Deep RNA-Seq profile reveals biodiversity, plant-microbe interactions and a large family of NBS-LRR resistance genes in walnut (Juglans regia) tissues.

Deep RNA-Seq profiling, a revolutionary method used for quantifying transcriptional levels, often includes non-specific transcripts from other co-existing organisms in spite of stringent protocols. Using the recently published walnut genome sequence as a filter, we present a broad analysis of the RNA-Seq derived transcriptome profiles obtained from twenty different tissues to extract the biodiversity and possible plant-microbe interactions in the walnut ecosystem in California. Since the residual nature of the transcripts being analyzed does not provide sufficient information to identify the exact strain, inferences made are constrained to the genus level. The presence of the pathogenic oomycete Phytophthora was detected in the root through the presence of a glyceraldehyde-3-phosphate dehydrogenase. Cryptococcus, the causal agent of cryptococcosis, was found in the catkins and vegetative buds, corroborating previous work indicating that the plant surface supported the sexual cycle of this human pathogen. The RNA-Seq profile revealed several species of the endophytic nitrogen fixing Actinobacteria. Another bacterial species implicated in aerobic biodegradation of methyl tert-butyl ether (Methylibium petroleiphilum) is also found in the root. RNA encoding proteins from the pea aphid were found in the leaves and vegetative buds, while a serine protease from mosquito with significant homology to a female reproductive tract protease from Drosophila mojavensis in the vegetative bud suggests egg-laying activities. The comprehensive analysis of RNA-seq data present also unraveled detailed, tissue-specific information of ~400 transcripts encoded by the largest family of resistance (R) genes (NBS-LRR), which possibly rationalizes the resistance of the specific walnut plant to the pathogens detected. Thus, we elucidate the biodiversity and possible plant-microbe interactions in several walnut (Juglans regia) tissues in California using deep RNA-Seq profiling

WP 2016-348

Using data from the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA), we estimate a dynamic model of health and employment. We estimate how transitory and persistent health shocks affect employment over time. In a first step we formulate and estimate a dynamic model of health. The procedure accounts for measurement error and the possibility that people might justify their employment status by reporting bad health. We find that health is well represented by the sum of a transitory white noise process and a persistent AR(1) process. Next, we use the method of simulated moments to estimate the employment response to these shocks. We find that persistent shocks have much bigger effects on employment than transitory shocks, and that these persistent shocks are long lived. For this reason employment is strongly correlated with lagged health, a fact that the usual cross sectional estimates do not account for. We also show that accounting for the dynamics of health and employment leads to larger estimates of the effect of health on employment than what simple OLS estimates of health on employment would imply. We argue that the dynamic effect of health on employment could be generated by a model with human capital accumulation, where negative health shocks slowly reduce the human capital stock, and thus gradually causes people to exit the labor market.Social Security Administration, RRC08098401, R-UM16-16http://deepblue.lib.umich.edu/bitstream/2027.42/134680/1/wp348.pdfDescription of wp348.pdf : Working pape

The impact of health on labor supply near retirement

Estimates of how health affects employment vary considerably. We assess how different methods and health measures impact estimates of the impact of health on employment using a unified framework for the US and England. We find that subjective and objective health measures, and subjective measures instrumented by objective measures produce similar estimates when using sufficiently rich objective measures. Moreover, a single health index can capture the relevant health variation for employment. Health deterioration explains up to 15% of the decline in employment between ages 50 and 70. Effects are larger for the US than England, and for the low educated

WP 2017-364

Working paperEstimates of effect of health on employment differ from study to study due to differences in methods, data, institutional background and health measure. We assess the importance of these differences, using a unified framework to interpret and contrast estimate for the US and England. We find that subjective and objective health measures, and subjective measures instrumented by objective measures produce similar estimates but only if a sufficiently large number of objective measures is used. Otherwise, objective measures produce downward biased estimates. Failure to account for initial conditions produces upward biased estimates. We find that a single subjective health index yields similar estimates to multiple measures. Overall, declines in health explain up to 15% of the decline in employment between ages 50 and 70. The effects drop with education and are larger in the US than in England. Cognition has little added explanatory power once we control for health.Social Security Administration, RRC08098401-09, R-UM17-02https://deepblue.lib.umich.edu/bitstream/2027.42/140752/1/wp364.pdfDescription of wp364.pdf : Working pape

YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software

YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut [version 2; referees: 3 approved]

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software

A systematic review and meta-analysis of e-Mental Health interventions to treat symptoms of post-traumatic stress

Quality control of RNA-seq data: Box Plot of transcript quantification levels. The distributions of FPKM scores across samples are visualized. (PDF 130 kb

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