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ENCORI Tutorialcover Pan-Cancer analysis
Li Cai, KeRen Zhou, Shun Liu, LiangHu Qu, JianHua Yang
© 2010-2019, Jianhua Yang at Sun Yat-sen University
For comments, suggestions on the starBase platform, please contact us: [email protected] and [email protected]
On commercial use, please contact: [email protected].
1. Brief introduction of ENCORI.
2. Study the miRNA-target interactions supported by Ago CLIP-seq data.
3. Decipher the miRNA-mRNA Interactions supported by degradome-seq data.
4. Uncover the RNA-RNA interactions derived from RNA-RNA interactome data.
5. Decode the ceRNA interaction Networks supported by CLIP-seq data.
6. Study the RBP-Target interactions supported by CLIP-seq data.
7. Reveal the RBP-Motif interactions with supported CLIP-seq Data.
8. Decrypt the connections between RBP-RNA interactions and somatic mutations in human diseases with supported CLIP-seq data.
9. Decipher the interactions of miRNA-targets supported by CLIP-seq data.
10. Study the Pan-Cancer networks of ncRNAs, RBPs and Genes.
11. Download the data of your interests.
ENCORI Tutorial Catalog
Brief introduction of ENCORI1
ENCORI is an open-source platform for studying the miRNA-mRNA, ncRNA-RNA, RBP-ncRNA and RBP-mRNA interactions from CLIP-seq, degradome-seq and RNARNA interactome data, which is released at http://rna.sysu.edu.cn.
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1.1 Brief introduction of ENCORI
Select a module of your interest.
A system-level overview of the ENCORI core framework.
Brief introduction of ENCORI.
How to site. For better browse experience, Chrome is strongly recommended. Firefox 61, Opera 54, Safari 5 and Microsoft Edge are also supported. IE 7-11 and IE-core browsers are not supported.
Statistics of ENCORI (2018 update).
ENCORI Citations.
1.2 Brief introduction of ENCORI
Study the miRNA-target interactions supported by Ago CLIP-seq data2
2.1 miRNA-Target — e.g. How many mRNAs could interact with miRNA hsa-miR-21-5p?
A particular miRNA can pair specifically with several target mRNAs
performing different biological functions. How could we know the mRNAs that
interact with the interested miRNA?
Here, take “hsa-miR-21-5p” (in human) which has been reported to interact with PDCD4 as an example
Oncogene volume 27, pages 2128–2136 (03 April 2008)
2.1 miRNA-Target — e.g. How many mRNAs could interact with miRNA hsa-miR-21-5p?
Click to edit parameters then select Quick Search to query
1. Select the sub-class of “miRNA-mRNA”
Usage and example
2.1 Choose the interested clade, genome and assembly
Export all query results as excel or txt format
Click the title of each column to sort
3. Non-zero numbers are highlighted. Click on them for more details.
2.2 Change the filters of interests
Filter the interested results in the search box
A single mRNA can be recognized and regulated by multiple miRNAs. In
most cases, we want to figure out our target gene can be potentially
regulated by how many miRNAs.
Take PDCD4 gene (in human) as an example
2.2 miRNA-Target — e.g. How many miRNAs may target PDCD4 gene?
2.2 miRNA-Target — e.g. How many miRNAs may target PDCD4 gene?
3. Click to edit parameters then select Quick Search to query
1. Select the sub-class “miRNA-mRNA”
Usage and example
Export all query results as excel or txt format
Click the title of each column to sort
5. Non-zero numbers are highlighted. Click on them for more details.
2. Choose the interested clade, genome and assembly
4. Change the filters of interests
Filter the interested results in the search box
Recently, cirRNAs have been reported to regulate gene expression by
sponging miRNAs.
Here, we instance cirRNA "HIPK3" (in human) , which is directly binds to miR-
124 and inhibits miR-124 activity, to explore miRNA-circRNA interactions (in
human).
2.3 miRNA-Target — e.g. How many miRNAs may target circRNA HIPK3?
2.3 miRNA-Target — e.g. How many miRNAs may target circRNA HIPK3?
3 Click to edit parameters then select Quick Search to query
1. Select the sub-class “miRNA-circRNA”
Usage and example
2. Choose the interested clade, genome and assembly
Export all query results as excel or txt format
Click the title of each column to sort
6. Non-zero numbers are highlighted. Click on them for more details.
4. Change the filters of interests
5. Filter the interested results in the search box
Decipher the miRNA-mRNA Interactions supported by degradome-seq data3
3.1 Degradome-RNA — e.g. Which mRNAs are natural targets of miR-196-directed cleavage?
A microRNA usually can exert
post-transcriptional control over
a broad set of cellular mRNAs.
