An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer
Ryall et al. BMC Genomics
An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer
Karen A Ryall 0 3
Jihye Kim 0 3
Peter J Klauck 0 3
Jimin Shin 0 3
Minjae Yoo 0 3
Anastasia Ionkina 0 3
Todd M Pitts 0 3
John J Tentler 0 3
Jennifer R Diamond 0 3
S Gail Eckhardt 0 3
Lynn E Heasley 2 3
Jaewoo Kang 1 3
Aik Choon Tan 0 1 3 4
0 Division of Medical Oncology, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus , Aurora CO 80045 USA
1 Department of Computer Science, Korea University , Seoul , Korea
2 Department of Craniofacial Biology, School of Dental Medicine, University of Colorado Anschutz Medical Campus , Aurora CO 80045 USA
3 Authors' details
4 Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus , Aurora CO 80045 USA
Background: Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. Results: We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. Wevalidated our predictions using published and new experimental data. Conclusions: In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.
From Joint 26th Genome Informatics Workshop and Asia Pacific Bioinformatics Network (APBioNet) 14th
International Conference on Bioinformatics (GIW/InCoB2015)
Tokyo, Japan. 9-11 September 2015
Triple-negative breast cancer (TNBC) is a subtype of
breast cancer that is lacking the expression ofestrogen
receptor (ER), progesterone receptor (PR) and HER2
]. TNBC, also known as basal-like breast
cancer, is an aggressive disease with a poor prognosis.
Unlike ER-positive, PR-positive, and HER2-amplified
breast cancer subtype patients, chemotherapy is the only
treatment option for TNBC patients. Advances in the
treatment of TNBC have been hampered by the lack of
novel effective targeted therapies due to the poor
understanding of the underlying molecular characteristics of
this disease. Recent large-scale molecular characterization
studies in breast cancer have revealed some frequently
mutated genes and altered pathways in TNBC[
genes and pathways include TP53, BRCA1/2, PIK3CA, and
PTEN mutations and activation of PI3K/AKT and RAS/
RAF/MEK signaling pathways. Many of these genes and
pathways are regulated by kinases (e.g. PIK3CA, RAS,
MAPKs); therefore providing an opportunity to identify
potential druggable targets by small moleculesfor TNBC
Protein kinases represent one of the largest
“druggable” and well-studied protein families in the human
]. This class of proteins (kinome) plays key
role in regulating various signaling pathways in cells.
There are>500 members of the human kinome which
can be classified into seven different kinase families
based on their conserved catalytic domain sequences [
In cancer cells, some kinases are mutated and have
acquired oncogenic properties to drive tumorgenesis.
Small molecules that inhibit these oncogenic kinases can
effectively kill cancer cells. Targeted cancer therapies
have exploited this “oncogene addiction” concept[
has lead to several successful clinical applications of
targeted therapies: BCR-ABL tyrosine kinase inhibition in
chronic myeloid leukemia by imatinib[
], inhibition of
EGFR in EGFR-mutated non-small cell lung cancers
(NSCLC) by erlotinib or gefitinib[
], inhibition of
BRAF in BRAF-mutated melanoma by vemurafenib [
and inhibition of ALK in EML4-ALK NSCLC by crizotinib
]. Furthermore, many of the small molecules inhibit
multiple kinases and could be repositioned or
repurposedfor other applications. For example, imatinib has been
repositioned to inhibit KIT and PDGFRA in
gastrointestinal stromal tumors [
] and crizotinib has been
repositioned to inhibit ROS1 in ROS1-fusion NSCLC patients
]. Large-scale quantitative in vitro kinase binding assays
have been developed to capture the complex interactions
between inhibitors and kinases[
High-throughput screening (HTS) provides a different
perspective to interrogate biological systems using
chemical biology. Large-scale HTS studies such as Cancer Cell
Line Encyclopedia (CCLE) [
], Genomics of Drug
Sensitivity in Cancer (GDSC) [
], Cancer Therapeutics
Response Portal (CTRP) , and NCI-60 Developmental
Therapeutic Program Screen [
] represent examples of
the HTS pharmacological profiling data sources. One
recent study has performed HTS of 180 kinase inhibitors
in 12 TNBC cell lines [
]. Typically, HTS was performed
on a panel of cancer cell lines screened with multiple
compounds to generate pharmacological profiling data. From
the pharmacological profiling data, one can correlate the
compound sensitivity with other molecular genomics data
to derive drug sensitivity signatures [
application of HTS pharmacological data is to correlate
with in vitro kinase binding assays to deconvolute kinase
dependency in biological systems . However, no efforts
have been made to integrate HTS pharmacological
profiling data, in vitro kinase binding data, and genomics data
for dissecting kinase dependency in cancer cells.
