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Background Jatropha curcas, a tropical shrub, is a promising biofuel crop, which produces seeds with high content of oil and protein. To better understand the maturation process of J. curcas seeds and to improve its agronomic performance, a two-step approach was performed in six different maturation stages of seeds: 1) generation of the entire transcriptome of J. curcas seeds using 454-Roche sequencing of a cDNA library, 2) comparison of transcriptional expression levels using a custom Agilent 8x60K oligonucleotide microarray. Results A total of 793,875 high-quality reads were assembled into 19,382 unique full-length contigs, of which 13,507 could be annotated with Gene Ontology (GO) terms. Microarray data analysis identified 9111 probes (out of 57,842 probes), which were differentially expressed between the six maturation stages. The expression results were validated for 75 selected transcripts based on expression levels, predicted function, pathway, and length. Result from cluster analyses showed that transcripts associated with fatty acid, flavonoid, and phenylpropanoid biosynthesis were over-represented in the early stages, while those of lipid storage were over-represented in the late stages. Expression analyses of different maturation stages of J. curcas seed showed that most changes in transcript abundance occurred between the two last stages, suggesting that the timing of metabolic pathways during seed maturation in J. curcas occurs in late stages. The co-expression results showed that the hubs (CB5-D, CDR1, TT8, DFR, HVA22) with the highest number of edges, associated with fatty acid and flavonoid biosynthesis, are showing a decrease in their expression during seed maturation. Furthermore, seed development and hormone pathways are significantly well connected. Conclusion The obtained results revealed differentially expressed sequences (DESs) regulating important pathways related to seed maturation, which could contribute to the understanding of the complex regulatory network during seed maturation with the focus on lipid, flavonoid and phenylpropanoid biosynthesis. This study provides detailed information on transcriptional changes during J. curcas seed maturation and provides a starting point for a genomic survey of seed quality traits. The results highlighted specific genes and processes relevant to the molecular mechanisms involved in Jatropha seed maturation. These data can also be utilized regarding other Euphorbiaceae species.

Mutagenesis in combination with Genotyping by Sequencing (GBS) is a powerful tool for introducing variation, studying gene function and identifying causal mutations underlying phenotypes of interest in crop plant genomes. About 400 million paired-end reads were obtained from 82 ethylmethane sulfonate (EMS) induced mutants and 14 wild-type accessions of for the detection of Single Nucleotide Polymorphisms (SNPs) and Insertion/Deletions (InDels) by two different approaches (nGBS and ddGBS) on an Illumina HiSeq 2000 sequencer. Using bioinformatics analyses, 1,452 induced SNPs and InDels were identified in coding regions, which were distributed across 995 genes. The predominantly observed mutations were G/C to A/T transitions (64%), while transversions were observed at a lower frequency (36%). Regarding the effect of mutations on gene function, 18% of the mutations were located in intergenic regions. In fact, mutants with the highest number of heterozygous SNPs were found in samples treated with 0.8% EMS for 3 h. Reconstruction of the metabolic pathways showed that in total 16 SNPs were located in six KEGG pathways by nGBS and two pathways by ddGBS. The most highly represented pathways were ether-lipid metabolism and glycerophospholipid metabolism, followed by starch and sucrose metabolism by nGBS and triterpenoid biosynthesis as well as steroid biosynthesis by ddGBS. Furthermore, high genome methylation was observed in , which might help to understand the plasticity of the genome in response to environmental factors. At last, the results showed that continuously vegetatively propagated tissue is a fast, efficient and accurate method to dissolve chimeras, especially for long-lived plants like . Obtained data showed that allelic variations and analyses of gene functions (gene function prediction), which control important traits, could be identified in mutant populations using nGBS and ddGBS. However, the handling of GBS data is more difficult and more challenging than the traditional TILLING strategy in mutated plants, since the genome sequence is incomplete, which makes alignment and variant analysis of target sequence reads challenging to perform and interpret. Therefore, providing a complete reference genome sequence with high quality should be a priority for any breeding program.

Gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Very few therapeutic options are currently available in this neoplasia. The use of 5-Aza-2′-deoxycytidine (5-AZAdC) was approved for the treatment of myelodysplastic syndromes, and this drug can treat solid tumours at low doses. Epigenetic manipulation of GC cell lines is a useful tool to better understand gene expression regulatory mechanisms for clinical applications. Therefore, we compared the gene expression profile of 5-AZAdC-treated and untreated GC cell lines by a microarray assay. Among the genes identified in this analysis, we selected NRN1 and TNFAIP3 to be evaluated for gene expression by RT-qPCR and DNA methylation by bisulfite DNA next-generation sequencing in 43 and 52 pairs of GC and adjacent non-neoplastic tissue samples, respectively. We identified 83 candidate genes modulated by DNA methylation in GC cell lines. Increased expression of NRN1 and TNFAIP3 was associated with advanced tumours (P < 0.05). We showed that increased NRN1 and TNFAIP3 expression seems to be regulated by DNA demethylation in GC samples: inverse correlations between the mRNA and DNA methylation levels in the promoter of NRN1 (P < 0.05) and the intron of TNFAIP3 (P < 0.05) were detected. Reduced NRN1 promoter methylation was associated with III/IV TNM stage tumours (P = 0.03) and the presence of Helicobacter pylori infection (P = 0.02). The identification of demethylated activated genes in GC may be useful in clinical practice, stratifying patients who are less likely to benefit from 5-AZAdC-based therapies.Key messagesHigher expression of NRN1 and TNFAIP3 is associated with advanced gastric cancer (GC).NRN1 promoter hypomethylation contributes to gene upregulation in advanced GC.TNFAIP3 intronic-specific CpG site demethylation contributes to gene upregulation in GC.These findings may be useful to stratify GC patients who are less likely to benefit from DNA demethylating-based therapies.

Animal mitochondrial genomic polymorphism occurs as low-level mitochondrial heteroplasmy and deeply divergent co-existing molecules. The latter is rare, known only in bivalvian mollusks. Here we show two deeply divergent co-existing mt-genomes in a vertebrate through genomic sequencing of the Tuatara (Sphenodon punctatus), the sole-representative of an ancient reptilian Order. The two molecules, revealed using a combination of short-read and long-read sequencing technologies, differ by 10.4% nucleotide divergence. A single long-read covers an entire mt-molecule for both strands. Phylogenetic analyses suggest a 7–8 million-year divergence between genomes. Contrary to earlier reports, all 37 genes typical of animal mitochondria, with drastic gene rearrangements, are confirmed for both mt-genomes. Also unique to vertebrates, concerted evolution drives three near-identical putative Control Region non-coding blocks. Evidence of positive selection at sites linked to metabolically important transmembrane regions of encoded proteins suggests these two mt-genomes may confer an adaptive advantage for an unusually cold-tolerant reptile.Robert Macey et al. use deep sequencing to reveal two co-existing, deeply divergent, fully functional mitochondrial genomes in a single Tuatara from Lady Alice Island, New Zealand. This finding will stimulate further investigation, having profound implications for our understanding of mitochondrial evolution and function.

The diversity of the hepatitis B surface antigen (HBsAg) has a significant impact on the performance of diagnostic screening tests and the clinical outcome of hepatitis B infection. Neutralizing or diagnostic antibodies against the HBsAg are directed towards its highly conserved major hydrophilic region (MHR), in particular towards its \"a\" determinant subdomain. Here, we explored, on a global scale, the genetic diversity of the HBsAg MHR in a large, multi-ethnic cohort of randomly selected subjects with HBV infection from four continents. A total of 1553 HBsAg positive blood samples of subjects originating from 20 different countries across Africa, America, Asia and central Europe were characterized for amino acid variation in the MHR. Using highly sensitive ultra-deep sequencing, we found 72.8% of the successfully sequenced subjects (n = 1391) demonstrated amino acid sequence variation in the HBsAg MHR. This indicates that the global variation frequency in the HBsAg MHR is threefold higher than previously reported. The majority of the amino acid mutations were found in the HBV genotypes B (28.9%) and C (25.4%). Collectively, we identified 345 distinct amino acid mutations in the MHR. Among these, we report 62 previously unknown mutations, which extends the worldwide pool of currently known HBsAg MHR mutations by 22%. Importantly, topological analysis identified the \"a\" determinant upstream flanking region as the structurally most diverse subdomain of the HBsAg MHR. The highest prevalence of \"a\" determinant region mutations was observed in subjects from Asia, followed by the African, American and European cohorts, respectively. Finally, we found that more than half (59.3%) of all HBV subjects investigated carried multiple MHR mutations. Together, this worldwide ultra-deep sequencing based genotyping study reveals that the global prevalence and structural complexity of variation in the hepatitis B surface antigen have, to date, been significantly underappreciated.

Traditional Sanger sequencing as well as Next-Generation Sequencing have been used for the identification of disease causing mutations in human molecular research. The majority of currently available tools are developed for research and explorative purposes and often do not provide a complete, efficient, one-stop solution. As the focus of currently developed tools is mainly on NGS data analysis, no integrative solution for the analysis of Sanger data is provided and consequently a one-stop solution to analyze reads from both sequencing platforms is not available. We have therefore developed a new pipeline called MutAid to analyze and interpret raw sequencing data produced by Sanger or several NGS sequencing platforms. It performs format conversion, base calling, quality trimming, filtering, read mapping, variant calling, variant annotation and analysis of Sanger and NGS data under a single platform. It is capable of analyzing reads from multiple patients in a single run to create a list of potential disease causing base substitutions as well as insertions and deletions. MutAid has been developed for expert and non-expert users and supports four sequencing platforms including Sanger, Illumina, 454 and Ion Torrent. Furthermore, for NGS data analysis, five read mappers including BWA, TMAP, Bowtie, Bowtie2 and GSNAP and four variant callers including GATK-HaplotypeCaller, SAMTOOLS, Freebayes and VarScan2 pipelines are supported. MutAid is freely available at https://sourceforge.net/projects/mutaid.

