Segmentation and automated genome annotation (SAGA) techniques, such as Segway and ChromHMM, assign labels to every part of the genome, identifying similar patterns across multiple genomic input signals. Inferring biological meaning in these patterns remains challenging. Doing so requires a time-consuming process of manually downloading reference data, running multiple analysis methods, and interpreting many individual results. To simplify these tasks, we developed the turnkey system Segzoo. As input, Segzoo only requires a genome annotation file in browser extensible data (BED) format. It automatically downloads the rest of the data required for comparisons. Segzoo performs analyses using these data and summarizes results in a single visualization. Availability and Implementation Source code for Python ≥3.7 on Linux freely available for download at https://github.com/hoffmangroup/segzoo under the GNU General Public License (GPL) version 2. Segzoo is also available in the Bioconda package segzoo: https://anaconda.org/bioconda/segzoo .

This multi-interest holder consensus effort highlights the essential influence and responsibility of Research-Performing Organizations (RPOs) in shaping the quality, transparency, and trustworthiness of scientific research. Despite the use of metrics to assess transparency and openness by academic journals and funders, most RPOs do not yet have metrics/indicators or monitoring that address deep-rooted shortcomings in research practice and assessment. These shortcomings manifest as restricted access to research outputs, poor availability of underlying data and materials, infrequent reproducibility checks and direct replications, and ongoing incidents of research misconduct all contributing to erosion of public trust in research. Such issues are exacerbated by institutional incentive structures that reward the number of publications and journal prestige, while neglecting research credibility, societal relevance, and transparent communication. To respond comprehensively to these challenges, our working group reached consensus on six core practices for RPOs to monitor: data, code, and material sharing; open access publishing; prospective study registration; reporting transparency; disclosures of interest and funding; and verification efforts. Collectively, these six practices offer a pragmatic and flexible framework that RPOs can tailor to their local context. The paper provides an implementation guide for these practices and calls for renewed leadership by RPOs in realigning research(er) assessment and incentives to reward quality, transparency, and trustworthiness in research, closing persistent gaps, and reinforcing science’s credibility and utility for society.

Human papillomavirus (HPV) DNA is detectable in the peripheral blood plasma from patients with locally advanced and metastatic cervical cancer. Levels of HPV circulating tumor DNA (ctDNA) in pretreatment plasma have weak associations with prognosis, but the significance of detecting HPV integration into the host genome or fragmentation features within HPV ctDNA has not been explored. We hypothesized that these molecular features of HPV ctDNA may serve as prognostic biomarkers and reflect HPV biology, independent from total ctDNA abundance. Plasma cell-free DNA was collected at baseline from 57 patients with locally advanced cervical cancer (stage IB-IVA) and 21 patients with metastatic cervical cancer. Whole viral genome sequencing was performed following hybrid capture. HPV ctDNA levels were expressed in copies/mL plasma. HPV integration sites were detected using SearcHPV. Progression-free survival (PFS) was evaluated using Kaplan-Meier analysis and log-rank tests. The normalized fragment midpoint coverage across the HPV-16 genome was calculated for individual samples and averaged across patient groups. NuPoP, was used to calculate the expected nucleosome occupancy at each base pair of the HPV-16 genome. Peaks corresponding to the observed nucleosome occupancy within our cohorts were called using pracma, and occupancy probabilities were calculated for each peak. Baseline HPV ctDNA levels were detected in 57/57 locally advanced patients (median=283 copies/mL) and 20/21 metastatic patients (median=299 copies/mL). Unique HPV integration sites were detected at varying levels in 21/57 locally advanced patients (median sites:1, range:0-23) and 12/21 metastatic patients (median sites:2, range:0-45) (Wilcoxon p=0.8). No significant differences in PFS were observed in locally advanced or metastatic patients when stratified by median ctDNA levels (p=0.7 and p=0.8 respectively). However, high confidence integration detected at baseline was associated with inferior PFS in locally advanced patients (p=0.01), but not metastatic (p=0.28). HPV ctDNA fragment coverage varied across the HPV-16 genome with the number of observed merged peaks being 32 in the locally advanced cohort (n=36) and 31 in the metastatic cohort (n=14). The median expected nucleosome occupancy probabilities across all peaks were 98.6% and 97.2%, respectively. Detection of viral integration within baseline HPV ctDNA was associated with significantly inferior PFS in locally advanced patients. Peaks in the average HPV-16 ctDNA coverage profiles corresponded to high expected nucleosome occupancy in both the locally advanced and metastatic cohorts, suggesting that ctDNA may reflect chromatin accessibility of the virus. These findings suggest that quantitative and qualitative features of HPV ctDNA from baseline plasma may reflect HPV biology associated with cervical cancer. Emma M. Collier, Lucas Penny, Jinfeng Zou, Zhen Zhao, Yangqiao Zheng, Pamela Soberanis Pina, Michelle McMullen, Eric Y. Stutheit-Zhao, Michael M. Hoffman, Sarah E. Ferguson, Kathy Han, Eric Leung, Stephanie Lheureux, Scott V. Bratman. Human papillomavirus circulating tumor DNA for risk stratification in cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4663.

The mechanisms that ensure developmental progression in the early human embryo remain largely unknown. Here, we show that the family of long interspersed nuclear element 1 (LINE1) transposons prevents the reversion of naive human embryonic stem cells (hESCs) to 8-cell-like cells (8CLCs). LINE1 RNA contributes to maintenance of H3K27me3 levels, particularly at chromosome 19 (Chr19). Chr19 is enriched for key 8C regulators, H3K27me3, and genes derepressed upon LINE1 knockdown or PRC2 inhibition. Moreover, Chr19 is strongly associated with the nucleolus in hESCs but less in 8CLCs. Direct inhibition of PRC2 activity induces the 8C program and leads to a relocalization of Chr19 away from the nucleolus. LINE1 KD or PRC2 inhibition induces nucleolar stress, and disruption of nucleolar architecture is sufficient to de-repress the 8C program. These results indicate that LINE1 RNA and PRC2 maintain H3K27me3-mediated gene repression and 3D nuclear organization to prevent developmental reversion of hESCs.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.

Diagnosing lymphoma relies on invasive tissue biopsies, which can yield insufficient material for histopathological evaluation and carry a risk of complications. Cell-free DNA (cfDNA) analysis from plasma represents a promising alternative for non-invasive lymphoma diagnosis, as DNA methylation patterns are both highly cell type–specific and characteristically altered in malignancy. We analyzed cfDNA methylation in 265 plasma samples (165 pre-treatment samples from lymphoma patients: 71 DLBCL, 46 FL, 48 HL; 48 non-lymphoma/non-malignant controls; and 52 post-cycle 1 or end-of-treatment [EOT] samples from 15 DLBCL and 12 FL patients) using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq). Most pre-treatment samples (84.2%) were obtained at diagnosis, and a small number of samples before second-line treatment (15.8%). The pre-treatment cohort was split into discovery (n=142) and validation (n=71) sets for model development and testing. Differential methylation analysis identified 13,934 lymphoma-associated hypermethylated regions, which were used to train regularized binomial generalized linear models. Enrichment analyses revealed these regions overlapped significantly with CpG islands and H3K27me3-marked genes. In the validation cohort, the binary classification model distinguishing lymphoma from controls achieved an accuracy of 0.88, with a positive predictive value (PPV) and negative predictive value (NPV) of 0.88. Subtype-specific models were subsequently developed: the DLBCL vs. control model reached an AUC of 0.96 and accuracy of 0.87 (PPV = 0.91, NPV = 0.84); the FL vs. control model yielded an AUC of 0.82 and accuracy of 0.74 (PPV = 0.82, NPV = 0.69); and the HL vs. control model achieved an AUC of 0.99 and accuracy of 0.96 (PPV = 0.94, NPV = 0.98). Stage-stratified analysis showed high classification performance for both limited and advanced-stage disease. The lymphoma vs. control model achieved AUCs of 0.96 (advanced-stage) and 0.94 (limited-stage). For DLBCL, AUCs were 0.98 and 0.94; for FL, 0.88 and 0.70; and for HL, 0.99 and 0.97, respectively. A three-class model distinguishing controls, HL, and a combined DLBCL/FL group showed robust overall performance. HL classification achieved an AUC of 0.99 and accuracy of 0.89 (PPV = 0.91, NPV = 0.89); the DLBCL/FL group reached an AUC of 0.95 and accuracy of 0.86 (PPV = 0.92, NPV = 0.82); and control classification had an AUC of 0.95 and accuracy of 0.80 (PPV = 0.69, NPV = 0.87). A four-class model distinguishing HL, DLBCL, FL, and controls showed that HL remained the most accurately identified subtype (AUC = 0.99, accuracy = 0.89, PPV = 0.92, NPV = 0.89), followed by DLBCL (AUC = 0.89, accuracy = 0.83) and FL (AUC = 0.80, accuracy = 0.79). DLBCL samples misclassified as FL were enriched for GCB-type mutations in EZH2 and BCL2 and lacked ABC-associated mutations such as TBL1XR1, BTG1, CCND3, and PRDM1. We calculated cfDNA methylation scores by averaging normalized methylation levels across lymphoma-associated hypermethylated regions. These scores were significantly associated with LDH levels (DLBCL: R = 0.53, p = 2.1×10⁻⁶; FL: R = 0.51, p = 2.9×10⁻⁴), IPI in DLBCL (p = 0.0077), FLIPI in FL (p = 9.1×10⁻⁸), cfDNA tumor burden, and metabolic tumor volume from PET-CT. Methylation scores from plasma samples taken after the first immunochemotherapy cycle (15 DLBCL, 10 FL) and at EOT (15 DLBCL, 12 FL) tracked treatment response as conveyed by PET-CT or CT scans. Increasing scores were observed alongside radiographic progression in 2 patients, and a patient with complete radiological response but with a slow declining methylation score post-cycle 1 had early progression 2 months after EOT. Five additional patients with low EOT methylation scores and complete metabolic response experienced either relapse or transformation. Four progression-free patients showed partial radiological response, but had low methylation scores at EOT. To the best of our knowledge, this is the first study to apply cfMeDIP-seq to plasma samples from lymphoma patients. cfDNA methylation profiling offers a sensitive, minimally invasive approach for lymphoma detection and subtype classification, with high classification performance even in early-stage disease for DLBCL and HL. cfDNA methylation correlates with tumor burden and clinical risk, supporting its potential role as a biomarker for predicting treatment response.

