NCI's Proteomic Data Commons: A Cloud-Based Proteomics Repository Empowering Comprehensive Cancer Analysis through Cross-Referencing with Genomic and Imaging Data.

Proteomics has emerged as a powerful tool for studying cancer biology, developing diagnostics, and therapies. With the continuous improvement and widespread availability of high-throughput proteomic technologies, the generation of large-scale proteomic data has become more common in cancer research, and there is a growing need for resources that support the sharing and integration of multi-omics datasets. Such datasets require extensive metadata including clinical, biospecimen, and experimental and workflow annotations that are crucial for data interpretation and reanalysis. The need to integrate, analyze, and share these data has led to the development of NCI's Proteomic Data Commons (PDC), accessible at https://pdc.cancer.gov. As a specialized repository within the NCI Cancer Research Data Commons (CRDC), PDC enables researchers to locate and analyze proteomic data from various cancer types and connect with genomic and imaging data available for the same samples in other CRDC nodes. Presently, PDC houses annotated data from more than 160 datasets across 19 cancer types, generated by several large-scale cancer research programs with cohort sizes exceeding 100 samples (tumor and associated normal when available). In this article, we review the current state of PDC in cancer research, discuss the opportunities and challenges associated with data sharing in proteomics, and propose future directions for the resource. SIGNIFICANCE: The Proteomic Data Commons (PDC) plays a crucial role in advancing cancer research by providing a centralized repository of high-quality cancer proteomic data, enriched with extensive clinical annotations. By integrating and cross-referencing with complementary genomic and imaging data, the PDC facilitates multi-omics analyses, driving comprehensive insights, and accelerating discoveries across various cancer types.

Silent Struggles: Traumatic Brain Injuries and Mental Health in Law Enforcement.

OBJECTIVE: To determine the prevalence of head injuries (HIs), posttraumatic stress disorder (PTSD), and depressive symptoms in law enforcement officers (LEOs) and (2) the association between HIs and psychological health conditions. SETTING: County-level survey administered via Research Electronic Data Capture. PARTICIPANTS: A total of 381 LEOs completed the survey (age = 43 ± 11 years; 40 [11%] females; time as LEO = 1-50 years, median = 15 years). DESIGN: Cross-sectional study. MAIN MEASURES: We examined the prevalence of HIs (the Ohio State University Traumatic Brain Injury Identification Method), PTSD (PTSD Checklist-Civilian [PCL-C]), and depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]). We used Mann-Whitney U and chi-square analyses to compare PTSD and depressive symptoms between those with and without a HI history. RESULTS: There were 282 (74%) participants who reported a lifetime history of 1 or more HIs; 116 (30%) sustained 1 or more HIs on the job. PCL-C scores ranged 17 to 85 (median = 27); 33 (10%) participants met or exceeded the clinical cutoff score of 50 to indicate a positive PTSD screening. Participants with a HI history (median = 29) had higher PCL-C scores than those with no HI history (median = 24; P < .001), but the proportion of participants who met the clinical cutoff for PTSD was not different between those with (n = 28, 11%) and without (n = 5, 5%) a HI history (X2 = 2.52, P = .112, odds ratio = 2.18; 95% confidence interval, 0.82-5.83). PHQ-9 scores ranged 0 to 20 (median = 3); 124 (36%) participants reported mild or greater depressive symptoms. Participants with a HI history (median = 3) had higher depressive symptoms than those with no HI history (median = 2; P = .012). The proportion of participants with mild or greater depressive symptoms was higher among those with a HI history (n = 99, 39%) than without (n = 25, 27%; X2 = 4.34, odds ratio = 1.74; 95% confidence interval, 1.03-2.93). CONCLUSION: HIs are prevalent in LEOs, which may have consequences for their performance, well-being, and career longevity. PTSD and depressive symptoms are higher in those with a HI history, suggesting LEOs need better traumatic brain injuries and mental health resources.

Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.

Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled "Functional impact of glycans and their curation" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.

A Framework for Concussion Management Strategies in Law Enforcement Officers.

