OBJECTIVE: Large language models (LLMs) must effectively communicate their uncertainty to be viable in clinical settings. As such, the need for reliable uncertainty estimation grows increasingly urgent with the expanding use of LLMs for information extraction from electronic health records. Previous token-level uncertainty estimators have only used token probabilities within a single output sequence. Here, by leveraging the constraints of JSON output structure, we instead consider all likely sequences and their respective probabilities to obtain a more robust measure of model confidence. We develop Certainty Retrieval Token Search (CeRTS), a new uncertainty estimator for structured information extraction. METHODS: We evaluated CeRTS against a previous gold-standard uncertainty estimator when extracting clinical features from lung cancer discharge summaries across eight open-source LLMs. Calibration (Brier score) and discrimination (AUROC) were used to quantify performance. RESULTS: CeRTS surpassed the previous gold-standard estimator in discriminatory power across every model and achieved better calibration in most cases. CeRTS had the strongest agreement between model confidence and accuracy with Qwen-2.5. CONCLUSION: CeRTS enhances LLM-based information extraction from unstructured clinical text by assigning well-calibrated confidence scores to each extracted item, providing medical researchers with a quantitative measure of reliability at minimal additional cost. Although its performance was generally robust, CeRTS struggled with DeepSeek-R1, which we attribute to the model's Chain-of-Thought reasoning steps. Our evaluation focused on clinical data, but CeRTS can be applied to any domain requiring reliable uncertainty estimation.

UNLABELLED: Visualizing and summarizing data from genomic studies continues to be a challenge. Here, we introduce the GenVisR package to addresses this challenge by providing highly customizable, publication-quality graphics focused on cohort level genome analyses. GenVisR provides a rapid and easy-to-use suite of genomic visualization tools, while maintaining a high degree of flexibility by leveraging the abilities of ggplot2 and Bioconductor. AVAILABILITY AND IMPLEMENTATION: GenVisR is an R package available via Bioconductor (https://bioconductor.org/packages/GenVisR) under GPLv3. Support is available via GitHub (https://github.com/griffithlab/GenVisR/issues) and the Bioconductor support website. CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Several tools exist to identify cancer driver genes based on somatic mutation data. However, these tools do not account for subclasses of cancer genes: oncogenes, which undergo gain-of-function events, and tumor suppressor genes (TSGs) which undergo loss-of-function. A method which accounts for these subclasses could improve performance while also suggesting a mechanism of action for new putative cancer genes. RESULTS: We develop a panel of five complementary statistical tests and assess their performance against a curated set of 99 HiConf cancer genes using a pan-cancer dataset of 1.7 million mutations. We identify patient bias as a novel signal for cancer gene discovery, and use it to significantly improve detection of oncogenes over existing methods (AUROC = 0.894). Additionally, our test of truncation event rate separates oncogenes and TSGs from one another (AUROC = 0.922). Finally, a random forest integrating the five tests further improves performance and identifies new cancer genes, including CACNG3, HDAC2, HIST1H1E, NXF1, GPS2 and HLA-DRB1. AVAILABILITY AND IMPLEMENTATION: All mutation data, instructions, functions for computing the statistics and integrating them, as well as the HiConf gene panel, are available at www.github.com/Bose-Lab/Improved-Detection-of-Cancer-Genes. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.

Order-preserving submatrix (OPSM) has been widely accepted as a biologically meaningful cluster model, capturing the general tendency of gene expression across a subset of experiments. In an OPSM, the expression levels of all genes induce the same linear ordering of the experiments. The OPSM problem is to discover those statistically significant OPSMs from a given data matrix. The problem is reducible to a special case of the sequential pattern mining problem, where a pattern and its supporting sequences uniquely specify an OPSM. Unfortunately, existing methods do not scale well to massive data sets containing thousands of experiments and hundreds of thousands of genes, which are common in today's gene expression analysis. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs in their discovery and are completely pruned off by existing methods. However, it is of particular interest of biologists to determine small groups of genes that are tightly coregulated across many experiments, and some pathways or processes may require as few as two genes to act in concert. In this paper, we study the discovery of deep OPSMs from massive data sets. We propose a novel best effort mining framework Kiwi that exploits two parameters k and w to bound the available computational resources and search a selected search space, and does what it can to find as many as possible deep OPSMs. Extensive biological and computational evaluations on real data sets demonstrate the validity and importance of the deep OPSM problem, and the efficiency and effectiveness of the Kiwi mining framework.

