Plant Pedia | Plant Biotechnology | Plant Science | Plant Tissue Culture

Sunday, March 9, 2014

Improving Heat Tolerance in Plants



A research group of the Universidad Politécnica de Madrid (UPM), led by Luis Gómez, a professor of the Forestry School and the Centre for Plant Biotechnology and Genomics (CBGP), is studying the tolerance of trees using molecular and biotechnological tools. The research work was published in the last issue of the journal Plant Physiology.

Concepts of QTL Analysis and Genomic Selection



The use of molecular genetic markers for selection and genetic improvement is based on genetic linkage between these markers and a quantitative trait locus (QTL) of interest. Thus, linkage analyses between markers and QTLs and between the proper multiple markers are essential for genetic selection from genomic information. It must be made clear that by definition, a QTL refers only to the statistical association between a genomic region and a trait.

Tuesday, February 25, 2014

Biometrics Applied to Molecular Analysis in Genetic Diversity




Studies about genetic diversity have been of great importance for the purposes of genetic improvement and to evaluate the impact of human activity on biodiversity. They are equally important in the understanding of the microevolutionary and macroevolutionary mechanisms that act in the diversification of the species, involving population studies, as well as in the optimization of the conservation of genetic diversity. They are also fundamental in understanding how natural populations are structured in time and space and the effects of anthropogenic activities on this structure and, consequently, on their chances of survival and/or extinction. This information provides an aid in finding the genetic losses generated by the isolation of the populations and of the individuals, which will be reflected in future generations, allowing for the establishment of better strategies to increase and preserve species diversity and diversity within the species.

Monday, February 24, 2014

How to Choice The Best Molecular Marker for Plant Breeding



The choice of the most appropriate molecular marker for genetic and plant breeding studies must be made on the basis of the ease of developing a useful technique coupled with the efficiency of data evaluation, interpretation, and analysis. The chosen marker must provide easy access and availability, rapid response and high reproducibility, and allow information exchange between laboratories and between populations and/or different species; it must also permit automation of data generation and subsequent analysis. Other desirable characteristics include a highly polymorphic nature, codominant inheritance (permitting the identification of homozygous and heterozygous individuals), frequent occurrence in the genome, and neutral selection (selection free from interference by management practices and environmental conditions). In addition to the characteristics of the marker, the goals of the project, the availability of financial, structural, and personal resources, convenience, and the availability of facilities for the development of the assay, as well as the genetic trait of the species under study, should all be considered.

Sunday, February 23, 2014

Evolution of Genetics and Plant Breeding



Since the beginning of agriculture in approximately 10,000 BC, people have consciously or unconsciously selected plants with superior characteristics for the cultivation of future generations. However, there is controversy regarding the time when breeding became a science. Some believe that this occurred after Mendel’s findings, while others argue that it occurred even before the “era of genetics.”

Sunday, January 26, 2014

Develop longer and stronger cotton fiber

The overwhelming majority of cotton harvested in the U.S. and worldwide is upland cotton, or Gossypium hirsutum, with more than 6.5 million acres planted in 2012 in Texas alone, according to the USDA. A higher-end cotton called Gossypium barbadense is more desirable because of greater fiber length and strength but is late-maturing, low-yielding and more difficult to grow because it requires dry climates with significant irrigation and is less resistant to pathogens and pests.

Friday, December 14, 2012

Plants Diseases

Diseases of Plants
I
INTRODUCTION
Diseases of Plants, deviations from the normal growth and development of plants incited by microorganisms, parasitic flowering plants, nematodes, viruses, or adverse environmental conditions. In the United States alone, known plant diseases attributable to these causes are estimated to number more than 25,000; the estimated annual losses therefrom add up to several billion dollars. Injuries to plant life due primarily to insects, mites, or animals other than nematodes are not regarded as plant diseases.

Thursday, December 13, 2012

Potatoes Plant

Potato, edible starchy tuber. It is produced by certain plants of a genus of the nightshade family, especially the common white potato. The name is also applied to the plants. The white-potato tuber is a food staple in most countries of the temperate regions of the world. The plant is grown as an annual herb. The stem attains a length of up to almost 1 m (almost 3 ft), erect or prostrate, with pointed leaves and white to purple flowers. 

