Analysis of genome organization, composition and microsynteny using 500 kb BAC sequences in chickpea

The small genome size (740 Mb), short life cycle (3 months) and high economic importance as a food crop legume make chickpea (Cicer arietinum L.) an important system for genomics research. Although several genetic linkage maps using various markers and genomic tools have become available, sequencing efforts and their use are limited in chickpea genomic research. In this study, we explored the genome organization of chickpea by sequencing approximately 500 kb from 11 BAC clones (three representing ascochyta blight resistance QTL1 (ABR-QTL1) and eight randomly selected BAC clones). Our analysis revealed that these sequenced chickpea genomic regions have a gene density of one per 9.2 kb, an average gene length of 2,500 bp, an average of 4.7 exons per gene, with an average exon and intron size of 401 and 316 bp, respectively, and approximately 8.6% repetitive elements. Other features analyzed included exon and intron length, number of exons per gene, protein length and %GC content. Although there are reports on high synteny among legume genomes, the microsynteny between the 500 kb chickpea and available Medicago truncatula genomic sequences varied depending on the region analyzed. The GBrowse-based annotation of these BACs is available at http://www.genome.ou.edu/plants_totals.html . We believe that our work provides significant information that supports a chickpea genome sequencing effort in the future.     

Microarray expression profiling resources for plant genomics

Large volumes of genomic data have been generated for several plant species over the past decade, including structural sequence data and functional annotation at the genome level. Various technologies such as expressed sequence tags (ESTs), massively parallel signature sequencing (MPSS) and microarrays have been used to study gene expression and to provide functional data for many genes simultaneously. This review focuses on recent advances in the application of microarrays in plant genomic research and in gene expression databases available for plants. Large sets of Arabidopsis microarray data are publicly available. Recently developed array platforms are currently being used to generate genome-wide expression profiles for several crop species. Coupled to these platforms are public databases that provide access to these large-scale expression data, which can be used to aid the functional discovery of gene function.     

Gene expression in Trypanosoma brucei: lessons from high-throughput RNA sequencing

Trypanosoma brucei undergoes major biochemical and morphological changes during its development from the bloodstream form in the mammalian host to the procyclic form in the midgut of its insect host. The underlying regulation of gene expression, however, is poorly understood. More than 60% of the predicted genes remain annotated as hypothetical, and the 5' and 3' untranslated regions important for regulation of gene expression are unknown for >90% of the genes. In this review, we compare the data from four recently published high-throughput RNA sequencing studies in light of the different experimental setups and discuss how these data can enhance genome annotation and give insights into the regulation of gene expression in T. brucei.     

Accurate and unambiguous tag-to-gene mapping in serial analysis of gene expression

Background  

In this study, we present a robust and reliable computational method for tag-to-gene assignment in serial analysis of gene expression (SAGE). The method relies on current genome information and annotation, incorporation of several new features, and key improvements over alternative methods, all of which are important to determine gene expression levels more accurately. The method provides a complete annotation of potential virtual SAGE tags within a genome, along with an estimation of their confidence for experimental observation that ranks tags that present multiple matches in the genome.     

Mass spectrometry at the interface of proteomics and genomics

With the onset of modern DNA sequencing technologies, genomics is experiencing a revolution in terms of quantity and quality of sequencing data. Rapidly growing numbers of sequenced genomes and metagenomes present a tremendous challenge for bioinformatics tools that predict protein-coding regions. Experimental evidence of expressed genomic regions, both at the RNA and protein level, is becoming invaluable for genome annotation and training of gene prediction algorithms. Evidence of gene expression at the protein level using mass spectrometry-based proteomics is increasingly used in refinement of raw genome sequencing data. In a typical "proteogenomics" experiment, the whole proteome of an organism is extracted, digested into peptides and measured by a mass spectrometer. The peptide fragmentation spectra are identified by searching against a six-frame translation of the raw genomic assembly, thus enabling the identification of hitherto unpredicted protein-coding genomic regions. Application of mass spectrometry to genome annotation presents a range of challenges to the standard workflows in proteomics, especially in terms of proteome coverage and database search strategies. Here we provide an overview of the field and argue that the latest mass spectrometry technologies that enable high mass accuracy at high acquisition rates will prove to be especially well suited for proteogenomics applications.