Publications by authors named "Ferran Reverter"

25 Publications

  • Page 1 of 1

BPF-Based Thermal Sensor Circuit for On-Chip Testing of RF Circuits.

Sensors (Basel) 2021 Jan 26;21(3). Epub 2021 Jan 26.

Electronic Engineering Department, Universitat Politècnica de Catalunya-BarcelonaTech, 08034 Barcelona, Spain.

A new sensor topology meant to extract figures of merit of radio-frequency analog integrated circuits (RF-ICs) was experimentally validated. Implemented in a standard 0.35 μm complementary metal-oxide-semiconductor (CMOS) technology, it comprised two blocks: a single metal-oxide-semiconductor (MOS) transistor acting as temperature transducer, which was placed near the circuit to monitor, and an active band-pass filter amplifier. For validation purposes, the temperature sensor was integrated with a tuned radio-frequency power amplifier (420 MHz) and MOS transistors acting as controllable dissipating devices. First, using the MOS dissipating devices, the performance and limitations of the different blocks that constitute the temperature sensor were characterized. Second, by using the heterodyne technique (applying two nearby tones) to the power amplifier (PA) and connecting the sensor output voltage to a low-cost AC voltmeter, the PA's output power and its central frequency were monitored. As a result, this topology resulted in a low-cost approach, with high linearity and sensitivity, for RF-IC testing and variability monitoring.
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http://dx.doi.org/10.3390/s21030805DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865451PMC
January 2021

Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome.

Nat Commun 2021 02 1;12(1):727. Epub 2021 Feb 1.

Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Catalonia, Spain.

Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome. Downstream analysis of this catalog provides insight into the mechanisms underlying splicing regulation. We report that a core set of sQTLs is shared across multiple tissues. sQTLs often target the global splicing pattern of genes, rather than individual splicing events. Many also affect the expression of the same or other genes, uncovering regulatory loci that act through different mechanisms. sQTLs tend to be located in post-transcriptionally spliced introns, which would function as hotspots for splicing regulation. While many variants affect splicing patterns by altering the sequence of splice sites, many more modify the binding sites of RNA-binding proteins. Genetic variants affecting splicing can have a stronger phenotypic impact than those affecting gene expression.
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http://dx.doi.org/10.1038/s41467-020-20578-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851174PMC
February 2021

The impact of sex on gene expression across human tissues.

Science 2020 09;369(6509)

Department of Statistics, University of Chicago, Chicago, IL, USA.

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.
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http://dx.doi.org/10.1126/science.aba3066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136152PMC
September 2020

Correction to: Enhancing SVM for survival data using local invariances and weighting.

BMC Bioinformatics 2020 Aug 27;21(1):371. Epub 2020 Aug 27.

Department of Global Health, Boston University, 801 Massachusetts Avenue, Boston, MA, 02118, USA.

An amendment to this paper has been published and can be accessed via the original article.
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http://dx.doi.org/10.1186/s12859-020-03558-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450564PMC
August 2020

A limited set of transcriptional programs define major cell types.

Genome Res 2020 07 29;30(7):1047-1059. Epub 2020 Jul 29.

Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, E-08003 Barcelona, Catalonia, Spain.

We have produced RNA sequencing data for 53 primary cells from different locations in the human body. The clustering of these primary cells reveals that most cells in the human body share a few broad transcriptional programs, which define five major cell types: epithelial, endothelial, mesenchymal, neural, and blood cells. These act as basic components of many tissues and organs. Based on gene expression, these cell types redefine the basic histological types by which tissues have been traditionally classified. We identified genes whose expression is specific to these cell types, and from these genes, we estimated the contribution of the major cell types to the composition of human tissues. We found this cellular composition to be a characteristic signature of tissues and to reflect tissue morphological heterogeneity and histology. We identified changes in cellular composition in different tissues associated with age and sex, and found that departures from the normal cellular composition correlate with histological phenotypes associated with disease.
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http://dx.doi.org/10.1101/gr.263186.120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397875PMC
July 2020

Enhancing SVM for survival data using local invariances and weighting.

BMC Bioinformatics 2020 May 19;21(1):193. Epub 2020 May 19.

