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Richard I. Kuo

dblp:278/5588 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.712023
nf-core/isoseq: simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing · Bioinform. 2023
Bioinformatics and computational biology › transcriptomics › transcript isoform analysis
isoform annotation
0.712023
nf-core/isoseq: simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing · Bioinform. 2023
Bioinformatics and computational biology › transcriptomics
transcriptome annotation
0.712023
nf-core/isoseq: simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing · Bioinform. 2023
Bioinformatics and computational biology › sequence analysis
long-read sequencing
0.212023
nf-core/isoseq: simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing · Bioinform. 2023

Methods — techniques the papers use, named apart from their topics

iso-seq · 0.7
YearPublicationVenuePosition
2023 nf-core/isoseq: simple gene and isoform annotation with PacBio Iso-Seq long-read sequencing
abstract
MOTIVATION: Iso-Seq RNA long-read sequencing enables the identification of full-length transcripts and isoforms, removing the need for complex analysis such as transcriptome assembly. However, the raw sequencing data need to be processed in a series of steps before annotation is complete. Here, we present nf-core/isoseq, a pipeline for automatic read processing and genome annotation. Following nf-core guidelines, the pipeline has few dependencies and can be run on any of platforms. AVAILABILITY AND IMPLEMENTATION: The pipeline is freely available online on the nf-core website (https://nf-co.re/isoseq) and on GitHub (https://github.com/nf-core/isoseq) under MIT License (DOI: 10.5281/zenodo.7116979).
Sébastien Guizard, Katarzyna Miedzinska, Jacqueline Smith, Richard I. Kuo, Megan Davey, Alan L. Archibald, Mick Watson
Bioinform.5
2021 Correction to: Avian Immunome DB: an example of a user‑friendly interface for extracting genetic information
abstract
Following publication of the original article [1], the authors identifed an error in the author name of Lél Eöry. Te incorrect author name is: Lél Eöery. Te correct author name is: Lél Eöry. Te author group has been updated above and the original article [1] has been corrected.
Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöery, Richard I. Kuo, Robert H. S. Kraus
BMC Bioinform.5
2020 Avian Immunome DB: an example of a user-friendly interface for extracting genetic information
abstract
BACKGROUND: Genomic and genetic studies often require a target list of genes before conducting any hypothesis testing or experimental verification. With the ever-growing number of sequenced genomes and a variety of different annotation strategies, comes the potential for ambiguous gene symbols, making it cumbersome to capture the "correct" set of genes. In this article, we present and describe the Avian Immunome DB (AVIMM) for easy gene property extraction as exemplified by avian immune genes. The avian immune system is characterised by a cascade of complex biological processes underlaid by more than 1000 different genes. It is a vital trait to study particularly in birds considering that they are a significant driver in spreading zoonotic diseases. With the completion of phase II of the B10K ("Bird 10,000 Genomes") consortium's whole-genome sequencing effort, we have included 363 annotated bird genomes in addition to other publicly available bird genome data which serve as a valuable foundation for AVIMM. CONSTRUCTION AND CONTENT: A relational database with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has been designed and set up. The foundation stone or the "seed" for the initial set of avian immune genes is based on the well-studied model organism chicken (Gallus gallus). Gene annotations, different transcript isoforms, nucleotide sequences and protein information, including amino acid sequences, are included. Ambiguous gene names (symbols) are resolved within the database and linked to their canonical gene symbol. AVIMM is supplemented by a command-line interface and a web front-end to query the database. UTILITY AND DISCUSSION: The internal mapping of unique gene symbol identifiers to canonical gene symbols allows for an ambiguous gene property search. The database is organised within core and feature tables, which makes it straightforward to extend for future purposes. The database design is ready to be applied to other taxa or biological processes. Currently, the database contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species. While the command-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end with download functionality offers sophisticated search functionalities and tracks the origin for each record. AVIMM is publicly accessible at https://avimm.ab.mpg.de .
Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöery, Richard I. Kuo, Robert H. S. Kraus
BMC Bioinform.5