On statistical treatment of the results of parallel trails with special reference to fishery research

Parallel trials form a most important part of the technique of scientific experimentation. Such trials may be divided into two; categories. In the first the results are comparable measurements of one kind or another. In the second the data consist of records of the number of times a certain 'event' has occurred in the two sets of trials compared. Only trials of the second category are dealt with here. In this paper all the reliable methods of testing for significance the results of parallel trials of a certain type with special reference to fishery research are described fully. Some sections relate to exact, others to approximate tests. The only advantage in the use of the latter lies in the fact that they are often the more expeditious. Apart from this it is always preferable to use exact methods

A comparison of forensic toolkits and mass market data recovery applications

Digital forensic application suites are large, expensive, complex software products, offering a range of functions to assist in the investigation of digital artifacts. Several authors have raised concerns as to the reliability of evidence derived from these products. This is of particular concern, given that many forensic suites are closed source and therefore can only be subject to black box evaluation. In addition, many of the individual functions integrated into forensic suites are available as commercial stand-alone products, typically at a much lower cost, or even free. This paper reports research which compared (rather than individually evaluated) the data recovery function of two forensic suites and three stand alone `non-forensic' commercial applications. The research demonstrates that, for this function at least, the commercial data recovery tools provide comparable performance to that of the forensic software suites. In addition, the research demonstrates that there is some variation in results presented by all of the data recovery tools

A temporal precedence based clustering method for gene expression microarray data

Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not. Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system. Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits

Impact of environmental inputs on reverse-engineering approach to network structures

Background: Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. Results: With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. Conclusion: We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations

Cell specific analysis of Arabidopsis leaves using fluorescence activated cell sorting

After initiation of the leaf primordium, biomass accumulation is controlled mainly by cell proliferation and expansion in the leaves1. However, the Arabidopsis leaf is a complex organ made up of many different cell types and several structures. At the same time, the growing leaf contains cells at different stages of development, with the cells furthest from the petiole being the first to stop expanding and undergo senescence1. Different cells within the leaf are therefore dividing, elongating or differentiating; active, stressed or dead; and/or responding to stimuli in sub-sets of their cellular type at any one time. This makes genomic study of the leaf challenging: for example when analyzing expression data from whole leaves, signals from genetic networks operating in distinct cellular response zones or cell types will be confounded, resulting in an inaccurate profile being generated. To address this, several methods have been described which enable studies of cell specific gene expression. These include laser-capture microdissection (LCM)2 or GFP expressing plants used for protoplast generation and subsequent fluorescence activated cell sorting (FACS)3,4, the recently described INTACT system for nuclear precipitation5 and immunoprecipitation of polysomes6. FACS has been successfully used for a number of studies, including showing that the cell identity and distance from the root tip had a significant effect on the expression profiles of a large number of genes3,7. FACS of GFP lines have also been used to demonstrate cell-specific transcriptional regulation during root nitrogen responses and lateral root development8, salt stress9 auxin distribution in the root10 and to create a gene expression map of the Arabidopsis shoot apical meristem11. Although FACS has previously been used to sort Arabidopsis leaf derived protoplasts based on autofluorescence12,13, so far the use of FACS on Arabidopsis lines expressing GFP in the leaves has been very limited4. In the following protocol we describe a method for obtaining Arabidopsis leaf protoplasts that are compatible with FACS while minimizing the impact of the protoplast generation regime. We demonstrate the method using the KC464 Arabidopsis line, which express GFP in the adaxial epidermis14, the KC274 line, which express GFP in the vascular tissue14 and the TP382 Arabidopsis line, which express a double GFP construct linked to a nuclear localization signal in the guard cells (data not shown; Figure 2). We are currently using this method to study both cell-type specific expression during development and stress, as well as heterogeneous cell populations at various stages of senescence

A specific group of genes respond to cold dehydration stress in cut Alstroemeria flowers whereas ambient dehydration stress accelerates developmental senescence expression patterns

