A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

Human Fetal-Derived Enterospheres Provide Insights on Intestinal Development and a Novel Model to Study Necrotizing Enterocolitis (NEC)

BACKGROUND & AIMS

Untreated necrotizing enterocolitis (NEC) can lead to massive inflammation resulting in intestinal necrosis with a high mortality rate in preterm infants. Limited access to human samples and relevant experimental models have hampered progress in NEC pathogenesis. Earlier evidence has suggested that bacterial colonization of an immature and developing intestine can lead to an abnormally high inflammatory response to bacterial bioproducts. The aim of our study was to use human fetal organoids to gain insights into NEC pathogenesis.

METHODS

RNA sequencing analysis was performed to compare patterns of gene expression in human fetal-derived enterospheres (FEnS) and adult-derived enterospheres (AEnS). Differentially expressed genes were analyzed using computational techniques for dimensional reduction, clustering, and gene set enrichment. Unsupervised cluster analysis, Gene Ontology, and gene pathway analysis were used to predict differences between gene expression of samples. Cell monolayers derived from FEnS and AEnS were evaluated for epithelium function and responsiveness to lipopolysaccharide and commensal bacteria.

RESULTS

Based on gene expression patterns, FEnS clustered according to their developmental age in 2 distinct groups: early and late FEnS, with the latter more closely resembling AEnS. Genes involved in maturation, gut barrier function, and innate immunity were responsible for these differences. FEnS-derived monolayers exposed to either lipopolysaccharide or commensal Escherichia coli showed that late FEnS activated gene expression of key inflammatory cytokines, whereas early FEnS monolayers did not, owing to decreased expression of nuclear factor-κB-associated machinery.

CONCLUSIONS

Our results provide insights into processes underlying human intestinal development and support the use of FEnS as a relevant human preclinical model for NEC. Accession number of repository for expression data: GSE101531.

Drug target inference through pathway analysis of genomics data

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues in this field, covering methodological developments, their pros and cons, as well as future research directions.

Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits

A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.