Por qué estudiar bioinformática, y por qué hacerlo en la Universidad de Málaga

1. Bioinformática en el
Grado de Ingeniería de la Salud
M. Gonzalo Claros Díaz
Dpto Biología Molecular y Bioquímica
Plataforma Andaluza de Bioinformática
Centro de Bioinnovación
http://about.me/mgclaros/
@MGClaros

2. Bioinformática solo se ofrece en la UMA
2http://www.uma.es/grado-en-ingenieria-de-la-salud

3. ¿Qué es la bioinformática?
3http://everydaylife.globalpost.com/medical-schools-bioinformatics-37686.html
La bioinformática es un
campo científico nuevo y muy
atractivo que está en la
interfase entre la informática,
la biología y las matemáticas
para descubrir informaciones
nuevas sobre las
enfermedades y el cuerpo
humano
La bioinformática utiliza la
biología y la informática para
descubrir cómo funcionan los
seres vivos y sus
enfermedades

4. ¿Qué es la bioinformática?
3http://everydaylife.globalpost.com/medical-schools-bioinformatics-37686.html
La bioinformática es un
campo científico nuevo y muy
atractivo que está en la
interfase entre la informática,
la biología y las matemáticas
para descubrir informaciones
nuevas sobre las
enfermedades y el cuerpo
humano
La bioinformática utiliza la
biología y la informática para
descubrir cómo funcionan los
seres vivos y sus
enfermedades

5. Se están definiendo las competencias del bioinformático
4
Message from ISCB
Bioinformatics Curriculum Guidelines: Toward a
Definition of Core Competencies
Lonnie Welch1
*, Fran Lewitter2
, Russell Schwartz3
, Cath Brooksbank4
, Predrag Radivojac5
, Bruno Gaeta6
,
Maria Victoria Schneider7
1 School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, United States of America, 2 Bioinformatics and Research Computing, Whitehead
Institute, Cambridge, Massachusetts, United States of America, 3 Department of Biological Sciences and School of Computer Science, Carnegie Mellon University,
Pittsburgh, Pennsylvania, United States of America, 4 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus,
Hinxton, Cambridge, United Kingdom, 5 School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America, 6 School of Computer
Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia, 7 The Genome Analysis Centre, Norwich Research Park, Norwich, United
Kingdom
Introduction
Rapid advances in the life sciences and
in related information technologies neces-
sitate the ongoing refinement of bioinfor-
matics educational programs in order to
maintain their relevance. As the discipline
of bioinformatics and computational biol-
ogy expands and matures, it is important
to characterize the elements that contrib-
ute to the success of professionals in this
field. These individuals work in a wide
variety of settings, including bioinformatics
core facilities, biological and medical re-
search laboratories, software development
organizations, pharmaceutical and instru-
ment development companies, and institu-
tions that provide education, service, and
training. In response to this need, the
Curriculum Task Force of the International
Society for Computational Biology (ISCB)
Education Committee seeks to define
curricular guidelines for those who train
and educate bioinformaticians. The previ-
ous report of the task force summarized a
survey that was conducted to gather input
regarding the skill set needed by bioinfor-
maticians [1]. The current article details a
The skill sets required for success in the
field of bioinformatics are considered by
several authors: Altman [2] defines five
broad areas of competency and lists key
technologies; Ranganathan [3] presents
highlights from the Workshops on Education
in Bioinformatics, discussing challenges and
possible solutions; Yale’s interdepartmental
PhD program in computational biology and
bioinformatics is described in [4], which lists
the general areas of knowledge of bioinfor-
matics; in a related article, a graduate of
Yale’s PhD program reflects on the skills
needed by a bioinformatician [5]; Altman
and Klein [6] describe the Stanford Bio-
medical Informatics (BMI) Training Pro-
gram, presenting observed trends among
BMI students; the American Medical Infor-
matics Association defines competencies in
the related field of biomedical informatics in
[7]; and the approaches used in several
German universities to implement bioinfor-
matics education are described in [8].
Several approaches to providing bioin-
formatics training for biologists are de-
scribed in the literature. Tan et al. [9]
report on workshops conducted to identify
a minimum skill set for biologists to be
able to address the informatics challenges
of the ‘‘-omics’’ era. They define a
requisite skill set by analyzing responses
to questions about the knowledge, skills,
and abilities that biologists should possess.
The authors in [10] present examples of
strategies and methods for incorporating
life sciences curricula. Pevzner and Shamir
[11] propose that undergraduate biology
curricula should contain an additional
course, ‘‘Algorithmic, Mathematical, and
Statistical Concepts in Biology.’’ Wingren
and Botstein [12] present a graduate
course in quantitative biology that is based
on original, pathbreaking papers in diverse
areas of biology. Johnson and Friedman
[13] evaluate the effectiveness of incorpo-
rating biological informatics into a clinical
informatics program. The results reported
are based on interviews of four students
and informal assessments of bioinformatics
faculty.
The challenges and opportunities rele-
vant to training and education in the
context of bioinformatics core facilities are
discussed by Lewitter et al. [14]. Relatedly,
Lewitter and Rebhan [15] provide guid-
ance regarding the role of a bioinformatics
core facility in hiring biologists and in
furthering their education in bioinfor-
matics. Richter and Sexton [16] describe
a need for highly trained bioinformaticians
in core facilities and provide a list of
requisite skills. Similarly, Kallioniemi et al.
[17] highlight the roles of bioinformatics
core units in education and training.
This manuscript expands the body of
knowledge pertaining to bioinformatics
curriculum guidelines by presenting the
results from a broad set of surveys (of core
facility directors, of career opportunities,
and of existing curricula). Although there
database management languages (e.g.,
Oracle, PostgreSQL, and MySQL), and
scientific and statistical analysis software
also desirable for a bioinformatician to have
modeling experience or background in one
or more specialized domains, such as
Preliminary Survey of Existing
Curricula
Table 1. Summary of the skill sets of a bioinformatician, identified by surveying bioinformatics core facility directors and
examining bioinformatics career opportunities.
Skill Category Specific Skills
General time management, project management, management of multiple projects, independence, curiosity, self-motivation, ability to
synthesize information, ability to complete projects, leadership, critical thinking, dedication, ability to communicate scientific
concepts, analytical reasoning, scientific creativity, collaborative ability
Computational programming, software engineering, system administration, algorithm design and analysis, machine learning, data mining, database
design and management, scripting languages, ability to use scientific and statistical analysis software packages, open source
software repositories, distributed and high-performance computing, networking, web authoring tools, web-based user interface
implementation technologies, version control tools
Biology molecular biology, genomics, genetics, cell biology, biochemistry, evolutionary theory, regulatory genomics, systems biology, next
generation sequencing, proteomics/mass spectrometry, specialized knowledge in one or more domains
Statistics and Mathematics application of statistics in the contexts of molecular biology and genomics, mastery of relevant statistical and mathematical
modeling methods (including experimental design, descriptive and inferential statistics, probability theory, differential equations and
parameter estimation, graph theory, epidemiological data analysis, analysis of next generation sequencing data using R and
Bioconductor)
Bioinformatics analysis of biological data; working in a production environment managing scientific data; modeling and warehousing of biological
data; using and building ontologies; retrieving and manipulating data from public repositories; ability to manage, interpret, and
analyze large data sets; broad knowledge of bioinformatics analysis methodologies; familiarity with functional genetic and genomic
data; expertise in common bioinformatics software packages, tools, and algorithms
doi:10.1371/journal.pcbi.1003496.t001
http://www.ploscompbiol.org/article/info:doi%2F10.1371%2Fjournal.pcbi.1003496
¿Qué tiene
que saber?
¿Qué puede
hacer?
06-04-14

