Linked Functional Annotation For Differentially
Expressed Gene (DEG) Demonstrated using
Illumina Body Map 2.0
Alokkumar Jha , Yasar Khan, Muntazir Mehdi, Aftab Iqbal, Achille Zappa,
Ratnesh Sahay, and Dietrich Rebholz-Schuhmann
Insight Centre for Data Analytics, NUI Galway, Ireland
Galway
{alokkumar.jha,yasar.khan,aftab.iqbal,achille.zappa,muntazir.mehdi,
ratnesh.sahay,rebholz}@insight-centre.org
Abstract. Semantic Web technologies are core for the integration of
disparate data resources. It can be used to exploit data from next gen-
eration sequencing (NGS) for therapeutic decisions regarding cancer. In
this manuscript, we describe how different data resources, which inform
on the expression of specific genes in a tissue and its variants, can be
brought together to indicate a risk for tissue-specific cancer for NGS
data. This approach can be used to judge patient genomic data against
public reference data resources.
The TCGA and COSMIC repositories are being processed to connect and
query information concerning the expression of genes, copy number vari-
ants (CNV), and somatic mutations. We annotated sets of differential ex-
pression data provided from the Illumina Body map 2.0 (HBM) concern-
ing 16 different tissue types and identify genes with an RPKM (Reads Per
Kilobase of transcript per Million mapped reads) value greater than 0.5
as measure indicating an associated risk for cancer. Thus, the differential
expressed genes from HBM can be associated with a tissue type and gene
expressions in COSMIC and TCGA leading to a potential biomarker for
that particular tissue specific cancer. In the case of ovarian cancer, we
retrieved the genomic positions (loci) and the associated genes of poten-
tial biomarker candidates, and suggest that this approach and platform
can serve future studies well.
Altogether, the presented linked annotation platform is the first approach
to represent the COSMIC data in an RDF format and to link the data
with the TCGA datasets. The proposed approach enriches mutations by
filling in missing links from COSMIC and TCGA datasets which in turn
helped to map mutations with associated phenotypes.
Keywords: Differentially Expressed genes(DEG),Linked data, Clinical
genomics,Copy Number Variation (CNV)
1 Introduction
Next Generation Sequencing (NGS) technologies open new diagnostic and ther-
apeutic ways for cancer research. However, the resulting high-throughput se-
quencing data has to be processed in complex data analytics pipelines including
annotation services. Unfortunately, there is not yet a well-integrated platform
available for both clinical and translational [12] research to fulfill these anno-
tation and analytical tasks. In addition, the large volumes of NGS data poses
another challenge, since the computational infrastructure for the biological in-
terpretation will have to cope with very large quantities of data originating from
clinical facilities. Last, but not least, the functional annotation of genomics data
for cancer has to take tissue specificity into consideration and thus has to avoid
ambiguity while aggregating clinical outcomes from disparate resources. In this
paper we focus on exploring gene expression patterns across different cancer and
tissue types. Our experiments are based on semantic integration of gene expres-
sion, CNV, complete mutation data from two disjoint resources, i.e. COSMIC1
and TCGA2 . By doing this we can assist in variant and mutation prioritization
using 16 different tissue types given by the Illumina Body Map 2.0 and evaluated
in a case study for Ovarian cancer.
In order to link and retrieve patterns of a gene and tissue specific information
from various cancer mutation (TCGA) and database with global mutation list
and mutation type (COSMIC), we encountered the following three challenges:
(i) to transform heterogeneous data repositories and their storage formats into
standard RDF; (ii) to discover associations (aka. links) by finding specific pat-
terns (i.e. correlations) for a gene with regards to CNV, mutation and its gene
expression data sets; and (iii) to query in a scalable way the large volume and
frequently updating datasets covering 16 different tissue types and the gene ex-
pression data from different repositories.
