Biology subjects

Reed, E.

Publications and source records attributed to Reed, E..

17 recordsLinked to original sources

bioRxiv2026

Sex Chromosomes and Gonadal Hormones Contribute to B Cell Antibody Responses in Aging and Alzheimer's Disease

It is increasingly appreciated that B cell populations in the brain shift during aging and Alzheimer's Disease (AD), contributing to neuroinflammation and cognitive decline. Though the underlying mechanisms remain unclear, biological sex is likely a key regulator since differences in B cell antibody responses are one of the most well conserved sex differences in immunology. Sex differences in B cell subtypes and antibody classes are significant because they drive sexually dimorphic immune responses and subsequent disease susceptibility. Despite extensive work evaluating the effects of gonadal hormones on B cells, the effects of aging and AD pathology, and the contributions of sex chromosomes are understudied. We evaluated B cell subtypes in the brain and bloodstream in both adult and 5xFAD mice via flow cytometry and antibody levels in the cortical brain region of both groups via ELISAs. We found sex differences mediated by both sex chromosomes and the gonadal hormone environment; these differences were primarily involved in antibody class-switching.

neuroscience↗

bioRxiv2026

Neotaphonomic characteristics of vertebrate site formation in underwater caves

Recovering well-preserved vertebrate remains from underwater caves has provided critical insights into archaeological and palaeontological records worldwide. However, understanding how bone assemblages form and are modified in underwater environments remains limited due to stable low energy burial conditions that produce time-averaged deposits, and underwater settings that hinder traditional recording and recovery methods. This study applies an actualistic taphonomic framework to three assemblages of domesticate animal bones (N = 231) from two underwater caves, Green Waterhole and Gouldens Sinkhole, near Mount Gambier, South Australia, encompassing known submerged (wet; N = 134) and dry (N = 97) burial conditions. The assemblages were examined to assess how wet and dry cave environments impact bone distribution, surface and microstructural modification. Radiocarbon dating of 41 specimens indicates that domesticate fauna were deposited over decadal and centennial timescales, allowing taphonomic signatures to be contextualised through time. Statistically significant differences were identified between wet and dry burial contexts. Bones recovered from wet contexts exhibit mostly better preservation, including skeletal elemental completeness, surface, and microstructure, than those from dry caves. However, some of the submerged specimens also have elevated frequencies of bone surface corrosion with macroscopic evidence for heterogenous black biological staining, algal or biofilm attack, and a distinctive form of circular etching. Histotaphonomy further reveals patterns of peripheral cyanobacterial tunnelling across most bones recovered from submerged contexts. Bones from dry environments were dominated by terrestrially linked tunnelling across all regions of the bone cortex. These findings can be explained by variation in light availability across different cave zones which influences biological activity and, in turn, the expression of taphonomic markers on bone externally and at the microstructural level. This is the first study to provide a benchmark bone dataset for reconstructing depositional histories and post-depositional reworking in underwater cave environments under a taphonomic framework.

paleontology↗

bioRxiv2025

Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies

Although aging is a universal event, some individuals are able to achieve extreme longevity. The Long-Life Family Study (LLFS) enrolls participants from families enriched with long-lived individuals, serves as a valuable dataset for studying ageing phenotypes and identify potential intervention targets. We analyzed the association between age at blood draw and 16,284 RNAseq-based blood transcriptomic data from 2,167 LLFS participants with ages ranging from 18 to 107, replicated the results in the Integrative Longevity Omics Study (ILO) dataset of 20,884 RNAseq-based blood transcriptomic data from 419 participants, with ages ranging from 60 to 108, and further compared our findings to a published reference aging signature. We identified 4,227 transcripts increasing and 4,044 transcripts decreasing with age, and enrichment analysis revealed age-related upregulation of inflammatory and senescence-related pathways, and downregulation of MYC and Wnt/{beta}-catenin targets, among others. Further, a subset of transcripts showed age associations unique to the longevity-enriched cohorts (LLFS and ILO). We also identified 314 transcripts significantly associated with mortality risk and found that pro-survival gene sets included NK cell-mediated cytotoxicity and GPCR signaling. Finally, increased transcriptomic age predicted using transcriptomic clock was strongly associated with increased mortality. In summary, this study identified robust transcriptomic signatures of aging and mortality in a longevity-enriched population, highlighting key biological pathways such as immune modulation, inflammation, and senescence. Authors notesThis manuscript has been peer-reviewed and accepted by GeroScience (Springer). This bioRxiv article reflects the published version, incorporating revisions made in response to reviewers comments. The main content, results, and conclusions remain unchanged from the previous version, while the Discussion section has been expanded to further address the functional annotation and clinical relevance of the identified markers. Copy of the acceptance letterThe editors are pleased to inform you that your manuscript, JAAA-D-25-01946R1 entitled "Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies" has been accepted for publication in GeroScience as an Original Article. The editors commend you on your outstanding contribution to the journal. Your manuscript can be published online ahead of print within approximately two weeks.

molecular biology↗

bioRxiv2025

Robust Serum Proteomic Signatures of APOE2

We previously identified a signature of 16 serum proteins that highlighted a role of the e2 allele of APOE in lipid regulation via apolipoprotein B (APOB) and apolipoprotein E (APOE), and in inflammation. The serum proteins were profiled using the aptamer-based Somalogic technology. Here, we validate and expand the serum protein signature of APOE using a combination of mass-spectrometry, ELISA, Luminex, antibody-based Olink proteomics, and blood transcriptomics. We replicate the association between APOB and the e2 allele of APOE, we correct the pattern of association between APOE genotypes and serum level of APOE, and we detect new associations between APOE genotypes and the complex of apolipoproteins APOC1, APOC4, APOC2, APOC3, APOE, APOF and APOL1. In addition, we discover 13 new proteins that correlate with APOE genotypes. This extended signature includes granule proteins CAMP, CTSG, DEFA3, and MPO secreted from neutrophils and points to olfactomedin 4 (OLFM4) as a new target for the prevention of Alzheimers disease.

genetics↗

bioRxiv2025

A highly resolved integrated single-cell atlas of HPV-negative head and neck cancer

Head and Neck Squamous Cell Carcinomas (HNSCC) are the seventh most prevalent form of cancer and are associated with human papilloma virus infection (HPV-positive) or with tobacco and alcohol use (HPV-negative). HPV-negative HNSCCs have a high recurrence rate, and individual patients responses to treatment vary greatly due to the high level of cellular heterogeneity of the tumor and its microenvironment. Here, we describe a HNSCC single cell atlas, which we created by integrating six publicly available datasets encompassing over 230,000 cells across 54 patients. We contextualized the relationships between existing signatures and cell populations, identified new subpopulations, and show the power of this large-scale resource to robustly identify associations between transcriptional signatures and clinical phenotypes that would not be possible to discover using fewer patients. We reveal a previously undefined myeloid population, sex-associated changes in cell type proportions, and novel interactions between CXCL8-positive fibroblasts and vascular endothelial cells. Beyond our findings, the atlas will serve as a public resource for the high-resolution characterization of tumor heterogeneity of HPV-negative HNSCC.

cancer biology↗

bioRxiv2025

DNA data (genome skims and metabarcodes) paired with chemical data demonstrate utility for retrospective analysis of forage linked to fatal poisoning of cattle

Prepared and stored feeds, fodder, silage, and hay may be contaminated by toxic plants resulting in the loss of livestock. Several poisonous plants have played significant roles in livestock deaths from forage consumption in recent years in the Western United States including Salvia reflexa. Metagenomic data, genome skims and metabarcodes, have been used for identification and characterization of plants in complex matrices including diet composition of animals, mixed forages, and herbal products. Here, chemistry, genome skims, and metabarcoding were used to retrospectively describe the composition of contaminated alfalfa hay from a case of Salvia reflexa poisoning that killed 165 cattle. Genome skims and metabarcoding provided similar estimates of the relative abundance of the Salvia in the hay samples when compared to chemical methods. Additionally, genome skims and metabarcoding provided similar estimates of species composition in the contaminated hay and rumen contents of poisoned animals. The data demonstrate that genome skims and DNA metabarcoding may provide useful tools for plant poisoning investigations.

genomics↗

bioRxiv2024

Paired metagenomic and chemical evaluation of an aflatoxin contaminated dog kibble

Identification of chemical toxins from complex or highly processed foods can present needle in the haystack challenges for chemists. Metagenomic data can guide chemical toxicity evaluations with DNA-based description of the wholistic composition (bacterial, eukaryotic, protozoal, viral, and antimicrobial resistance) of any food suspected to harbor toxins, allergens, or pathogens. This approach can focus chemistry-based diagnostics, improve risk assessment, and address data gaps. There is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity. Here we evaluate an aflatoxin contaminated kibble with known levels of specific mycotoxins and demonstrate that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with levels of aflatoxin quantified by Liquid Chromatography Mass Spectrometry (LCMS). Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. Metagenomic data paired with chemical data provides a novel modality to address current data gaps pertaining to mycotoxin toxicity exposures, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

microbiology↗

bioRxiv2023

A Simple Strategy for Identifying Conserved Features across Non-independent Omics Studies

False discovery is an ever-present concern in omics research, especially for burgeoning technologies with unvetted specificity of their biomolecular measurements, as such unknowns obscure the ability to characterize biologically informative features from studies performed with any single platform. Accordingly, performing replication studies of the same samples using different omics platforms is a viable strategy for identifying high-confidence molecular associations that are conserved across studies. However, an important caveat of replication studies that include the same samples is that they are inherently non-independent, leading to overestimating conservation if studies are treated otherwise. Strategies for accounting for such inter-study dependencies have been proposed for meta-analysis methods devised to increase statistical power to detect molecular associations in one or more studies. Still, they are not immediately suited for identifying conserved molecular associations across multiple studies. Here, we present a unifying strategy for performing inter-study conservation analysis as an alternative to meta-analysis strategies for aggregating summary statistical results of shared features across complementary studies while accounting for inter-study dependency. This method, which we call "adjusted maximum p-value" (AdjMaxP), is easy to implement with inter-study dependency and conservation estimated directly from the p-values from each studys molecular feature-level association testing results. Through simulation-based assessment, we demonstrate AdjMaxPs improved performance for accurately identifying conserved features over a related meta-analysis strategy for non-independent studies. AdjMaxP offers an easily implementable strategy for improving the precision of analyses for biomarker discovery from cross-platform omics study designs, thereby facilitating the adoption of such protocols for robust inference from emerging omics technologies.

bioinformatics↗

bioRxiv2023

Fecal microbiomes of laboratory beagles receiving antiparasitic formulations in an experimental setting

Here we describe the fecal microbiome of laboratory beagles in a non-invasive and humane experiment designed to contrast in vivo versus invitro bioequivalence in response to antiparasitic drug administration. The experiment provided a unique opportunity to describe the fecal microbiota of dogs in an experimental setting prior to their adoption. These data are contributed as a resource for the scientific community by the Center for Veterinary Medicine (CVM) of the U.S. Food and Drug Administration (FDA).

microbiology↗

bioRxiv2023

Metagenomic survey of antimicrobial resistance (AMR) in Maryland surface waters differentiated by high and low human impact

In alignment with the One Health paradigm, surface waters are being evaluated as a modality to better understand baseline antimicrobial resistance (AMR) across the environment to supplement existing AMR monitoring in pathogens associated with humans, foods, and animals. Here, we use metagenomic and quasimetagenomic sequence data to describe AMR in Maryland surface waters from developed (high human impact) and natural (low human impact) classifications by the National Land Cover Database (NLCD). Critically important {beta}-lactamase genes were observed in twice as many high human impact zones. All data are available under BioProject PRJNA79347. https://www.ncbi.nlm.nih.gov/bioproject/794347

microbiology↗

bioRxiv2023

bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting.

