MoccaDB - an integrative database for functional, comparative and diversity studies in the Rubiaceae family

<p>Abstract</p> <p>Background</p> <p>In the past few years, functional genomics information has been rapidly accumulating on Rubiaceae species and especially on those belonging to the <it>Coffea </it>genus (coffee trees). An increasing number of expressed sequence tag (EST) data and EST- or genomic-derived microsatellite markers have been generated, together with Conserved Ortholog Set (COS) markers. This considerably facilitates comparative genomics or map-based genetic studies through the common use of orthologous loci across different species. Similar genomic information is available for e.g. tomato or potato, members of the Solanaceae family. Since both Rubiaceae and Solanaceae belong to the Euasterids I (lamiids) integration of information on genetic markers would be possible and lead to more efficient analyses and discovery of key loci involved in important traits such as fruit development, quality, and maturation, or adaptation. Our goal was to develop a comprehensive web data source for integrated information on validated orthologous markers in Rubiaceae.</p> <p>Description</p> <p>MoccaDB is an online MySQL-PHP driven relational database that houses annotated and/or mapped microsatellite markers in Rubiaceae. In its current release, the database stores 638 markers that have been defined on 259 ESTs and 379 genomic sequences. Marker information was retrieved from 11 published works, and completed with original data on 132 microsatellite markers validated in our laboratory. DNA sequences were derived from three <it>Coffea </it>species/hybrids. Microsatellite markers were checked for similarity, <it>in vitro </it>tested for cross-amplification and diversity/polymorphism status in up to 38 Rubiaceae species belonging to the Cinchonoideae and Rubioideae subfamilies. Functional annotation was provided and some markers associated with described metabolic pathways were also integrated. Users can search the database for marker, sequence, map or diversity information through multi-option query forms. The retrieved data can be browsed and downloaded, along with protocols used, using a standard web browser. MoccaDB also integrates bioinformatics tools (CMap viewer and local BLAST) and hyperlinks to related external data sources (NCBI GenBank and PubMed, SOL Genomic Network database).</p> <p>Conclusion</p> <p>We believe that MoccaDB will be extremely useful for all researchers working in the areas of comparative and functional genomics and molecular evolution, in general, and population analysis and association mapping of Rubiaceae and Solanaceae species, in particular.</p

Genomics-assisted breeding for crop improvement

Genomics research is generating new tools, such as functional molecular markers and informatics, as well as new knowledge about statistics and inheritance phenomena that could increase the efficiency and precision of crop improvement. In particular, the elucidation of the fundamental mechanisms of heterosis and epigenetics, and their manipulation, has great potential. Eventually, knowledge of the relative values of alleles at all loci segregating in a population could allow the breeder to design a genotype in silico and to practice whole genome selection. High costs currently limit the implementation of genomics-assisted crop improvement, particularly for inbreeding and/or minor crops. Nevertheless, marker-assisted breeding and selection will gradually evolve into ‘genomics-assisted breeding’ for crop improvement

MolabIS - An integrated information system for storing and managing molecular genetics data

BACKGROUND: Long-term sample storage, tracing of data flow and data export for subsequent analyses are of great importance in genetics studies. Therefore, molecular labs do need a proper information system to handle an increasing amount of data from different projects. RESULTS: We have developed a molecular labs information management system (MolabIS). It was implemented as a web-based system allowing the users to capture original data at each step of their workflow. MolabIS provides essential functionality for managing information on individuals, tracking samples and storage locations, capturing raw files, importing final data from external files, searching results, accessing and modifying data. Further important features are options to generate ready-to-print reports and convert sequence and microsatellite data into various data formats, which can be used as input files in subsequent analyses. Moreover, MolabIS also provides a tool for data migration. CONCLUSIONS: MolabIS is designed for small-to-medium sized labs conducting Sanger sequencing and microsatellite genotyping to store and efficiently handle a relative large amount of data. MolabIS not only helps to avoid time consuming tasks but also ensures the availability of data for further analyses. The software is packaged as a virtual appliance which can run on different platforms (e.g. Linux, Windows). MolabIS can be distributed to a wide range of molecular genetics labs since it was developed according to a general data model. Released under GPL, MolabIS is freely available at http://www.molabis.org

Bioinformatics: A Way Forward to Explore “Plant Omics”

