The accuracy of absolute differential abundance analysis from relative count data.

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with a median sensitivity (true positive rates) of 0.91 and specificity (1-false positive rates) of 0.89, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give reasonable predictions of discrepancy in differential abundance calling in simulated data and can provide useful bounds for worst-case outcomes in real data.

A topological data analytic approach for discovering biophysical signatures in protein dynamics.

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.

Impact of COVID-19 pandemic on rheumatology trainees: an online survey.

To assess the impact of the COVID-19 pandemic on the training of rheumatology trainees. We conducted an observational cross-sectional study using an online survey-based questionnaire sent to rheumatology trainees in India. Rheumatology trainees from India, including DM/DNB residents and fellows, were included. A total of 78 trainees from 24 institutes in 12 states participated in the study. An overwhelming majority of residents (84%) felt COVID-19 Pandemic Negatively impacted their residency and their Physical (65%), Mental (74%) and Social well-being (80%); 79% of trainees felt burnt out. Majority of trainees felt the pandemic negatively impacted their training with clinical teaching (91%), Clinical examination skills (74%), current (80%) and future (70%) research opportunities suffering during the pandemic. Most had significant reduction in the overall footfall (72%) of patients in rheumatology including OPD (77%) and indoor (67%) admissions along with academics (35%), procedures (66%) and exposure to musculoskeletal ultrasound (71%). Almost 60% and 40% of trainees had OPDs, and indoor admissions stopped during COVID-19 pandemic of these 20% had OPDs, and Admissions closed for more than 6 months. 85% of participants had one or the other psychological symptoms with almost half experiencing anxiety (44%), low mood (47%) or lack of sleep (41%). We found The COVID-19 Pandemic has significantly affected the physical, social and mental well-being of Rheumatology trainees. Academic and clinical training reduced, current and future Research became difficult, disruptions in OPDs and Admissions, recurrent COVID postings and reduction in patient footfall, procedures and MSK-US have been detrimental to trainees.

Generalized Bayes approach to inverse problems with model misspecification.

We propose a general framework for obtaining probabilistic solutions to PDE-based inverse problems. Bayesian methods are attractive for uncertainty quantification but assume knowledge of the likelihood model or data generation process. This assumption is difficult to justify in many inverse problems, where the specification of the data generation process is not obvious. We adopt a Gibbs posterior framework that directly posits a regularized variational problem on the space of probability distributions of the parameter. We propose a novel model comparison framework that evaluates the optimality of a given loss based on its "predictive performance". We provide cross-validation procedures to calibrate the regularization parameter of the variational objective and compare multiple loss functions. Some novel theoretical properties of Gibbs posteriors are also presented. We illustrate the utility of our framework via a simulated example, motivated by dispersion-based wave models used to characterize arterial vessels in ultrasound vibrometry.

A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors.

MOTIVATION: Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. RESULTS: We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model's ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Measuring and mitigating PCR bias in microbiota datasets.

PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

Asymmetric 1,4-Michael Addition in Aqueous Medium Using Hydrophobic Chiral Organocatalysts.

Organic transformations exclusively in water as an environmentally friendly and safe medium have drawn significant interest in the recent years. Moreover, transition metal-free synthesis of enantiopure molecules in water will have a great deal of attention as the system will mimic the natural enzymatic reactions. In this work, a new set of proline-derived hydrophobic organocatalysts have been synthesized and utilized for asymmetric Michael reactions in water as the sole reaction medium. Among the various catalysts screened, the catalyst 1 is indeed efficient for stereoselective 1,4-conjugated Michael additions (dr: >97:3, ee up to >99.9%) resulting in high chemical yields (up to 95%) in a very short reaction time (1 h) at room temperature. This methodology provides a robust, green, and convenient protocol and can thus be an important addition to the arsenal of the asymmetric Michael addition reaction. Upon successful implementation, the present strategy also led to the formation of an optically active octahydroindole, the key component found in many natural products.

Combinations of DIPs and Dprs control organization of olfactory receptor neuron terminals in Drosophila.