Wednesday, September 26, 2012

Computational Tools and Resources in Plant Genome Informatics

Though all biologists deal with information, only recently have the computational challenges of systematically collecting, storing, organising, man-ipulating, visualising and analysing large amounts of biological information come to be widely appreciated. The cause of this is the explosive growth of genomics. The term bioinformatics was originally coined for the application of information technology to large volumes of biological, and particularly genomic, data. The field of bioinformatics has come to be intermingled with traditional computational biology and biostatistics, which are strictly concerned not with how to handle the information itself, but rather with how to extract biological meaning from it. Thus, bioinformatics, in its broad sense, can be seen as providing both the infrastructure and the scientific framework in which biologists take information and use computers to help convert it into knowledge.

Despite the relative youth of the field as a recognised discipline, there is an impressive diversity of bioinformatics resources currently available. By necessity, we only focus on a small slice of this diversity here. We pay particular attention to sequence analysis because of its centrality to genomics. We also do not attempt to provide specific protocols, as the specific needs of users vary greatly. The resources we describe range drastically in sophistication from little tested programs posted on graduate student web pages to very stable and complex databases maintained by governmental agencies. The better ones typically provide manuals and tutorials, often containing descriptions of the underlying principles. The reader is strongly advised to consult the documentation available for each tool.

Though a wide array of commercial resources exist, some of which are ideally suited to specific tasks, many of the most fundamental and long-lived bioinformatics tools are freely available. For this reason, we describe primarily non-commercial software in this chapter. Many of the databases and analysis tools we describe are hosted by government or academic research centres and can be accessed via user-friendly web interfaces.
 
Collectively, online databases allow access to a staggering quantity of data. This partly reflects the way much biological data are now collected. Genome projects popularised the concept of high-throughput, highly automated biological data factories, in which data are systematically collected with the express purpose of facilitating as-yet-unknown downstream applications. As a result, the value of such data is only realised when it is made accessible to the research community as a whole.

The growth in the size of Genbank (Benson et al., 2002), the DNA and protein sequence repository jointly maintained by the National Center for Biotechnology Information (NCBI), the European Molecular Biology Laboratory (EMBL) and the DNA Databank of Japan (DDBJ), is legendary. Genbank contained 14.4 billion base pairs by the end of 2001, 200 times the number of base pairs in the database just 10 years earlier. In step with the growth in sequence data, a wide variety of different types of data have become available. These run the gamut from raw sequence data to highly derived computational predictions of protein structure and biomolecular interactions.

Unlike Genbank, which archives sequence data from all organisms, many database resources are organism specific. A variety of crop and model-plant specific genomic databases are accessible through UKCropNet. These include GrainGenes (which holds molecular and phenotypic information on wheat, barley, oats, rye and sugarcane) and MaizeDB (which performs a similar service for maize). Some databases are specific to somewhat larger taxonomic assemblages. For example, the Gramene database is a recent effort that aims to integrate genomic information from among all grasses using the rice genomic sequence as a focal point (Ware et al., 2002).

It can be helpful to recognise a distinction between primary data repositories, on the one hand, and derivative databases that offer a regularly updated analysis of data from primary repositories, on the other. Genbank is an example of a primary repository. Pfam, a protein sequence signature database, is an example of one that is derived. Derived databases in plant genomics frequently only include those plant systems having the most abundant data. One example is the set of Gene Indices at The Institute for Genomic Research (TIGR), which is a collection of very focussed databases, each covering a different plant, animal, protist or fungal species (Quackenbush et al., 2001). Each Gene Index computationally assembles the non-redundant set of gene sequences for that organism, with links to expression, homology and other information. Those plants for which there exist sufficient publicly available sequence data are included. This includes 14 species at the time of writing. Because it was the first plant nuclear genome to be sequenced in its entirety, Arabidopsis thaliana is sometimes the sole plant representative in other genomic databases. An example of this is MODBASE, which contains homology modelled protein structures using predicted amino acid sequences from a variety of completed genomes.

