10X Genomics, Inc.

23Andme, Inc.

Bio-Rad Laboratories, Inc.

23Andme Genomics LLC

Jeffrey Mellen

Linda Avey

Anne Wojcicki

Alexander Wong

Oleksiy Khomenko

Jack Tihon

Marcela Miyazawa

Brian Lee Hawthorne

Michael P Polcari

Kevin J Wu

Jasper Staab

Kevin D Ness

Serge Saxonov

Nicholas Heredia

Phillip Belgrader

Benjamin J Hindson

Michael Ybarra Lucero

John Frederick Regan

Simant Dube

Paul Ryvkin

Neil Ira Weisenfeld

Austin So

Billy Wayne Colston, Jr

Patrick Marks

Zachary Bent

Yifeng Yin

Alexander Y Wong

Florian Baumgartner

Brynn Claypoole

Vijay Kumar Sreenivasa Gopalan

Nicolaus Lance Hepler

Preyas Shah

Niranjan Srinivas

Phil Belgrader

Billy W Colston

Paul Wyatt

Narek Dshkhunyan

Stephen R Williams

Shawn Paul Hodges

Camille Bodley Troup

Michael Polcari

Bill Colston

Mike Lucero

Ben Hindson

Nicholas Jack Heredia

Ian Fiddes

Erik Leonard Henrik Borgstrom

Sharing data is disclosed. In some cases, sharing data includes receiving a request to share data from a first account to a second account, receiving an indication of a plurality of first account profiles associated with the first account to share with the second account, and establishing sharing from the plurality of first account profiles to the second account, wherein sharing comprises the second account having read access to a subset of nonpublic data associated with the plurality of first account profiles.

Genome sharing

A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

Systems and methods for identifying morphological patterns in tissue samples

The present disclosure provides methods and compositions for detecting polynucleotides in a sample and for quantifying polynucleotide load in a sample. The polynucleotides can be associated with a disease, disorder, or condition. In some applications, methylated DNA is quantified, e.g., in order to determine the load of polynucleotides in a sample. The present disclosure also provides methods and compositions for determining the load of fetal polynucleotides in a biological sample, e.g., the load of fetal polynucleotides (e.g., DNA, RNA) in maternal plasma. The present disclosure provides methods and compositions for detecting cellular processes such as cellular viability, growth rates, and infection rates. This disclosure also provides compositions and methods for detecting differences in copy number of a target polynucleotide. In some embodiments, the methods and compositions provided herein are useful for diagnosis of fetal genetic abnormalities, when the starting sample is maternal tissue (e.g., blood, plasma). The methods and materials described apply techniques for allowing detection of small, but statistically significant, differences in polynucleotide copy number.

Methods and compositions for detecting genetic material

A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.

Systems and methods for visualization of single-cell resolution characteristics

A visualization system comprising a persistent memory, storing a dataset, and a non-persistent memory implements a pattern visualizing method. The dataset contains discrete attribute values for each first entity of a first type in a plurality of first entities of the first type and discrete attribute values for each first entity of a second type in a plurality of first entities of the second type for each second entity in a plurality of second entities. The dataset is compressed by blocked compression and represents discrete attribute values in both compressed sparse row and column formats. The discrete attribute values are clustered to assign each second entity to a cluster in a plurality of clusters.

Systems and methods for visualizing a pattern in a dataset

Methods for screening clonotypes are provided. Data representing a plurality of cells from a single subject is obtained. The data represents a plurality of clonotypes. The data includes a plurality of contigs for each respective clonotype in the plurality of clonotypes. Each respective contig in the plurality of contigs comprises (i) an indication of chain type for the respective contig and (ii) a contig sequence of an mRNA of the respective cell. There is determined, using the data, for each respective clonotype in the plurality of clonotypes, a number of the plurality of cells that represent the respective clonotype. In some instances, more than one cell in the plurality of cells have the same clonotype in the plurality of clonotypes. In some instances, the plurality of clonotypes comprises 25 clonotypes and where the plurality of cells includes at least one cell for each clonotype in the plurality of clonotypes.

Systems and methods for clonotype screening

Systems and methods for identifying morphological patterns in tissue samplers

Method of haplotype analysis. In an exemplary method, an aqueous phase containing nucleic acid may be partitioned into a plurality of discrete volumes. At least one allele sequence may be amplified in the volumes from each of a first polymorphic locus and a second polymorphic locus that exhibit sequence variation in the nucleic acid. At least one measure of co-amplification of allele sequences from both loci in the same volumes may be determined. A haplotype of the first and second loci may be selected based on the at least one measure of co-amplification.

Analysis of nucleic acids

Growing community of inventors

San Francisco, CA, United States of America

Jeffrey Mellen