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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].

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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.

Note: 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.

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




2. Choose the interested clade, genome and assembly

4. Change the filters of interests


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






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)



Usage and example2.2 Change the filters of interests






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



Usage and example4. Change the filters of interests





3.2 Degradome-RNA — e.g. How many miRNAs may mediate the degradation of MYB33 gene?


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?


1. Select mRNA-RNA of RNA-RNA

Usage and example







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?


1. Select the sub-class of “mRNA-ceRNA”

Usage and example

s


6. Filter the interested results in the search box

4. Click the title of each column to sort



5.2 ceRNA-Net — e.g. What are the ceRNA interaction networks of PTEN?


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


1. Select “RBP-mRNA” of RBP-Target

Usage and example


5. Click the title of each column to sort




6. RBP-Target — e.g. How many RBPs could regulate TP53?


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



Source and reference



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




5. Non-zero numbers are highlighted. Click on them for more details


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?


1. Select “mirTarPathway”of Pathway

Usage and example Filter the interested results in the search box


4. Select the pathway of your interest

6. Click to link the GSEA website


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?

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?


2. Select one platform, here, we select “miRNA Target CoExpression”


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


5.2 Click a point on the survival curves to get more information


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?


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?


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



10.3.3 Gene Survival Analysis (Results Page) — e.g. What is the association

between HOTAIR expression and survival rates in Adrenocortical Carcinoma?

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?


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?


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


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&degraExpNum=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

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