Research with Computational Biology (ReComBio)

General Instructions

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2024 Bioinformatics Bridge Course

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Research in Computational Biology

• Introduction

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• Research in Computational Biology (ReComBio) PyaR tutorials were thought as a systematic approach under the mission of the Science Internship Program (SIP) and the Genomics Institute at UCSC, to create equity in STEAM (Science, Technology, Engineering, Arts and Mathematics)

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• Initially, Bioinformatics projects mentored by Gepoliano Chaves were ran at the UCSC main campus in Santa Cruz (CA) as part of the Science Internship Program (SIP) during summers

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• SIP projects included a wet lab component where students were exposed to experiments such as DNA and protein extraction, PCR, RT-PCR, western blot, DNA sequencing and/or nanopipette technology demonstration

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Research in Computational Biology

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• Due to the COVID-19 pandemic, SIP was first ran remotely in 2020

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• Over time, it became necessary to provide further support to interns willing to use bioinformatics tools in their research projects

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• Support is necessary for software installation (R, RStudio and Bash) in Windows, MacBook and (potentially) other machine systems

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SIP 2020, 2021 and 2022 Projects

• Links to description of projects identifying SARS-CoV-2 variants can be found at the BME Department at UCSC section of the SIP website.

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• The link below is a good starting point to start digging in the research of these projects:
• https://sip.ucsc.edu/2021-research-projects/

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• Variant detection projects, including Huntington’s Disease projects, can be found at the SIP web-page indicated above

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Computational tasks: Algorithms

• The next section covers tasks that can be accomplished using the computational concept of an Algorithm

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The concept of a Computational Algorithm

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Pseudocode and code steps

1. Describe in human language what the question is
1. How does a gene set affect the EFS and OS survival rates?
2. Start putting in R language, mixed with human language
3. Write your command or script

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Clustering and Classification: the concept of Algorithm

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• How many paws does the animal have?

(2 or 4)

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• Does the animal have feather or wool

(1 or 2)

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SARS-CoV-2 Clustering based on Mutation Frequency (Incidence - Epidemiology)

• A Bash (Linux) pipeline calculates mutation frequency in VCF files

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• This frequency is used to cluster SARS-CoV-2 sequences from different regions of the globe

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Algorithm or Pipeline

The algorithm (also called a pipeline) needs to objectively explain how we go about answering our question or solving a problem

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• Align to reference sequence (FASTA)

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• Compare alignment to reference (SAM)

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• Annotate differences (mutations) (VCF)

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• Extract mutations from VCF (Frequency Table)

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Bioinformatics Software Development

1. Software development considers the analytical steps in human language
• What are the exact steps that are necessary for execution of the analysis?

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• Then, the software product considers the steps the machine will execute
• How files are produced and what are the processing steps?
• Where in the computational infra-structure are the files stored?
• How can these files be accessed?
• What information is contained in the files?

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Bioinformatics Software Development

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1. The present program is about how a scientific question is answered, not what the final answer is

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1. If how the question is answered is not addressed, opportunity is lost in terms of information that is embedded in the process of data analysis

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1. This is an important notion to have when developing computational tools that answer a scientific question

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Project Assignments

By the end of next week I would like you to have decided which of these groups you want to join:

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• Group 1: Huntington’s Disease Visualization

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• Group 2: Pipeline development (Variant Call)

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• Group 3: Data-Base Construction (SARS-CoV-2 and Neuroblastoma)

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• Group 4: Dashboard Visualization (SARS-CoV-2 and Neuroblastoma)

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• SARS-CoV-2 Data visualization
• Neuroblastoma Data Visualization

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Sub-Group 1: Pipeline development

• Download data from GISAID

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• Define regions of interest and samples to be processed (bat and pangolin samples)

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• Think critically about the pipeline design, the commands and the command order

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• Work in the Bash environment

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Sub-Group 2: Data-Base Construction

• Organize the information of the FASTQ and/or FASTA files downloaded

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• Extract Mutation information from the VCF files

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• Design strategies to handle/process the numbers of files processed by the pipeline of Sub-Group 1

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• Construct a Frequency Table for Classification of SARS-CoV-2 samples by geographic regions

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BME02 Sub-groups notebooks

• Every Sub-Group should have a code notebook

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• By the 4th or 5th week I expect to see your code and notebooks with Descriptors of what the code does
• The Descriptor should express the task assigned to each Sub-group

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• I should help you in documenting your code on Github/Gitlab

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GISAID Sample Download

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• Proceed to Slide 45

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• Part 3: Variant Call Pipeline using GISAID data

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• Start addressing GISAID sample data download

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Study Plan for 09/29/2021

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Study Plan for 09/30/2021

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Study Plan for 10/01/2021

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Study Plan for Week 1

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Study Plan for Week 2

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Study Plan for Week 3

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• Introduction to Python and Machine Learning prediction of neuroblastoma

Example of Hierarchical Clustering Visualization

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Cluster 1: China, Bat, Pangolin

Cluster 2: Germany-Argentina

Cluster 3: Australia

Study Plan for 10/01/2021

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Operating System (OS)