Bioinformatics, a computer-assisted science aiming at managing a huge volume of genomic data, is an emerging discipline that combines the power of computers, mathematical algorithms, and statistical concepts to solve multiple genetic/biological puzzles. This science has progressed parallel to the evolution of genome-sequencing tools, for example, the next-generation sequencing technologies, that resulted in arranging and analyzing the genome-sequencing information of large genomes. Synergism of “plant omics” and bioinformatics set a firm foundation for deducing ancestral karyotype of multiple plant families, predicting genes, etc. Second, the huge genomic data can be assembled to acquire maximum information from a voluminous “omics” data. The science of bioinformatics is handicapped due to lack of appropriate computational procedures in assembling sequencing reads of the homologs occurring in complex genomes like cotton (2n = 4x = 52), wheat (2n = 6x = 42), etc., and shortage of multidisciplinary-oriented trained manpower. In addition, the rapid expansion of sequencing data restricts the potential of acquisitioning, storing, distributing, and analyzing the genomic information. In future, inventions of high-tech computational tools and skills together with improved biological expertise would provide better insight into the genomes, and this information would be helpful in sustaining crop productivities on this planet

Genic molecular markers in plants: Development and applications

The current advancement in plant biology research encompassing: generation of huge amount of molecular-genetic data, development of impressive methodological skills in molecular biology experimentation, and systems analyses, has set the stage to search for ways/means to utilize the available resources to strengthen interdisciplinary efforts to find solutions to the challenging goals of plant breeding efforts (such as abiotic stress tolerance) ultimately leading to gainful applications in crop improvement. A positive fall out of such a realization and efforts has been the identification/development of a new class of very useful DNA markers called genic molecular markers (GMMs) utilizing the ever-increasing archives of gene sequence information being accumulated under the EST sequencing projects on a large number of plant species in the recent years. These markers being part of the cDNA/EST-sequences, are expected to represent the functional component of the genome i.e., gene(s), in contrast to all other random DNA-based markers (RDMs) that are developed/generated from the anonymous genomic DNA sequences/domains irrespective of their genic content/information. Therefore, identifying DNA sequences that demonstrate large effects on adaptive plant behavior remains fundamental to the development of GMMs. The few recent studies have now demonstrated the utility of these markers in genetic studies, and also shown that GMMs may be superior than RDMs for use in the marker-assisted selection, comparative mapping, and exploration of the functional genetic diversity in the germplasm adapted to different environments. The only constraint of GMMs is their low level of polymorphism as compared to the RDMs expected of their origin from the relatively conserved functional portion of the genome. This chapter provides a critical review of the development and various applications of the GMMs

Genomics-assisted crop improvement: An overview

In recent years, a truly impressive number of advances in genetics and genomics have greatly enhanced our understanding of structural and functional aspects of plant genomes but at the same time have challenged us with many compelling avenues of investigation. The complete genome sequences of Arabidopsis, rice, sorghum and poplar as well as an enormous number of plant expressed sequence tags (ESTs) have become available. In the next few years, the entire genomes or at least gene space will likely be sequenced for most major crops. However, improved varieties, not sequences per se, contribute to improved economic return to the farmer. Functional genomics and systems biology research are facilitating the identification of gene networks that are involved in controlling genetic variation for agronomically valuable traits in elite breeding populations. Furthermore* combining the new knowledge from genomic research with conventional breeding methods is essential for enhancing response to selection, hence crop improvement. Superior varieties can result from the discovery of novel genetic variation, improved selection techniques, and/or the identification of genotypes with improved attributes due to superior combinations of alleles at multiple loci assembled through marker-assisted selection. Although it is clear that genomics research has great potential to revolutionize the discipline of plant breeding, high costs invested in/associated with genomics research currently limit the implementation of genomics-assisted crop improvement, especially for inbreeding and/or minor crops. A critical assessment of the status and availability of genomic resources and genomics research in model and crop plants, and devising the strategies and approaches for effectively exploiting genomics research for crop improvement have been presented in two volumes of the book. While Volume 1, entitled "Genomics approaches and platforms", compiles chapters providing readers with an overview of the available genomics tools, approaches and platforms, Volume 2, entitled "Genomics applications in crop improvement", presents a timely and critical overview on applications of genomics in crop improvement. An overview on the highlights of the chapters of these two volumes has been presented in the present introductory chapter

