Setting up for Metagenome functions

by Maria Jane Poncardas

written: September 4, 2019


SETTING UP FOR DETERMINING METAGENOME FUNCTIONS

  1. Download Oracle virtual box from https://www.virtualbox.org/wiki/Downloads > VirtualBox 6.0.8 platform packages > Windows hosts.

  2. Install the Virtual Box (which was downloaded from step 1). You would receive a warning, but do skip ahead.

  3. Download the 64-bit QIIME Virtual Box, which is linked from http://qiime.org/home_static/dataFiles.html. Grab the latest QIIME Virtual Box image, in our case, QIIME 1.9.1 64-bit. Its file size is around 4GB. This is downloaded as a compressed file, hence you need to unzip it. You should see a red cube icon for this file and is a VDI file extension (Virtual disk image).

  4. Launch the installed Virtual box (from step 2) and create a new machine by pressing the “New” button.

    1. A new window will show up and then click ‘Next’

    2. Select the Linux as operating system and Ubuntu (64 bit) as its version, then proceed.

    3. Select the amount of RAM (memory), in our case, we allocated 4GB (from a 16GB RAM machine).

    4. Select “Use existing hard drive, and click the folder icon next to the selector. Click “Add”, and then select the QIIME hard drive that was downloaded from step 3.

    5. Click Finish.

  5. Select the virtual machine and give a name to it, (e.g. QIIME), and boot it for the first time by clicking on the “Start” button on the top panel.

  6. Now install QIIME inside this virtual machine (Ubuntu 64 bit).

    1. Click on the “Devices” on the topmost menu inside the Linux machine, then select Insert Guest Additions CD image. Basically, they have a CD in this virtual computer.

    2. A new window appears and simply click Run.

    3. Another prompt is expected to open as it will request a password. Just type, “qiime”, in all lower case letters to authenticate.

    4. Once done, click on the CD icon on the left side panel of the Ubuntu and it will show the contents of the CD. After this, also open the terminal.

    5. Inside the terminal, type in “sudo”, without the quotation marks, and on the folder in step d., drag over the VBoxLinuxAdditions (this file name may vary over time).

    6. In the terminal line, remove the apostrophes before and after the file name and then press Enter.

    7. It will ask you to write the password for qiime. Enter the same password as mentioned above (step c). This will not show you asterisks or any typical way on typing passwords. And click enter after that.

    8. Shut down your virtual machine. Make sure that it is powered off inside the Oracle VM virtual machine.

  7. Create a Shared folder that you can use to transfer files to and from your virtual machine.

    1. Click on the Shared Folders inside the Oracle VM.

    2. Click on the blue envelop button with a green cross in it.

    3. On the “Folder Path”, select the location which you could transfer your files to your virtual machine and make sure that the “Folder Name” is named as “Shared_Folder”, following the exact capitalizations.

    4. Once you open your QIIME virtual machine, you should see the changes on the folder appearance of the Shared_Folder on the Desktop.

  8. Now, it is recommended to install the Miniconda which could hold the Bioconda library. The easiest way to install PICRUSt is using the bioconda. This Bioconda supports only 64-bit Linux and Mac OSX. (see https://bioconda.github.io/) 

citation: Grüning, Björn, Ryan Dale, Andreas Sjödin, Brad A. Chapman, Jillian Rowe, Christopher H. Tomkins-Tinch, Renan Valieris, the Bioconda Team, and Johannes Köster. 2018. “Bioconda: Sustainable and Comprehensive Software Distribution for the Life Sciences”. Nature Methods, 2018 doi:10.1038/s41592-018-0046-7.

  1. Inside the virtual machine, download Python 2.7 for Linux 64-bit from https://docs.conda.io/en/latest/miniconda.html 

  2. In the terminal, navigate to the downloaded file of miniconda by changing its directory (use cd), and then type in “bash”, without the quotation marks, and type the file name.

  3. Once you are completed, close the terminal.

  1. Then let us install the Bioconda channel through the terminal in this order:

conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

  1. Install the packages:

conda install bwa

conda install -c bioconda picrust

download_picrust_files.py

Comments

Post a Comment

Popular posts from this blog

Using QGIS to convert .shp file to .geojson (GADM)

Image
by Maria Jane S. Poncardas, May 16, 2019      At the moment of writing, there is no direct method on how to utilize raw file from Database of Global Administrative Areas (GADM) into Python script to create charts in Bokeh package. GADM provides maps and spatial data for all countries and their sub-divisions. The goal here is to extract administrative areas in Mindanao (Southern Philippines) as it will be used for spatial and temporal analyses on terrorism (with which utilizes Bokeh for visualization). Sure, there are plenty of workarounds (such as using Plotly or simply Seaborn), but this article focuses on such as it deals with maps. I hope a few portion of this article may help you. CONVERT .SHP TO .GEOJSON USING QGIS Download shapefile from Database of Global Administrative Areas https://gadm.org/download_country_v3.html Extract downloaded file by right-clicking on the file then from the dropdown menu, select “Extract files to <filename>\ ” In the case of Ph...
Read more

LSTM pseudocode

Image
by Maria Jane Poncardas, July 19, 2019 Forecasting models are generated based on the training of historical data of six (6) <confidential hydroelectric company> plants, which is from January 1, 2011 to May 4, 2014. The author used Python Jupyter notebook to construct training codes for RNN-LSTM forecasting method. To perform such training, missing values need to be treated hence imputation methods were employed such as Kalman filtering, per plant’s maximum rated capacity and the triple exponential smoothing. The following are the steps resembling information of the code for the forecasting: PREPROCESSING : Import necessary packages and modules for the training: pandas - is used to extract power generated out from the consolidated dataset of excel file Tensorflow’s keras is utilized to build the architecture of RNN and is also used to initiate the model sklearn is for transforming features of data by scaling to a given range, in this case, between zero and one. The dataset being r...
Read more

On-the-Job training

Image
by Maria Jane Poncardas      When I was scrolling through my documents in Physics. I stumbled upon the daily journals of my on-the-job training weekly experiences as we were mandated to write our journey. I thought I would share my immersion experience in the National Institute of Physics, University of the Philippines - Diliman through this blog as it is a precious moment in my physics life.      When we were looking for a company and laboratory to have our OJTs, we were encouraged by our seniors to gain relevant research skills through the Condensed Matter Physics laboratory (CMPL) in National Institute of Physics, UP Diliman, since the physics laboratory in our university (Mindanao State University - Iligan Institute of Technology) has a solid foundation in the field of Materials Science research. We have bountiful of options to choose from the National Institute of Physics, as they are engaged in various research areas: condensed matter, instrumentation...
Read more