Department of Global Health, Boston University, 801 Massachusetts Avenue, Boston, MA, 02118, USA.

Background: The necessity to analyze medium-throughput data in epidemiological studies with small sample size, particularly when studying biomedical data may hinder the use of classical statistical methods. Support vector machines (SVM) models can be successfully applied in this setting because they are a powerful tool to analyze data with large number of predictors and limited sample size, especially when handling binary outcomes. However, biomedical research often involves analysis of time-to-event outcomes and has to account for censoring. Methods to handle censored data in the SVM framework can be divided into two classes: those based on support vector regression (SVR) and those based on binary classification. Methods based on SVR seem to be suboptimal to handle sparse data and yield results comparable to Cox proportional hazards model and kernel Cox regression. The limited work dedicated to assess methods based on of SVM for binary classification has been based on SVM learning using privileged information and SVM with uncertain classes.

Results: This paper proposes alternative methods and extensions within the binary classification framework, specifically, a conditional survival approach for weighting censored observations and a semi-supervised SVM with local invariances. Using simulation studies and some real datasets, we evaluate those two methods and compare them with a weighted SVM model, SVM extensions found in the literature, kernel Cox regression and Cox model.

Conclusions: Our proposed methods perform generally better under a wide variety of realistic scenarios about the structure of biomedical data. Specifically, the local invariances method using the conditional survival approach is the most robust method under different scenarios and is a good approach to consider as an alternative to other time-to-event methods. When analysing real data is a method to be considered and recommended since outperforms other methods in proportional and non-proportional scenarios and sparse data, which is something usual in biomedical data and biomarkers analysis.
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http://dx.doi.org/10.1186/s12859-020-3481-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7236493PMC
May 2020

A Compact Thévenin Model for a Rectenna and Its Application to an RF Harvester with MPPT.

Sensors (Basel) 2019 Apr 6;19(7). Epub 2019 Apr 6.

e-CAT Research Group, Department of Electronic Engineering, Castelldefels School of Telecommunications and Aerospace Engineering, Universitat Politècnica de Catalunya, c/ Esteve Terradas, 7, 08860 Castelldefels (Barcelona), Spain.

This paper proposes a compact Thévenin model for a rectenna. This model is then applied to design a high-efficiency radio frequency harvester with a maximum power point tracker (MPPT). The rectenna under study consists of an L-matching network and a half-wave rectifier. The derived model is simpler and more compact than those suggested so far in the literature and includes explicit expressions of the Thévenin voltage () and resistance and of the power efficiency related with the parameters of the rectenna. The rectenna was implemented and characterized from -30 to -10 dBm at 808 MHz. Experimental results agree with the proposed model, showing a linear current⁻voltage relationship as well as a maximum efficiency at /2, in particular 60% at -10 dBm, which is a remarkable value. An MPPT was also used at the rectenna output in order to automatically work at the maximum efficiency point, with an overall efficiency near 50% at -10 dBm. Further tests were performed using a nearby transmitting antenna for powering a sensor node with a power consumption of 4.2 µW.
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http://dx.doi.org/10.3390/s19071641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479741PMC
April 2019

Seat Occupancy Detection Based on a Low-Power Microcontroller and a Single FSR.

Sensors (Basel) 2019 Feb 8;19(3). Epub 2019 Feb 8.

Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC)⁻BarcelonaTech, Castelldefels, 08860 Barcelona, Spain.

This paper proposes a microcontroller-based measurement system to detect and confirm the presence of a subject in a chair. The system relies on a single Force Sensing Resistor (FSR), which is arranged in the seat of the chair, that undergoes a sudden resistance change when a subject/object is seated/placed over the chair. In order to distinguish between a subject and an inanimate object, the system also monitors small-signal variations of the FSR resistance caused by respiration. These resistance variations are then directly measured by a low-cost general-purpose microcontroller unit (MCU) without using either an analogue processing stage or an analogue-to-digital converter. Two versions of such a MCU-based circuit are presented: one to prove the concept of the measurement, and another with a smart wake-up (generated by the sudden resistance change) intended to reduce the energy consumption. The feasibility of the proposed measurement system is experimentally demonstrated with subjects of different weight sitting at different postures, and also with objects of different weight. The MCU-based circuit with a smart wake-up shows a standby current consumption of 800 nA, and requires an energy of 125 µJ to carry out the measurement after the wake-up.
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http://dx.doi.org/10.3390/s19030699DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387014PMC
February 2019

SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.