Here, we take “miR-196” (in
human) that has been reported
to interact with HOXB8 as an
example to explore miRNA-mRNA interactions
Nature volume 438, pages 671–674 (01 December 2005)
Click to edit parameters then select Quick Search to query
1. Select the sub-class “miRNA-mRNA”
Usage and example2.2 Change the filters of interests
2.1 Choose the interested clade, genome and assembly
Export all query results as excel or txt format
Click the title of each column to sort
3. Non-zero numbers are highlighted. Click on them for more details.
Filter the interested results in the search box
3.1 Degradome-RNA — e.g. Which mRNAs are natural targets of miR-196-directed cleavage?
MiRNAs play a keen role in targeted gene silencing by
facilitating posttranscriptional and translational repression.
Here, we take the cleavage site of ath-miR159a on “MYB33”
(in A. thaliana) as an example to study miRNA-mRNA interactions
Plant Cell. 2005 Mar; 17(3): 705–721.
3.2 Degradome-RNA — e.g. How many miRNAs may mediate the degradation of MYB33 gene?
Fig. Model for the Control of MYB33 Expression
3. Click to edit parameters then select Quick Search to query
1. Select the sub-class “miRNA-mRNA”
Usage and example4. Change the filters of interests
2. Choose the interested clade, genome and assembly
Export all query results as excel or txt format
Filter the interested results in the search box
Click the title of each column to sort
3.2 Degradome-RNA — e.g. How many miRNAs may mediate the degradation of MYB33 gene?
5. Non-zero numbers are highlighted. Click on them for more details.
Uncover the RNA-RNA interactions derived from RNA-RNA interactome data4
lncRNAs have well established
functions and much remains to be
done towards decipherment of their
biological roles. One of the least
studied aspects of lncRNAs biology is
their engagement in gene expression
regulation through RNA-RNA
interactions.
We explore lncRNA-RNA interaction network on H19 as an example.
Int J Mol Sci. 2017 Feb; 18(2): 450.
4. RNA-RNA — e.g. How many lncRNAs could H19 interact with?
Click to edit parameters then select Quick Search to query
1. Select mRNA-RNA of RNA-RNA
Usage and example
2.1 Choose the interested clade, genome and assembly
Export all query results as excel or txt format
Filter the interested results in the search box
Click the title of each column to sort
3. Non-zero numbers are highlighted. Click on them for more details.
2.2 Change the filters of interests
4. RNA-RNA — e.g. How many lncRNAs could H19 interact with?
Decode the ceRNA interaction Networks supported by CLIP-seq data5
Crosstalk between RNAs mediated
by shared miRNAs represents a
novel layer of gene regulation,
which plays important roles in
cellular scenarios.
Study the PTEN-centered (in
human) ceRNA networks as an
example to explore the module
Cell. 2011 Oct 14;147(2):382-95.
5.1 ceRNA-Net — e.g. What are the ceRNA interaction networks of PTEN?
3. Click to edit parameters then select Quick Search to query
1. Select the sub-class of “mRNA-ceRNA”
Usage and example
s
Export all query results as excel or txt format
6. Filter the interested results in the search box
4. Click the title of each column to sort
5. Non-zero numbers are highlighted. Click on them for more details.
2.1 Choose the interested clade, genome and assembly
5.2 ceRNA-Net — e.g. What are the ceRNA interaction networks of PTEN?
2.2 Change the filters of interests
Study the RBP-Target interactions supported by CLIP-seq data6
RBPs are crucial functional components of the molecular
machineries involved in posttranscriptional events, such as
premRNA splicing, mRNA editing.
We navigate the TP53-mRNA (in human) interactions as an
example.
Proc Natl Acad Sci U S A. (2007); 104(13): 5389–5394.
6. RBP-Target — e.g. How many RBPs could regulate TP53?
The Ink4a/Arf locus and p53 mediate Cbx7 effects in lymphomagenesis
3. Click to edit parameters then select Quick Search to query
1. Select “RBP-mRNA” of RBP-Target
Usage and example
Export all query results as excel or txt format
5. Click the title of each column to sort
6. Non-zero numbers are highlighted. Click on them for more details.
2. Choose the interested clade, genome and assembly
Filter the interested results in the search box
6. RBP-Target — e.g. How many RBPs could regulate TP53?
4. Change the filters of interests
Reveal the RBP-Motif interactions with supported CLIP-seq Data7
RBPs are crucial functional components of the molecular machineries
involved in posttranscriptional events, such as premRNA splicing,
mRNA editing. Sequence-specific associations between RBPs and
their RNA targets are typically regulated by one or more motifs,
however, few of them have been studied in detail.