• I am assuming people use either an Apple MacBook or a Windows machine

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• Both Windows and MacBook support RStudio usage

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• The advantage of a MacBook is the built-in Terminal interface which can be easily accessed and used by the student/researcher

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• If your machine is different from the machines described above, please talk to me so we can find a solution

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Operating System (OS)

• Windows users need to install Putty, Git or WSL
• Putty seems the easiest solution for Windows users
• I used Putty when I was a Windows user

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• For Putty installation, follow the recommendations in the Amazon Machine Image link (same link as syllabus) in the Data Carpentry Genomics Workshop
• https://datacarpentry.org/genomics-workshop/AMI-setup/

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• For Ubuntu (WSL) installation, follow the recommendations in the link below.
• It should guide you to install Bash for the R Markdown notebooks:

https://ubuntu.com/tutorials/ubuntu-on-windows#1-overview

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• More instructions on WSL and Ubuntu installation can be found in the slack channel and in the next slide

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Sub-Group 3: Dashboard Visualization

• Construct visualization tools

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• Visualization through a Dashboard or a web application

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• Use Shiny (R) or Python to visualize information provided by Sub-Groups 1 and 2

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• Understand the processing steps taken by Sub-Groups 1 and 2

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• Make the application available in the internet

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• Make the application take user's input

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Installing Ubuntu (Windows users)

• Windows users need to install Windows Subsystem for Linux Installation (WSL) to have access to the Linux/Bash Terminal

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• Follow instructions in the following link for Ubuntu installation
• https://docs.microsoft.com/en-us/windows/wsl/install-win10

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• After installing Ubuntu, you can access the AMI, by using ssh from the Ubuntu Linux Terminal

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WSL/Ubuntu installation (Windows)

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Complete the 6 steps in the instructions

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Download and Install R and RStudio

• R

The R Project for Statistical Computing

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• RStudio

https://www.rstudio.com/products/rstudio/download/

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R, RStudio and R Markdown installation (Windows users)

• Watch the ReComBio video on “Bash in Windows for R Markdown”, recorded on Monday, 07/19/2021

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• The video starts with instructions on how to download R and RStudio

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• 00:15:00 - Access .Rmd file
• 00:17:00 - Install packages in R
• 00:21:00 - Bash chunks (need Bash/Ubuntu installed in Windows)
• 00:50:00 - Dealing with path problems in R in Windows
• 01:14:00 - 01:28:00- Ubuntu and WSL installation
• 01:24:00 - Troubleshooting Ubuntu Password
• 01:26:00 - Connecting Ubuntu to R Markdown (RStudio needs to be re-started)
• 01:27:00 - Terminal -> Terminal Options -> New Terminals open with -> Bash (WSL)

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R Markdown installation for Windows users

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• The critical point of Ubuntu installation for Windows users is around 01:27:00
• Seen in Video “Bash in Windows for R Markdown”, recorded on Monday, 07/19/2021

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• As seen in the next slide, once Ubuntu is installed, Bash can be used from Rmd following these steps
• Terminal -> Terminal Options -> New Terminals open with -> Bash (WSL)

R Markdown installation for Windows users

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Downloading Git

• In helping the user to install Bash for R Markdown in Windows, we notice that Bash is not installed in their Windows Machine

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• After installing Git, the user will have the challenge of downloading bwa, samtools, bcftools

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• Downloading these softwares is necessary to run Bash from inside the R Markdown notebooks that

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• Alternatively, the user can run the alignment pipeline from the Command Line

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• 00:35:00 - bash not recognized error, but not addressed

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Download Git (Windows Users)

• Google Search: rstudio git bash terminal

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• https://www.bioinformatics.babraham.ac.uk/training/RStudio_GitHub/Initial_setup.html

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Installing Shiny in a machine running Git

• The link bellow helped me while installing Shiny in a Windows machine

https://community.rstudio.com/t/install-packages-unable-to-access-index-for-repository-try-disabling-secure-download-method-for-http/16578

• For this question in R: Update all/some/none? [a/s/n]:
• Start with “n”, the same as “No”

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R Markdown installation and Layout

Once properly installed, these are the main advantages of R Markdown:

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• Documentation of the code with comments and notes

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• Publication-quality figures and slides

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• User is free to use multiple languages in parallel
• Python
• R
• Bash

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R Markdown Layout

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Insert Chunk

Comments about code

Code Chunk

Console

Plots

R Markdown Cook-Book

• https://bookdown.org/yihui/rmarkdown/

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• R Markdown cheat-sheet:
• https://rmarkdown.rstudio.com/lesson-15.HTML

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Part 1: The Data Carpentry Genomics Workshop

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• Data Carpentry Genomics workshop

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• Following onboarding slides instructions should be sufficient for these two interactions

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Part 2: Three Iterations of Software Installation

• There should be plenty of information on installation in the recorded videos and with instructors

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• Software to be installed by 09/03:
• R and RStudio (1)
• Ubuntu/Bash/WSL and R Markdown (2)

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• SARS-CoV-2 Pipeline
• splitfasta
• Anaconda (3)
• bwa
• Samtools
• Samtools and Bcftools (Alternative)