BMC Bioinformatics 2018 Nov 19;19(1):432. Epub 2018 Nov 19.

Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Diagonal, 643, 08028, Barcelona, Catalonia, Spain.

Background: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research. Currently, substantial work has been done to allow assessment of variable importance in SVM models but this work has focused on SVM implemented with linear kernels. The power of SVM as a prediction model is associated with the flexibility generated by use of non-linear kernels. Moreover, SVM has been extended to model survival outcomes. This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis.

Results: The proposed algorithms allows visualization of each one the RFE iterations, and hence, identification of the most relevant predictors of the response variable. Using simulation studies based on time-to-event outcomes and three real datasets, we evaluate the three methods, based on pseudo-samples and kernel principal component analysis, and compare them with the original SVM-RFE algorithm for non-linear kernels. The three algorithms we proposed performed generally better than the gold standard RFE for non-linear kernels, when comparing the truly most relevant variables with the variable ranks produced by each algorithm in simulation studies. Generally, the RFE-pseudo-samples outperformed the other three methods, even when variables were assumed to be correlated in all tested scenarios.

Conclusions: The proposed approaches can be implemented with accuracy to select variables and assess direction and strength of associations in analysis of biomedical data using SVM for categorical or time-to-event responses. Conducting variable selection and interpreting direction and strength of associations between predictors and outcomes with the proposed approaches, particularly with the RFE-pseudo-samples approach can be implemented with accuracy when analyzing biomedical data. These approaches, perform better than the classical RFE of Guyon for realistic scenarios about the structure of biomedical data.
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http://dx.doi.org/10.1186/s12859-018-2451-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245920PMC
November 2018

Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk.

Nat Genet 2018 09 20;50(9):1327-1334. Epub 2018 Aug 20.

New York Genome Center, New York, NY, USA.

Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed 'variable penetrance'. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPR/Cas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants.
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http://dx.doi.org/10.1038/s41588-018-0192-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119105PMC
September 2018

The effects of death and post-mortem cold ischemia on human tissue transcriptomes.

Nat Commun 2018 02 13;9(1):490. Epub 2018 Feb 13.

Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, Barcelona, E-08003, Catalonia, Spain.

Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.
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http://dx.doi.org/10.1038/s41467-017-02772-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5811508PMC
February 2018

Measuring Dynamic Signals with Direct Sensor-to-Microcontroller Interfaces Applied to a Magnetoresistive Sensor.

Sensors (Basel) 2017 May 18;17(5). Epub 2017 May 18.

e-CAT Research Group, Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC)-BarcelonaTech, C/Esteve Terradas 7, C4, 08860 Castelldefels, Spain.

This paper evaluates the performance of direct interface circuits (DIC), where the sensor is directly connected to a microcontroller, when a resistive sensor subjected to dynamic changes is measured. The theoretical analysis provides guidelines for the selection of the components taking into account both the desired resolution and the bandwidth of the input signal. Such an analysis reveals that there is a trade-off between the sampling frequency and the resolution of the measurement, and this depends on the selected value of the capacitor that forms the RC circuit together with the sensor resistance. This performance is then experimentally proved with a DIC measuring a magnetoresistive sensor exposed to a magnetic field of different frequencies, amplitudes, and waveforms. A sinusoidal magnetic field up to 1 kHz can be monitored with a resolution of eight bits and a sampling frequency of around 10 kSa/s. If a higher resolution is desired, the sampling frequency has to be lower, thus limiting the bandwidth of the dynamic signal under measurement. The DIC is also applied to measure an electrocardiogram-type signal and its QRS complex is well identified, which enables the estimation, for instance, of the heart rate.
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http://dx.doi.org/10.3390/s17051150DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5470896PMC
May 2017

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Sci Rep 2017 01 27;7:41544. Epub 2017 Jan 27.

Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such "driver lncRNAs" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nucleotide variants (SNVs) across panels of tumour genomes. Heterogeneity in mutational signatures between cancer types and individuals is accounted for using a simple local trinucleotide background model, which yields high precision and low computational demands. We use ExInAtor to predict drivers from the GENCODE annotation across 1112 entire genomes from 23 cancer types. Using a stratified approach, we identify 15 high-confidence candidates: 9 novel and 6 known cancer-related genes, including MALAT1, NEAT1 and SAMMSON. Both known and novel driver lncRNAs are distinguished by elevated gene length, evolutionary conservation and expression. We have presented a first catalogue of mutated lncRNA genes driving cancer, which will grow and improve with the application of ExInAtor to future tumour genome projects.
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http://dx.doi.org/10.1038/srep41544DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5269722PMC
January 2017

Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx.

Genome Biol 2016 12 14;17(1):251. Epub 2016 Dec 14.

CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain.

Background: Genomic studies of endangered species provide insights into their evolution and demographic history, reveal patterns of genomic erosion that might limit their viability, and offer tools for their effective conservation. The Iberian lynx (Lynx pardinus) is the most endangered felid and a unique example of a species on the brink of extinction.

Results: We generate the first annotated draft of the Iberian lynx genome and carry out genome-based analyses of lynx demography, evolution, and population genetics. We identify a series of severe population bottlenecks in the history of the Iberian lynx that predate its known demographic decline during the 20th century and have greatly impacted its genome evolution. We observe drastically reduced rates of weak-to-strong substitutions associated with GC-biased gene conversion and increased rates of fixation of transposable elements. We also find multiple signatures of genetic erosion in the two remnant Iberian lynx populations, including a high frequency of potentially deleterious variants and substitutions, as well as the lowest genome-wide genetic diversity reported so far in any species.

Conclusions: The genomic features observed in the Iberian lynx genome may hamper short- and long-term viability through reduced fitness and adaptive potential. The knowledge and resources developed in this study will boost the research on felid evolution and conservation genomics and will benefit the ongoing conservation and management of this emblematic species.
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http://dx.doi.org/10.1186/s13059-016-1090-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5155386PMC
December 2016

Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test.

BMC Bioinformatics 2016 Jun 6;17 Suppl 5:205. Epub 2016 Jun 6.

Department of Statistics, University of Barcelona, Diagonal, 643, Barcelona, 08028, Spain.

Background: Pathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two samples are from the same distribution, its implementation being greatly simplified using the kernel method.

Results: An MMD-based test successfully detected the differential expression between two conditions, specifically the expression of a set of genes involved in certain fatty acid metabolic pathways. Furthermore, we exploited the ability of the kernel method to integrate data and successfully added hepatic fatty acid levels to the test procedure.

Conclusion: MMD is a non-parametric test that acquires several advantages when combined with the kernelization of data: 1) the number of variables can be greater than the sample size; 2) omics data can be integrated; 3) it can be applied not only to vectors, but to strings, sequences and other common structured data types arising in molecular biology.
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http://dx.doi.org/10.1186/s12859-016-1046-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4905616PMC
June 2016

CARMEN, a human super enhancer-associated long noncoding RNA controlling cardiac specification, differentiation and homeostasis.

J Mol Cell Cardiol 2015 Dec 28;89(Pt A):98-112. Epub 2015 Sep 28.

Experimental Cardiology Unit, Department of Medicine, University of Lausanne Medical School, Lausanne, Switzerland. Electronic address:

