Biology subjects

Schmidt, B. M.

Publications and source records attributed to Schmidt, B. M..

3 recordsLinked to original sources

bioRxiv2023

The landscape of biomedical research

The number of publications in biomedicine and life sciences has rapidly grown over the last decades, with over 1.5 million papers now being published every year. This makes it difficult to keep track of new scientific works and to have an overview of the evolution of the field as a whole. Here we present a 2D map of the entire corpus of biomedical literature, and argue that it provides a unique and useful overview of the life sciences research. We based our atlas on the abstract texts of 21 million English articles from the PubMed database. To embed the abstracts into 2D, we used the large language model PubMedBERT, combined with t-SNE tailored to handle samples of our size. We used our atlas to study the emergence of the Covid-19 literature, the evolution of the neuroscience discipline, the uptake of machine learning, the distribution of gender imbalance in academic authorship, and the distribution of retracted paper mill articles. Furthermore, we present an interactive web version of our atlas that allows easy exploration and will enable further insights and facilitate future research.

scientific communication and education↗

bioRxiv2021

ALLSorts: a RNA-Seq classifier for B-Cell Acute Lymphoblastic Leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patients subtype would have clear clinical utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose. Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes. ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/). Key PointsO_LIALLSorts is a gene expression classifier for B-cell acute lymphoblastic leukemia, which predicts 18 distinct genomic subtypes - including those designated by the World Health Organisation (WHO) and provisional entities. C_LIO_LITrained and validated on over 2300 B-ALL samples, representing each subtype and a variety of clinical features. C_LIO_LICorrectly identified subtypes in 91% of cases in a held-out dataset and between 82-93% across a newly combined cohort of paediatric and adult samples. C_LIO_LIALLSorts assigned subtypes to samples with previously unknown driver events. C_LI ALLsorts is an accurate, comprehensive and freely available classification tool that distinguishes subtypes of B-cell acute lymphoblastic leukemia from RNA-sequencing.

bioinformatics↗

bioRxiv2017

Clinker: visualising fusion genes detected in RNA-seq data

Genomic profiling efforts have revealed a rich diversity of oncogenic fusion genes, and many are emerging as important therapeutic targets. While there are many ways to identify fusion genes from RNA-seq data, visualising these transcripts and their supporting reads remains challenging. Clinker is a bioinformatics tool written in Python, R and Bpipe, that leverages the superTranscript method to visualise fusion genes. We demonstrate the use of Clinker to obtain interpretable visualisations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell Acute Lymphoblastic Leukaemia (B-ALL).\n\nAvailability and ImplementationClinker is freely available from Github https://github.com/Oshlack/Clinker under a MIT License.\n\nContactalicia.oshlack@mcri.edu.au

bioinformatics↗