معرفی شرکت ها


PathogenTrack-0.2.3


Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر
Card image cap
تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

A pipeline to identify pathogenic microorganisms from scRNA-seq raw data
ویژگی مقدار
سیستم عامل OS Independent
نام فایل PathogenTrack-0.2.3
نام PathogenTrack
نسخه کتابخانه 0.2.3
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Wei Zhang
ایمیل نویسنده admin@ncrna.net
آدرس صفحه اصلی https://github.com/ncrna/PathogenTrack
آدرس اینترنتی https://pypi.org/project/PathogenTrack/
مجوز MIT
[![Build Status](https://app.travis-ci.com/ncrna/PathogenTrack.svg?branch=master)](https://app.travis-ci.com/ncrna/PathogenTrack) [![PYPI](https://img.shields.io/pypi/v/pathogentrack.svg)](https://pypi.org/project/pathogentrack/) [![The MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/ncrna/PathogenTrack/blob/master/LICENSE) # PathogenTrack PathogenTrack is an unsupervised computational software that uses `unmapped single-cell RNAseq reads` to characterize `intracellular pathogens` at the single-cell level. It is a python-based script that can be used to identify and quantify intracellular pathogenic `viruses` and `bacteria` reads at the single-cell level. PathogenTrack has been tested on various scRNA-seq datasets derived from simulated and real datasets and performed robustly. The detailes are described in our paper *`Decoding Intracellular Pathogens of scRNA-seq experiments with PathogenTrack and SCKIT`*. ### System Requirements PathogenTrack has been tested on Linux platform with CentOS 7 operation system. The RAM is 120 GB, with 40 computational threads. ## Installation ### PathogenTrack can be installed in two steps: 1 . Installing Miniconda on Linux Platform. For details, please refer to [Miniconda Installation](https://conda.io/projects/conda/en/latest/user-guide/install/linux.html#install-linux-silent). ```sh wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh ``` 2 . Installing PathogenTrack. ```sh conda env create -f environment.yml ``` Users can install the dependencies manually. The dependencies and test versions are listed below. Package|Version --|:--: python|3.6.10 biopython|1.78 star|2.7.5a umi_tools|1.1.1 kraken2|2.1.1 ## Databases Preparation ### 1. Prepare the Human genome database Download the Human GRCh38 genome and genome annotation file, and then decompress them: ```sh wget ftp://ftp.ensembl.org/pub/release-101/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz gzip -d Homo_sapiens.GRCh38.dna.toplevel.fa.gz wget ftp://ftp.ensembl.org/pub/release-101/gtf/homo_sapiens/Homo_sapiens.GRCh38.101.gtf.gz gzip -d Homo_sapiens.GRCh38.101.gtf.gz ``` Build STAR Index with the following command: ```sh STAR --runThreadN 16 --runMode genomeGenerate --genomeDir ./ \ --genomeFastaFiles ./Homo_sapiens.GRCh38.dna.toplevel.fa \ --sjdbGTFfile ./Homo_sapiens.GRCh38.101.gtf \ --sjdbOverhang 100 ``` ### 2. Prepare Kraken2 database ```sh wget ftp://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken_8GB_202003.tgz tar zxf minikraken_8GB_202003.tgz ``` ## How to use PathogenTrack? Before running this tutorial, you should run `cellranger` or `alevin` to get the single cells' gene expression matrix. Here, we take the simulated 10X sequencing data as an example: First, we use cellranger to get scRNA-seq expression matrix and valid barcodes: ```sh cellranger count --id cellranger_out --transcriptom /path/to/cellranger_database/ ``` Then we run PathogenTrack to identify and quantify pathogen expression at the single-cell level: ```sh conda activate PathogenTrack python PathogenTrack.py count --project_id PathogenTrack_out --pattern CCCCCCCCCCCCCCCCNNNNNNNNNN \ --min_reads 10 --confidence 0.11 --star_index ~/database/STAR_index/ \ --kraken_db ~/database/minikraken_8GB_20200312/ --barcode barcodes.tsv \ --read1 simulation_S1_L001_R1_001.fastq.gz \ --read2 simulation_S1_L001_R2_001.fastq.gz ``` **IMPORTANT**: The Read 1 in the example is made up of 16 bp CB and 10 bp UMI, so the --pattern is *CCCCCCCCCCCCCCCCNNNNNNNNNN* (16C and 10N). Users must adjust the pattern with their own Read 1 accordingly. *Note:* It may take 4-6 hours to complete one sample, and it depends on the performance of computational resources and the size of the raw single-cell data. ### Please see [QUICK_START.md](https://github.com/ncrna/PathogenTrack/blob/master/doc/QUICK_START.md) for a complete tutorial. ## Questions For questions and suggestions about the pipeline or the code, please contact [admin@ncrna.net](mailto:admin@ncrna.net) and [ty12260@rjh.com.cn](mailto:ty12260@rjh.com.cn). We will try our best to provide support, address new issues, and keep improving this software.


نیازمندی

مقدار نام
>=1.78 biopython


زبان مورد نیاز

مقدار نام
>=3 Python


نحوه نصب


نصب پکیج whl PathogenTrack-0.2.3:

    pip install PathogenTrack-0.2.3.whl


نصب پکیج tar.gz PathogenTrack-0.2.3:

    pip install PathogenTrack-0.2.3.tar.gz