Sistema de avaliação de práticas psicológicas Aluízio Lopes de Brito (SAPP) sobre políticas de cuidado / Aluízio Lopes de Brito psychological practices assessment system (SAPP) on care policies / Sistema de evaluación de prácticas psicológicas Aluízio Lopes de Brito (SAPP) sobre políticas de cuidado

O objetivo do presente manuscrito é caracterizar e descrever os fluxos do Sistema de Avaliação de Práticas Psicológicas Aluízio Lopes de Brito (SAPP), dispositivo instituído no âmbito do Sistema Conselhos de Psicologia e regulamentado pelo Conselho Federal de Psicologia através da Resolução CFP nº 15, de 18 de agosto de 2023. O SAPP surge da necessidade premente de orientação e qualificação profissionais frente às práticas emergentes que produzem o saber/ fazer da psicologia. Nesse sentido, trata-se de processo que busca orientar, qualificar e fazer conhecer práticas que sejam compatíveis ou não com o exercício profissional em psicologia. Com o trabalho realizado no SAPP serão produzidos pareceres que contribuirão minimamente para o conhecimento das fronteiras que delimitam os campos da psicologia e, por excelência, conheceremos melhor nossas próprias formas de atuação. Através da consideração do trinômio teoria-prática-ética, o CFP espera com o SAPP abrir diálogos com grupos, práticas e saberes fronteiriços e constantemente relegados pela psicologia hegemônica. Para tanto, parte do pressuposto de que os saberes e fazeres destas populações podem refinar as teorias psicológicas e fazer a psicologia avançar como ciência e profissão.(AU)

This manuscript aims to characterize and describe the flows of the Aluízio Lopes de Brito Psychological Practices Assessment System (SAPP), an instrument established within the framework of the Psychology Council System and regulated by the Federal Council of Psychology (CFP) with Resolution CFP No. 15, of August 18, 2023. The SAPP arises from the pressing need for professional guidance and qualification in the face of emerging practices that shape the knowledge/practice of psychology. In this sense, it is a process that seeks to guide, qualify, and make known practices that are compatible or not with the professional practice of psychology. The work carried out in the SAPP will produce opinions that will contribute minimally to the understanding of the boundaries that delimit the fields of psychology and, by excellence, we will better understand our own modes of operation. Considering the trinity of theory-practice-ethics, the CFP hopes with the SAPP to open dialogues with groups, practices, and knowledge that are in the borders and are constantly relegated by hegemonic psychology. To this end, it assumes that the knowledge and practices of these populations can refine psychological theories and advance psychology as a science and profession.(AU)

El objetivo de este manuscrito es caracterizar y describir los flujos del Sistema de Evaluación de Prácticas Psicológicas Aluízio Lopes de Brito (SAPP), un dispositivo establecido en el marco del Sistema de Consejos de Psicología y regulado por el Consejo Federal de Psicología a través de la Resolución CFP n.º 15, con fecha del 18 de agosto de 2023. El SAPP surge de la necesidad apremiante de orientación y calificación profesional frente a las prácticas emergentes que configuran el conocimiento y las habilidades de la psicología. En este sentido, es un proceso que busca orientar, calificar y dar a conocer prácticas que sean compatibles o no con el ejercicio profesional de la psicología. El trabajo realizado en el SAPP generará opiniones que contribuirán mínimamente a la comprensión de los límites que delimitan los campos de la psicología y, por excelencia, comprender mejor nuestras propias formas de actuación. A través de la consideración de la tríada teoría-práctica-ética, el CFP espera que con el SAPP puede llevar a cabo el diálogo con grupos, prácticas y conocimientos fronterizos y constantemente pasados por alto por la psicología hegemónica. Con este fin, se asume que los conocimientos y prácticas de estas poblaciones pueden refinar las teorías psicológicas y hacer avanzar la psicología como ciencia y profesión.(AU)

Transcriptome analysis of gene expression profiling from the deep sea in situ to the laboratory for the cold seep mussel Gigantidas haimaensis.