Athletic trainers are increasingly utilized in non-traditional settings, such as in law enforcement, where they can contribute to healthcare management, including concussion management of law enforcement officers (LEOs). Despite the prevalence of concussions among LEOs, there is a notable gap in concussion management guidelines for this population. LEOs may lack the education and resources necessary for concussion recognition and proper management. Drawing on advancements in concussion management in athletes and military personnel, here we present a comprehensive framework for concussion management in LEOs encompassing concussion education, a graduated return to duty (RTD) protocol, and considerations for implementation and documentation specific to law enforcement. We also present several barriers and facilitators to implementation. Due to job requirements, it is critical for law enforcement organizations and their medical providers to adopt a concussion management strategy. Without proper concussion management, LEOs may risk subsequent injury and/or suffer from prolonged recovery and adverse long-term outcomes.

ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs.

Motivation: Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants. Results: We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest. Availability: ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.

Characterizing Head Acceleration Events in Law Enforcement Cadets During Subject Control Technique Training.

Law enforcement cadets (LECs) complete weeks of subject control technique training. Similar sport-related combat training has been shown to expose participants to head acceleration events (HAEs) that have potential to result in short- and long-term impairments. The purpose of this study was to describe the number and magnitude of HAEs in LECs throughout their training. 37 LECs (7 females; age = 30.6 ± 8.8 years; BMI = 30.0 ± 6.0) were recruited from a law enforcement organization. Participants wore instrumented mouthguards, which recorded all HAEs exceeding a resultant 5 g threshold for training sessions with the potential for HAEs. Participants completed three defensive tactics (DT) training sessions, a DT skill assessment (DTA), and three boxing sessions. Outcome measures included the number of HAEs, peak linear acceleration (PLA), and peak rotational velocity (PRV). There were 2758 true-positive HAEs recorded across the duration of the study. Boxing sessions accounted for 63.7% of all true-positive HAEs, while DT accounted for 31.4% and DTA accounted for 4.9%. Boxing sessions resulted in a higher number of HAEs per session (F2,28 = 48.588, p < 0.001, ηp2 = 0.776), and higher median PLA (F2,28 = 8.609, p = 0.001, ηp2 = 0.381) and median PRV (F2,28 = 11.297, p < 0.001, ηp2 = 0.447) than DT and DTA. The LECs experience a high number of HAEs, particularly during boxing sessions. Although this training is necessary for job duties, HAE monitoring may lead to modifications in training structure to improve participant safety and enhance recovery.

Use of accelerometers and inertial measurement units to quantify movement of tactical athletes: A systematic review.

The dynamic work environments of tactical athletes are difficult to replicate in a laboratory. Accelerometers and inertial measurement units provide a way to characterize movement in the field. This systematic review identified how accelerometers and inertial measurement units are currently being used to quantify movement patterns of tactical athletes. Seven research and military databases were searched, producing 26,228 potential articles with 78 articles included in this review. The articles studied military personnel (73.1%), firefighters (19.2%), paramedics (3.8%), and law enforcement officers (3.8%). Accelerometers were the most used type of sensor, and physical activity was the primarily reported outcome variable. Seventy of the studies had fair or poor quality. Research on firefighters, emergency medical services, and law enforcement officers was limited. Future research should strive to make quantified movement data more accessible and user-friendly for non-research personnel, thereby prompting increased use in tactical athlete groups, especially first responder agencies.

A Preliminary Investigation into the Neural Correlates of Knee Loading during a Change of Direction Task in Individuals after Anterior Cruciate Ligament Reconstruction.

Background: Central nervous system (CNS) function after ACLR, quantified by the blood oxygen level dependent (BOLD) response, is altered in regions of sensory function during knee movement after ACLR. However, it is unknown how this altered neural response may manifest in knee loading and response to sensory perturbations during sport specific movements. Purpose: To investigate the relationship among CNS function and lower extremity kinetics, under multiple visual conditions, during 180° change of direction task in individuals with a history of ACLR. Methods: Eight participants, 39.3 ± 37.1 months after primary, left ACLR performed repetitive active knee flexion and extension of their involved knee during fMRI scanning. Participants separately performed 3D motion capture analysis of a 180° change of direction task under full vision (FV) and stroboscopic vision (SV) conditions. A neural correlate analysis was performed to associate BOLD signal to knee loading of the left lower extremity. Results: Involved limb peak internal knee extension moment (pKEM) was significantly lower in the SV condition (1.89 ± 0.37 N*m/Kg) compared to the FV condition (2.0 ± 0.34 N*m/Kg) (p = .018). Involved limb pKEM during the SV condition was positively correlated with BOLD signal in the contralateral precuneus and superior parietal lobe (Voxels: 53; p = .017; z-stat max: 6.47; MNI peak: 6, -50, 66). Conclusion: There is a positive association between involved limb pKEM in the SV condition and BOLD response in areas of visual-sensory integration. Activation of contralateral precuneus and superior parietal lobe brain regions may be a strategy to maintain joint loading when vision is perturbed. Level of Evidence: Level 3.