The introduction of next-generation DNA sequencing devices into the field of oncology provides an unprecedented mechanism to determine the underlying genetic changes that have occurred within a tumor and also the changes that accrue during treatment. An enhanced understanding of the oncogenic mechanisms could have an immediate clinical role in the treatment of rare tumors - where treatment protocols do not exist and their rarity would indicate that clinical trials would be unlikely to be undertaken for their establishment. We have investigated the utility of massively parallel sequencing to characterize a rare adenocarcinoma of the tongue, before and after treatment. In the pre-treatment tumor we identified 7,629 genes within regions of copy number gain, 1,078 genes exhibited increased expression relative to the blood and unrelated tumors and four genes contained somatic protein-coding mutations. Our analysis suggested the tumor cells were driven by the RET oncogene and its other pathway constituents. Genes whose protein products are targeted by the RET inhibitors sunitinib and sorafenib correlated with being amplified and or highly expressed. Consistent with our observations subsequent administration of sunitinib was associated with stable disease lasting 4 months, after which the lung lesions began to grow. Administration of sorafenib and sulindac provided disease stabilization for an additional 3 months after which the cancer progressed and new lesions appeared. A metastasis recurring in the skin was determined to possess 7,288 genes within copy number amplicons, 385 genes exhibiting increased expression relative to other tumours and 9 new somatic protein coding mutations. The observed mutations and amplifications were found to be consistent with resistance to therapy arising through further activation of RET pathway and nascent activation of the AKT pathway. Our results provide evidence for the clinical utility of complete genomic characterization and direct in-vivo genome-wide characterization of the mutations accruing within a tumor under drug selection.

Advances in the computational identification of functional noncoding polymorphisms will aid in cataloging novel determinants of health and identifying genetic variants that explain human evolution. To date, however, the development and evaluation of such techniques has been limited by the availability of known regulatory polymorphisms. We have attempted to address this by assembling, from the literature, a computationally tractable set of regulatory polymorphisms within the ORegAnno database (http://www.oreganno.org). We have further used 104 regulatory single-nucleotide polymorphisms from this set and 951 polymorphisms of unknown function, from 2-kb and 152-bp noncoding upstream regions of genes, to investigate the discriminatory potential of 23 properties related to gene regulation and population genetics. Among the most important properties detected in this region are distance to transcription start site, local repetitive content, sequence conservation, minor and derived allele frequencies, and presence of a CpG island. We further used the entire set of properties to evaluate their collective performance in detecting regulatory polymorphisms. Using a 10-fold cross-validation approach, we were able to achieve a sensitivity and specificity of 0.82 and 0.71, respectively, and we show that this performance is strongly influenced by the distance to the transcription start site.

Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of conditions. In an OPSM, the expression levels of all genes induce the same linear ordering of the conditions. OPSM mining is reducible to a special case of the sequential pattern mining problem, in which a pattern and its supporting sequences uniquely specify an OPSM cluster. Those small twig clusters, specified by long patterns with naturally low support, incur explosive computational costs and would be completely pruned off by most existing methods for massive datasets containing thousands of conditions and hundreds of thousands of genes, which are common in today's gene expression analysis. However, it is in particular interest of biologists to reveal such small groups of genes that are tightly coregulated under many conditions, and some pathways or processes might require only two genes to act in concert. In this paper, we introduce the KiWi mining framework for massive datasets, that exploits two parameters k and w to provide a biased testing on a bounded number of candidates, substantially reducing the search space and problem scale, targeting on highly promising seeds that lead to significant clusters and twig clusters. Extensive biological and computational evaluations on real datasets demonstrate that KiWi can effectively mine biologically meaningful OPSM subspace clusters with good efficiency and scalability.

MOTIVATION: Our understanding of gene regulation is currently limited by our ability to collectively synthesize and catalogue transcriptional regulatory elements stored in scientific literature. Over the past decade, this task has become increasingly challenging as the accrual of biologically validated regulatory sequences has accelerated. To meet this challenge, novel community-based approaches to regulatory element annotation are required. SUMMARY: Here, we present the Open Regulatory Annotation (ORegAnno) database as a dynamic collection of literature-curated regulatory regions, transcription factor binding sites and regulatory mutations (polymorphisms and haplotypes). ORegAnno has been designed to manage the submission, indexing and validation of new annotations from users worldwide. Submissions to ORegAnno are immediately cross-referenced to EnsEMBL, dbSNP, Entrez Gene, the NCBI Taxonomy database and PubMed, where appropriate. AVAILABILITY: ORegAnno is available directly through MySQL, Web services, and online at http://www.oreganno.org. All software is licensed under the Lesser GNU Public License (LGPL).