eran yanowski, eran hornstein’s: monitor drug impact on the transcriptome of mouse beta cells...

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Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report I

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Page 1: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Eran Yanowski, Eran Hornstein’s:

Monitor drug impact on the transcriptome of mouse beta cells

(primary and cell-line) using Transeq/RNA-Seq

17.08.15Report I

Page 2: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Overview of basic parameters of your

NGS run (per sample):

samples origin: Mouse Beta cells,

line

SR 60

Page 3: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

FASTQC report for sample 1000_0A(randomly chosen)

Page 4: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

FASTQC report for sample 1000_0A : Most reads start with 5’ GGG, typical in Transeq

procedure

Page 5: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

FASTQC report for sample :overrepresentation of Ins2 reads

Page 6: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Pre-Pipeline data processing

Þ Need to remove 5’GGG sequences ÞNeed to remove reads containing either poly-A or poly-T

Page 7: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Reads summary following removal of poly-A & poly-T reads (Mouse beta cells, line)

Sample Name # total reads # filtered based on polyA# filtered based on

poly T

# filtered based on poly A/T

% reads lost due to polyA/T read

content

# of reads post-polyA/T exclusion

(input for mapping)

1000_0A 4522647 213478 42572 256050 5.66% 42665971000_0B 4157116 180098 38352 218450 5.25% 39386661000_0C 3566132 151943 30774 182717 5.12% 3383415

1000_12A 5799749 275383 51874 327257 5.64% 54724921000_12B 5056278 240559 46506 287065 5.68% 47692131000_12C 5194233 233382 44016 277398 5.34% 49168351000_1A 4533427 199358 35904 235262 5.19% 42981651000_1B 5526596 238100 52237 290337 5.25% 52362591000_1C 5515394 250700 46548 297248 5.39% 52181461000_6A 14039334 661855 96809 758664 5.40% 132806701000_6B 5168398 232440 38577 271017 5.24% 48973811000_6C 5600189 265320 47521 312841 5.59% 5287348100__0A 4922399 239109 54170 293279 5.96% 4629120100_0B 5200307 262254 49176 311430 5.99% 4888877100_0C 5252913 266526 58464 324990 6.19% 4927923

100_12A 3748359 163938 33309 197247 5.26% 3551112100_12B 5900067 263716 48099 311815 5.28% 5588252100_12C 5346386 239766 47480 287246 5.37% 5059140100_1A 5809281 312301 61939 374240 6.44% 5435041100_1B 4582181 202274 46677 248951 5.43% 4333230100_1C 5494900 274244 51519 325763 5.93% 5169137100_6A 5075956 251945 49379 301324 5.94% 4774632100_6B 5073253 261229 46717 307946 6.07% 4765307100_6C_ 4875816 227749 46709 274458 5.63% 4601358

Page 8: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

FASTQC report following removal of poly-A & poly-T reads & 5’ GGG trimming: (sample

1000_0A )

‘normal’ frequency of reads’ 5’ GGG reduced frequency of

%A

Page 9: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Data processing (pipeline) workflow (done using Mouse_mm9_v1 base repository)

1. If each sample has more than one fastq file (per sequencing read) then fastq files merging-step is performed

2. Transeq Reads pre-processing (5’ GGG trimming & polyA & polyT removal)

3. Processed-reads Mapping (using TopHat)4. TES reads coverage profile (Transeq protocol QC step) 5. Reads Count (per 3’UTR) (using HTSeq-count)6. Data Normalization and Differential Gene Expression (using DESeq2)7. QC: Principal Component Analysis (PCA) & Hierarchical Clustering

Page 10: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Reads mapping summary_Exp4_1_Mouse Beta cells, line

Sample Name# of reads post-polyA/T

exclusion (input for mapping)

# mapped reads % mapping# Uniquely

mapped reads% unique mapping Total reads count % read counted/mapped %read counted/Total

1000_0A 4266597 2878974 67.50% 2659357 62.33% 2134781 74.15% 47.20%1000_0B 3938666 2611453 66.30% 2416329 61.35% 1932450 74.00% 46.49%1000_0C 3383415 2289845 67.70% 2084541 61.61% 1661576 72.56% 46.59%

