Biology subjects

Schadt, E.

Publications and source records attributed to Schadt, E..

At least 19 recordsLinked to original sources

bioRxiv2025

A collection of patient-derived intestinal organoid lines reveals epithelial phenotypes associated with genetic drivers of pediatric inflammatory bowel disease

Pediatric Inflammatory Bowel Disease (IBD) is a chronic condition characterized by per-sistent intestinal inflammation in children and adolescents. Despite a rising global incidence, the underlying causes and optimal management strategies for pediatric IBD are still not fully understood. Compared to adult IBD, pediatric IBD frequently presents with distinct disease phenotypes, and is more commonly linked to rare monogenic variants that cause intestinal epithelial barrier dysfunction or affect the function of mucosal immune cells. While more than 100 genes have been associated with early-onset IBD, the roles of many of these genes in the intestinal epithelium and the mechanisms by which genetic variants contribute to disease remain poorly defined. Here we aimed to improve our understanding of intestinal epithelium dysfunction in early-onset IBD by conducting extensive molecular and cellular characterization to gain insights into patient-specific epithelial phenotypes and identify therapeutic targets. We generated intestinal epithelial organoids (IEOs) from 94 pediatric IBD patients, representing diverse clinical characteristics and including those with monogenic variants (BTK n=4, TTC7A n=3, IL10RA n=1, LRBA n=1, STXBP2 n=1, TTC37 n=1, TRNT1 n=1, PLCG2 n=1, DKC1 n=1, POLA1 n=1), and 46 non-IBD controls. This effort resulted in the largest RNA-seq dataset of pediatric IBD intestinal epithelial organoids to date, encompassing both baseline conditions and post-immunological stimulation, serving as a valuable resource for future research. We observed that IEOs effectively initiate inflammation upon stimulation with bacterial lysate, regardless of disease status, origin, or mutation status. Inflammatory stimulation triggered single-gene upregulation of IBD-linked SERPINA1 and LIFR across the IBD population compared to controls, suggesting their role in intestinal epithelial innate immune responses. However, co-expression network analysis showed no consistent transcriptional signatures across the entire IBD group at the systems level. Instead, differences emerged between controls and specific genotypes (TTC7A, STXBP2, LRBA), with STXBP2 and LRBA sharing an upregulated transcriptional response of IL-1 and SLC30-mediated zinc trafficking pathways. These findings underscore the potential of IEOs as a valuable model for studying IBD and offer key insights that could guide the development of targeted therapies for both monogenic and non-monogenic forms of IBD. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/659052v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@10a4d9org.highwire.dtl.DTLVardef@b6cdadorg.highwire.dtl.DTLVardef@179216dorg.highwire.dtl.DTLVardef@181a4e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2025

Multiple Types of Context-Specific Brain Causal Regulatory Networks and their Applications to Autism Spectrum Disorder

One of the primary goals of systems biology is to understand how different cell types are regulated at various developmental stages and how dysregulation in these cells contributes to disease. With advances in high-throughput omics profiling technologies, vast amounts of both bulk tissue and single-cell data have been generated to study cellular regulation. To investigate the types of gene-gene regulatory interactions that can be uncovered from bulk versus single-cell profiling data, we constructed multiple context-specific brain gene regulatory networks using brain bulk and single-cell RNA-seq datasets and applied two distinct computational methods: an integrative Bayesian Network (BN) and a deep learning model, Geneformer. A total of 39 networks were generated, comprising 13 brain region-specific Bayesian networks from bulk RNA-seq data (bulk_BNs), 13 cell-type and developmental stage-specific Bayesian networks, and 13 corresponding Geneformer-based networks from scRNA-seq data (sc_BNs and sc_GFs, respectively). We compared these networks in terms of their topological features, biological pathway regulation, and known regulatory relationships among disease-related genes. Our analysis revealed distinct network properties driven by data source type (bulk vs. single-cell RNA-seq) and network inference method (integrative Bayesian Network vs. deep learning Geneformer). Neurodegenerative disease-related pathways were co-regulated in bulk_BNs, while sc_BNs and sc_GFs captured pathways associated with neuroactive ligand-receptor interactions. To demonstrate the utility of these networks, we leveraged them to investigate potential disease mechanisms of Autism Spectrum Disorder (ASD). We found that ASD risk genes were enriched among hub genes common across networks but were more frequently hubs in context-specific networks, underscoring the significance of inhibitory interneurons during early developmental stages in ASD. Additionally, we showed that these networks could effectively recapitulate context-specific ASD gene signatures. In summary, this study highlights the complementary value of integrating multiple data types through different computational approaches to advance our understanding of disease-related molecular mechanisms, such as those involved in ASD. Our findings provide a framework for using context-specific gene regulatory networks to identify disease-causal genes and key regulators of signaling pathways, potentially guiding future therapeutic strategies.