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Background Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult. Results Here, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs. Conclusions Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

The family of LINE1 transposable elements underwent a massive expansion in mammalian genomes. While traditionally viewed as a mutagenic selfish element, recent studies point to roles for LINE1 in early mouse development, T cell quiescence and neurogenesis. Here we show that human LINE1 RNA is essential for self-renewal and identity of human embryonic stem cells (hESCs). Silencing of LINE1 using either antisense oligonucleotides or CRISPR interference in naïve hESCs leads to a strong induction of 8C-like cells (8CLCs). We found that genes derepressed upon LINE1 KD are not uniformly distributed across the genome, with an enrichment for chromosome 19, which includes key markers of the 8C state such as TPRX1 . Silencing of TPRX1 , but not other putative 8C regulators p53 or H3.XY , suppresses the induction of the 8C program in LINE1 KD hESCs. We found that LINE1 RNA is preferentially localized to the lamina and periphery of the nucleolus in hESCs. Sequencing of Lamina-Associated Domains (LADs) and Nucleolus-Associated Domains (NADs) reveals a preferential association of chromosome 19 with NADs in hESCs. However, 8CLCs have a distinct nucleolar morphology and a lower association of chromosome 19 and TPRX1 loci with the nucleolus relative to naïve and primed hESCs, suggesting a role for nucleolar dynamics in the 8CLC-hESC transition. In agreement, LINE1 KD leads to disruption of nucleolar architecture with signs of nucleolar stress. Independent perturbations of the nucleolus induce the 8C program in hESCs. Genes induced by LINE1 KD are enriched for targets of Polycomb Repressive Complex (PRC2), and inhibition of PRC2 leads to a strong induction of 8C genes. Our results indicate that LINE1 coordinates nuclear compartmentalization and chromatin-mediated gene repression to prevent developmental reversion of hESCs. Highlights ### Competing Interest Statement The authors have declared no competing interest.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases.

ObjectiveThe San Francisco Declaration on Research Assessment (DORA) advocates for assessing biomedical research quality and impact, yet academic organizations continue to employ traditional measures such as Journal Impact Factor. We aimed to identify and prioritize measures for assessing research quality and impact.MethodsWe conducted a review of published and grey literature to identify measures of research quality and impact, which we included in an online survey. We assembled a panel of researchers and research leaders, and conducted a two-round Delphi survey to prioritize measures rated as high (rated 6 or 7 by ≥ 80% of respondents) or moderate (rated 6 or 7 by ≥ 50% of respondents) importance.ResultsWe identified 50 measures organized in 8 domains: relevance of the research program, challenges to research program, or productivity, team/open science, funding, innovations, publications, other dissemination, and impact. Rating of measures by 44 panelists (60%) in Round One and 24 (55%) in Round Two of a Delphi survey resulted in consensus on the high importance of 5 measures: research advances existing knowledge, research plan is innovative, an independent body of research (or fundamental role) supported by peer-reviewed research funding, research outputs relevant to discipline, and quality of the content of publications. Five measures achieved consensus on moderate importance: challenges to research productivity, potential to improve health or healthcare, team science, collaboration, and recognition by professional societies or academic bodies. There was high congruence between researchers and research leaders across disciplines.ConclusionsOur work contributes to the field by identifying 10 DORA-compliant measures of research quality and impact, a more comprehensive and explicit set of measures than prior efforts. Research is needed to identify strategies to overcome barriers of use of DORA-compliant measures, and to "de-implement" traditional measures that do not uphold DORA principles yet are still in use.

Background Human papillomavirus (HPV) drives almost all cervical cancers and up to 70% of head and neck cancers. Frequent integration into the host genome occurs predominantly in tumorigenic types of HPV. We hypothesize that changes in chromatin state at the location of integration can result in changes in gene expression that contribute to the tumorigenicity of HPV. Results We find that viral integration events often occur along with changes in chromatin state and expression of genes near the integration site. We investigate whether introduction of new transcription factor binding sites due to HPV integration could invoke these changes. Some regions within the HPV genome, particularly the position of a conserved CTCF binding site, show enriched chromatin accessibility signal. ChIP-seq reveals that the conserved CTCF binding site within the HPV genome binds CTCF in 4 HPV + cancer cell lines. Significant changes in CTCF binding pattern and increases in chromatin accessibility occur exclusively within 100 kbp of HPV integration sites. The chromatin changes co-occur with out-sized changes in transcription and alternative splicing of local genes. Analysis of The Cancer Genome Atlas (TCGA) HPV + tumors indicates that HPV integration upregulates genes which have significantly higher essentiality scores compared to randomly selected upregulated genes from the same tumors. Conclusions Our results suggest that introduction of a new CTCF binding site due to HPV integration reorganizes chromatin state and upregulates genes essential for tumor viability in some HPV + tumors. These findings emphasize a newly recognized role of HPV integration in oncogenesis.

The state of open science needs to be monitored to track changes over time and identify areas to create interventions to drive improvements. In order to monitor open science practices, they first need to be well defined and operationalized. To reach consensus on what open science practices to monitor at biomedical research institutions, we conducted a modified 3-round Delphi study. Participants were research administrators, researchers, specialists in dedicated open science roles, and librarians. In rounds 1 and 2, participants completed an online survey evaluating a set of potential open science practices, and for round 3, we hosted two half-day virtual meetings to discuss and vote on items that had not reached consensus. Ultimately, participants reached consensus on 19 open science practices. This core set of open science practices will form the foundation for institutional dashboards and may also be of value for the development of policy, education, and interventions.

AbstractEnhancer reprogramming has been proposed as a key source of transcriptional dysregulation during tumorigenesis, but the molecular mechanisms underlying this process remain unclear. Here, we identify an enhancer cluster required for normal development that is aberrantly activated in breast and lung adenocarcinoma. Deletion of the SRR124–134 cluster disrupts expression of the SOX2 oncogene, dysregulates genome-wide transcription and chromatin accessibility and reduces the ability of cancer cells to form colonies in vitro. Analysis of primary tumors reveals a correlation between chromatin accessibility at this cluster and SOX2 overexpression in breast and lung cancer patients. We demonstrate that FOXA1 is an activator and NFIB is a repressor of SRR124–134 activity and SOX2 transcription in cancer cells, revealing a co-opting of the regulatory mechanisms involved in early development. Notably, we show that the conserved SRR124 and SRR134 regions are essential during mouse development, where homozygous deletion results in the lethal failure of esophageal–tracheal separation. These findings provide insights into how developmental enhancers can be reprogrammed during tumorigenesis and underscore the importance of understanding enhancer dynamics during development and disease.

Summary:Chromatin immunoprecipitation-sequencing is widely used to find transcription factor binding sites, but suffers from various sources of noise. Knocking out the target factor mitigates noise by acting as a negative control. Paired wild-type and knockout (KO) experiments can generate improved motifs but require optimal differential analysis. We introduce peaKO-a computational method to automatically optimize motif analyses with KO controls, which we compare to two other methods. PeaKO often improves elucidation of the target factor and highlights the benefits of KO controls, which far outperform input controls. Availability and implementation:PeaKO is freely available at https://peako.hoffmanlab.org. Contact:michael.hoffman@utoronto.ca.