The experiments conducted in this paper is aligned to the transcriptome
study based on the Human Body Map 2.0 (HBM)3 from Illumina which covers
the following tissues: adrenal, adipose, brain, breast, colon, heart, kidney, liver,
lung, lymph, ovary, prostate, skeletal muscle, testes, thyroid, and white blood
cells. The HBM provides gene specific information across one or more tissue types
and intends to support the identification of potential biomarker for targeted
therapy. In this study our results not only depicts novel biological outcomes but
also provides a linked annotation framework that assimilates clinical outcomes
from related data repositories.
The rest of the paper is structured as follows: Section 2 motivates our working
scenario exploring on the HBM use case and the annotation databases; Section 3
presents the methodology and architecture of the proposed functional annotation
framework; Section 4 gives an evaluation of the functional annotation framework;
Section 6 presents the related work in linking the TCGA repository and Section 7
draws the conclusion from our work.
2 Motivation
In order to understand the outbreak of disease, in particular cancer, it is one ap-
proach to compare normal and diseased tissue samples to interpret the changes in
the expression patterns of the genes with regards to the observed disease status.
1
http://cancer.sanger.ac.uk/cosmic
2
https://tcga-data.nci.nih.gov/tcga/
3
https://www.ebi.ac.uk/gxa/experiments/E-MTAB-513
In our case, HBM serves the purpose to identify similarities in gene expression
patterns using the studies across different tissue types, where HBM discloses
the similarities between human tissues on the molecular and genetic level. Due
to overlaps between cancer behaviours, progression and mutated genes, we have
annotated top 100 genes distilled by our filtering criteria with COSMIC to ex-
plore previously observed studies from TCGA database, e.g., somatic mutation,
genomic loci and other mutations linked to these genes retrieved from healthy
tissues.
Human Body Map (HBM) 2.0 from Illumina: HBM covers data from
transcriptome studies for 16 tissue types (see above). Samples for these 16 tis-
sue types have been processed, aligned and finally expression level have been
determined [1]. Sequencing has been performed to provide both paired-end and
single-end libraries (read-length of 50bp and 75bp). Therefore, the data process-
ing platform requires a list of differentially expressed genes as input, which is
the outcome of the RNA seq data analysis pipeline.
The gene expression data extracted from HBM samples returns a very large
list of more than 52000 genes. For data processing reasons we chose to reduce
the list and therefore defined the cut off for each RPKM value according to the
method suggested by Sandberg et. al[8]. As a result, the data for each tissue
type includes both the coverages and the RPKM values as the corresponding
expression level. In addition, the RNA seq data set provides further relevant data
such as CNV, fusion genes, structural variation, differentially expressed genes,
novel mutations, splice junctions and trascriptome variations [4]. Identifying the
associations and relations between these datasets, i.e. the logical connections,
enables further insightful research into the cancer disease the biological and
clinical interpretation of given data.
Annotation databases (COSMIC & TCGA): The main focus of this work
is the identification of patterns for cancer mutations (given by TCGA) and glob-
ally known mutations and their types (given by COSMIC) for selected differen-
tially expressed genes across different tissue types. Figure 1 shows the correspon-
dences, i.e., the associations or connections that have been established between
the TCGA and COSMIC databases for this purpose. For this task our primary
concern have been the associations between the CNV, the known mutations and
the gene expression data.
GENE_EXPRESSION COMPLETE_MUTATION CNV(COPY NUMBER)
GENE_EXPRESSION COMPLETE_MUTATION CNV(COPY NUMBER)
ID_SAMPLE Gene name ID_SAMPLE
Composite Element Hugo_Symbol ID_tumour
Chromosome SAMPLE_NAME Sample name
REF Protein Start GENE_NAME Tumour origin Primary site
Expression Entrez_Gene_Id Histology subtype
End REGULATION TCGA_ID
Segment_Mean ID_tumour MUT_TYPE
ChromosomeStart_Position Chromosome:G_Start..G_Stop
Sample source
UID ……..