bioinformatics↗

bioRxiv2024

Transcriptomic landscape of human induced pluripotent stem cell-derived osteogenic differentiation identifies a regulatory role of KLF16

Osteogenic differentiation is essential for bone development, metabolism, and repair; however, the underlying regulatory relationships among genes remain poorly understood. To elucidate the transcriptomic changes and identify novel regulatory genes involved in osteogenic differentiation, we differentiated mesenchymal stem cells (MSCs) derived from 20 human iPSC lines into preosteoblasts (preOBs) and osteoblasts (OBs). We then performed transcriptome profiling of MSCs, preOBs and OBs. The iPSC-derived MSCs and OBs showed similar transcriptome profiles to those of primary human MSCs and OBs, respectively. Differential gene expression analysis revealed global changes in the transcriptomes from MSCs to preOBs, and then to OBs, including the differential expression of 840 genes encoding transcription factors (TFs). TF regulatory network analysis uncovered a network comprising 451 TFs, organized into five interactive modules. Multiscale embedded gene co-expression network analysis (MEGENA) identified gene co-expression modules and key network regulators (KNRs). From these analyses, KLF16 emerged as an important TF in osteogenic differentiation. We demonstrate that overexpression of Klf16 in vitro inhibited osteogenic differentiation and mineralization, while Klf16+/- mice exhibited increased bone mineral density, trabecular number, and cortical bone area. Our study underscores the complexity of osteogenic differentiation and identifies novel regulatory genes such as KLF16, which plays an inhibitory role in osteogenic differentiation both in vitro and in vivo.

molecular biology↗

bioRxiv2020

A Library Of Induced Pluripotent Stem Cells From Clinically Well-Characterized, Diverse Healthy Human Individuals

A library of well-characterized human induced pluripotent stem cell (hiPSC) lines from clinically healthy human subjects could serve as a useful resource of normal controls for in vitro human development, disease modeling, genotype-phenotype association studies, and drug response evaluation. We report generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically healthy human individuals who range in age from 22-61. The hiPSCs match the karyotype and short tandem repeat identity of their parental fibroblasts, and have a transcription profile characteristic of pluripotent stem cells. We provide whole genome sequencing data for one hiPSC clone from each individual, genomic ancestry determination, and analysis of Mendelian disease genes and risks. We document similar transcriptomic profiles, single-cell RNA-seq derived cell clusters and physiology of cardiomyocytes differentiated from multiple independent hiPSC lines. This extensive characterization makes this hiPSC library a valuable resource for many studies on human biology.

cell biology↗

bioRxiv2020

Prioritization prostate cancer to immune checkpoint therapy by ranking tumors along IFN-γ axis and identification of immune resistance mechanisms

We propose priotirization of prostate cancer patients to PD-L1 checkpoint therapy by assessing activity of IFN-{gamma}/PD-L1 signaling in tumor from transcriptional profile. To this end, we introduced a new approach for inferring pathway activity and suppression (IPAS) by assessing significance of positioning pathways genes expression levels at top (activation) or bottom (suppression) in gene expression profile of a given tumor. By ordering tumors along IFN-{gamma}/PD-L1 axis, we determined distinct "IFN-{gamma}-depleted" and "IFN-{gamma}-enriched" immune subtypes, genes involved in immune evasion and potential targets for combination therapy. Using IPAS scoring method, we proposed biomarker panels for accurate ranking tumors along IFN-{gamma}/PD-L1 axis.

bioinformatics↗

bioRxiv2020

Detecting and phasing minor single-nucleotide variants from long-read sequencing data

Cellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, co-infection of multiple pathogens. Detecting and phasing minor variants, which is to determine whether multiple variants are from the same haplotype, play an instrumental role in deciphering cellular genetic heterogeneity, but are still difficult because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, have provided an unprecedented opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrated that iGDA can accurately reconstruct haplotypes in closely-related strains of the same species (divergence[≥] 0.011%) from long-read metagenomic data. Our approach, therefore, presents a significant advance towards the complete deciphering of cellular genetic heterogeneity.

bioinformatics↗

bioRxiv2020

Myeloid cell-associated resistance to PD-1/PD-L1 blockade in urothelial cancer revealed through bulk and single-cell RNA sequencing