Plant biologists are, of course, also interested in plant symbionts and disease causing organisms. A number of plant pathogenic bacteria and fungi have either been sequenced in their entirety, including Agrobacterium tumefaciens (Goodner et al., 2001), Ralstonia solanacearum (Salanoubat et al., 2002) and Xylella fastidiosa (Simpson et al., 2000), or are the subject of ongoing sequencing projects, such as Magnaporthe grisea (Zhu et al., 1997), Pseudomonas syringae pv. tomato and Xanthomonas campestris. Completed sequence is also available for the legume nodule-associated mutualist Sinorhizobium meliloti (Capela et al., 2001). In addition, a variety of plant viral genomes have been deposited in Genbank. The Genomes OnLine Database (GOLD) is a regularly updated on-line listing of prokaryotic and eukaryotic genome projects that have been completed or that are under way. TIGR offers what it calls the Comprehensive Microbial Resource database, which allows exploration and comparison of the annotated microbial sequences. Unfortunately, genomic information for metazoan plant symbionts, such as pathogenic nematodes and insect herbivores, is much less abundant and likely to remain that way for some time.

An excellent resource to the world of genomic databases is the annual database issue of the journal Nucleic Acids Research, published on the 1st of January each year (www3.oup.co.uk/nar/database/c/). In addition to written descriptions of dozens of different databases, a list of links to hundreds of databases, organised by category, is maintained online. Publications describing online databases quickly become obsolete as new databases spring up and old ones change, and no list (online or otherwise) could hope to be comprehensive, but this is a good place to start. Website addresses (URLs) for databases and resources discussed in this chapter are provided in Table 12.1, while major web jump stations for genomics and bioinformatics are given in Table 12.2.
 

The Growing Role of Standards

The meanings of biological terms are often slippery and operational. For instance, ‘gene function’ can easily mean different things to different practitioners. Although it may be preferable, in some cases, to allow for ambiguity rather than force misguided precision, computers are not at all adept at handling ambiguity. Thus, there has been much effort expended in adopting standardised terminologies, with clear relationships defined among the terms. Such language standards are referred to as controlled vocabularies, or ontologies. Ontologies provide transparency of meaning to users and greatly facilitate inter-communication among databases.
One of the oldest systematic attempts to standardise plant gene nomenclature is the Mendel Plant Gene Names Database and its derivatives, which provide a useful categorisation of known plant genes and their sequences (Lonsdale et al., 2001; Price et al., 2001). The Enzyme Commission Database, which is taxonomically broader, offers a heavily used classification system that organises enzymes hierarchically by function. An even more ambitious effort is that of the Gene Ontology (GO) Consortium, which works to produce a dynamic controlled vocabulary, valid across all organisms, that can accommodate accumulating and changing knowledge of gene function (The Gene Ontology Consortium 2001). GO recognises three independent ontologies for genes and gene products:
  1. Molecular function, which is specific to an individual gene product (e.g. DNA helicase)
  2. Biological process, which is coordinated by multiple products (e.g. mitosis)
  3. Cellular component, which describes the physical localisation of a gene product (e.g. nucleus)
Controlled vocabularies are not restricted to gene or protein function. A number of plant databases (including TAIR—The Arabidopsis Information Resource, Gramene and MaizeDB) are collaborating to provide a controlled vocabulary for plant-specific terms such as anatomy, morphology and development (The Plant Ontology Consortium, in press).


In addition to controlled vocabularies, there is an important role for standards that define the salient features of particular kinds of data. For example, a group has been working to develop a standard for the minimum information about microarray experiments (MIAME). The diversity of experimental and analytical approaches to microarray expression data could potentially be a major barrier to the verification and integration of such data by the research community as a whole. MIAME is a set of evolving guidelines designed to ‘facilitate the establishment of databases and public repositories and enable the development of data analysis tools’ (Brazma et al., 2001).

Each of these approaches at facilitating transparent communication among multiple users and databases has slightly different goals and guiding philosophies. Some of the earliest and most successful initiatives to date in this area have tackled the practical, and limited, goal of establishing concrete relationships among the entities in a small number of related databases. The InterPro database, for example, provides a single point of entry for searching a large number of different protein signature (motif and domain) databases, including PROSITE, PRINTS, ProDom and Pfam, SMART, and TIGRFams (Apweiler et al., 2001).