lsbnb/docexpress_2022 repository overview

⁠Description (same as DOCExpress but Reads count workflow [1-4 & 1-8 & 2-2] added)

It is a galaxy docker designed for RNA-seq data analysis based on Genome reference. There are two components to complete the whole analysis.

⁠Part One: three workflows for estimating expression profiling in "all workflows"

⁠1-1 : Transcript_FPKM (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Pair end RNA-seq data)

⁠1-2 : Gene_FPMK (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Pair end RNA-seq data)

⁠1-3 : Gene_TPM (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Pair end RNA-seq data)

⁠1-4 : Gene_Counts (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Pair end RNA-seq data)

⁠1-5 : Transcript_Counts (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Pair end RNA-seq data)

⁠1-6 : Transcript_FPKM_Single_End (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Single End RNA-seq data)

⁠1-7 : Gene_FPMK_Single_End (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Single end RNA-seq data)

⁠1-8 : Gene_TPM_Single_End (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Single end RNA-seq data)

⁠1-9 : Gene_Counts_Single_End (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Single end RNA-seq data)

⁠1-10 : Transcript_Counts_Single_End (Main Key as ENSEMBL Transcript ID or User defined Transcript ID) (Input: Single end RNA-seq data)

Depend on which kind of values you want to analyze, you can choose one of workflows from "Part One" to deal with all of your RNA-seq data. After analyzing RNA-seq data in part one, you will obtain FPKM/TPM values of each paired-end RNA-seq, then use the workflow, Expression_table_for_MOLAS in "Part Two":

⁠Part Two: Expression_table_for_Molas

⁠2 : Expression_Table_for_Molas (output .tab)

Please merge all FPKM/TPM values generated by above pipelines to an expression table. This table can be used as the input of MOLAS, Multi-Omics onLine Analysis System, which is a web platform with intuitive interface to analyze and visualize the expression outputs and decipher the biological meaning hidden behind. More detail about MOLAS, please visit http://molas.iis.sinica.edu.tw/molas.html⁠

⁠Install & Usage

Step 1. docker pull lsbnb/docexpress_2022

Step 2. docker run -d -t -i -p 8080:80 -p 8021:21 -p 8022:22 -v $('pwd')/galaxy_guest/:/root/galaxy/database/ftp/[email protected]/ lsbnb/docexpress_2022 /bin/bash

Step 3. Open your browser and type your host IP address, ex. http://your.host.IP.address:8080/⁠

Step 4. login as [email protected]⁠ with password rnaanalysis. And please take the section about "how to upload data" to import the data for galaxy.

Step 5. Then select the workflow from left panel according to Paired End / Single end and the way to estimate the expression level.

Step 6. Start with your RNA-seq raw data, if you are new to Galaxy →Please start your jouourney from "https://galaxyproject.org/tutorials/g101/"⁠

Step 7. Select the uploaded files to fit the requests from workflows like 1) reads in fastq, 2) Reference Genome and 3) Gene Transfer Format (GTF).

Step 8. Submit the table of expression profiling to MOLAS (http://molas.iis.sinica.edu.tw⁠) for deep analysis in web GUI.

⁠How to upload files

A. If the size of file is less than 2GB, please use "Tools for RNA-seq/ upload file/ choose local file".

B. For those files > 2GB, two approaches below you can choose.

B1. Use "Tools for RNA-seq/ upload file/ paste and fetch data" to paste the URLs to get the files. ex. ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX247357/SRR776587_1.fastq.bz2⁠

B2. Use "Tools for RNA-seq/ upload file/ choose FTP file". In our script for starting the docker (lsbnb/galaxy-rnaanalysis), we have mounted the local directory named as "galaxy_guest" at localhost. So you can find the directory in the home directory you executed the DOCKER image. Please copy / ftp those files for analysis in this directory, then you can find them when you click the button "choose FTP site".

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⁠Demo files

1. ⁠Human GRCH 38 (Genome)

   (ftp://ftp.ensembl.org/pub/current_fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz⁠)

2. ⁠GTF for Human GRCH 38

   (ftp://ftp.ensembl.org/pub/release-78/gtf/homo_sapiens/Homo_sapiens.GRCh38.78.gtf.gz⁠) Notice: Please "Edit Dataset Attributes/ Data type" as "gtf" if Galaxy has incorrectly guessed the type of your dataset.

3. ⁠Fastq in RNA-seq (Please shorten the file names after download, like SSRXXX_1.fq)

⁠Library A: (SRR776587):

R1: ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX247357/SRR776587_1.fastq.bz2⁠
R2: ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX247357/SRR776587_2.fastq.bz2⁠

⁠Library B: (SRR776592):

R1: ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX250141/SRR776592_1.fastq.bz2⁠
R2: ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX250141/SRR776592_2.fastq.bz2⁠

⁠Library C: (SRR776593)

R1: ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX250142/SRR776593_1.fastq.bz2⁠
R2: ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA068/SRA068307/SRX250142/SRR776593_2.fastq.bz2⁠