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Background Next Generation Sequencing technologies have facilitated differential gene expression analysis through RNA-seq and Tag-seq methods. RNA-seq has biases associated with transcript lengths, lacks uniform coverage of regions in mRNA and requires10-20 times more reads than a typical Tag-seq. Most existing Tag-seq methods either have biases or not high throughput due to use restriction enzymes or enzymatic manipulation of 5' ends of mRNA or use of RNA ligations. Results We have developed EXpression Profiling through Randomly Sheared cDNA tag Sequencing (EXPRSS) that employs acoustic waves to randomly shear cDNA and generate sequence tags at a relatively defined position (~150-200 bp) from the 3' end of each mRNA. Implementation of the method was verified through comparative analysis of expression data generated from EXPRSS, NlaIII-DGE and Affymetrix microarray and through qPCR quantification of selected genes. EXPRSS is a strand specific and restriction enzyme independent tag sequencing method that does not require cDNA length-based data transformations. EXPRSS is highly reproducible, is high-throughput and it also reveals alternative polyadenylation and polyadenylated antisense transcripts. It is cost-effective using barcoded multiplexing, avoids the biases of existing SAGE and derivative methods and can reveal polyadenylation position from paired-end sequencing. Conclusions EXPRSS Tag-seq provides sensitive and reliable gene expression data and enables highthroughput expression profiling with relatively simple downstream analysis. [ABSTRACT FROM AUTHOR]

Background: Wheat yellow (stripe) rust caused by Puccinia striiformis f. sp. tritici (PST) is one of the most devastating diseases of wheat worldwide. To design effective breeding strategies that maximize the potential for durable disease resistance it is important to understand the molecular basis of PST pathogenicity. In particular, the characterisation of the structure, function and evolutionary dynamics of secreted effector proteins that are detected by host immune receptors can help guide and prioritize breeding efforts. However, to date, our knowledge of the effector repertoire of cereal rust pathogens is limited. Results: We re-sequenced genomes of four PST isolates from the US and UK to identify effector candidates and relate them to their distinct virulence profiles. First, we assessed SNP frequencies between all isolates, with heterokaryotic SNPs being over tenfold more frequent (5.29 ± 2.23 SNPs/kb) than homokaryotic SNPs (0.41 ± 0.28 SNPs/kb). Next, we implemented a bioinformatics pipeline to integrate genomics, transcriptomics, and effector-focused annotations to identify and classify effector candidates in PST. RNAseq analysis highlighted transcripts encoding secreted proteins that were significantly enriched in haustoria compared to infected tissue. The expression of 22 candidate effector genes was characterised using qRT-PCR, revealing distinct temporal expression patterns during infection in wheat. Lastly, we identified proteins that displayed non-synonymous substitutions specifically between the two UK isolates PST-87/7 and PST-08/21, which differ in virulence to two wheat varieties. By focusing on polymorphic variants enriched in haustoria, we identified five polymorphic effector candidates between PST-87/7 and PST-08/21 among 2,999 secreted proteins. These allelic variants are now a priority for functional validation as virulence/avirulence effectors in the corresponding wheat varieties. Conclusions: Integration of genomics, transcriptomics, and effector-directed annotation of PST isolates has enabled us to move beyond the single isolate-directed catalogues of effector proteins and develop a framework for mining effector proteins in closely related isolates and relate these back to their defined virulence profiles. This should ultimately lead to more comprehensive understanding of the PST pathogenesis system, an important first step towards developing more effective surveillance and management strategies for one of the most devastating pathogens of wheat. [ABSTRACT FROM AUTHOR]

Background: The SAMtools utilities comprise a very useful and widely used suite of software for manipulating files and alignments in the SAM and BAM format, used in a wide range of genetic analyses. The SAMtools utilities are implemented in C and provide an API for programmatic access, to help make this functionality available to programmers wishing to develop in the high level Ruby language we have developed bio-samtools, a Ruby binding to the SAMtools library. Results: The utility of SAMtools is encapsulated in 3 main classes, Bio::DB::Sam, representing the alignment files and providing access to the data in them, Bio::DB::Alignment, representing the individual read alignments inside the files and Bio::DB::Pileup, representing the summarised nucleotides of reads over a single point in the nucleotide sequence to which the reads are aligned. Conclusions: Bio-samtools is a flexible and easy to use interface that programmers of many levels of experience can use to access information in the popular and common SAM/BAM format. [ABSTRACT FROM AUTHOR]

