Integrative bioinformatics analyses of genome-wide RNAi screens

In past few years, genome-wide RNAi screens have identified many novel genes involved in diseases for many viruses such as Human Immunodeficiency Virus-1 (HIV-1), Hepatitis C virus (HCV), West Nile Virus (WNV) and Influenza virus (IV). However, due to difference in experimental conditions, usage of different viral strains and inherent biological noise, these screens have shown low number of common or overlapping hits for a virus. Moreover, this overlap gets poorer for similar studies on viruses of different families. Although these overlaps are significant, their lower size restricts a comprehensive insight from a comparative analysis. Thus, a direct comparison of gene hit-lists of RNAi screens may not always give meaningful results. To address this problem we propose an integrative bioinformatics pipeline that allows for network based meta-analysis of viral HT-RNAi screens. Initially, human protein interaction network (PIN) generated by collating data from various public repositories, is subjected to unsupervised clustering to determine functional modules. Those modules that are significantly enriched in host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are then interpreted for their biological significance from enrichment analyses. With our approach we could predict Tankyrase-1 as a potential novel hit within the functional subnetworks, within the human PIN for Hepatitis C virus (HCV). and Human Immunodeficiency Virus-1 (HIV-1), based on HDFs and HRFs identified in the corresponding genome-wide RNAi screens of these viruses. Thus, our approach allows for a network based meta-analysis of genome-wide screens to develop plausible hypotheses for novel regulatory mechanisms in virus-host interactions based on RNAi screens

Rif2 Promotes a Telomere Fold-Back Structure through Rpd3L Recruitment in Budding Yeast

Using a genome-wide screening approach, we have established the genetic requirements for proper telomere structure in Saccharomyces cerevisiae. We uncovered 112 genes, many of which have not previously been implicated in telomere function, that are required to form a fold-back structure at chromosome ends. Among other biological processes, lysine deacetylation, through the Rpd3L, Rpd3S, and Hda1 complexes, emerged as being a critical regulator of telomere structure. The telomeric-bound protein, Rif2, was also found to promote a telomere fold-back through the recruitment of Rpd3L to telomeres. In the absence of Rpd3 function, telomeres have an increased susceptibility to nucleolytic degradation, telomere loss, and the initiation of premature senescence, suggesting that an Rpd3-mediated structure may have protective functions. Together these data reveal that multiple genetic pathways may directly or indirectly impinge on telomere structure, thus broadening the potential targets available to manipulate telomere function

KnetMiner:A comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists’ own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org

Whole-genome sequencing reveals new Alzheimer's disease-associated rare variants in loci related to synaptic function and neuronal development

Introduction Genome-wide association studies have led to numerous genetic loci associated with Alzheimer's disease (AD). Whole-genome sequencing (WGS) now permits genome-wide analyses to identify rare variants contributing to AD risk. Methods We performed single-variant and spatial clustering–based testing on rare variants (minor allele frequency [MAF] ≤1%) in a family-based WGS-based association study of 2247 subjects from 605 multiplex AD families, followed by replication in 1669 unrelated individuals. Results We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2. Discussion Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity and amyloid processing. These loci have not been associated previously with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of the exome

Meta-Analysis of the Alzheimer\u27s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

We present a consensus atlas of the human brain transcriptome in Alzheimer\u27s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington\u27s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies

An integrative approach for a network based meta-analysis of viral RNAi screens.

BACKGROUND: Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. RESULTS: To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules. CONCLUSIONS: We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-015-0035-7) contains supplementary material, which is available to authorized users

High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay

Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread. Identification of host cell factors (HCFs) required for these processes is a major challenge for researchers, but it enables the identification of new, highly selective targets for anti viral therapeutics. To this end, the establishment of platforms enabling genome-wide high-throughput RNA interference (HT-RNAi) screens has led to the identification of several key factors involved in the viral life cycle. A number of genome-wide HT-RNAi screens have been performed for major human pathogens. These studies enable first inter-viral comparisons related to HCF requirements. Although several cellular functions appear to be uniformly required for the life cycle of most viruses tested (such as the proteasome and the Golgi-mediated secretory pathways), some factors, like the lipid kinase Phosphatidylinositol 4-kinase III\u3b1 in the case of hepatitis C virus, are selectively required for individual viruses. However, despite the amount of data available, we are still far away from a comprehensive understanding of the interplay between viruses and host factors. Major limitations towards this goal are the low sensitivity and specificity of such screens, resulting in limited overlap between different screens performed with the same virus. This review focuses on how statistical and bioinformatic analysis methods applied to HT-RNAi screens can help overcoming these issues thus increasing the reliability and impact of such studies

GET_PANGENES: calling pangenes from plant genome alignments confirms presence-absence variation.

Crop pangenomes made from individual cultivar assemblies promise easy access to conserved genes, but genome content variability and inconsistent identifiers hamper their exploration. To address this, we define pangenes, which summarize a species coding potential and link back to original annotations. The protocol get_pangenes performs whole genome alignments (WGA) to call syntenic gene models based on coordinate overlaps. A benchmark with small and large plant genomes shows that pangenes recapitulate phylogeny-based orthologies and produce complete soft-core gene sets. Moreover, WGAs support lift-over and help confirm gene presence-absence variation. Source code and documentation: https://github.com/Ensembl/plant-scripts