1. Bulk RNA-Seq Analysis Using CLCGenomics Workbench
June 26, 2019
SRI CHAPARALA
Ansuman Chattopadhyay

2. Topics Brief introduction to RNA-Seq experiments Analyze RNA-seq data
Dexamethasone treatment on airway smooth muscle cells (Himes et al. PLos One 2014)
Download seq reads from EBI-ENA/NCBI SRA
Import reads to CLC Genomics Workbench
Align reads to Reference Genome
Estimate expressions in the gene level
Estimate expressions in the transcript isoform level
Statistical analysis of the differential expressed genes and transcripts
Create Heat Map, Volcano Plots, and Venn Diagram

3. Differential Gene Expressions
Raw Reads
Venn Diagram
Volcano Plot

4. Workshop Page

5. Pitt

6. HSLS MolBio

7. SUMMER 2019 Workshop Schedule

8. Partek Flow : Software for scRNA-Seq Data Analysis

9. NGS Software @ HSLS MolBio
NGS Analysis
Sanger Seq Analysis

10. RNA-Seq Software @ HSLS MolBio
Enrichment Analysis
Deferentially Expressed Genes
CLC Genomics
Work Bench
Ingenuity
Pathway Analysis
Functions
Diseases
Pathways
Key Pathway Advisor
Upstream Regulators
Volcano Plot
PCA Plot
Venn
Diagram
Heat Map
Any Organism
Illumina BaseSpace
Correlation Engine
Correlated Expression Studies
RNA-Seq
Reads
Variant Detection
Ingenuity
Variant Analysis
Variant Annotation
and Prioritization
RNA-Seq Analysis
Down Stream Analysis

12. RNA-Seq data analysis support through HSLS MBIS

13. RNA Seq Questionnaire
What is the scientific objective of the RNA Seq experiment? How many classes will be compared?
Are only coding RNA (mRNA) or long non coding RNA, miRNA expected to be detected?
Did all the samples pass RNA quality checks before sequencing?
Are there biological replicates? If so how many?
What type of sequencing platform was used to sequence the reads? Illumina, Ion torrent, Solid
Where was the sequencing performed? Facility name and contact info
When was the sequencing performed? Year/date
Which RNA – extraction method was used in the experiment? Total RNA/ poly A/ rRNA depletion method and kit name and if possible, link to protocol
Whether the protocol is strand specific or not? Unstranded/ forward/reverse, kit name and if possible link to protocol
Whether the data is single end or paired end?
What is the expected read length?
Do the reads contain adapters or removed? If not please provide adapter sequence, if available, or link (usually can get this info from facility)
What are the experimental conditions to perform differential expression analysis?
Which organism and the reference genome to be used for analysis?

14. CLC Genomics Workbench

15. CLCGx 12
Genomics Workbench
BioMedical Workbench

16. Install Plugins

17. CLCbio Genomics Workbench
System Requirements
Windows Vista, Windows 7, Windows 8, Windows 10, Windows Server 2012 or 2016
Mac: OS X 10.10, and macOS 10.12, 10.13, 10.14
Linux: RHEL 7 and later, Suse Linux Enterprise Server 11 and later. (The software is expected to run without problem on other recent Linux systems, but we do not guarantee this.)
8 GB RAM required
16 GB RAM recommended
1024 x 768 display required
1600 x 1200 display recommended
Intel or AMD CPU required
500GB disc space required in the CLC Genomics server

18. CLCBio Genomics Workbench Server
- You can connect your CLC Genomics Workbench software to the core HTC cluster available to University of Pittsburgh researchers through the Center for Research Computing (CRC).
- This allows you to transparently migrate data from your workstation to the cluster, and run analyses on the cluster, which then run independently of your workstation (i.e. you can shutdown your machine and your analyses will continue unabated).

19. Center for Research computing (CRC)

20. Request access to CRC

21. CLC Genomics Workbench
Ensure you have the most up-to-date version of the CLCbio Genomics Workbench (the software should tell you if there's a more recent version when you start it, or you can check on the CLCbio website)
If you have not already done so, request a user account/allocation on the Center for Research Computing (CRC) for HTC cluster by filling out the required information
If your computer is not connected to the Pitt network (e.g. you are working from home or on a trip), or you are working from a laptop that is connected to the Pitt wireless system, make sure you setup Pitt VPN, so that you can communicate with the CLC Bioserver on HTC cluster.
Start the CLC Genomics Workbench

22. Connect to CLC Server @ CRC

23. Access to CRC-HTC Cluster – CLC Server
If you DO NOT HAVE CRC-HTC account:
Use the following for a limited access
UserID: hslsmolb
PW: library1#
Server host: clcbio.crc.pitt.edu
Server port: 7777
If you have CRC-HTC account
Use – pitt user name; pitt password
Server host: clcbio.crc.pitt.edu
Server host: 7777

24. File Structure at CRC CLC Gx Server

25. Pre-analyzed Results

26. RNA-Seq Data

27. Bulk RNA-seq Study

29. NCBI SRA

30. NCBI SRA

31. NCBI SRA
Untreated Vs DEX

32. RNA-Seq Basics

33. Bulk RNA-seq Basic Steps
convert to cDNA fragments
adaptors ligation
short seq reads
align reads to reference genome
Wang Z, Gerstein M, Snyder M.
RNA-Seq: a revolutionary tool for transcriptomics.
Nat Rev Genet Jan;10(1):57–63.

34. Bulk RNA-Seq Data Analysis Workflow

35. Must Read
Cresko Lab, University of Oregon

36. RNA-Seq vs. Microarrays
covers more dynamic range
allows to discover novel transcripts
able to detect SNPs
more costly ($300-$1000/sample) than Microarray ($100-$200/sample)
Generates times larger dataset than Microarray
uncompressed RNA-Seq raw files: >5GB
Microarray
RNA-Seq
Riki Kawaguchi’s Blog:
Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS ONE Jan 16;9(1):e78644.