Background: Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales. Results: We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters. Conclusions: NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA. [ABSTRACT FROM AUTHOR]

Background: Pseudomonas syringae is a widespread bacterial pathogen that causes disease on a broad range of economically important plant species. Pathogenicity of P. syringae strains is dependent on the type III secretion system, which secretes a suite of up to about thirty virulence 'effector' proteins into the host cytoplasm where they subvert the eukaryotic cell physiology and disrupt host defences. P. syringae pathovar tabaci naturally causes disease on wild tobacco, the model member of the Solanaceae, a family that includes many crop species as well as on soybean. Results: We used the 'next-generation' Illumina sequencing platform and the Velvet short-read assembly program to generate a 145X deep 6,077,921 nucleotide draft genome sequence for P. syringae pathovar tabaci strain 11528. From our draft assembly, we predicted 5,300 potential genes encoding proteins of at least 100 amino acids long, of which 303 (5.72%) had no significant sequence similarity to those encoded by the three previously fully sequenced P. syringae genomes. Of the core set of Hrp Outer Proteins that are conserved in three previously fully sequenced P. syringae strains, most were also conserved in strain 11528, including AvrE1, HopAH2, HopAJ2, HopAK1, HopAN1, HopI, HopJ1, HopX1, HrpK1 and HrpW1. However, the hrpZ1 gene is partially deleted and hopAF1 is completely absent in 11528. The draft genome of strain 11528 also encodes close homologues of HopO1, HopT1, HopAH1, HopR1, HopV1, HopAG1, HopAS1, HopAE1, HopAR1, HopF1, and HopW1 and a degenerate HopM1'. Using a functional screen, we confirmed that hopO1, hopT1, hopAH1, hopM1', hopAE1, hopAR1, and hopAI1' are part of the virulence-associated HrpL regulon, though the hopAI1' and hopM1' sequences were degenerate with premature stop codons. We also discovered two additional HrpL-regulated effector candidates and an HrpL-regulated distant homologue of avrPto1. Conclusion: The draft genome sequence facilitates the continued development of P. syringae pathovar tabaci on wild tobacco as an attractive model system for studying bacterial disease on plants. The catalogue of effectors sheds further light on the evolution of pathogenicity and host-specificity as well as providing a set of molecular tools for the study of plant defence mechanisms. We also discovered several large genomic regions in Pta 11528 that do not share detectable nucleotide sequence similarity with previously sequenced Pseudomonas genomes. These regions may include horizontally acquired islands that possibly contribute to pathogenicity or epiphytic fitness of Pta 11528. [ABSTRACT FROM AUTHOR]

Background: We have created a software implementation of a published and verified method for assigning probabilities to potential phosphorylation sites on peptides using mass spectrometric data. Our tool, named PhosCalc, determines the number of possible phosphorylation sites and calculates the theoretical masses for the b and y fragment ions of a user-provided peptide sequence. A corresponding user-provided mass spectrum is examined to determine which putative b and y ions have support in the spectrum and a probability score is calculated for each combination of phosphorylation sites., Findings: We test the implementation using spectra of phosphopeptides from bovine beta-casein and we compare the results from the implementation to those from manually curated and verified phosphopeptides from our own experiments. We find that the PhosCalc scores are capable of helping a user to identify phosphorylated sites and can remove a bottleneck in high throughput proteomics analyses., Conclusion: PhosCalc is available as a web-based interface for examining up to 100 peptides and as a downloadable tool for examining larger numbers of peptides. PhosCalc can be used to speed up identification of phosphorylation sites and can be easily integrated into data handling pipelines making it a very useful tool for those involved in phosphoproteomic research.