Validation Status
……
Fig. 1: Links between COSMIC and TCGA repositories
As part of this work, specific basic curation for data refinements have been
performed: we had to identify instances to link two databases or a couple of
events within the databases (see fig. 1). For example, MUTATION and GENE
EXPRESSION data in COSMIC could be linked to GENE NAME but CNV
had SAMPLE IDs as expected. Later we used SAMPLE IDs after first iteration
with GENE NAME. Also, chr:start end position and GENE NAME were used
to link COSMIC and TCGA (see green arrows in Figure 1). The RDFized
version (see section 3.1) has kept this redundancy problem to have FDR rate as
low as possible.
3 Methodology & Architecture
The annotation architecture is summarized in Figure 2 showing all three major
components. First, the RDFization component that generates Linked Data from
the TCGA and COSMIC databases leading to several SPARQL endpoints for
public use. Second, the linking component that searches and discovers correspon-
dences between selected datasets (TCGA variants, COSMIC diff expression,
COSMIC Mutation, etc.). The links discovered by this component have an
effect on the efficiency in the source selection, on the query planning, and on
the overall query execution in a decentralized setting. Third, the scalable query
federation component: it a single-point-of-access through which distributed data
sources can be queried in concerto.
Illumina Body Map 2.0
Differentially expressed genes (DEG)
TCGA_variants
SPARQL Query COSMIC
SAFE Query Federation Engine
Linked TCGA and COSMIC Repositories
GENE_NAME
Mutation _ID
Sample_ID
Source Tumor_ID
Chr_start
COSMIC_diff_expression
Selection Chr_end
TCGA_ID
Data
Summaries
GENE_EXPRESSION
RDFization
COMPLETE_MUTATION
Query
COSMIC_Mutation
CNV(COPY NUMBER)
Planning
TCGA_ID
Chr_end
Chr_start
Access
TCGA_protein_expression
Policy Model Variant_type
Ref_Protien
Query Expression
Execution
TCGA
Results
Fig. 2: Linking and functional annotation of gene expression data
The scalable query federation is based on the SPARQL query federation en-
gine called SAFE [7], which has been developed for accessing distributed clinical
trial repositories. SAFE has been adapted to improve the efficient integration of
data from the different TCGA and COSMIC SPARQL endpoints. More specifi-
cally, SAFE makes use of a favourable distribution of data to reduce the number
of sources required for processing federated SPARQL queries (without compro-
mising recall). This approach is based on the principle that integrated data
sources allow querying of multiple data sources in a single search, independently
of their status being distributed or centralized, whereas traditional methods of
data integration rather map the data models to a single, unified, model. Such
methods tend to resolve syntactic differences between models, but do not ad-
dress possible inconsistencies in the concepts defined in those models. Semantic
integration resolves the syntactic heterogeneity present in multiple data models
as well as the semantic heterogeneity among similar concepts across those data
models.
3.1 RDFization
COSMIC raw data files are delivered as tab separated text (tsv) and are being
processed with the COSMIC RDFizer tool that generates the N3 triples for
the SPARQL endpoint and statistical information related to the data. Only
three types of data have been included, i.e., gene expression data, gene mutation
and CNV data. Table 1 shows the overall statistics of the RDFization: row 1
represents for the COSMIC gene expression data the number of records (column
2), it’s size (column 4), the corresponding triples generated (column 2) and again
it’s size. The other two rows represent the same type of data for the COSMIC
gene mutation and CNV data. A total of 154 million records has been RDFized,
producing approximately 1.2 billion triples. Row 5 represents the statistics for
the RDF version of TCGA-OV (TCGA Ovarian), which forms a subset of the
linked TCGA4 data. The RDF file for the COSMIC data can be made available
inline with the COSMIC data policy.