Long noncoding RNAs (lncRNAs) are emerging as important regulators of developmental pathways. However, their roles in human cardiac precursor cell (CPC) remain unexplored. To characterize the long noncoding transcriptome during human CPC cardiac differentiation, we profiled the lncRNA transcriptome in CPCs isolated from the human fetal heart and identified 570 lncRNAs that were modulated during cardiac differentiation. Many of these were associated with active cardiac enhancer and super enhancers (SE) with their expression being correlated with proximal cardiac genes. One of the most upregulated lncRNAs was a SE-associated lncRNA that was named CARMEN, (CAR)diac (M)esoderm (E)nhancer-associated (N)oncoding RNA. CARMEN exhibits RNA-dependent enhancing activity and is upstream of the cardiac mesoderm-specifying gene regulatory network. Interestingly, CARMEN interacts with SUZ12 and EZH2, two components of the polycomb repressive complex 2 (PRC2). We demonstrate that CARMEN knockdown inhibits cardiac specification and differentiation in cardiac precursor cells independently of MIR-143 and -145 expression, two microRNAs located proximal to the enhancer sequences. Importantly, CARMEN expression was activated during pathological remodeling in the mouse and human hearts, and was necessary for maintaining cardiac identity in differentiated cardiomyocytes. This study demonstrates therefore that CARMEN is a crucial regulator of cardiac cell differentiation and homeostasis.
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http://dx.doi.org/10.1016/j.yjmcc.2015.09.016DOI Listing
December 2015

Human genomics. The human transcriptome across tissues and individuals.

Science 2015 May;348(6235):660-5

Center for Genomic Regulation (CRG), Barcelona, Catalonia, Spain. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain. Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Catalonia, Spain. Joint CRG-Barcelona Super Computing Center (BSC)-Institut de Recerca Biomedica (IRB) Program in Computational Biology, Barcelona, Catalonia, Spain.

Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.
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http://dx.doi.org/10.1126/science.aaa0355DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547472PMC
May 2015

Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes.

Brain 2015 Apr 13;138(Pt 4):918-31. Epub 2015 Feb 13.

13 Department of Neurology, Medical University of Lublin, Lublin, Poland 14 Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland.

Chitinase 3-like 1 (CHI3L1) has been proposed as a biomarker associated with the conversion to clinically definite multiple sclerosis in patients with clinically isolated syndromes, based on the finding of increased cerebrospinal fluid CHI3L1 levels in clinically isolated syndrome patients who later converted to multiple sclerosis compared to those who remained as clinically isolated syndrome. Here, we aimed to validate CHI3L1 as a prognostic biomarker in a large cohort of patients with clinically isolated syndrome. This is a longitudinal cohort study of clinically isolated syndrome patients with clinical, magnetic resonance imaging, and cerebrospinal fluid data prospectively acquired. A total of 813 cerebrospinal fluid samples from patients with clinically isolated syndrome were recruited from 15 European multiple sclerosis centres. Cerebrospinal fluid CHI3L1 levels were measured by enzyme-linked immunosorbent assay. Multivariable Cox regression models were used to investigate the association between cerebrospinal fluid CHI3L1 levels and time to conversion to multiple sclerosis and time to reach Expanded Disability Status Scale 3.0. CHI3L1 levels were higher in patients who converted to clinically definite multiple sclerosis compared to patients who continued as clinically isolated syndrome (P = 8.1 × 10(-11)). In the Cox regression analysis, CHI3L1 levels were a risk factor for conversion to multiple sclerosis (hazard ratio = 1.7; P = 1.1 × 10(-5) using Poser criteria; hazard ratio = 1.6; P = 3.7 × 10(-6) for McDonald criteria) independent of other covariates such as brain magnetic resonance imaging abnormalities and presence of cerebrospinal fluid oligoclonal bands, and were the only significant independent risk factor associated with the development of disability (hazard ratio = 3.8; P = 2.5 × 10(-8)). High CHI3L1 levels were associated with shorter time to multiple sclerosis (P = 3.2 × 10(-9) using Poser criteria; P = 5.6 × 10(-11) for McDonald criteria) and more rapid development of disability (P = 1.8 × 10(-10)). These findings validate cerebrospinal fluid CHI3L1 as a biomarker associated with the conversion to multiple sclerosis and development of disability and reinforce the prognostic role of CHI3L1 in patients with clinically isolated syndrome. We propose that determining cerebrospinal fluid chitinase 3-like 1 levels at the time of a clinically isolated syndrome event will help identify those patients with worse disease prognosis.
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http://dx.doi.org/10.1093/brain/awv017DOI Listing
April 2015

Kernel-PCA data integration with enhanced interpretability.

BMC Syst Biol 2014 13;8 Suppl 2:S6. Epub 2014 Mar 13.

Background: Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task.

Results: We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed.