Adaptive immunity and tumor-promoting inflammation exist in delicate balance in individual tumor microenvironments; however, the role of this balance in defining sensitivity and resistance to PD-1/PD-L1 blockade therapy in urothelial cancer and other malignancies is poorly understood. We pursued an unbiased systems biology approach using bulk RNA sequencing data to examine pre-treatment molecular features associated with sensitivity to PD-1/PD-L1 blockade in patients with metastatic urothelial cancer and identified an adaptive_immune_response module associated with response and an inflammatory_response module and stromal module associated with resistance. We mapped these gene modules onto single-cell RNA sequencing data demonstrating the adaptive_immune_response module emanated predominantly from T, NK, and B cells, the inflammatory_response module from monocytes/macrophages, and the stromal module from fibroblasts. The adaptive_immune_response:inflammatory_response module expression ratio in individual tumors, reflecting the balance between antitumor immunity and tumor-associated inflammation and coined the 2IR score, best correlated with clinical outcomes and was validated in an independent cohort. Individual monocytes/macrophages with low 2IR scores demonstrated upregulation of proinflammatory genes including IL1B and downregulation of antigen presentation genes, were unrelated to classical M1 versus M2 polarization, and were enriched in pre-treatment peripheral blood from patients with PD-L1 blockade-resistant metastatic urothelial cancer. Single sentence summaryProinflammatory monocytes/macrophages, present in tumor and blood, are associated with resistance to immune checkpoint blockade in urothelial cancer.

genomics↗

bioRxiv2020

Single Cell-type Integrative Network Modeling IdentifiedNovel Microglial-specific Targets for the Phagosome inAlzheimer's disease

SummaryLate-Onset Alzheimer’s Disease (LOAD) results from a complex pathological process influenced by genetic variation, aging and environment factors. Genetic susceptibility factors indicate that myeloid cells such as microglia play a significant role in the onset of LOAD. Here, we developed a computational systems biology approach to construct probabilistic causal and predictive network models of genetic regulatory programs of microglial cells under LOAD diagnosis by integrating two independent brain transcriptome and genome-wide genotype datasets from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MAYO) studies in the AMP-AD consortium. From this network model, we identified and replicated novel microglial-specific master regulators predicted to modulate network states associated with LOAD. We experimentally validated three microglial master regulators (FCER1G, HCK and LAPTM5) in primary human microglia-like cells (MDMi) by demonstrating the molecular impact these master regulators have on modulating downstream genomic targets identified by our top-down/bottom-up method and the causal relations among the three key drivers. These master regulators are involved in phagocytosis, a process associated with LOAD. Thus, we propose three new master regulator (key driver) genes that emerged from our network analyses as robust candidates for further evaluation in LOAD therapeutic development efforts.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

A Consensus Proteomic Analysis of Alzheimer’s Disease Brain and Cerebrospinal Fluid Reveals Early Changes in Energy Metabolism Associated with Microglia and Astrocyte Activation

Our understanding of the biological changes in the brain associated with Alzheimers disease (AD) pathology and cognitive impairment remains incomplete. To increase our understanding of these changes, we analyzed dorsolateral prefrontal cortex of control, asymptomatic AD, and AD brains from four different centers by label-free quantitative mass spectrometry and weighted protein co-expression analysis to obtain a consensus protein co-expression network of AD brain. This network consisted of 13 protein co-expression modules. Six of these modules correlated with amyloid-{beta} plaque burden, tau neurofibrillary tangle burden, cognitive function, and clinical functional status, and were altered in asymptomatic AD, AD, or in both disease states. These six modules reflected synaptic, mitochondrial, sugar metabolism, extracellular matrix, cytoskeletal, and RNA binding/splicing biological functions. The identified protein network modules were preserved in a community-based cohort analyzed by a different quantitative mass spectrometry approach. They were also preserved in temporal lobe and precuneus brain regions. Some of the modules were influenced by aging, and showed changes in other neurodegenerative diseases such as frontotemporal dementia and corticobasal degeneration. The module most strongly associated with AD pathology and cognitive impairment was the sugar metabolism module. This module was enriched in AD genetic risk factors, and was also highly enriched in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from the sugar metabolism module were increased in cerebrospinal fluid from asymptomatic AD and AD cases, highlighting their potential as biomarkers of the altered brain network. In this study of >2000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brain that may serve as therapeutic targets and fluid biomarkers for the disease.

systems biology↗

bioRxiv2019

Molecular Networks and Key Regulators of the Dysregulated Neuronal System in Alzheimer’s Disease