Precise and rapid identification of Salmonella serotypes from suspect food matrices is critical for successful source attribution of illness outbreaks (Scallan et al., 2011). Currently, close to 3% of U.S. foodborne Salmonella outbreaks have been attributed to multiple Salmonella serotypes (2.85%, 2000 - 2020) (CDC, 2022). Recent foodborne outbreaks that have been attributed to multiple Salmonella serotypes force us to question whether these are rare events or if previous methods simply did not have the throughput to provide an accurate picture of the complex ecology that is connected to outbreak etiologies. (Hassan et al., 2019; FDA, 2021; Whitney et al., 2021). An in-silico benchmark dataset, comprising 29 unique Salmonella, 46 non-Salmonella bacterial and 10 viral genomes, was generated with varying read depths. For outbreak samples, analysis was performed on previously sequenced pre-enrichments and selective enrichments of papayas and peaches (fruits and leaves) that led to the identification of multiple serovars. Data analyses was performed using a custom-built k-mer tool, SeqSero2, Kallisto and bettercallsal. The in-silico dataset analyzed with bettercallsal had accuracy, recall and specificity of 95%, 96% and 98 % respectively. In the papaya outbreak samples, bettercallsal identified multiple serovar presence in concordance with Bioplex assay results and the genome hits assigned to the samples are Salmonella isolates from the papaya outbreak as evident by NCBI SNP cluster information. In peach outbreak samples, bettercallsal identified both the serovars (Alachua and Gaminara) in concordance with k-mer analysis and the Luminex xMap assay. bettercallsal outperformed k-mer, Kallisto and Seqsero2 in identifying multiple serovars from enrichment cultures using shotgun metagenomics sequencing. Most Salmonella subtyping work has relied upon WGS methods which focuses on the high-resolution analysis of single genomes, or multiple single genomes picked from colonies on agar. Here we introduce laboratory and bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time in a much higher throughput approach. bettercallsal is one of the first analysis tools that can potentially identify multiple Salmonella spp. serotypes from a metagenomic or quasi-metagenomic datasets with accuracy and can provide early insights into the etiology of the sample.

genomics↗

bioRxiv2022

Plasma Proteome Perturbation for CMV DNAemia in Kidney Transplantation

BackgroundCytomegalovirus (CMV) infection, either de novo or as reactivation after allotransplantation and chronic immunosuppression, is recognized to cause detrimental alloimmune effects, inclusive of higher susceptibility to graft rejection and substantive impact on chronic graft injury and reduced transplant survival. To obtain further insights into the evolution and pathogenesis of CMV infection in an immunocompromised host we evaluated changes in the circulating host proteome serially, before and after transplantation, and during and after CMV DNA replication (DNAemia), as measured by quantitative polymerase chain reaction (QPCR). MethodsLC-MS-based proteomics was conducted on 168 serially banked plasma samples, from 62 propensity score-matched kidney transplant recipients. Patients were stratified by CMV replication status into 31 with CMV DNAemia and 31 without CMV DNAemia. Patients had blood samples drawn at protocol times of 3- and 12-months post-transplant. Additionally, blood samples were also drawn before and 1 week and 1 month after detection of CMV DNAemia. Plasma proteins were analyzed using an LCMS 8060 triple quadrupole mass spectrometer. Further, public transcriptomic data on time matched PBMCs samples from the same patients was utilized to evaluate integrative pathways. Data analysis was conducted using R and Limma. ResultsSamples were segregated based on their proteomic profiles with respect to their CMV Dnaemia status. A subset of 17 plasma proteins was observed to predict the onset of CMV at 3 months post-transplant enriching platelet degranulation (FDR, 4.83E-06), acute inflammatory response (FDR, 0.0018), blood coagulation (FDR, 0.0018) pathways. An increase in many immune complex proteins were observed at CMV infection. Prior to DNAemia the plasma proteome showed changes in the anti-inflammatory adipokine vaspin (SERPINA12), copper binding protein ceruloplasmin (CP), complement activation (FDR=0.03), and proteins enriched in the humoral (FDR=0.01) and innate immune responses (FDR=0.01). ConclusionPlasma proteomic and transcriptional perturbations impacting humoral and innate immune pathways are observed during CMV infection and provide biomarkers for CMV disease prediction and resolution. Further studies to understand the clinical impact of these pathways can help in the formulation of different types and duration of anti-viral therapies for management of CMV infection in the immunocompromised host.

