1. Bulk RNA-Seq Analysis Using CLCGenomics Workbench
2019
Ansuman Chattopadhyay, PhD
Asst Director, Molecular Biology information service
Health sciences library system
University of pittsburgh
Sri Chaparala, MS
Bioinformatics Specialist
Health Sciences Library System
University of Pittsburgh

2. Topics Brief introduction to RNA-Seq experiments Analyze RNA-seq data
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. Scaife Hall, Falk Library, Classroom 2
Descriptions & Registration:
4th Single Cell RNA-Seq
10-11am Genomics Research Core
11am-12pm Overview
1-3pm Hands-On
SEPTEMBER
11th ChIP-Seq & CLC Genomics
10am-12pm Overview & 1-3pm Hands-On
25th Pathway Analysis—IPA & MetaCore
10am-12pm Overview & 1-3pm Hands-On
2nd Bulk RNA-Seq
10-11am Genomics Research Core
11am-12pm Overview
1-3pm Hands-On
Fall 2019
HSLS MolBio Workshops
OCTOBER
9th Pathway Analysis—Open Access Tools
10am-12pm Overview & 1-3pm Hands-On
23rd ChIP-Seq & Partek Flow 1-4pm
30th Gene Regulation 1-4pm
Scaife Hall, Falk Library, Classroom 2
6th Single Cell RNA-Seq
10am-12pm Overview & 1-3pm Hands-On
NOVEMBER
13th Gene Expression Visualization 1-4pm
20th Pathway Analysis—IPA & MetaCore
10am-12pm Overview & 1-3pm Hands-On
4th Bulk RNA-Seq
10-11am Genomics Research Core
11am-12pm Overview
1-3pm Hands-On
DECEMBER
11th Genetic Variation
10am-12pm Overview & 1-3pm Hands-On

5. CRC Workshops

6. Pitt

8. HSLS MolBio

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

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

11. 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
RNA-Seq
Reads
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. HPC Partnership with CRC to Mitigate Computational Bottleneck
NGS Pitt
HSLS License Server

19. 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).

20. Center for Research computing (CRC)

21. Request Access to CRC

22. 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

23. Connect to CLC Server @ CRC

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

25. File Structure at CRC CLC Gx Server
folders organized by PI’s
name

26. Pre-analyzed Results

27. RNA-Seq Data

28. Bulk RNA-seq Study

30. NCBI SRA

31. NCBI SRA

32. NCBI SRA
Untreated Vs DEX

33. RNA-Seq Basics

34. 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.

35. convert to cDNA fragments
adaptors ligation
short seq reads
align reads to reference genome

37. Bulk RNA-Seq
fragmentation of RNA before cDNA synthesis was shown to reduce 3ʹ:5ʹ bias4, and strand-specific library preparation methods, which allow sense and antisense transcripts to be differentiated, were shown to provide a more accurate estimate of transcript abundance

38. Bulk RNA-Seq Data Analysis Workflow

39. Bulk RNA-Seq Data Analysis Steps
Command Line Tools
Graphical User Interface
 In workflow A, aligners such as TopHat112, STAR113 or HISAT2 (ref.114) use a reference genome to map reads to genomic locations, and then quantification tools, such as HTSeq133 and featureCounts134, assign reads to features. After normalization (usually using methods embedded in the quantification or expression modelling tools, such as trimmed mean of M-values (TMM)142), gene expression is modelled using tools such as edgeR143, DESeq2 (ref.155) and limma+voom156, and a list of differentially expressed genes or transcripts is generated for further visualization and interpretation. In workflow B, newer, alignment-free tools, such as Kallisto119 and Salmon120, assemble a transcriptome and quantify abundance in one step. The output from these tools is usually converted to count estimates (using tximport130 (TXI)) and run through the same normalization and modelling used in workflow A, to output a list of differentially expressed genes or transcripts. Alternatively, workflow C begins by aligning the reads (typically performed with TopHat112, although STAR113 and HISAT114 can also be used), followed by the use of CuffLinks131to process raw reads and the CuffDiff2 package to output transcript abundance estimates and a list of differentially expressed genes or transcripts. Other tools in common use include StringTie116, which assembles a transcriptome model from TopHat112(or similar tools) before the results are passed through to RSEM105 or MMSEQ132 to estimate transcript abundance, and then to Ballgown157 to identify differentially expressed genes or transcripts, and SOAPdenovo-trans117, which simultaneously aligns and assembles reads for analysis via the path of choice. Taken from Stark etal., Nat Rev Genet 2019 paper
Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. (2019). doi: /s

