Strand-specific transcriptome profiling with directly labeled RNA on genomic tiling microarrays

Detection and mitigation of spurious antisense expression with RoSA

Antisense transcription is known to have a range of impacts on sense gene expression, including (but not limited to) impeding transcription initiation, disrupting post-transcriptional processes, and enhancing, slowing, or even preventing transcription of the sense gene. Strand-specific RNA-Seq protocols preserve the strand information of the original RNA in the data, and so can be used to identify where antisense transcription may be implicated in regulating gene expression. However, our analysis of 199 strand-specific RNA-Seq experiments reveals that spurious antisense reads are often present in these datasets at levels greater than 1% of sense gene expression levels. Furthermore, these levels can vary substantially even between replicates in the same experiment, potentially disrupting any downstream analysis, if the incorrectly assigned antisense counts dominate the set of genes with high antisense transcription levels. Currently, no tools exist to detect or correct for this spurious antisense signal. Our tool, RoSA (Removal of Spurious Antisense), detects the presence of high levels of spurious antisense read alignments in strand-specific RNA-Seq datasets. It uses incorrectly spliced reads on the antisense strand and/or ERCC spikeins (if present in the data) to calculate both global and gene-specific antisense correction factors. We demonstrate the utility of our tool to filter out spurious antisense transcript counts in an Arabidopsis thaliana RNA-Seq experiment. Availability: RoSA is open source software available under the GPL licence via the Barton Group GitHub page https://github.com/bartongroup.

Expression Profiling of Differentially Regulated Genes in Fanconi Anemia

Gene expression analysis mainly helps to study gene quantification methods by using various downstream detection approaches like imaging, amplification, probe hybridization, or sequencing. With respect to DNA, which is less static, mRNA levels vary over time, between cell types under divergent conditions. Gene expression analysis is principally focused on determination of mRNA levels transcribed from DNA. DNA microarrays are one of the robust and powerful tools to detect changes in multiple transcripts in larger cohorts in parallel. The basic principle of DNA microarray hybridization is complementary base pairing of single-stranded nucleic-acid sequences. On a microarray platform (also called a chip), known sequences called targets are attached at fixed locations (spots) to a solid surface such as glass using robotic spotting. Since a large number of samples (variables) are used in a typical hybridization experiment, which often leads to impreciseness for example, target mRNA transcribed from the same source should be identical every time. In such cases, developing an optimized protocol for microarray platform to study the expression profiling of differentially regulated genes is a challenging task. Thus genome-wide expression array analysis yields data about candidate genes that may be involved in disease acquisition progression, and helps in better understanding the pathophysiology of the disease. In this chapter we describe in detail the microarray technique, a well-accepted method for understanding the development and progression of Fanconi anemia (FA), a genetic disorder which is characterized by progressive bone marrow failure and a predisposition to cancer.

Genomic and transcriptomic landscape of Escherichia coli BL21(DE3)

Escherichia coli BL21(DE3) has long served as a model organism for scientific research, as well as a workhorse for biotechnology. Here we present the most current genome annotation of E. coli BL21(DE3) based on the transcriptome structure of the strain that was determined for the first time. The genome was annotated using multiple automated pipelines and compared to the current genome annotation of the closely related strain, E. coli K-12. High-resolution tiling array data of E. coli BL21(DE3) from several different stages of cell growth in rich and minimal media were analyzed to characterize the transcriptome structure and to provide supporting evidence for open reading frames. This new integrated analysis of the genomic and transcriptomic structure of E. coli BL21(DE3) has led to the correction of translation initiation sites for 88 coding DNA sequences and provided updated information for most genes. Additionally, 37 putative genes and 66 putative non-coding RNAs were also identified. The panoramic landscape of the genome and transcriptome of E. coli BL21(DE3) revealed here will allow us to better understand the fundamental biology of the strain and also advance biotechnological applications in industry.

Thermodynamically optimal whole-genome tiling microarray design and validation

Background

Microarray is an efficient apparatus to interrogate the whole transcriptome of species. Microarray can be designed according to annotated gene sets, but the resulted microarrays cannot be used to identify novel transcripts and this design method is not applicable to unannotated species. Alternatively, a whole-genome tiling microarray can be designed using only genomic sequences without gene annotations, and it can be used to detect novel RNA transcripts as well as known genes. The difficulty with tiling microarray design lies in the tradeoff between probe-specificity and coverage of the genome. Sequence comparison methods based on BLAST or similar software are commonly employed in microarray design, but they cannot precisely determine the subtle thermodynamic competition between probe targets and partially matched probe nontargets during hybridizations.

Findings

Using the whole-genome thermodynamic analysis software PICKY to design tiling microarrays, we can achieve maximum whole-genome coverage allowable under the thermodynamic constraints of each target genome. The resulted tiling microarrays are thermodynamically optimal in the sense that all selected probes share the same melting temperature separation range between their targets and closest nontargets, and no additional probes can be added without violating the specificity of the microarray to the target genome.

Conclusions

This new design method was used to create two whole-genome tiling microarrays for Escherichia coli MG1655 and Agrobacterium tumefaciens C58 and the experiment results validated the design.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-016-2113-4) contains supplementary material, which is available to authorized users.

