Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida.

Purpose: Spina bifida (SB) arises from complex genetic interactions that converge to interfere with neural tube closure. Understanding the precise patterns conferring SB risk requires a deep exploration of the genomic networks and molecular pathways that govern neurulation. This study aims to delineate genome-wide regulatory signatures underlying SB pathophysiology.

Lower respiratory tract microbiome composition and community interactions in smokers.

The lung microbiome impacts on lung function, making any smoking-induced changes in the lung microbiome potentially significant. The complex co-occurrence and co-avoidance patterns between the bacterial taxa in the lower respiratory tract (LRT) microbiome were explored for a cohort of active (AS), former (FS) and never (NS) smokers. Bronchoalveolar lavages (BALs) were collected from 55 volunteer subjects (9 NS, 24 FS and 22 AS).

CIC missense variants contribute to susceptibility for spina bifida.

Neural tube defects (NTDs) are congenital malformations resulting from abnormal embryonic development of the brain, spine, or spinal column. The genetic etiology of human NTDs remains poorly understood despite intensive investigation. CIC, homolog of the Capicua transcription repressor, has been reported to interact with ataxin-1 (ATXN1) and participate in the pathogenesis of spinocerebellar ataxia type 1.

Systems biology analysis of human genomes points to key pathways conferring spina bifida risk.

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance, and environmental influences that hamper genome-wide association studies approaches to untargeted discovery.

Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis.

The novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells.

Genome-wide investigation identifies a rare copy-number variant burden associated with human spina bifida.

Purpose: Next-generation sequencing has implicated some risk variants for human spina bifida (SB), but the genome-wide contribution of structural variation to this complex genetic disorder remains largely unknown. We examined copy-number variant (CNV) participation in the genetic architecture underlying SB risk.

Methods: A high-confidence ensemble approach to genome sequences (GS) was benchmarked and employed for systematic detection of common and rare CNVs in two separate ancestry-matched SB case-control cohorts.

A data-driven, high-throughput methodology to determine tissue-specific differentially methylated regions able to discriminate body fluids.

Tissue-specific differentially methylated regions (tDMRs) are regions of the genome with methylation patterns that modulate gene expression in those tissue types. The detection of tDMRs in forensic evidence can permit the identification of body fluids at trace levels. In this report, we have performed a bioinformatic analysis of an existing array dataset to determine if new tDMRs could be identified for use in body fluid identification from forensic evidence.

Single-cell sperm transcriptomes and variants from fathers of children with and without autism spectrum disorder.

The human sperm is one of the smallest cells in the body, but also one of the most important, as it serves as the entire paternal genetic contribution to a child. Investigating RNA and mutations in sperm is especially relevant for diseases such as autism spectrum disorders (ASD), which have been correlated with advanced paternal age. Historically, studies have focused on the assessment of bulk sperm, wherein millions of individual sperm are present and only high-frequency variants can be detected.

Loss of RAD9B impairs early neural development and contributes to the risk for human spina bifida.

DNA damage response (DDR) genes orchestrating the network of DNA repair, cell cycle control, are essential for the rapid proliferation of neural progenitor cells. To date, the potential association between specific DDR genes and the risk of human neural tube defects (NTDs) has not been investigated. Using whole-genome sequencing and targeted sequencing, we identified significant enrichment of rare deleterious RAD9B variants in spina bifida cases compared to controls (8/409 vs.

Author Correction: Threshold for neural tube defect risk by accumulated singleton loss-of-function variants.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

MATria: a unified centrality algorithm.

Background: Computing centrality is a foundational concept in social networking that involves finding the most "central" or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm.

Results: We instead generalize the results of any k centrality algorithms through our iterative algorithm MATRIA, producing a single ranked and unified set of central nodes.

ATria: a novel centrality algorithm applied to biological networks.

Background: The notion of centrality is used to identify "important" nodes in social networks. Importance of nodes is not well-defined, and many different notions exist in the literature. The challenge of defining centrality in meaningful ways when network edges can be positively or negatively weighted has not been adequately addressed in the literature.

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis.

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued.

Gene expression patterns in transgenic mouse models of hypertrophic cardiomyopathy caused by mutations in myosin regulatory light chain.

Using microarray and bioinformatics, we examined the gene expression profiles in transgenic mouse hearts expressing mutations in the myosin regulatory light chain shown to cause hypertrophic cardiomyopathy (HCM). We focused on two malignant RLC-mutations, Arginine 58→Glutamine (R58Q) and Aspartic Acid 166 → Valine (D166V), and one benign, Lysine 104 → Glutamic Acid (K104E)-mutation. Datasets of differentially expressed genes for each of three mutants were compared to those observed in wild-type (WT) hearts.

Unbiased high-throughput characterization of mussel transcriptomic responses to sublethal concentrations of the biotoxin okadaic acid.

Background. Harmful Algal Blooms (HABs) responsible for Diarrhetic Shellfish Poisoning (DSP) represent a major threat for human consumers of shellfish. The biotoxin Okadaic Acid (OA), a well-known phosphatase inhibitor and tumor promoter, is the primary cause of acute DSP intoxications.

Evolutionary computation and QSAR research.

The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments.

Applied computational techniques on schizophrenia using genetic mutations.

Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study.

The CHROMEVALOA database: a resource for the evaluation of Okadaic Acid contamination in the marine environment based on the chromatin-associated transcriptome of the mussel Mytilus galloprovincialis.

Okadaic Acid (OA) constitutes the main active principle in Diarrhetic Shellfish Poisoning (DSP) toxins produced during Harmful Algal Blooms (HABs), representing a serious threat for human consumers of edible shellfish. Furthermore, OA conveys critical deleterious effects for marine organisms due to its genotoxic potential. Many efforts have been dedicated to OA biomonitoring during the last three decades.

Biomedical data integration in computational drug design and bioinformatics.

In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources.

Random Forest classification based on star graph topological indices for antioxidant proteins.

Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard, there exists a tiny subset of molecules in nature, named antioxidant proteins that may influence the aging process.

Exploring patterns of epigenetic information with data mining techniques.

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate.