DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies

A Case Study of Knockdown and Upregulated FLI1 in Breast Cancer Cells

Katherine Duchinski

1, ∗

, Margaret Antonio

2, ∗

, Dennis Watson

and Paul Anderson

1, †

Department of Computer Science, College of Charleston, 66 George Street, Charleston, SC, U.S.A.

Department of Biology, Boston College, 140 Commonwealth Ave., Chestnut Hill, MA, U.S.A.

Department of Genetics, Medical University of South Carolina, 68 President Street, Charleston, SC, U.S.A.

Keywords:

RNA-sequencing, RNA-Seq, Meta-analysis, Differential Expression, Transcriptomics, Comparative Analysis.

Abstract:

Understanding the genetic basis of disease may lead to the development of life-saving diagnostics and thera-

peutics. RNA-sequencing (RNA-seq) gives a snapshot of cellular processes via high-throughput transcriptome

sequencing. Meta-analysis of multiple RNA-Seq experiments has the potential to (a) elucidate gene function

under different conditions and (b) compare results in replicate experiments. To simplify such meta-analyses,

we created the Dataset Exploration And Curation Tool (DEACT), an interactive, user-friendly web applica-

tion. DEACT allows users to (1) interactively visualize RNA-Seq data, (2) select genes of interest through

the user interface, and (3) download subsets for downstream analyses. We tested DEACT using two com-

plementary RNA-seq studies resulting from knockdown and gain-of-function FLI1 in an aggressive breast

cancer cell line. We performed ﬁxed gene-set enrichment analysis on four subsets of genes selected through

DEACT. Each subset implicated different metabolic pathways, demonstrating the power of DEACT in driving

downstream analysis of complementary RNA-Seq studies.

1 INTRODUCTION

Recent advances in next-generation sequencing have

enabled researchers to collect genomic data more

quickly and cost-efﬁciently than ever before. RNA-

sequencing (RNA-Seq) utilizes next-generation se-

quencing to identify and quantify transcripts in a cell.

Analyzing transcriptome changes between healthy

and abnormal cells, or other contrasting phenotypes,

is key in understanding diseases and in developing

novel molecular therapies and drugs.

Given a reference genome, one of the ﬁrst stages

of analyzing RNA-Seq data involves aligning and

quantifying sequenced reads. Next, differential ex-

pression is determined by comparing paired groups

(e.g., treatment vs control). Several tools have been

developed and are widely used for identifying, quan-

tifying, and assessing differential expression of tran-

scripts from the sequenced reads (Trapnell et al.,

2012; Zhou et al., 2014; Robinson et al., 2010;

Ritchie et al., 2015; Love et al., 2014).

Typically, the following stage in RNA-Seq anal-

∗

These authors contributed equally to this work

†

Corresponding author

ysis is pathway enrichment, identiﬁcation of path-

ways based on the differentially expressed transcripts

in those pathways. Popular tools for pathway en-

richment analysis and visualization include iPath-

wayGuide, GAGE, and Pathview (Draghici et al.,

2007; Luo et al., 2009; Luo and Brouwer, 2013).

The transition between these two stages can be

problematic for researchers who want to analyze two

or more parallel or independent RNA-Seq experi-

ments. Parallel studies may convey, for example, a

spectrum of severity of a cell phenotype, while inde-

pendent studies may contrast two conditions, such as

loss-of-function versus gain-of-function of a gene. In

such cases, researchers are interested in a subset of

transcripts that stand out comparatively across exper-

iments. Subsets of interesting genes could then be fed

to downstream pathway enrichment tools.

Visualizing and creating subsets of genes across

experiments is difﬁcult for researchers who are not

computationally trained. Biclustering is a popular ap-

proach for identifying functional genes that behave

a similar way in multiple experimental conditions

(Pontes et al., 2015). However, successful use of bi-

clustering algorithms depends on having a amount of

large data, which is not always the case in compar-

154

Duchinski K., Antonio M., Watson D. and Anderson P.

DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast Cancer Cells.

DOI: 10.5220/0006152901540159

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 154-159

ISBN: 978-989-758-214-1

Figure 1: Screenshot of the DEACT web application. DEACTs user interface contains (A) ﬁle upload; (B) visualization

options; (C) the main panel, in which the user may manage, plot, view, and download the data; and (D) an overview of

signiﬁcant genes in the dataset.

ing only two experimental conditions, or in RNA-Seq

analysis in general. Secondly, the lack of an ”optimal”

bi-clustering algorithm, as in the case of sequence

alignment, is a challenge to experimental biologists

who do not have the training to run and compare mul-

tiple algorithms.

