Data Analysis Tools

• Online Array CGH Analysis Tool (under construction)
• Primer Database:  Contains validated genome-wide sequencing from a published article by Sjoblom, et al. Science 314:268-74, 2006
• NB-cMAP:  NCI-Neuroblastoma Connectivity Map

Bioinformatic Analysis Training

Code locations: 

   ChIP-seq: /data/khanlab/projects/ChIP_seq/projects/

   RNA-seq: /data/khanlab/projects/ChIP_seq/RNA_DATA/RNA_projects/

Section 1: Navigating Biowulf, Pipeline Output, and Unsupervised Correlation 

Video 1.1: What is biowulf? What is the pipeline? 

• ChIPseq Bioinformatic Pipeline Overview (0:00)

• Locate your data (2:45)

• Input metadata into ChIP_seq_samples.xlsx (4:20)

• Example of pipeline output (8:15)

• Viewing files in IGV (8:50)

• Load from ENCODE (10:10)

• Choosing a p-value (12:10 and 21:40)

• GREAT ontology (14:35) 

• Viewing summary files to determine total number of peaks (20:15)

• Homer motif analysis (23:10)

• Identifying mutations from ATACseq data (27:50)

Video 1.2: Summarize peaks 

• Where to find scripts (1:10)

• Logging on to biowulf and running scripts (2:15)

  • in putty, type biowulf2.nih.gov and click “Open”

  • answer username and password prompts

• Navigating in biowulf (4:40)

  • type: cd /data/khanlab/projects/ChIP_seq/

• Code_Builder (8:40)

  • ***code_builder created during these videos is saved onto the google drive***

• Summarize peaks across samples (10:40)

  • Find ChIP_seq_samples: khanlab/projects/ChIP_seq/manage_samples

• Using summarized peaks to determine p-value (21:40)

• Summary of other analyses that can be done with ATACseq data (28:00)

Video 1.3: Run an unsupervised correlation 

• Figures produced by unbiased correlation of ATACseq data (0:00)

• What is the folder path for correlations? (0:45)

• What files are generated? (1:30)

• Code_Builder for correlation (2:30)

• Create sample files using RStudio (4:00)

• Defining different p-values for your samples for all downstream analysis (14:00)

• Run correlation on created sample files using biowulf (30:15)

  • ***Ultimately does not work, skip to Video 1.4 ***

• Checking your “queue” on biowulf (31:40)

• Output of correlation (38:00)

Video 1.4: 

• What was done in Video 1.3 (0:00)

• Code_builder explanation (1:10)

• How to format bed, bam, and sample list files to be Unix compatible (1:30)

• Checking the slurm (5:50)

• Viewing peaks from generated beds in IGV (11:00)

Section 2: Running BCHNV 

Video 2.1: 

• What is BCHNV? (0:00)

• Gathering necessary (configuration, run, etc.) files (7:00)

• Code_Builder BCHNV (9:20)

• Customizing BCHNV run parameters in the shell script and code_builder (11:50)

• Editing configuration file (22:20)

• Determining color scheme (31:20)

• Gathering .bed files (38:00)

• Edit permissions (49:00)

Section 3: Gathering, Merging, and Combining Bed Files

Video 3.1: “bed tools” 

• Using ChIP_seq_samples.xlsx to gather beds (0:00)

• Intro to RStudio (2:30)

• Gathering .beds with RStudio (6:00)

• Merge .beds using bedtools merge, how to count, etc (20:00)

• Running script on biowulf (29:00)

Video 3.2: Combining beds with “.cat” 

• How to combine bed files that are not in the ChIPseq_samples excel spreadsheet

• Shortcut to combine all files with the same ending in a folder (2:40)

Section 4: Downloading Data from Gene Expression Omnibus (GEO)

Video 4.1: RNA data

• Downloading RNA data using ChIPstack and the ChIPseq pipeline (0:00)

  • https://www.ncbi.nlm.nih.gov/geo/

Video 4.2: ChIPseq or ATACseq data 

• Downloading ChIP or ATAC data using ChIPstack and the ChIPseq pipeline (0:00)

  • https://www.ncbi.nlm.nih.gov/geo/

Section 5: Processing and Analyzing RNAseq Data (NOT main Khan Lab Pipeline)

Video 5.1: 

• Overview on pipeline output (0:00)

• RNAseq Code Builder (3:40)

• Putting RNA samples in ChIP_seq_samples.xlsx (6:45)

Video 5.2: 

• Review of RNAseq pipeline outputs (0:00)

• Locating R scripts (1:40)

• TPM vs. FKPM (4:30)

  • https://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/

• Viewing values in R (7:55)

• Build Matrix (11:00)

• Make a gene expression heat map (13:40)

• Create a comparison scatter plot (16:20)

