TF-Cluster: A pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM)

<p>Abstract</p> <p>Background</p> <p>Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed.</p> <p>Results</p> <p>We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and <it>Arabidopsis </it>roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest.</p> <p>Conclusions</p> <p>TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.</p

Soil physico-chemical properties are critical for predicting carbon storage and nutrient availability across Australia

Soil carbon and nutrient availability play crucial roles in ecosystem sustainability, and they are controlled by the interaction of climatic, biotic, and soil physico-chemical variables. Although soil physico-chemical properties have been recognized as vital variables for predicting soil organic carbon (SOC) and nutrients, their relative influence across broad geographical scales has yet to be evaluated when simultaneously considering many other drivers. Using boosted regression tree and structural equation modelling analyses of observations from topsoil (0-10 cm) and subsoil (20-30 cm) at 628 sites across Australia, we investigated the effects and relative influence of climate (mean annual temperature and aridity index), plant productivity, soil biodiversity (bacterial and fungal richness), and soil physical (clay and silt) and chemical (pH and iron) properties on SOC content and nutrient availability (i.e. nitrogen, phosphorus, and potassium). Among these variables, we found that soil physico-chemical properties primarily predicted the continent-scale SOC storage and nutrient availability. In contrast, climate, plant productivity, and soil biodiversity played relatively small roles. The importance of physico-chemical properties was evident across soil depths and ecosystem types (i.e. tropical, temperate, arid, and cropland). Our findings point to the need to better understand the role of soil physico-chemical properties in soil carbon and nutrient cycling and including these variables in predictions of SOC and nutrient dynamics at the ecosystem to continental scale

Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery

Background Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. Methods and Results In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding\u27s D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. Conclusions We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction

Multi-Institutional Dosimetric Evaluation of Modern Day Stereotactic Radiosurgery (SRS) Treatment Options for Multiple Brain Metastases.

Purpose/Objectives: There are several popular treatment options currently available for stereotactic radiosurgery (SRS) of multiple brain metastases: 60Co sources and cone collimators around a spherical geometry (GammaKnife), multi-aperture dynamic conformal arcs on a linac (BrainLab Elements™ v1.5), and volumetric arc therapy on a linac (VMAT) calculated with either the conventional optimizer or with the Varian HyperArc™ solution. This study aimed to dosimetrically compare and evaluate the differences among these treatment options in terms of dose conformity to the tumor as well as dose sparing to the surrounding normal tissues. Methods and Materials: Sixteen patients and a total of 112 metastases were analyzed. Five plans were generated per patient: GammaKnife, Elements, HyperArc-VMAT, and two Manual-VMAT plans to evaluate different treatment planning styles. Manual-VMAT plans were generated by different institutions according to their own clinical planning standards. The following dosimetric parameters were extracted: RTOG and Paddick conformity indices, gradient index, total volume of brain receiving 12Gy, 6Gy, and 3Gy, and maximum doses to surrounding organs. The Wilcoxon signed rank test was applied to evaluate statistically significant differences (p \u3c 0.05). Results: For targets ≤ 1 cm, GammaKnife, HyperArc-VMAT and both Manual-VMAT plans achieved comparable conformity indices, all superior to Elements. However, GammaKnife resulted in the lowest gradient indices at these target sizes. HyperArc-VMAT performed similarly to GammaKnife for V12Gy parameters. For targets ≥ 1 cm, HyperArc-VMAT and Manual-VMAT plans resulted in superior conformity vs. GammaKnife and Elements. All SRS plans achieved clinically acceptable organs-at-risk dose constraints. Beam-on times were significantly longer for GammaKnife. Manual-VMATA and Elements resulted in shorter delivery times relative to Manual-VMATB and HyperArc-VMAT. Conclusion: The study revealed that Manual-VMAT and HyperArc-VMAT are capable of achieving similar low dose brain spillage and conformity as GammaKnife, while significantly minimizing beam-on time. For targets smaller than 1 cm in diameter, GammaKnife still resulted in superior gradient indices. The quality of the two sets of Manual-VMAT plans varied greatly based on planner and optimization constraint settings, whereas HyperArc-VMAT performed dosimetrically superior to the two Manual-VMAT plans