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Download Anaconda

Docs instructions:

https://docs.google.com/document/d/1OWulKk9-9cJTExe4Bo5xq-VKgWrdw2Uy54RndjyrzJM/edit

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Anaconda installation allows downloas of bwa and split-fasta

https://anaconda.org/bioconda/bwa

https://pypi.org/project/split-fasta/

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The following link will download a Shell Script (Bash) that will be run with the Bash command to install Anaconda in the machine. One option is (64-Bit Command Line Installer (584 MB))

https://www.anaconda.com/products/distribution

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The Following link (Documents the steps to download Anaconda)

https://docs.anaconda.com/anaconda/install/mac-os/

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Part 3: Variant Call Pipeline using GISAID data

• Students need to request access prior to downloading data from GISAID

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• Global Initiative on Sharing Avian Influenza Data

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• Request access to GISAID:
• https://www.gisaid.org/register/
• This step may take some time

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GISAID Download Instructions

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• GISAID download Instructions are available in the SIP2022 BME02 Recording of 2022-06-24

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• Time of the recording was 17.03.17
• there are two recordings for 2022-06-24)
• this image is from 17.03.17 (5:03:17 PM)

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• Download instructions start at 01:12:55 of the video
• Make this video available in ReComBio folder, not only SIP2022

GISAID Download Instructions

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• Note the Search Tab indicated by the red arrow
• You should be able to click the Search Tab

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• After logging into GISAID, you need to click the Search tab

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• After clicking Search, follow the sequence: Search -> Location -> Collection Date -> Submission Date to filter the files of the region of your interest
• Note how this is done in the image in the left

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• The image on the left illustrates filtering GISAID data using the following parameters:
• Location: Brazil
• Collection: 2020-07-01 and
• Submission: 2020-12-31

Access to GitHub Markdown Notebooks

• Go to
• https://github.com/gepolianochaves/Espanol
• https://github.com/gepolianochaves/English
• https://github.com/gepolianochaves/Portugues

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• As shown in the next slide, click “Code”, then Download Zip

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• The notebook should be ready to be used in your computer
• To run the notebook, you will need to have R and RStudio installed

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Download R Markdown Notebook from GitHub

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SIP2022 BME02 Markdown Notebooks

• Notebooks for SIP2022 can be found at
• https://gitlab.com/genomics-research/recombio

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• Identify the Download button, near the Clone button as indicated in the next slide

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• You need to decide on which of the 4 notebooks you want to download

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• You can download for instance the 1-Introduction repository or all of the 4 repositories at the same time

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SIP2022 BME02 Markdown Notebooks

• If you click the download button, you will be lead to download the main repository, containing the 4 sub-repositories indicated by the green arrows

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• You can also click one of the four repositories (1-Introduction, 2-Variant-Call-Pipeline, etc) instead

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• This last action will download each sub-repository at a time

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• The image below is the screen from https://gitlab.com/genomics-research/recombio

Gitlab Commit Documentation

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• Commits are used to record modifications made to repository

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• Show example of commit in Gitlab Repository

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• Notebook descriptor

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Git clone repository in the local machine

• Be careful because you may delete files when using Git in the command line

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• git clone $path in the directory of interest will mount the GitHub repository in the local machine

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• Later, we will be able to collaborate as we progress to develop our coding abilities

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• Collaboration is done through GitHub Branching

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Create Branches and Push Modifications to gitlab Repo

• git checkout -b rachelle_shiny_r_script YourBranch

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• git add ./

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• git commit -m “Rachelle Shiny Application Notebook”
• Git only allows you to push the Origin as upstream
• You need to use git push --set-upstream origin YourBranch

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• git push --set-upstream origin YourBranch

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Create a Merge Request

• In blue pop-up click “Create Merge Request”
• Assign it to me

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• git add ./

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• git commit -m “Rachelle Shiny Application Notebook”
• Git only allows you to push the Origin as upstream
• You need to use git push --set-upstream origin YourBranch

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• git push --set-upstream origin YourBranch

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Instructions for R Markdown Notebook Use

• Notebook 1: Introduction to R and Genotype-Phenotype Association

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• Notebook 2: SARS-CoV-2 Variant Call Pipeline - NB2
• Requires Anaconda installation
• Requires several software installation steps
• This may be different for Windows and Linux (MacOS) systems
• After software installation, folders fasta_reference_file, SARS-CoV-2_Regions need to be filled with FASTA files from the region of interest

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• Notebook 3: Hierarchical Clustering of SARS-CoV-2 Mutations
• Need to decide whether will incorporate the Web Application development aspect
• This was awarded 1st place in the Poster Presentation of a Research Symposium

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Alex’s Lemonade Stand Foundation (ALSF) Git Workshop

Course Website

• https://alexslemonade.github.io/reproducible-research/

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Schedule (Contains Slides at the end of page)

• https://alexslemonade.github.io/reproducible-research/workshop-schedule.html

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Resources List (Variety of relevant resources that may be of interest)

• https://alexslemonade.github.io/reproducible-research/reproducibility_resources.html

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