111 research outputs found
Mark Twain: Mysterious stranger
No full textOne hundred years after his death, the curators of this exhibition in The Rare Book & Manuscript Library, have explored the enormous holdings of the Library to assemble and present this glimpse of Mark Twain the author, publisher, erstwhile tycoon, and world-wide celebrity … and Sam Clemens the husband, the father, and the friend.not peer reviewedSubmitted by Dennis Sears ([email protected]) on 2010-06-02T16:17:26Z
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Previous issue date: 2010-04-16published or submitted for publicatio
TiSAn: Estimating Tissue Specific Effects of Coding and Noncoding Variants
No full textAbstractMeasures of general deleteriousness, like CADD or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these measures say little about where in the organism these deleterious effects will be most apparent. An additional, complementary measure is needed to link deleterious variants (as determined by e.g., CADD) to tissues in which their effect will be most meaningful. Here, we introduce TiSAn (Tissue Specific Annotation), a tool that predicts how related a genomic position is to a given tissue (http://github.com/kevinVervier/TiSAn). TiSAn uses machine learning on genome-scale, tissue-specific data to discriminate variants relevant to a tissue from those having no bearing on the development or function of that tissue. Predictions are then made genome-wide, and these scores can then be used to contextualize and filter variants of interest in whole genome sequencing or genome wide association studies (GWAS). We demonstrate the accuracy and versatility of TiSAn by introducing predictive models for human heart and human brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find that TiSAn is better able to prioritize genetic variants according to their tissue-specific action than the current state of the art method, GenoSkyLine.</jats:p
TiSAn: estimating tissue-specific effects of coding and non-coding variants
No full textAbstract
Motivation
Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations.
Results
Herein, we introduce TiSAn, a tool that integrates multiple genome-scale data sources, defined by expert knowledge. TiSAn uses machine learning to discriminate variants relevant to a tissue from those with no bearing on the function of that tissue. Predictions are made genome-wide, and can be used to contextualize and filter variants of interest in whole genome sequencing or genome-wide association studies. We demonstrate the accuracy and flexibility of TiSAn by producing predictive models for human heart and brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find the multiomics TiSAn model is better able to prioritize genetic variants according to their tissue-specific action than the current state-of-the-art method, GenoSkyLine.
Availability and implementation
Software and vignettes are available at http://github.com/kevinVervier/TiSAn.
Supplementary information
Supplementary data are available at Bioinformatics online.
</jats:sec
Large-scale metagenomic analysis of oral microbiomes reveals markers for autism spectrum disorders
No full textUsing Deep Learning to Quantify Neuronal Activation from Single-Cell and Spatial Transcriptomic Data
No full textData for reproduction of figures in "Using Deep Learning to Quantify Neuronal Activation from Single-Cell and Spatial Transcriptomic Data
Data-driven assessment of eQTL mapping methods
No full textAbstract Background The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis. Results Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods. Conclusions Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.</p
Class of 1899
No full textClass composite photograph for Chicago College of Law Lake Forest University class of 1899.
Students and faculty pictured (incomplete):
Faculty
Elmer E. Barrett
Charles A. Brown
Edmund Whitney Burke
Orrin N. Carter
John Gibbons
M. Henry Guerin
Adelbert Hamilton
C.E. Kramer
Jno. C. Mathis
Thomas A. Moran
William J. Pringle
Frank F. Reed
Henry M. Shepard
Simeon P. Shope
Students
Harry J. Aaron
Henry C. Adams
Edward J. Ader
Richard Todd Archer
Waldemar Bauer
Frederick C. Becker
R.L. Beers
R.A. Bertucci
Isadore S. Blumenthal
Thomas C. Boyd
Clara Breese
Herman H. Breidt
W.L. Bryan
R.L. Campbell
F.J. Carr
J.L. Carroll
John R. Caverly
Walter B. Cole
Ernst. E. Cole
Melvin E. Coleman