To study the molecular mechanisms driving the pathogenesis and identify novel therapeutic targets of late onset Alzheimers Disease (LOAD), we performed an integrative network analysis of whole-genome DNA and RNA sequencing profiling of four cortical areas, including the parahippocampal gyrus, across 364 donors spanning the full spectrum of LOAD-related cognitive and neuropathological disease severities. Our analyses revealed thousands of molecular changes and uncovered for the first-time multiple neuron specific gene subnetworks most dysregulated in LOAD. ATP6V1A, a critical subunit of vacuolar-type H+-ATPase (v-ATPase), was predicted to be a key regulator of one neuronal subnetwork and its role in disease-related processes was evaluated through CRISPR-based manipulation of human induced pluripotent stem cell derived neurons and RNAi-based knockdown in transgenic Drosophila models. This study advances our understanding of LOAD pathogenesis by providing the global landscape and detailed circuits of complex molecular interactions and regulations in several key brain regions affected by LOAD and the resulting network models provide a blueprint for developing next generation therapeutics against LOAD.

neuroscience↗

bioRxiv2019

Complete genome screening of clinical MRSA isolates identifies lineage diversity and provides full resolution of transmission and outbreak events

Whole-genome sequencing (WGS) of Staphylococcus aureus is increasingly used as part of infection prevention practices, but most applications are focused on conserved core genomic regions due to limitations of short-read technologies. In this study we established a long-read technology-based WGS screening program of all first-episode MRSA blood infections at a major urban hospital. A survey of 132 MRSA genomes assembled from long reads revealed widespread gain/loss of accessory mobile genetic elements among established hospital- and community-associated lineages impacting >10% of each genome, and frequent megabase-scale inversions between endogenous prophages. We also characterized an outbreak of a CC5/ST105/USA100 clone among 3 adults and 18 infants in a neonatal intensive care unit (NICU) lasting 7 months. The pattern of changes among complete outbreak genomes provided full spatiotemporal resolution of its origins and progression, which was characterized by multiple sub-transmissions and likely precipitated by equipment sharing. Compared to other hospital strains, the outbreak strain carried distinct mutations and accessory genetic elements that impacted genes with roles in metabolism, resistance and persistence. This included a DNA-recognition domain recombination in the hsdS gene of a Type-I restriction-modification system that altered DNA methylation. RNA-Seq profiling showed that the (epi)genetic changes in the outbreak clone attenuated agr gene expression and upregulated genes involved in stress response and biofilm formation. Overall our findings demonstrate that long-read sequencing substantially improves our ability to characterize accessory genomic elements that impact MRSA virulence and persistence, and provides valuable information for infection control efforts.

microbiology↗

bioRxiv2019

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Alzheimers disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

systems biology↗

bioRxiv2018

Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti-TNF therapy

Clinical benefits to cytokine blockade in ileal Crohns disease (iCD) have been limited to a subset of patients. Whether cellular and molecular heterogeneity contributes to variability in treatment responses has been unclear. Using single cell technologies combining scRNAseq, CyTOF and multiplex tissue imaging, we mapped the cellular landscape of inflamed ileum lesions, adjacent non-inflamed ileum and matched circulating blood cells of iCD patients. In inflamed tissues, we identified a pathogenic module characterized by an inflammatory mononuclear phagocyte (Inf.MNP)-associated cellular response organized around inflammatory macrophages and mature dendritic cells in a subset of iCD patients. We confirmed the Inf.MNP-associated cellular response in 4 independent iCD cohorts (n=441) and showed that presence of this pathogenic module at diagnosis correlated with primary resistance to anti-TNF therapy. Single cell mapping of iCD tissues identifies a complex cellular signature of anti-TNF resistance thereby revealing novel biomarkers of treatment response and tailored therapeutic opportunities.

immunology↗

bioRxiv2018

Multiparameter cell characterization using nanofluidic technology facilitates real-time phenotypic and genotypic elucidation of intratumor heterogeneity

Genetic and functional complexity of bulk tumor has become evident through rapid advances in sequencing technologies. As a unique integrated approach to characterizing tumor heterogeneity, we demonstrate the multifaceted capabilities of a novel nanofluidic platform to enable single-cell phenotypic and genetic profiling of ovarian cancer patient-derived tumor cells. This approach has enabled increased resolution of tumor cell phenotypic and genetic heterogeneity, providing a better understanding of underlying biological drivers of the disease. A range of CA-125 expression levels is observed within cells from individuals, demonstrating clonal diversity consistent with other phenotypic data. Further, TP53 mutation analysis demonstrates a sub-population of cells exhibiting high mutation frequency that likely drives downstream growth kinetics and protein expression. Finally, genomic data is orthogonally used to address clonal heterogeneity across ovarian tumors when compared to bulk sequencing, illustrating the potential for single-cell sequencing data integrated with cellular functional and growth data toward future therapeutic intervention.