Table 1: COSMIC data statistics
No. Data Records Triples Original Size RDFized Size
1 COSMIC GE 149 M 1185 M 7.5 GB 20 GB
2 COSMIC GM 3.7 M 84 M 916 MB 1.44 GB
3 COSMIC CNV 0.9 M 11 M 82 MB 161 MB
4 Total 154M 1.28 B 8.5 GB 22 GB
5 TCGA-OV 18M 100 M 2.15 GB 5 GB
3.2 COSMIC and TCGA linking
The main integration is based on owl:sameAs constructs as can be seen in the
listing 1.1 where two COSMIC sample ids have been identified as being identical
to two TCGA patient bar code ids. These links are at the core of facilitating
data integration and the data analysis tasks.
Listing 1.1: COSMIC and TCGA Linking Example
COSMIC
TCGA−OV
h t t p : / / c o s m i c . s e l s . i n s i g h t . o r g / schema / ID Sample /TCGA−13−0920
owl : sameAs
h t t p : / / t c g a . d e r i . i e /TCGA−13−0920
COSMIC
TCGA−OV
4
http://tcga.deri.ie/
h t t p : / / c o s m i c . s e l s . i n s i g h t . o r g / schema / ID Sample /TCGA−24−1850
owl : sameAs
h t t p : / / t c g a . d e r i . i e /TCGA−24−1850
3.3 Scalable query federation
SAFE has been developed for accessing sensitive clinical data in data cubes at
different locations [7]. Two main changes have been introduced to SAFE for
efficiently querying the TCGA and COSMIC SPARQL endpoints. First, stan-
dardize RDF query representation: in the initial versions, SAFE issues queries
for statistical clinical information stored within distinct names graphs for RDF
data cubes [3]. Therefore, the internal query processing (i.e., source selection,
query planning, query execution) had to be adapted to query the regular RD-
Fized versions of the TCGA and COSMIC repositories. Second, access control
had to be disabled: SAFE imposes restrictions for data-access as a feature (de-
fined as Access Policy Model [7]) while federating queries over multiple clinical
site, i.e. imposing the data restrictions for different data repositories. Since, ex-
periments conducted in this paper mainly involve public repositories this feature
has been disabled.
The listing 1.2 shows a sample SPARQL query, which federates across COS-
MIC and TCGA data asking for genomic loci of a mutated gene by chro-
mosome start points which then returns the disease metastasis information
along with the mutation type. Answering such a query requires the integra-
tion of COSMIC with TCGA and merging results from both TCGA and COS-
MIC, and thus has to make use of query federation. The results for the first
four triples in the given query (i.e. cosmic-s:ID Sample, cosmic-s:Gene Name,
cosmic-s:Chrom start) are fetched from COSMIC and the results for the next
three triples (i.e. tcga:tcga id, tcga:start) are fetched from TCGA. To pro-
duce the required information, both results are merged on the basis of the last
triple which integrates COSMIC with TCGA. Sample results for this query can
be seen in Figure 5.
Listing 1.2: Federated SPARQL Query
PREFIX c o s m i c−s :
SELECT ∗ WHERE {
? c o s m i c r e s u l t a c o s m i c−s : r e s u l t ;
c o s m i c−s : ID Sample ? i d s a m p l e ; c o s m i c−s : Gene Name ? g e n e ;
c o s m i c−s : c h r o m s t a r t ? c h r o m s c o s m i c .
? t c g a r e s u l t a tcga : r e s u l t ;
tcga : Id ? t c g a i d ; tcga : S t a r t ? t c g a c h r o m s t a r t .
? i d s a m p l e owl : sameAs ? t c g a i d .
}
4 Biological questions and annotation results from HBM
We analysed all genes have an RPKM > 0.5 [8] and that are differentially ex-
pressed in all tissue types. Figure 3 [2] is a schematic representation which
satisfies all mentioned conditions and delivers 99 genes per query. We have iden-
tified potential cancer types based on gene patterns for different tissues and
further helped to understand the behaviour of most amplified cancer types.
