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Jack M. Gardiner

dblp:64/3798 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0002-1804-0110ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 3 · 1 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
3 papers
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › comparative genomics › pangenomics
pan-genome analysis
0.812024
PanEffect: a pan-genome visualization tool for variant effects in maize · Bioinform. 2024
Bioinformatics and computational biology › protein sequence analysis › protein sequence representation
protein language model
0.812024
PanEffect: a pan-genome visualization tool for variant effects in maize · Bioinform. 2024
Bioinformatics and computational biology › statistical genetics
variant effect prediction
0.812024
PanEffect: a pan-genome visualization tool for variant effects in maize · Bioinform. 2024
Bioinformatics and computational biology › statistical genetics
genetic mapping
0.012003
Development of an integrated laboratory information management system for the maize mapping project · Bioinform. 2003
Bioinformatics and computational biology
laboratory information management
0.012003
Development of an integrated laboratory information management system for the maize mapping project · Bioinform. 2003

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

protein language model · 0.8ESM · 0.8relational database · 0.0map graphic browser · 0.0database management · 0.0
YearPublicationVenuePosition
2024 PanEffect: a pan-genome visualization tool for variant effects in maize
abstract
SUMMARY: Understanding the effects of genetic variants is crucial for accurately predicting traits and functional outcomes. Recent approaches have utilized artificial intelligence and protein language models to score all possible missense variant effects at the proteome level for a single genome, but a reliable tool is needed to explore these effects at the pan-genome level. To address this gap, we introduce a new tool called PanEffect. We implemented PanEffect at MaizeGDB to enable a comprehensive examination of the potential effects of coding variants across 50 maize genomes. The tool allows users to visualize over 550 million possible amino acid substitutions in the B73 maize reference genome and to observe the effects of the 2.3 million natural variations in the maize pan-genome. Each variant effect score, calculated from the Evolutionary Scale Modeling (ESM) protein language model, shows the log-likelihood ratio difference between B73 and all variants in the pan-genome. These scores are shown using heatmaps spanning benign outcomes to potential functional consequences. In addition, PanEffect displays secondary structures and functional domains along with the variant effects, offering additional functional and structural context. Using PanEffect, researchers now have a platform to explore protein variants and identify genetic targets for crop enhancement. AVAILABILITY AND IMPLEMENTATION: The PanEffect code is freely available on GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/PanEffect). A maize implementation of PanEffect and underlying datasets are available at MaizeGDB (https://www.maizegdb.org/effect/maize/).
Carson M. Andorf, Olivia C. Haley, Rita K. Hayford, John L. Portwood II, Stephen Harding, Shatabdi Sen, Ethalinda K. S. Cannon, Jack M. Gardiner, Hye-Seon Kim, Margaret Woodhouse
Bioinform.8
2003 iMap: a database-driven utility to integrate and access the genetic and physical maps of maize
abstract
MOTIVATION: Because of the unique biological features, a bioinformatic platform for the integrated genetic and physical map of maize is required for storing, integrating, accessing and visualizing the underlying data. RESULTS: The goal of the Maize Mapping Project is to develop a fully integrated genetic and physical map for maize. To display this integrated map, we have developed iMap. iMap has three main components: a relational database (iMapDB), a map graphic browser (iMap Viewer) and a search utility (iMap Search). iMapDB is populated with current genetic and physical map data, describing relationships among genetic loci, molecular markers and bacterial artificial chromosome (BAC) contigs. The database also contains integrated information produced by applying a set of anchoring rules to assign BAC contigs to specific locations on the genetic map. The iMap Viewer and iMap Search functions are combined in the user interface to allow viewing and retrieving many types of genetic and physical map data. The iMap Viewer features side-by-side chromosome-based displays of the genetic map and associated BAC contigs. For each genetic locus, information about marker type or contig can be viewed via pop-up windows that feature links to external data resources. Searches can be conducted for genetic locus, probe or sequence accession number; search results include relevant map positions, anchored BAC contigs and links to the graphical display of relevant chromosomes. iMap can be accessed at http://www.maizemap.org AVAILABILITY: The iMap utility package is available for non-commercial use upon request from the authors.
Zhiwei Fang, Karen C. Cone, Hector Sanchez-Villeda, Mary L. Polacco, Michael D. McMullen, Steven G. Schroeder, Jack M. Gardiner, Georgia L. Davis, Seth A. Havermann, Young-Sun Yim, Irie Vroh-Bi, Edward H. Coe
Bioinform.7
2003 Development of an integrated laboratory information management system for the maize mapping project
abstract
MOTIVATION: The development of an integrated genetic and physical map for the maize genome involves the generation of an enormous amount of data. Managing this data requires a system to aid in genotype scoring for different types of markers coming from both local and remote users. In addition, researchers need an efficient way to interact with genetic mapping software and with data files from automated DNA sequencing. They also need ways to manage primer data for mapping and sequencing and provide views of the integrated physical and genetic map and views of genetic map comparisons. RESULTS: The MMP-LIMS system has been used successfully in a high-throughput mapping environment. The genotypes from 957 SSR, 1023 RFLP, 189 SNP, and 177 InDel markers have been entered and verified via MMP-LIMS. The system is flexible, and can be easily modified to manage data for other species. The software is freely available. AVAILABILITY: To receive a copy of the iMap or cMap software, please fill out the form on our website. The other MMP-LIMS software is freely available at http://www.maizemap.org/bioinformatics.htm.
Hector Sanchez-Villeda, Steven G. Schroeder, Mary L. Polacco, Michael D. McMullen, Seth A. Havermann, Georgia L. Davis, Irie Vroh-Bi, Karen C. Cone, Natasha Sharopova, Young-Sun Yim, Linda Schultz, Ngozi A. Duru, Theresa A. Musket, Kate Houchins, Zhiwei Fang, Jack M. Gardiner, Edward H. Coe
Bioinform.16