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Background: Toxoplasma gondii is an intracellular protozoan that affects most species of endothermic animals including humans with a great infection rate. The vertical transmission of T. gondii causes abortion, constituting a serious threat to humans and leading to great losses in livestock production. Distinct from population structure of T. gondii in North America and Europe, Chinese 1 (ToxoDB #9) is a dominant genotype prevalent in China. Among the isolates of Chinese 1, the Wh3 and Wh6 have different virulence and pathogenicity in mice. However, little has been known about their difference at the genomic level. Thus the next-generation sequencing (NGS) approach was used to discover the association of the phenotypical variations with the genome sequencing data and the expression and polymorphisms of the key effectors. Results: We successfully sequenced the genome of Chinese 1 strains of Wh3 and Wh6. The average sequencing depths were 63.91 and 63.61 for Wh3 and Wh6, respectively. The variations of both isolates were identified in comparison with reference genome of type I GT1 strain. There were 505,645 and 505,856 SNPs, 30,658 and 30,004 indels, 4661 and 2320 SVs, and 1942 and 3080 CNVs for Wh3 and Wh6, respectively. In target search variations of particular factors of T. gondii, the dense granule protein 3 (GRA3) and rhoptry neck protein 3 (RON3) were found to have 35 SNPs, 2 indels and 89 SNPs, 6 indels, respectively. GRA3 and RON3 were both found to have higher expression levels in less virulent Wh6 than in virulent Wh3. Both strains of type Chinese 1 share polymorphic GRA15II and ROPI/III with type I, II, and III strains. Conclusions: Sequencing of the two strains revealed that genome structure of Chinese 1 and type I strains has considerable genomic variations. Sequencing and qRT-PCR analyses of 26 effectors displayed a remarkable variation that may be associated with phenotype and pathogenic differences. [ABSTRACT FROM AUTHOR]

Background: Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE. Results: A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to 1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·105 (oligoarrays), 1.1·105 (MPSS) and 7.6·104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1·106 (oligoarrays), 2.8·105 (MPSS) and 3.8·105 (SAGE). Conclusion: The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands. [ABSTRACT FROM AUTHOR]

Background: High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing). Results: The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery. Conclusion: Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling. [ABSTRACT FROM AUTHOR]

Background: Bacterial vaginosis (BV) is an ecological disorder of the vaginal microbiota that affects millions of women annually, and is associated with numerous adverse health outcomes including pre-term birth and the acquisition of sexually transmitted infections. However, little is known about the overall structure and composition of vaginal microbial communities; most of the earlier studies focused on predominant vaginal bacteria in the process of BV. In the present study, the diversity and richness of vaginal microbiota in 50 BV positive and 50 healthy women from China were investigated using culture-independent PCR-denaturing gradient gel electrophoresis (DGGE) and barcoded 454 pyrosequencing methods, and validated by quantitative PCR. Results: Our data demonstrated that there was a profound shift in the absolute and relative abundances of bacterial species present in the vagina when comparing populations associated with healthy and diseased conditions. In spite of significant interpersonal variations, the diversity of vaginal microbiota in the two groups could be clearly divided into two clusters. A total of 246,359 high quality pyrosequencing reads was obtained for evaluating bacterial diversity and 24,298 unique sequences represented all phylotypes. The most predominant phyla of bacteria identified in the vagina belonged to Firmicutes, Bacteroidetes, Actinobacteria and Fusobacteria. The higher number of phylotypes in BV positive women over healthy is consistent with the results of previous studies and a large number of low-abundance taxa which were missed in previous studies were revealed. Although no single bacterium could be identified as a specific marker for healthy over diseased conditions, three phyla - Bacteroidetes, Actinobacteria and Fusobacteria, and eight genera including Gardnerella, Atopobium, Megasphaera, Eggerthella, Aerococcus, Leptotrichia/Sneathia, Prevotella and Papillibacter were strongly associated with BV (p < 0.05). These genera are potentially excellent markers and could be used as targets for clinical BV diagnosis by molecular approaches. Conclusions: The data presented here have clearly profiled the overall structure of vaginal communities and clearly demonstrated that BV is associated with a dramatic increase in the taxonomic richness and diversity of vaginal microbiota. The study also provides the most comprehensive picture of the vaginal community structure and the bacterial ecosystem, and significantly contributes to the current understanding of the etiology of BV. [ABSTRACT FROM AUTHOR]

