Hasil untuk "Analysis"

S. Eberhart

J. Zou

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human ‘knockout’ variants in protein-coding genes.

Yong Zhang, Tao Liu, Clifford A. Meyer et al.

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

D. Huang, Brad T. Sherman, R. Lempicki

X. Qu

K. Tamura, J. Dudley, M. Nei et al.

E. Pettersen, Thomas D. Goddard, Conrad C. Huang et al.

Ø. Hammer, D. Harper, P. Ryan

Jeffrey M. Woodbridge

A. Delorme, S. Makeig

A. Subramanian, P. Tamayo, V. Mootha et al.

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Ø. Hammer, D. Harper, P. Ryan et al.

H. Shen

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

R. Peakall, P. Smouse

S. Raudenbush, A. Bryk

M. Egger, G. Smith, Martin Schneider et al.

P. Lachenbruch

L. Fávero, P. Belfiore

of substrate modification on wetting and wicking behavior

M. Pesaran, Y. Shin, Richard J. Smith

R. Dersimonian, R. Dersimonian, N. Laird et al.