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Bioinformatics
Journal Prestige (SJR): 6.14

Citation Impact (citeScore): 8
Number of Followers: 288

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1367-4803 - ISSN (Online) 1460-2059
Published by Oxford University Press

[425 journals]

Improving the filtering of false positive single nucleotide variations by
combining genomic features with quality metrics
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  First page: btad694
  Abstract: AbstractMotivationTechnical errors in sequencing or bioinformatics steps and difficulties in alignment at some genomic sites result in false positive (FP) variants. Filtering based on quality metrics is a common method for detecting FP variants, but setting thresholds to reduce FP rates may reduce the number of true positive variants by overlooking the more complex relationships between features. The goal of this study is to develop a machine learning-based model for identifying FPs that integrates quality metrics with genomic features and with the feature interpretability property to provide insights into model results.ResultsWe propose a random forest-based model that utilizes genomic features to improve identification of FPs. Further examination of the features shows that the newly introduced features have an important impact on the prediction of variants misclassified by VEF, GATK-CNN, and GARFIELD, recently introduced FP detection systems. We applied cost-sensitive training to avoid errors in misclassification of true variants and developed a model that provides a robust mechanism against misclassification of true variants while increasing the prediction rate of FP variants. This model can be easily re-trained when factors such as experimental protocols might alter the FP distribution. In addition, it has an interpretability mechanism that allows users to understand the impact of features on the model’s predictions.Availability and implementationThe software implementation can be found at https://github.com/ideateknoloji/FPDetect.
  PubDate: Wed, 29 Nov 2023 00:00:00 GMT
  DOI: 10.1093/bioinformatics/btad694
  Issue No: Vol. 39, No. 12 (2023)
cellsig plug-in enhances CIBERSORTx signature selection for multidataset
transcriptomes with sparse multilevel modelling
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  First page: btad685
  Abstract: AbstractMotivationThe precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation.ResultsHere, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks.Availability and implementationCodes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.
  PubDate: Sat, 11 Nov 2023 00:00:00 GMT
  DOI: 10.1093/bioinformatics/btad685
  Issue No: Vol. 39, No. 12 (2023)

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