United States

Next-Generation Genomics 2021

Milo: differential abundance testing on single-cell data using KNN graphs

Recent years have given rise to breathtaking advances in genomics, nano-technology, and computation. These fields continue to evolve rapidly, with applications across nearly all biological disciplines. In this conference, we aim to showcase the ‘next-generation’ of genomic methods and technologies, and in particular to provide a platform for junior faculty to present recent discoveries and innovations. This virtual conference will feature advances in both computational and experimental methods, including single cell analysis, genetic engineering, population genetics, and statistical and deep learning. 

The meeting will take on September 27-28 from 10:00AM-5:30PM EDT, and we will release a detailed agenda prior to the conference.

We gratefully acknowledge funding from the Chan Zuckerberg Initiative that enables us to offer open and free registration for Next-Generation Genomics 2021.  
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**Registration is free and open to all attendees**. Please use [this link](https://ngg2021reg.paperform.co/) to register and reserve your attendance at the conference.



Free registration

This conference will feature advances in both computational and experimental methods, including single cell analysis, genetic engineering, population genetics, and statistical and deep learning.

technical paper

Synthetic genomic reconstitution reveals principles of mammalian Hox cluster regulation

Long-term DNA recording with ordered insertion mutations

Single-cell multi-omics defines the cell-type specific impact of splicing factor mutations on human hematopoietic differentiation

Cross-tissue organization of resident macrophage heterogeneity

A human paralog genetic interaction map with implications for cancer therapy and tumor evolution

Single-cell multi-modal intracellular epitope and transcript detection revealing signal transduction dynamics

Delivering RNA drugs outside the liver using DNA barcoded lipid nanoparticles

Mining the epigenome: computational tools to interpret DNA methylation data

Computational methods for characterizing spatial gene expression heterogeneity in tissues

Spatial and Single Cell Technologies for Next Generation Neuropathology

https://d3smihljt9218e.cloudfront.net/transcript/23435/subtitle/7e8787e9fc4d66a7c8f830cc6e77b2d4.vtt

English (automatic)

Milo: differential abundance testing on single-cell data using KNN graphs

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Next from Next-Generation Genomics 2021

Synthetic genomic reconstitution reveals principles of mammalian Hox cluster regulation

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A wild-derived mutator locus drives germline mutation spectrum differences in a genetically diverse murine family

Milo: differential abundance testing on single-cell data using KNN graphs

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Next from Next-Generation Genomics 2021

Synthetic genomic reconstitution reveals principles of mammalian Hox cluster regulation

Similar lecture

A wild-derived mutator locus drives germline mutation spectrum differences in a genetically diverse murine family

Downloads