The Glycan Structure Dictionary-a dictionary describing commonly used glycan structure terms.

Recent technological advances in glycobiology have resulted in a large influx of data and the publication of many papers describing discoveries in glycoscience. However, the terms used in describing glycan structural features are not standardized, making it difficult to harmonize data across biomolecular databases, hampering the harvesting of information across studies and hindering text mining and curation efforts. To address this shortcoming, the Glycan Structure Dictionary has been developed as a reference dictionary to provide a standardized list of widely used glycan terms that can help in the curation and mapping of glycan structures described in publications. Currently, the dictionary has 190 glycan structure terms with 297 synonyms linked to 3,332 publications. For a term to be included in the dictionary, it must be present in at least 2 peer-reviewed publications. Synonyms, annotations, and cross-references to GlyTouCan, GlycoMotif, and other relevant databases and resources are also provided when available. The purpose of this effort is to facilitate biocuration, assist in the development of text mining tools, improve the harmonization of search, and browse capabilities in glycoinformatics resources and help to map glycan structures to function and disease. It is also expected that authors will use these terms to describe glycan structures in their manuscripts over time. A mechanism is also provided for researchers to submit terms for potential incorporation. The dictionary is available at https://wiki.glygen.org/Glycan_structure_dictionary.

Development and validation of LC-MS/MS method for quantification of protease inhibitor Pepstatin A to monitor its robust clearance in vaccine downstream process.

Pepstatin A reversibly inhibits aspartic acid proteases and minimizes the impact of protease-induced degradation in recombinant protein manufacturing process. Pepstatin A is considered as a process-related impurity and must be characterized and controlled during manufacturing. Herein we describe the development and validation of an LC-MS/MS method for the quantitation of pepstatin A to monitor its robust clearance in vaccine purification process. Analyte extraction from process intermediates was carried out using 10% acetonitrile/water extraction method. Acetyl-pepstatin was used as internal standard (IS). Pepstatin A and IS were resolved on a C18 column using 10 mM ammonium acetate in water and methanol/acetonitrile mobile phase system. A triple quadrupole mass spectrometer operating in the positive electrospray ionization mode with multiple reaction monitoring was used to detect Pepstatin A and IS transitions of m/z 686.5 to 229.3 and 644.5 to 229.3, respectively. The method was validated for specificity, linearity, accuracy, repeatability (precision), intermediate precision, and assay robustness. The assay was linear over the range of calibration standards 0.5-100 ng/mL. The Lower-limit-of-quantification (LLOQ) of the method was 0.50 ng/mL.

SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.

MOTIVATION: In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS: We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AVAILABILITY AND IMPLEMENTATION: SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Normative Performance Profiles of College Aged Esport Athletes in a Pilot Study.

Aims: This study aimed to holistically assess the physical and cognitive attributes of esport athletes. Methods and Results: Forty-six adults between 18 and 32 years old with experience playing videogames were enrolled in this study. Participants completed assessments in five areas: demographics, self-report questionnaires, cognitive performance, physical performance, and gaming performance. Participants self-reported Overwatch ranking and physical activity participation (Pediatric Physical Activity Measure), and grip strength was measured with a handheld dynamometer. Seven domains of physical, mental, and social health and well-being were measured with the Patient Reported Outcomes Measurement Information System (PROMIS-29). The List Sorting Working Memory Test and Picture Sequence Memory Test from the National Institutes of Health (NIH) Toolbox Cognition Batteries were used to measure cognitive performance. Finally, esports performance was measured using a series of tasks through Alienware Academy and AIM Booster to record accuracy, reaction time, and targets hit. Participants were separated into high and low ranking groups for comparisons. This sample of esport athletes was similar to the general population for grip strength, each of the PROMIS-29 metrics, the List Sorting Working Memory Test, and the Picture Sequence Memory Test. Reaction time was the variable with the only significant difference between ranking groups. Conclusion: This study represents a primary investigation of esport athletes using a holistic approach. By incorporating physical and cognitive components, the most important factors to esport athletes' health and performance can be better understood and applied.