The goal of this study was to determine the kinase
dependency in TNBC cell lines and to predict
compounds that could inhibit these kinases using integrative
bioinformatics analysis. In this study, we used publicly
available gene expression data, HTS pharmacological
profiling data, and quantitative in vitro kinase binding
data. We employed our recently developed Kinase
Addiction Ranker (KAR) to integrate these data sourcesto
dissect kinase dependency in TNBC cell lines [
]. We then
used the kinase dependency predicted by KAR to query
] for connecting compounds with kinases
for individual TNBC lines. For validation, we performed
literature search on published experimental data and
tested K-Map predictionsin cell lines. Our research
strategy for this study is illustrated in Figure 1.
Pharmacological profiling data
We obtained the HTS pharmacological profiling data of
12 TNBC cell lines from a recently published paper [
The 12 TNBC cell lines are: BT20, BT549, CAL148,
HCC38, HCC70, HCC1143, HCC1187, HCC1806,
Hs578T, MDA-MB231, MDA-MB468 and MFM223.
These cell lines were screened with 180 kinase inhibitors.
The drug sensitivity read out from this dataset is half
maximal effective concentration values (EC50).
Quantitative kinase inhibition data
We obtained comprehensive quantitative kinase
inhibition data for 72 of the 180 screened kinase inhibitors
from literature. Drug sensitivity data from these 72
drugs were used in our algorithm. This comprehensive
inhibition data allows for better interpretation of
highthroughput screening results as most kinase inhibitors
interact with far more kinases than the ones that are
most commonly reported [
Microarray gene expression data
We obtained the TNBC microarray gene expression data
from the Cancer Cell Line Encyclopedia (GSE36133).
These cell lines were profiled using Affymetrix
HGU133 Plus 2.0 microarrays. Raw CEL files for these cell
lines were normalized using Robust Multiarray Average
(RMA) approach in Affymetrix Power Tools (APT).
Kinase Addiction Ranker (KAR)
We have recently developed KAR (Kinase Addiction
Ranker), a novel computational method that integrates
high-throughput drug screening data, quantitative kinase
binding data, and transcriptomics data to define kinase
dependency for individual cancer cell lines [
each cell line, KAR first assigns compounds in the
highthroughput drug screen to1 of 5 bins based on drug
sensitivity. The bin number determines how many
points each kinase target of the drug receives by the
scoring algorithm. Targets of compounds in Bin 1
receive 20 points, Bin 2 targets receive 10 points, Bin 3
targets receive 5 points, Bin 4 targets receive 0 points,
and Bin 5 targets receive -10 points. Bin 4 and 5
therefore contain drugs that do not meet the threshold for
drug sensitivity in the sample, with compounds in Bin 5
receiving a negative penalty.
Next, quantitative kinase binding data is dichotomized
as inhibited or not inhibited for each compound based
on user-defined threshold (default: a kinase is considered
as inhibited by the compound if IC50/Kd< 1 μM or percent
of inhibition > 85%). Transcriptomics data is used to filter
out low expressed kinases. Kinases are scored by adding or
subtracting points based on the sensitivity bin of each drug
that inhibits the kinase. Finally, p-values are computed
using chi-square and Fisher’s exact tests to determine if
there is a significant association between a kinase being
inhibited and the drug being sensitive (Sensitivity Bins
1-3) in the cell line. KAR returns the ranked list of kinases
based on p-values and scaled scores. Kinases with p < 0.05
will be deemed as significant and dependent by the cancer
cell line. We have implemented KAR in two programming
languages: python and Matlab. KAR is freely available at:
Kinase Connectivity Map (K-Map) Analysis
We recently developed and implemented K-Map that
systematically connects a kinase profile with a reference
kinase inhibitor database and predicts the most effective
inhibitor for a queried kinase profile [
K-Map consists of three key components: (1) a reference
database that contains a set of kinase inhibitors profiles;
(2) a query signature; and (3) a pattern matching
algorithm or similarity metric defined between a query
signature and a reference kinase inhibitor profile to
quantify the connection (or similarity) between the
interactions of kinases and inhibitors.
