Ning Leng

EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments

MOTIVATION Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others. RESULTS Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes. AVAILABILITY AND IMPLEMENTATION An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/kendzior/EBSEQ/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Comparative RNA-seq Analysis in the Unsequenced Axolotl: The Oncogene Burst Highlights Early Gene Expression in the Blastema

The salamander has the remarkable ability to regenerate its limb after amputation. Cells at the site of amputation form a blastema and then proliferate and differentiate to regrow the limb. To better understand this process, we performed deep RNA sequencing of the blastema over a time course in the axolotl, a species whose genome has not been sequenced. Using a novel comparative approach to analyzing RNA-seq data, we characterized the transcriptional dynamics of the regenerating axolotl limb with respect to the human gene set. This approach involved de novo assembly of axolotl transcripts, RNA-seq transcript quantification without a reference genome, and transformation of abundances from axolotl contigs to human genes. We found a prominent burst in oncogene expression during the first day and blastemal/limb bud genes peaking at 7 to 14 days. In addition, we found that limb patterning genes, SALL genes, and genes involved in angiogenesis, wound healing, defense/immunity, and bone development are enriched during blastema formation and development. Finally, we identified a category of genes with no prior literature support for limb regeneration that are candidates for further evaluation based on their expression pattern during the regenerative process.

Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments

Oscillatory gene expression is fundamental to development, but technologies for monitoring expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applying Oscope to a number of data sets, we demonstrated its utility and also identified a potential artifact in the Fluidigm C1 platform.

The osteoblast to osteocyte transition: epigenetic changes and response to the vitamin D3 hormone.

Osteocytes are derived from osteoblast lineage cells that become progressively embedded in mineralized bone. Development of the osteocytogenic cell line IDG-SW3 has enabled a temporal and mechanistic investigation of this process. Through RNA-sequencing analyses, we show that although substantial changes in gene expression occur during the osteoblast to osteocyte transition, the majority of the transcriptome remains qualitatively osteoblast like. Genes either up-regulated or expressed uniquely in the osteocyte include local and systemic factors such as Sost and Fgf23 as well as genes implicated in neuronal, muscle, vascular, or regulatory function. As assessed by chromatin immunoprecipitation coupled to high-throughput sequencing, numerous changes in epigenetic histone modifications also occur during osteocytogenesis; these are largely qualitative rather than quantitative. Specific epigenetic changes correlate with altered gene expression patterns that are observed during the transition. These genomic changes likely influence the highly restricted transcriptomic response to 1,25(OH)(2)D(3) that occurs during differentiation. VDR binding in osteocytes revealed an extensive cistrome co-occupied by retinoid X receptor and located predominantly at sites distal to regulated genes. Although sites of VDR binding were apparent near many 1,25(OH)(2)D(3)-regulated genes, the expression of others adjacent to VDR-binding sites were unaffected; lack of VDR binding was particularly prevalent at down-regulated genes. Interestingly, 1,25(OH)(2)D(3) was found to induce the Boc and Cdon coreceptors that are active in hedgehog signaling in osteocytes. We conclude that osteocytogenesis is accompanied by changes in gene expression that may be driven by both genetic and epigenetic components. These changes are likely responsible for the osteocyte phenotype and may contribute to reduced sensitivity to 1,25(OH)(2)D(3).

EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data. Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression. Availability and implementation: An R package containing examples and sample datasets is available at Bioconductor. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

SCnorm: robust normalization of single-cell RNA-seq data

The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

Collaborative rewiring of the pluripotency network by chromatin and signalling modulating pathways

Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) represents a profound change in cell fate. Here, we show that combining ascorbic acid (AA) and 2i (MAP kinase and GSK inhibitors) increases the efficiency of reprogramming from fibroblasts and synergistically enhances conversion of partially reprogrammed intermediates to the iPSC state. AA and 2i induce differential transcriptional responses, each leading to the activation of specific pluripotency loci. A unique cohort of pluripotency genes including Esrrb require both stimuli for activation. Temporally, AA-dependent histone demethylase effects are important early, whereas Tet enzyme effects are required throughout the conversion. 2i function could partially be replaced by depletion of components of the epidermal growth factor (EGF) and insulin growth factor pathways, indicating that they act as barriers to reprogramming. Accordingly, reduction in the levels of the EGF receptor gene contributes to the activation of Esrrb. These results provide insight into the rewiring of the pluripotency network at the late stage of reprogramming.

A Wireless Spectrum Analyzer in Your Pocket

We propose Snoopy, a system that can translate ones mobile phone or tablet into a low-cost, yet effective RF spectrum analyzer. Since typical spectrum analyzers are specialized hardware that is both expensive to acquire and cumbersome to carry around, they are rarely available for quick-and-easy spectrum sensing while on the go. To address this challenge, Snoopy augments popular mobile devices with a small attachable hardware unit (RF frequency translator) that can provide a reasonable view of the wireless spectrum across different frequency bands. It achieves this by leveraging the spectral scan functionality available in certain 802.11 NICs (e.g., the Atheros 9280 family of chipsets), which provides an unique lens towards the WiFi spectrum (2.4 GHz). Through the use of suitable frequency translators, such a view can be flexibly shifted to other spectrum bands. Although such a construction might not match the precision of the most sophisticated but expensive spectrum analyzers, we show that by leveraging some carefully designed spectral features, Snoopy can achieve decent accuracy in determining TV whitespaces (512 -- 698 MHz) -- it can detect primary signals at up to - 90dBm with an error rate of <15%, while achieving a median error of < 4dB in estimating the power of these signals. These promising results suggest that Snoopy is an intriguing option in bringing the ability of spectrum sensing to the masses, thereby truly enabling crowdsourcing options in this domain.

Analysis of embryonic development in the unsequenced axolotl: Waves of transcriptomic upheaval and stability

The axolotl (Ambystoma mexicanum) has long been the subject of biological research, primarily owing to its outstanding regenerative capabilities. However, the gene expression programs governing its embryonic development are particularly underexplored, especially when compared to other amphibian model species. Therefore, we performed whole transcriptome polyA+ RNA sequencing experiments on 17 stages of embryonic development. As the axolotl genome is unsequenced and its gene annotation is incomplete, we built de novo transcriptome assemblies for each stage and garnered functional annotation by comparing expressed contigs with known genes in other organisms. In evaluating the number of differentially expressed genes over time, we identify three waves of substantial transcriptome upheaval each followed by a period of relative transcriptome stability. The first wave of upheaval is between the one and two cell stage. We show that the number of differentially expressed genes per unit time is higher between the one and two cell stage than it is across the mid-blastula transition (MBT), the period of zygotic genome activation. We use total RNA sequencing to demonstrate that the vast majority of genes with increasing polyA+ signal between the one and two cell stage result from polyadenylation rather than de novo transcription. The first stable phase begins after the two cell stage and continues until the mid-blastula transition, corresponding with the pre-MBT phase of transcriptional quiescence in amphibian development. Following this is a peak of differential gene expression corresponding with the activation of the zygotic genome and a phase of transcriptomic stability from stages 9-11. We observe a third wave of transcriptomic change between stages 11 and 14, followed by a final stable period. The last two stable phases have not been documented in amphibians previously and correspond to times of major morphogenic change in the axolotl embryo: gastrulation and neurulation. These results yield new insights into global gene expression during early stages of amphibian embryogenesis and will help to further develop the axolotl as a model species for developmental and regenerative biology.

OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data

Summary: A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations which allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform. Availability and implementation: OEFinder is freely available at https://github.com/lengning/OEFinder Contact: rstewart@morgridge.org or lengning1@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.