Biology subjects

Bertrand, D.

Publications and source records attributed to Bertrand, D..

3 recordsLinked to original sources

bioRxiv2018

A MinION-based pipeline for fast and cost-effective DNA barcoding

DNA barcodes are useful for species discovery and species identification, but obtaining barcodes currently requires a well-equipped molecular laboratory, is time-consuming, and/or expensive. We here address these issues by developing a barcoding pipeline for Oxford Nanopore MinION and demonstrate that one flowcell can generate barcodes for [~]500 specimens despite high base-call error rates of MinION. The pipeline overcomes the errors by first summarizing all reads for the same tagged amplicon as a consensus barcode. These barcodes are overall mismatch-free but retain indel errors that are concentrated in homopolymeric regions. We thus complement the barcode caller with an optional error correction pipeline that uses conserved amino-acid motifs from publicly available barcodes to correct the indel errors. The effectiveness of this pipeline is documented by analysing reads from three MinION runs that represent three different stages of MinION development. They generated data for (1) 511 specimens of a mixed Diptera sample, (2) 575 specimens of ants, and (3) 50 specimens of Chironomidae. The run based on the latest chemistry yielded MinION barcodes for 490 specimens which were assessed against reference Sanger barcodes (N=471). Overall, the MinION barcodes have an accuracy of 99.3%-100% and the number of ambiguities ranges from <0.01-1.5% depending on which correction pipeline is used. We demonstrate that it requires only 2 hours of sequencing to gather all information that is needed for obtaining reliable barcodes for most specimens (>90%). We estimate that up to 1000 barcodes can be generated in one flowcell and that the cost of a MinION barcode can be <USD 2.

bioinformatics↗

bioRxiv2017

Predicting Cancer Drug Response Using a Recommender System

MotivationAs we move towards an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets.\n\nResultsWe propose a method based on ideas from \"recommender systems\" (CaDRReS) that predicts cancer drug responses for unseen cell-lines/patients based on learning projections for drugs and cell-lines into a latent \"pharmacogenomic\" space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE, GDSC) shows that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes, and further characterize drug-pathway associations.\n\nAvailabilitySource code and datasets are available at https://github.com/CSB5/CaDRReS\n\nContactnagarajann@gis.a-star.edu.sg\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

ConsensusDriver Improves Upon Individual Algorithms For Predicting Driver Alterations In Different Cancer Types And Individual Patients -- A Toolbox For Precision Oncology

BackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nMethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC) and methods that use gene interaction network-based analysis (INA).\n\nResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

bioinformatics↗