The current K-Map reference database was builtbased on
two recently published comprehensive analyses of kinase
inhibitor selectivity [
]. The first study systematically
interrogated 178 commercially available inhibitors
against a panel of 300 protein kinases using a radiometric
phospho-transfer method to assess the percent kinase
inhibition (IC50) . The second study measured the
selectivity and potency of 72 inhibitors against 442
kinases using direct binding affinities between inhibitors
and kinases (Kd)[
]. These datasets were converted into
rank-ordered lists according to the inhibitors’ potencies
against the kinases and used as the K-Map reference
profiles for matching query kinases.
For each TNBC line, the top five kinases ranked by KAR
were used as the query kinase profile and connected
through the K-Map in this study.
Pattern Matching Algorithm
K-Map implementsthe pattern matching strategy based on
the Kolmogorov-Smirnov (KS) statistics. The KS-test is a
non-parametric, rank-based pattern-matching approach
implemented in the connectivity map[
]. The algorithm
aims to correlate kinase inhibitors, based on kinase
inhibition profiles in the reference database, with a given query
(i.e. top five kinases ranked by KAR). For every inhibitor
in the reference database, the KS statistic is computed and
a “connectivity score” is defined where it ranges from 1
(maximal efficacy) to 0 (minimal efficacy). K-Map then
returnsthe ranked list of kinase inhibitors that best inhibit
the list of queried kinases sorted by their “connectivity
scores”. We used K-Map to connect the top five kinases
for 12 TNBC cell lines with drugs in this study. K-Map is
freely available at: http://tanlab.ucdenver.edu/kMap.
Cell lines and culture
HCC1806 was obtained from the American Type Culture
Collection (ATCC). The cell line has been authenticated
as previously described [
]. Cell cultures were
prepared as previously described [
Erlotinib and bosutinib were obtained commercially
(Selleck Chemicals) and prepared according to the
Cell proliferation assay
To evaluate the drug effects in TNBC cells, we used the
CellTiter-Glo assay. In brief, cell viability assayswere
performed using CellTiter-Glo (Promega) according to
manufacturer’s instructions. TNBC cells were seeded at
4000 cells/well in a 96-well plate, and exposed to
increasing concentrations of erlotinib or bosutinib from
0 - 10 μmol/L for 96 hours. CellTiter-Glo measurements
were obtained for these different concentrations to
determine cell viability. Cell viability curves were derived
from the data and IC50 values calculated from a minimum
of three experiments.
Results and discussion
Kinase Addiction Ranker for ranking kinase dependency in TNBC cell lines
To identify kinase dependency in TNBC, we first
analyzed a HTS pharmacological profiling data set of 180
kinase inhibitors profiled across 12 TNBC cell lines [
We selected 72 of the 180 profiled drugs based on
availability of a published quantitative in vitrokinase
inhibition profile and inhibition of at least one kinase above
threshold (Kd/EC50<1 μM or >85% inhibition). We used
KAR (Kinase Addiction Ranker), a novel bioinformatics
approach, which integrates gene expression, drug
sensitivity, and kinase inhibition data to generate a ranked list
of kinase dependency in these TNBC cell lines. As
described in the methods section, KAR integrates three
data sources (pharmacological profiling data, kinase
inhibition data and gene expression data) to delineate kinase
dependency in individual cell lines. On average, KAR
identified 24 kinases with a significant association with
drug sensitivity in each cell line (range: 9 - 46) (Table 1).