genomics↗

bioRxiv2018

Biology and Bias in Cell Type-Specific RNAseq of Nucleus Accumbens Medium Spiny Neurons

Isolation of cell populations is untangling complex biological interactions, but studies comparing methodologies lack in vivo complexity and draw limited conclusions about the types of transcripts identified by each technique. Furthermore, few studies compare FACS-based techniques to ribosomal affinity purification, and none do so genome-wide. We addressed this gap by systematically comparing nuclear-FACS, whole cell-FACS, and RiboTag affinity purification in the context of D1 or D2 dopamine receptor-expressing medium spiny neuron (MSN) subtypes of the nucleus accumbens (NAc), a key brain reward region. We find that nuclear-FACS-seq generates a substantially longer list of differentially expressed genes between these cell types, and a significantly larger number of neuropsychiatric GWAS hits than the other two methods. RiboTag-seq has much lower coverage of the transcriptome than the other methods, but very efficiently distinguishes D1- and D2-MSNs. We also demonstrate differences between D1- and D2-MSNs with respect to RNA localization, suggesting fundamental cell type differences in mechanisms of transcriptional regulation and subcellular transport of RNAs. Together, these findings guide the field in selecting the RNAseq method that best suits the scientific questions under investigation.

neuroscience↗

bioRxiv2018

A novel approach of human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases

A key goal of geroscience research is to discover effective interventions to extend human healthspan, the years of healthy life. Currently, majority of the geroprotectors are found by testing compounds in model organisms; whether these compounds will be effective in humans is largely unknown. Here we present a novel strategy called ANDRU (aging network based drug discovery) to help the discovery of human geroprotectors. Instead of relying on model organisms, this approach is driven by human genomic and pharmacogenomic data. It first identifies human aging subnetworks that putatively function at the interface between aging and age-related diseases; it then screens for pharmacological or genetic interventions that may \"reverse\" the age-associated transcriptional changes seen in these subnetworks. We applied ANDRU to human adipose and artery tissues. In adipose tissue, PTPN1, a target for diabetes treatment and APOE, a known genetic factor for human longevity and diseases like Alzheimers disease, were ranked at the top. For small molecules, conjugated linoleic acid and metformin, a drug commonly used to treat type 2 diabetes, were ranked among the top compounds. In artery tissue, N-methyl-D-aspartate antagonists and curcumin were ranked at the top. In summary, ANDRU represents a promising human data-driven strategy that may speed up the discovery of interventions to extend human healthspan.

systems biology↗

bioRxiv2018

B cell defects observed in Nod2 knockout mice are a consequence of a Dock2 mutation frequently found in inbred strains

Phenotypic differences among substrains of laboratory mice due to spontaneous mutations or pre-existing genetic variation confound the interpretation of targeted mutagenesis experiments, and contribute to challenges with reproducibility across institutions. Notably, C57BL/6NHsd mice and gene-targeted mice that have been backcrossed to this substrain have been reported to harbor a duplication in exons 28 and 29 of Dock2. Here, we demonstrate the presence of this Dock2 variant in the widely used Nod2-/- mice. NOD2 is a cytosolic innate immune receptor that has been the subject of intense investigation due to its association with inflammatory bowel disease (IBD) susceptibility. Consistent with a role of NOD2 in an immunological disorder, Nod2-/- mice bred at our institution displayed multiple B cell defects including deficiencies in recirculating B cells, marginal zone B cells and B1a cells. However, we found that these effects are due to the Dock2 variant and are independent of Nod2 deletion. Despite originating from the same gene-targeted founder mice, Nod2-/- mice from another source did not harbor the Dock2 variant or B cell defects. Finally, we show that Dock2-/- mice display the same B cell defects as mice harboring the Dock2 variant, confirming that the variant is a loss-of-function mutation and is sufficient to explain the alterations to the B cell compartment observed in Nod2-/- mice. Our findings highlight the effects of confounding mutations from widely-used inbred strains on gene-targeted mice and reveal new functions of DOCK2 in B cells.

immunology↗

bioRxiv2017

STAR Chimeric Post For Rapid Detection Of Circular RNA And Fusion Transcripts

MotivationThe biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer, and recently discovered circular RNA are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, very little software is available for circular RNA detection and quantification.\n\nResultsHere we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circular RNA and high precision fusions in a rapid, efficient, and scalable manner that is appropriate for high dimensional medical omics datasets.\n\nAvailability and ImplementationSTARChip is available at https://github.com/LosicLab/STARChip\n\nContactsbojan.losic@mssm.edu or nicholas.kipp.akers@gmail.com\n\nSupplementary InformationSupplementary figures and tables are available online.

bioinformatics↗