1. Use Case 1: Exogenous exRNA in plasma of patients with Colorectal Cancer and Ulcerative Colitis Wednesday, Nov 5 th, 2014 6:00 – 8:30 pm Organized and Hosted by the Data Management and Resource Repository (DMRR) Data kindly provided by David Galas, Pacific Northwest Diabetes Research Institute (PNDRI) ERCC Data Analysis Workshop

2. Background: Comparison of human plasma small RNA profiles of patients with colorectal cancer to those with ulcerative colitis, indicated that a large fraction of reads were not mapping to the human genome. This raised the question as to what was the origin of those small RNAs? Results: Mapping suggested that a significant fraction of small RNA reads were derived from bacterial, fungal, and plant sources. Use Case 1: Exogenous exRNA Wang K., Hong L., Yuan Y., Etheridge A., Zhou Y., Huang D., Wilmes P., & Galas D. (2012) The Complex Exogenous RNA Spectra in Human Plasma: An Interface with Human Gut Biota? PLoS ONE 7: e51009. 2

3. We will use the Genboree Workbench to check what fraction of reads do not map to the human genome. We will also use the output of the small RNA-seq Pipeline to answer the following questions: Do all plasma small RNAs map to the human genome (slide 17)? Which miRNAs are normally present in human plasma (slide 18)? What are the sources of small RNAs found in human plasma that do not map to the human genome (exercise)? Use Case 1: Exogenous exRNA Wang K., Hong L., Yuan Y., Etheridge A., Zhou Y., Huang D., Wilmes P., & Galas D. (2012) The Complex Exogenous RNA Spectra in Human Plasma: An Interface with Human Gut Biota? PLoS ONE 7: e51009. 3

4. Biological Samples to Be Analyzed Patient NumberSampleInput File NameBiosample Metadata # in KB #1Plasma (Colorectal) SM1_crc1_sequence.fastq.gz EXR-022273PF-BS #2Plasma (Colorectal) SM2_crc2_sequence.fastq.gz EXR-022163PF-BS #3Plasma (Colorectal) SM3_crc3_sequence.fastq.gz EXR-022299PM-BS #4Plasma (Ulcerative) SM6_uc1_sequence.fastq.gz EXR-93163PMC-BS #5Plasma (Ulcerative) SM7_uc2_sequence.fastq.gz EXR-93164PMC-BS #6Plasma (Ulcerative) SM8_uc3_sequence.fastq.gz EXR-93166PFC-BS #7Plasma (Control) SM11_norm1_sequence.fastq.gz EXR-D3340PMN-BS #8Plasma (Control) SM12_norm2_sequence.fastq.gz EXR-D3176PFN-BS #9Plasma (Control) SM3_norm3 _sequence.fastq.gz EXR-D3142PFN-BS Input files are located in the Data Selector in the following Group  Database  Folder: Group: exRNA Metadata Standards Database: Use Case 1: Exogenous exRNA in Colorectal Cancer and Ulcerative Colitis Folder: 1. Inputs (FASTQ) 4 Use Case 1: Exogenous exRNA

5. Genboree Workbench – Getting Started Getting Started –http://genboree.org/theCommons/projects/pub lic-commons/wiki/Getting_startedhttp://genboree.org/theCommons/projects/pub lic-commons/wiki/Getting_started Genboree Workbench Icons Explanation –http://genboree.org/theCommons/projects/pub lic-commons/wiki/genboree_iconshttp://genboree.org/theCommons/projects/pub lic-commons/wiki/genboree_icons FAQs –http://genboree.org/theCommons/ezfaq/index/ public-commonshttp://genboree.org/theCommons/ezfaq/index/ public-commons 5

6. Genboree Workbench – Create Database Create a Genboree Workbench Database –http://genboree.org/theCommons/ezfaq/show/ public-commons?faq_id=491http://genboree.org/theCommons/ezfaq/show/ public-commons?faq_id=491 hg19 6 Note: - You will be using this newly created Genboree Workbench Database to hold the output of tool runs. This will be the database that we’re referring to when we say ‘your database’. Note: - You will be using this newly created Genboree Workbench Database to hold the output of tool runs. This will be the database that we’re referring to when we say ‘your database’.