The kinases most commonly associated with drug
sensitivity in the TNBC cell lines were MAP4K4 and
PRKD3, which were each significant in 10/12 TNBC cell
lines. MAP4K4 (also known as HGK or NIK) activates
MAPK8/JNK signaling. MAP4K4 is involved in cell
migration and invasion in melanoma, ovarian, breast and
]. Moreover, overexpression of
MAP4K4 correlated with larger tumor size, increased
lymph node involvement, and recurrence in pancreatic
]. PRKD3 has been shown to
promote proliferation and chemoresistance in TNBC
]. Since MAP4K4 and PRKD3 are so frequently
associated with drug sensitivity in this dataset, they may
represent targets that could benefit larger populations of
TNBC patients. MAP4K4 and PRKD3 are simultaneously
inhibited by CDK1/2 inhibitor III and PKR inhibitor,
which were sensitive in nearly all of the 12 cell lines.
Next, we performed hierarchical clustering on the
scaled KAR scores to reveal relationships between the
12 TNBC cell lines and kinases (Figure 2). Clustering
included the 89 kinases that had a significant association
with drug sensitivity in at least one of the 12 cell lines
analyzed. No correlation of TNBC subtypes [
found in these clusters, this is similar to the previous
published pharmacological profiling data. This suggests
the heterogeneity of the molecular subtypes of TNBC
andthat understanding the kinase dependency could
provide better treatment strategy for this disease.
From Figure 2, the cluster analysis reveals three main
groups of TNBC cell lines (Figure 2A). The first group
contains HCC1806, BT20, MDA-MB-468, HCC38 and
BT549, the second group contains HCC70, HCC1187
and CAL148 and the third group contains MFM223,
MDA-MB-231, HCC1143 and Hs578T. Within the first
group, HCC1806 and BT20 show a unique dependence
on EGFR when compared to the other three lines
(MDA-MB-468, HCC38 and BT549). Interestingly,
EGFR also does not cluster with any of the other kinases
analyzed, indicating EGFR dependence is a fairly unique
marker in a cell line compared to other kinases.
We also clustered the pharmacological profiling data
(EC50) from all 180 drugs [
] (Figure 2B). As with the
kinase score, EGFR-dependent BT20 and HCC1806
grouped together. The other cell lines show less distinct
groupings with many cell lines being paired with
different cell lines than when clustered based on kinase score.
This suggests that the kinase dependency relationships
derived from KAR are different from the relationships
derived from clustering pharmacological profiling data.
Validating kinase dependency in TNBC cell lines
Here, we validate the kinases with high KAR rankings
(Table 1) in a subset of the TNBC cell lines studied
based on previously published studies.
KAR ranks Epidermal Growth Factor Receptor (EGFR),
as the top kinase for this cell line. Indeed, previously
published papers have verified that this cell line
expressed high levels of EGFR[
], however, this cell
line is not sensitive to EGFR inhibitors such as erlotinib
or gefitinib [
]. This indicates that there may be
some other kinases driving the proliferation of this cell
For this cell line, KAR ranks YES1 and LYN in the top 5
kinases. Both of these kinases are SRC kinase family
members. Indeed, previous studies have demonstrated
that this cell line is highly sensitive to dasatinib (FDA
approved SRC inhibitor) [
]. Interestingly, KAR also
ranks EGFR as one of the kinase dependent in this cell
line. From the clustering of kinase dependency score
(Figure 2), BT20 and HCC1806 clustered together.
KAR ranks Aurora Kinase A (AURKA) as one of the top
five kinases in this cell line. Previously, we have tested
two different Aurora Kinase inhibitors across a large panel
of TNBC cell lines, and found that HCC70 is very sensitive
to MLN8237 (IC50 = 0.1 μM)[
] and ENMD2076 (IC50 =
]. This supports AURKA dependence in this
cell line. In fact, there is an ongoing Phase II clinical trial
of treating TNBC patients with ENMD2076
(http://ClinicalTrials.gov ID: NCT01639248).
