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Copy file name to clipboardExpand all lines: docs/1_pipeline_setup/2_database.md
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There are **FOUR** files required for database preparation. Three of them can be directly downloaded from online resources:
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-**<u>*annotation.gtf*</u>**: Gene Annotation file in [GTF](https://biocorecrg.github.io/PhD_course_genomics_format_2021/gtf_format.html) (Gene Transfer Format) format.
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-***<u>annotation.gtf</u>***: Gene Annotation file in [GTF](https://biocorecrg.github.io/PhD_course_genomics_format_2021/gtf_format.html) (Gene Transfer Format) format.
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-***<u>transcriptome.fa</u>***: Transcriptome sequence file in [FASTA](https://www.ncbi.nlm.nih.gov/genbank/fastaformat/) format
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Copy file name to clipboardExpand all lines: docs/2_quick_tutorial/quick_tutorial.md
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permalink: /docs/2_quick_tutorial/quick_tutorial
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---
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### A quick tutorial to run this pipeline
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#Quick Tutorial for Running the Pipeline
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---
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Welcome to the **quick tutorial**of bulk RNA-Seq quantification pipeline! This tutorial aims to provide **immediate and practical guidance**on running this pipeline with your own data. As a quick tutorial, this is mainly for the users who are familar with the basic concepts and tools of RNA-Seq quantification analysis. If you are new to this field, we highly recommend you to start from the **Full Tutorial** that provides more detailed documentation of this pipeline.
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Welcome to the **Quick Tutorial**for the bulk RNA-seq quantification pipeline! This tutorial aims to provide **immediate and practical guidance**for running this pipeline with your own data. As a quick tutorial, it is intended for users who are already familar with the basic concepts and tools of RNA-seq quantification analysis. If you are new to this field, we highly recommend starting with the **Full Tutorial**, which provides comprehensive documentation and step-by-step guidance.
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To get started, please run the command below to activate the conda environment for this pipeline:
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To get started, activate the conda environment for this pipeline using the following commands:
If you have your own conda environment, change the commands accordingly. To set up a conda environment for this pipeline, pelase refer to [Pipeline Setup](https://jyyulab.github.io/bulkRNAseq_quantification_pipeline/docs/pipeline_setup/index) tutorial.
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If you are using a different conda environment, please change the path accordingly. To set up a conda environment for this pipeline, please refer to the[Pipeline Setup](https://jyyulab.github.io/bulkRNAseq_quantification_pipeline/docs/pipeline_setup/index) tutorial.
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####I. Prepare the sample table
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## I. Prepare the sample table
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Below is an example of the sample table for this pipeline:
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It is a **tab-delimited text file with 6 columns**:
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1)**<u>sampleID</u>**: name of samples. Some rules apply:
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- Should contain **letters**, **numbers** or **underscores** only
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- Should NOT **start with numbers**
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- Should contain **letters**, **numbers** or **underscores** only;
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- Should NOT **start with numbers**.
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2.**<u>libraryType</u>**: type of libraries, `[PE | SE]`.
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2.**<u>libraryType</u>**: type of libraries, paired-end or single-end, **`[PE | SE]`**.
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-My input files are in BAM/SAM format, and I'm not sure about the library type of them? The command below can tell that:
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-For BAM/SAM file inputs, not sure about their library type? You can use the command below to determine the library type:
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```bash
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## To tell the BAM/SAM files are single- or paired-end
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# It returns 0 for single-end sequeing. Otherwise, the input bam/sam file is paired-end.
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```
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3. **<u>phredMethod</u>**: Phred quality score encoding method, `[Phred33 | Phred64]`. Not sure about the answer? These two rules can help tell that:
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- For FASTQ file inputs, you typically have two paired files for**`PE`** data or one single file for **`SE`**data. An exception, though exceedingly rare, is an **interleaved** FASTQ file, where both **mate1** and **mate2** reads are combinedin a single file. ***This pipeline does not support interleaved FASTQ files as standard input.*** If you data is in this format, you will need to split it into two seperate FASTQ files before including them in your sample table.