In order to address this need, we developed a

user-friendly web application designed to visualize,

compare, select, categorize, and subset differentially

expressed transcripts in two complementary experi-

ments. The Dataset Exploration And Curation Tool

(DEACT) enables researchers to compare two RNA-

Seq studies using a simple interface for managing,

viewing, and plotting data. Here we demonstrate

its use with a case study of gain-of-function (GOF)

and loss-of-function (LOF) of the FLI1 gene, which

has been associated with hematological and epithelial

cancers (Watson et al., 2010; Scheiber et al., 2014).

DEACT allows users to subset data based on gene

categories (i.e., whether the gene is signiﬁcant in both

experiments, or has a contrasting expression between

two conditions).

Many tools that support multiple studies, such

as RNASeqViewer, visualize users data through heat

maps, while DEACT allows for more quantitative

comparisons. Other applications that utilize scatter-

plots, like GRACOMICS, do not allow the user to

create custom selections or easily export predeﬁned

subsets (Rog

e and Zhang, 2013; Seo et al., 2015).

DEACT offers an interactive data visualization fea-

ture which retains quantitative and transcript annota-

tion information. It allows users to quickly and easily

compare experimental conditions by visualizing and

deﬁning gene subsets that can easily be exported for

further analysis.

2 IMPLEMENTATION

2.1 Software

DEACT is an interactive web application imple-

mented using the R shiny package. It uses the

R package plotly to create scatterplots (RStudio,

Inc, 2014; Plotly Technologies Inc., 2015). DE-

ACT can be used with up to 5 MB of data at

https://kduchinski.shinyapps.io/DEACT/. For larger

datasets or for deploying customized versions, the

source code to use DEACT locally is available at

https://github.com/kduchinski/DEACT.

2.2 Workﬂow

The DEACT workﬂow consists of three main sections

which can be easily navigated using a tab bar in the

main panel interface: (a) manage data, (b) view data,

and (c) plot data (Figure 1C).

In the MANAGE DATA section, users can upload

differential expression data from transcriptome proﬁl-

ing in comma-separated value (csv) or tab-delimited

(txt) formats (Figure 1A). Each differential expres-

sion observation should have (1) a gene identiﬁca-

tion number and/or gene symbol, (2) differential ex-

DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast

Cancer Cells

155

Figure 2: A sample plot generated by DEACT with the Fli1 data. The graph has been zoomed in on the center and an area has

been selected through the lasso tool. The hover tag is displayed for one of the points, a gene signiﬁcantly affected by only the

condition named ”Gain” by the user.

pression measured in Fragments Per Kilobase of tran-

script per Million mapped reads (fpkm) or log fold

change, and (3) a measure of signiﬁcance (i.e. p-value

or q-value). Upon uploading data, the ﬁelds for these

three components must be speciﬁed for both experi-

mental conditions using a drop-down selection menu

of the data columns (Figure 1C). DEACT automati-

cally subsets transcripts into categories based on their

differential expression in the two experimental condi-

tions being studied (Figure 1D). These subsets can be

selectively added to the data viewing table. Custom

subsets may be added directly from the scatterplot.

The second section, PLOT DATA, plots the entire

data set on an interactive scatterplot, which includes

zoom, pan, and hover controls (Figure 1). Each sig-

niﬁcantly differentially expressed gene is plotted by

the change in its expression, either in fragments per

kilobase per million (fpkm) or by log

fold change,

which was speciﬁed in the MANAGE DATA section.

The plot displays the gene symbol/id and differential

expression value when the user hovers over a data

point (Figure 2). Data can be selected by box (rect-

angular) or lasso (free shape) selection. All points

within the selection will be included in the data view-

ing table. If plotting by fold change, transcripts with

inﬁnite fold changes will not be graphed, but the user

may choose to include them in the data viewing table.

After managing data (uploading data, specifying

ﬁelds, and selecting categories), users can use the

VIEW DATA feature which displays select data in a

table format with search and sorting options. This fea-

ture allows users to preview their data before down-

Finally, the user-curated dataset can be down-

loaded as a tab-delimited text ﬁle for further analy-

sis, such as pathway analysis. The user may choose

which columns from the dataset to include in the ﬁle.

3 USAGE EXAMPLE

DEACT was tested using RNA-Seq data from

Friend Leukemia Virus Integration 1 (FLI1) research.

Speciﬁcally, two independent RNA-Seq studies were

analyzed with DEACT: (1) gain-of-function (GOF)

and (2) loss-of-function by knockdown (LOF) exper-

iments in MDA MB231, an aggressive human breast

cancer cell line. FLI1 belongs to the ETS family of

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

156

transcription factors, which are associated with key

cellular processes dysregulated in cancer cells (Wat-

son, 2010). Although aberrant expression of FLI1 has

been observed in hematological cancers, it was re-

cently found to also be dysregulated in breast cancer,

an epithelial-derived cancer (Scheiber et al., 2014).