1000_12A 5472492 3787628 69.20% 3497840 63.92% 2816961 74.37% 48.57%1000_12B 4769213 3190164 66.90% 2899057 60.79% 2314249 72.54% 45.77%1000_12C 4916835 3445350 70.10% 3184374 64.76% 2590155 75.18% 49.87%1000_1A 4298165 3001892 69.80% 2777304 64.62% 2245920 74.82% 49.54%1000_1B 5236259 3554120 67.90% 3279772 62.64% 2623326 73.81% 47.47%1000_1C 5218146 3594977 68.90% 3316174 63.55% 2670961 74.30% 48.43%1000_6A 13280670 6544448 49.30% 6061735 45.64% 4841756 73.98% 34.49%1000_6B 4897381 3386634 69.20% 3080472 62.90% 2481236 73.27% 48.01%1000_6C 5287348 3617658 68.40% 3286712 62.16% 2642159 73.04% 47.18%100__0A 4629120 3052511 65.90% 2771379 59.87% 2202901 72.17% 44.75%100_0B 4888877 3234356 66.20% 2987823 61.11% 2394370 74.03% 46.04%100_0C 4927923 3266384 66.30% 2959649 60.06% 2355123 72.10% 44.83%

100_12A 3551112 2315894 65.20% 2139258 60.24% 1707222 73.72% 45.55%100_12B 5588252 3511934 62.80% 3191989 57.12% 2551211 72.64% 43.24%100_12C 5059140 3403456 67.30% 3091671 61.11% 2474196 72.70% 46.28%100_1A 5435041 3607340 66.40% 3284228 60.43% 2616466 72.53% 45.04%100_1B 4333230 2860763 66.00% 2601528 60.04% 2066053 72.22% 45.09%100_1C 5169137 3455625 66.90% 3187479 61.66% 2545546 73.66% 46.33%100_6A 4774632 3237959 67.80% 2999976 62.83% 2410481 74.44% 47.49%100_6B 4765307 3222634 67.60% 2982099 62.58% 2417139 75.01% 47.64%100_6C_ 4601358 3096982 67.30% 2817727 61.24% 2258438 72.92% 46.32%

Page 11: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Reads mapping summary_Exp4_1_Mouse Beta cells, line: 23-24% of total reads count were mapped to Ins2/Ins1 genes

Sample Name(Ins2+Ins1) read

count%(Ins2+Ins1) read

count/Total read countTotal_Read_counts_without_(Ins2+I

ns1)

%Read_counts_without_(Ins2+Ins1)/mappe

d

# of UMI-filtered reads counted

% UMI-filtered reads counted

Experiment code

1000_0A 521168 24.4% 1613613 56.05% 1303102 49.00% Exp4_1_beta cell-line1000_0B 465186 24.1% 1467264 56.19% 1193399 49.39% Exp4_1_beta cell-line1000_0C 369931 22.3% 1291645 56.41% 1049199 50.33% Exp4_1_beta cell-line

1000_12A 677079 24.0% 2139882 56.50% 1670334 47.75% Exp4_1_beta cell-line1000_12B 517458 22.4% 1796791 56.32% 1451853 50.08% Exp4_1_beta cell-line1000_12C 613419 23.7% 1976736 57.37% 1550960 48.71% Exp4_1_beta cell-line1000_1A 543266 24.2% 1702654 56.72% 1354037 48.75% Exp4_1_beta cell-line1000_1B 634653 24.2% 1988673 55.95% 1558003 47.50% Exp4_1_beta cell-line1000_1C 648519 24.3% 2022442 56.26% 1610963 48.58% Exp4_1_beta cell-line1000_6A 1155934 23.9% 3685822 56.32% 2897404 47.80% Exp4_1_beta cell-line1000_6B 551344 22.2% 1929892 56.99% 1524828 49.50% Exp4_1_beta cell-line1000_6C 596020 22.6% 2046139 56.56% 1611762 49.04% Exp4_1_beta cell-line100__0A 497816 22.6% 1705085 55.86% 1380827 49.82% Exp4_1_beta cell-line100_0B 578813 24.2% 1815557 56.13% 1461077 48.90% Exp4_1_beta cell-line100_0C 544482 23.1% 1810641 55.43% 1451192 49.03% Exp4_1_beta cell-line

100_12A 404082 23.7% 1303140 56.27% 1067542 49.90% Exp4_1_beta cell-line100_12B 574329 22.5% 1976882 56.29% 1568245 49.13% Exp4_1_beta cell-line100_12C 568210 23.0% 1905986 56.00% 1507206 48.75% Exp4_1_beta cell-line100_1A 618801 23.7% 1997665 55.38% 1576598 48.01% Exp4_1_beta cell-line100_1B 467712 22.6% 1598341 55.87% 1277871 49.12% Exp4_1_beta cell-line100_1C 618901 24.3% 1926645 55.75% 1519110 47.66% Exp4_1_beta cell-line100_6A 577676 24.0% 1832805 56.60% 1465024 48.83% Exp4_1_beta cell-line100_6B 586016 24.2% 1831123 56.82% 1440583 48.31% Exp4_1_beta cell-line100_6C_ 518860 23.0% 1739578 56.17% 1386532 49.21% Exp4_1_beta cell-line

