Plant Genetics, NGS and Bioinformatics

Bioinformatics is a very young field of research. Its growth started in earnest after the publication of human genome, when many scientists saw immense medical value of such vast genetic data and decided to explore further. Thirteen years passed since then and the same message reached all parts of the world. Therefore checking how many people from around the globe are interested in bioinformatics today is possibly an indication of progress they are making in education and economic matters.

A draft sequence of the staple crop kabuli chickpea, together with resequencing and analysis of 90 additional lines from 10 countries, provides a resource for breeders.

Chickpea (Cicer arietinum) is the second most widely grown legume crop after soybean, accounting for a substantial proportion of human dietary nitrogen intake and playing a crucial role in food security in developing countries. We report the ~738-Mb draft whole genome shotgun sequence of CDC Frontier, a kabuli chickpea variety, which contains an estimated 28,269 genes. Resequencing and analysis of 90 cultivated and wild genotypes from ten countries identifies targets of both breeding-associated genetic sweeps and breeding-associated balancing selection. Candidate genes for disease resistance and agronomic traits are highlighted, including traits that distinguish the two main market classes of cultivated chickpea—desi and kabuli. These data comprise a resource for chickpea improvement through molecular breeding and provide insights into both genome diversity and domestication.

The timing of the induction of flowering determines to a large extent the reproductive success of plants. Plants integrate diverse environmental and endogenous signals to ensure the timely transition from vegetative growth to flowering. Carbohydrates are thought to play a crucial role in the regulation of flowering, and trehalose-6-phosphate (T6P) has been suggested to function as a proxy for carbohydrate status in plants. The loss of TREHALOSE-6-PHOSPHATE SYNTHASE 1 (TPS1) causes Arabidopsis thaliana to flower extremely late, even under otherwise inductive environmental conditions. This suggests that TPS1 is required for the timely initiation of flowering. We show that the T6P pathway affects flowering both in the leaves and at the shoot meristem, and integrate TPS1 into the existing genetic framework of flowering-time control.

Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, small size, short generation time, small genome size, and wide geographic distribution make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire A. thaliana community. To facilitate this, we present GWAPP, an interactive Web-based application for conductingGWAS in A. thaliana. Using an efficient implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with a mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and user-friendly interface that includes interactive Manhattan plots and linkage disequilibrium plots. It also facilitates exploratory data analysis by implementing features such as the inclusion of candidate polymorphisms in the model as cofactors.

Most crops were first domesticated about 13 000 to 11 000 years ago. Humans are dependent on crops for survival, and from the beginnings of agriculture have been energetically involved in developing crops that better serve their needs (Allard 1999). During the last decades breeding has contributed approximately a 50% contribution to increasing the world's food crop production. However, plant breeding only began to adopt a scientific approach in the 1900s, when Mendel's hybridization experiment was rediscovered. Mendelian genetics and the development of the statistical concepts of randomization and replication had considerable impact on plant breeding methods (Hallauer et al. 1988). In spite of the fact that scientific crop breeding has only existed for one century, it is a discipline that is developing very quickly. The major objective of crop breeding programs is to develop new genotypes that are genetically superior to those currently available for specific environments. To achieve this objective, breeders employ a range of selection methods and technologies (Hallauer et al. 1988; Falconer and Mackay 1996; Allard 1999).

As the world's population continues to grow rapidly and becomes more demanding, the pressure on resources is increasing, whilst climate change poses further challenges. The balance between the supply and demand of the major food crops is fragile, fueling concerns for long-term global food security. The need to accelerate plant breeding for increased yield potential and better adaptation to drought and other abiotic stresses is an issue of increasing urgency. The global population is facing a common challenge of providing safe, nutritious and affordable food, given the constraints of land, water, and energy and in the face of climate change. The sustainable exploitation of biological resources for a secure and healthy food supply, animal feed and a wide range of sustainable materials and technical products will require careful husbandry of land and a shift to systems that produce more from less in a sustainable manner. With this common goal, OPTICHINA (Breeding to Optimize Chinese Agriculture), an EU-China partnership initiative in crop breeding was launched in June of 2011. The first project workshop was held shortly after the launch, and focused on new technologies and methods in crop molecular breeding. This special issue of Journal of Integrative Plant Biology publishes key presentations and topics addressed in this workshop.

The Cuffdiff 2 algorithm improves analysis of RNA-Seq data by accounting for sample-to-sample biological variability and the complexity of transcript isoforms.

The cost of DNA sequencing has undergone a dramatical reduction in the past decade. As a result, sequencing technologies have been increasingly applied to genomic research. RNA-Seq is becoming a common technique for surveying gene expression based on DNA sequencing. As it is not clear how increased sequencing capacity has affected measurement accuracy of mRNA, we sought to investigate that relationship.

We empirically evaluate the accuracy of repeated gene expression measurements using RNA-Seq. We identify library preparation steps prior to DNA sequencing as the main source of error in this process. Studying three datasets, we show that the accuracy indeed improves with the sequencing depth. However, the rate of improvement as a function of sequence reads is generally slower than predicted by the binomial distribution. We therefore used the beta-binomial distribution to model the overdispersion. The overdispersion parameters we introduced depend explicitly on the number of reads so that the resulting statistical uncertainty is consistent with the empirical data that measurement accuracy increases with the sequencing depth. The overdispersion parameters were determined by maximizing the likelihood. We shown that our modified beta-binomial model had lower false discovery rate than the binomial or the pure beta-binomial models.

We proposed a novel form of overdispersion guaranteeing that the accuracy improves with sequencing depth. We demonstrated that the new form provides a better fit to the data.

Creative projects often require us to pick up new skills – and fast. Learn how the Flatiron School accelerates the learning process through collaboration.

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