Single Workflow Research Articles

SaseR: Juggling offsets unlocks RNA-seq tools for fast and Scalable differential usage, Aberrant Splicing and Expression Retrieval.

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and restrictions. Indeed, tools for differential transcript usage, or diagnosing patients with rare diseases through splicing and expression outliers, either lack in performance, discard information, or do not scale to massive data compendia. Here, we show that replacing the normalisation offsets unlocks bulk RNA-seq workflows for scalable differential usage, aberrant splicing and expression analyses. Our method, saseR, is much faster than state-of-the-art methods, dramatically outperforms these to detect aberrant splicing, and provides a single workflow for various short- and long-read RNA-seq applications.

Exploring nanopore direct sequencing performance of forensic STRs, SNPs, InDels, and DNA methylation markers in a single assay

IntroductionThe field of forensic DNA analysis has undergone rapid advancements in recent decades. The integration of massively parallel sequencing (MPS) has notably expanded the forensic toolkit, moving beyond identity matching to predicting phenotypic traits and biogeographical ancestry. This shift is of particular significance in cases where conventional DNA profiling fails to identify a single suspect. Supplementing forensic analyses with estimated biological age may be valuable but involves a complex and time-consuming DNA methylation analysis. This study explores and validates the performance of a comprehensive forensic third-generation sequencing assay utilizing Oxford Nanopore Technologies (ONT) in an adaptive and direct sequencing approach. We incorporated the most widely used forensic markers, i.e., STRs, SNPs, InDels, mitochondrial DNA (mtDNA), and two methylation-based clock classifiers, thereby combining forensic genetic and epigenetic analysis in one single workflow. Methods and resultsIn our investigation, DNA from six anonymous individuals was sequenced using the ONT standard adaptive direct sequencing approach, reaching a mean percentage of on-target reads ranging from 6.6 % to 7.7 % per sample. ONT data was compared to standard MPS data and Illumina EPIC DNA methylation profiles. Basecalling employed recommended ONT software packages. TREAT was used for ONT-based analysis of autosomal and Y-chromosome STRs, achieving 90–92 % correct calls depending on allelic read depth thresholds. InDel analyses for two lower-quality samples proved challenging due to inadequate read depth, while the remaining four samples significantly contributed to the observed percentage markers (60.9 %) and correct calls (97.8 %). SNP analysis achieved a 98 % call rate, with only two mismatches and two missed alleles. ONT-generated DNA methylation data demonstrated Pearson’s correlation coefficients with EPIC data ranging from 0.67 to 0.97 for Horvath’s clock. Additional age-associated markers exhibited Pearson’s correlation coefficients with chronological age between 0.14 (ELOVL2) and 0.96 (FHL2) at read depths of <30 and <20, respectively. Despite excluding mtDNA from our targeted sequencing approach, adaptive proof-reading fragments covered the complete mtDNA with an average read depth of 21–72, showing 100 % concordance with reference data. DiscussionOur exploratory study using ONT adaptive sequencing for conventional forensic and age associated DNA methylation markers showed high sequencing accuracy for a significant number of markers, showcasing ONT as a promising (epi)genetic forensic method. Future studies must address three critical aspects: determining clear quantity and quality measures and detection thresholds for accuracy, optimizing input DNA quantity for forensic casework expectations, and addressing ethical considerations associated with phenotype and ancestry analysis to prevent ethnic biases.

Multiplexed chromatin immunoprecipitation sequencing for quantitative study of histone modifications and chromatin factors.

ChIP-seq is a widely used technique for studying histone post-translational modifications and DNA-binding proteins. DNA fragments associated with a specific protein or histone modification epitope are captured by using antibodies, sequenced and mapped to a reference genome. Albeit versatile and popular, performing many parallel ChIP-seq experiments to compare different conditions, replicates and epitopes is laborious, is prone to experimental variation and does not allow quantitative comparisons unless adequate spike-in chromatin is included. We present a detailed protocol for performing and analyzing a multiplexed quantitative chromatin immunoprecipitation-sequencing experiment (MINUTE-ChIP), in which multiple samples are profiled against multiple epitopes in a single workflow. Multiplexing not only dramatically increases the throughput of ChIP-seq experiments (e.g., profiling 12 samples against multiple histone modifications or DNA-binding proteins in a single experiment), but also enables accurate quantitative comparisons. The protocol consists of four parts: sample preparation (i.e., lysis, chromatin fragmentation and barcoding of native or formaldehyde-fixed material), pooling and splitting of the barcoded chromatin into parallel immunoprecipitation reactions, preparation of next-generation sequencing libraries from input and immunoprecipitated DNA and data analysis using our dedicated analysis pipeline. This pipeline autonomously generates quantitatively scaled ChIP-seq tracks for downstream analysis and visualization, alongside necessary quality control indicators. The entire workflow requires basic knowledge in molecular biology and bioinformatics and can be completed in 1 week. MINUTE-ChIP empowers biologists to perform every ChIP-seq experiment with an appropriate number of replicates and control conditions, delivering more statistically robust, exquisitely quantitative and biologically meaningful results.

Towards a taxonomy of team workflow structures

AbstractTeam workflow represents interactions between individuals and specific actions or tasks. Individuals’ interactions have important effects on fellow teammates’ actions by expanding or constraining actions available to them. For example, teammates may avoid performing the same action to avoid duplication of effort or they may perform their actions sequentially if one task’s completion is a prerequisite for another task. Complex dependencies embedded in these interactions suggest the need to understand team workflows from a relational perspective. As workflow structures are shaped by elements of organizational design, cognitive factors, and features of the task environment, no single workflow structure is optimal for all teams, and team workflows may manifest in countless distinct configurations. Through a systematic, network-based representation of team workflows, this paper uses a sample of 139,500 teams on GitHub to identify common patterns of team workflows. Each team is represented as a two-mode network where individuals form ties to up to fifteen distinct actions capturing productivity, discussion, and team management. Several node-level and graph-level centrality indices highlight patterns of differentiation across team workflows, and a k-means clustering algorithm detects three distinct clusters of team workflow structures: small teams of highly active generalists, small teams with a moderately active mix of focused and generalist members, and large, segmented teams of focused individuals collectively engaging in a few extremely popular actions. These results demonstrate how a structural representation of team workflows provides unique insight into team behavior and highlights distinctions that may otherwise be lost when examining team activity in aggregate.

Clustering-based multi-objective optimization considering fairness for multi-workflow scheduling on clouds

Distributed computing, such as cloud computing, provides promising platforms for orchestrating scientific workflows' tasks based on their sequences and dependencies. Workflow scheduling plays an important role in optimizing concerned objectives for distributed computing, such as minimizing the makespan and cost. Many researchers have focused on optimizing a specific single workflow with multiple objectives. Currently, there are few studies on multi-workflow scheduling, with most research focusing on objectives such as cost and makespan. However, multi-workflow scheduling requires the design of specific objectives that reflect the unique characteristics of multiple workflows. On the other hand, clustering-based approaches have garnered significant attention in the field of workflow scheduling over distributed computing resources due to their advantage in reducing data communication among tasks. Despite this, the effectiveness of clustering-based algorithms has not been extensively studied and validated in the context of multi-objective multi-workflow scheduling models. Motivated by these factors, we propose an approach for multiple workflows' multi-objective optimization (MOO), considering the new defined metric, fairness. We first mathematically formulate the fairness and define a fairness-involved MOO model. Then, we propose an advanced clustering-based resource optimization strategy in multiple workflow runs. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.

The Use of Online Consultation Systems and Patient Experience of Primary Care: Cross-Sectional Analysis Using the General Practice Patient Survey.