6. El bioinformático puede ejercer de varias formas
• Como un ingeniero y usuario
• Facilitar tareas difíciles o tediosas
• Flujos de trabajo y automatización
• Como un informático
• Mejorar los algoritmos existentes
• Crear algoritmos nuevos
• Ensamblaje de secuencias
• Como un clínico
• Descubrir información biológica con
el ordenador
• Relacionar enfermedades aparentemente
inconexas
5
Inf
Ing
Clin

7. El perfil de un bioinformático australiano
6http://www.ebi.edu.au/news/braembl-community-survey-report-2013
¿Dónde trabaja? ¿Quién es el bioinformático?
Esto es un usuario
Otro usuario
Este es el bioinformático Y este también

8. El bioinformático no tiene problemas de movilidad
7

9. La «info» no logra ponerse al ritmo de la tecnología «bio»
8

10. Si no aumentan los recursos, habrá que dedicar más gente
a analizar los datos
9
Se necesitan
bioinformáticos

11. …y se necesitan cada vez más
10
http://www.indeed.com/jobtrends?q=molecular+biology,
+bioinformatics,+biomedical+engineering&l=&relative=1
El estallido de la crisis
provocó grandes diferencias
El bioinformático
es el de mejores
perspectivas
El bioinformático no vive solo de los hospitales

12. Todos los días hay nuevas peticiones de bioinformáticos
11
30-dic-13

13. Todos los días hay nuevas peticiones de bioinformáticos
11
30-dic-13

14. Y también en España y Europa
12http://www.eurosciencejobs.com/jobs/bioinformatics

15. Si lo que quieres es ganar dinero, también
13
Puedes anunciarte
aquídesde 50euros
Contacta:633601207
publicidad@lamarea.com
LaMareatieneunCÓDIGO
ÉTICO consensuadoconlos
sociospararegularlasinser-
cionespublicitarias.Larevista
nuncapublicaráanunciosque
entrenencontradiccióncon
nuestrosprincipios.Noacep-
tamospublicidadconconte-
nidossexistas,racistasoque
frutossecosylegumbres.Todocondeno-
minacióndeagriculturaecológica.
Ctra.AV923,km.0,5.
Mombeltrán.Ávila.
Teléfono:920370297
Genoma4u
Conocertugenomayeldetushijosesla
llavedelamedicinapersonalizada.
www.genoma4u.com
ElCanterodeLetur
Alimentoslácteosecológicosdealtaca-
lidad.Eslógico.Esecológico.
Teléfono:967426066
www.elcanterodeletur.com
¿Sepuede
cambiar
Europa
através
delvoto?
ElParlamentodelaUE
ganapoderperocarecede
competenciasparacontrolar
organismoscomolatroika
ABRIL2014
LA
REV
ISTA
M
ENSUA
L
DELA
COOPERATIVA
M
Á
SPÚ
BLICO
MERCADONA
Elreydelos
supermercados
imponesuspropias
condicioneslaborales
AGUA
ElGobiernoultima
laprivatización
demanantialesyde
caudalesderíos
22-M
LasMarchas
delaDignidad,
unsímbolodeunidad
ypoderpopular
ABRIL2014 | Nº15 | 3€

16. Se les paga bien, al menos en el extranjero
14
Se paga mejor
linux y OSX
que Windows
http://www.r-bloggers.com/r-skills-attract-the-highest-salaries/
En la rama de
bioinformática
de GIS se
estudia R
http://www.r-users.com

17. Merece la pena estudiar
bioinformática en la UMA
15

18. El descubrimiento de nuevos fármacos «era» carísimo
16
Hay que sintetizar cada
compuesto y comprobarlo
en los animales
Método clásico Método bioinformático
Solo se sintetizan los
candidatos. Ahorro en
síntesis, tiempo y animales
Ligand
database