BACKGROUND: The deep-sea mussel Gigantidas haimaensis is a representative species from the Haima cold seep ecosystem in the South China Sea that establishes endosymbiosis with chemotrophic bacteria. During long-term evolution, G. haimaensis has adapted well to the local environment of cold seeps. Until now, adaptive mechanisms responding to environmental stresses have remained poorly understood. RESULTS: In this study, transcriptomic analysis was performed for muscle tissue of G. haimaensis in the in situ environment (MH) and laboratory environment for 0 h (M0), 3 h (M3) and 9 h (M9), and 187,368 transcript sequences and 22,924 annotated differentially expressed genes (DEGs) were generated. Based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, these DEGs were enriched with a broad spectrum of biological processes and pathways, including those associated with antioxidants, apoptosis, chaperones, immunity and metabolism. Among these significantly enriched pathways, protein processing in the endoplasmic reticulum and metabolism were the most affected metabolic pathways. These results may imply that G. haimaensis struggles to support the life response to environmental change by changing gene expression profiles. CONCLUSION: The present study provides a better understanding of the biological responses and survival strategies of the mussel G. haimaensis from deep sea in situ to the laboratory environment.

Signaling factors potentially associated to the pathogenesis of Adult T-cell leukemia /lymphoma: A network-analysis and novel findings assessment.

Adult T-cell leukemia/lymphoma (ATLL) is a human T-cell leukemia virus (HTLV) type 1-associated disease of TCD4+ cell transformation. Despite extensive studies on ATLL development and progression, the fundamental processes of HTLV-1 oncogenicity are yet to be understood. This study aimed to integrate high-throughput microarray datasets to find novel genes involved in the mechanism of ATLL progression. For this purpose, five microarray datasets were downloaded from the Gene Expression Omnibus database and then profoundly analyzed. Differentially expressed genes and miRNAs were determined using the MetaDE package in the R software and the GEO2R web tool. The STRING database was utilized to construct the protein-protein interaction network and explore hub genes. Gene ontology and pathway enrichment analysis were carried out by employing the EnrichR web tool. Furthermore, flow cytometry was employed to assess the CD4/CD8 ratio, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to confirm the high-throughput data analysis results. Four miRNAs, including hsa-mir-146, hsa-mir-451, hsa-mir-31, and hsa-mir-125, were among the statistically significant differentially expressed miRNAs between healthy individuals and ATLL patients. Moreover, 924 differentially expressed genes were identified between normal and ATLL samples. Further network analysis highlighted 59 hub genes mainly regulating pathways implicated in viral interferences, immunological processes, cancer, and apoptosis pathways. Among the identified hub genes, RhoA and PRKACB were most considerable in the high-throughput analysis and were further validated by qRT-PCR. The RhoA and PRKACB expression were significantly down-regulated in ATLL patients compared to asymptomatic carriers (p<0.0001 and p=0.004) and healthy subjects (p=0.043 and p=0.002). Therefore, these corresponding miRNAs and proteins could be targeted for diagnosis purposes and designing effective treatments.

Clinical assessment and molecular mechanism of the upregulation of Toll-like receptor 2 (TLR2) in myocardial infarction.

OBJECTIVE: The prevalence and mortality of cardiovascular diseases remain ranked first worldwide. Myocardial infarction (MI) is the central cause of death from cardiovascular diseases, seriously endangering human health. The clinical implication of toll-like receptor 2 (TLR2) remains contradictory, and its mechanism is still unknown. Hence, the objective of this study was to elucidate the clinical value and molecular mechanism of TLR2 in MI. METHODS: All high-throughput datasets and eligible literature were screened, and the expression levels of TLR2 were collected from the MI. The integrated expression level of TLR2 was displayed by calculating the standardized mean difference (SMD) and the area under the curve (AUC) of the summary receiver operating characteristic curve (sROC). The related TLR2 genes were sent for pathway analyses by gene ontology (GO), Kyoto encyclopedia of genes and genome (KEGG), and disease ontology (DO). Single-cell RNA-seq was applied to ascertain the molecular mechanism of TLR2 in MI. RESULTS: Nine microarrays and four reported data were available to calculate the comprehensive expression level of TLR2 in MI, including 325 cases of MI and 306 cases of controls. The SMD was 2.55 (95% CI = 1.35-3.75), and the AUC was 0.76 (95% CI = 0.72-0.79), indicating the upregulation of TLR2 in MI. The related TLR2 genes were primarily enriched in the pathways of atherosclerosis, arteriosclerotic cardiovascular disease, and arteriosclerosis, suggesting the clinical role of TLR2 in the progression of MI. Afterward, TLR2 was upregulated in myeloid cells in MI. CONCLUSIONS: TLR2 may have a crucial role in progressing from coronary atherosclerosis to MI. The upregulation of TLR2 may have a favorable screening value for MI.

Hierarchical multi-label classification based on LSTM network and Bayesian decision theory for LncRNA function prediction.

