A spatial genome aligner for analyzing multiplexed DNA-FISH imaging data.

Related tags

Deep Learningjie
Overview

jie

jie is a spatial genome aligner. This package parses true chromatin imaging signal from noise by aligning signals to a reference DNA polymer model.

The codename is a tribute to the Chinese homophones:

  • 结 (jié) : a knot, a nod to the mysterious and often entangled structures of DNA
  • 解 (jiĕ) : to solve, to untie, our bid to uncover these structures amid noise and uncertainty
  • 姐 (jiĕ) : sister, our ability to resolve tightly paired replicated chromatids

Installation

Step 1 - Clone this repository:

git clone https://github.com/b2jia/jie.git
cd jie

Step 2 - Create a new conda environment and install dependencies:

conda create --name jie -f environment.yml
conda activate jie

Step 3 - Install jie:

pip install -e .

To test, run:

python -W ignore test/test_jie.py

Usage

jie is an exposition of chromatin tracing using polymer physics. The main function of this package is to illustrate the utility and power of spatial genome alignment.

jie is NOT an all-purpose spatial genome aligner. Chromatin imaging is a nascent field and data collection is still being standardized. This aligner may not be compatible with different imaging protocols and data formats, among other variables.

We provide a vignette under jie/jupyter/, with emphasis on inspectability. This walks through the intuition of our spatial genome alignment and polymer fiber karyotyping routines:

00-spatial-genome-alignment-walk-thru.ipynb

We also provide a series of Jupyter notebooks (jie/jupyter/), with emphasis on reproducibility. This reproduces figures from our accompanying manuscript:

01-seqFISH-plus-mouse-ESC-spatial-genome-alignment.ipynb
02-seqFISH-plus-mouse-ESC-polymer-fiber-karyotyping.ipynb
03-seqFISH-plus-mouse-brain-spatial-genome-alignment.ipynb
04-seqFISH-plus-mouse-brain-polymer-fiber-karyotyping.ipynb
05-bench-mark-spatial-genome-agignment-against-chromatin-tracing-algorithm.ipynb

A command-line tool forthcoming.

Motivation

Multiplexed DNA-FISH is a powerful imaging technology that enables us to peer directly at the spatial location of genes inside the nucleus. Each gene appears as tiny dot under imaging.

Pivotally, figuring out which dots are physically linked would trace out the structure of chromosomes. Unfortunately, imaging is noisy, and single-cell biology is extremely variable. The two confound each other, making chromatin tracing prohibitively difficult!

For instance, in a diploid cell line with two copies of a gene we expect to see two spots. But what happens when we see:

  • Extra signals:
    • Is it noise?
      • Off-target labeling: The FISH probes might inadvertently label an off-target gene
    • Or is it biological variation?
      • Aneuploidy: A cell (ie. cancerous cell) may have more than one copy of a gene
      • Cell cycle: When a cell gets ready to divide, it duplicates its genes
  • Missing signals:
    • Is it noise?
      • Poor probe labeling: The FISH probes never labeled the intended target gene
    • Or is it biological variation?
      • Copy Number Variation: A cell may have a gene deletion

If true signal and noise are indistinguishable, how do we know we are selecting true signals during chromatin tracing? It is not obvious which spots should be connected as part of a chromatin fiber. This dilemma was first aptly characterized by Ross et al. (https://journals.aps.org/pre/abstract/10.1103/PhysRevE.86.011918), which is nothing short of prescient...!

jie is, conceptually, a spatial genome aligner that disambiguates spot selection by checking each imaged signal against a reference polymer physics model of chromatin. It relies on the key insight that the spatial separation between two genes should be congruent with its genomic separation.

It makes no assumptions about the expected copy number of a gene, and when it traces chromatin it does so instead by evaluating the physical likelihood of the chromatin fiber. In doing so, we can uncover copy number variations and even sister chromatids from multiplexed DNA-FISH imaging data.

