Single-cell RNA-seq's (scRNA-seq) unprecedented cellular resolution at a genome wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts.However, scRNA-seq datasets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present SAKE (Single-cell RNA-seq Analysis and Klustering Evaluation): a robust method for scRNA-seq analysis that provides quantitative statistical metrics at each step of the scRNA-seq analysis pipeline.Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors. Single-cell RNA-seq Cold Spring Harbor Laboratory Press on June 7, 2019 -Published by genome.cshlp.org Downloaded from data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. These experimentally validated markers of BRAFi resistance share overlap with previous analysis in different melanoma cell lines, demonstrating the generality of these findings and highlighting the utility of single-cell analysis to elucidate mechanisms of BRAFi resistance.
The advent of next-generation sequencing has allowed for higher-throughput determination of which species live within a specific location. Here we establish that three analysis methods for estimating diversity within samples—namely, Operational Taxonomic Units; the newer Amplicon Sequence Variants; and a method commonly found in sequence analysis, minhash—are affected by various properties of these sequence data. Using simulations we show that the presence of Single Nucleotide Polymorphisms and the depth of coverage from each species affect the correlations between these approaches. Through this analysis, we provide insights which would affect the decisions on the application of each method. Specifically, the presence of sequence read errors and variability in sequence read coverage deferentially affects these processing methods.
IntroductionPersonalising menopausal healthcare with tailored information and shared decision making between the health professional and woman is the ideal. This should begin before the menopause transition. This review will explore the evolving opportunity that the Internet affords in enabling this process.Over the past decade, the management of woman's healthcare has changed from the alleviation of menopausal symptoms to include the management of other lifechanging medical issues falling under the aegis of post-reproductive health; A lifecourse approach 1 aimed at both maximising health and minimising, if not preventing, negative sequelae in this third stage of a woman's life, provides an important framework for care. This framework is recognised by the Royal College ofObstetricians and Gynaecologists as a standard in which to understand and mitigate against the long-term effects of earlier biological, behavioural and social exposures.A life-course approach in women's health also acknowledges gender-specific medicine.Gender-specific medicine is the study of how diseases differ between men and women in terms of prevention, clinical signs, therapeutic approach, prognosis, psychological and social impact -it is a neglected dimension of medicine. 2 Gender differences in susceptibility to complex diseases; e.g. asthma, diabetes and depression, come under the umbrella of epigenetics. 3 Epigenetics is the study of factors which control gene expression other than the genetic code itself, and epigenetic markers have been shown to be passed on to children and grandchildren 4 challenging the paradigm that disease caused by lifestyle can have no effect on offspring. The Barker hypothesis 5 was a forerunner to epigenetics and proposed programming in utero and in infancy as a mechanism for later poor health. Links are well established between reduced birth weight and increased risk of coronary heart disease, diabetes, hypertension, and stroke in adulthood. The possibilities of understanding these complex relationships are being explored in the emerging fields of systems biology 6 and Big Data analytics 7. Mitigating against this "toxic" information and other future diseases that have epigenetic origins fits within a lifecourse framework, not least maximising post reproductive health. 8 The Internet is expected to be increasingly utilised as a tool for pathological and biological testing.Direct to consumer genetic testing is already available and the market is expected to grow and to date, negative effects on consumers or health benefits have yet to be observed.9
Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year—with 180,000 resulting deaths—mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations.
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