19. Ha valido para el Nobel de química en 2013
17
Por el desarrollo de modelos
computacionales para conocer
y predecir procesos químicos
Químico teórico Biofísico
Bioquímico
http://blogs.plos.org/biologue/2013/10/18/the-significance-of-
the-2013-nobel-prize-in-chemistry-and-the-challenges-ahead/
Bioquímico

20. Ha valido para el Nobel de química en 2013
17
Por el desarrollo de modelos
computacionales para conocer
y predecir procesos químicos
Químico teórico Biofísico
Bioquímico
http://blogs.plos.org/biologue/2013/10/18/the-significance-of-
the-2013-nobel-prize-in-chemistry-and-the-challenges-ahead/
Bioquímico
This Nobel Prize is the first given to work in
computational biology, indicating that the field has
matured and is on a par with experimental biology
The blog of PLOS Computational Biology

21. Diseño de fármacos sobre dianas en compartimentos
18
Send Orders for Reprints to reprints@benthamscience.net
Current Pharmaceutical Design, 2014, 20, 293-300 293
Biocomputational Resources Useful For Drug Discovery Against Compartmentalized
Targets
Francisca Sánchez-Jiménez*,#
, Armando Reyes-Palomares#
, Aurelio A. Moya-García, Juan AG Ranea and
Miguel Ángel Medina
Department of Molecular Biology and Biochemistry and unit 741 of “Centro de Investigación en Red en Enfermedades Raras”
(CIBERER), Faculty of Sciences, University of Malaga, 29071 Malaga, Spain
Abstract: It has been estimated that the cost of bringing a new drug onto the market is 10 years and 0.5-2 billions of dollars, making it a
non-profitable project, particularly in the case of low prevalence diseases. The advances in Systems Biology have been absolutely deci-
sive for drug discovery, as iterative rounds of predictions made from in silico models followed by selected experimental validations have
resulted in a substantial saving of time and investments. Many diseases have their origins in proteins that are not located in the cytosol
but in intracellular compartments (i.e. mitochondria, lysosome, peroxisome and others) or cell membranes. In these cases, biocomputa-
tional approaches present limitations to their study. In the present work, we review them and propose new initiatives to advance towards
a safer, more efficient and personalized pharmacology. This focus could be especially useful for drug discovery and the reposition of
known drugs in rare and emergent diseases associated with compartmentalized proteins.
Keywords: Systems biology, diseasomes, compartmentalized proteins, drug discovery, rare diseases, lysosome, mitochondria, peroxisome.
SYSTEMS PHARMACOLOGY CONCEPTS AND AIMS
During the second half of the 20th
century both conceptual and
technological developments have made it possible to establish rela-
tionships between specific molecules (genes, proteins, metabolites,
drugs) related to different human diseases applying reductionist
approaches [1].
Following this strategy, the volume of molecular data from the
analyses of human samples under different pathophysiological con-
ditions and pharmacological testing was exponentially increasing.
Despite these impressive research efforts, the molecular basis of
many diseases remains far from being well characterized, since they
are complex problems influenced by both genome and environment
[2]. Although most genetic diseases are monogenic, around 20% of
them are polygenic, as deduced from genetic disorder databases
(OMIM, www.ncbi.nlm.nih.gov/omim; and Orphanet, www.orpha.net).
In addition, next-generation sequencing is revealing novel causal
variants and candidates genes involved in Mendelian disorders
[3,4]. The majority of human diseases are the result of interactions
between at least two types of overlapped, dynamic and very com-
plex molecular networks at the cellular level (metabolic interaction
and signaling networks).
At present, it is well known that the huge amounts of molecular
information obtained from fragmented subsystems -studied by re-
ductionist strategies- need to be integrated, organized and even
formalized in algorithms in order to be re-analyzed [5]. The idea
that it is not possible to reach the full characterization of biological
processes from only the sum of the properties of their partial sub-
Although there have been significant advances in the construc-
tion and analysis of biological networks in different organisms, the
current state of the art still remains far from this holistic perspec-
tive. The main restrictions are due to the inherent complexity of
biological systems, but also by the limitations of computational
approaches. The lack of systematic platforms of analysis for re-
searches and the disregarded -or unavailability of- information
could produce an unveiled bias in the problem under study [10].
In spite of all these difficulties, network biology has been pro-
posed as an efficient computational tool to identify multi-scale
mechanisms related to biomedical processes [9] and drug interven-
tion strategies [11]. The structure and dynamics of these networks
for each individual determine the effectiveness of the therapeutic
strategies. Thus, pharmacogenomics is considered essential to iden-
tify individualized responses to drug treatments (personalized
medicine/pharmacology) based on systemic information. Moreover,
the success in discovery and characterization of new drugs also
depends on the degree of knowledge on the structure and dynamics
of these networks. Thus, systems pharmacology is an emerging
field that collects all the above mentioned concepts to discover and
analyze potential drugs, network based-methods playing an essen-
tial role in their development; in fact, network pharmacology is a
new scientific field devoted to studying multiple active relation-
ships between drugs and targets, to validate drug combinations and
to predict new targets [12,13].
BIOCOMPUTATIONAL TOOLS, AN ESSENTIAL SUP-
PORT FOR SYSTEMS PHARMACOLOGY
298 Current Pharmaceutical Design, 2014, Vol. 20, No. 2 Sánchez-Jiménez et al.
Fig. (2). Metabolic network of amine metabolism and their cellular compartments. This scheme illustrates a major re-ordering of metabolic interactions
between genes associated with the amine metabolism (gene ontology term, GO:0009308) disregarding (A) or considering their location in cellular compart-
• Bioquímica estructural
• Biología de sistemas
Bioquímica
estructural
Inf
Ing

22. Las enfermedades y los biomarcadores
19
Chen and Wang Journal of Clinical Bioinformatics 2011 1:35 doi:10.1186/2043-9113-1-35
Se necesita la bioinformática
para descubrir los candidatos
Bioinformática
pura y dura
Con la bioinformática se
descubren:

