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Narges Bani Asadi

Researcher at Hoffmann-La Roche

Publications -  19
Citations -  859

Narges Bani Asadi is an academic researcher from Hoffmann-La Roche. The author has contributed to research in topics: Reconfigurable computing & Markov chain Monte Carlo. The author has an hindex of 15, co-authored 19 publications receiving 742 citations. Previous affiliations of Narges Bani Asadi include University of Michigan & Stanford University.

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Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

TL;DR: An extensive study analysing a broad spectrum of RNA-seq workflows and proposing a comprehensive analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy, which could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.
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MetaSV: an accurate and integrative structural-variant caller for next generation sequencing

TL;DR: MetaSV is proposed, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution and analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs.
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An ensemble approach to accurately detect somatic mutations using SomaticSeq

TL;DR: SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatics mutation calls for both single nucleotide variants and small insertions and deletions that achieves better overall accuracy than any individual tool incorporated.

MetaSV: an accurate and integrative structural-variant caller for next generation

TL;DR: MetaSV as mentioned in this paper combines multiple orthogonal SV signals for high accuracy and resolution by merging SVs from multiple tools for all types of SVs and analyzes soft-clipped reads from alignment to detect insertions accurately.
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VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications

TL;DR: A novel map data structure to validate read alignments, a strategy to compare variants binned in size ranges and a lightweight, interactive, graphical report to visualize validation results with detailed statistics make VarSim the most comprehensive validation tool for secondary analysis in next generation sequencing.