Growing evidence shows that long noncoding RNAs (lncRNAs) play an important role in cellular biological processes at multiple levels, such as gene imprinting, immune response, and genetic regulation, and are closely related to diseases because of their complex and precise control. However, most functions of lncRNAs remain undiscovered. Current computational methods for exploring lncRNA functions can avoid high-throughput experiments, but they usually focus on the construction of similarity networks and ignore the certain directed acyclic graph (DAG) formed by gene ontology annotations. In this paper, we view the function annotation work as a hierarchical multilabel classification problem and design a method HLSTMBD for classification with DAG-structured labels. With the help of a mathematical model based on Bayesian decision theory, the HLSTMBD algorithm is implemented with the long-short term memory network and a hierarchical constraint method DAGLabel. Compared with other state-of-the-art algorithms, the results on GOA-lncRNA datasets show that the proposed method can efficiently and accurately complete the label prediction work.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

The first insight into the genetic structure of the population of modern Serbia.

The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.

Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods.

Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely 'PathTurbEr' (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\beta $ singalling pathways. Next, the aberration in TGF-$\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler that potentially yield robust inference with lower chances of getting stuck at local optima and faster convergence compared to other state-of-art methods. Next, we examined the structural features of the optimal BN as a statistical process that generates the global structure using $p_1$-model, a special class of Exponential Random Graph Models (ERGMs), and MCMC methods for their hyper-parameter sampling. This step enabled key drivers identification that drive the aberration within the perturbed BN structure of STP, and yielded 34, 34 and 23 perturbation driver genes out of 80 constituent genes of three perturbed STP models of TGF-$\beta $ signalling inferred by NS, HAR and MH sampling methods, respectively. Functional-relevance and disease-relevance analyses suggested their significant associations with breast cancer progression/resistance.

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

PharmGKB Tutorial for Pharmacogenomics of Drugs Potentially Used in the Context of COVID-19.

Pharmacogenomics (PGx) is a key area of precision medicine, which is already being implemented in some health systems and may help guide clinicians toward effective therapies for individual patients. Over the last 2 decades, the Pharmacogenomics Knowledgebase (PharmGKB) has built a unique repository of PGx knowledge, including annotations of clinical guideline and regulator-approved drug labels in addition to evidence-based drug pathways and annotations of the scientific literature. All of this knowledge is freely accessible on the PharmGKB website. In the first of a series of PharmGKB tutorials, we introduce the PharmGKB coronavirus disease 2019 (COVID-19) portal and, using examples of drugs found in the portal, demonstrate some of the main features of PharmGKB. This paper is intended as a resource to help users become quickly acquainted with the wealth of information stored in PharmGKB.

Relation Prediction of Co-Morbid Diseases Using Knowledge Graph Completion.

Co-morbid disease condition refers to the simultaneous presence of one or more diseases along with the primary disease. A patient suffering from co-morbid diseases possess more mortality risk than with a disease alone. So, it is necessary to predict co-morbid disease pairs. In past years, though several methods have been proposed by researchers for predicting the co-morbid diseases, not much work is done in prediction using knowledge graph embedding using tensor factorization. Moreover, the complex-valued vector-based tensor factorization is not being used in any knowledge graph with biological and biomedical entities. We propose a tensor factorization based approach on biological knowledge graphs. Our method introduces the concept of complex-valued embedding in knowledge graphs with biological entities. Here, we build a knowledge graph with disease-gene associations and their corresponding background information. To predict the association between prevalent diseases, we use ComplEx embedding based tensor decomposition method. Besides, we obtain new prevalent disease pairs using the MCL algorithm in a disease-gene-gene network and check their corresponding inter-relations using edge prediction task.

A Gene Rank Based Approach for Single Cell Similarity Assessment and Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

Met-Controlled Allosteric Module of Neural Generation as A New Therapeutic Target in Rodent Brain Ischemia.

OBJECTIVE: To investigate a Met-controlled allosteric module (AM) of neural generation as a potential therapeutic target for brain ischemia. METHODS: We selected Markov clustering algorithm (MCL) to mine functional modules in the related target networks. According to the topological similarity, one functional module was predicted in the modules of baicalin (BA), jasminoidin (JA), cholic acid (CA), compared with I/R model modules. This functional module included three genes: Inppl1, Met and Dapk3 (IMD). By gene ontology enrichment analysis, biological process related to this functional module was obtained. This functional module participated in generation of neurons. Western blotting was applied to present the compound-dependent regulation of IMD. Co-immunoprecipitation was used to reveal the relationship among the three members. We used IF to determine the number of newborn neurons between compound treatment group and ischemia/reperfusion group. The expressions of vascular endothelial growth factor (VEGF) and matrix metalloproteinase 9 (MMP-9) were supposed to show the changing circumstances for neural generation under cerebral ischemia. RESULTS: Significant reduction in infarction volume and pathological changes were shown in the compound treatment groups compared with the I/R model group (P<0.05). Three nodes in one novel module of IMD were found to exert diverse compound-dependent ischemic-specific excitatory regulatory activities. An anti-ischemic excitatory allosteric module (AME) of generation of neurons (AME-GN) was validated successfully in vivo. Newborn neurons increased in BJC treatment group (P<0.05). The expression of VEGF and MMP-9 decreased in the compound treatment groups compared with the I/R model group (P<0.05). CONCLUSIONS: AME demonstrates effectiveness of our pioneering approach to the discovery of therapeutic target. The novel approach for AM discovery in an effort to identify therapeutic targets holds the promise of accelerating elucidation of underlying pharmacological mechanisms in cerebral ischemia.