We identify the motifs of IGF2BP2 (in human) as an example.
7. RBP-Motif — e.g. What are the motifs of IGF2BP2?
1. Select RBP-Motif
Usage and example
Export all query results as excel or txt format
Filter the interested results in the search box
Source and reference
Click the title of each column to sort
2.1 Choose the interested clade, genome and assembly
7. RBP-Motif — e.g. What are the motifs of IGF2BP2?
2.2 Input a RBP , select a cell/tissue and a property
3. Download data of a particular motif
Decrypt the connections between RBP-RNA interactions and somatic mutations in human
diseases with supported CLIP-seq data8
RBPs are a class of post-transcriptional regulatory molecules which
are increasingly documented to accumulate functionally impactful
mutations in cancer genomes. However, our current understanding of
their relationships with somatic mutations is limited.
Using the RBP-Disease module, we can dig the deeper connections
between RBP-BRCA1 interactions and somatic mutations in human
diseases .
8. RBP-Disease — e.g. How are the correlations between RBP-BRCA1 interactions and
somatic mutations?
3. Click to edit parameters then select Quick Search to query 1. Select RBP-Disease
Usage and example
Export all query results as excel or txt format
Filter the interested results in the search box
Click the title of each column to sort
5. Non-zero numbers are highlighted. Click on them for more details
2. Choose the interested clade, genome and assembly
8. RBP-Disease — e.g. How are the correlations between RBP-BRCA1 interactions and
somatic mutations?
4. Input the tissue and disease
Decipher the interactions of miRNA-targets supported by CLIP-seq data9
RBPs has diverse cellular post-transcriptional regulatory functions.
Pathway analysis of RBPs can reveal their potential biological roles.
We do the enrichment analysis of hsa-let-7a-5p in KEGG pathways.
9. Pathway — e.g. What are the potential biological roles of hsa-let-7a-5p?
3. Click to edit parameters then select Quick Search to query
1. Select “mirTarPathway”of Pathway
Usage and example Filter the interested results in the search box
Click the title of each column to sort
4. Select the pathway of your interest
6. Click to link the GSEA website
2. Choose the interested clade, genome and assembly
9. Pathway — e.g. What are the potential biological roles of hsa-let-7a-5p?
5. Change the filter of interests
Study the Pan-Cancer networks of ncRNAs, RBPs and Genes10
10.1 What is the expression pattern of miR-34a in cancer?
10.2 What is the relationship between hsa-mir-21-5p and PDCD4 in colorectal cancer?
10.3 What is the association between HOTAIR expression and survival rates in Adrenocortical Carcinoma?
10.4 What is the correlation between IGF2BP1 and MYC in ovarian cancer?
10. Pan-Cancer Analysis Catalog
An increasing number of studies have identified miRNAs as potential
biomarkers for human cancer diagnosis, prognosis and therapeutic
targets or tools, which needs further investigation and validation.
MiRNAs may function as either oncogenes or tumor suppressors
under certain conditions. Take “miR-34a” as an example.
10.1.1 What is the expression pattern of miR-34a in cancer?
Description of Pan-Cancer Analysis Platform 1. Select Pan-Cancer
2. Select one platform, here, we select “miRNA Differential Expression”
Description and Usage
10.1.2 miRNA Differential Expression(Query page) — e.g. What is the expression
pattern of miR-34a in cancer?
3. Select miRNA “hsa-miR-34a-5p / MIMAT0000062”
Description and Usage 5. Click a single point to get more sample information
4. Select a Cancer Type 6. Export the figureClick the title of column to sort
Filter the results of your interests
10.1.3 miRNA Differential Expression(Results page) — e.g. What is the
expression pattern of miR-34a in cancer?
10.1 What is the expression pattern of miR-34a in cancer?
10.2 What is the relationship between hsa-mir-21-5p and PDCD4 in colorectal cancer?
10.3 What is the association between HOTAIR expression and survival rates in Adrenocortical Carcinoma?
10.4 What is the correlation between IGF2BP1 and MYC in ovarian cancer?
10. Pan-Cancer Analysis Catalog
Abnormalities in non-coding genes, including miRNAs, can determine
biological alterations in tumor cells to execute the invasion-metastasis
cascade.
We seek to investigate the relationship between miR-21 ang PDCD4
in colorectal cancer regulation.
10.2.1 What is the relationship between hsa-mir-21-5p and PDCD4 in
colorectal cancer?
Description of Pan-Cancer Analysis Platform 1. Select Pan-Cancer
2. Select one platform, here, we select “miRNA Target CoExpression”
Description and Usage
10.2.2 miRNA Target CoExpression (Query page) — e.g. What is the relationship
between hsa-mir-21-5p and PDCD4 in colorectal cancer??
