R
Rob Patro
Researcher at University of Maryland, College Park
Publications - 118
Citations - 10703
Rob Patro is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: De Bruijn graph & Computer science. The author has an hindex of 28, co-authored 103 publications receiving 7105 citations. Previous affiliations of Rob Patro include Carnegie Mellon University & Stony Brook University.
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Salmon provides fast and bias-aware quantification of transcript expression
TL;DR: Salmon is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
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Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms
TL;DR: Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data, exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.
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TransRate: reference-free quality assessment of de novo transcriptome assemblies
TL;DR: TransRate is a tool for reference-free quality assessment of de novo transcriptome assemblies using only the sequenced reads and the assembly as input and it is revealed that variance in the quality of the input data explains 43% of the variance inThe quality of published de noVO transcriptome assembly assemblies.
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Sailfish: Alignment-free Isoform Quantification from RNA-seq Reads using Lightweight Algorithms
TL;DR: Sailfish as mentioned in this paper is a novel computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data, which avoids mapping reads, which is a timeconsuming step in all current methods.
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Global network alignment using multiscale spectral signatures
Rob Patro,Carl Kingsford +1 more
TL;DR: GHOST is introduced, a global pairwise network aligner that uses a novel spectral signature to measure topological similarity between subnetworks and is able to recover larger and more biologically significant, shared subnets between species.