The Rat Genome Database (RGD): developments towards a phenome database

The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms

Multiplexed CRISPR/Cas9 Targeting of Genes Implicated in Retinal Regeneration and Degeneration

Thousands of genes have been implicated in retinal regeneration, but only a few have been shown to impact the regenerative capacity of Müller glia—an adult retinal stem cell with untapped therapeutic potential. Similarly, among nearly 300 genetic loci associated with human retinal disease, the majority remain untested in animal models. To address the large-scale nature of these problems, we are applying CRISPR/Cas9-based genome modification strategies in zebrafish to target over 300 genes implicated in retinal regeneration or degeneration. Our intent is to enable large-scale reverse genetic screens by applying a multiplexed gene disruption strategy that markedly increases the efficiency of the screening process. To facilitate large-scale phenotyping, we incorporate an automated reporter quantification-based assay to identify cellular degeneration and regeneration-deficient phenotypes in transgenic fish. Multiplexed gene targeting strategies can address mismatches in scale between “big data” bioinformatics and wet lab experimental capacities, a critical shortfall limiting comprehensive functional analyses of factors implicated in ever-expanding multiomics datasets. This report details the progress we have made to date with a multiplexed CRISPR/Cas9-based gene targeting strategy and discusses how the methodologies applied can further our understanding of the genes that predispose to retinal degenerative disease and which control the regenerative capacity of retinal Müller glia cells

A study of the relationships between oligonucleotide properties and hybridization signal intensities from NimbleGen microarray datasets

Well-defined relationships between oligonucleotide properties and hybridization signal intensities (HSI) can aid chip design, data normalization and true biological knowledge discovery. We clarify these relationships using the data from two microarray experiments containing over three million probes from 48 high-density chips. We find that melting temperature (Tm) has the most significant effect on HSI while length for the long oligonucleotides studied has very little effect. Analysis of positional effect using a linear model provides evidence that the protruding ends of probes contribute more than tethered ends to HSI, which is further validated by specifically designed match fragment sliding and extension experiments. The impact of sequence similarity (SeqS) on HSI is not significant in comparison with other oligonucleotide properties. Using regression and regression tree analysis, we prioritize these oligonucleotide properties based on their effects on HSI. The implications of our discoveries for the design of unbiased oligonucleotides are discussed. We propose that isothermal probes designed by varying the length is a viable strategy to reduce sequence bias, though imposing selection constraints on other oligonucleotide properties is also essential

FCC Physics Opportunities: Future Circular Collider Conceptual Design Report Volume 1

We review the physics opportunities of the Future Circular Collider, covering its e+e-, pp, ep and heavy ion programmes. We describe the measurement capabilities of each FCC component, addressing the study of electroweak, Higgs and strong interactions, the top quark and flavour, as well as phenomena beyond the Standard Model. We highlight the synergy and complementarity of the different colliders, which will contribute to a uniquely coherent and ambitious research programme, providing an unmatchable combination of precision and sensitivity to new physics

HE-LHC: The High-Energy Large Hadron Collider: Future Circular Collider Conceptual Design Report Volume 4

In response to the 2013 Update of the European Strategy for Particle Physics (EPPSU), the Future Circular Collider (FCC) study was launched as a world-wide international collaboration hosted by CERN. The FCC study covered an energy-frontier hadron collider (FCC-hh), a highest-luminosity high-energy lepton collider (FCC-ee), the corresponding 100 km tunnel infrastructure, as well as the physics opportunities of these two colliders, and a high-energy LHC, based on FCC-hh technology. This document constitutes the third volume of the FCC Conceptual Design Report, devoted to the hadron collider FCC-hh. It summarizes the FCC-hh physics discovery opportunities, presents the FCC-hh accelerator design, performance reach, and staged operation plan, discusses the underlying technologies, the civil engineering and technical infrastructure, and also sketches a possible implementation. Combining ingredients from the Large Hadron Collider (LHC), the high-luminosity LHC upgrade and adding novel technologies and approaches, the FCC-hh design aims at significantly extending the energy frontier to 100 TeV. Its unprecedented centre-of-mass collision energy will make the FCC-hh a unique instrument to explore physics beyond the Standard Model, offering great direct sensitivity to new physics and discoveries