Andrew J. Corcoran
Alfred E. Croft
Harry V. Culp
Henry Davis
L.W. Denison
Homer T. Dick
Otto Dobroth
Leonidas B. Dyer
Henry Eckhardt
John H. Engwall
Thomas F. Enright
C.M. Everett
Thomas J. Ford
Henry J. Frercks
William L. Gahan
Roy S. Gaskill
F.H. George
Henry M. Goldsmith
Agnes A. Graham
John F. Haas
Albert Haentze
Harvey L. Hanson
James D. Hawkes
Daniel M. Healy
John F. Higgins
Patrick H. Holland
John A. Irrman
Arthur Irwin
George E. J. Johnson
Stephen Janowicz
J.W. Jedlan
Will H. Jung
Nathan D. Kaplan/Kaplin
William D. Kelley/Kelly
Martin C. Koebel
Charles W. Kopf
Louis P. Kraft
William O. La Monte / LaMont
Harry M. Lavers
Harry C. Levinson
Samuel K. Markham
Frank S. Matousek
John R. McCabe
W. F. McCarthy
Jacob .E. Michaelson
Edward B. Millett
John P. Moran
John W. Morsbach
Michael J. Neeman/Neenan
Edward C. Nettles/Nettels
Gilbert Noxton/Noxon
O.P. Olson
Noel B. Palmer
Charles Payne
George G. Peironnet
William L. Pettigrew
May F. Powers
Abraham Privat
Joseph H. Pyle
C. Randolph
Alfred Roessner
William M. Rosenthal
George K. Rudol
Robert F. Runzel
W.J. Schwari
Ernst G. Shisbert/Schubert
F. Steigmeyer
William P. Swain
F.H.T. Potter
R.L. Taylor
Charles P. Thompson
Edward R. Tobin
William E. Tucker
Robert Turnbull
T. Gifford Vance
Walter J. Vanderslice
John V. Walsh
Charles B. Whittemore
J.J. Williams
Albert E. Wolfe
Henry J. Zechlin
Richard Todd Archer
Homer T. Dick
John A. Irrman
O.P. Olson
William P. Swain
Frank F. Reed
Roy S. Gaskill
George G. Peironnet
Isadore S. Blumenthal
F.H. George
Daniel M. Healy
George E. J. Johnson
Edward R. Tobin
Henry M. Shepard
Noel B. Palmer
Harry J. Aaron
John F. Higgins
William L. Pettigrew
Charles B. Whittemore
Harry M. Lavers
Frederick C. Becker
J.W. Jedlan
William L. Gahan
R.L. Campbell
John V. Walsh
Henry M. Goldsmith
Edward B. Millett
Abraham Privat
Arthur Irwin
John R. McCabe
William E. Tucker
Edward J. Ader
Ernst G. Shisbert / Schubert
Henry C. Adams
Samuel K. Markham
Henry J. Zechlin
John F. Haas
F. H. T. Potter
Adelbert Hamilton
Frank S. Matousek
George K. Rudol
William M. Rosenthal
J.J. Williams
J.L. Carroll
John R. Caverly
Edward C. Nettles / Nettels
R.A. Bertucci
R.L. Taylor
Jacob .E. Michaelson
Andrew J. Corcoran
Walter B. Cole
Leonidas B. Dyer
Louis P. Kraft
Henry Davis
May F. Powers
Harvey L. Hanson
Ernst. E. Cole
Simeon P. Shope
Albert E. Wolfe
L.W. Denison
Michael J. Neeman / Neenan
Charles Payne
Thomas C. Boyd
C.E. Kramer
William O. La Monte / LaMont
W. F. McCarthy
Harry V. Culp
James D. Hawkes
William D. Kelley / Kelly
M. Henry Guerin
Orrin N. Carter
C.M. Everett
W.J. Schwari
Thomas J. Ford
Agnes A. Graham
Charles P. Thompson
C. Randolph
T. Gifford Vance
Clara Breese
Robert Turnbull
Otto Dobroth
Joseph H. Pyle
F.J. Carr
Walter J. Vanderslice
Waldemar Bauer
Stephen Janowicz
E.W. Burke
Elmer E. Barrett
W.L. Bryan
Will H. Jung
Robert F. Runzel
John H. Engwall
R.L. Beers
John P. Moran
Charles W. Kopf
Alfred Roessner
Henry Eckhardt
Albert Haentze
F. Steigmeyer
William J. Pringle
Nathan D. Kaplan / Kaplin
Melvin E. Coleman
Patrick H. Holland
Thomas A. Moran
Charles A. Brown
Harry C. Levinson
Alfred E. Croft
Henry J. Frercks
Thomas F. Enright
Herman H. Breidt
Martin C. Koebel
John W. Morsbach
Gilbert Noxton / Noxonhttps://scholarship.kentlaw.iit.edu/composites/1004/thumbnail.jp
High frequencies of de novo cnvs in bipolar disorder and schizophrenia
No full textWhile it is known that rare copy-number variants (CNVs) contribute to risk for some neuropsychiatric disorders, the role of CNVs in bipolar disorder is unclear. Here, we reasoned that a contribution of CNVs to mood disorders might be most evident for de novo mutations. We performed a genome-wide analysis of de novo CNVs in a cohort of 788 trios. Diagnoses of offspring included bipolar disorder (n = 185), schizophrenia (n = 177), and healthy controls (n = 426). Frequencies of de novo CNVs were significantly higher in bipolar disorder as compared with controls (OR = 4.8 [1.4,16.0], p = 0.009). De novo CNVs were particularly enriched among cases with an age at onset younger than 18 (OR = 6.3 [1.7,22.6], p = 0.006). We also confirmed a significant enrichment of de novo CNVs in schizophrenia (OR = 5.0 [1.5,16.8], p = 0.007). Our results suggest that rare spontaneous mutations are an important contributor to risk for bipolar disorder and other major neuropsychiatric diseases. © 2011 Elsevier Inc
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