Ching-Yu Shih1, Amrita Chattopadhyay1, Chien-Hui Wu2,3, Yu-Wen Tien3, Tzu-Pin Lu4,5. 1. Bioinformatics and Biostatistics Core, Centre of Genomic and Precision Medicine, National Taiwan University, Taipei, 10055, Taiwan. 2. Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 10055, Taiwan. 3. Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan. 4. Bioinformatics and Biostatistics Core, Centre of Genomic and Precision Medicine, National Taiwan University, Taipei, 10055, Taiwan. tplu@ntu.edu.tw. 5. Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 10055, Taiwan. tplu@ntu.edu.tw.
Abstract
BACKGROUND: An individual's genetics play a role in how RNA transcripts are generated from DNA and consequently in their translation into protein. Transcriptional and translational profiling of patients furnishes the information that a specific marker is present; however, it fails to provide evidence whether the marker correlates with response to a therapeutic agent. A comparative analysis of the frequency of genetic variants, such as single nucleotide polymorphisms (SNPs), in diseased and general populations can identify pathogenic variants in individual patients. This is in part because SNPs have considerable effects on protein function and gene expression when they occur in coding regions and regulatory sequences, respectively. Therefore, a tool that can help users to obtain the allele frequency for a corresponding transcript is the need of the day. Several annotation tools such as SNPnexus and VariED are publicly available; however, none of them can use transcript IDs as input and provide the corresponding genomic positions of variants. RESULTS: In this study, we developed an R package, called transcript annotation tool (TransAT), that provides (i) SNP ID and genomic position for a user-provided transcript ID from patients, and (ii) allele frequencies for the SNPs from publicly available global populations. All data elements are extracted, collected, and displayed in an easily downloadable format in two simple command lines. TransAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It is available at https://github.com/ShihChingYu/TransAT and can be downloaded and installed using devtools::install_github("ShihChingYu/TransAT", force=T) on the R execution page. Thereafter, all functions can be executed by loading the package into R with library(TransAT). CONCLUSIONS: TransAT is a novel tool that seamlessly provides genetic annotations for queried transcripts. Such easily obtainable information would be greatly advantageous for physicians, assisting them to make individualized decisions about specific drug treatments. Moreover, allele frequencies from user-chosen global ethnic populations will highlight the importance of ethnicity and its effect on patient pathogenicity.
BACKGROUND: An individual's genetics play a role in how RNA transcripts are generated from DNA and consequently in their translation into protein. Transcriptional and translational profiling of patients furnishes the information that a specific marker is present; however, it fails to provide evidence whether the marker correlates with response to a therapeutic agent. A comparative analysis of the frequency of genetic variants, such as single nucleotide polymorphisms (SNPs), in diseased and general populations can identify pathogenic variants in individual patients. This is in part because SNPs have considerable effects on protein function and gene expression when they occur in coding regions and regulatory sequences, respectively. Therefore, a tool that can help users to obtain the allele frequency for a corresponding transcript is the need of the day. Several annotation tools such as SNPnexus and VariED are publicly available; however, none of them can use transcript IDs as input and provide the corresponding genomic positions of variants. RESULTS: In this study, we developed an R package, called transcript annotation tool (TransAT), that provides (i) SNP ID and genomic position for a user-provided transcript ID from patients, and (ii) allele frequencies for the SNPs from publicly available global populations. All data elements are extracted, collected, and displayed in an easily downloadable format in two simple command lines. TransAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It is available at https://github.com/ShihChingYu/TransAT and can be downloaded and installed using devtools::install_github("ShihChingYu/TransAT", force=T) on the R execution page. Thereafter, all functions can be executed by loading the package into R with library(TransAT). CONCLUSIONS:TransAT is a novel tool that seamlessly provides genetic annotations for queried transcripts. Such easily obtainable information would be greatly advantageous for physicians, assisting them to make individualized decisions about specific drug treatments. Moreover, allele frequencies from user-chosen global ethnic populations will highlight the importance of ethnicity and its effect on patient pathogenicity.
Entities:
Keywords:
Allele frequency; R package; TransAT; Transcript annotation; Variant annotation
Authors: Andrew D Yates; Premanand Achuthan; Wasiu Akanni; James Allen; Jamie Allen; Jorge Alvarez-Jarreta; M Ridwan Amode; Irina M Armean; Andrey G Azov; Ruth Bennett; Jyothish Bhai; Konstantinos Billis; Sanjay Boddu; José Carlos Marugán; Carla Cummins; Claire Davidson; Kamalkumar Dodiya; Reham Fatima; Astrid Gall; Carlos Garcia Giron; Laurent Gil; Tiago Grego; Leanne Haggerty; Erin Haskell; Thibaut Hourlier; Osagie G Izuogu; Sophie H Janacek; Thomas Juettemann; Mike Kay; Ilias Lavidas; Tuan Le; Diana Lemos; Jose Gonzalez Martinez; Thomas Maurel; Mark McDowall; Aoife McMahon; Shamika Mohanan; Benjamin Moore; Michael Nuhn; Denye N Oheh; Anne Parker; Andrew Parton; Mateus Patricio; Manoj Pandian Sakthivel; Ahamed Imran Abdul Salam; Bianca M Schmitt; Helen Schuilenburg; Dan Sheppard; Mira Sycheva; Marek Szuba; Kieron Taylor; Anja Thormann; Glen Threadgold; Alessandro Vullo; Brandon Walts; Andrea Winterbottom; Amonida Zadissa; Marc Chakiachvili; Bethany Flint; Adam Frankish; Sarah E Hunt; Garth IIsley; Myrto Kostadima; Nick Langridge; Jane E Loveland; Fergal J Martin; Joannella Morales; Jonathan M Mudge; Matthieu Muffato; Emily Perry; Magali Ruffier; Stephen J Trevanion; Fiona Cunningham; Kevin L Howe; Daniel R Zerbino; Paul Flicek Journal: Nucleic Acids Res Date: 2020-01-08 Impact factor: 16.971