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DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 ( https://rnbeads.org/ ) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.

The raccoon dog (Nyctereutes procyonoides) is known to be an opportunistic generalist who feeds on a wide variety of foods. Historically, their diet has been investigated by morphological observation of undigested remains in feces, requiring specialized knowledge such as osteology, zoology, and phytology. Here, we used DNA metabarcoding of vertebrate 12S rRNA gene and invertebrate 16S rRNA gene to investigate their fecal contents. Additionally, we developed a blocking oligonucleotide that specifically inhibits the amplification of the canid 12S rRNA gene. We confirmed that the blocking oligonucleotide selectively inhibit the amplification of raccoon dog's DNA without significantly changing the composition of the preys' DNA. We found that the main foods of raccoon dogs in our study area, the waterside of paddy fields in Korea, were fishes such as Cyprinidae and insects such as mole crickets, which makes sense given the Korean fauna and their well-known opportunistic feeding behaviors. As a method to conveniently and objectively investigate feeding habits of raccoon dogs, this study provided baseline information on DNA metabarcoding. By using DNA metabarcoding, it is expected that the diet habits and ecology of raccoon dogs will be better understood by future research.

[This corrects the article DOI: 10.1371/journal.pone.0259518.].

Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, immobilization techniques, and detection strategies. The aim of this review is to explore the development of biomolecule-based microarray applications since 2018 onwards. Here, we have covered a different array of printing strategies, substrate surface modification, biomolecule immobilization strategies, detection techniques, and biomolecule-based microarray applications. The period of 2018–2022 focused on using biomolecule-based microarrays for the identification of biomarkers, detection of viruses, differentiation of multiple pathogens, etc. A few potential future applications of microarrays could be for personalized medicine, vaccine candidate screening, toxin screening, pathogen identification, and posttranslational modifications.

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

This protocol describes DAP-seq, a transcription-factor binding site discovery assay that can be used to produce cistrome and epicistrome maps for any organism. To enable low-cost, high-throughput generation of cistrome and epicistrome maps for any organism, we developed DNA affinity purification sequencing (DAP-seq), a transcription factor (TF)-binding site (TFBS) discovery assay that couples affinity-purified TFs with next-generation sequencing of a genomic DNA library. The method is fast, inexpensive, and more easily scaled than chromatin immunoprecipitation sequencing (ChIP-seq). DNA libraries are constructed using native genomic DNA from any source of interest, preserving cell- and tissue-specific chemical modifications that are known to affect TF binding (such as DNA methylation) and providing increased specificity as compared with in silico predictions based on motifs from methods such as protein-binding microarrays (PBMs) and systematic evolution of ligands by exponential enrichment (SELEX). The resulting DNA library is incubated with an affinity-tagged in vitro -expressed TF, and TF–DNA complexes are purified using magnetic separation of the affinity tag. Bound genomic DNA is eluted from the TF and sequenced using next-generation sequencing. Sequence reads are mapped to a reference genome, identifying genome-wide binding locations for each TF assayed, from which sequence motifs can then be derived. A researcher with molecular biology experience should be able to follow this protocol, processing up to 400 samples per week.

Here, the authors describe genetically engineering the Pseudomonas genome by two-step allelic exchange. Suicide vector-encoded alleles are used to generate mutations by homologous recombination at the single base pair level. Allelic exchange is an efficient method of bacterial genome engineering. This protocol describes the use of this technique to make gene knockouts and knock-ins, as well as single-nucleotide insertions, deletions and substitutions, in Pseudomonas aeruginosa . Unlike other approaches to allelic exchange, this protocol does not require heterologous recombinases to insert or excise selective markers from the target chromosome. Rather, positive and negative selections are enabled solely by suicide vector–encoded functions and host cell proteins. Here, mutant alleles, which are flanked by regions of homology to the recipient chromosome, are synthesized in vitro and then cloned into allelic exchange vectors using standard procedures. These suicide vectors are then introduced into recipient cells by conjugation. Homologous recombination then results in antibiotic-resistant single-crossover mutants in which the plasmid has integrated site-specifically into the chromosome. Subsequently, unmarked double-crossover mutants are isolated directly using sucrose-mediated counter-selection. This two-step process yields seamless mutations that are precise to a single base pair of DNA. The entire procedure requires ∼2 weeks.

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org .

Insects often overcome unfavorable seasons in a hormonally regulated state of diapause during which their activity ceases, development is arrested, metabolic rate is suppressed, and tolerance of environmental stress is bolstered. Diapausing insects pass through a stereotypic succession of eco-physiological phases termed “diapause development.” The phasing is varied in the literature, and the whole concept is sometimes criticized as being too artificial. Here we present the results of transcriptional profiling using custom microarrays representing 1,042 genes in the drosophilid fly, Chymomyza costata. Fully grown, third-instar larvae programmed for diapause by a photoperiodic (short-day) signal were assayed as they traversed the diapause developmental program. When analyzing the gradual dynamics in the transcriptomic profile, we could readily distinguish distinct diapause developmental phases associated with induction/initiation, maintenance, cold acclimation, and termination by cold or by photoperiodic signal. Accordingly, each phase is characterized by a specific pattern of gene expression, supporting the physiological relevance of the concept of diapause phasing. Further, we have dissected in greater detail the changes in transcript levels of elements of several signaling pathways considered critical for diapause regulation. The phase of diapause termination is associated with enhanced transcript levels in several positive elements stimulating direct development (the 20-hydroxyecdysone pathway: Ecr, Shd, Broad; the Wnt pathway: basket, c-jun) that are countered by up-regulation in some negative elements (the insulin-signaling pathway: Ilp8, PI3k, Akt; the target of rapamycin pathway: Tsc2 and 4EBP; the Wnt pathway: shaggy). We speculate such up-regulations may represent the early steps linked to termination of diapause programming.

Background As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. Results The standard index of DNA methylation at any specific CpG site is β = M /( M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas ( β s) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Conclusions Careful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.