivated cross-linkers to enhance protein structural elucidation.

High-throughput next-generation sequencing (NGS) systems have allowed for large scale collection of transcriptomic data with single cell resolution. Within this data lies variability allowing researchers to characterize and/or infer certain morphological aspects of interest, such as single cell type, cell state, cell growth trajectories, and inter-cellular gene regulatory networks. All of these qualities are important parts of understanding how cells interact with one another, both for building better cellular models in vitro and for understanding biological processes in vivo. While the size of single cell data has increased massively, NGS techniques for key pieces of analysis have not kept pace, using slow, manual pipelines of domain experts for initial clustering. Attempts to improve NGS classification performance have fallen short as the numbers of cell types (often asymmetric) and cell subtypes have increased while the number of samples per label has become small. The technical variability between NGS experiments can make robust classification between multiple tissue samples difficult. Moreover, the high-dimensional nature of NGS transcriptomic data makes this type of analysis statistically and computationally intractable.

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Researchers at UC Berkeley have developed a machine learning model that can aide in the design of more efficient viral vector libraries.Directed evolution of biomolecules to generate large numbers of randomized variants is an important innovation in biochemistry. This methodology can be applied to myriad biomolecules of interest, including viruses. In the case of viral variants, this method may be used to select viral variants or viral vectors with specific properties such as tissue type specificity, increased replication capacity, or enhanced evasion of the immune system. However, testing large numbers of viral variants for specific properties is inherently time consuming and limits potential innovation.The inventors have devised a new method to optimize the functionality of viral libraries with many random variants. Specifically, this methodology comprises a machine learning model that systematically designs more effectively starting libraries by optimizing for a chosen factor. This method works by using a training set of viruses that can be evaluated experimentally for the chosen optimization factor (e.g., packaging efficiency, infectivity of a cell line, etc.). These experiments will then provide a fitness value for each viral variant, and the fitness value matched with viral variant sequences will in turn be used in a supervised machine learning model to select sequences for a larger library that is optimized for the chosen factor.

Researchers at the University of California, Davis have developed a single-use chip for the identification of protein crystals using X-ray based instruments.

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This invention leverages the nuclease activity of CRISPR proteins for the direct, sensitive detection of specific nucleic acid sequences. This all-in-one detection modality includes an internal Nuclease Chain Reaction (NCR), which possesses an amplifying, feed-forward loop to generate an exponential signal upon detection of a target nucleic acid.Cas13 or Cas12 enzymes can be programmed with a guide RNA that recognizes a desired target sequence, activating a non-specific RNase or DNase activity. This can be used to release a detectable label. On its own, this approach is inherently limited in sensitivity and current methods require an amplification of genetic material before CRISPR-base detection. 

Techniques such as genome editing, gene therapy, and CRISPR-based gene expression require robust methods of delivering genetic information. The effectiveness of delivery depends on the amount of DNA or RNA that can be delivered.  In some cases there is a strict upper-limit on the amount of DNA or RNA that can be delivered.  For example, AAV vectors for mammalian gene delivery are limited to genetic cargos of < 5 kb.  In general, and irrespective of the delivery vector, larger DNA constructs are delivered less efficiently and so it is advantageous to use smaller constructs where possible. It is therefore advantageous to compress constructs. Methods of compression that do not require removal of genetic elements (“lossless compression”) are very desirable since size requirements can be met without compromising functionality.     In order to reduce the number of bases (DNA or RNA) required to encode larger constructs, UC Berkeley researchers have developed a method for compressing genetic information.   The method can be applied to two elements which be encoded in the same or different reading and can also be applied to a single genetic elements. 

UCLA researchers in the Department of Molecular and Medical Pharmacology have developed a classifier for the identification and treatment of small cell neuroendocrine cancers and small-round-blue cell tumors not previously identified.

To develop a novel imaging-based single cell RNA-sequencing (scRNA-Seq) platform that allows capturing of spatiotemporal information and cellular behavior of the sequenced cells within tissue.