immunology↗

bioRxiv2022

Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity

Age-related changes in immune cell composition and functionality are associated with multimorbidity and mortality. However, many centenarians delay the onset of aging-related disease suggesting the presence of elite immunity that remains highly functional at extreme old age. To identify immune-specific patterns of aging and extreme human longevity, we analyzed novel single cell profiles from the peripheral blood mononuclear cells (PBMCs) of 7 centenarians (mean age 106) and publicly available single cell RNA-sequencing (scRNA-seq) datasets that included an additional 7 centenarians as well as 52 people at younger ages (20-89 years). The analysis confirmed known shifts in the ratio of lymphocytes to myeloid cells, and noncytotoxic to cytotoxic cell distributions with aging, but also identified significant shifts from CD4+ T cell to B cell populations in centenarians suggesting a history of exposure to natural and environmental immunogens. Our transcriptional analysis identified cell type signatures specific to exceptional longevity that included genes with age-related changes (e.g., increased expression of STK17A, a gene known to be involved in DNA damage response) as well as genes expressed uniquely in centenarians PBMCs (e.g., S100A4, part of the S100 protein family studied in age-related disease and connected to longevity and metabolic regulation). Collectively, these data suggest that centenarians harbor unique, highly functional immune systems that have successfully adapted to a history of insults allowing for the achievement of exceptional longevity.

immunology↗

bioRxiv2022

Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods

Surface waters present a unique challenge for the monitoring of critically important antimicrobial resistance. Metagenomic approaches provide unbiased descriptions of taxonomy and antimicrobial resistance genes in many environments, but for surface water, culture independent data is insufficient to describe critically important resistance. To address this challenge and expand resistome reporting capacity of antimicrobial resistance in surface waters, we apply metagenomic and quasimetagenomic (enriched microbiome) data to examine and contrast water from two sites, a creek near a hospital, and a reservoir used for recreation and municipal water. Approximately 30% of the National Antimicrobial Resistance Monitoring Systems critically important resistance gene targets were identified in enriched data contrasted to only 1% in culture independent data. Four different analytical approaches consistently reported substantially more antimicrobial resistance genes in quasimetagenomic data compared to culture independent data across most classes of antimicrobial resistance. Statistically significant differential fold changes (p<0.05) of resistance determinants were used to infer microbiological differences in the waters. Important pathogens associated with critical antimicrobial resistance were described for each water source. While the single time-point for only two sites represents a small pilot project, the successful reporting of critically important resistance determinants is proof of concept that the quasimetagenomic approach is robust and can be expanded to multiple sites and timepoints for national and global monitoring and surveillance of antimicrobial resistance in surface waters.

microbiology↗

bioRxiv2021

Genomic Evidence of Environmental and Resident Salmonella Senftenberg and Montevideo Contamination in the Pistachio Supply-chain