7. Running the Pipeline: Select Input Files 7 Note: You will input (1) fastq file per tool run. So, for each fastq file you wish to analyze, you will need to repeat the process shown on the next 3 slides. Note: You will input (1) fastq file per tool run. So, for each fastq file you wish to analyze, you will need to repeat the process shown on the next 3 slides.

8. 8 Running the Pipeline: Select Output Database Note: Drag Your newly created database to Output Targets. Note: Drag Your newly created database to Output Targets.

9. 9 Running the Pipeline: Select Tool

10. 10 Running the Pipeline: Submit Job

11. 11 Post-processing: Select Input Files Note: These zip files will be in your database, in the folder that you named: Files/smallRNAseqPipeline/[your analysis name]/ Note: These zip files will be in your database, in the folder that you named: Files/smallRNAseqPipeline/[your analysis name]/

12. 12 Post-processing: Select Output Database Note: Drag Your newly created database to Output Targets. Note: Drag Your newly created database to Output Targets.

13. 13 Post-processing: Select Tool

14. 14 Post-processing: Submit Job

15. 15 Post-processing: Begin Analysis (Excel) Note: The processed files to the left will be in your database, but will be in the folder that you named: Files/processPipelineRuns/ [your analysis name]/ Note: The processed files to the left will be in your database, but will be in the folder that you named: Files/processPipelineRuns/ [your analysis name]/

16. Use Case 1: Pipeline Results - Number of Input Reads 16 Case 3) Sample ID inputclipped calibrato rrRNAnot_rRNAgenome miRNA sense miRNA antisensetRNA sense tRNA antisense piRNA sense piRNA antisense snoRNA sense snoRNA antisense Rfam sense Rfam antisense miRNA plantVirus sense norm127,002,90110,349,566NA3,483,7066,865,8603,154,174156,6701214,75144730162111220012,323 norm227,957,1859,872,947NA3,253,5516,619,3962,969,73072,6381211,75651609264118176009,776 norm328,214,2619,316,527NA2,929,0746,387,4532,901,24791,6611512,4923873219713023008,048 crc221,132,6744,455,562NA1,508,6052,946,9571,307,65747,20485,4941850419885168003,266 crc323,547,3685,737,688NA1,901,3563,836,3321,721,24855,287107,9501866717177282004,076 crc122,729,8582,431,702NA704,8871,726,815767,52313,76841,779624362244001,817 uc120,626,9935,265,060NA1,714,1803,550,8801,553,15821,834611,40025662229184180002,915 uc218,186,2595,742,022NA1,937,7193,804,3031,642,32921,30747,0661766614858168004,510 uc328,426,8197,095,086NA2,447,3024,647,7842,099,770155,573810,29633720239133171005,218 Summary Table from small RNAseq Pipeline Wang et al (2012)

17. 17 Sample not_rRNAgenome Mapped Fraction Unmapped Fraction norm16865860315417446%54% norm26619396296973045%55% norm36387453290124745%55% crc22946957130765744%56% crc33836332172124845%55% crc1172681576752344%56% uc13550880155315844%56% uc23804303164232943%57% uc34647784209977045%55% Fraction mapping to the human genome = genome / not_rRNA Summary Table from small RNA-Seq Pipeline Use Case 1: Pipeline Results - Do all plasma small RNAs map to the human genome? Wang et al (2012)