Citation

Contact

Author: Bojing (Blair) Jia
Email: b2jia at eng dot ucsd dot edu
Position: MD-PhD Student, Ren Lab

For other work related to single-cell biology, 3D genome, and chromatin imaging, please visit Prof. Bing Ren's website: http://renlab.sdsc.edu/

Owner
Bojing Jia
How do we better describe the world around us?
Bojing Jia
Ağ tarayıcı.Gönderdiği paketler ile ağa bağlı olan cihazların IP adreslerini gösterir.

NetScanner.py Ağ tarayıcı.Gönderdiği paketler ile ağa bağlı olan cihazların IP adreslerini gösterir. Linux'da Kullanımı: git clone https://github.com/

4 Aug 23, 2021
Official repo for our 3DV 2021 paper "Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements".

Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements Yu Rong, Jingbo Wang, Ziwei Liu, Chen Change Loy Paper. Pr

Yu Rong 41 Dec 13, 2022
Arbitrary Distribution Modeling with Censorship in Real Time 59 2 60 3 Bidding Advertising for KDD'21

Arbitrary_Distribution_Modeling This repo implements the Neighborhood Likelihood Loss (NLL) and Arbitrary Distribution Modeling (ADM, with Interacting

7 Jan 03, 2023
Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

Open Compound Domain Adaptation [Project] [Paper] [Demo] [Blog] Overview Open Compound Domain Adaptation (OCDA) is the author's re-implementation of t

Zhongqi Miao 137 Dec 15, 2022
External Attention Network

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks paper : https://arxiv.org/abs/2105.02358 EAMLP will come soon Jitto

MenghaoGuo 357 Dec 11, 2022
It's a implement of this paper:Relation extraction via Multi-Level attention CNNs

Relation Classification via Multi-Level Attention CNNs It's a implement of this paper:Relation Classification via Multi-Level Attention CNNs. Training

Aybss 2 Nov 04, 2022
:fire: 2D and 3D Face alignment library build using pytorch

Face Recognition Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D an

Adrian Bulat 6k Dec 31, 2022
Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

Zero-shot-Fact-Verification-by-Claim-Generation This repository contains code and models for the paper: Zero-shot Fact Verification by Claim Generatio

Liangming Pan 47 Jan 01, 2023
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
Py-faster-rcnn - Faster R-CNN (Python implementation)

py-faster-rcnn has been deprecated. Please see Detectron, which includes an implementation of Mask R-CNN. Disclaimer The official Faster R-CNN code (w

Ross Girshick 7.8k Jan 03, 2023
Predictive Modeling on Electronic Health Records(EHR) using Pytorch

Predictive Modeling on Electronic Health Records(EHR) using Pytorch Overview Although there are plenty of repos on vision and NLP models, there are ve

81 Jan 01, 2023
Official repository for "Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring".

RNN-MBP Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring (AAAI-2022) by Chao Zhu, Hang Dong, Jinshan Pan

SIV-LAB 22 Aug 31, 2022
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

EMOShip This repository contains the EMO-Film dataset described in the paper "Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis

1 Nov 18, 2022
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
ROS Basics and TurtleSim

Waypoint Follower Anna Garverick This package draws given waypoints, then waits for a service call with a start position to send the turtle to each wa

Anna Garverick 1 Dec 13, 2021
Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

SelfTask-GNN A PyTorch implementation of "Self-supervised Learning on Graphs: Deep Insights and New Directions". [paper] In this paper, we first deepe

Wei Jin 85 Oct 13, 2022
PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM) This repo is the official PyTorch implementation for the paper Pseudo Numerical Meth

Luping Liu (刘路平) 196 Jan 05, 2023
Video Corpus Moment Retrieval with Contrastive Learning (SIGIR 2021)

Video Corpus Moment Retrieval with Contrastive Learning PyTorch implementation for the paper "Video Corpus Moment Retrieval with Contrastive Learning"

ZHANG HAO 42 Dec 29, 2022
Code of paper: "DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks"

DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks Abstract: Adversarial training has been proven to

倪仕文 (Shiwen Ni) 58 Nov 10, 2022
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER

WangRui 46 Dec 29, 2022