Median 1831964Average 1879375

Page 12: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Reads summary following removal of polyA and polyT reads (Exp4_2_Mouse Primary Beta cells):

Sample Name # total reads # filtered based on polyA# filtered based on

poly T

# filtered based on poly A/T

% reads lost due to polyA/T read

content

# of reads post-polyA/T exclusion

(input for mapping)0A 5588409 224404 20705 245109 4.39% 53433000B 5780324 203388 17500 220888 3.82% 55594360C 8911138 306635 25285 331920 3.72% 8579218

1000_12A 5116948 178673 14436 193109 3.77% 49238391000_12B 6016116 250518 22761 273279 4.54% 57428371000_12C 10153149 379818 27848 407666 4.02% 97454831000_1A 5646586 224835 19976 244811 4.34% 54017751000_1B 6618582 243771 19876 263647 3.98% 63549351000_1C 11190335 345687 32945 378632 3.38% 108117031000_6A 5424645 219720 17175 236895 4.37% 51877501000_6B 6477781 289338 21210 310548 4.79% 61672331000_6C 12366201 375283 28681 403964 3.27% 11962237100_12A 5467491 253599 19289 272888 4.99% 5194603100_12B 5310245 217038 18173 235211 4.43% 5075034100_12C 9919613 369879 27390 397269 4.00% 9522344100_1A 4642696 190171 15133 205304 4.42% 4437392100_1B 7499436 292209 27384 319593 4.26% 7179843100_1C 8211277 281292 21373 302665 3.69% 7908612100_6A 4517430 257981 18671 276652 6.12% 4240778100_6B 6134348 257537 20423 277960 4.53% 5856388100_6C 8622327 330255 32769 363024 4.21% 8259303

Page 13: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Reads mapping summary_Exp4_2_Mouse Primary Beta cells:

Sample Name# of reads post-

polyA/T exclusion (input for mapping)

# mapped reads % mapping# Uniquely

mapped reads% unique mapping Total reads count % read counted/mapped %read counted/Total

0A 5343300 4780133 89.50% 4594190 85.98% 4080129 85.36% 73.01%0B 5559436 5030565 90.50% 4756610 85.56% 4293252 85.34% 74.27%0C 8579218 7836698 91.30% 7542463 87.92% 6749550 86.13% 75.74%

1000_12A 4923839 4436601 90.10% 4224644 85.80% 3795044 85.54% 74.17%1000_12B 5742837 5141665 89.50% 4873186 84.86% 4314688 83.92% 71.72%1000_12C 9745483 8899011 91.30% 8470029 86.91% 7536704 84.69% 74.23%1000_1A 5401775 4848188 89.80% 4580392 84.79% 4053938 83.62% 71.79%1000_1B 6354935 5755635 90.60% 5452595 85.80% 4877657 84.75% 73.70%1000_1C 10811703 9906774 91.60% 8920246 82.51% 7843983 79.18% 70.10%1000_6A 5187750 4656630 89.80% 4427373 85.34% 3970277 85.26% 73.19%1000_6B 6167233 5540137 89.80% 5258449 85.26% 4735818 85.48% 73.11%1000_6C 11962237 11026944 92.20% 10616369 88.75% 9468819 85.87% 76.57%100_12A 5194603 4659567 89.70% 4464784 85.95% 3988557 85.60% 72.95%100_12B 5075034 4546346 89.60% 4373427 86.18% 3894705 85.67% 73.34%100_12C 9522344 8709660 91.50% 8273383 86.88% 7335857 84.23% 73.95%100_1A 4437392 3975240 89.60% 3762015 84.78% 3324750 83.64% 71.61%100_1B 7179843 6492766 90.40% 6239520 86.90% 5596841 86.20% 74.63%100_1C 7908612 7196482 91.00% 6828275 86.34% 6135099 85.25% 74.72%100_6A 4240778 3727434 87.90% 3587523 84.60% 3195721 85.74% 70.74%100_6B 5856388 5274621 90.10% 5079855 86.74% 4550876 86.28% 74.19%100_6C 8259303 7470850 90.50% 7080063 85.72% 6245548 83.60% 72.43%

Page 14: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Reads mapping summary_Exp4_2_Mouse Primary Beta cells: % of total reads count were mapped to Ins2/Ins1 genes

Sample Name(Ins2+Ins1) read

count%(Ins2+Ins1) read count

Total_Read_counts_without_(Ins2+Ins1)

%Read_counts_without_(Ins2+I

ns1)/mapped

# of UMI-filtered reads counted

% UMI-filtered reads counted

Experiment code

0A 2079103 51.0% 2001026 41.86% 1250310 27.22% Exp4_2_primary beta cells0B 2272032 52.9% 2021220 40.18% 1256347 26.41% Exp4_2_primary beta cells0C 3659420 54.2% 3090130 39.43% 1829003 24.25% Exp4_2_primary beta cells