23. Mejorar los algoritmos de detección de biomarcadores
20
•Minería de datos
•Análisis de expresión génica
Aprendizaje
computacional
93
94
95
96
97
98
Leukemia
accuracy(%)
0
10
20
30
40
50
60
70
80
90
100
robustness(%)
05340
04640
04662
Filter+G
A
04670
05200
G
A
04062
accuracy
robustness
95
96
97
98
99
100
Lung
accuracy(%)
0
10
20
30
40
50
60
70
80
90
100
robustness(%)
04530
04144
04010
Filter+G
A
04514
04610
05200
G
A
accuracy
robustness
89
90
91
92
93
Prostate
accuracy(%)
10
20
30
40
50
60
70
80
90
100
robustness(%)
accuracy
robustness
Robust gene signatures from microarray data using genetic algorithms
enriched with biological pathway keywords
R.M. Luque-Baena a,⇑
, D. Urda a,b
, M. Gonzalo Claros c
, L. Franco a,b
, J.M. Jerez a,b
a
Departmento de Lenguajes y Ciencias de la Computación, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, Spain
b
Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain
c
Supercomputing and Bioinformatics Centre, University of Málaga, C/ Severo Ochoa, 34, 29590 Málaga, Spain
a r t i c l e i n f o
Article history:
Received 24 July 2013
Accepted 16 January 2014
Available online 27 January 2014
Keywords:
DNA analysis
Evolutionary algorithms
Biological enrichment
Feature selection
a b s t r a c t
Genetic algorithms are widely used in the estimation of expression profiles from microarrays data. How-
ever, these techniques are unable to produce stable and robust solutions suitable to use in clinical and bio-
medical studies. This paper presents a novel two-stage evolutionary strategy for gene feature selection
combining the genetic algorithm with biological information extracted from the KEGG database. A com-
parative study is carried out over public data from three different types of cancer (leukemia, lung cancer
and prostate cancer). Even though the analyses only use features having KEGG information, the results
demonstrate that this two-stage evolutionary strategy increased the consistency, robustness and accuracy
of a blind discrimination among relapsed and healthy individuals. Therefore, this approach could facilitate
the definition of gene signatures for the clinical prognosis and diagnostic of cancer diseases in a near
future. Additionally, it could also be used for biological knowledge discovery about the studied disease.
Ó 2014 Elsevier Inc. All rights reserved.
1. Introduction domain of DNA microarrays. Genetic algorithms (GAs) [13–18],
as a particular case of evolutionary models, use classification tech-
Journal of Biomedical Informatics 49 (2014) 32–44
Contents lists available at ScienceDirect
Journal of Biomedical Informatics
journal homepage: www.elsevier.com/locate/yjbin
• Bases de datos biológicas
• Herramientas y algoritmos
• Análisis de expresión génica
Combinar biología e informática
es lo que da mejores resultados
Inf