NHS England encourages the use of online consultation (OC) systems alongside traditional access methods for patients to contact their general practice online and for practices to manage workflow. Access is a key driver of patients' primary care experience. The use of online technology and patient experience vary by sociodemographic characteristics. This study aims to assess the association between OC system use and patient experience of primary care in English general practice and how that varies by OC system model and practice sociodemographic characteristics (rurality, deprivation, age, and ethnicity). We categorized practices as "low-use" or "high-use" according to the volume of patient-initiated contacts made via the OC system. We considered practices using one of 2 OC systems with distinct designs and implementation models-shorter "free text" input with an embedded single workflow OC system (FT practices) and longer "mixed text" input with variation in implemented workflow OC system (MT practices). We used 2022 General Practice Patient Survey data to capture 4 dimensions of patient experience-overall experience, experience of making an appointment, continuity of care, and use of self-care before making an appointment. We used logistic regression at the practice level to explore the association between OC system use and patient experience, including interaction terms to assess sociodemographic variation. We included 287,194 responses from 2423 MT and 170 FT practices. The proportions of patients reporting positive experiences at MT and FT practices were similar or better than practices nationally, except at high-use MT practices. At high-use MT practices, patients were 19.8% (odds ratio [OR] 0.802, 95% CI 0.782-0.823) less likely to report a good overall experience; 24.5% (OR 0.755, 95% CI 0.738-0.773) less likely to report a good experience of making an appointment; and 18.9% (OR 0.811, 95% CI 0.792-0.83) less likely to see their preferred general practitioner; but 27.8% (OR 1.278, 95% CI 1.249-1.308) more likely to use self-care, compared with low-use MT practices. Opposite trends were seen at FT practices. Sociodemographic inequalities in patient experience were generally lower at high-use than low-use practices; for example, gaps in overall experience between practices with the most and fewest White patients decreased by 2.7 percentage points at MT practices and 6.4 percentage points at FT practices. Trends suggested greater improvements in experience for traditionally underserved groups-patients from urban and deprived areas, younger patients, and non-White patients. An OC system with shorter free text input and an integrated single workflow can enhance patient experience and reduce sociodemographic inequalities. Variation in patient experience between practices with different sociodemographic characteristics and OC systems underscores the importance of tailored design and implementation. Generalizing results across different OC systems is difficult due to variations in how they are integrated into practice workflows and communicated to patients.

Combined Metagenomic Viral Detection and Donor-Derived Cell-Free DNA Quantification in Plasma From Kidney Transplant Recipients

BackgroundKidney transplant recipients require potent immunosuppression and are predisposed to opportunistic infections, many of which have a viral etiology. Currently, viral assays detect and quantify single pathogens using PCR or qPCR. An unbiased sequencing method with comparable accuracy would allow simultaneous monitoring of multiple viral pathogens and nonpathogenic Anelloviridae. The quantification of donor-derived cell-free DNA (dd-cfDNA) is an established method for the detection of allograft rejection, and a single workflow combining dd-cfDNA quantification and viral detection represents an opportunity to improve patient monitoring and management. MethodsWhole genome sequencing of cell-free DNA was performed using 1,980 plasma samples from 256 subjects enrolled in a multi-center study. Non-human sequences underwent reference-assisted assembly and taxonomic annotation of the viral DNA pathogens. ResultsOf the 1,980 samples tested, 1,453 (73.4%) had ≥1 viral detection(s), either a known viral pathogen or torque teno virus (TTV), with positivity rates generally declining 12–18 months post-transplant. Concordance of metagenomic NGS (mNGS) viral detection with qPCR detection was 97.7% (94.1% sensitivity, 98.2% specificity), and a linear relationship was demonstrated between mNGS viral quantitation and qPCR results. BK virus, cytomegalovirus, and Epstein-Barr virus were detected by sequencing up to 60 days prior to independently established clinical diagnoses. ConclusionsWhole-genome sequencing allows simultaneous quantification of dd-cfDNA as well as sensitive and early detection of viral infection through secondary analysis of the same sequencing results. In combination with dd-cfDNA, mNGS viral detection may provide additional pathogen surveillance results and serve as a useful biomarker for both over- and under-immunosuppression.

Prognostic and predictive value of ctDNA-based MRD and actionable biomarkers in patients with resectable colorectal cancer: CIRCULATE-Japan GALAXY.