CCL21
JCHAIN
IGHA1
IGHM
IGKV1−5
IGHV1−2
IGKV3−20
IGLV3−25
IGHG1
IGKV4−1
IGHG2
IGHV3−23
IGKC
IGLV3−19
IGLC2
IGLC3
IGFBP4
RPS11
RPS27
MYL9
SEMG1
TXNIP
FABP4
EEF1A1
ACTB
RPS12
S100A8
S100A9
LYZ
CD74
HLA−DRA
TMSB10
SRGN
TMSB4X
B2M
FTL
HLA−E
SCGB1A1
HBB
SPP1
GPX3
MALAT1
MT−ND6
MTATP6P1
MT−ATP8
MT−ND4L
MT−CO1
MT−ND5
MB
FABP3
MYL2
MYH7
ACTC1
MYL3
PLN
MT−ATP6
MT−CYB
MT−ND3
MT−CO3
MT−ND4
MT−CO2
MT−ND2
MT−ND1
MT−RNR2
MT−RNR1
ACTA1
GAPDH
CKM
MYBPC1
KLHL41
MT−TP
PDK4
DES
TNP1
PRM2
PRM1
TG
SCD
C3
RBP4
SAA1
AGT
APOA1
SERPINA1
SAA2
FGG
FGB
HP
ORM2
ORM1
CRP
APOA2
FGL1
GC
APOH
APOC3
FGA
ALB
AMBP
APCS
thyroid
ovary
prostate
testis
brain
breast
colon
adipose
kidney
adrenal
leukocyte
lung
lymph node
skeletal muscle
heart
Fig. 3: List of Genes expressed in all tissues and highly expressed. liver
5 Results
The overall goal of this study is to understand the relevance of mutations and
genes along with their associated expression levels measured in data sets for
normal tissues e.g. HBM 2.0, and then evaluate (e.g., query) them against the
mutations retrieved and linked from somatic and patient specific data e.g COS-
MIC, TCGA. Further focus of this work is put to the linked annotations where
a single query can retrieve all other possibly relevant annotations.
Initially, we have sampled 99 genes that are highly expressed in all 16 tis-
sues as shown in Figure 3 to retrieve their CNV, mutation and gene expression
annotations from cBIO portal (for TCGA) and CNV annotator (for COSMIC)
to determine current state of the art and provide a baseline comparison for
the proposed linked annotation solution. The results for TCGA 4 clearly indi-
cate an elevated distribution of these genes in uterine and ovarian cancer with
a large number of mutations and CNVs. As an outcome, ovarian cancer has
been selected as a good candidate for further investigation due to its elevated
amplification rate and its multiple repetition in different experiments. Further
studies have been retrieved that were conducted to understand the somatic rel-
evance and the loci(genomic position) of these genes, and further detailed in-
formation for mutations could be retrieved as well. This study demonstrated
a focus to genes such as ACTC1, B2M, CRP, FABP3, FABP4, FGA, FGB,
GC, MYH7, RPRH2, SLC26A3, TG, TXNIP which form most relevant driver
genes transforming healthy human tissues into ovarian cancer. The same 99
genes have been queried against 99 genes from HBM 2.0 to get the results for
the somatic mutations in cancer. This repeat annotation will not only provide
detailed statistics reported in COSMIC but also a validation for out earlier ex-
periments. Table 2 clearly indicates that locations chr1,chr4,chr14 and genes
CRP,FGA,FABP3,MYH7 could be potential genes with high relevance for
the development of ovarian cancer.