24. miRNA biomarcadores de supervivencia del cáncer de mama
21
A microRNA Signature Associated with Early Recurrence
in Breast Cancer
Luis G. Pe´rez-Rivas1.
, Jose´ M. Jerez2.
, Rosario Carmona3
, Vanessa de Luque1
, Luis Vicioso4
,
M. Gonzalo Claros3,5
, Enrique Viguera6
, Bella Pajares1
, Alfonso Sa´nchez1
, Nuria Ribelles1
,
Emilio Alba1
, Jose´ Lozano1,5
*
1 Laboratorio de Oncologı´a Molecular, Servicio de Oncologı´a Me´dica, Instituto de Biomedicina de Ma´laga (IBIMA), Hospital Universitario Virgen de la Victoria, Ma´laga,
Spain, 2 Departamento de Lenguajes y Ciencias de la Computacio´n, Universidad de Ma´laga, Ma´laga, Spain, 3 Plataforma Andaluza de Bioinforma´tica, Universidad de
Ma´laga, Ma´laga, Spain, 4 Servicio de Anatomı´a Patolo´gica, Instituto de Biomedicina de Ma´laga (IBIMA), Hospital Universitario Virgen de la Victoria, Ma´laga, Spain,
5 Departmento de Biologı´a Molecular y Bioquı´mica, Universidad de Ma´laga, Ma´laga, Spain, 6 Departmento of Biologı´a Celular, Gene´tica y Fisiologı´a Animal, Universidad de
Ma´laga, Ma´laga, Spain
Abstract
Recurrent breast cancer occurring after the initial treatment is associated with poor outcome. A bimodal relapse pattern
after surgery for primary tumor has been described with peaks of early and late recurrence occurring at about 2 and 5 years,
respectively. Although several clinical and pathological features have been used to discriminate between low- and high-risk
patients, the identification of molecular biomarkers with prognostic value remains an unmet need in the current
management of breast cancer. Using microarray-based technology, we have performed a microRNA expression analysis in
71 primary breast tumors from patients that either remained disease-free at 5 years post-surgery (group A) or developed
early (group B) or late (group C) recurrence. Unsupervised hierarchical clustering of microRNA expression data segregated
tumors in two groups, mainly corresponding to patients with early recurrence and those with no recurrence. Microarray
data analysis and RT-qPCR validation led to the identification of a set of 5 microRNAs (the 5-miRNA signature) differentially
expressed between these two groups: miR-149, miR-10a, miR-20b, miR-30a-3p and miR-342-5p. All five microRNAs were
down-regulated in tumors from patients with early recurrence. We show here that the 5-miRNA signature defines a high-risk
group of patients with shorter relapse-free survival and has predictive value to discriminate non-relapsing versus early-
relapsing patients (AUC = 0.993, p-value,0.05). Network analysis based on miRNA-target interactions curated by public
databases suggests that down-regulation of the 5-miRNA signature in the subset of early-relapsing tumors would result in
an overall increased proliferative and angiogenic capacity. In summary, we have identified a set of recurrence-related
microRNAs with potential prognostic value to identify patients who will likely develop metastasis early after primary breast
surgery.
Citation: Pe´rez-Rivas LG, Jerez JM, Carmona R, de Luque V, Vicioso L, et al. (2014) A microRNA Signature Associated with Early Recurrence in Breast Cancer. PLoS
ONE 9(3): e91884. doi:10.1371/journal.pone.0091884
Editor: Sonia Rocha, University of Dundee, United Kingdom
Received November 11, 2013; Accepted February 14, 2014; Published March 14, 2014
Copyright: ß 2014 Pe´rez-Rivas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by a grant from the Spanish Society of Medical Oncology (SEOM, to NR) and by grants from the Spanish Ministerio de
Economı´a, (SAF2010-20203 to J.L and TIN2010-16556 to J.J) and from the Junta de Andalucı´a (TIN-4026, to JJ). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: jlozano@uma.es
. These authors contributed equally to this work.
Introduction years, respectively, followed by a nearly flat plateau in which the
Introduction
Breast cancer comprises a group of heterogeneous diseases that
can be classified based on both clinical and molecular features [1–
5]. Improvements in the early detection of primary tumors and the
development of novel targeted therapies, together with the
systematic use of adjuvant chemotherapy, has drastically reduced
mortality rates and increased disease-free survival (DFS) in breast
cancer. Still, about one third of patients undergoing breast tumor
excision will develop metastases, the major life-threatening event
which is strongly associated with poor outcome [6,7].
The risk of relapse after tumor resection is not constant over
time. A detailed examination of large series of long-term follow-up
studies over the last two decades reveals a bimodal hazard function
with two peaks of early and late recurrence occurring at 1.5 and 5
years, respectively, followed by a nearly flat plateau in which the
risk of relapse tends to zero [8–10]. A causal link between tumor
surgery and the bimodal pattern of recurrence has been proposed
by some investigators (i.e. an iatrogenic effect) [11]. According to
that model, surgical removal of the primary breast tumor would
accelerate the growth of dormant metastatic foci by altering the
balance between circulating pro- and anti-angiogenic factors
[9,11–14]. Such hypothesis is supported by the fact that the two
peaks of relapse are observed regardless other factors than surgery,
such as the axillary nodal status, the type of surgery or the
administration of adjuvant therapy. Although estrogen receptor
(ER)-negative tumors are commonly associated with a higher risk
of early relapse [15], the bimodal distribution pattern is observed
with independence of the hormone receptor status [16]. Other
studies also suggest that the dynamics of tumor relapse may be a
PLOS ONE | www.plosone.org 1 March 2014 | Volume 9 | Issue 3 | e91884
• Bioquímica estructural
• Biología Molecular
Table 2). MiR-
RT-qPCR data
). Next, we re-
signature. As
B were clearly
d most of the
A in cluster 1b
k). Of note, the
up C (72.8%),
ure specifically
discriminates tumors with an overall higher risk of early
recurrence.
The 5-miRNA signature
MiR-149 was the most significant miRNA downregulated in
group B, as determined by microarray hybridization and by RT-
qPCR. This miRNA has been described as a TS-miR that
regulates the expression of genes associated with cell cycle,
invasion or migration and its downregulation has been observed in
several tumor diseases, including gastric cancer and breast cancer
[70,77–81]. Down-regulation of miR-149 can occur epigenetical-
early recurrence in breast cancer. Hierarchical clustering of the 71 tumor samples based
r expression levels of the 5-miRNA signature defines a distinct cluster 2b wich mainly includes
trary, most patients with good prognosis (group A) had tumors with normal or higher-than
erent cluster 1b (‘‘low risk’’).
atients with diferent RFS. A) Kaplan-Meier graph for the whole patient cohort included in
overall down-regulation of the 5-miRNA signature (i.e. those from cluster 2b in Fig. 2) were
FS was calculated (red line). RFS was also calculated for the remaining patients in the cohort
at the 5-miRNA signature specifically discriminates tumors with an overall higher risk of early
post-recurrence survival [100], likely because it targets AKT1
mRNA [101].
In sum, the available bibliographic data suggests that down-
regulation of miR-149, miR-30a-3p, miR-20b, miR-10a and
miR342-5p in primary breast tumors could confer them enhanced
proliferative, angiogenic and invasive potentials.
Prognostic value of the 5-miRNA signature. The relation-
ship between expression of the 5-miRNA signature and RFS was
examined by a survival analysis. Figure 3A shows a Kaplan-Meier
graph for the whole series of patients included in the study. Due to
the intrinsic characteristics of the cohort, decreases in the RFS are
only observed in the intervals 0–24 and 50–60 months
(corresponding to groups B and C, respectively). We next grouped
the tumors according to their 5-miRNA signature status in two
different groups. One group included those tumors with all five
miRNAs simultaneously downregulated, (FC.2 and p,0.05) and
a second group included those tumors not having all five miRNAs
downregulated. A survival analysis was performed using clinical
data from the corresponding patients. As shown in Figure 3B, the
Kaplan-Meier graphs for the two groups demonstrate that the 5-
miRNA signature defines a ‘‘high risk’’ group of patients with a
Figure 4. Receiver operating characteristic curve (ROC) for
early breast cancer recurrence by the 5-miRNA signature
status. ROC curves generated using the prognosis information and
expression levels of the 5-miRNA signature can discriminate between
A miRNA Signature Predictive of Early RecurrenceA miRNA Signature Predictive of Early Recurrence
Ing Clin

25. La bioinformática se ha vuelto imprescindible
22http://pubs.niaaa.nih.gov/publications/arh311/5-11.htm
Through integration and modeling, these studies would allow us to better exploit the complexity
of genomic and functional genomic data and to extract their biological and clinical significance

