'Expression levels' presentation slideshows

Web Technologies in Bioinformatics

Web Technologies in Bioinformatics. T.J. Esposito April 28, 2005 Advanced Bioinformatics Computing. Project Goal. To make the normalized Frisina data easy and convenient to work with To avoid having to work with enormous text files of seemingly meaningless numbers. Project Goals.

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Exploring the Role of Non-Coding DNA in the Function of the Human Genome through Variation.

Exploring the Role of Non-Coding DNA in the Function of the Human Genome through Variation. Christine Bird cpb@sanger.ac.uk. Hypothesis: Conserved non-coding DNA has a function in the human genome. Does human variation data suggest selection is acting on noncoding DNA?

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T helper cells in IBD

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BI 83201: The Literature of Computational Genomics

BI 83201: The Literature of Computational Genomics. Instructor: Prof. Jeffrey Chuang Meeting Time: Fridays 10:30-11:45 Higgins 465 Requirements: Read and discuss 1-2 papers per week. Grading will be based on participation. Attendance at all sessions is mandatory.

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Methods for Micro-Array Analysis Data Mining & Machine Learning Approaches

Methods for Micro-Array Analysis Data Mining & Machine Learning Approaches. What is Micro-Array Analysis?. Analysis of Micro-Array Data. Challenges posed Typical characteristic of micro array data is the large number of variables relative to the number of observations .

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Acid-Induced Gene Expression in Helicobacter pylori : Study in Genomic Scale by Microarray

Acid-Induced Gene Expression in Helicobacter pylori : Study in Genomic Scale by Microarray. Sunny Ang, Cha-Ze Lee, Komam Peck, Michelle Sindice, Udayakumar Matrubutham, Martin A. Gleeson, and Jin-Town Wang

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Transcriptome Sequencing with Reference

Transcriptome Sequencing with Reference. To be continue. Transcriptome Sequencing with Reference. Overview of RNA- Seq. Bioinformatics Analysis Pipeline for Transcritpome Sequencing with Reference. Gene structure refinement. UTRs by RNA? Seq in yeast. (Nagalakshmi, U. et al.,2008).

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Post-Genomic Computational Methods for Functional Annotation

Post-Genomic Computational Methods for Functional Annotation. presented by: Ashok Palaniappan November 5, 2002. Overview of the methods. Phylogenetic profiling Rosetta-Stone method (the name derives from the key discovery to decrypting Egyptian hieroglyphics) that detects domain fusions

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Gene Length & Proximity to Neighbours affect Genome Wide Expression Levels

Gene Length & Proximity to Neighbours affect Genome Wide Expression Levels. Authors: Chiaromonte, Miller and Bouhassira. Content. Introduction Aims of the paper Methods Data Preparation and Processing Statistical Analysis Methods Results Points of Interest Topics for Discussion.

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Estimating the False Discovery Rate in Genome-wide Studies

Estimating the False Discovery Rate in Genome-wide Studies. BMI/CS 576 www.biostat.wisc.edu/bmi576/ Colin Dewey cdewey@biostat.wisc.edu Fall 2008. Expression in BRCA1 and BRCA2 Mutation-Positive Tumors.

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Melanie Dunn 9/20/2011

Single nucleotide polymorphisms at the TRAF1/C5 locus are associated with rheumatoid arthritis in a Han Chinese population. Melanie Dunn 9/20/2011. What is Rheumatoid Arthritis?.

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HIF-2 ?

Figure S1. parp-1 +/+. parp-1 -/-. H. N. H. N. HIF-2 ?. DAPI.

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Figure S1

Figure S1.

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0.5

a. Atria. Ventricles. Ang II (hr):. 0.5. 2. 5. 24. 0. 0.5. 2. 5. 24. 0. ATF3. Tubulin. 1. 2. 3. 4. 5. 1. 2. 3. 4. 5. (hr):. (hr):. Atria. Ventricles. Figure S1: Kinetics of ATF3 expression in response to angiotensin II in atria versus ventricles. a.

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cont

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Conclusions:

PCa ( GS3+4 pT3N0). Non- PCa (n=22). PCa (n=28). Characteristic. control. mock. mock. si -c ont rol. c ont rol. 66 ? 53-72 ? 6.7 (4.3-19.5). 72 (63-88) 228 (7.23-2640) 8 (29%) 12 (42%) 8 (29%) 9 (32%) 19 (68%) 10 (36%) 18 (64%) 2 (7%) 14 (50%) 11 (39%) 1 (4%).

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(hr)

Supplemental Figure 1 Komori H. et al. A. B. (mg/mL). 0. 0.1. 0.25. 0.5. 1.0. 2.0. (hr). 0. 6. 12. 24. 36. 48. CD163. CD163. b -actin. b -actin. CD163/18S rRNA (THP-1). CD163/18S rRNA (THP-1). 7. 7. 6. 6. 5. 5. 4. 4. Relative quantity of CD163 mRNA.

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TCGA-AN-A04D

A. B. C. D. RB1. CAB39L. SETDB2. EBPL. TCGA-AB-A09V. TCGA-AB-A09V. TCGA-BH-A0EE. TCGA-AB-A09V. TCGA-BH-A0EE. TCGA-AN-A04D. TCGA-AN-A04D. TCGA-AN-A04D. E. F. Supplementary Figure 8 - Meta-analysis of TCGA gene expression data

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ZD Blount et al. Nature 000 , 1-6 (2012) doi:10.1038/nature11514

Expression levels from native citT , native rnk and evolved rnk-citT regulatory regions during aerobic metabolism. ZD Blount et al. Nature 000 , 1-6 (2012) doi:10.1038/nature11514.

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Quantitative multi-gene expression profiling of primary prostate cancer*

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