Mining functional modules in genetic networks with decomposable graphical models

In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count.     

Databases, models, and algorithms for functional genomics

A variety of patterns have been observed on the DNA and protein sequences that serve as control points for gene expression and cellular functions. Owing to the vital role of such patterns discovered on biological sequences, they are generally cataloged and maintained within internationally shared databases. Furthermore,the variability in a family of observed patterns is often represented using computational models in order to facilitate their search within an uncharacterized biological sequence. As the biological data is comprised of a mosaic of sequence-levels motifs, it is significant to unravel the synergies of macromolecular coordination utilized in cell-specific differential synthesis of proteins. This article provides an overview of the various pattern representation methodologies and the surveys the pattern databases available for use to the molecular biologists. Our aim is to describe the principles behind the computational modeling and analysis techniques utilized in bioinformatics research, with the objective of providing insight necessary to better understand and effectively utilize the available databases and analysis tools. We also provide a detailed review of DNA sequence level patterns responsible for structural conformations within the Scaffold or Matrix Attachment Regions (S/MARs).     

Zebrafish models for the functional genomics of neurogenetic disorders

In this review, we consider recent work using zebrafish to validate and study the functional consequences of mutations of human genes implicated in a broad range of degenerative and developmental disorders of the brain and spinal cord. Also we present technical considerations for those wishing to study their own genes of interest by taking advantage of this easily manipulated and clinically relevant model organism. Zebrafish permit mutational analyses of genetic function (gain or loss of function) and the rapid validation of human variants as pathological mutations. In particular, neural degeneration can be characterized at genetic, cellular, functional, and behavioral levels. Zebrafish have been used to knock down or express mutations in zebrafish homologs of human genes and to directly express human genes bearing mutations related to neurodegenerative disorders such as spinal muscular atrophy, ataxia, hereditary spastic paraplegia, amyotrophic lateral sclerosis (ALS), epilepsy, Huntington's disease, Parkinson's disease, fronto-temporal dementia, and Alzheimer's disease. More recently, we have been using zebrafish to validate mutations of synaptic genes discovered by large-scale genomic approaches in developmental disorders such as autism, schizophrenia, and non-syndromic mental retardation. Advances in zebrafish genetics such as multigenic analyses and chemical genetics now offer a unique potential for disease research. Thus, zebrafish hold much promise for advancing the functional genomics of human diseases, the understanding of the genetics and cell biology of degenerative and developmental disorders, and the discovery of therapeutics. This article is part of a Special Issue entitled Zebrafish Models of Neurological Diseases.     

The missing graphical user interface for genomics

The Galaxy package empowers regular users to perform rich DNA sequence analysis through a much-needed and user-friendly graphical web interface.     

A factor analysis model for functional genomics

Background  

Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories.     

Empirical comparison study of approximate methods for structure selection in binary graphical models

Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine, and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. In the binary case, however, exact inference is generally very slow or even intractable because of the form of the so‐called log‐partition function. In this paper, we review various approximate methods for structure selection in binary graphical models that have recently been proposed in the literature and compare them through an extensive simulation study. We also propose a modification of one existing method, that is shown to achieve good performance and to be generally very fast. We conclude with an application in which we search for associations among causes of death recorded on French death certificates.     

A versatile toolkit for high throughput functional genomics with Trichoderma reesei

Background  

The ascomycete fungus, Trichoderma reesei (anamorph of Hypocrea jecorina), represents a biotechnological workhorse and is currently one of the most proficient cellulase producers. While strain improvement was traditionally accomplished by random mutagenesis, a detailed understanding of cellulase regulation can only be gained using recombinant technologies.     

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.     

A role for oligonucleotide-based RNA-knock down technologies in functional genomics

Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.     