1000_12A 1806555 47.6% 1988489 44.82% 1239960 29.35% Exp4_2_primary beta cells1000_12B 2137245 49.5% 2177443 42.35% 1362445 27.96% Exp4_2_primary beta cells1000_12C 3755984 49.8% 3780720 42.48% 2156596 25.46% Exp4_2_primary beta cells1000_1A 2003041 49.4% 2050897 42.30% 1275397 27.84% Exp4_2_primary beta cells1000_1B 2543057 52.1% 2334600 40.56% 1414740 25.95% Exp4_2_primary beta cells1000_1C 3897421 49.7% 3946562 39.84% 2244427 25.16% Exp4_2_primary beta cells1000_6A 1875296 47.2% 2094981 44.99% 1289816 29.13% Exp4_2_primary beta cells1000_6B 2438176 51.5% 2297642 41.47% 1403013 26.68% Exp4_2_primary beta cells1000_6C 4863790 51.4% 4605029 41.76% 2545816 23.98% Exp4_2_primary beta cells100_12A 1954496 49.0% 2034061 43.65% 1271547 28.48% Exp4_2_primary beta cells100_12B 1935262 49.7% 1959443 43.10% 1222657 27.96% Exp4_2_primary beta cells100_12C 3682705 50.2% 3653152 41.94% 2108318 25.48% Exp4_2_primary beta cells100_1A 1655797 49.8% 1668953 41.98% 1067918 28.39% Exp4_2_primary beta cells100_1B 2929336 52.3% 2667505 41.08% 1602695 25.69% Exp4_2_primary beta cells100_1C 3302584 53.8% 2832515 39.36% 1692040 24.78% Exp4_2_primary beta cells100_6A 1569769 49.1% 1625952 43.62% 1069131 29.80% Exp4_2_primary beta cells100_6B 2364828 52.0% 2186048 41.44% 1347814 26.53% Exp4_2_primary beta cells100_6C 3103130 49.7% 3142418 42.06% 1851135 26.15% Exp4_2_primary beta cells

Median 2186048Average 2578990

Page 15: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Hierarchical clustering: Mouse beta cells, line

Drug Con=1000Drug con=100

Page 16: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Hierarchical clustering: Mouse Primary beta cells

Drug Con=1000Drug con=100

Separated by processing day: A/B/C ?

Page 17: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

• Differential gene expression data is assessed by DESeq2• DESeq output is summarized in a single

sheet per experiment• Genes differentially-expressed during each

time series were called by two independent means:

I. Using pairwise comparison vs. time zeroII. Using a tool named: maSigPro

Page 18: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

RNA-Seq drug dose response (1000 and 100)/time series (0, 1, 6 and 12 hrs) gene filtering criteria

• The data filters used during the last analysis performed (12.08.15):I. maSigPro: • NormCounts of genes meeting MaxRawCount>50 served as input; • maSigPro output was further filtered against potential outlier genes (flagged

by maSigPro) • Genes showing FC greater than 1.5 (at least in one of the paired-comparisons); • By default maSigPro requires (BH) adjusted p-value <0.05 II. Pairwise comparison criteria:

MaxRawCount>50, adjusted-p-value<0.05 and FC greater than 1.5 (at least in one of the paired-comparisons);

Page 19: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Exp 4.2: Mouse primary beta cells:Genes meeting criteria: Paired-comparison yields higher

100/1000 gene overlap (than the one obtained with maSigPro)

maSigPro filtered output Pairwise-comparison filtered output

Page 20: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Exp 4.2: Mouse primary beta cells: Most of maSigPro shared-genes are included

in the group of paired shared genes

To determine which output is preferred data validation using an orthogonal method is essential

Page 21: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Partitioning clustering of genes responsive in both drug concentrations

Paired comparison, con=1000, shared_genes partitioning clustering

Paired comparison, con=100, shared_genes partitioning clustering

Exp 4.2: Mouse primary beta cells:

Page 22: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Exp4.1: Mouse Beta Cells, cell line

• Generally this experiment yielded less significant results• When applying the same filters used for the primary beta-cells

datasets, very few genes pass; • The possibility of using p-value (instead of adjusted-p-value should be

tested by the investigator)• The level of 100/1000 intersection (shared-genes) is lower here

compared to the one observed in the primary cells experiment

Page 23: Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report

Venn diagram of unfiltered maSigPro outputs of both the primary (100, 1000) and the cell-line

TranSeq datasets

• Low overall intersection between the primary and the cell-line ‘significant’

genes;• Relatively low intersection between the

two drug concentrations tested on the beta cell line