Researchers led by Yi Xing have developed a novel deep learning algorithm to detect alternative splicing patterns in RNA-seq data

Researchers at the University of California, Davis have developed a device that can capture, analyze, and monitor volatile organic compounds (VOCs) emitted by cell cultures through a bioreactor exhaust line – thus eliminating the need to contact the cell culture directly.

UCLA researchers in the Department of Radiological Sciences have invented a novel interactive tool that can rapidly focus and zoom on a large number of images using eye tracking technology.

UCLA researchers in the Department of Radiological Sciences have developed a novel computation system that uses large imaging datasets to aid in clinical diagnosis and prognosis.

UCLA researchers in the Division of Cardiology at the Geffen School of Medicine have developed a bioinformatics methodology to identify and functionally annotate novel endocrine pathways.

Pharmacophore analysis through examination of Joint Pharmacophore Space of chemical compounds, targets, and chemical/biological properties.

UCLA researchers in the Department of Electrical Engineering have developed a high-throughput, label-free cell classification method based on time-stretch quantitative phase imaging.

Media that contains nutrients and growth factors is necessary to grow all types of cells, a process that is widely used in many fields of research. Such media should be routinely changed either to different media or a fresh batch of the same media. This change currently involves either using a pipette to transfer cells from their current dish of media to a new dish, or aspirating the media out of the dish and replacing it with new media. Both methods have inherent risks to stressing and damaging the cells. Researchers at UCI have developed a unique dish for growing cells that allows for safer aspiration of the old media, which reduces stress and damage to the cells.

This invention consists of software that facilitates modeling and interactive visualization of molecular structures and related data.

The technology is a method for analysis and mapping of a broad range of omic data.It features maps and visualizes interactions between omic data, such as how the circadian metabolome, transcriptome, and proteome operate in concert.With this technology, users can use non-public and public data, per tissue/organ data and data across multiple conditions.

Sequence census experiments utilize next-generation sequence data to estimate the relative abundance of target sequences.  Since the samples are often short DNA fragments, they must first be assigned to the correct transcripts and genes that produced them, and this alignment or mapping step currently takes up the majority of computing power and time in most expression analyses. To avoid this costly process, UC Berkeley researchers have developed a software program (kallisto) for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads based on pseudo-alignment for rapidly determining the compatibility of reads with targets, without the need for alignment. Pseudo-alignment of reads preserves the key information needed for quantification. 

DNA sequence assembly software is used for designing the construction of longer DNA molecules from fragments of shorter DNA molecules. Current methods of sequence assembly are expensive, slow and prone to failure. Investigators at UC Berkeley have developed a sequence assembly tool, Design Evolver, which is cheaper, faster, easier to implement and less prone to failure than alternate tools. The software works in conjunction with the j5 DNA assembly tool developed and licensed by the Joint BioEnergy Institute (JBEI). Design Evolver’s algorithm can further optimize designs that have been processed by j5.

Researchers at University of California, Irvine, have responded to the worldwide growing demand for fast 3D microscopy in bioimaging, by creating iSPIM Box (Inclined Single Plane Imaging Microscope Box), an adapter for commercial body microscopes, which can be used to achieve high spatial and temporal resolution in live cell imaging with only simple sample preparation in common culture dishes.

Researchers at the University of California, Berkeley have developed an invention that consists of an angular interferometer able to measure angle variations of a coherent, collimated light source with an accuracy below 30 nrad. The optical setup is compact and consists of a few simple optical components. The novelty of this innovation lies in the use of a simple, cost-effect technique to amplify the sensitivity of the instrument. The disclosed invention is in principle capable of being integrated into more compact, high-sensitivity commercial instruments for a fraction of the cost of current, state-of-the-art instruments (currently exceeding $30,000).   Commercial devices used to measure the angular deviation of a single beam include autocollimators and interferometers. The highest resolution offered by a commercial system is 25 nrad. The disclosed angular interferometer is able to measure relative angle variations (of a sample beam relative to a reference beam) below 30 nrad, though the resolution is known to currently be limited by the specific details of the current application and can therefore be further reduced with minor, inexpensive improvements.

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