Targeted sequencing of PCR amplicons generated from bisulfite deaminated DNA is a flexible, cost-effective way to study methylation of a sample at single CpG resolution and perform subsequent multi-target, multi-sample comparisons. Currently, no platform specific protocol, support, or analysis solution is provided to perform targeted bisulfite sequencing on a Personal Genome Machine (PGM). Here, we present a novel tool, called TABSAT, for analyzing targeted bisulfite sequencing data generated on Ion Torrent sequencers. The workflow starts with raw sequencing data, performs quality assessment, and uses a tailored version of Bismark to map the reads to a reference genome. The pipeline visualizes results as lollipop plots and is able to deduce specific methylation-patterns present in a sample. The obtained profiles are then summarized and compared between samples. In order to assess the performance of the targeted bisulfite sequencing workflow, 48 samples were used to generate 53 different Bisulfite-Sequencing PCR amplicons from each sample, resulting in 2,544 amplicon targets. We obtained a mean coverage of 282X using 1,196,822 aligned reads. Next, we compared the sequencing results of these targets to the methylation level of the corresponding sites on an Illumina 450k methylation chip. The calculated average Pearson correlation coefficient of 0.91 confirms the sequencing results with one of the industry-leading CpG methylation platforms and shows that targeted amplicon bisulfite sequencing provides an accurate and cost-efficient method for DNA methylation studies, e.g., to provide platform-independent confirmation of Illumina Infinium 450k methylation data. TABSAT offers a novel way to analyze data generated by Ion Torrent instruments and can also be used with data from the Illumina MiSeq platform. It can be easily accessed via the Platomics platform, which offers a web-based graphical user interface along with sample and parameter storage. TABSAT is freely available under a GNU General Public License version 3.0 (GPLv3) at https://github.com/tadkeys/tabsat/ and http://demo.platomics.com/.

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.

Background Since its introduction quantitative real-time polymerase chain reaction (qPCR) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline. Results QPCR is a versatile web-based Java application that allows to store, manage, and analyze data from relative quantification qPCR experiments. It comprises a parser to import generated data from qPCR instruments and includes a variety of analysis methods to calculate cycle-threshold and amplification efficiency values. The analysis pipeline includes technical and biological replicate handling, incorporation of sample or gene specific efficiency, normalization using single or multiple reference genes, inter-run calibration, and fold change calculation. Moreover, the application supports assessment of error propagation throughout all analysis steps and allows conducting statistical tests on biological replicates. Results can be visualized in customizable charts and exported for further investigation. Conclusion We have developed a web-based system designed to enhance and facilitate the analysis of qPCR experiments. It covers the complete analysis workflow combining parsing, analysis, and generation of charts into one single application. The system is freely available at http://genome.tugraz.at/QPCR

Background: Bacillus Calmette-Guérin (BCG) immunotherapy, the standard adjuvant intravesical therapy for some intermediate and most high-risk non-muscle invasive bladder cancers (NMIBCs), suffers from a heterogenous response rate. Molecular markers to help guide responses are scarce and currently not used in the clinical setting. Methods: To identify novel biomarkers and pathways involved in response to BCG immunotherapy, we performed a genome-wide DNA methylation analysis of NMIBCs before BCG therapy. Genome-wide DNA methylation profiles of DNA isolated from tumors of 26 BCG responders and 27 failures were obtained using the Infinium MethylationEPIC BeadChip. Results: Distinct DNA methylation patterns were found by genome-wide analysis in the two groups. Differentially methylated CpG sites were predominantly located in gene promoters and gene bodies associated with bacterial invasion of epithelial cells, chemokine signaling, endocytosis, and focal adhesion. In total, 40 genomic regions with a significant difference in methylation between responders and failures were detected. The differential methylation state of six of these regions, localized in the promoters of the genes GPR158, KLF8, C12orf42, WDR44, FLT1, and CHST11, were internally validated by bisulfite-sequencing. GPR158 promoter hypermethylation was the best predictor of BCG failure with an AUC of 0.809 (p-value < 0.001). Conclusions: Tumors from BCG responders and BCG failures harbor distinct DNA methylation profiles. Differentially methylated DNA regions were detected in genes related to pathways involved in bacterial invasion of cells or focal adhesion. We identified candidate DNA methylation biomarkers that may help to predict patient prognosis after external validation in larger, well-designed cohorts.