26. Análisis de transcriptómica en la UMA
23
DATABASE Open Access
EuroPineDB: a high-coverage web database for
maritime pine transcriptome
Noé Fernández-Pozo1
, Javier Canales1
, Darío Guerrero-Fernández2
, David P Villalobos1
, Sara M Díaz-Moreno1
,
Rocío Bautista2
, Arantxa Flores-Monterroso1
, M Ángeles Guevara3
, Pedro Perdiguero4
, Carmen Collada3,4
,
M Teresa Cervera3,4
, Álvaro Soto3,4
, Ricardo Ordás5
, Francisco R Cantón1
, Concepción Avila1
, Francisco M Cánovas1
and M Gonzalo Claros1,2*
Abstract
Background: Pinus pinaster is an economically and ecologically important species that is becoming a woody
gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply.
Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to
be stored in dedicated databases.
Description: EuroPineDB is the largest sequence collection available for a single pine species, Pinus pinaster
(maritime pine), since it comprises 951 641 raw sequence reads obtained from non-normalised cDNA libraries and
high-throughput sequencing from adult (xylem, phloem, roots, stem, needles, cones, strobili) and embryonic
(germinated embryos, buds, callus) maritime pine tissues. Using open-source tools, sequences were optimally pre-
processed, assembled, and extensively annotated (GO, EC and KEGG terms, descriptions, SNPs, SSRs, ORFs and
InterPro codes). As a result, a 10.5× P. pinaster genome was covered and assembled in 55 322 UniGenes. A total of
32 919 (59.5%) of P. pinaster UniGenes were annotated with at least one description, revealing at least 18 466
different genes. The complete database, which is designed to be scalable, maintainable, and expandable, is freely
available at: http://www.scbi.uma.es/pindb/. It can be retrieved by gene libraries, pine species, annotations,
UniGenes and microarrays (i.e., the sequences are distributed in two-colour microarrays; this is the only conifer
database that provides this information) and will be periodically updated. Small assemblies can be viewed using a
dedicated visualisation tool that connects them with SNPs. Any sequence or annotation set shown on-screen can
be downloaded. Retrieval mechanisms for sequences and gene annotations are provided.
Conclusions: The EuroPineDB with its integrated information can be used to reveal new knowledge, offers an
easy-to-use collection of information to directly support experimental work (including microarray hybridisation),
and provides deeper knowledge on the maritime pine transcriptome.
1 Background
Conifers (Coniferales), the most important group of
gymnosperms, represent 650 species, some of which are
the largest, tallest, and oldest non-clonal terrestrial
Given that trees are the great majority of conifers, they
provide a different perspective on plant genome biology
and evolution taking into account that conifers are sepa-
rated from angiosperms by more than 300 million years
Fernández-Pozo et al. BMC Genomics 2011, 12:366
http://www.biomedcentral.com/1471-2164/12/366
• Bases de datos biológicas
• Herramientas y algoritmos
• Análisis de expresión génica
• Biotecnología
• Genómica, proteómica, metabolómica
Alumnas de 1.ªpromoción
GIS-Bioinformática
Frontiers)in)Journal) ! Original!Research!
2015204221!
ReprOlive: a Database with Linked-Data for the Olive Tree (Olea1!
europaea L.) Reproductive Transcriptome2!
ReprOlive:*an*olive*tree*reproductive*transcriptome*database*3!
Rosario)Carmona1,2,§
,)A.)Zafra1,§
,)Pedro)Seoane3
,)A.)Castro1
,)Darío)Guerrero@Fernández2
,)Trinidad)Castillo@4!
Castillo4
,)Ana)Medina@García4
,)Francisco)M.)Cánovas3
,)José)F.)Aldana@Montes4
,)Ismael)Navas@Delgado4
,)5!
Juan)D.)Alché1
,)M.)Gonzalo)Claros2,3,*
)6!
1"
Department"of"Biochemistry,"Cell"and"Molecular"Biology"of"Plants."Estación"Experimental"del"Zaidín."CSIC."Granada."7!
Spain."8!
2"
Plataforma"Andaluza"de"Bioinformática,"Edificio"de"Bioinnovación,"Universidad"de"Málaga."Spain"9!
3!
Departamento"de"Biología"Molecular"y"Bioquímica,"Universidad"de"Málaga."Málaga."Spain"10!
4
"Departamento"de"Lenguajes"y"Ciencias"de"la"Computación,"Universidad"de"Málaga."Spain."11!
§
These"authors"contributed"equally"to"this"work)12!
*)Correspondence:)M."Gonzalo"Claros,"Departamento"de"Biología"Molecular"y"Bioquímica,"Facultad"de"Ciencias,"13!
Universidad"de"Málaga."29071"Málaga."Spain."EWmail:"claros@uma.es!14!
• Bases de datos biológicas
• Herramientas y algoritmos
• Biología de sistemas
Ing