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma.

BACKGROUND We constructed a predictive risk model of esophageal carcinoma (EC) for prognostic prediction. MATERIAL AND METHODS Immune genes and the expression data were downloaded from the ImmPort database and The Cancer Genome Atlas database. Univariate analysis, Lasso regression, and multivariate analysis were applied to screen the ultimately included prognostic immune genes for the model based on the training cohort. Survival analysis and receiver operating characteristic (ROC) curve were applied to evaluate the model. The model was further validated in the testing and entire cohorts, and the clinical utility of the model and its ability to assess the subtypes of EC were evaluated in the entire cohort. RESULTS We detected 297 differentially expressed immune genes, including 241 upregulated genes and 56 downregulated genes in EC patients. Based on these genes, we developed a 7-immune gene model of EC, including HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. The area under the curve (AUC) of the model at 1 year was 0.825. Similarly, the AUC values for the validating cohorts were 0.813 and 0.816, respectively. Pathological stage and risk score of the model were independent prognostic factors. This model was effective for both subtypes of EC. CONCLUSIONS We constructed a 7-gene model consisting of HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. This risk model could be used for prognostic prediction of EC.

Molecular assessment and transcriptome profiling of wild fish populations of Oryzias mekongensis and O. songkhramensis (Adrianichthyidae: Beloniformes) from Thailand.

Among the fish of the genus Oryzias, two species are frequently used as model animals in biological research. In Thailand, Oryzias mekongensis is usually found in natural freshwater near the Mekong Basin in the northeast region, while O. songkhramensis inhabits the Songkhram Basin. For differential morphological identification, the coloured bands on the dorsal and ventral margins of the caudal fin are used to distinguish O. mekongensis from O. songkhramensis. However, these characteristics are insufficient to justify species differentiation, and little molecular evidence is available to supplement them. This study aimed to investigate the molecular population and transcriptome profiles of adult O. mekongensis and O. songkhramensis. In the molecular tree based on cytochrome b sequences, O. mekongensis exhibited four clades that were clearly distinguished from O. songkhramensis. Clade 1 of the O. mekongensis population was close to the Mekong River and lived in the eastern portion of the upper northeast region. Clade 2 was far from the Mekong River and inhabited the middle region of the Songkhram River. Clade 3 was positioned to the west of the Songkhram River, and clade 4 was to the south of the Songkhram River Basin. After RNA sequencing using an Illumina HiSeq 2500 platform, the gene category annotations hardly differentiated the species and were discussed in the text. Based on the present findings, population dispersal of these Oryzias species might be associated with geographic variations of the upper northeast region. Molecular genetics and transcriptome profiling might advance our understanding of the evolution of teleost fish.

Exploring disease comorbidity in a module-module interaction network.

Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module-module interaction (MMI) network, which represents how biological functions influence each other. To construct the MMI network, we detected gene modules - sets of genes that have a higher probability of taking part in specific functions - and established a link between these modules. Subsequently, we constructed disease-related networks in the MMI network to understand inherent disease mechanisms and calculated comorbidity scores of disease pairs using Gene Ontology (GO) terms. Our results show that we can obtain further information on disease mechanisms by considering interactions between functional modules instead of between genes. In addition, we verified that predicted comorbid relationships of disease pairs based on the MMI network are more significant than those based on the protein-protein interaction (PPI) network. This study can be useful to elucidate the mechanisms underlying comorbidities for further study, which will provide a broader insight into the pathogenesis of diseases.

Discovery of novel biomarkers for atherosclerotic aortic aneurysm through proteomics-based assessment of disease progression.

