Study overview and analysis workflow.

Slides:

Advertisements

Similar presentations

B USINESS IN M EDICINE H OW ARE WE USING T ELEMETRY ? Asad Qasim MD, MPH – PGY2.

Advertisements

Prediction Models in Medicine Clinical Decision Support The Road Ahead Chapter 10.

The Microbiome and Metagenomics

References 1.Salazar R, Roepman P, Capella G et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J.

ASSURED criteria (WHO) for the evaluation of point-of-care devices CharacteristicExample target specification Affordable Less than US$ 500 per machine,

Case Study: Characterizing Diseased States from Expression/Regulation Data Tuck et al., BMC Bioinformatics, 2006.

Engineering magnetosomes to express novel proteins Which ones? Tweaking p18 Linker Deleting or replacing GFP Something else? TRZN Oxalate decarboxylases.

D EPARTMENT OF EPIDEMIOLOGY AND PREVENTIVE MEDICINE.

Shotgun sequencing reveals transkingdom alterations in immunodeficiency associated enteropathy Xiaoxi Dong (Oregon State University), Jialu Hu (Oregon.

Bacterial and viral infections in patients requiring hospitalization : effect of mixed infections on clinical outcome J. Petitjean Lecherbonnier 1, F.

Comparison between pathogen directed antibiotic treatment and empiri cal broad spectrum antibiotic treatment in patients with community acquired pneumonia.

The Effects of Airway Microbiome on Corticosteroid Responsiveness in Asthma AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE Nov 15, 2013 R2.

R3 조 욱 Salivary Transcriptomic Biomarkers for Detection of Resectable Pancreatic Cancer Articles LEI ZHANG, JAMES J. FARRELL, HUI ZHOU, DAVID ELASHOFF,

David Amar, Tom Hait, and Ron Shamir

Background Post-transplant outcomes in adult Fontan patients remain poorly defined. Available studies limited to sub-groups within published registry data.

Statistical Applications in Biology and Genetics

Gene expression.

Three lessons I learned

dentistry deintyddiaeth SCHOOL K-3364 ysgol

Summary of genetic alterations in resistant biopsies among patients progressing on ceritinib or alectinib. Summary of genetic alterations in resistant.

(A) Heat map showing expression changes during in vitro differentiation of nscs to astrocytes. (A) Heat map showing expression changes during in vitro.

Distribution of mutations in different groups of genes, in cancer with extreme ML values. Distribution of mutations in different groups of genes, in cancer.

Cg-OxyR crystal structure shows a more symmetric subunit arrangement compared with the asymmetric relation between the RD and DBD homodimers of Pa-OxyR.

Transient overexpression of FveCYP707A4a delayed fruit ripening.

Cg-OxyR controls catalase transcription, recovery from lag phase, survival to H2O2 stress, and H2O2 scavenging rate. Cg-OxyR controls catalase transcription,

Differential expression of select miRNAs was validated in a second cohort of patients. Differential expression of select miRNAs was validated in a second.

Patients with SLE have elevated ERV expression.

Estimation of R(t). Estimation of R(t). (A) Daily R(t) as inferred from the daily incidence of new infections for one stochastic model realization. Tg.

Figure 1 Metabolic profiling as a tool for studying rheumatic diseases

Predict Failures with Developer Networks and Social Network Analysis

Distribution of respiratory pathogens identified in patients using clinician-ordered diagnostics versus mNGS. Distribution of respiratory pathogens identified.

Host transcriptional profiling distinguishes patients with acute LRTI (LRTI+C+M) from those with noninfectious acute respiratory illness (no-LRTI). Host.

RNA-seq analysis of iWAT and eWAT.

WPV1 complete VP1 gene phylogeny including the RC2010 viruses.

Combined LRTI prediction metric integrating pathogen detection and host gene expression. Combined LRTI prediction metric integrating pathogen detection.

WES detects a limited number of clinically targetable alterations in patients with advanced cancer. WES detects a limited number of clinically targetable.

Overall survival from enrolment according to operating rule and scenario of organ scarcity for a) scenario R96 (i.e. 96 organs for 100 virtual patients.