Conclusions: The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.
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http://dx.doi.org/10.1186/1752-0509-8-S2-S6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101706PMC
March 2015

Kinematic parameters that influence the aesthetic perception of beauty in contemporary dance.

Perception 2013 ;42(4):447-58

National Institute of Physical Education of Catalunya (INEFC), Universitat de Lleida, E-25192 Lleida, Spain.

Some experiments have stablished that certain kinematic parameters can influence the subjective aesthetic perception of the dance audience. Neave, McCarty, Freynik, Caplan, Hönekopp, and Fink (2010, Biology Letters 7 221-224) reported eleven movement parameters in non-expert male dancers, showing a significant positive correlation with perceived dance quality. We aim to identify some of the kinematic parameters of expert dancers' movements that influence the subjective aesthetic perception of observers in relation to specific skills of contemporary dance. Four experienced contemporary dancers performed three repetitions of four dance-related motor skills. Motion was captured by a VICON-MX system. The resulting 48 animations were viewed by 108 observers. The observers judged beauty using a semantic differential. The data were then subjected to multiple factor analysis. The results suggested that there were strong associations between higher beauty scores and certain kinematic parameters, especially those related to amplitude of movement.
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http://dx.doi.org/10.1068/p7117DOI Listing
July 2013

TNFRSF1A polymorphisms rs1800693 and rs4149584 in patients with multiple sclerosis.

Neurology 2013 May 26;80(22):2010-6. Epub 2013 Apr 26.

Department of Neurology-Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya, Cemcat, Hospital Universitari Vall d´Hebron, Barcelona, Spain.

Objectives: To investigate the roles of 2 polymorphisms of the tumor necrosis factor (TNF) receptor superfamily member 1A (TNFRSF1A) gene, rs1800693 (a common variant) and rs4149584 (a coding polymorphism that results in an amino acid substitution-R92Q), as genetic modifiers of multiple sclerosis (MS), and to evaluate their potential functional implications in the disease.

Methods: The effects of rs1800693 and rs4149584 on 2 measures of disease severity, age at disease onset and Multiple Sclerosis Severity Score, were analyzed in 2,032 patients with MS. In a subgroup of patients, serum levels of the soluble form of TNF-R1 (sTNF-R1) were measured by ELISA; mRNA expression levels of the full-length TNF-R1 and Δ6-TNF-R1 isoform were investigated in peripheral blood mononuclear cells (PBMC) by real-time PCR; cell surface expression of the TNF-R1 was determined in T cells by flow cytometry.

Results: For rs4149584, R92Q carriers were younger at disease onset and progressed slower compared to noncarriers. However, no association with disease severity was observed for rs1800693. Serum levels of sTNF-R1 and mRNA expression levels of the full-length receptor were significantly increased in patients with MS carrying the R92Q mutation (p = 0.003 and p = 0.011, respectively), but similarly distributed among rs1800693 genotypes; cell surface TNF-R1 expression in T cells did not differ between rs4149584 and rs1800693 genotypes. The truncated soluble Δ6-TNF-R1 isoform was identified in PBMC from patients carrying the risk allele for rs1800693.

Conclusions: These findings suggest that both rs1800693 and rs4149584 TNFRSF1A polymorphisms have functional consequences in the TNF-R1.
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http://dx.doi.org/10.1212/WNL.0b013e318294b2d6DOI Listing
May 2013

Transcriptomics: mRNA and alternative splicing.

J Neuroimmunol 2012 Jul 22;248(1-2):23-31. Epub 2012 May 22.

Departament d'Estadística, Universitat de Barcelona, Spain.

Transcriptomics has emerged as a powerful approach for biomarker discovery. In the present review, the two main types of high throughput transcriptomic technologies - microarrays and next generation sequencing - that can be used to identify candidate biomarkers are briefly described. Microarrays, the mainstream technology of the last decade, have provided hundreds of valuable datasets in a wide variety of diseases including multiple sclerosis (MS), in which this approach has been used to disentangle different aspects of its complex pathogenesis. RNA-seq, the current next generation sequencing approach, is expected to provide similar power as microarrays but extending their capabilities to aspects up to now more difficult to analyse such as alternative splicing and discovery of novel transcripts.
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http://dx.doi.org/10.1016/j.jneuroim.2012.04.008DOI Listing
July 2012

Mining gene expression profiles: an integrated implementation of kernel principal component analysis and singular value decomposition.