MolabIS: A Labs Backbone for Storing, Managing and Evaluating Molecular Genetics Data

Using paper lab books and spreadsheets to store and manage growing datasets in a file system is inefficient, time consuming and error-prone. Therefore, the overall purpose of this study is to develop an integrated information system for small laboratories conducting Sanger sequencing and microsatellite genotyping projects. To address this, the thesis has investigated the following three issues. First, we proposed a uniform solution using the workflow approach to efficiently collect and store data items in different labs. The outcome is the design of the formalized data framework which is the basic to create a general data model for biodiversity studies. Second, we designed and implemented a web-based information system (MolabIS) allowing lab people to store all original data at each step of their workflow. MolabIS provides essential tools to import, store, organize, search, modify, report and export relevant data. Finally, we conducted a case study to evaluate the performance of MolabIS with typical operations in a production mode. Consequently, we can propose the use of virtual appliance as an efficient solution for the deployment of complex open-source information systems like MolabIS. The major result of this study, along with the publications, is the MolabIS software which is freely released under GPL license at http://www.molabis.org. With its general data model, easy installation process and additional tools for data migration, MolabIS can be used in a wide range of molecular genetics labs

Bioinformatics tools for crop research and breeding

Crop improvement has always been, but will be even more so in the twenty-first century, an information intensive process. For effective and efficient improvement, a range of activities from molecular biology to genetics to indirect selection must now be involved. The rate of progress made by any breeding programme depends as much on the efficient integration of information from these activities as it does on the activities themselves. Plant breeders are now realizing the importance of innovative approaches that include the use of a range of molecular methods and their outputs, and the possibilities of transferring this information from model species to cultivated crops. The use of these high throughput methods in model crops has already generated a large amount of public resources such as databases containing genetic resource, genomic and genetic information; tools for the effective analysis, data mining and visualization of such information; and semantic web resources for data integration. In this chapter, we highlight the role and contributions of bioinformatics to crop research and breeding by focusing on the bioinformatics resources that are available for crop science research and breeding, and indicating gaps that need to be bridged that will allow scientists to access, transfer and integrate data with eas

Expanding the ancient DNA bioinformatics toolbox, and its applications to archeological microbiomes

The 1980s were very prolific years not only for music, but also for molecular biology and genetics, with the first publications on the microbiome and ancient DNA. Several technical revolutions later, the field of ancient metagenomics is now progressing full steam ahead, at a never seen before pace. While generating sequencing data is becoming cheaper every year, the bioinformatics methods and the compute power needed to analyze them are struggling to catch up. In this thesis, I propose new methods to reduce the sequencing to analysis gap, by introducing scalable and parallelized softwares for ancient DNA metagenomics analysis. In manuscript A, I first introduce a method for estimating the mixtures of different sources in a sequencing sample, a problem known as source tracking. I then apply this method to predict the original sources of paleofeces in manuscript B. In manuscript C, I propose a new method to scale the lowest common ancestor calling from sequence alignment files, which brings a solution for the computational intractability of fitting ever growing metagenomic reference database indices in memory. In manuscript D, I present a method to statistically estimate in parallel the ancient DNA deamination damage, and test it in the context of de novo assembly. Finally, in manuscript E, I apply some of the methods developed in this thesis to the analyis of ancient wine fermentation samples, and present the first ancient genomes of ancient fermentation bacteria. Taken together, the tools developed in this thesis will help the researchers working in the field of ancient DNA metagenomics to scale their analysis to the massive amount of sequencing data routinely produced nowadays

Genomics-Assisted Crop Improvement, Vol 1: Genomics Approaches and Platforms

Genomics research has great potential to revolutionize the discipline of plant breeding. This two-volume set provides a critical assessment of genomics tools and approaches for crop breeding. Volume 1, entitled "Genomics Approaches and Platforms", illustrates state-of-the-art genomics approaches and platforms presently available for crop improvement. Volume 2, entitled "Genomics Applications in Crops", compiles crop-specific studies that summarize both the achievements and limitations of genomics research for crop improvement. We hope that these two volumes, while providing new ideas and opportunities to those working in crop breeding, will help graduate students and teachers to develop a better understanding of the applications of crop genomics to plant research and breeding