37. CLC Genomics Software User Interface

38. Contact CLCBio Support Team
CLCGX 12.0 User Mannual:
manuals/clcgenomicsworkbench/current/index.php?manual=Introduction_CLC_Genomics_Workbench.html

39. Create a Folder in CRC-HTC Cluster1 2

40. Create Workshop Folder@ HTC-CLC Server1 2 3

41. CLCGX Tools for RNA-Seq Data Analysis

42. Import FASTQ Reads to CLCGx
Step 1
Import FASTQ Reads to CLCGx

43. Download from SRA or EBI and import into CLC NCBI SRA download in CLC
Import FASTQ Reads
Download from SRA or EBI and import into CLC
NCBI SRA download in CLC
Import own data from local computer or from CRC servers

44. Illumina 5,043429 NGS Technologies ABI SoLid 25,882 Ion Torrent 74,684
NCBI Seq Read Archive
Illumina 5,043429
ABI SoLid ,882
Ion Torrent ,684
PacBio ,410
MinIon ,102
Jan 14, 2019

45. STEP 1: Import Reads to CLC2

46. STEP 1: Import Reads to CLC3 4 5

47. STEP 1: Import Reads to CLC; Download from NCBI SRA2

48. CLC SRA Download

49. EBI ENA

50. EBI-ENA

51. Help : Import Illumina Reads

52. FASTQ Format

53. Results By CLC : Imported Illumina Reads
TrainingMaterials
Workshops
Pre-analyzed Results_RNA-Seq
RNASeq _Workshop_DExvsUNT
DexvsUnt
Reads

54. Results By CLC: Imported Illumina Reads

55. QC for Sequencing Reads
Step2
QC for Sequencing Reads

56. https://galaxyproject. github

57. FASTQC Project

58. Phred Score
wikipedia

59. Taken from Introduction to ChIP-Seq by HPC Tutorial by HBC Training

60. Taken from Introduction to ChIP-Seq by HPC Tutorial by HBC Training

61. Assessing Sequence Data Quality (led by Dawei Lin and Simon Andrews)

62. Step 2: Create Seq QC Report1 2

63. Results By CLC: Read QC Report

64. Read Trimming (based on quality of reads or adapters)

65. Trim Reads

66. Annotate Reads: Create a Metadata Table

67. Step 3 :Create a Metadata Table

68. Import Metadata

69. Import Metadata 2 1 3

70. Import Metadata
Select All

71. STEP 4: Read Mapping

72. Read Mapping
Wikipedia

73. Read Mapping
Ozsolak et al.
Nature Review Genetics

74. CLC Read Mapper Documentation

75. Best Practices

76. Popular Software

77. STEP 4: Read Mapping 5

78. STEP 4: Reads Mapping 7

79. STEP 4: Reads Mapping 8

80. Reference Genome

81. Reference Genomes https://www.ncbi.nlm.nih.gov/grc

82. Reference Genome Human : Grch38 Mouse: mm10 -- C57BL/6J
Mouse 16 other strains are
now available

83. Step 4: Read Mapping

84. Step 4: Read Mapping 9

85. Step 4: Reads Mapping 10

86. Step 4: Reads Mapping

87. Normalization and Expression Values
TMM: weighted trimmed mean of the log expression ratios
(trimmed mean of M values (TMM)
used by EDGER and CLCGx

88. Normalization Methods

89. Step 4: Reads Mapping

90. Results By CLC: Reads Mapping

91. Step 5 :Create a Combined RNA-Seq Report

92. Reads Mapping; Gene expression Track GE

93. Reads Mapping; Transcript Level Gene expression Track - TE

94. Transcript Level Expression

95. Step6: Create a PCA Plot - QC at the sample level

96. Step 7: Differential Expression
Differential Expressions Between Two Groups – ex: Treated vs Untreated, KO vs WT
Differential Expressions between Multiple Groups

97. Differential Expressions Between Two Groups – Treated vs Untreated
First, select mapped reads from
Test Samples
Then, select mapped reads from
control samples

99. Differential Expressions Between Two Groups – Treated vs Untreated
TMM Normalization (Trimmed Mean of M values) calculates effective libraries sizes, which are then used as part of the per-sample normalization. TMM normalization adjusts library sizes based on the assumption that most genes are not differentially expressed.

100. Differential Expressions between Multiple Groups
Use the metadata table to define groups

101. Step8: Differential Expressions

102. Differential Expressions; Dex vs Unt Gene Level

103. GraphPad Statistics Guide :

104. Differential Expressions; Dex vs Unt Transcript Level

105. Step8: Differential Expressions; Dex vs Unt Volcano Plot

106. Step9: Create a HeatMap

107. Step9: Create a HeatMap

108. Step9 : Create a HeatMap

109. Step9 : Create a HeatMap

110. Step 10: Create a Venn Diagram

111. Step 10: Create a Venn Diagram

112. Step 10: Create a Venn Diagram

113. Step 11: Create a Track

114. Step 12: Expression Browser – all in one large spread sheet

115. Downstream Analysis

116. Downstream Analysis DEG Annotates differentially expressed genes from
an RNA-seq experiment, using the curated
public data from GEO

117. NextBio Research

118. Export Data from CLC

119. Find Correlated Gene Expression Studies from GEO

120. Find Correlated Gene Expression Studies from GEO

121. Ingenuity IPA Analysis

122. Thanks To…. HSLS Carrie Iwema David Leung Michael Sweezer Qiagen
Shawn Prince
Center for Research Computing
Kim F Wong
Mu Fangping