3609 Background: The role of biomarker-guided therapy for resectable colorectal cancer (CRC) remains unestablished. Circulating tumor DNA (ctDNA) has emerged as a minimally invasive biomarker for detecting molecular residual disease (MRD) and predicting recurrence. Tumor-informed ctDNA testing offers the advantage of tumor sequencing and MRD detection in a single workflow, suggesting its potential for facilitating integration of biomarker-guided therapy into perioperative treatment. Methods: GALAXY, a large-scale prospective observational study, monitored post-surgical ctDNA MRD status in patients with clinical stage II to IV or relapsed CRC (UMIN000039205). Tumor tissue and matched normal DNA were processed for whole-exome sequencing (WES) to identify and track patient-specific somatic single nucleotide variants (SNVs), for ctDNA detection in the associated patients’ plasma samples. Actionable biomarkers having OncoKB therapeutic levels 1-3 were explored and identified separately from the Signatera test (through WES data). Results: Of the 4,590 patients enrolled between May 2020, and October 2022, 2,695 who were not enrolled in randomized trials and had available WES data were included in this analysis. Identified actionable biomarkers included RAS/ BRAF wild-type in 1,369 (50.8%) patients, tumor mutational burden (TMB) high in 256 (10.3%), MSI high in 236 (9.5%), BRAF V600E in 176 (6.5%), KRAS G12C in 61 (2.3%), ERBB2 amplification in 47 (1.7%), TP53 Y220C in 33 (1.2%), NTRK1/ 2/ 3 fusion in 1 (0.04%), and RET fusion in 1 (0.04%). ctDNA positivity during the MRD window (2 to 10 weeks post-surgery) varied from 5.1% in those with TMB high and MSI high to 29.8% in those with ERBB2 amplification. ERBB2 amplification was the only actionable biomarker significantly associated with ctDNA positivity during the MRD window (odds ratio 2.20). Positive ctDNA MRD status correlated with significantly worse DFS across all actionable biomarkers, with hazard ratios ranging from 8.46 for TP53 Y220C to 5.53e+8 for BRAF V600E. In MSI high disease, recurrence was observed in only 3 of 225 (1.3%) patients with negative ctDNA compared to 5 of 12 (41.7%) with positive ctDNA during the MRD window. Adjustment by the propensity score revealed that adjuvant chemotherapy provided survival benefits in ctDNA-positive patients with pathological stage II and III disease across various biomarkers. In patients with one or more actionable mutations selected among the16-plex of the ctDNA MRD assay, those variants were detected at time of disease recurrence. Conclusions: Our study highlights the importance of ctDNA-based MRD as both a prognostic and predictive tool for CRC across different actionable biomarkers. These findings suggest the potential of MRD-driven targeted therapeutic strategies for CRC with specific actionable biomarkers. Clinical trial information: UMIN000039205.

High-throughput system for the thermostability analysis of proteins.

Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.

Titer and charge-based heterogeneity multiattribute monitoring of mAbs in cell culture harvest using 2D ProA CEX MS

Robust monitoring of heterogeneity in biopharmaceutical development is crucial for producing safe and efficacious biotherapeutic products. Multiattribute monitoring (MAM) has emerged as an efficient tool for monitoring of mAb heterogeneities like deamidation, sialylation, glycosylation, and oxidation. Conventional biopharma analysis during mAb development relies on use of one-dimensional methods for monitoring titer and charge-based heterogeneity using non-volatile solvents without direct coupling with mass spectrometry (MS). This approach requires analysis of mAb harvest by ProA for titer estimation followed by separate cation exchange chromatography (CEX) analysis of the purified sample for estimating charge-based heterogeneity. This can take up to 60–90 min due to the required fraction collection and buffer exchange steps. In this work, a native two-dimensional liquid chromatography (2DLC) mass spectrometry method has been developed with Protein A chromatography in the first dimension for titer estimation and cation exchange chromatography (CEX) in the second dimension for charge variant analysis. The method uses volatile salts for both dimensions and enables easy coupling to MS. The proposed 2DLC method exhibits a charge variant profile that is similar to that observed via the traditional methods and takes only 15 min for mass identification of each variant. A total of six charge variants were separated by the CEX analysis after titer estimation, including linearity assessment from 5 μg to 160 μg of injected mAb sample. The proposed method successfully estimated charge variants for the mAb innovator and 4 of its biosimilars, showcasing its applicability for biosimilarity exercises. Hence, the 2D ProA CEX MS method allows direct titer and charge variant estimation of mAbs in a single workflow.

A single workflow for multi-species blood transcriptomics.

Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Additive manufacturing of membrane-type acoustic metamaterials

Traditional membrane-type acoustic metamaterials have been proven to act as effective lightweight sound insulation materials. However, their geometries cannot be easily incorporated into other acoustic designs in a single workflow. Here, we leverage the enhanced customizability that additive manufacturing provides by introducing 3D printed membrane-type metamaterials that can one day be seamlessly incorporated into a variety of acoustic designs without the need for post-processing. In this study, we present the fabrication workflow to 3D print membranes of varying thicknesses and offer an analysis on print quality. Using a four-microphone normal-incidence impedance tube, we provide preliminary results on the sound transmission loss behavior of these membrane-type metamaterials. Our results show the membranes are fabricated without any significant holes or gaps, and their thicknesses can be reliably controlled.

Automated method for quantification of20aminoacids in cell culture media during biopharmaceutical development.