In Drosophila, 50 classes of olfactory receptor neurons (ORNs) connect to 50 class-specific and uniquely positioned glomeruli in the antennal lobe. Despite the identification of cell surface receptors regulating axon guidance, how ORN axons sort to form 50 stereotypical glomeruli remains unclear. Here we show that the heterophilic cell adhesion proteins, DIPs and Dprs, are expressed in ORNs during glomerular formation. Many ORN classes express a unique combination of DIPs/dprs, with neurons of the same class expressing interacting partners, suggesting a role in class-specific self-adhesion between ORN axons. Analysis of DIP/Dpr expression revealed that ORNs that target neighboring glomeruli have different combinations, and ORNs with very similar DIP/Dpr combinations can project to distant glomeruli in the antennal lobe. DIP/Dpr profiles are dynamic during development and correlate with sensilla type lineage for some ORN classes. Perturbations of DIP/dpr gene function result in local projection defects of ORN axons and glomerular positioning, without altering correct matching of ORNs with their target neurons. Our results suggest that context-dependent differential adhesion through DIP/Dpr combinations regulate self-adhesion and sort ORN axons into uniquely positioned glomeruli.

Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways.

INTRODUCTION: The study of Alzheimer's disease (AD) has revealed biological pathways with implications for disease neuropathology and pathophysiology. These pathway-level effects may also be mediated by individual characteristics or covariates such as age or sex. Evaluation of AD biological pathways in the context of interactions with these covariates is critical to the understanding of AD as well as the development of model systems used to study the disease. METHODS: Gene set enrichment methods are powerful tools used to interpret gene-level statistics at the level of biological pathways. We introduce a method for quantifying gene set enrichment using likelihood ratio-derived test statistics (gsLRT), which accounts for sample covariates like age and sex. We then use our method to test for age and sex interactions with protein expression levels in AD and to compare the pathway results between human and mouse species. RESULTS: Our method, based on nested logistic regressions is competitive with the existing standard for gene set testing in the context of linear models and complex experimental design. The gene sets we identify as having a significant association with AD-both with and without additional covariate interactions-are validated by previous studies. Differences between gsLRT results on mouse and human datasets are observed. DISCUSSION: Characterizing biological pathways involved in AD builds on the important work involving single gene drivers. Our gene set enrichment method finds pathways that are significantly related to AD while accounting for covariates that may be relevant to disease development. The method highlights commonalities and differences between human AD and mouse models, which may inform the development of higher fidelity models for the study of AD.

Evolution of DNA Methylation in Papio Baboons.

Changes in gene regulation have long been thought to play an important role in primate evolution. However, although a number of studies have compared genome-wide gene expression patterns across primate species, fewer have investigated the gene regulatory mechanisms that underlie such patterns, or the relative contribution of drift versus selection. Here, we profiled genome-scale DNA methylation levels in blood samples from five of the six extant species of the baboon genus Papio (4-14 individuals per species). This radiation presents the opportunity to investigate DNA methylation divergence at both shallow and deeper timescales (0.380-1.4 My). In contrast to studies in human populations, but similar to studies in great apes, DNA methylation profiles clearly mirror genetic and geographic structure. Divergence in DNA methylation proceeds fastest in unannotated regions of the genome and slowest in regions of the genome that are likely more constrained at the sequence level (e.g., gene exons). Both heuristic approaches and Ornstein-Uhlenbeck models suggest that DNA methylation levels at a small set of sites have been affected by positive selection, and that this class is enriched in functionally relevant contexts, including promoters, enhancers, and CpG islands. Our results thus indicate that the rate and distribution of DNA methylation changes across the genome largely mirror genetic structure. However, at some CpG sites, DNA methylation levels themselves may have been a target of positive selection, pointing to loci that could be important in connecting sequence variation to fitness-related traits.

Cyclin-dependent kinases are regulators and effectors of oscillations driven by a transcription factor network.

During embryonic cell cycles, B-cyclin-CDKs function as the core component of an autonomous oscillator. Current models for the cell-cycle oscillator in nonembryonic cells are slightly more complex, incorporating multiple G1, S phase, and mitotic cyclin-CDK complexes. However, periodic events persist in yeast cells lacking all S phase and mitotic B-cyclin genes, challenging the assertion that cyclin-CDK complexes are essential for oscillations. These and other results led to the proposal that a network of sequentially activated transcription factors functions as an underlying cell-cycle oscillator. Here we examine the individual contributions of a transcription factor network and cyclin-CDKs to the maintenance of cell-cycle oscillations. Our findings suggest that while cyclin-CDKs are not required for oscillations, they do contribute to oscillation robustness. A model emerges in which cyclin expression (thereby, CDK activity) is entrained to an autonomous transcriptional oscillator. CDKs then modulate oscillator function and serve as effectors of the oscillator.

Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects-the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the "MArginal ePIstasis Test", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.

Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia.

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Pyrrolidine-Oxadiazolone Conjugates as Organocatalysts in Asymmetric Michael Reaction.

Pyrrolidine-oxadiazolone based organocatalysts are envisaged, synthesized, and utilized for asymmetric Michael reactions. Results of the investigations suggest that some of the catalysts are indeed efficient for stereoselective 1,4-conjugated Michael additions (dr: >97:3, ee up to 99%) in high chemical yields (up to 97%) often in short reaction time. As an extension, one enantiopure Michael adduct has been utilized to synthesize optically active octahydroindole.

Differential expression analysis for RNAseq using Poisson mixed models.

Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.

Copper Catalyzed C-N Cross-Coupling Reaction of Aryl Boronic Acids at Room Temperature through Chelation Assistance.

A copper-catalyzed selective C-N cross-coupling has been developed based on chelation-assisted amidation of readily available aryl boronic acids at room-temperature under open-flask conditions. The reaction is scalable and tolerates a wide spectrum of functional groups delivering fully substituted unsymmetrical amides in high yields (up to 96%). The C-N cross coupling also established with aryl silanes, extending the palette of coupling partners of this strategy.

DNase-seq predicts regions of rotational nucleosome stability across diverse human cell types.

DNase-seq is primarily used to identify nucleosome-depleted DNase I hypersensitive (DHS) sites genome-wide that correspond to active regulatory elements. However, ≈ 40 yr ago it was demonstrated that DNase I also digests with a ≈ 10-bp periodicity around nucleosomes matching the exposure of the DNA minor groove as it wraps around histones. Here, we use DNase-seq data from 49 samples representing diverse cell types to reveal this digestion pattern at individual loci and predict genomic locations where nucleosome rotational positioning, the orientation of DNA with respect to the histone surface, is stably maintained. We call these regions DNase I annotated regions of nucleosome stability (DARNS). Compared to MNase-seq experiments, we show DARNS correspond well to annotated nucleosomes. Interestingly, many DARNS are positioned over only one side of annotated nucleosomes, suggesting that the periodic digestion pattern attenuates over the nucleosome dyad. DARNS reproduce the arrangement of nucleosomes around transcription start sites and are depleted at ubiquitous DHS sites. We also generated DARNS from multiple lymphoblast cell line (LCL) samples. We found that LCL DARNS were enriched at DHS sites present in most of the original 49 samples but absent in LCLs, while multi-cell-type DARNS were enriched at LCL-specific DHS sites. This indicates that variably open DHS sites are often occupied by rotationally stable nucleosomes in cell types where the DHS site is closed. DARNS provide additional information about precise DNA orientation within individual nucleosomes not available from other nucleosome positioning assays and contribute to understanding the role of chromatin in gene regulation.

Complexity in congestive heart failure: A time-frequency approach.

Reconstruction of phase space is an effective method to quantify the dynamics of a signal or a time series. Various phase space reconstruction techniques have been investigated. However, there are some issues on the optimal reconstructions and the best possible choice of the reconstruction parameters. This research introduces the idea of gradient cross recurrence (GCR) and mean gradient cross recurrence density which shows that reconstructions in time frequency domain preserve more information about the dynamics than the optimal reconstructions in time domain. This analysis is further extended to ECG signals of normal and congestive heart failure patients. By using another newly introduced measure-gradient cross recurrence period density entropy, two classes of aforesaid ECG signals can be classified with a proper threshold. This analysis can be applied to quantifying and distinguishing biomedical and other nonlinear signals.

Random walks on simplicial complexes and harmonics.

In this paper, we introduce a class of random walks with absorbing states on simplicial complexes. Given a simplicial complex of dimension d, a random walk with an absorbing state is defined which relates to the spectrum of the k-dimensional Laplacian for 1 ≤ k ≤ d. We study an example of random walks on simplicial complexes in the context of a semi-supervised learning problem. Specifically, we consider a label propagation algorithm on oriented edges, which applies to a generalization of the partially labelled classification problem on graphs. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 49, 379-405, 2016.

Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.

Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene-disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoma and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.