Comprehensive Transcriptome Profiles of Streptococcus mutans UA159 Map Core Streptococcal Competence Genes

In Streptococcus mutans, an oral colonizer associated with dental caries, development of competence for natural genetic transformation is triggered by either of two types of peptide pheromones, competence-stimulating peptides (CSPs) (18 amino acids [aa]) or SigX-inducing peptides (XIPs) (7 aa). Competence induced by CSP is a late response to the pheromone that requires the response regulator ComE and the XIP-encoding gene comS. XIP binds to ComR to allow expression of the alternative sigma factor SigX and the effector genes it controls. While these regulatory links are established, the precise set of effectors controlled by each regulator is poorly defined. To improve the definition of all three regulons, we used a high-resolution tiling array to map global changes in gene expression in the early and late phases of the CSP response. The early phase of the CSP response was limited to increased gene expression at four loci associated with bacteriocin production and immunity. In the late phase, upregulated regions expanded to a total of 29 loci, including comS and genes required for DNA uptake and recombination. The results indicate that the entire late response to CSP depends on the expression of comS and that the immediate transcriptional response to CSP, mediated by ComE, is restricted to just four bacteriocin-related loci. Comparison of the new data with published transcriptome data permitted the identification of all of the operons in each regulon: 4 for ComE, 2 for ComR, and 21 for SigX. Finally, a core set of 27 panstreptococcal competence genes was identified within the SigX regulon by comparison of transcriptome data from diverse streptococcal species. IMPORTANCES. mutans has the hard surfaces of the oral cavity as its natural habitat, where it depends on its ability to form biofilms in order to survive. The comprehensive identification of S. mutans regulons activated in response to peptide pheromones provides an important basis for understanding how S. mutans can transition from individual to social behavior. Our study placed 27 of the 29 transcripts activated during competence within three major regulons and revealed a core set of 27 panstreptococcal competence-activated genes within the SigX regulon.

The Antisense Transcriptome and the Human Brain

The transcriptome of a cell is made up of a varied array of RNA species, including protein-coding RNAs, long non-coding RNAs, short non-coding RNAs, and circular RNAs. The cellular transcriptome is dynamic and can change depending on environmental factors, disease state and cellular context. The human brain has perhaps the most diverse transcriptome profile that is enriched for many species of RNA, including antisense transcripts. Antisense transcripts are produced when both the plus and minus strand of the DNA helix are transcribed at a particular locus. This results in an RNA transcript that has a partial or complete overlap with an intronic or exonic region of the sense transcript. While antisense transcription is known to occur at some level in most organisms, this review focuses specifically on antisense transcription in the brain and how regulation of genes by antisense transcripts can contribute to functional aspects of the healthy and diseased brain. First, we discuss different techniques that can be used in the identification and quantification of antisense transcripts. This is followed by examples of antisense transcription and modes of regulatory function that have been identified in the brain.

Hypothesis: Artifacts, Including Spurious Chimeric RNAs with a Short Homologous Sequence, Caused by Consecutive Reverse Transcriptions and Endogenous Random Primers

Recent RNA-sequencing technology and associated bioinformatics have led to identification of tens of thousands of putative human chimeric RNAs, i.e. RNAs containing sequences from two different genes, most of which are derived from neighboring genes on the same chromosome. In this essay, we redefine "two neighboring genes" as those producing individual transcripts, and point out two known mechanisms for chimeric RNA formation, i.e. transcription from a fusion gene or trans-splicing of two RNAs. By our definition, most putative RNA chimeras derived from canonically-defined neighboring genes may either be technical artifacts or be cis-splicing products of 5'- or 3'-extended RNA of either partner that is redefined herein as an unannotated gene, whereas trans-splicing events are rare in human cells. Therefore, most authentic chimeric RNAs result from fusion genes, about 1,000 of which have been identified hitherto. We propose a hypothesis of "consecutive reverse transcriptions (RTs)", i.e. another RT reaction following the previous one, for how most spurious chimeric RNAs, especially those containing a short homologous sequence, may be generated during RT, especially in RNA-sequencing wherein RNAs are fragmented. We also point out that RNA samples contain numerous RNA and DNA shreds that can serve as endogenous random primers for RT and ensuing polymerase chain reactions (PCR), creating artifacts in RT-PCR.

Physico-chemical foundations underpinning microarray and next-generation sequencing experiments

Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized.

Comprehensive transcriptome analysis of the periodontopathogenic bacterium Porphyromonas gingivalis W83

High-density tiling microarray and RNA sequencing technologies were used to analyze the transcriptome of the periodontopathogenic bacterium Porphyromonas gingivalis. The compiled P. gingivalis transcriptome profiles were based on total RNA samples isolated from three different laboratory culturing conditions, and the strand-specific transcription profiles generated covered the entire genome, including both protein coding and noncoding regions. The transcription profiles revealed various operon structures, 5'- and 3'-end untranslated regions (UTRs), differential expression patterns, and many novel, not-yet-annotated transcripts within intergenic and antisense regions. Further transcriptome analysis identified the majority of the genes as being expressed within operons and most 5' and 3' ends to be protruding UTRs, of which several 3' UTRs were extended to overlap genes carried on the opposite/antisense strand. Extensive antisense RNAs were detected opposite most insertion sequence (IS) elements. Pairwise comparative analyses were also performed among transcriptome profiles of the three culture conditions, and differentially expressed genes and metabolic pathways were identified. With the growing realization that noncoding RNAs play important biological functions, the discovery of novel RNAs and the comprehensive transcriptome profiles compiled in this study may provide a foundation to further understand the gene regulation and virulence mechanisms in P. gingivalis. The transcriptome profiles can be viewed at and downloaded from the Microbial Transcriptome Database website, http://bioinformatics.forsyth.org/mtd.