Background: The maize kernel row number (KRN) is a key component that contributes to grain yield and has high broad-sense heritability (H2). Quantitative trait locus/loci (QTL) mapping using a high-density genetic map is a powerful approach to detecting loci that are responsible for traits of interest. Bulked segregant ribonucleic acid (RNA) sequencing (BSR-seq) is another rapid and cost-effective strategy to identify QTL. Combining QTL mapping using a high-density genetic map and BSR-seq may dissect comprehensively the genetic architecture underlying the maize KRN. Results: A panel of 300 F2 individuals derived from inbred lines abe2 and B73 were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) method. A total of 4,579 high-quality polymorphic SLAF markers were obtained and used to construct a high-density geneticmap with a total length of 2,123 centimorgan (cM) and an average distance between adjacent markers of 0.46 cM. Combining the genetic map and KRN of F2 individuals, four QTL (qKRN1, qKRN2, qKRN5, and qKRN8-1) were identified on chromosomes 1, 2, 5, and 8, respectively. The physical intervals of these four QTL ranged from 4.36 Mb for qKRN8-1 to 7.11 Mb for qKRN1 with an average value of 6.08 Mb. Based on high-throughput sequencing of two RNA pools bulked from leaves of plants with extremely high and low KRNs, two QTL were detected on chromosome 8 in the 10-25 Mb (BSR_QTL1) and 60-150 Mb (BSR_QTL2) intervals. According to the physical positions of these QTL, qKRN8-1 was included by BSR_QTL2. In addition, qKRN8-1 was validated using QTL mapping with a recombinant inbred lines population that was derived from inbred lines abe2 and B73. Conclusions: In this study, we proved that combining QTL mapping using a high-density genetic map and BSR-seq is a powerful and cost-effective approach to comprehensively revealing genetic architecture underlying traits of interest. The QTL for the KRN detected in this study, especially qKRN8-1, can be used for performing fine mapping experiments and marker-assisted selection in maize breeding. [ABSTRACT FROM AUTHOR]

Background: The morphogenesis of the cerebral cortex depends on the precise control of gene expression during development. Small non-coding RNAs, including microRNAs and other groups of small RNAs, play profound roles in various physiological and pathological processes via their regulation of gene expression. A systematic analysis of the expression profile of small non-coding RNAs in developing cortical tissues is important for clarifying the gene regulation networks mediating key developmental events during cortical morphogenesis. Results: Global profiling of the small RNA transcriptome was carried out in rat cerebral cortex from E10 till P28 using next-generation sequencing technique. We found an extraordinary degree of developmental stage-specific expression of a large group of microRNAs. A group of novel microRNAs with functional hints were identified, and brain-enriched expression and Dicer-dependent production of high-abundant novel microRNAs were validated. Profound editing of known microRNAs at "seed" sequence and flanking sequence was observed, with much higher editing events detected at late postnatal stages than embryonic stages, suggesting the necessity of microRNA editing for the fine tuning of gene expression during the formation of complicated synaptic connections at postnatal stages. Conclusion: Our analysis reveals extensive regulation of microRNAs during cortical development. The dataset described here will be a valuable resource for clarifying new regulatory mechanisms for cortical development and diseases and will greatly contribute to our understanding of the divergence, modification, and function of microRNAs [ABSTRACT FROM AUTHOR]

Background: A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20-200 micro-grams total RNA or 0.5-2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material. Results: Using analysis of variance (ANOVA) and multiple hypothesis testing, we estimated the impact of amplification on the preservation of gene expression ratios. Both methods showed that the gene expression ratios were not completely preserved between amplified and non-amplified material. We also compared the expression ratios between the two cell lines for the amplified material with expression ratios between the two cell lines for the non-amplified material for each gene. With the aid of multiple t-testing with a false discovery rate of 5%, we found that 10% of the genes investigated showed significantly different expression ratios. Conclusion: Although the ratios were not fully preserved, amplification may prove to be extremely useful with respect to characterizing low expressing genes. [ABSTRACT FROM AUTHOR]