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Monozygotic or 'identical' twins have been widely studied to dissect the relative contributions of genetics and environment in human diseases. In multiple sclerosis (MS), an autoimmune demyelinating disease and common cause of neurodegeneration and disability in young adults, disease discordance in monozygotic twins has been interpreted to indicate environmental importance in its pathogenesis. However, genetic and epigenetic differences between monozygotic twins have been described, challenging the accepted experimental model in disambiguating the effects of nature and nurture. Here we report the genome sequences of one MS-discordant monozygotic twin pair, and messenger RNA transcriptome and epigenome sequences of CD4(+) lymphocytes from three MS-discordant, monozygotic twin pairs. No reproducible differences were detected between co-twins among approximately 3.6 million single nucleotide polymorphisms (SNPs) or approximately 0.2 million insertion-deletion polymorphisms. Nor were any reproducible differences observed between siblings of the three twin pairs in HLA haplotypes, confirmed MS-susceptibility SNPs, copy number variations, mRNA and genomic SNP and insertion-deletion genotypes, or the expression of approximately 19,000 genes in CD4(+) T cells. Only 2 to 176 differences in the methylation of approximately 2 million CpG dinucleotides were detected between siblings of the three twin pairs, in contrast to approximately 800 methylation differences between T cells of unrelated individuals and several thousand differences between tissues or between normal and cancerous tissues. In the first systematic effort to estimate sequence variation among monozygotic co-twins, we did not find evidence for genetic, epigenetic or transcriptome differences that explained disease discordance. These are the first, to our knowledge, female, twin and autoimmune disease individual genome sequences reported.

Genomics and bioinformatics are expected to revolutionize crop improvement We have to admit, however, that while information from the various 'omics' is being used by developmental and evolutionary biologists, the information is not being used routinely by translational plant biologists or applied plant breeders. This is due to a failure to provide information from omics technologies in formats that can be used to develop more effective and efficient assays that the breeder can use for selection. A similar situation exists in biomedical research where more efficacious therapies will be realized if omics information is translated into biomarker-based diagnostics. Because data are still lacking in plant science, herein we describe the development of an integrated web-based system that supports translational research using a biomedical example that can serve as a model for translational plant research. We also discuss now the bioinformatic system s agnostic with respect to data content and is capable of accepting omics data that eventually will be generated by plant biologists for use by plat breeders to develop diagnostic biomarkers for use in selection.

Next-generation sequencing techniques, such as RNA sequencing, have provided a wealth of genomic information for nonmodel species. Transcriptomic information can be used to quantify the patterns of gene expression, which can identify how environmental differences invoke organismal stress responses and provide a gauge in predicting species adaptability. In our study, we used RNA sequencing to characterize the first transcriptome from a naupliar tadpole shrimp ( Triops newberryi) to identify the genes expressed during the early life history stages and which could be important for future genomic studies. RNA was extracted from naupliar T. newberryi that were reared in a laboratory-controlled setting and in two different water types, a native and a non-native condition. A total of six replicates, three per condition, were sequenced with the Illumina Hi-Seq 2000 achieving 365 M 50-nt reads. High-quality reads were produced and de novo assembly was used to construct a T. newberryi transcriptome that was approximately 24.8 M base pairs. More than 10 000 peptides were predicted from the assembly, and genes were sorted into gene ontology categories. The use of different water conditions allowed for a preliminary differential gene expression analysis in order to compare the changes in gene expression between conditions. There were 299 differentially expressed genes between water conditions that might serve as a focal point for future genomic studies of Triops acclimation to different environments. The Triops transcriptome could serve as vital genomic information for additional studies on Branchiopod crustaceans. [ABSTRACT FROM AUTHOR]

A correction to the article "RNA-Seq Comparison of Larval and Adult Malpighian Tubules of the Yellow Fever Mosquito Aedes aegypti Reveals Life Stage-Specific Changes in Renal Function" published in a previous issue of the journal is presented.

High-throughput DNA sequencing has enabled systems biology to begin to address areas in health, agricultural and basic biological research. Concomitant with the opportunities is an absolute necessity to manage significant volumes of high-dimensional and inter-related data and analysis. Alpheus is an analysis pipeline, database and visualization software for use with massively parallel DNA sequencing technologies that feature multi-gigabase throughput characterized by relatively short reads, such as Illumina-Solexa (sequencing-by-synthesis), Roche-454 (pyrosequencing) and Applied Biosystem's SOLiD (sequencing-by-ligation). Alpheus enables alignment to reference sequence(s), detection of variants and enumeration of sequence abundance, including expression levels in transcriptome sequence. Alpheus is able to detect several types of variants, including non-synonymous and synonymous single nucleotide polymorphisms (SNPs), insertions/deletions (indels), premature stop codons, and splice isoforms. Variant detection is aided by the ability to filter variant calls based on consistency, expected allele frequency, sequence quality, coverage, and variant type in order to minimize false positives while maximizing the identification of true positives. Alpheus also enables comparisons of genes with variants between cases and controls or bulk segregant pools. Sequence-based differential expression comparisons can be developed, with data export to SAS JMP Genomics for statistical analysis.