27. Incluyen el diseño y comprobación de flujos de trabajo
24
AutoFlow, a Versatile Workflow Engine Illustrated by Assembling an
Optimised de novo Transcriptome for a Non-Model Species, such as Faba
Bean (Vicia faba)
Running title: AutoFlow, a versatile workflow engine
Pedro Seoane1
, Sara Ocaña2
, Rosario Carmona3
, Rocío Bautista3
, Eva Madrid4
,
Ana M. Torres2
, M. Gonzalo Claros1,3,*
1 Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, E-29071, Malaga,
Spain
contigs
Full-LengtherNext
Non-coding
#1
Short reads
SeqTrimNext
(pre-processing)
Oases
(pre-assembling)
kmer 23 & 47
paired-end + single
CD-HIT
99%
Miss-assembly
rejection#3
#2 Rejected
#1 S.
senegalensis
long-reads
SeqTrimNext
(pre-processing)
MIRA
(pre-assembling)
EULER-SR
(pre-assembling)
CAP3
(reconciliation)
Unmapped
contigs
UNIGENES
S.senegalensis
v4
#6
Mapped
contigs
#4
Contigs
Debris
Non-coding
#7 Coding
unmapped
contigs
BOWTIE 2
(mapping test)
#3
B #2 Rejected
#9
#10 #11
Full-LengtherNext
Missassemblies
#12
Contigs
#8
MOWServ: a web client for integration of
bioinformatic resources
Sergio Ramı´rez1
, Antonio Mun˜ oz-Me´ rida1
, Johan Karlsson1
, Maximiliano Garcı´a1
,
Antonio J. Pe´ rez-Pulido2
, M. Gonzalo Claros3
and Oswaldo Trelles1,
*
1
Departamento Arquitectura de Computadores, Escuela Te´ cnica Superior de Ingenierı´a Informa´ tica,
Universidad de Ma´ laga, Ma´ laga, 2
Centro Andaluz de Biologı´a del Desarrollo (CSIC-UPO), Universidad Pablo
de Olavide, Sevilla and 3
Departamento de Biologı´a Molecular y Bioquı´mica, Facultad de Ciencias,
Universidad de Ma´ laga, Ma´ laga, Spain
Received February 5, 2010; Revised May 12, 2010; Accepted May 18, 2010
ABSTRACT INTRODUCTION
Published online 4 June 2010 Nucleic Acids Research, 2010, Vol. 38, Web Server issue W671–W676
doi:10.1093/nar/gkq497
Do
Técnicas y modelos algorítmicos
Inf
Ing

28. Relación entre genes, enfermedades y fenotipos
25
Using Pathological Phenotypes for Human Diseasomes
Global Analysis of the Human Pathophenotypic
Similarity Gene Network Merges Disease Module
Components
Armando Reyes-Palomares1,2
, Rocı´o Rodrı´guez-Lo´ pez1,2
, Juan A. G. Ranea1,2
, Francisca
Sa´nchez Jime´nez1,2
, Miguel Angel Medina1,2
*
1 Department of Molecular Biology and Biochemistry, Faculty of Sciences, University of Ma´laga, Ma´laga, Spain, 2 CIBER de Enfermedades Raras (CIBERER), Ma´laga, Spain
Abstract
The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules.
For this purpose, many disease network models have been created and analyzed. We highlight two of them, ‘‘the human
diseases networks’’ (HDN) and ‘‘the orphan disease networks’’ (ODN). However, in these models, each single node
represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces
the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define
pathophenotypic connections between disease-causing genes improve our understanding of the molecular events
originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and
compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network
models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic
similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in
the ‘‘Human Phenotype Ontology’’. The resulting network contains 1075 genes (nodes) and 26197 significant
pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing
significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between
genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential
genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities
and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a
coherent pathological module. Our results indicate that pathophenotypes contribute to identify underlying co-
dependencies among disease-causing genes that are useful to describe disease modularity.
Citation: Reyes-Palomares A, Rodrı´guez-Lo´pez R, Ranea JAG, Jime´nez FS, Medina MA (2013) Global Analysis of the Human Pathophenotypic Similarity Gene
Network Merges Disease Module Components. PLoS ONE 8(2): e56653. doi:10.1371/journal.pone.0056653
Editor: Steve Horvath, University of California Los Angeles, United States of America
Received August 29, 2012; Accepted January 12, 2013; Published February 21, 2013
Copyright: ß 2013 Reyes-Palomares et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors’ experimental work is supported by grants SAF2011/26518, SAF2009/09839, PI12/01096 and PS09/02216 (Spanish Ministry of Economy and
Competitiveness and FEDER), and PIE P08-CTS-3759, CVI-6585 and funds from group BIO-267 (Andalusian Government and FEDER). JR acknowledges grants
SAF2009-09839 and SAF2012-33110 and FSJ acknowledges funds from an INTERCONNECTA-AMER grant (Spanish Ministry of Economy and Competitiveness and
FEDER). The ‘‘CIBER de Enfermedades Raras’’ is an initiative from the ISCIII (Spain). The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: MAM is a PLOS ONE Editorial board member. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and
materials. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential
conflict of interest.
Bioquímica estructural
• Bioquímica estructural
• Biología de sistemas
Clin
Inf