Hammoud et al. Am J Nephrol 2016;44: (DOI: / )

Workflow for distinguishing LRTI pathogens from commensal respiratory microbiota using an algorithmic approach. Workflow for distinguishing LRTI pathogens.

Questionnaire & physical exam

Figure 2 A model for the future analysis

Structural overview. Structural overview. (A) Domain organization of LRRK2. The residue boundary of the WD40 domain and locations of three recurrent disease.

PCA of RNAseq data from CTL TB and EOPE TB (GSE119265).

Validation of bioinformatic screening of a patient cohort.

Single Cell Regulatory Variation

Figure 1 Mutations in SPG7 in a family with primary lateral sclerosis

Validation of RNA transcription profile differential expression using real-time quantitative PCR. Relative gene expression in cases compared with controls.

Radiological evolution of acute respiratory distress syndrome over the first week in a 57-year-old male with non-Hodgkin’s lymphoma and H1N1 infection.

Bioinformatics Core Director

RNA-seq results at the SAM

E. Tacconelli Clinical Microbiology and Infection

Pyoverdine production measured as RFUs standardized by OD significantly decreases with length of infection in years. Pyoverdine production measured as.

Simplified Markov diagram of a patient admitted to the ward with no respiratory support. Simplified Markov diagram of a patient admitted to the ward with.

(Upper) Blind-to-controls generalization performance: prediction accuracies observed when the classifier was trained on the auditory trials from the blind.

Integrative analysis incorporating histopathology, transcript-based assessment of AR signaling and NEPC score, TP53 and RB1 genomic status, and clinical.

RNA-Seq analysis of CYR1 cells in the adaptation to acid pH.

Dissection of oligoclonality.

Landscape of genomic alterations in 444 tumors from 429 patients with mCRPC. Landscape of genomic alterations in 444 tumors from 429 patients with mCRPC.

Schematic representation of our random walk model with environmental state-dependent dynamics. Schematic representation of our random walk model with environmental.

Study institution’s PEWS

The up-regulation of Galnt3 by Pi requires activation of the ERK pathway and induction of Egr1 and Etv5. The up-regulation of Galnt3 by Pi requires activation.

(A) Spatial distribution of plague spread during the Third Pandemic.

Gene expression is reduced when RNAP binds a promoter too tightly.

Schematic representation of a transcriptomic evaluation approach.

Pro-inflammatory biomarkers in decompensated and stable HFpEF patients

Fig. 5. Vitamin B12 supplementation in the host altered the transcriptome of P. acnes in the skin microbiota. Vitamin B12 supplementation in the host altered.

EN1 expression in breast cancer and clinical outcome.

Transcriptomic fingerprint of advanced idiopathic pulmonary fibrosis (IPF) lung. Transcriptomic fingerprint of advanced idiopathic pulmonary fibrosis (IPF)

JAK3 mutations and ectopic expression of HOXA9 are significantly associated in clinical T-ALL cases. JAK3 mutations and ectopic expression of HOXA9 are.

General overview of the bioinformatic pipelines for the 16S rRNA gene microbial profiling and shotgun metagenomics. General overview of the bioinformatic.

Presentation transcript:

Study overview and analysis workflow. Study overview and analysis workflow. Patients with acute respiratory failure were enrolled within 72 h of ICU admission, and TA samples were collected and underwent both RNA sequencing (RNA-seq) and shotgun DNA sequencing (DNA-seq). Post hoc clinical adjudication blinded to mNGS results identified patients with LRTI defined by clinical and microbiologic criteria (LRTI+C+M); LRTI defined by clinical criteria only (LRTI+C); patients with noninfectious reasons for acute respiratory failure (no-LRTI); and respiratory failure due to unknown cause (unk-LRTI). The LRTI+C+M and no-LRTI groups were divided into derivation and validation cohorts. To detect pathogens and differentiate them from a background of commensal microbiota, we developed two models: a rules-based model (RBM) and a logistic regression model (LRM). LRTI probability was next evaluated with (i) a pathogen metric, (ii) a lung microbiome diversity metric, and (iii) a 12-gene host transcriptional classifier. Models were then combined and optimized for LRTI rule out. Charles Langelier et al. PNAS doi:10.1073/pnas.1809700115 ©2018 by National Academy of Sciences