10.2.3 miRNA Target CoExpression (Results page) — e.g. What is the
relationship between hsa-mir-21-5p and PDCD4 in colorectal cancer??3. Select miRNA “hsa-mir-21-5p” and Target Gene “PDCD4”
Description and Usage 5.1 r value and p-value of Pearson correlation analysis
4. Select a Cancer Type 6. Export the figureClick the title of column to sort
5.2 Click a point on the survival curves to get more information
Filter the results of your interests
10.1 What is the expression pattern of miR-34a in cancer?
10.2 What is the relationship between hsa-mir-21-5p and PDCD4 in colorectal cancer?
10.3 What is the association between HOTAIR expression and survival rates in Adrenocortical Carcinoma?
10.4 What is the correlation between IGF2BP1 and MYC in ovarian cancer?
10. Pan-Cancer Analysis Catalog
LncRNAs have emerged as primary regulators of carcinogenesis.
Thus, lncRNAs may represent the novel prognostic and diagnostic
markers of cancer, in addition to being the potential therapeutic targets.
Here, we use “Gene Survival Analysis” module to explore overall
survival rates in patients with Adrenocortical Carcinoma according to
high or low HOTAIR expression levels.
10.3.1 What is the association between HOTAIR expression and
survival rates in Adrenocortical Carcinoma?
Description of Pan-Cancer Analysis Platform 1. Select Pan-Cancer
2. Select one platform, here, we select “Gene Survival Analysis”
10.3.2 Gene Survival Analysis (Query Page) — e.g. What is the association between
HOTAIR expression and survival rates in Adrenocortical Carcinoma?
Description and Usage
3. Input “HOTAIR” or “ENSG00000228630”
Description and Usage 5.1 Information of survival analysis
5.2 Click a point on the survival curves to get more information
4. Select a Cancer Type 6. Export the figureClick the title of column to sort
Filter the results of your interests
10.3.3 Gene Survival Analysis (Results Page) — e.g. What is the association
between HOTAIR expression and survival rates in Adrenocortical Carcinoma?
10.1 What is the expression pattern of miR-34a in cancer?
10.2 What is the relationship between hsa-mir-21-5p and PDCD4 in colorectal cancer?
10.3 What is the association between HOTAIR expression and survival rates in Adrenocortical Carcinoma?
10.4 What is the correlation between IGF2BP1 and MYC in ovarian cancer?
10. Pan-Cancer Analysis Catalog
Intermolecular RNA-RNA interactions are used by many noncoding
RNAs (ncRNAs) to achieve their diverse functions.
We develop the “RNA-RNA CoExpression” module to systematically
analyze RNA-RNA interactions across 32 types of cancers and apply it
to explore two ncRNAs implicated in ovarian cancer: IGF2BP1 and
MYC.
10.4.1 What is the correlation between IGF2BP1 and MYC in ovarian
cancer?
Description of Pan-Cancer Analysis Platform 1. Select Pan-Cancer
2. Select one platform, here, we select “RNA-RNA CoExpression”
10.4.2 RNA-RNA CoExpression (Query page) — e.g. What is the correlation
between IGF2BP1 and MYC in ovarian cancer?
Description and Usage
3. Select Query Gene “IGF2BP1” and Target Gene “MYC”
Description and Usage 5.1 r value and p-value of Pearson correlation analysis
5.2 Click a single point to get more sample information
4. Select single Cancer Type 6. Export the figure
Click the title of column to sort
Filter the results of your interests
10.4.3 RNA-RNA CoExpression (Results page) — e.g. What is the correlation
between IGF2BP1 and MYC in ovarian cancer?
Download the data of your interests11
In “Wed API” module, we provide the direct programmatic access to all
regulatory data stored within the server. This enables you to easily
access data from your favorite programming language, such as Shell,
Python, or Perl.
Here we give an example explaining how to download all miRNA data
for TP53 (in human) derived from CLIP-seq.
11.1 Web API module introduction
11.2 Download all miRNA data for TP53 (in human) used in ENCORI
curl
'http://rna.sysu.edu.cn/encori/api/miRNATarget/?assembly=hg19&gene
Type=mRNA&miRNA=all&clipExpNum=5°raExpNum=0&pancanc
erNum=0&programNum=1&program=PITA,RNA22&target=TP53&cell
Type=HeLa' > ENCORIV3_hg19_CLIP-seq_all_TP53.txt
Note: Here we use curl (command line tool) for communicating with
the ENCORI APIs.
THANKS