80%
70%
Alteration frequency
60%
50%
40%
30%
20%
10%
0%
Cancer type
Mutation data + + + + + + + + + + + + + + + + - + + + + - - + + + + + + + + + + + + + + + + + + + +
CNV data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
(TC )
a (T A)
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Mutation Deletion Amplification Multiple alterations
Fig. 4: TCGA query output from cBIO Portal[5]
We now query the same 99 genes from before using the linked annotation
mechanism. The result snippet for ovarian cancer is depicted in Figure 5 with
detailed functional annotations together with the TCGA ids, again for ovarian
cancer.
cs:result cs:Sample_Name c:TCGA-13-0920-01;
cs:Gene_Name c:MYH7;
ts:result ts:bcr_patient_barcode t:TCGA-13-0920;
cs:Regulation "over".
ts:beta_value "0.0419..";
cs:chr_no c:1;
ts:chromosome t:14;
cs:chrom_start_m c:23418303;
ts:chromosome t:1;
cs:chrom_stop_m c:23418303;
ts:start t:1288070;
cs:chr_no_m c:14;
ts:stop t:1293914;
cs:mut_type "GAIN";
ts:scaled_estimate "773.555".
cs:Primary_Site c:ovary;
cs:Primary_Histology c:carcinoma;
Fig. 5: Linked annotations for MYH7 - COSMIC
Figure 5 represents the COSMIC and TCGA annotations, respectively.
MYH7 corresponds to chr-1 which is evident from previous annotations and
replicated again in our study along with its TCGA ID:TCGA-13-0920-01. Its
mutation type is primarily the GAIN type of a mutation for chr1 and chr14 which
is a dominant mutation with all its regulation of over, under and normally ex-
pressed. Translational researchers may want to repeat and re-validate the study
for Pubmed ID:1398522 additionally with beta value (measure of methylation)
of 0.041999536 and scaled estimation (Tumour purity) of 773.555 also supports
this gene from the epigenetic point of view. Further multiple genomic locations
will help clinical practitioners to track CNV for the targeted study and which
ultimately leads the direction towards a better prognosis.
Table 2: loci information for highly expressed gene in ovarian cancer from HBM 2.0
CHROMOSOME Mutation Type Pubmed GENES
chr1:246407146-246740944 CNV 17122850 CNST, LOC255654, SMYD3, TFB2M
chr1:159683086-159684133 Loss 20364138 CRP
chr1:31842502-31849609 Deletion 20482838 FABP3
chr1:31842502-31849609 Deletion 20482838 FABP3
chr4:155506556-155506859 Insertion 20981092 FGA
chr14:23857082-23886607 Loss 20981092 MIR208A, MYH6, MYH7
chr14:23857092-23886486 Loss 20981092 MIR208A, MYH6, MYH7
6 Related Work
Kandoth et al. [6] performed a cancer study with 12 cancer types to enable
logical classifications for the large amoung of data generated by TCGA and
ICGC. Saleem et. al. [11] have covered TCGA database with few cancer types
and for a limited number of patient data.
Likewise a reduced version of the COSMIC database has been RDFized to
explore on the mechanism of TP53 [13] further for CNV explains the linked
infrastructure to annotated CNV. The federation platform [10] called “TopFed”
is being developed to measure the query execution time on TCGA data set, which
then has been further extended to cover the biological outcomes identified from
Medline abstracts [9]. Our work covers all CNVs, mutations and gene expression
data and has been extended with TCGA for the same type of data, thus forming
a proof of concept for an annotation platform that covers comprehensive linked
life science data. It is important to note that the work presented in this paper
is a preliminary approach for transforming COSMIC into the RDF format and
link it with the TCGA datasets.
7 Conclusion
In this paper we have presented a linked data infrastructure for functional an-
notation which enables querying different types of mutations and genomic al-
terations to contribute to molecular and clinical insights of cancer by defining
most relevant variants and their prioritization. This knowledge could be highly
advantageous for a targeted therapy and personalized medicine based on gene
expression data. The presented experiments are based on TCGA, COSMIC and
HBM 2.0 datasets and have been used to identify sets of genes with relevance for
ovarian cancer and with comprehensive set of mutations. Similar studies have
to be performed for other cancer types. We have covered CNV, gene expression
and mutation data from COSMIC and TCGA (only for ovarian cancer). We
have processed 1.2 billion COSMIC triples and 100 million TCGA triples which
in turn generated 27 GB of data. In future, this work will be expanded to cover
level 1, 2 and 3 along with other datasets from COSMIC to provide in-depth
biological insight for each queried gene.
