Making microarray and RNA-seq gene expression data comparable

Measuring gene expression levels in the cell is an important tool in biomedical sciences. It can be used in new drug development, disease diagnostics and many other areas. Currently, two most popular platforms for measuring gene expression are microarrays and RNA-sequencing (RNA-seq). Making the gene expression results more comparable between these two platforms is an important topic which has not yet been investigated enough. In this thesis, we present a novel method, called PREBS, that addresses this issue. Our method adjusts RNA-seq data computational processing in a way that makes the resulting gene expression measures more similar to microarray based gene expression measures. We compare our method against two other RNA-seq processing methods, RPKM and MMSEQ, and evaluate each method's agreement with microarrays by calculating correlations between the platforms. We show that our method reaches the highest level of agreement among all of the methods in absolute expression scale and has a similar level of agreement as the other methods in differential expression scale. Additionally, this thesis provides some background on gene expression, its measurement and computational analysis of gene expression data. Moreover, it gives a brief literature review on the past microarray{RNA-seq comparisons

Estrés y desempeño laboral de los profesionales no médicos del hospital Santa María - Cutervo, 2019

La presente tesis titulada “estrés y desempeño laboral de los profesionales no médicos del Hospital Santa María – Cutervo, 2019, cuyo propósito fue construir conocimiento científico que proporcionen herramientas para el diseño de acciones de mejora. El objetivo general fue determinar la relación entre el nivel de estrés y el nivel de desempeño laboral de los profesionales no médicos del Hospital Santa María – Cutervo, 2019. El estudio fue cuantitativo, tipo descriptivo correlacional. La población fue de 111 trabajadores de salud con una muestra no probabilística poblacional a quienes se les aplicó encuestas adaptados, consistentes en la Escala de estrés laboral de la OIT – OMS y Escala de Desempeño Individual de Williams & Anderson. Los resultados obtenidos fueron que el 82,9% (90) del personal de salud no médico sufre de estrés severo y en cuanto al desempeño, el 55% (61) logró un desempeño bueno y regular un 45% (50); el 78,9% (15) de personal de salud no médico que sufre un estrés moderado tiene un desempeño regular y el 50% (46) de personal que sufre de estrés severo tiene desempeño regular. Se concluyó que hay una relación entre el estrés laboral y el desempeño de los trabajadores de salud no médicos que laboran en el Hospital Santa María de Cutervo (P=0,075)

Estrés y desempeño laboral de los profesionales no médicos del hospital Santa María - Cutervo, 2019

La presente tesis titulada “estrés y desempeño laboral de los profesionales no médicos del Hospital Santa María – Cutervo, 2019, cuyo propósito fue construir conocimiento científico que proporcionen herramientas para el diseño de acciones de mejora. El objetivo general fue determinar la relación entre el nivel de estrés y el nivel de desempeño laboral de los profesionales no médicos del Hospital Santa María – Cutervo, 2019. El estudio fue cuantitativo, tipo descriptivo correlacional. La población fue de 111 trabajadores de salud con una muestra no probabilística poblacional a quienes se les aplicó encuestas adaptados, consistentes en la Escala de estrés laboral de la OIT – OMS y Escala de Desempeño Individual de Williams & Anderson. Los resultados obtenidos fueron que el 82,9% (90) del personal de salud no médico sufre de estrés severo y en cuanto al desempeño, el 55% (61) logró un desempeño bueno y regular un 45% (50); el 78,9% (15) de personal de salud no médico que sufre un estrés moderado tiene un desempeño regular y el 50% (46) de personal que sufre de estrés severo tiene desempeño regular. Se concluyó que hay una relación entre el estrés laboral y el desempeño de los trabajadores de salud no médicos que laboran en el Hospital Santa María de Cutervo (P=0,075)

Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package "prebs."Peer reviewe

Methods for estimation of model accuracy in CASP12

Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this paper, the most successful groups in CASP12 describe their latest methods for Estimates of Model Accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the three top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). While the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact based model quality measures (CAD, lDDT) the single model quality methods perform relatively better

Accurate template-based modeling in CASP12 using the IntFOLD4-TS, ModFOLD6, and ReFOLD methods

Our aim in CASP12 was to improve our Template-Based Modeling (TBM) methods through better model selection, accuracy self-estimate (ASE) scores and refinement. To meet this aim, we developed two new automated methods, which we used to score, rank, and improve upon the provided server models. Firstly, the ModFOLD6_rank method, for improved global Quality Assessment (QA), model ranking and the detection of local errors. Secondly, the ReFOLD method for fixing errors through iterative QA guided refinement. For our automated predictions we developed the IntFOLD4-TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple-template models that were generated using a number of alternative sequence-structure alignments. Overall, our selection of top models and ASE scores using ModFOLD6_rank was an improvement on our previous approaches. In addition, it was worthwhile attempting to repair the detected errors in the top selected models using ReFOLD, which gave us an overall gain in performance. According to the assessors' formula, the IntFOLD4 server ranked 3rd/5th (average Z-score > 0.0/–2.0) on the server only targets, and our manual predictions (McGuffin group) ranked 1st/2nd (average Z-score > −2.0/0.0) compared to all other groups

Estimating the quality of 3D protein models using the ModFOLD7 server

Assessing the accuracy of 3D models has become a keystone in the protein structure prediction field. ModFOLD7 is our leading resource for Estimates of Model Accuracy (EMA), which has been upgraded by integrating a number of the pioneering pure-single- and quasi-single-model approaches. Such an integration has given our latest version the strengths to accurately score and rank predicted models, with higher consistency compared to older EMA methods. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (1) ModFOLD7_rank, which is optimized for ranking/selection, (2) ModFOLD7_cor, which is optimized for correlations of predicted and observed scores, and (3) ModFOLD7 global for balanced performance. ModFOLD7 has been ranked among the top few EMA methods according to independent blind testing by the CASP13 assessors. Another evaluation resource for ModFOLD7 is the CAMEO project, where the method is continuously automatically evaluated, showing a significant improvement compared to our previous versions. The ModFOLD7 server is freely available at http://www.reading.ac.uk/bioinf/ModFOLD/

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

Computational modelling of proteins has been a major catalyst in structural biology. Bioinformatics groups have exploited the repositories of known structures to predict high-quality structural models with high efficiency at low cost. This article provides an overview of comparative modelling, reviews recent developments and describes resources dedicated to large-scale comparative modelling of genome sequences. The value of subclustering protein domain superfamilies to guide the template-selection process is investigated. Some recent cases in which structural modelling has aided experimental work to determine very large macromolecular complexes are also cited