De novo RNA-Seq assembly facilitates the study of transcriptomes for species without sequenced genomes, but it is challenging to select the most accurate assembly in this context. To address this challenge, we developed a model-based score, RSEM-EVAL, for evaluating assemblies when the ground truth is unknown. We show that RSEM-EVAL correctly reflects assembly accuracy, as measured by REF-EVAL, a refined set of ground-truth-based scores that we also developed. Guided by RSEM-EVAL, we assembled the transcriptome of the regenerating axolotl limb; this assembly compares favorably to a previous assembly. A software package implementing our methods, DETONATE, is freely available at http://deweylab.biostat.wisc.edu/detonate.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0553-5) contains supplementary material, which is available to authorized users.
Background Emerging data suggest variability in susceptibility and outcome to COVID-19 infection. Identifying risk-factors associated with infection and outcomes in cancer patients is necessary to develop healthcare recommendations. Methods We analyzed electronic health records of the US Veterans Affairs healthcare system and assessed the prevalence of COVID-19 infection in cancer patients. We evaluated the proportion of cancer patients tested for COVID-19 who were positive, as well as outcome attributable to COVID-19, and stratified by clinical characteristics including demographics, comorbidities, cancer treatment and cancer type. All statistical tests are two-sided. Results Of 22914 cancer patients tested for COVID-19, 1794 (7.8%) were positive. The prevalence of COVID-19 was similar across age. Higher prevalence was observed in African-American (AA) (15.0%) compared to White (5.5%; P<.001) and in patients with hematologic malignancy compared to those with solid tumors (10.9% vs 7.8%; P<.001). Conversely, prevalence was lower in current smokers and patients who recently received cancer therapy (<6 months). The COVID-19 attributable mortality was 10.9%. Higher attributable mortality rates were observed in older patients, those with higher Charlson comorbidity score, and in certain cancer types. Recent (<6 months) or past treatment did not influence attributable mortality. Importantly, AA patients had 3.5-fold higher COVID-19 attributable hospitalization, however had similar attributable mortality as White patients. Conclusion Pre-existence of cancer affects both susceptibility to COVID-19 infection and eventual outcome. The overall COVID-19 attributable mortality in cancer patients is affected by age, comorbidity and specific cancer types, however, race or recent treatment including immunotherapy does not impact outcome.
This cohort study assesses the association between SARS-CoV-2 vaccination and SARS-CoV-2 infections among a population of Veterans Affairs (VA) patients with cancer.
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