Genomics Proteomics Bioinformatics 2010 Sep;8(3):200-10

Department of Statistics, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.

The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualization tools are used to identify genes with similar profiles in microarray studies. Given the large number of genes recorded in microarray experiments, gene expression data are generally displayed on a low dimensional plot, based on linear methods. However, microarray data show nonlinearity, due to high-order terms of interaction between genes, so alternative approaches, such as kernel methods, may be more appropriate. We introduce a technique that combines kernel principal component analysis (KPCA) and Biplot to visualize gene expression profiles. Our approach relies on the singular value decomposition of the input matrix and incorporates an additional step that involves KPCA. The main properties of our method are the extraction of nonlinear features and the preservation of the input variables (genes) in the output display. We apply this algorithm to colon tumor, leukemia and lymphoma datasets. Our approach reveals the underlying structure of the gene expression profiles and provides a more intuitive understanding of the gene and sample association.
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http://dx.doi.org/10.1016/S1672-0229(10)60022-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054124PMC
September 2010

Concentration of Cd, Cu, Pb, Zn, Al, and Fe in soils of Manresa, NE Spain.

Environ Monit Assess 2008 Oct 15;145(1-3):257-66. Epub 2007 Dec 15.

Soil Science, University of Barcelona, Avda. Diagonal 645, E-08028 Barcelona, Spain.

The aims of this study were to determine the contents of cadmium (Cd), copper (Cu), lead (Pb), zinc (Zn), aluminium (Al), and iron (Fe) (aqua regia-extractable) in 27 soil plots (54 samples) from Manresa, NE Spain, and to establish relationships between heavy metals and some soil properties. The main soil types were surveyed and the median concentrations (mg kg(-1)) obtained were Cd 0.28, Cu 20.3, Pb 18.6, Zn 67.4, Al 22,572, and Fe 21,551. Element concentrations for these soils were lower than the published values for the Valencia region (Spain) and Torrelles and Sant Climent municipal districts (Catalonia, Spain). In terms of soil properties, the results of this study suggest that, in Manresa soils, both trace element adsorption and retention are influenced by several properties such as clay minerals, carbonates, organic matter, and pH. All element contents were positively correlated with clay content. Pb and Zn were negatively correlated with pH and CaCO(3).
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http://dx.doi.org/10.1007/s10661-007-0035-2DOI Listing
October 2008

Baseline concentrations of trace elements in surface soils of the Torrelles and Sant Climent Municipal Districts (Catalonia, Spain).

Environ Monit Assess 2005 Sep;108(1-3):309-22

University of Barcelona, Avda. Diagonal 645, Barcelona, Spain.

An investigation was conducted to study the baseline levels of Ba, Cd, Cu, Cr, Ni, Pb, Sr, V and Zn (aqua regia-extractable) based on 51 representative soils of the Torrelles and Sant Climent Municipal Districts (Catalonia, Spain). The baseline concentrations of those elements were (mg kg(-1)): Ba 73.9-617.9, Cr 9.2-120.2, Cu 4.0-111.6, Ni 6.1-118.6, Pb 5.6-217.5, Sr 19.6-128.8, V 12.1-101.2, and Zn 16.8-326.8, respectively. Forty-nine samples were reported as having less than the 0.67 mg kg(-1) detection limit for cadmiun and were therefore not useful for baseline determination; however, these results suggest that the baseline average is probably below 0.67 mg kg(-1). Upper baseline values for most of the elements corresponded with those reported in the literature, except for Pb and Zn, which were two to four times greater. Soil properties, including clay fraction, OC, CEC and pH(w) were related to metal concentration using correlation and factorial analysis. R-mode factor analysis separates the soil analysis data into three factors. These factors explain 67.3% of the total variance, suggesting that metal concentration was controlled by soil composition.
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http://dx.doi.org/10.1007/s10661-005-4331-4DOI Listing
September 2005
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