Biology subjects

Lever, J.

Publications and source records attributed to Lever, J..

6 recordsLinked to original sources

bioRxiv2025

CIViC MCP: Integrating Large Language Models with the Clinical Interpretations of Variants in Cancer

SummaryThe Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biological and clinical significance of molecular variants in cancer. To enable users to make complex connections between CIViC information, we developed the CIViC Model Context Protocol (MCP) server, which allows users to interface with the CIViC API through natural language via large language models (LLMs), facilitating the rapid summarization of expertly curated cancer variant interpretations. Availability and implementationThe CIViC MCP server is detailed at https://github.com/griffithlab/civic-mcp-server. The repository includes instructions for accessing the server through the Claude desktop app (our recommended approach; Supplementary Figure 1) and hosting it locally with GPT-5, as well as a Python script for directly querying the MCP server. We also provide an MCP-supported Chatbot for CIViC users at https://civicdb.org/mcp-chat. Supplemental informationSupplementary data are available at Bioinformatics Advances online.

cancer biology↗

bioRxiv2021

Evolution of the open-access CIViC knowledgebase is driven by the needsof the cancer variant interpretation community

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. With nearly 300 contributors, CIViC contains peer-reviewed, published literature curated and expert-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViCs functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new evidence types (predisposing, oncogenic and functional). The growing CIViC knowledgebase distributes clinically-relevant cancer variant data currently representing >2500 variants in >400 genes from >2800 publications.

genomics↗

bioRxiv2020

Analyzing the vast coronavirus literature with CoronaCentral

The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research. To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as "Long Covid" and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention. This resource, available at https://coronacentral.ai, is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.

bioinformatics↗

bioRxiv2018

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated biomarkers and their clinical associations discussed in 800 sentences and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase (http://bionlp.bcgsc.ca/civicmine/) extracting 128,857 relevant sentences from PubMed abstracts and Pubmed Central Open Access full text papers. CIViCmine contains over 90,992 biomarkers associated with 7,866 genes, 402 drugs and 557 cancer types, representing 29,153 abstracts and 40,551 full-text publications. Through integration with CIVIC, we provide a prioritised list of curatable biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general.

bioinformatics↗

bioRxiv2018

CancerMine: A literature-mined resource for drivers, oncogenes and tumor suppressors in cancer

Understanding a mutation in cancer requires knowledge of the different roles that genes play in cancer as drivers, oncogenes and tumor suppressors. We present CancerMine, a high-quality text-mined knowledgebase that catalogues over 856 genes as drivers, 2,421 as oncogenes and 2,037 as tumor suppressors in 426 cancer types. We compile 3,485 genes that are not in the IntOGen resource of drivers and complement the Cancer Gene Census with 3,136 new genes identified as oncogenes and tumor suppressors. CancerMine provides a method for gene-centric clustering of cancer types illustrating genetic similarities between cancer types of different organs and was validated against data from the Cancer Genome Atlas (TCGA) project. Finally with 178 novel cancer gene mentions in publications each month, this resource will be updated monthly, pre-empting the need to manually curate the ever-increasing number of novel cancer associated genes. CancerMine is viewable through a web portal (http://bionlp.bcgsc.ca/cancermine/) and available for download (https://github.com/jakelever/cancermine).

bioinformatics↗

bioRxiv2017

LIONS: Analysis Suite for Detecting and Quantifying Transposable Element Initiated Transcription from RNA-seq

SummaryTransposable Elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-initiation events contribute to the transcriptome has been marred by computational and methodological deficiencies. We developed LIONS for the analysis of paired-end RNA-seq data to specifically detect and quantify TE-initiated transcripts.\n\nAvailabilitySource code, container, test data and instruction manual are freely available at www.github.com/ababaian/LIONS.\n\nContactababaian@bccrc.ca or mahdi.karimi@lms.mrc.ac.uk or dmager@bccrc.ca.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