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Ugrappa Nagalakshmi

Researcher at Yale University

Publications -  13
Citations -  10285

Ugrappa Nagalakshmi is an academic researcher from Yale University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 8, co-authored 8 publications receiving 9659 citations.

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Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

Ewan Birney, +320 more
- 14 Jun 2007 - 
TL;DR: Functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project are reported, providing convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts.
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The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

TL;DR: A quantitative sequencing-based method is developed for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome, and it is demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed.
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The ENCODE (ENCyclopedia of DNA elements) Project

Elise A. Feingold, +196 more
- 22 Oct 2004 - 
TL;DR: The ENCyclopedia Of DNA Elements (ENCODE) Project is organized as an international consortium of computational and laboratory-based scientists working to develop and apply high-throughput approaches for detecting all sequence elements that confer biological function.
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RNA-Seq: A Method for Comprehensive Transcriptome Analysis

TL;DR: This unit describes protocols for performing RNA‐Seq using the Illumina sequencing platform, and has been used successfully to precisely quantify transcript levels, confirm or revise previously annotated 5′ and 3′ ends of genes, and map exon/intron boundaries.
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Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome

TL;DR: Overall, the performance improves with more data points per locus, coupled with statistical scoring approaches that properly take advantage of this, where this larger number of data points arises from higher genomic tiling density and the use of replicate arrays and mismatches.