Multiple models for Rosaceae genomics

The plant family Rosaceae consists of over 100 genera and 3,000 species that include many important fruit, nut, ornamental, and wood crops. Members of this family provide high-value nutritional foods and contribute desirable aesthetic and industrial products. Most rosaceous crops have been enhanced by human intervention through sexual hybridization, asexual propagation, and genetic improvement since ancient times, 4,000 to 5,000 B.C. Modern breeding programs have contributed to the selection and release of numerous cultivars having significant economic impact on the U.S. and world markets. In recent years, the Rosaceae community, both in the United States and internationally, has benefited from newfound organization and collaboration that have hastened progress in developing genetic and genomic resources for representative crops such as apple (Malus spp.), peach (Prunus spp.), and strawberry (Fragaria spp.). These resources, including expressed sequence tags, bacterial artificial chromosome libraries, physical and genetic maps, and molecular markers, combined with genetic transformation protocols and bioinformatics tools, have rendered various rosaceous crops highly amenable to comparative and functional genomics studies. This report serves as a synopsis of the resources and initiatives of the Rosaceae community, recent developments in Rosaceae genomics, and plans to apply newly accumulated knowledge and resources toward breeding and crop improvement.     

Deploying MMEJ using MENdel in precision gene editing applications for gene therapy and functional genomics

Gene-editing experiments commonly elicit the error-prone non-homologous end joining for DNA double-strand break (DSB) repair. Microhomology-mediated end joining (MMEJ) can generate more predictable outcomes for functional genomic and somatic therapeutic applications. We compared three DSB repair prediction algorithms – MENTHU, inDelphi, and Lindel – in identifying MMEJ-repaired, homogeneous genotypes (PreMAs) in an independent dataset of 5,885 distinct Cas9-mediated mouse embryonic stem cell DSB repair events. MENTHU correctly identified 46% of all PreMAs available, a ∼2- and ∼60-fold sensitivity increase compared to inDelphi and Lindel, respectively. In contrast, only Lindel correctly predicted predominant single-base insertions. We report the new algorithm MENdel, a combination of MENTHU and Lindel, that achieves the most predictive coverage of homogeneous out-of-frame mutations in this large dataset. We then estimated the frequency of Cas9-targetable homogeneous frameshift-inducing DSBs in vertebrate coding regions for gene discovery using MENdel. 47 out of 54 genes (87%) contained at least one early frameshift-inducing DSB and 49 out of 54 (91%) did so when also considering Cas12a-mediated deletions. We suggest that the use of MENdel helps researchers use MMEJ at scale for reverse genetics screenings and with sufficient intra-gene density rates to be viable for nearly all loss-of-function based gene editing therapeutic applications.     

RNAi for plant functional genomics

A major challenge in the post-genome era of plant biology is to determine the functions of all the genes in the plant genome. A straightforward approach to this problem is to reduce or knock out expression of a gene with the hope of seeing a phenotype that is suggestive of its function. Insertional mutagenesis is a useful tool for this type of study, but it is limited by gene redundancy, lethal knock-outs, nontagged mutants and the inability to target the inserted element to a specific gene. RNA interference (RNAi) of plant genes, using constructs encoding self-complementary 'hairpin' RNA, largely overcomes these problems. RNAi has been used very effectively in Caenorhabditis elegans functional genomics, and resources are currently being developed for the application of RNAi to high-throughput plant functional genomics.     

Boolean dynamics of Kauffman models with a scale-free network

We studied the Boolean dynamics of the "quenched" Kauffman models with a directed scale-free network, comparing with that of the original directed random Kauffman networks and that of the directed exponential-fluctuation networks. We have numerically investigated the distributions of the state cycle lengths and its changes as the network size N and the average degree k of nodes increase. In the relatively small network (N approximately 150), the median, the mean value and the standard deviation grow exponentially with N in the directed scale-free and the directed exponential-fluctuation networks with k=2, where the function forms of the distributions are given as an almost exponential. We have found that for the relatively large N approximately 10(3) the growth of the median of the distribution over the attractor lengths asymptotically changes from algebraic type to exponential one as the average degree k goes to k=2. The result supports the existence of the transition at k(c)=2 derived in the annealed model.