KAR ranks MAP3K7, which is commonly known as the
Transforming growth factor beta-activated kinase 1
(TAK1), as the top kinase for this cell line. Interestingly,
this cell line is the only TNBC cell line analyzed that is
a KRAS mutant (p.G13D) and is highly dependent on
KRAS (”KRAS-dependent” cell line)[
]. Indeed, previous
studies have demonstrated that this cell line has high
MAP3K7 expression, and is sensitive to the TAK1 kinase
inhibitor 5Z-7-oxozeaenol [
]. Previous studies in
colorectal cancer cell lines have suggested that TAK1 could
be a therapeutic target in KRAS-dependent lines [
This confirms that KAR could identify relevant kinases
for individual cell lines.
KAR results indicate that the other TNBC cell lines
seem highly dependent on MAPKs (e.g. MAP4K2,
MAP4K3) and CDK kinases (e.g. CDK1, CDK2, CDK3,
CDK5, CDK6). A previous study evaluating kinase
expression in Estrogen Receptor (ER) positive vs. negative breast
cancer samples identified a subgroup in the ER-negative
samples also enriched with MAPKs[
Predicting compounds for individual TNBC cell lines by using K-Map
For each TNBC cell lines, we used the top five ranking
kinases (lowest chi-square p-values) as the query to
K-Map for predicting effective compounds. Compounds
with p < 0.05 are selected and sorted by connectivity
scores. Table 2 lists the top five compounds predicted by
K-map based on the top five kinases for each TNBC
Staurosporine, a multi-kinase inhibitor used as the
positive control in the K-Map, was predicted as an effective
compound for ten TNBC cell lines (Table 2). This is likely
because a highly non-specific compound like Staurosporine
can inhibit > 400 kinases by itself.
Validating compounds predicted by K-Map in TNBC cell lines
From Table 2 K-Map predicts bosutinib as one of the
compounds that targets the top five ranking kinases of
(100, 8.05 × 10-5)
(85, 9,47 × 10-5)
CDK6 / cyclin D1
BT20 and HCC1806. Both cell lines have EGFR
dependency as determined by KAR, and one of the targets of
bosutinib is EGFR. In addition to EGFR, bosutinib also
inhibits top ranking kinases YES1, TNK2, MAP4K4, and
LYN (Table 1). Therefore, bosutinib inhibits each top
ranking kinase for HCC1806 while erlotinib only inhibits
one of the top five (EGFR). We would therefore predict
that HCC1806 would be more sensitive to bosutinib than
erlotinib. To validate this prediction, we tested HCC1806
in vitro with bosutinib and erlotinib using a CellTiter-Glo
assay. As depicted in Figure 3, the IC50 of bosutinib(3 μM)
is lower than erlotinib (>10 μM) in HCC1806. This
validates the K-Map prediction that this cell line is more
sensitive to bosutinibthan erlotinib. We also validated BT20
with bosutinib and erlotinib, and found that bosutinib also
exhibited lower IC50 than erlotinib. (data not shown).
K-Map predicts SU11652 and sunitinib as potential
compounds to be effective against Hs578T. Both
compounds are PDGFR inhibitors, where PDGFRB is the top
dependent kinase predicted by KAR for this cell line.
Indeed, previous studies have demonstrated that
Hs578T has high expression of PDGFRB (both at
mRNA and protein levels), and this cell line is more
sensitive to sunitinib[
CDK 1/2 inhibitor was predicted by K-Map as one of
the compounds that inhibits the top five kinases in nine
TNBC cell lines (BT549, CAL148, HCC38, HCC70,
HCC1143, HCC1187, MDA-MB-231, MDA-MB-468
and MFM-223). Recent studies have suggested that
MYC-dependence is synthetic lethal with CDK inhibitor
in TNBC cell lines[
]. Indeed, six of these cell lines
(BT549, HCC38, HCC70, HCC1143, MDA-MB-231 and
MDA-MB-468) were MYC-dependent [
supports that the K-Map prediction of CDK1/2 inhibitor
could be a potential therapeutic for these TNBC cell
Similar to Fink et al.’s analysis of the pharmacological
profiling data [
], we observed heterogeneity of kinase
dependence among the 12 TNBC cell lines and no
coclustering of cell lines of the same molecular subtype.