Herein, a step-by-step protocol for simultaneous detection of 20 amino acids commonly present incell culture media is described. The protocol facilitates detection of both primary and secondary amino acids through a two-step precolumn derivatization strategy usingortho-phthalaldehyde and 9-fluorenylmethyl chloroformate as derivatizing agents. The separation of derivatized amino acids with varying hydrophobicity is achieved through reverse-phase chromatography. The amino acids are simultaneously detected in a single workflow through the use of Variable Wavelength Detector at 338 and 262nm. The protocol is applicable for both mammalian and bacterial cell culture matrices with an option for automation of precolumn derivatization.

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

With the ongoing digitization in archives, an increasing number of historical data becomes available for research. This includes historical aerial images which provide detailed information about the depicted area. Among the applications enabled by these images are change detection of land use, land cover, glaciers, and coastal environments as well as the observation of land degradation, and natural hazards. Studying the depicted areas and occurring 3D deformations requires the generation of a digital surface model (DSM) which is usually obtained via photogrammetric Structure-from-Motion (SfM). However, conventional SfM workflows often fail in registering historical aerial images due to their radiometric characteristics introduced by digitization, original image quality, or vast temporal changes between epochs. We demonstrate that the feature matching step in the Structure from Motion (SfM) pipeline is particularly crucial. To address this issue, we apply the two synergetic neural network methods SuperGlue and DISK, improving feature matching for historical aerial images. This requires several modifications to enable rotational invariance and leveraging the high resolution of aerial images. In contrast to other studies our workflow does not require any prior information such as DSMs, flight height, focal lengths, or scan resolution which are often no more extent in archives. It is shown that our methods using adapted parameter settings are even able to deal with quasi texture-less images. This enables the simultaneous processing of various kind of mono-temporal and multi-temporal data handled in a single workflow from data preparation over feature matching through to camera parameter estimation and the generation of a sparse point cloud. It outperforms conventional strategies in the number of correct feature matches, number of registered images and calculated 3D points and allows the generation of multi-temporal DSMs with high quality.With the flexibility of the method, it enables the automatic processing of formerly unusable or only to be interactively processed data, e.g. aerial images where the flight route is unknown, or with difficult radiometric properties. This makes it possible to go back even further in time, where the data quality usually decreases, and enables a holistic monitoring and comparison of environments of high interest. The code is made publicly available at https://github.com/tudipffmgt/HAI-SFM.

Analytical and Clinical Validation of Duoseq, a Novel Assay for Rapid, on-Site Clinical DNA and RNA Sequencing of Hematologic Malignancies

