README.md

A cloud based resource for research and training

About BISR

Faculty and staff at Georgetown University Innovation Center For Biomedical Informatics (Georgetown-ICBI) provide expert support services and work with researchers to select what data analysis approach could be most insightful for different studies, clinical datasets organization, and running data through bioinformatics pipelines to help answer research questions.

Consultations often involves study guidance, information about available resources, systems and tools, and analytic services depending on the scope of the project. The general areas of consultation include: next generation sequencing analysis, molecular profiling analysis, big data analytics, data integration, visualization, systems biology analysis, G-DOC, and bioinformatics software training.

These services are offered to the Georgetown Univiersity community through the Biomedical Informatics Shared Resource (BISR)

Learn more about BISR and how to request a consultation:

• https://icbi.georgetown.edu/services-new/
• https://lombardi.georgetown.edu/research/sharedresources/bbsr/

Learn more about Georgetown-ICBI:

• https://icbi.georgetown.edu

Contact information

• Yuriy Gusev PhD, Co-Director (yg63 at georgetown dot edu)
• Adil Alaoui MS MBA, Co-Director (alaoui at georgetown dot edu)
• Matt McCoy, PhD, Assistant Professor
• Krithika Bhuvaneshwar MS, Senior Bioinformatics Scientist

Suggested tutorials for beginners

• Introduction to R
  • NIH /NIAID webinar: https://bioinformatics.niaid.nih.gov/resources#20.3.1
  • From UCLA : https://qcb.ucla.edu/collaboratory/workshops/w3-intro-to-r/
  • Datacamp courses
• Introduction to Data Science
  • Coursera courses from Johns Hopkins
• Introduction to command line unix
  • From UCLA: https://qcb.ucla.edu/collaboratory/workshops/w1-intro-to-unix-command-line/
  • Datacamp courses
• Introduction to NGS data and analysis
  • From UCLA: https://qcb.ucla.edu/collaboratory/workshops/w4-galaxy-for-ngs-data-analysis/
  • Genomic Data Science courses in Coursera from Johns Hopkins
• Intoduction to Python for Biologists
  • NIH /NIAID webinar by Richard Squires : https://bioinformatics.niaid.nih.gov/resources#70.3.1
• Online platform for data analysis
  • Chipster - User-friendly open source tool for data analysis : https://chipster.rahtiapp.fi/home
  • Chipster tutorials: https://www.youtube.com/@ChipsterTutorials
• Other Bioinformatics Resources for learning
  • https://github.com/mdozmorov?tab=repositories
  • https://github.com/seandavi?tab=repositories

Advanced Tutorials available from Georgetown-BISR (requires programming experience)

A folder has been created in this github repo for the following topics. Each folder has description summaries and tutorials with R code for hands-on exercises.

RNA-seq

• RNA-seq summary: https://github.com/ICBI/BISR_Tutorials/blob/main/RNA_seq/README_RNAseq.md
• RNA-seq group comparison analyis using Google Colab in R: https://github.com/ICBI/BISR_Tutorials/blob/main/RNA_seq/RNAseq_CompareGroups.ipynb

Single Cell RNA-seq

• Summary of single cell RNA-seq (scRNA-seq) https://github.com/ICBI/BISR_Tutorials/blob/main/scRNA_seq/README_scRNAseq.md
• Analysis of Single-Cell RNA-seq data using Seurat toolkit using Google Colab in R https://github.com/ICBI/BISR_Tutorials/blob/main/scRNA_seq/Notebook_SCRNAseq_Seurat.ipynb
• Analysis of Single-Cell RNA-seq data using Seurat toolkit using Google Colab in R (mouse genome example) https://github.com/ICBI/BISR_Tutorials/blob/main/scRNA_seq/SCRNAseq_Seurat_Tutorial2.ipynb
• Analysis of Single-Cell RNA-seq data using Monocle3 using Google Colab in R --> under construction

Machine learning concepts

• Tutorials about machine learning concepts using using Google Colab in Python: https://github.com/ICBI/AIMAHEAD_GU/tree/main/Courses/ML_Concepts

ChIP-Seq

General information and resrouces: https://github.com/ICBI/BISR_Resource/blob/main/ChIP-Seq.md

Spatial transcriptomics

General information and resources: https://github.com/ICBI/BISR_Resource/blob/main/sptial_transcriptomics/About_spatial_tx.md

Imaging (future)