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Ivo Palli

Researcher at Erasmus University Rotterdam

Publications -  6
Citations -  323

Ivo Palli is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Whole genome sequencing & Mutation (genetic algorithm). The author has an hindex of 6, co-authored 6 publications receiving 321 citations. Previous affiliations of Ivo Palli include Erasmus University Medical Center.

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CGtag: complete genomics toolkit and annotation in a cloud-based Galaxy

TL;DR: In this article, the authors have implemented a complete genomics toolkit and annotation in a cloud-based Galaxy, called CGtag (Complete Genomics Toolkit and Annotation in a Cloudbased Galaxy), for the selection of candidate mutations from Complete Genomics data.
Journal Article

CGtag: complete genomics toolkit and annotation in a cloud-based Galaxy : Technical Note

TL;DR: This work has implemented the open-source Complete Genomics tool set, CGATools, in Galaxy, and implemented some of the most popular command-line annotation and visualisation tools to allow research scientists to select candidate pathological mutations.
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Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer

TL;DR: An open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the `hottest’ hotspot areas in adrenocortical carcinoma.
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Huvariome: a web server resource of whole genome next-generation sequencing allelic frequencies to aid in pathological candidate gene selection

TL;DR: Huvariome is a simple to use resource for validation of resequencing results obtained by NGS experiments and it is demonstrated that Huvariome can provide accurate reference allele frequencies to disambiguate sequencing inconsistencies produced in resequenced experiments.
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iFUSE: integrated fusion gene explorer

TL;DR: iFUSE (integrated fusion gene explorer), an online visualization tool that provides a fast and informative view of structural variation data and prioritizes those breaks likely representing fusion genes, is presented.