18. 18 Fraction of reads mapping to miRNA = miRNA_sense / not_rRNA Summary Table from Pipeline Use Case 1: Pipeline Results – Reads Mapping to miRNA sampleinputclippedrRNA not rRNAgenome miRNA sense miRNA antisensetRNA sense tRNA antisense piRNA sense piRNA antisense snoRNA sense snoRNA antisense miRNA plantVirus sense miRNA sense average norm1393%151%51%100%46%2.28%0.0002%0.2148%0.0006%0.0106%0.0024%0.0016%0.0003%0.1795%1.60% norm2422%149%49%100%45%1.10%0.0002%0.1776%0.0008%0.0092%0.0040%0.0018%0.0027%0.1477% norm3442%146%46%100%45%1.44%0.0002%0.1956%0.0006%0.0115%0.0031%0.0020%0.0004%0.1260% crc2717%151%51%100%44%1.60%0.0003%0.1864%0.0006%0.0171%0.0067%0.0029%0.0057%0.1108%1.28% crc3614%150%50%100%45%1.44%0.0003%0.2072%0.0005%0.0174%0.0045%0.0020%0.0074%0.1062% crc11316%141%41%100%44%0.80%0.0002%0.1030%0.0003%0.0141%0.0036%0.0014%0.0002%0.1052% uc1581%148%48%100%44%0.61%0.0002%0.3210%0.0007%0.0186%0.0064%0.0052%0.0051%0.0821%1.51% uc2478%151%51%100%43%0.56%0.0001%0.1857%0.0004%0.0175%0.0039%0.0015%0.0044%0.1185% uc3612%153%53%100%45%3.35%0.0002%0.2215%0.0007%0.0155%0.0051%0.0029%0.0037%0.1123% Wang et al (2012)

19. 19 We can look for the answer to this question in the processed pipeline output file DG_miRNA_Quantifications_RPM.txt. Use Case 1: Which miRNAs are normally present in human plasma? miRNAnorm1norm2norm3crc1crc2crc3uc1uc2uc3normcrcuccrc/normuc/norm hsa-let-7b-5p1984.61502.21442.2363.61670.51659.6614.0674.84728.01643.01231.22005.60.74941.2207 hsa-miR-451a864.2996.12765.01610.32740.23287.0994.1294.38648.21541.82545.83312.21.65122.1483 hsa-let-7a-5p1553.6871.31151.9320.2906.21069.6410.7341.22000.51192.3765.3917.50.64190.7695 hsa-miR-378a-3p904.51330.3938.4516.21462.7478.7162.4286.5865.11057.8819.2438.00.77450.4141 hsa-miR-143-3p1471.7714.1935.41326.81050.5508.2198.5101.8179.71040.4961.8160.00.92450.1538 hsa-let-7f-5p1547.2490.0765.8602.3540.2517.3215.3262.01125.8934.3553.3534.30.59220.5719 hsa-miR-486-5p1047.0612.5708.8472.8634.51246.0246.2207.64135.3789.4784.41529.70.99371.9376 hsa-miR-12071.122.131.914.225.534.5332.7202.073.4708.424.7202.70.03490.2861 hsa-miR-1841864.811.114.021.07.87.7134.922.613.8630.012.257.10.01930.0906 hsa-miR-1246854.3296.5226.1306.6161.780.0369.7221.2577.3458.9182.8389.40.39820.8485 hsa-miR-423-5p817.4242.4141.61147.0323.5274.652.9115.8715.2400.5581.7294.61.45250.7357 hsa-miR-24-3p531.6279.3318.8130.9356.8291.7122.3158.7190.9376.5259.8157.30.69000.4178 hsa-miR-3168318.9324.2276.611.5626.0390.4527.2299.1592.4306.6342.7472.91.11761.5424 hsa-miR-146a-5p418.8220.8257.835.3250.0231.182.3133.9160.4299.1172.1125.50.57550.4196 hsa-miR-21-5p304.1210.8372.584.1313.2268.8162.7195.6477.3295.8222.0278.50.75070.9416 hsa-miR-140-3p260.4242.1381.3214.3417.8379.7117.767.51215.9294.6337.3467.01.14491.5853 hsa-miR-122-5p480.783.7301.613.6285.2116.962.1457.0768.4288.7138.5429.10.47991.4865 hsa-miR-148a-3p445.7112.6287.972.6304.0238.571.2149.0283.2282.1205.0167.80.72680.5948 hsa-let-7g-5p374.1183.9221.142.7212.1209.878.984.8284.3259.7154.9149.30.59640.5750 We added columns for averages and differential expression, and then sorted by the average expression level in normal plasma. Average s Diff. Expr.

20.  55-60% of miRNA reads do not map to the human genome.  ~1.5% of reads map to human miRNA.  A fraction of a percent of reads map to plant or viral miRNA. Use Case 1: Summary 20