We also showed EGFR dependence for BT-20 and
HCC1806, but our experiments showed much lower
sensitivity to Erlotinibthan Fink et al. (IC50 0.2 μM, our
experiments: >10 μM). Fink et al.’s clustering of the
drug sensitivity datarevealed co-clustering of HCC70,
BT549, and MDA-MD468, and reported increased
sensitivity of this group to PI3K pathway inhibitors[
KAR revealed significant association between PIK3CB
inhibition and drug sensitivity in HCC70 and BT549,
but much higher correlations with drug sensitivity for
other kinases (Table 1). Fink et al. also report that
another group of cell lines which co-cluster (HCC38,
HCC1143, HCC1187, HS578T, MDA-MB231, and
MFM-223), are generally resistant to kinase inhibition
with no kinase target being selectively toxic to this
]. Our approach incorporating more
comprehensive target lists for each drug, however, was able to
find kinases with significant associations with drug
sensitivity for each cell line in this group. Moreover,
MAPK4K4, which was one of the kinases most
commonly associated with drug sensitivity in the 12 TNBC
cell lines, is significant in all but HCC1187in this group
of cell lines.
Here, we presented examples of how the KAR
algorithm and K-Map research tool can be integrated to
determine kinase dependency and predict effective cancer
drugs for TNBC. KAR aids greatly in preventing
misinterpretation of HTS data, as kinase inhibitors typically
inhibit many more targets than are commonly reported.
KAR therefore helps uncover kinase dependency that
may be overlooked if only focusing on the commonly
reported drug targets. Moreover, incorporation of gene
expression data can help ensure that high-ranking
kinases will be translationally applicable. Compared to
], KAR benefits from
producing scores and p-values that can be easily interpreted
by biologists without computational backgrounds,
incorporation of transcriptomics data, increased accessibility
(MATLAB and python functions available at
http://tanlab.ucdenver.edu/KAR), and does not require preliminary
optimization of the drug screening list. K-Map allows for
quick connection of essential kinases revealed by KAR to
compounds for experimental testing. K-Map can help
reveal drugs that may not have been part of the original
screening set. While we used this approach with TNBC
cell lines, a similar strategy can be used with patient
samples to predict effective kinase inhibitor therapies and
We presented an integrative bioinformatics analysis to
determine kinase dependency in TNBC. We integrated
three different high-throughput data sources with the
KAR algorithm: HTS pharmacological profiling data,
quantitative in vitro kinase binding data, and gene
expression data. We then queried the top five kinases
from each TNBC cell lines to K-Map to predict
compounds that could inhibit these sets of kinases. We
validated the KAR and K-Map predictions using
experiments and published studies. Using the integrative
bioinformatics analysis, we have discovered kinase dependency
in these TNBC cell lines. The data provide candidate
kinases and drugs for further pharmacological and
List of abbreviations used
AURKA: Aurora Kinase A; EGFR: Epidermal Growth Factor Receptor; HTS:
High-throughput Screening; KAR: Kinase Addiction Ranker; NSCLC: Non-small
cell lung cancer; TNBC: Triple Negative Breast Cancer.
The authors declare that they have no competing interests.
KAR and ACT conceived the study, coordinated the experiments, performed
acquisition of data, participated in data analysis, and drafted the manuscript.
LEH and JK contributed to the study design. KAR, JK, JS, MY and ACT
conducted bioinformatics analysis. PK, AI, TMP, JTT, JRD and SGE conducted
experimental validation. All authors read and approved the final manuscript.
We thank the PETT lab members for constructive comments on the
This work is partly supported by the National Institutes of Health under Ruth
L. Kirschstein National Research Service Award T32CA17468 (K.A.R.), the
National Institutes of Health P30CA046934, Cancer League of Colorado, the
David F. and Margaret T. Grohne Family Foundation. Its contents are solely
the responsibility of the authors and do not necessarily represent the official
views of the funders.
Publication charges for this article have been funded bythe David F. and
Margaret T. Grohne Family Foundation.
This article has been published as part of BMC Genomics Volume 16
Supplement 12, 2015: Joint 26th Genome Informatics Workshop and 14th
International Conference on Bioinformatics: Genomics. The full contents of
the supplement are available online at http://www.biomedcentral.com/
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