40. CommandLine vs Graphical User Interface
CLI
GUI

41. CLC Genomics Software User Interface

42. Contact CLCBio Support Team
CLCGX 12.0 User Manual:

43. Create a Folder in CRC-HTC Cluster1 2

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

45. CLCGX Tools for RNA-Seq Data Analysis1 2

46. Import FASTQ Reads to CLCGx

47. Import FASTQ Reads to CLCGx
Import your saved data from local computer or from CRC servers
NCBI SRA download in CLC

48. Illumina 6,235591 NGS Technologies ABI SoLid 27,315 Ion Torrent 88,946
NCBI Seq Read Archive
Illumina 6,235591
ABI SoLid ,315
Ion Torrent ,946
PacBio ,538
MinIon ,404

49. Import Reads Stored in Local Computer Files to CLCGx1 2

50. Import Reads to CLC 3 4 5

51. Import Reads from CRC Server
Select Grid
option – HTC Data
CRC can assign each group (faculty) an import/export directory on the server. Member of the group shared this import/export directory with read/write permissions. Please open a support ticket on CRC website if you do not find a folder matching your group.

52. Download Reads from NCBI SRA database

53. NCBI SRA download in CLC

54. Download FASTQ Reads from EBI ENA

55. Help : Import Illumina Reads

56. FASTQ Format

57. Results By CLC : Imported Illumina Reads
TrainingMaterials
Workshops
CBF_AMLeukemiaProject
RNASeq _GSE101788
RNASeq_DifferentialExpression
Reads
Reads are already downloaded. You can find the reads in Server Folder – TraingMaterials – Pre-analyzed Result_RNA-Seq

58. Imported Illumina Reads

59. A Preprocessing includes experimental design, sequencing design, and quality control steps.

60. Number of Replicates
Filtering out genes that are expressed at low levels prior to differential expression analysis reduces the severity of the correction and may improve the power of detection [20]. Increasing sequencing depth also can improve statistical power for lowly expressed genes.

61. QC for Sequencing Reads

62. https://galaxyproject. github

63. FASTQC Project

64. Phred Score
wikipedia

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

66. Taken from Introduction to ChIP-Seq by HPC Tutorial by HBC Training
Acceptable duplication, k-mer or GC content levels are experiment- and organism-specific, but these values should be homogeneous for samples in the same experiments. We recommend that outliers with over 30 % disagreement to be discarded. –
As a general rule, read quality decreases towards the 3’end of reads, and if it becomes too low, bases should be removed to improve mappability.
Taken from Introduction to ChIP-Seq by HPC Tutorial by HBC Training

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

68. Create a Seq QC Report 1 2

69. Results By CLC: Read QC Report

70. Acceptable duplication, k-mer or GC content levels are experiment- and organism-specific, but these values should be homogeneous for samples in the same experiments. We recommend that outliers with over 30 % disagreement to be discarded.
As a general rule, read quality decreases towards the 3’end of reads, and if it becomes too low, bases should be removed to improve mappability.

