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Lawrimore, J.

Publications and source records attributed to Lawrimore, J..

4 recordsLinked to original sources

labapi: a Python object model for the LabArchives electronic lab notebook

labapi is a Python library that enables computational workflows to connect to LabArchives' electronic lab notebook (ELN). Without an Application Programming Interface (API) connection, researchers must manually add workflow outputs through the LabArchives web interface, navigating to the appropriate page and uploading each output so that it appears with the experimental notes that provide context. labapi translates the flat LabArchives API into a Python object model following the existing hierarchy of the web interface, allowing workflows to navigate and modify notebook content through familiar paths. labapi enables researchers to build interconnected workflows that both read in and write data to LabArchives' ELN automatically. Researchers can inspect those outputs in the notebook, and later analysis code can read them back for another stage of analysis.

bioinformatics↗

Who Funds Open Data Sharing? Analysis of data availability statements in biomedical publications

Open data sharing is increasingly mandated by research funders, journals, and institutions, yet large-scale compliance measurement remains challenging. We analyzed 951,949 open access biomedical research articles published between January 2024 and June 2025 using a dual-source pipeline: PDF-based text extraction (MinerU) where PDFs were available and PMC XML otherwise, followed by algorithmic detection of data sharing statements (oddpub v7.2.3), enriched with funder, journal, and institutional metadata from OpenAlex. The corpus included 294,172 PDF-covered articles (30.9%) and 657,777 XML-only articles (69.1%). We found an over-all open data rate of 8.7%, rising to 11.7% among funder-linked articles (those with at least one funder identified in the metadata). Rates varied more than tenfold across the research ecosystem: leading major funders reached observed open data rates of 20-24%, while top journals reached observed rates of 70-86%, with corrected estimates as high as 92.9% (Nature Genetics) after adjusting for XML-only coverage limitations. PDF-based detection identified approximately 52% more data sharing statements than XML-based methods on the same articles. These observed rates differ markedly across funders and journals, and current overall sharing remains far below universal compliance. These patterns provide an empirical baseline against which future policy changes can be measured. An interactive dashboard at https://www.opensciencemetrics.org enables stakeholders to explore and benchmark these results.

scientific communication and education↗

Transient crosslinking kinetics optimize gene cluster interactions

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through \"flexible\" clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, \"rigid\" clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions.\n\nAuthor SummaryThe spatiotemporal organization of the genome plays an important role in cellular processes involving DNA, but remains poorly understood, especially in the nucleolus, which does not facilitate conventional techniques. Polymer chain models have shown ability in recent years to make accurate predictions of the dynamics of the genome. We consider a polymer bead-chain model of the full yeast genome during the interphase portion of the cell cycle, featuring special dynamic crosslinking to model the effects of structural maintenance proteins in the nucleolus, and investigate how the kinetic timescale on which the crosslinks bind and unbind affects the resulting dynamics inside the nucleolus. It was previously known that when this timescale is sufficiently short, large, stable clusters appear, but when it is long, there is no resulting structure. We find that there additionally exists a range of timescales for which flexible clusters appear, in which beads frequently enter and leave clusters. Furthermore, we demonstrate that these flexible clusters maximize the cross-communication between beads in the nucleolus. Finally, we apply network temporal community detection algorithms to identify what beads are in what communities at what times, in a way that is more robust and objective than conventional visual-based methods.

genomics↗

Transfer RNA Genes Affect Chromosome Structure and Function via Local Effects

The genome is packaged and organized in an ordered, non-random manner and specific chromatin segments contact nuclear substructures to mediate this organization. While transfer RNA genes (tDNAs) are essential for the generation of tRNAs, these loci are also binding sites for transcription factors and architectural proteins and are thought to play an important role in the organization of the genome. In this study, we investigate the role of tDNAs in genomic organization and chromosome function by editing a chromosome so that it lacks any tDNAs. Surprisingly our analyses of this tDNA-less chromosome show that loss of tDNAs does not grossly affect chromosome folding or chromosome tethering. However, loss of tDNAs affects local nucleosome positioning and the binding of SMC proteins at these loci. The absence of tDNAs also leads to changes in centromere clustering and a reduction in the frequency of long range HML-HMR heterochromatin clustering. We propose that the tDNAs primarily affect local chromatin structure that result in effects on long-range chromosome architecture.

molecular biology↗