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Lior Pachter

Researcher at California Institute of Technology

Publications -  308
Citations -  95965

Lior Pachter is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 69, co-authored 281 publications receiving 83783 citations. Previous affiliations of Lior Pachter include University of Miami & University of Oxford.

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Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

TL;DR: The results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
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TopHat: discovering splice junctions with RNA-Seq

TL;DR: The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer.
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Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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Initial sequencing and comparative analysis of the mouse genome.

Robert H. Waterston, +222 more
- 05 Dec 2002 - 
TL;DR: The results of an international collaboration to produce a high-quality draft sequence of the mouse genome are reported and an initial comparative analysis of the Mouse and human genomes is presented, describing some of the insights that can be gleaned from the two sequences.
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Near-optimal probabilistic RNA-seq quantification

TL;DR: Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases, which removes a major computational bottleneck in RNA-seq analysis.