High-throughput sequencing enables unbiased profiling of microbial communities, universal pathogen detection, and host response to infectious diseases. However, computation times and algorithmic inaccuracies have hindered adoption.

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.

Genomic mutations caused by cytotoxic agents used in cancer chemotherapy may cause secondary malignancies as well as contribute to the evolution of treatment-resistant tumour cells. The stable diploid genome of the chicken DT40 lymphoblast cell line, an established DNA repair model system, is well suited to accurately assay genomic mutations.

Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being hard to trace to their basis in the data. Here, we use both published and novel single-cell RNA sequencing (RNA-seq) data to understand fundamental drivers of gene-gene connectivity and replicability in co-expression networks.

The most widely utilized approaches for quantifying DNA methylation involve the treatment of genomic DNA with sodium bisulfite; however, this method cannot distinguish between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Previous studies have shown that 5hmC is enriched in the brain, although little is known about its genomic distribution and how it differs between anatomical regions and individuals. In this study, we combine oxidative bisulfite (oxBS) treatment with the Illumina Infinium 450K BeadArray to quantify genome-wide patterns of 5hmC in two distinct anatomical regions of the brain from multiple individuals.

Recent evidence suggests that RNA interaction can regulate the activity and localization of chromatin-associated proteins. However, it is unknown if these observations are specialized instances for a few key RNAs and chromatin factors in specific contexts, or a general mechanism underlying the establishment of chromatin state and regulation of gene expression.

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.

Although genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis.

Recent advances in single-cell techniques have provided the opportunity to finely dissect cellular heterogeneity within populations previously defined by "bulk" assays and to uncover rare cell types. In human hematopoiesis, megakaryocytes and erythroid cells differentiate from a shared precursor, the megakaryocyte-erythroid progenitor (MEP), which remains poorly defined.

Expression quantitative trait loci (eQTL) mapping is a widely used tool to study the genetics of gene expression. Confounding factors and the burden of multiple testing limit the ability to map distal trans eQTLs, which is important to understand downstream genetic effects on genes and pathways. We propose a two-stage linear mixed model that first learns local directed gene-regulatory networks to then condition on the expression levels of selected genes. We show that this covariate selection approach controls for confounding factors and regulatory context, thereby increasing eQTL detection power and improving the consistency between studies. GNet-LMM is available at: https://github.com/PMBio/GNetLMM.

The planarian Schmidtea mediterranea is a master regenerator with a large adult stem cell compartment. The lack of transgenic labeling techniques in this animal has hindered the study of lineage progression and has made understanding the mechanisms of tissue regeneration a challenge. However, recent advances in single-cell transcriptomics and analysis methods allow for the discovery of novel cell lineages as differentiation progresses from stem cell to terminally differentiated cell.

Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.

And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.

Cells detect and adapt to hypoxic and nutritional stress through immediate transcriptional, translational and metabolic responses. The environmental effects of ischemia on chromatin nanostructure were investigated using single molecule localization microscopy of DNA binding dyes and of acetylated histones, by the sensitivity of chromatin to digestion with DNAseI, and by fluorescence recovery after photobleaching (FRAP) of core and linker histones.

Genetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTLs).

Microbes are main drivers of biogeochemical cycles in oceans and lakes. Although the genome is a foundation for understanding the metabolism, ecology and evolution of an organism, few bacterioplankton genomes have been sequenced, partly due to difficulties in cultivating them.

Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy causing systolic dysfunction and heart failure. Rare variants in more than 30 genes, mostly encoding sarcomeric proteins and proteins of the cytoskeleton, have been implicated in familial DCM to date. Yet, the majority of variants causing DCM remain to be identified. The goal of the study is to identify novel mutations causing familial dilated cardiomyopathy.

The role of cytokines in establishing specific transcriptional programmes in innate immune cells has long been recognized. However, little is known about how these extracellular factors instruct innate immune cell epigenomes to engage specific differentiation states. Human monocytes differentiate under inflammatory conditions into effector cells with non-redundant functions, such as dendritic cells and macrophages. In this context, interleukin 4 (IL-4) and granulocyte macrophage colony-stimulating factor (GM-CSF) drive dendritic cell differentiation, whereas GM-CSF alone leads to macrophage differentiation.

Although the chemopreventive effects of aspirin have been extensively investigated, the roles of many cell components, such as long non-coding RNAs, in these effects are still not completely understood.

Analysis of somatic mutations provides insight into the mutational processes that have shaped the cancer genome, but such analysis currently requires large cohorts. We develop deconstructSigs, which allows the identification of mutational signatures within a single tumor sample.