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- Phred64 was retired in late 2011. Data genrated after that should be in Phred33.
- We recommend to use `hg38`for human samples, and `mm39`for mouse samples. The assembly, `hg19` and `mm10`, are mainly used to match some legacy data.
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- For other species or genome assembly, you will need to manually create the required reference files following [this tutorial](https://jyyulab.github.io/bulkRNAseq_quantification_pipeline/docs/pipeline_setup/reference.html).
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If you are using a different conda environment, please change the paths accordingly. To set up a reference genome database, please refer to the [Database Preparation](https://jyyulab.github.io/bulkRNAseq_quantification_pipeline/docs/1_pipeline_setup/2_database.html) tutorial.
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5. **<u>input</u>**: input files for quantification. This pipeline accepts:
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- We recommend to use **`hg38`**for human samples, and **`mm39`**for mouse samples. The other assemblies, **`hg19`** and **`mm10`**, are mainly used to match some legacy data.
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- If you can't acccess to the paths listed above, or if you require other genome assemblies, you will need to manually create the reference files by following the [Database Preparation](https://jyyulab.github.io/bulkRNAseq_quantification_pipeline/docs/1_pipeline_setup/2_database.html) tutorial.
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- **<u>Standard FASTQ files</u>**: both paired-end (sample1) and single-end (sample2). Filenames should be ended with **`.fq`**, **`.fastq`**, **`.fq.gz`** or **`.fastq.gz`**.
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5. **<u>input</u>**: input files for quantification. This pipeline accepts the following formats:
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- **<u>FASTQ files of multple lanes</u>**: both paired-end (sample3) and single-end (sample4). Filenames should be ended with **`.bam`**or **`.sam`**.
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- **<u>Standard FASTQ files</u>**: both paired-end (e.g., sample1) and single-end (e.g., sample2). Filenames must be ended with **`.fq`**, **`.fastq`**, **`.fq.gz`** or **`.fastq.gz`**.
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- **<u>BAM/SAM files</u>**: single file only (sample 5 and sample6). For samples with multiple BAM/SAM files (usually splited ones), please merge them first:
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- **<u>FASTQ files of multple lanes</u>**: both paired-end (e.g., sample3) and single-end (e.g., sample4). Filenames must be ended with **`.bam`** or **`.sam`**.
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- **<u>BAM/SAM files</u>**: alignment files with filenames ended with **`.bam`** or **`.sam`** (e.g., sample 5, sample6). Only single files for each sample are accepted. If your sample consists of multiple BAM/SAM files (such as splited files), please merge them before proceeding:
6. **<u>output</u>**: path to save the output files. This pipeline will create a folder named by the `sampleID` under this directory.
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6. **<u>output</u>**: the directory where output files will be saved. This pipeline will create a subfolder named by the **`sampleID`** within this directory.
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Below are the two ways we recommend to generate the sample table:
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* Any coding language you prefer, e.g. BASH, R, Python, Perl *et. al.*
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* Excel or VIM. And foryou convince, we have a templete avaible on HPC: `/research_jude/rgs01_jude/groups/yu3grp/projects/software_JY/yu3grp/conda_env/bulkRNAseq_2025/git_repo/testdata/sampleTable.testdata.txt`. You can simply copy it to your folder and edit itin VIM. (To insert TAB in VIM: on INSERT mode press control + v + TAB)
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* **Excel** or **VIM**. And for you convince, we have a templete avaible here: `/research_jude/rgs01_jude/groups/yu3grp/projects/software_JY/yu3grp/conda_env/bulkRNAseq_2025/pipeline/testdata/sampleTable.testdata.txt`. You can simply copy it to your own folder and edit it using VIM. For those who can't access to the path, download the template file here.
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### II. Data preprocessing
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## II. Data preprocessing
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The purpose of data preprocessing is to prepare the FASTQ files that can be directly used for down-stream quantification analysis. It contains two steps:
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After these two steps in data preprocessing, you should have the standard FASTQ files with clean sequences. They will be directly used in subsequent quantification analysis.
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### III: Quantification
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## III: Quantification
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In this pipeline, we provide five quantification methods:
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