Susan J. Brown

BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences

BindN () takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.

The i5K Initiative: Advancing Arthropod Genomics for Knowledge, Human Health, Agriculture, and the Environment

Insects and their arthropod relatives including mites, spiders, and crustaceans play major roles in the worlds terrestrial, aquatic, and marine ecosystems. Arthropods compete with humans for food and transmit devastating diseases. They also comprise the most diverse and successful branch of metazoan evolution, with millions of extant species. Here, we describe an international effort to guide arthropod genomic efforts, from species prioritization to methodology and informatics. The 5000 arthropod genomes initiative (i5K) community met formally in 2012 to discuss a roadmap for sequencing and analyzing 5000 high-priority arthropods and is continuing this effort via pilot projects, the development of standard operating procedures, and training of students and career scientists. With university, governmental, and industry support, the i5K Consortium aspires to deliver sequences and analytical tools for each of the arthropod branches and each of the species having beneficial and negative effects on humankind.

Using RNAi to investigate orthologous homeotic gene function during development of distantly related insects

Gene product distribution is often used to infer developmental similarities and differences in animals with evolutionarily diverse body plans. However, to address commonalties of developmental mechanisms, what is really needed is a method to assess and compare gene function in divergent organisms. This requires mutations eliminating gene function. Such mutations are often difficult to obtain, even in organisms amenable to genetic analysis. To address this issue we have investigated the use of double‐stranded RNA interference to phenocopy null mutations. We show that RNA interference can be used to phenocopy mutations of the Deformed orthologues in Drosophila and Tribolium. We discuss the possible use of this technique for comparisons of developmental mechanisms in organisms with differing ontogenies.

Early Postnatal Death and Motor Disorders in Mice Congenitally Deficient in Calnexin Expression

ABSTRACT Calnexin is a ubiquitously expressed type I membrane protein which is exclusively localized in the endoplasmic reticulum (ER). In mammalian cells, calnexin functions as a chaperone molecule and plays a key role in glycoprotein folding and quality control within the ER by interacting with folding intermediates via their monoglucosylated glycans. In order to gain more insight into the physiological roles of calnexin, we have generated calnexin gene-deficient mice. Despite its profound involvement in protein folding, calnexin is not essential for mammalian-cell viability in vivo: calnexin gene knockout mice were carried to full term, although 50% died within 48 h and the majority of the remaining mice had to be sacrificed within 4 weeks, with only a very few mice surviving to 3 months. Calnexin gene-deficient mice were smaller than their littermates and showed very obvious motor disorders, associated with a dramatic loss of large myelinated nerve fibers. Thus, the critical contribution of calnexin to mammalian physiology is tissue specific.

Creating a Buzz About Insect Genomes

WHEN E. O. WILSON PROCLAIMED THAT INSECTS ARE THE “little creatures who run the world” (1), he was simply reaffi rming the long-recognized dominance of the largest class of animals on our planet. Insects constitute approximately 53% of all living species, with one group alone (the ants), accounting for almost a quarter of terrestrial animal biomass (2). These tiny creatures also exert outsized impacts on human affairs. By serving as pollinators to more than 75% of fl owering plant species (3), insects are essential to the maintenance and productivity of natural and agricultural ecosystems. But other insects consume or damage more than 25% of all agricultural, forestry, and livestock production in the United States, costing our economy more than

piggyBac-mediated germline transformation in the beetle Tribolium castaneum

30 billion annually (4). These losses occur despite more than 150 years of concerted efforts to prevent them. Insects and other arthropods not only affect our food supply, they also carry disease. Parasites and pathogens carried by insects and their relatives have led to more loss of human life than all wars in recorded history; even today, insect-borne diseases are a leading cause of death of children under the age of 5 (5). The annual cost of vector-borne diseases worldwide is estimated at almost

Incremental genetic K-means algorithm and its application in gene expression data analysis

50 billion (6). Clearly, our health and well-being depend on our ability to understand and manage arthropods of agricultural, medical, and veterinary importance. In the past decade, biomedical research has increasingly relied on information obtained from sequencing the human genome, and early genome-enabled successes have inspired a new vision of genomic medicine (7). We believe that genomics also can improve our lives by contributing to a better understanding of insect biology and transforming our ability to manage arthropods that threaten our health, food supply, and economic security. Because of the overwhelming diversity and abundance of insects, achieving these goals will require a project of grand scale. Therefore, we, the undersigned, are pleased to announce the launch of the “i5k” initiative to sequence the genomes of 5000 species of insects and other arthropods during the next 5 years (8). This project is aimed at sequencing and analyzing the genomes of all species known to be important to worldwide agriculture and food safety, medicine, and energy production; all species used as models in biology; the most abundant insects in world ecosystems; and, to achieve a deep understanding of arthropod evolution, representatives of insect relatives in every major branch of arthropod phylogeny. The i5k initiative will be broad and inclusive, seeking to involve scientists from around the world and obtain funding from academia, governments, industry, and private sources. We also aim to encourage new collaborative research by computer scientists, bioinformaticians, and biologists to overcome the challenges of handling this unprecedented volume of data and derive meaning from these genomes. GENE E. ROBINSON,* KEVIN J. HACKETT, MARY PURCELL-MIRAMONTES, SUSAN J. BROWN, JAY D. EVANS, MARIAN R. GOLDSMITH, DANIEL LAWSON, JACK OKAMURO, HUGH M. ROBERTSON, DAVID J. SCHNEIDER Department of Entomology, University of Illinois at UrbanaChampaign, Urbana, IL 61801, USA. USDA Agricultural Research Service, Beltsville, MD 20705, USA. USDA National Institute of Food and Agriculture, Washington, DC 20250, USA. Division of Biology, Kansas State University, Manhattan, KS 66506–4190, USA. Arthropod Genomics Consortium and European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. College of the Environment and Life Sciences, University of Rhode Island, Kingston, RI 02881, USA. USDA Agricultural Research Service, Ithaca, NY 14853, USA.

FGKA: a Fast Genetic K-means Clustering Algorithm

The lepidopteran transposable element piggyBac can mediate germline insertions in at least four insect orders. It therefore shows promise as a broad‐spectrum transformation vector, but applications such as enhancer trapping and transposon‐tag mutagenesis are still lacking. We created, cloned, sequenced and genetically mapped a set of piggyBac insertions in the red flour beetle, Tribolium castaneum. Transpositions were precise, and specifically targeted the canonical TTAA recognition sequence. We detected several novel reporter‐expression domains, indicating that piggyBac could be used to identify enhancer regions. We also demonstrated that a primary insertion of a non‐autonomous element can be efficiently remobilized to non‐homologous chromosomes by injection of an immobile helper element into embryos harbouring the primary insertion. These developments suggest potential for more sophisticated methods of piggyBac‐mediated genome manipulation.

BeetleBase in 2010: revisions to provide comprehensive genomic information for Tribolium castaneum

BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

Multiple Wnt Genes Are Required for Segmentation in the Short-Germ Embryo of Tribolium castaneum

In this paper, we propose a new clustering algorithm called Fast Genetic K-means Algorithm (FGKA). FGKA is inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty in 1999 but features several improvements over GKA. Our experiments indicate that, while K-means algorithm might converge to a local optimum, both FGKA and GKA always converge to the global optimum eventually but FGKA runs much faster than GKA.