Since aortic aneurysms (AAs) are mostly asymptomatic, but they have a high mortality rate upon rupture, their detection and progression evaluation are clinically important issues. To discover diagnostic biomarkers for AA, we performed proteome analysis of aortic media from patients with thoracic atherosclerotic AA (TAAA), comparing protein levels between the aneurysm and normal tissue areas. After hierarchical clustering analysis of the proteome analysis data, tissue samples were classified into three groups, regardless of morphological features. This classification was shown to reflect disease progression stage identified by pathological examination. This proteomics-based staging system enabled us to identify more significantly altered proteins than the morphological classification system. In subsequent data analysis, Niemann-Pick disease type C2 protein (NPC2) and insulin-like growth factor-binding protein 7 (IGFBP7) were selected as novel biomarker candidates for AA and were compared with the previously reported biomarker, thrombospondin 1 (THBS1). Blood concentrations of NPC2 and IGFBP7 were significantly increased, while THBS1 levels were decreased in TAAA and abdominal atherosclerotic AA patients. Receiver operating characteristic analysis of AA patients and healthy controls showed that NPC2 and IGFBP7 have higher specificity and sensitivity than THBS1. Thus, NPC2 and IGFBP7 are promising biomarkers for the detection and progression evaluation of AA.

GOMCL: a toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions.

BACKGROUND: Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically used to describe the main functional attributes of the gene set. However, these lists of overrepresented GO terms are often too large and contains redundant overlapping GO terms hindering informative functional interpretations. RESULTS: We developed GOMCL to reduce redundancy and summarize lists of GO terms effectively and informatively. This lightweight python toolkit efficiently identifies clusters within a list of GO terms using the Markov Clustering (MCL) algorithm, based on the overlap of gene members between GO terms. GOMCL facilitates biological interpretation of a large number of GO terms by condensing them into GO clusters representing non-overlapping functional themes. It enables visualizing GO clusters as a heatmap, networks based on either overlap of members or hierarchy among GO terms, and tables with depth and cluster information for each GO term. Each GO cluster generated by GOMCL can be evaluated and further divided into non-overlapping sub-clusters using the GOMCL-sub module. The outputs from both GOMCL and GOMCL-sub can be imported to Cytoscape for additional visualization effects. CONCLUSIONS: GOMCL is a convenient toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions. GOMCL helps researchers to reduce time spent on manual curation of large lists of GO terms, minimize biases introduced by redundant GO terms in data interpretation, and batch processing of multiple GO enrichment datasets. A user guide, a test dataset, and the source code of GOMCL are available at https://github.com/Guannan-Wang/GOMCL and www.lsugenomics.org.

In Silico Study of Rett Syndrome Treatment-Related Genes, MECP2, CDKL5, and FOXG1, by Evolutionary Classification and Disordered Region Assessment.

Rett syndrome (RTT), a neurodevelopmental disorder, is mainly caused by mutations in methyl CpG-binding protein 2 (MECP2), which has multiple functions such as binding to methylated DNA or interacting with a transcriptional co-repressor complex. It has been established that alterations in cyclin-dependent kinase-like 5 (CDKL5) or forkhead box protein G1 (FOXG1) correspond to distinct neurodevelopmental disorders, given that a series of studies have indicated that RTT is also caused by alterations in either one of these genes. We investigated the evolution and molecular features of MeCP2, CDKL5, and FOXG1 and their binding partners using phylogenetic profiling to gain a better understanding of their similarities. We also predicted the structural order-disorder propensity and assessed the evolutionary rates per site of MeCP2, CDKL5, and FOXG1 to investigate the relationships between disordered structure and other related properties with RTT. Here, we provide insight to the structural characteristics, evolution and interaction landscapes of those three proteins. We also uncovered the disordered structure properties and evolution of those proteins which may provide valuable information for the development of therapeutic strategies of RTT.

Utilizing iVariantGuide for Variant Assessment of Next-Generation Sequencing.

Molecular genetic testing provides the capability for personalized prediction, diagnosis, and pharmacological treatments of disease and disorders. Variant assessment of next-generation sequencing (NGS) is a crucial component of genetic testing for clinicians to counsel patients on risk and management. The iVariantGuide application is a dynamic Web-based application made for the tertiary analysis of NGS. Along with variant assessment, iVariantGuide provides a unique interactive pathway impact analysis of genetic variants, as well as a unique Gene Ontology (GO) analysis. Here we provide a step-by-step guide on how to utilize iVariantGuide, employing a publicly available NGS dataset consisting of a cohort of germline DNAs from high-risk serous ovarian cancer (OVCA) patients. The application will be used to exhibit the ease in filtering down to a set of compelling novel variants and their impact on biological pathways and GO terms. © 2019 by John Wiley & Sons, Inc.