Pistachios have been implicated in two salmonellosis outbreaks and multiple recalls in the U.S. This study performed a retrospective data analysis of Salmonella associated with pistachios and a storage study to evaluate the survivability of Salmonella on inoculated inshell pistachios to further understand the genetics and microbiological dynamics of this commodity-pathogen pair. The retrospective data analysis on isolates associated with pistachios was performed from both short-read and long-read sequencing technologies. The sequence data were analyzed using the FDAs Center for Food Safety and Applied Nutrition Single Nucleotide Polymorphism (SNP) analysis and Whole Genome Multi-locus Sequence Typing (wgMLST) pipeline. The storage study evaluated the survival of five strains of Salmonella on pistachios, both in a cocktail as well as individually. Our results demonstrate: i) evidence of persistent Salmonella Senftenberg and Salmonella Montevideo strains in pistachio environments, some of which may be due to clonal resident strains and some of which may be due to preharvest contamination; ii) presence of the Copper Homeostasis and Silver Resistance Island (CHASRI) in Salmonella Senftenberg and Montevideo strains in the pistachio supply chain; and iii) different serovars of Salmonella enterica, including Salmonella Senftenberg and Salmonella Montevideo, are able to survive in pistachios over an extended period of time. ImportancePistachios have been linked to multistate outbreaks caused by Salmonella serovar Senftenberg (2013, 2016) and serovar Montevideo (2016). This comprehensive study of whole-genome-sequence (WGS) data from Senftenberg and Montevideo isolates associated with pistachio outbreaks, recalls, and investigations over a nine-year period (2009-2018) was conducted to provide insights into evolutionary relationships and persistence. Multiple analytical approaches were applied including MLST, wgMLST and phylogenetic SNP analysis. The CHASRI was identified in all isolates associated with the sequence type 14 (ST14) and ST316, suggesting an adaptation in response to extrinsic pressures, such as in the farm environment. There is evidence of persistent Salmonella Senftenberg and Montevideo strains in pistachio pre-harvest and post-harvest environments. Utilizing metagenomic sequencing during an inoculated storage study of pistachios allowed for determination of surviving serovars over an extended period of time. Defining the mechanisms of persistence of these strains is of high importance to public health.

microbiology↗

bioRxiv2019

A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify Emerging Metabolic Health Threats

BackgroundChemicals in disparate structural classes activate specific subsets of PPAR{gamma}s transcriptional programs to generate adipocytes with distinct phenotypes. ObjectivesOur objectives were to 1) establish a novel classification method to predict PPAR{gamma} ligands and modifying chemicals, and 2) create a taxonomy to group chemicals based on their effects on PPAR{gamma}s transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic PPAR{gamma} ligands, would induce white but not brite adipogenesis. Methods3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, nuclear receptor ligands known to influence adipocyte biology, potential environmental PPAR{gamma} ligands). Differentiation was assessed by measuring lipid accumulation. mRNA expression was determined by RNA-Seq and validated by RT-qPCR. A novel classification model was developed using an amended random forest procedure. A subset of environmental contaminants identified as strong PPAR{gamma} agonists were analyzed by their effects on lipid handling, mitochondrial biogenesis and cellular respiration in 3T3-L1 cells and human preadipocytes. ResultsWe used lipid accumulation and RNA sequencing data to develop a classification system that 1) identified PPAR{gamma} agonists, and 2) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong PPAR{gamma} activation. Two known environmental PPAR{gamma} ligands, tetrabromobisphenol A and triphenyl phosphate, which sorted distinctly from therapeutic ligands, induced white adipocyte genes but failed to induce Pgc1a and Ucp1, and induced fatty acid uptake but not mitochondrial biogenesis in 3T3-L1 cells. Moreover, two chemicals identified as highly ranked PPAR{gamma} agonists, tonalide and quinoxyfen, induced white adipogenesis without the concomitant health-promoting characteristics of brite adipocytes in mouse and human preadipocytes. DiscussionA novel classification procedure accurately identified environmental chemicals as PPAR{gamma} ligands distinct from known PPAR{gamma}-activating therapeutics. The computational and experimental framework has general applicability to the classification of as-yet uncharacterized chemicals.

pharmacology and toxicology↗

bioRxiv2018

Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine samples into single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq and microarray, as well as another low-cost highly multiplexed technique known as 3 digital gene expression (3DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from in vivo lung cancer mutation signatures.

bioinformatics↗