Dataset Information

Ranked Choice Voting for Representative Transcripts with TRaCE.

ABSTRACT:

Summary

Genome sequencing projects annotate protein-coding gene models with multiple transcripts, aiming to represent all of the available transcript evidence. However, downstream analyses often operate on only one representative transcript per gene locus, sometimes known as the canonical transcript. To choose canonical transcripts, TRaCE (Transcript Ranking and Canonical Election) holds an 'election' in which a set of RNA-seq samples rank transcripts by annotation edit distance. These sample-specific votes are tallied along with other criteria such as protein length and InterPro domain coverage. The winner is selected as the canonical transcript, but the election proceeds through multiple rounds of voting to order all the transcripts by relevance. Based on the set of expression data provided, TRaCE can identify the most common isoforms from a broad expression atlas or prioritize alternative transcripts expressed in specific contexts.

Availability and implementation

Transcript ranking code can be found on GitHub at {{https://github.com/warelab/TRaCE}}.

Supplementary information

Additional data are available in the GitHub repository.

SUBMITTER: Olson AJ

PROVIDER: S-EPMC8696091 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Variant annotation is a crucial step in the analysis of genome sequencing data. Functional annotation results can have a strong influence on the ultimate conclusions of disease studies. Incorrect or incomplete annotations can cause researchers both to overlook potentially disease-relevant DNA variants and to dilute interesting variants in a pool of false positives. Researchers are aware of these issues in general, but the extent of the dependency of final results on the choice of transcripts and software used for annotation has not been quantified in detail.This paper quantifies the extent of differences in annotation of 80 million variants from a whole-genome sequencing study. We compare results using the RefSeq and Ensembl transcript sets as the basis for variant annotation with the software Annovar, and also compare the results from two annotation software packages, Annovar and VEP (Ensembl's Variant Effect Predictor), when using Ensembl transcripts.We found only 44% agreement in annotations for putative loss-of-function variants when using the RefSeq and Ensembl transcript sets as the basis for annotation with Annovar. The rate of matching annotations for loss-of-function and nonsynonymous variants combined was 79% and for all exonic variants it was 83%. When comparing results from Annovar and VEP using Ensembl transcripts, matching annotations were seen for only 65% of loss-of-function variants and 87% of all exonic variants, with splicing variants revealed as the category with the greatest discrepancy. Using these comparisons, we characterised the types of apparent errors made by Annovar and VEP and discuss their impact on the analysis of DNA variants in genome sequencing studies.Variant annotation is not yet a solved problem. Choice of transcript set can have a large effect on the ultimate variant annotations obtained in a whole-genome sequencing study. Choice of annotation software can also have a substantial effect. The annotation step in the analysis of a genome sequencing study must therefore be considered carefully, and a conscious choice made as to which transcript set and software are used for annotation.

Project description:ObjectiveTo investigate rates of adherence to the UK's test, trace, and isolate system over the initial 11 months of the covid-19 pandemic.DesignSeries of cross sectional online surveys.Setting37 nationally representative surveys in the UK, 2 March 2020 to 27 January 2021.Participants74 697 responses from 53 880 people living in the UK, aged 16 years or older (37 survey waves, about 2000 participants in each wave).Main outcome measuresIdentification of the main symptoms of covid-19 (cough, high temperature or fever, and loss of sense of smell or taste), self-reported adherence to self-isolation if symptoms were present and intention to self-isolate if symptoms were to develop, requesting a test for covid-19 if symptoms were present and intention to request a test if symptoms were to develop, and intention to share details of close contacts.ResultsOnly 51.5% of participants (95% confidence interval 51.0% to 51.9%, n=26 030/50 570) identified the main symptoms of covid-19; the corresponding values in the most recent wave of data collection (25-27 January 2021) were 50.8% (48.6% to 53.0%, n=1019/2007). Across all waves, duration adjusted adherence to full self-isolation was 42.5% (95% confidence interval 39.7% to 45.2%, n=515/1213); in the most recent wave of data collection (25-27 January 2021), it was 51.8% (40.8% to 62.8%, n=43/83). Across all waves, requesting a test for covid-19 was 18.0% (95% confidence interval 16.6% to 19.3%, n=552/3068), increasing to 22.2% (14.6% to 29.9%, n=26/117) from 25 to 27 January. Across all waves, intention to share details of close contacts was 79.1% (95% confidence interval 78.8% to 79.5%, n=36 145/45 680), increasing to 81.9% (80.1% to 83.6%, n=1547/1890) from 25 to 27 January. Non-adherence was associated with being male, younger age, having a dependent child in the household, lower socioeconomic grade, greater financial hardship during the pandemic, and working in a key sector.ConclusionsLevels of adherence to test, trace, and isolate are low, although some improvement has occurred over time. Practical support and financial reimbursement are likely to improve adherence. Targeting messaging and policies to men, younger age groups, and key workers might also be necessary.