Fli1

Knockdown

Overexpression

GOF

Control

LOF Control

Differential

expression analysis

Differential

expression analysis

DEACT subsets

GOF only

LOF only

↑↓GOF, ↑↓LOF

Custom

↑GOF, ↑LOF

↓GOF, ↓LOF

↑GOF, ↓LOF

↓GOF, ↑LOF

Figure 3: Diagram of the FLI1 loss- (LOF) and gain-of-

function (GOF) studies. The quantiﬁed differential expres-

sion data from both were subsetted by DEACT into cate-

gories with possible biological relevance.

An shRNA constructed in lentiviruses was used in

the FLI1 LOF experiment, while adenovirus was used

in the FLI1 GOF experiment (Figure 3). Three bi-

ological replicates each were prepared for the LOF,

GOF, and lentival and adenoviral controls. RNA

was extracted and sequenced using the Illumina

HiSeq2500. Sequenced reads were ﬁltered for quality,

processed, mapped, and quantiﬁed using the Tuxedo

Suite (Bioinformatics, 2011; Bolger and Giorgi,

2014; Trapnell et al., 2012). Probability values were

corrected for the false discovery rate. CummeRbund

was used to prepare a ﬁle for DEACT from CuffDiff

data from each experiment.

DEACT was able to visualize and subset data from

both studies. DEACT categorized signiﬁcant (α =

0.05) genes affected in each FLI1 experiment (Table

Table 1: Genes categorized by regulation direction across

two FLI1 expression studies. DEACT identiﬁed signiﬁ-

cantly dysregulated transcripts.

Gain-of-

Function FLI1

Loss-of-

Function

Genes

3318

1305

956

271

199

223

263

↑

↓

↑

↓

↑

↓

↑

↓

↑

↓

↑

↓

1). This information may prompt preliminary bio-

logical conclusions from the data. For example, take

the 271 genes that were up-regulated and the 199 that

were down-regulated in both conditions. From these

subsets, it can be concluded that of genes that were af-

fected by both conditions, 470 (49%) were regulated

in the same direction. This result is counter-intuitive;

GOF and LOF studies are rarely performed for the

same gene in part because their results are expected

to be non-informative due to an expected contrast.

The subsets that show contraregulation between the

gain-of-function and the loss-of-function may be of

particular interest to researchers because expression

of these genes may be dependent upon expression of

FLI1. Similarly, pathway analysis of these subsets

may show which cellular functions are correlated with

FLI1 expression.

Figure 4: A visual representation of pathway analysis by

subset. Small subsets from Table 1 are denoted with signif-

icant metabolic pathways related to those genes.

The genes categorized into the last four rows of

Table 1 were downloaded as four subsets. These se-

DEACT: An Online Tool for Analysing Complementary RNA-Seq Studies - A Case Study of Knockdown and Upregulated FLI1 in Breast

Cancer Cells

157

Table 2: Pathway categorized by regulation direction across

two FLI1 expression studies.

Gain-of-

Function FLI1

Loss-of-

Function

Pathways

153

↑

↓

↑

↓

↑

↓

↑

↓

↑

↓

↑

↓

lections represent the possible regulation patterns for

genes affected by both conditions. Each subset, as

well as the full dataset, were subjected to pathway

analysis using iPathwayGuide (Draghici et al., 2007).

Each subset is labeled with its signiﬁcant (α = 0.05)

pathways (Figure 4).

The top 100 pathways for genes up-regulated in

GOF, down-regulated in GOF, and so on for LOF

were identiﬁed and similarly categorized in Table 2.

These tables demonstrate how subsets of genes may

translate into subsets of pathways. For example, of

the original 400 pathways, 33 (8.25%) involve genes

up-regulated in GOF and down-regulated in LOF.

Closer investigation is necessary to determine if each

pathway as a whole was up- or down-regulated. Path-

ways that are up-regulated in one condition and down-

regulated by the other are often of particular interest,

as these may indicate which cellular processes are di-

rectly correlated with a condition.

4 DISCUSSION

DEACT’s interactive user interface for rapid visual-

ization and categorization of expression data is intu-

itive for researchers with little or no programming ex-

perience. It supports any two complementary studies

and can compare biological replicates or contrasting

experimental conditions. DEACT automatically cat-

egorizes signiﬁcant data points by their response to

each condition. These practically relevant categories

may be downloaded as subsets for further study, for

example, in a ﬁle format accepted by iPathwayGuide

and other programs. Alternatively, unique subsets can

be selected directly from the user interface. The re-

sponsive user interface allows users to instantly iden-

tify and select any set of genes, thus achieving a level

of engagement that neither scripts nor traditional plots

offer.