Introduction: Next generation sequencing (NGS) has become a critical component of the workup of malignancies. NGS can provide important diagnostic information including mutations, chromosomal copy number alterations and translocations (from DNAseq) as well as gene expression and fusions (from RNAseq). Incorporation of NGS in the clinic remains difficult. DNAseq and RNAseq remain distinct assays with challenging bioinformatics and turnaround times (TAT) that can last several weeks. Duoseq is a novel assay for combined DNA- and RNAseq in a single workflow that detects single nucleotide variants (SNVs), insertions/deletions (indels), splice site variants (SSVs), structural variants (SVs) and Epstein-Barr virus (EBV) status. Initially implemented for hematologic malignancies, Duoseq assesses nearly all known gene drivers (N=475) and is compatible with both fresh-frozen and formalin-fixed, paraffin-embedded (FFPE) tissues. It is available as a reagent kit and includes cloud-based bioinformatics, enabling any clinical lab with an Illumina sequencer to perform NGS in-house with a TAT of 2-3 days. Methods/Results: Duoseq was validated for concordance with commercial DNA- and RNAseq kits (New England BioLabs (NEB)). Sequencing libraries were prepared on DNA alone, RNA alone and total nucleic acid (TNA) from cell lines in accordance with kit protocols and captured using the Duoseq panel. Results were equivalent between Duoseq and individual (NEB) library preparations with nearly complete concordance in DNA variants (99.4%, P&amp;lt;10 -6) and RNA expression (R 2=0.97, P&amp;lt;10 -6). Separately, copy number alteration calls across 42 cases had high concordance between Duoseq and Affymetrix array approaches (P&amp;lt;10 -6). Analytical validation was performed at two CLIA laboratories to establish assay precision and reproducibility, sensitivity (LoD), and specificity. Analytical validation was performed on well characterized and control samples that were selected to harbor an array of clinically important variants at known allele frequencies. Precision and reproducibility testing confirmed Duoseq's ability to produce accurate results independent of external variables. We tested inter-lab reproducibility, intra-day and inter-day repeatability and technician-to-technician reproducibility. In each case, Duoseq achieved &amp;gt;98% precision and reproducibility for each variant class (SNVs, indels/SSVs and SVs). We confirmed the LoD using TNA from the same samples diluted in normal TNA to create 3-point curves that spanned our expected LoD. We confirmed the assay to be 100% accurate down to ≥5% mutant allele fraction (MAF) for SNVs, ≥10% MAF for indels/SSVs, and ≥20% tumor purity for SVs. Specificity was additionally assessed by including normal cell lines in each run to determine the incidence of cross-contamination indicated by false positives. Analytical specificity was 100%, indicating no instances of sample cross-contamination. We assessed the clinical accuracy of Duoseq using FFPE myeloid and lymphoid specimens (N=153) collected at both laboratories and characterized via orthogonal methods including Foundation Medicine (SNVs and indels/SSVs), fluorescent in situ hybridization (translocations) and in situ hybridization (EBV status). Duoseq achieved a positive predictive agreement of 98% for SNVs (N=139), 96% for indels/SSVs (N=55), 95% for translocations (N=282), and 100% for EBV status (N=58). Cell of origin (COO) for diffuse large B cell lymphoma (n=42) using Hans criteria and had high concordance (90%) with the COO calls rendered from Duoseq expression data. Given Duoseq's array of measurements and short TAT, we plan a potential application in “match” clinical trials where patients are enrolled in treatment arms based on molecular profiling. In ongoing work to test a potential trial workflow, we enrolled four institutions that will provide cases to a central site for pathology review and molecular profiling enabling trial arm assignment. Specimen enrollment is anticipated to be completed to allow for results to be presented at the meeting. Conclusions: Duoseq is comparable to current methods and provides a cost and time effective option for library preparation.The large gene panel enables testing a broad range of clinical features in a single assay, including genes required for diagnosis and prognostication in heme malignancies. These results support clinical use of Duoseq.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

3-D printed membrane-type acoustic metamaterials with tailored performance

While traditional membrane-type acoustic metamaterials act as effective lightweight sound insulation materials, their geometries can neither be easily customized nor incorporated into other acoustic designs in a single workflow. Here, we leverage our established techniques at 3-D printing thin membranes to create custom acoustic metamaterials with tailored performance that can be easily implemented into other acoustic designs without any post-processing. In this study, we present the workflow to additively manufacture flexible membranes with added textural features using low-cost extrusion-based printing methods. Using a custom G-code, we print membranes with controlled mass, stiffness, and porosity while superimposing various surface pattern designs. Using a four-microphone normal-incidence impedance tube setup, we demonstrate the effect of each parameter on the membrane’s sound transmission loss behavior and compare the results against traditional acoustic mass law.

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

Accurate and fast graph-based pangenome annotation and clustering with ggCaller.

Bacterial genomes differ in both gene content and sequence mutations, which underlie extensive phenotypic diversity, including variation in susceptibility to antimicrobials or vaccine-induced immunity. To identify and quantify important variants, all genes within a population must be predicted, functionally annotated, and clustered, representing the "pangenome." Despite the volume of genome data available, gene prediction and annotation are currently conducted in isolation on individual genomes, which is computationally inefficient and frequently inconsistent across genomes. Here, we introduce the open-source software graph-gene-caller (ggCaller). ggCaller combines gene prediction, functional annotation, and clustering into a single workflow using population-wide de Bruijn graphs, removing redundancy in gene annotation and resulting in more accurate gene predictions and orthologue clustering. We applied ggCaller to simulated and real-world bacterial data sets containing hundreds or thousands of genomes, comparing it to current state-of-the-art tools. ggCaller has considerable speed-ups with equivalent or greater accuracy, particularly with data sets containing complex sources of error, such as assembly contamination or fragmentation. ggCaller is also an important extension to bacterial genome-wide association studies, enabling querying of annotated graphs for functional analyses. We highlight this application by functionally annotating DNA sequences with significant associations to tetracycline and macrolide resistance in Streptococcus pneumoniae, identifying key resistance determinants that were missed when using only a single reference genome. ggCaller is a novel bacterial genome analysis tool with applications in bacterial evolution and epidemiology.