• GSEA rank list maker (24:30)

• Download GSEA (39:40)

  • software.broadinstitute.org/gsea/login.jsp

• Heat map for smaller gene sets. (48:00)

• GSEA Output (57:55)

Video 5.3: 

• Where to find/What is RNA_projects (0:00)

  • Notes: 

    • Clear environment in R = “Ctrl” + “Shift” + “F10”

    • View a “value” in R = start typing the name, “up arrow”, “enter”

• Create TPM Matrix for RNAseq data (1:45)

• Inversion grep (14:09)

  • “invert=T” 

• Visualize with a heatmap (18:10)

• Install “pheatmap” (19:08)

• Heatmap with only genes of interest (23:20)

  • Change subset, meaning select columns from matrix (25:30)

• Confirming expression level with IGV (29:20)

  • VERY important when using Access RNAseq data

  • Look for 5’UTRs and long exons without probes 

• Identify housekeeping genes (34:00)

  • K:/projects/ChIP_seq/RNA_DATA/RNA_projects/Genesets/Qlucore_format/HouseKeeping_genes.txt 

  • From Cancer Discovery paper… a compilation of a variety of normal tissues

• Define housekeeping genes within your own sample set (39:00)

• Use a list to compare 2 gene sets (44:00)

• Transcription Factors gene set - Based on the Broad’s + Berkley’s additions (49:00)

  • K:/projects/ChIP_seq/RNA_DATA/RNA_projects/Genesets/Qlucore_format/TFs_Epimachines.genelist.txt 

• Find specialized TFs for your sample set (52:30) 

• Edit heat map’s scale (1:08:15)

• Overview of TF identification strategy (1:13:00)

• Video summary/where to find files (1:19:40) 

Video 5.4 (Continuation of Video 5.3): 

• Load in second set of samples (1:00)

• Identify maximum in a matrix (3:30)

• List out genes in a defined gene set (9:00)

• Explanation of how we could theoretically ignore UTRs and compare multiple RNAseq data types (20:00)

• What you can compare using Access RNAseq data (21:40)

• Ideas of how to look at your data after you’ve filtered out multiple gene lists (26:00)

• Using Microsoft Excel to look at your data and run T test, log scale, etc (36:30)

Section 6: ChIPseq qPCR, Pipeline, and Analysis 

Video 6.1: Designing ChIP qPCR Primers

• Rationale on where to design primers (0:20)

• Visualizing data in IGV (3:00)

• Choosing locations (8:00)

• “Designing Primers for ChIP.docx” (10:00)

  • follow links and instructions in file

• Go to www.idt.dna.com/pages 

  • use Young’s account info, and tell him you have primers to order

  • 25 nm DNA Oligo with Standard Desalting

• Add primers to qPCR_primers_v2.bed using genome.ucsc.edu (18:30)

Video 6.2: How to run ChIP qPCR

• Where do I find the files and which files are there? (1:00)

• Setting up the plate in excel (3:00)

• Run qPCR follow protocol in “ChIP-qPCR ViiA7 protocol.docx” (12:00)

Video 6.3: Auto-launching ChIPseq Pipeline 

• See “Video 1.1” for a more detailed overview of the pipeline output

• Input metadata (0:10)

  • preferably before sequencing run

• Importance of file naming (3:00)

• How to manually launch the pipeline (7:30)

  • SEE YOUNG OR JUN FIRST! (not for a normal situation)

Video 6.4: Gathering Homer Motifs and Summarizing Enhancers

• Where to find files and creating input file (1:00) 

• Viewing all motif data based on all called peaks in excel ( 3:50)

  • ***Note mistake made in sorting… corrected at (16:00)

• Look at homerMotifs.R to see scripts that you can use to manipulate the data (7:00)

  • can upload the created matrix into R and use pheatmap 

• Correlating to Coltron Output (13:30) 

  • “DEGREE_Table” 

  • Use to tease out which TFs are likely more significant 

    • what is expressed? 

    • what has super enhancers? 

Video 6.5: Compare motif enrichment across various groups of samples

• R script = gatherBCHN_motifs.R (0.15)

  • Gather files (7:00) and grep

  • Make matrix (11:00)

  • Heatmap data (13:30)

  • Filter for highly enriched motifs (15:00)

• “Wide format”? (11:30)

• Using generated “allmotifs.wide.text” file with RNAseq data (21:50)

Section 7: Making and Editing Figures 

Video 7.1: Figures for Western Blots in Adobe Illustrator 

• “masking” Illustrator (0:00)

  • object > clipping mask > make 

  • double click inside the box to see inside mask (9:50)

• “rectangle tool” (3:00)

• “rotation” of a bitmap (4:00)

• “align” (9:00)

  • Can make any line straight by using the “Shift” ke