This tutorial is written for those with a Cloud Assembly license, whether purchased or from a free trial. The tutorial shows how to quantify differential gene expression by comparing two experimental E. coli bacteria samples and two wild-type samples to the E. coli K12 MG1655 reference sequence. In this example, the flhD gene has been knocked out in the experimental samples, causing a loss of gene function. You don’t need to download any data to follow this tutorial, as it is provided in your Cloud Data Drive.

To perform the assembly:

  1. Launch SeqMan NGen. If SeqMan NGen is already open as the result of setting up a previous assembly, close it and open it again before starting the tutorial.
  1. In the Begin Project screen, select Assembly on the DNASTAR cloud. At the prompt, enter your Email and the Password sent when you purchased or requested free trials of DNASTAR Cloud Assembly. Press Next.
  1. In the Cloud Assembly screen, click Next.
  1. In the Choose Assembly Workflow screen, select Transcriptome / RNA-Seq and click Next.
  1. In the Choose Assembly Type screen, keep the default Reference based assembly and click Next.
  1. In the Input Reference Sequences screen:

    1. Press the Add Genome Package button.

    2. In the pop-up, select Escherichia coli K12 MG1655 and click Select.

    3. Back in the wizard, press Next.
  1. In the Input Sequence Files and Define Experiments or Individual Replicates screen:

    1. Press Cloud Demo Data.

    2. In the pop-up, use Shift+click to select all four rows beginning with 2 Ecoli RNA-seq and click Select.

    3. Back in the wizard, select Illumina from the Read technology drop-down menu. Note that the Paired-end data and Multi-sample data boxes are checked automatically.

    4. Check the box next to Samples have replicates.

    5. In the Individual Replicates column, click twice on the uppermost cell to enable editing and type in the name flhD 1. Repeat with each of the other three cells, naming the replicates flhD 2, WT 1, and WT 2.

    6. Click Next.
  1. In the Group Individual Replicates into Replicate Sets screen:

    1. Use Shift+click to select the first two rows.

    2. Press Group Selected.

    3. In the pop-up, type the name flhD and click OK.

    4. Repeat steps a-c for the bottom two rows, naming them WT (“wild type).

    5. Click Next.
  1. In the Set Up Experiments screen, check the box to the right of WT and click Next.
  1. In the Assembly Options screen, choose the Normalization method named DESeq2 and click Next. Note that this method requires that both experimental and control samples have replicates, which are present in the current case.

  1. In the Assembly Output screen:

    1. Enter the project name Ecoli RNA-Seq.

    2. Press the Browse button to launch the DNASTAR Cloud Data Drive window:

      1. Within the Cloud Data Drive, press the Home tool ().

      2. If you see a folder named Assemblies, skip to Step iii. Otherwise, click the Create a new folder on the cloud tool on the top right. In the pop-up box, type in Assemblies and press Done.

      3. Single click on the Assemblies folder to select it, then press the green check mark in the bottom right corner of the DNASTAR Cloud Data Drive window.

    3. Back in the Assembly Output screen, press Next.
  1. In the “Your assembly is ready to begin” screen, press Start Assembly to begin the assembly.
  1. Assembly for this tutorial should take approximately 30-60 minutes, and you can check the status of the assembly at any time. Due to the size of the Tutorial 4 data set, we do not recommend starting another tutorial until the current assembly has finished.
  1. When the Tutorial 4 assembly is complete, compare the sample mutations by downloading the 2MB Assemblies/Ecoli RNA-Seq project folder and double-clicking on the file Assemblies/Ecoli RNA-Seq/Ecoli RNA-Seq-qng/Ecoli RNA-Seq.astar to analyze the results in ArrayStar. For a similar analysis that involves a comparison between flhC, flhD and wt, see Tutorial 3, Part B.

In addition, be sure to open ArrayStar’s “Scatterplot” to see the differential gene expression results from the DESeq2 analysis. The differentially expressed genes stand out clearly, as shown in the image below:

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