Petal development and senescence entails a normally irreversible process. It starts with petal expansion and pigment production, and ends with nutrient remobilization and ultimately cell death. In many species this is accompanied by petal abscission. Post-harvest stress is an important factor in limiting petal longevity in cut flowers and accelerates some of the processes of senescence such as petal wilting and abscission. However, some of the effects of moderate stress in young flowers are reversible with appropriate treatments. Transcriptomic studies have shown that distinct gene sets are expressed during petal development and senescence. Despite this, the overlap in gene expression between developmental and stress-induced senescence in petals has not been fully investigated in any species. Here a custom-made cDNA microarray from Alstroemeria petals was used to investigate the overlap in gene expression between developmental changes (bud to first sign of senescence) and typical post-harvest stress treatments. Young flowers were stressed by cold or ambient temperatures without water followed by a recovery and rehydration period. Stressed flowers were still at the bud stage after stress treatments. Microarray analysis showed that ambient dehydration stress accelerates many of the changes in gene expression patterns that would normally occur during developmental senescence. However, a higher proportion of gene expression changes in response to cold stress were specific to this stimulus and not senescence related. The expression of 21 transcription factors was characterized, showing that overlapping sets of regulatory genes are activated during developmental senescence and by different stresses

Expression of the β-oxidation gene 3-ketoacyl-CoA thiolase 2 (KAT2) is required for the timely onset of natural and dark-induced leaf senescence in Arabidopsis

The onset of leaf senescence is regulated by a complex mechanism involving positive and negative regulators. Among positive regulators, jasmonic acid (JA) accumulates in senescing leaves and the JA-insensitive coi1-1 mutant displays delayed leaf senescence in Arabidopsis. A strong activated expression of the gene coding for the JA-biosynthetic β-oxidation enzyme 3-ketoacyl-CoA thiolase 2 (KAT2) in natural and dark-induced senescing leaves of Arabidopsis thaliana is reported here. By using KAT2::GUS and KAT2::LUC transgenic plants, it was observed that dark-induced KAT2 activation occurred both in excised leaves as well as in whole darkened plants. The KAT2 activation associated with dark-induced senescence occurred soon after a move to darkness, and it preceded the detection of symptoms and the expression of senescence-associated gene (SAG) markers. Transgenic plants with reduced expression of the KAT2 gene showed a significant delayed senescence both in natural and dark-induced processes. The rapid induction of the KAT2 gene in senescence-promoting conditions as well as the delayed senescence phenotype and the reduced SAG expression in KAT2 antisense transgenic plants, point to KAT2 as an essential component for the timely onset of leaf senescence in Arabidopsis

Plant Genetics and Molecular Biology: An Introduction

The rapidly evolving technologies can serve as a potential growth engine in agriculture as many of these technologies have revolutionized several industries in the recent past. The tremendous advancements in biotechnology methods, cost-effective sequencing technology, refinement of genomic tools, and standardization of modern genomics-assisted breeding methods hold great promise in taking the global agriculture to the next level through development of improved climate-smart seeds. These technologies can dramatically increase our capacity to understand the molecular basis of traits and utilize the available resources for accelerated development of stable high-yielding, nutritious, input-use efficient, and climate-smart crop varieties. This book aimed to document the monumental advances witnessed during the last decade in multiple fields of plant biotechnology such as genetics, structural and functional genomics, trait and gene discovery, transcriptomics, proteomics, metabolomics, epigenomics, nanotechnology, and analytical tools. This book will serve to update the scientific community, academicians, and other stakeholders in global agriculture on the rapid progress in various areas of agricultural biotechnology. This chapter provides a summary of the book, “Plant Genetics and Molecular Biology.

Conserved noncoding sequences highlight shared components of regulatory networks in dicotyledonous plants

Conserved noncoding sequences (CNSs) in DNA are reliable pointers to regulatory elements controlling gene expression. Using a comparative genomics approach with four dicotyledonous plant species (Arabidopsis thaliana, papaya [Carica papaya], poplar [Populus trichocarpa], and grape [Vitis vinifera]), we detected hundreds of CNSs upstream of Arabidopsis genes. Distinct positioning, length, and enrichment for transcription factor binding sites suggest these CNSs play a functional role in transcriptional regulation. The enrichment of transcription factors within the set of genes associated with CNS is consistent with the hypothesis that together they form part of a conserved transcriptional network whose function is to regulate other transcription factors and control development. We identified a set of promoters where regulatory mechanisms are likely to be shared between the model organism Arabidopsis and other dicots, providing areas of focus for further research

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request