In situ Soil Moisture Sensors in Undisturbed Soils.

Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture data has been slow due to inconsistent data collection, poor standardization, and typically short record duration. Soil moisture, or quantitatively volumetric soil water content (SWC), is measured using buried, in situ sensors that infer SWC from an electromagnetic response. This signal can vary considerably with local site conditions such as clay content and mineralogy, soil salinity or bulk electrical conductivity, and soil temperature; each of these can have varying impacts depending on the sensor technology. Furthermore, poor soil contact and sensor degradation can affect the quality of these readings over time. Unlike more traditional environmental sensors, there are no accepted standards, maintenance practices, or quality controls for SWC data. As such, SWC is a challenging measurement for many environmental monitoring networks to implement. Here, we attempt to establish a community-based standard of practice for in situ SWC sensors so that future research and applications have consistent guidance on site selection, sensor installation, data interpretation, and long-term maintenance of monitoring stations. The videography focuses on a multi-agency consensus of best-practices and recommendations for the installation of in situ SWC sensors. This paper presents an overview of this protocol along with the various steps essential for high-quality and long-term SWC data collection. This protocol will be of use to scientists and engineers hoping to deploy a single station or an entire network.

Extracellular Heparan 6-O-Endosulfatases SULF1 and SULF2 in Head and Neck Squamous Cell Carcinoma and Other Malignancies.

Pan-cancer analysis of TCGA and CPTAC (proteomics) data shows that SULF1 and SULF2 are oncogenic in a number of human malignancies and associated with poor survival outcomes. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. These heparan sulfate editing enzymes were considered largely functional redundant but single-cell RNAseq (scRNAseq) shows that SULF1 is secreted by cancer-associated fibroblasts in contrast to the SULF2 derived from tumor cells. Our RNAScope and patient-derived xenograft (PDX) analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer even though its expression in cancer cells is low. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.

Non-neoplastic pathologies mimicking sarcoma - Experience from a tertiary referral centre multidisciplinary team.

OBJECTIVE: Distinguishing non-neoplastic tumour-mimicking pathologies from bone and soft tissue tumours is one of the fundamental aims of a tertiary centre sarcoma multidisciplinary team (MDT) service. In this study, we aim to analyse the incidence of non-neoplastic lesions referred to a tertiary referral service as suspected sarcoma, and to analyse the spectrum of conditions comprising these tumour-mimicking pathologies. MATERIALS AND METHODS: We conducted a retrospective observational study compiling the biopsy-proven non-neoplastic outcomes of suspected sarcoma cases referred to our MDT in the last year. We identified all referrals made to our service between 1st January 2020 and 31st December 2020 and compiled their histological diagnoses. RESULTS: A total of 976 new cases were referred to our MDT as suspected sarcoma in one year. Of these referrals, 8.6% (84/976) received a biopsy-proven outcome of non-neoplastic pathology. These non-neoplastic outcomes were categorised into the following types of pathology: 32.1% vascular, 31.0% inflammatory, 14.3% traumatic, 6.0% degenerative, 6.0% idiopathic, 4.8% infective, 3.6% metabolic, 1.2% autoimmune, and 1.2% genetic. CONCLUSION: A significant proportion of pathologies referred to a tertiary centre sarcoma MDT are non-neoplastic in nature. These lesions are made up of a range of pathologies, with vascular and inflammatory conditions being the most common. Our study, the first of its kind, offers clinicians an insight into tumour-mimicking pathologies encountered by a tertiary centre.

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson's correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data.

BACKGROUND: Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. RESULTS: To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. CONCLUSIONS: SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.