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

72. Trim Reads

73. Read Trimming

74. Annotate Reads: Create a Metadata Table

75. Create and Import a Metadata Table
Spread Sheet

76. Import Metadata

77. Import Metadata

78. Read Mapping

79. Read Mapping
Wikipedia

80. Read Mapping
Ozsolak et al.
Nature Review Genetics

81. CLC Read Mapper Documentation

82. Read Mapping 5

83. Reads Mapping 7

84. Reads Mapping 8

85. Reference Genome

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

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

88. Read Mapping

89. Read Mapping 9

90. Reads Mapping 10

91. Reads Mapping

92. Mapping Result
GE : Gene Expression; TE: Transcript Expression; FG: Fusion Gene

93. Reads Mapping

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

95. Normalization Methods

96. Reads Mapping GE

97. Transcript Expression

98. Read Mapping Report – SRR5861494
An important mapping quality parameter is the percentage of mapped reads, which is a global indicator of the overall sequencing accuracy and of the presence of contaminating DNA. For example, we expect between 70 and 90 % of regular RNA-seq reads to map onto the human genome (depending on the read mapper used) [15], with a significant fraction of reads mapping to a limited number of identical regions equally well (‘multi-mapping reads’).

99. Transcript Level Expression

100. The percentage of mapped reads is a global indicator of the overall sequencing accuracy and of the presence of contaminating DNA.
We expect between 70 and 90 % of regular RNA-seq reads to map onto the human genome (depending on the read mapper used) with a significant fraction of reads mapping to a limited number of identical regions equally well (‘multi-mapping reads’).
Other important parameters are the uniformity of read coverage on exons and the mapped strand. If reads primarily accumulate at the 3’end of transcripts in poly(A)-selected samples, this might indicate low RNA quality in the starting material. The GC content of mapped reads may reveal PCR biases.

101. Create a Combined RNA-Seq Report

103.  The biotypes are "as a percentage of all transcripts" or "as a percentage of all genes". For a poly-A enrichment experiment, it is expected that the majority of reads correspond to proteincoding regions. For an rRNA depletion protocol, a variety of non-coding RNA regions may also be observed. The percentage of reads mapping to rRNA should usually be <15%. If over 15% of the reads mapped to rRNA, it could be that the poly-A enrichment/rRNA depletion protocol failed. The sample can still be used for differential expression and variant calling, but expression values such as TPM and RPKM may not be comparable to those of other samples. To troubleshoot the issues in future experiments, check for rRNA depletion prior to library preparation. Also, if an rRNA depletion kit was used, check that the kit matches the species being studied.

104. For a poly-A enrichment experiment, it is expected that the majority of reads correspond to protein coding regions. For an rRNA depletion protocol, a variety of non-coding RNA regions may also be observed. The percentage of reads mapping to rRNA should usually be <15%.
If over 15% of the reads mapped to rRNA, it could be that the poly-A enrichment/rRNA depletion protocol failed.
The sample can still be used for differential expression and variant calling, but expression values such as TPM and RPKM may not be comparable to those of other samples.
To troubleshoot the issues in future experiments, check for rRNA depletion prior to library preparation. Also, if an rRNA depletion kit was used, check that the kit matches the species being studied.
CLC Gx Manual

105. A Preprocessing includes experimental design, sequencing design, and quality control steps.

106. Create a PCA Plot - QC at the sample level

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

108. Differential Expressions Between Two Groups – Treated vs Untreated
First, select mapped reads from
Test Samples
Then, select mapped reads from
Control Samples

109. Commonly Used Tools for Differential Gene Expression Analysis

110. Differential Gene Expression – 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.

111. Differential Expression - Treated vs Untreated
Use the metadata table to
define groups

112. Differential Gene Expression

113. Differential Expression – Gene level

114. Fold Change in Natural Scale vs Log Scale
GraphPad Statistics Guide :

115. Data Visualization

116. Differential Expression - Volcano Plot

117. Create a HeatMap

118. Create a HeatMap

119. Create a HeatMap

120. Running CLC Genomics software on CRC HTC Cluster

121. Create a Track

122. Expression Browser – all in one large spread sheet

123. Downstream Analysis

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

125. NextBio Research

126. Export Data from CLC

127. Find Correlated Gene Expression Studies from GEO

128. Find Correlated Gene Expression Studies from GEO

129. Ingenuity IPA Analysis

130. Suggested MBIS Workshops