29. O sea, el bioinformático encuentra la aguja del pajar
26

30. La bioinformática conecta enfermedades inconexas
27
Se sabía que los enfermos de alzhéimer sufrían menos
cáncer que el resto de la población
Molecular Evidence for the Inverse Comorbidity between
Central Nervous System Disorders and Cancers Detected
by Transcriptomic Meta-analyses
Kristina Iba´n˜ ez1.
, Ce´sar Boullosa1.
, Rafael Tabare´s-Seisdedos2
, Anaı¨s Baudot3
*, Alfonso Valencia1
*
1 Structural Biology and Biocomputing Programme, Spanish National Cancer, Research Centre (CNIO), Madrid, Spain, 2 Department of Medicine, University of Valencia,
CIBERSAM, INCLIVA, Valencia, Spain, 3 Aix-Marseille Universite´, CNRS, I2M, UMR 7373, Marseille, France
Abstract
There is epidemiological evidence that patients with certain Central Nervous System (CNS) disorders have a lower than
expected probability of developing some types of Cancer. We tested here the hypothesis that this inverse comorbidity is
driven by molecular processes common to CNS disorders and Cancers, and that are deregulated in opposite directions. We
conducted transcriptomic meta-analyses of three CNS disorders (Alzheimer’s disease, Parkinson’s disease and Schizophrenia)
and three Cancer types (Lung, Prostate, Colorectal) previously described with inverse comorbidities. A significant overlap was
observed between the genes upregulated in CNS disorders and downregulated in Cancers, as well as between the genes
downregulated in CNS disorders and upregulated in Cancers. We also observed expression deregulations in opposite
directions at the level of pathways. Our analysis points to specific genes and pathways, the upregulation of which could
increase the incidence of CNS disorders and simultaneously lower the risk of developing Cancer, while the downregulation
of another set of genes and pathways could contribute to a decrease in the incidence of CNS disorders while increasing the
Cancer risk. These results reinforce the previously proposed involvement of the PIN1 gene, Wnt and P53 pathways, and
reveal potential new candidates, in particular related with protein degradation processes.
Citation: Iba´n˜ez K, Boullosa C, Tabare´s-Seisdedos R, Baudot A, Valencia A (2014) Molecular Evidence for the Inverse Comorbidity between Central Nervous
System Disorders and Cancers Detected by Transcriptomic Meta-analyses. PLoS Genet 10(2): e1004173. doi:10.1371/journal.pgen.1004173
Editor: Marshall S. Horwitz, University of Washington, United States of America
Received September 16, 2013; Accepted December 30, 2013; Published February 20, 2014
Copyright: ß 2014 Iba´n˜ez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by a Fellowship from Obra Social la Caixa grant to KI (http://obrasocial.lacaixa.es/laCaixaFoundation/home_en.html), FPI grant
BES-2008-006332 to CB and grant BIO2012 to AV Group. The funders had no role in study design, data collection and analysis, decision to publish, or preparation
of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: anais.baudot@univ-amu.fr (AB); avalencia@cnio.es (AV)
. These authors contributed equally to this work.
Introduction
Epidemiological evidences point to a lower-than-expected
probability of developing some types of Cancer in certain CNS
Results and Discussion
For each CNS disorder and Cancer type independently, we
undertook meta-analyses from a large collection of microarray
rnal factors (for review, see [3–7]). In
e deregulation in opposite directions of a
nd pathways as an underlying cause of
logical plausibility of this hypothesis, a
establish the existence of inverse gene
i.e., down- versus up-regulations) in CNS
owards this objective, we have performed
of collections of gene expression data,
AD, PD and SCZ, and Lung (LC),
Prostate (PC) Cancers. Clinical and
eviously reported inverse comorbidities for
according to population studies assessing
patients with CNS disorders [8–17].
significant overlaps (Fisher’s exact test, corrected p-value (q-
value),0.05, see Methods) between the DEGs upregulated in
CNS disorders and those downregulated in Cancers. Similarly,
DEGs downregulated in CNS disorders overlapped significantly
with DEGs upregulated in Cancers (Figure 1A). Significant
overlaps between DEGs deregulated in opposite directions in CNS
disorders and Cancers are still observed while setting more
stringent cutoffs for the detection of DEGs (qvalues lower than
0.005, 0.0005, 0.00005 and 0.000005, Figure S1). A significant
overlap between DEGs deregulated in the same direction was only
identified in the case of CRC and PD upregulated genes
(Figure 1A).
A molecular interpretation of the inverse comorbidity between CNS
disorders and Cancers could be that the downregulation of certain
genetics.org 1 February 2014 | Volume 10 | Issue 2 | e1004173
Comparación de genes
con expresión diferencialWorkflow
El flujo de
trabajo
Cánceres
Enfermedadesmentales
Ing
Clin

31. Se ve con claridad
28
(Figure 2, Figure S2, Table S3). The inverse relationship
between the levels of expression deregulations of these pathways
possibly suggests opposite roles in CNS disorders and Cancers.
Figure 3). Hence, global regulations of cellular activity may
account for a protective effect between inversely comorbid
diseases.
Figure 2. KEGG pathways significantly deregulated in Central Nervous System (CNS) disorders and Cancer types. KEGG pathways [24]
significantly up- and downregulated in each disease were identified using the GSEA method [34] (q-value,0.05). The significant pathways were
compared between the 6 diseases and combined in a network representation. Node pie charts are coloured according to the pathway status as
Cancer upregulated (yellow), Cancer downregulated (blue), CNS disorder upregulated (green) and CNS disorder downregulated (red). The green/blue
and yellow/red associations thus correspond to pathways deregulated in opposite directions in CNS disorders and Cancers. Pathway labels are
coloured according to their classifications provided by KEGG [24], as: Metabolism (green), Genetic Information Processing (yellow), Cellular Process
(pink), Environmental Information Processing (red) and Organismal Systems (dark red). All networks are available at bioinfo.cnio.es/people/cboullosa/
validation/cytoscape/Ibanezetal.zip, in cytoscape format (http://www.cytoscape.org/).
doi:10.1371/journal.pgen.1004173.g002
PLOS Genetics | www.plosgenetics.org 4 February 2014 | Volume 10 | Issue 2 | e1004173
El cáncer (próstata, colorrectal, pulmón) comparte 93
genes con otras enfermedades del sistema nervioso
central (párkinson, alzhéimer, esquizofrenia)
↑↑ cáncer
↓↓ SNC enfermo
74 genes19 genes
cáncer ↓↓
SNC enfermo↑↑
Genes exclusivos
del cáncer
Genes exclusivos
del SNC enfermo

32. Conclusión: la formación del bioinformático le impide
amedrentarse ante lo desconocido
29
Bioinformatician
Biotechnologist
Other
scientists

33. Nuestro pequeño grupo interdisciplinar
30
Think & design Coding
Testing
Noé
B
Rocío
B
Hicham
B
Biólogos, médicos y tal
Ing. Informático
B
C
IS Bioinformáticos
Los
bioinformáticos
IS
C
Rafa
Gonzalo
B
Darío
C
David
B
Isabel
B
Pedro
B
Rosario
B
Marina
B
Macarena
B