8 Acknowledgment
This publication has emanated from research supported by the research grant
from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.
References
1. Y. W. Asmann, B. M. Necela, K. R. Kalari, A. Hossain, T. R. Baker, J. M. Carr,
C. Davis, J. E. Getz, G. Hostetter, X. Li, et al. Detection of redundant fusion
transcripts as biomarkers or disease-specific therapeutic targets in breast cancer.
Cancer research, 72(8):1921–1928, 2012.
2. E. bioinformatics institute. Illumina body map 2.0 european bioinformatics insti-
tute.
3. J. J. Carroll, C. Bizer, P. Hayes, and P. Stickler. Named graphs, provenance and
trust. In WWW, pages 613–622, 2007.
4. J. J. Crowley, V. Zhabotynsky, W. Sun, S. Huang, I. K. Pakatci, Y. Kim, J. R.
Wang, A. P. Morgan, J. D. Calaway, D. L. Aylor, et al. Analyses of allele-specific
gene expression in highly divergent mouse crosses identifies pervasive allelic imbal-
ance. Nature genetics, 2015.
5. J. Gao, B. A. Aksoy, U. Dogrusoz, G. Dresdner, B. Gross, S. O. Sumer, Y. Sun,
A. Jacobsen, R. Sinha, E. Larsson, et al. Integrative analysis of complex cancer
genomics and clinical profiles using the cbioportal. Science signaling, 6(269):pl1–
pl1, 2013.
6. C. Kandoth, M. D. McLellan, F. Vandin, K. Ye, B. Niu, C. Lu, M. Xie, Q. Zhang,
J. F. McMichael, M. A. Wyczalkowski, et al. Mutational landscape and significance
across 12 major cancer types. Nature, 502(7471):333–339, 2013.
7. Y. Khan, M. Saleem, A. Iqbal, M. Mehdi, A. Hogan, A. N. Ngomo, S. Decker, and
R. Sahay. SAFE: policy aware SPARQL query federation over RDF data cubes.
In Proceedings of the 7th International Workshop on Semantic Web Applications
and Tools for Life Sciences, Berlin, Germany, December 9-11, 2014., 2014.
8. D. Ramskold, E. T. Wang, C. B. Burge, and R. Sandberg. An abundance of
ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS
Comput Biol, 5(12):e1000598, 2009.
9. M. Saleem, M. R. Kamdar, A. Iqbal, S. Sampath, H. F. Deus, and A.-C. N. Ngomo.
Big linked cancer data: Integrating linked tcga and pubmed. Web Semantics:
Science, Services and Agents on the World Wide Web, 27:34–41, 2014.
10. M. Saleem, M. R. Kamdar, A. Iqbal, S. Sampath, H. F. Deus, and A.-C. Ngonga.
Fostering serendipity through big linked data. Semantic Web Challenge at ISWC,
2013.
11. M. Saleem, S. S. Padmanabhuni, A.-C. N. Ngomo, J. S. Almeida, S. Decker, and
H. F. Deus. Linked cancer genome atlas database. In Proceedings of the 9th
International Conference on Semantic Systems, pages 129–134. ACM, 2013.
12. J. Xuan, Y. Yu, T. Qing, L. Guo, and L. Shi. Next-generation sequencing in the
clinic: promises and challenges. Cancer letters, 340(2):284–295, 2013.
13. A. Zappa, A. Splendiani, and P. Romano. Towards linked open gene mutations
data. BMC bioinformatics, 13(Suppl 4):S7, 2012.