Demographic and clinical characteristics, seizure disorders, and antiepileptic drug usage in different types of corpus callosum disorders: a comparative study in children.

BACKGROUND: This study aimed to investigate the demographic and clinical characteristics, types of seizure disorders, and antiepileptic drug usage among individuals with different types of corpus callosum disorders. METHODS: A total of 73 individuals were included in the study and divided into three groups based on the type of corpus callosum abnormality: hypoplasia (H), agenesis (A), and dysgenesis (D). Demographic data, including gender and preterm birth, as well as clinical characteristics such as seizure disorders, attention deficit hyperactivity disorder (ADHD), severe developmental delay/intellectual disability, and other brain malformations, were analyzed. The types of seizure disorders and antiepileptic drugs used were also examined. RESULTS: The H group had the highest number of participants (n = 47), followed by the A group (n = 11) and the D group (n = 15). The A group had the highest percentage of males and preterm births, while the D group had the highest percentage of seizure disorders, other brain malformations, and severe developmental delay/intellectual disability. The A group also had the highest percentage of ADHD. Focal seizures were observed in all three groups, with the highest proportion in the A group. Focal impaired awareness seizures (FIAS) were present in all groups, with the highest proportion in the D group. Generalized tonic-clonic seizures (GTCS) were observed in all groups, with the highest proportion in the H group. Different types of antiepileptic drugs were used among the groups, with variations in usage rates for each drug. CONCLUSION: This study provided insights into the demographic and clinical characteristics, seizure disorders, and antiepileptic drug usage among individuals with different types of corpus callosum disorders. Significant differences were found between the groups, indicating the need for tailored management approaches. However, the study has limitations, including a small sample size and a cross-sectional design. Further research with larger sample sizes and longitudinal designs is warranted to validate these findings and explore the relationship between corpus callosum abnormality severity and clinical outcomes.

Assessing the impact of human genome annotation choice on RNA-seq expression estimates.

BACKGROUND: Genome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus. RESULTS: We define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates. CONCLUSIONS: Based on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

An integrative approach for the large-scale identification of human genome kinases regulating cancer metastasis.

Kinases become one of important groups of drug targets. To identify more kinases being potential for cancer therapy, we developed an integrative approach for the large-scale screen of functional genes capable of regulating the main traits of cancer metastasis. We first employed self-assembled cell microarray to screen functional genes that regulate cancer cell migration using a human genome kinase siRNA library. We identified 81 genes capable of significantly regulating cancer cell migration. Following with invasion assays and bio-informatics analysis, we discovered that 16 genes with differentially expression in cancer samples can regulate both cell migration and invasion, among which 10 genes have been well known to play critical roles in the cancer development. The remaining 6 genes were experimentally validated to have the capacities of regulating cell proliferation, apoptosis and anoikis activities besides cell motility. Together, these findings provide a new insight into the therapeutic use of human kinases. FROM THE CLINICAL EDITOR: This team of authors have utilized a self-assembled cell microarray to screen genes that regulate cancer cell migration using a human genome siRNA library of kinases. They validated previously known genes and identified novel ones that may serve as therapeutic targets.

Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction.

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine.

OBJECTIVE: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. METHODS: In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. RESULTS: To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. CONCLUSION: Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine. SIGNIFICANCE: Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

The Selection of Quantification Pipelines for Illumina RNA-seq Data Using a Subsampling Approach.

RNA sequencing, or (RNA-seq for short,, is a widely applied technology that for extractings gene and transcript expression from biological samples. Given numerous quantification pipelines for RNA-seq data, one fundamental challenge is to determine identify a pipeline that can produce the most accurate estimate the most accurate gene and/or transcript expression. Exploring all available pipelines requires tremendous extensive computational resources, so. Therefore, we propose to use a subsampling approach that can improve speed up the pipeline evaluation and selection the efficiency process of pipeline performance evaluation for a given RNA-seq dataset. We applied our approach to one simulated and two real RNA-seq datasets and found that expression estimates derived from subsampled data are close surrogates for those derived from original data. In addition, the ranking of quantification pipelines based on the subsampled data was highly correlated concordant with that based on the original data. Therefore, we conclude that subsampling is a valid approach to facilitating efficient quantification pipeline selection using RNA-seq data.

Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls.

Sequencing errors are a major issue for several next-generation sequencing-based applications such as de novo assembly and single nucleotide polymorphism detection. Several error-correction methods have been developed to improve raw data quality. However, error-correction performance is hard to evaluate because of the lack of a ground truth. In this study, we propose a novel approach which using ERCC RNA spike-in controls as the ground truth to facilitate error-correction performance evaluation. After aligning raw and corrected RNA-seq data, we characterized the quality of reads by three metrics: mismatch patterns (i.e., the substitution rate of A to C) of reads aligned with one mismatch, mismatch patterns of reads aligned with two mismatches and the percentage increase of reads aligned to reference. We observed that the mismatch patterns for reads aligned with one mismatch are significantly correlated between ERCC spike-ins and real RNA samples. Based on such observations, we conclude that ERCC spike-ins can serve as ground truths for error correction beyond their previous applications for validation of dynamic range and fold-change response. Also, the mismatch patterns for ERCC reads aligned with one mismatch can serve as a novel and reliable metric to evaluate the performance of error-correction tools.

Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.

The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

The impact of RNA-seq aligners on gene expression estimation.

While numerous RNA-seq data analysis pipelines are available, research has shown that the choice of pipeline influences the results of differentially expressed gene detection and gene expression estimation. Gene expression estimation is a key step in RNA-seq data analysis, since the accuracy of gene expression estimates profoundly affects the subsequent analysis. Generally, gene expression estimation involves sequence alignment and quantification, and accurate gene expression estimation requires accurate alignment. However, the impact of aligners on gene expression estimation remains unclear. We address this need by constructing nine pipelines consisting of nine spliced aligners and one quantifier. We then use simulated data to investigate the impact of aligners on gene expression estimation. To evaluate alignment, we introduce three alignment performance metrics, (1) the percentage of reads aligned, (2) the percentage of reads aligned with zero mismatch (ZeroMismatchPercentage), and (3) the percentage of reads aligned with at most one mismatch (ZeroOneMismatchPercentage). We then evaluate the impact of alignment performance on gene expression estimation using three metrics, (1) gene detection accuracy, (2) the number of genes falsely quantified (FalseExpNum), and (3) the number of genes with falsely estimated fold changes (FalseFcNum). We found that among various pipelines, FalseExpNum and FalseFcNum are correlated. Moreover, FalseExpNum is linearly correlated with the percentage of reads aligned and ZeroMismatchPercentage, and FalseFcNum is linearly correlated with ZeroMismatchPercentage. Because of this correlation, the percentage of reads aligned and ZeroMismatchPercentage may be used to assess the performance of gene expression estimation for all RNA-seq datasets.

A Review of Emerging Technologies for the Management of Diabetes Mellitus.

OBJECTIVE: High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. METHODS: A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. RESULTS: Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. CONCLUSION: Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. SIGNIFICANCE: The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.