Motivation Planarians are a widespread model for studying regeneration. Major efforts for studying gene function in planarian regeneration produced massive datasets, including transcriptome-wide gene expression analyses from hundreds of conditions. However, the accessibility of gene expression datasets to investigators is limited because of the need for expertise in gene expression analysis in this model, the requirement for computational resources, and the lack of a curated planarian gene expression metadata resource associating samples and their controls. Results We implemented a computational resource, PLANAtools, that is available online and provides a portal to the analysis of over 160 gene expression analyses. Planarian gene expression datasets from the last decade were processed using a standardized pipeline based on curated planarian metadata. PLANAtools generates plots, annotations, and analyses of gene expression data, based on user parameters. Availability PLANAtools is implemented using the R/Shiny framework and is accessible from https://wurtzellab.org/planatools

ABSTRACT Cell-free chromatin (cf-chromatin) is a rich source of biomarkers across various conditions, including cancer. Tumor-derived circulating cf-chromatin can be profiled for epigenetic features, including nucleosome positioning and histone modifications that govern cell type-specific chromatin conformations. However, the low fractional abundance of tumor-derived cf-chromatin in blood and constrained access to plasma samples pose challenges for epigenetic biomarker discovery. Conditioned media from preclinical tissue culture models could provide an unencumbered source of pure tumor-derived cf-chromatin, but large cf-chromatin complexes from such models do not resemble the nucleosomal structures found predominantly in plasma, thereby limiting the applicability of many analysis techniques. Here, we developed a robust and generalizable framework for simulating cf-chromatin with physiologic nucleosomal distributions using an optimized nuclease treatment. We profiled the resulting nucleosomes by whole genome sequencing and confirmed that inferred nucleosome positioning reflected gene expression and chromatin accessibility patterns specific to the cell type. Compared with plasma, simulated cf-chromatin displayed stronger nucleosome positioning patterns at genomic locations of accessible chromatin from patient tissue. We then utilized simulated cf-chromatin to develop methods for genome-wide profiling of histone post-translational modifications associated with heterochromatin states. Cell-free chromatin immunoprecipitation and sequencing (cf-ChIP-Seq) of H3K27me3 identified heterochromatin domains associated with repressed gene expression, and when combined with H3K4me3 cfChIP-Seq revealed bivalent domains consistent with an intermediate state of transcriptional activity. Combining cfChIP-Seq of both modifications provided more accurate predictions of transcriptional activity from the cell of origin. Altogether, our results demonstrate the broad applicability of preclinical simulated cf-chromatin for epigenetic liquid biopsy biomarker discovery.

Background In prior research, we identified and prioritized ten measures to assess research performance that comply with the San Francisco Declaration on Research Assessment, a principle adopted worldwide that discourages metrics-based assessment. Given the shift away from assessment based on Journal Impact Factor, we explored potential barriers to implementing and adopting the prioritized measures. Methods We identified administrators and researchers across six research institutes, conducted telephone interviews with consenting participants, and used qualitative description and inductive content analysis to derive themes. Results We interviewed 18 participants: 6 administrators (research institute business managers and directors) and 12 researchers (7 on appointment committees) who varied by career stage (2 early, 5 mid, 5 late). Participants appreciated that the measures were similar to those currently in use, comprehensive, relevant across disciplines, and generated using a rigorous process. They also said the reporting template was easy to understand and use. In contrast, a few administrators thought the measures were not relevant across disciplines. A few participants said it would be time-consuming and difficult to prepare narratives when reporting the measures, and several thought that it would be difficult to objectively evaluate researchers from a different discipline without considerable effort to read their work. Strategies viewed as necessary to overcome barriers and support implementation of the measures included high-level endorsement of the measures, an official launch accompanied by a multi-pronged communication strategy, training for both researchers and evaluators, administrative support or automated reporting for researchers, guidance for evaluators, and sharing of approaches across research institutes. Conclusions While participants identified many strengths of the measures, they also identified a few limitations and offered corresponding strategies to address the barriers that we will apply at our organization. Ongoing work is needed to develop a framework to help evaluators translate the measures into an overall assessment. Given little prior research that identified research assessment measures and strategies to support adoption of those measures, this research may be of interest to other organizations that assess the quality and impact of research.