The simple, interactive design makes DEACT an

effective collaboration tool for research laboratories.

Unlike biclustering, DEACT can be effectively used

to interpret small datasets and does not require the

additional time or training required to optimize a bi-

clustering algorithm. Instead, it is built to quickly an-

swer preliminary questions about new RNA-seq data

to prompt downstream analyses and encourage a ﬂow

of discussion. With DEACT, researchers may easily

create highly customizable datasets to ﬁt any ques-

tion, however speciﬁc. In the future, pathway analysis

may be integrated with DEACT in order to visualize

cellular responses on the pathway level as well as the

gene level. This feature may be incorporated into the

user interface, as shown in Figure 3.

DEACT adds to a growing set of meta-analytical

tools for RNA-Seq data. With a tool like DEACT,

non-computationally trained researchers can mine

their data for novel insights on gene expression and

function. Such meta-analyses not only augment our

understanding of cellular processes, but they have the

potential to lead to novel life-saving therapeutics.

ACKNOWLEDGEMENTS

The authors would like to thank the College of

Charleston for hosting the NSF Omics REU which

is supervised by the National Science Foundation

DBI Award 1359301. We also acknowledge sup-

port from the Genomics Shared Resource, Hollings

Cancer Center, Medical University of South Car-

olina. This shared resource is supported in part by the

Hollings Cancer Center, Medical University of South

Carolina Support Grant (P30 CA 138313).

REFERENCES

Bioinformatics, B. (2011). Fastqc a quality control tool

for high throughput sequence data. Cambridge, UK:

Babraham Institute.

Bolger, A. and Giorgi, F. (2014). Trimmomatic: a ﬂex-

ible read trimming tool for illumina ngs data. URL

http://www. usadellab. org/cms/index. php.

Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A.,

Voichita, C., Georgescu, C., and Romero, R. (2007).

A systems biology approach for pathway level analy-

sis. Genome research, 17(10):1537–1545.

Love, M. I., Huber, W., and Anders, S. (2014). Moderated

estimation of fold change and dispersion for rna-seq

data with deseq2. Genome biology, 15(12):1.

Luo, W. and Brouwer, C. (2013). Pathview: an

r/bioconductor package for pathway-based data

integration and visualization. Bioinformatics,

29(14):1830–1831.

BIOINFORMATICS 2017 - 8th International Conference on Bioinformatics Models, Methods and Algorithms

158

Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D.,

and Woolf, P. J. (2009). Gage: generally applicable

gene set enrichment for pathway analysis. BMC bioin-

formatics, 10(1):1.

Plotly Technologies Inc. (2015). Collaborative data science.

Pontes, B., Gir

aldez, R., and Aguilar-Ruiz, J. S. (2015). Bi-

clustering on expression data: A review. Journal of

biomedical informatics, 57:163–80.

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W.,

Shi, W., and Smyth, G. K. (2015). limma pow-

ers differential expression analyses for rna-sequencing

and microarray studies. Nucleic acids research, page

gkv007.

Robinson, M. D., McCarthy, D. J., and Smyth, G. K. (2010).

edger: a bioconductor package for differential expres-

sion analysis of digital gene expression data. Bioin-

formatics, 26(1):139–140.

Rog

e, X. and Zhang, X. (2013). Rnaseqviewer: visualiza-

tion tool for rna-seq data. Bioinformatics, page btt649.

RStudio, Inc (2014). Easy web applications in R. URL:

http://www.rstudio.com/shiny/.

Scheiber, M. N., Watson, P. M., Rumboldt, T., Stanley, C.,

Wilson, R. C., Findlay, V. J., Anderson, P. E., and Wat-

son, D. K. (2014). Fli1 expression is correlated with

breast cancer cellular growth, migration, and invasion

and altered gene expression. Neoplasia, 16(10):801–

813.

Seo, M., Yoon, J., and Park, T. (2015). Gracomics: soft-

ware for graphical comparison of multiple results with

omics data. BMC genomics, 16(1):1.

Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kel-

ley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L.,

and Pachter, L. (2012). Differential gene and tran-

script expression analysis of rna-seq experiments with

tophat and cufﬂinks. Nature protocols, 7(3):562–578.

Watson, D. K., Turner, D. P., Scheiber, M. N., Findlay, V. J.,

and Watson, P. M. (2010). Ets transcription factor

expression and conversion during prostate and breast

cancer progression. Open Cancer J, 3:24–39.

Zhou, X., Lindsay, H., and Robinson, M. D. (2014).

Robustly detecting differential expression in rna se-

quencing data using observation weights. Nucleic

acids research, 42(11):e91–e91.

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