Biology subjects

Gursoy, G.

Publications and source records attributed to Gursoy, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments

Cerebrospinal fluid (CSF) contains brain derived proteins that can be leveraged as biomarkers for Alzheimer's disease (AD) and other neurodegenerative diseases. However, converting protein intensities to a disease signal remains a challenge because physiology driven changes to the localization of proteins across CSF subcompartments are obscured when CSF is analyzed alone. Where subcompartment data, such as that from extracellular vesicles (EVs), are also measured, separate but parallel analyses of EVs and CSF can obscure their shared disease signal. Here, we present an integrated matrix factorization approach that combines proteomic signals from matched CSF and phosphatidylserine exposed EVs and nanoparticles (EVps). In a ROS/MAP cohort of 48 participants, integrating CSF and EVps signals improved biological concordance across compartments and increased the number of candidate proteins distinguishing participants with AD from controls. Notably, the joint signal identified as many brain related biomarker candidates as studies using substantially larger cohorts. By demonstrating that integration of CSF and EVps proteomes maximizes information from limited samples, this framework offers a path toward more efficient biomarker discovery across two compartments increasingly leveraged in neurodegenerative disease research.

systems biology↗

bioRxiv2026

African Green Monkey Cerebrospinal Fluid miRNome Captures Conserved miRNAs Relevant to Human Neurodegenerative Disease

BackgroundThe African green monkey (AGM) is increasingly used as a model for early-stage Alzheimers disease (AD), with cerebrospinal fluid (CSF) targeted for biomarker discovery and longitudinal disease monitoring of shifts in the central nervous system. MicroRNAs (miRNAs) are particularly informative indicators of early neuropathological change. Despite the complementary value of an early-stage disease model and a molecular marker capable of capturing early change, the miRNA composition (miRNome) of AGM remains undefined. We established the AGM CSF miRNome from antemortem samples using miRNA sequencing and a qRT-PCR-based array. We also developed a hierarchical annotation pipeline to classify miRNAs as either family-conserved or unclassified and to assess sequence alignment across humans and other species. ResultsWe used untargeted miRNA sequencing to characterize the AGM CSF miRNome and identified 205 miRNAs that could be classified into three family-conserved categories: canonical, noncanonical, and 3'-terminal variants. Of these, 150 were also detected using a human-targeted qRT-PCR array, providing independent support for the sequence-derived miRNome. Sequencing abundance and qRT-PCR array Ct values showed significant cross-platform concordance overall, although concordance was lower for 3'-terminal isomiRs than for canonical miRNAs. Comparison with human GTEx tissue-expression data indicated that several human homologs of AGM CSF miRNAs exhibited brain-preferential expression. Notably, predicted targets of many of these miRNAs were enriched for pathways implicated in neurodegenerative disease. Finally, we identified 20 unclassified candidates that could not be assigned to established miRNA families, two of which we propose as putatively novel miRNAs. ConclusionThe AGM CSF miRNome is substantially conserved with the human miRNome but also contains 3'-terminal isomiRs and unclassified miRNA candidates. AGM CSF contains miRNAs homologous to human miRNAs associated with AD and other neuropathologies, highlighting the translational potential of this model. However, our study also reveals challenges related to species-specific sequence variation and reduced cross-platform concordance for isomiRs. Thus, comparative studies will be needed to validate the functional and biomarker relevance of these miRNAs across species. More generally, this initial miRNome provides a reference resource for future studies of miRNAs in AGM across disease-related, physiological, experimental, and evolutionary contexts.

genomics↗

bioRxiv2026

The SEA-AD DREAM Challenge: Community benchmarking human and AI agent solutions for Alzheimer's disease neuropathology prediction from single-nucleus transcriptomics

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

neuroscience↗

bioRxiv2026

Privacy-Preserving Pangenome Graphs

The human pangenome reference, often represented as a graph, promises to capture genetic diversity across populations, but open release of individual haplotypes raises significant privacy concerns, including risks of re-identification and inference of sensitive traits. To address these challenges, we introduce PanMixer, a framework for privacy-preserving pangenome graph releases that selectively obfuscates an individuals haplotypes while retaining the utility of the reference graph. PanMixer formulates the privacy-utility trade-off as a knapsack problem, where privacy risk is quantified using information theory and utility is measured using graph properties. Using the recently released draft human pangenome containing 47 individuals, we show that PanMixer robustly reduces re-identification risk under linkage attacks and genome reconstruction attempts. We also show that PanMixer preserves the accuracy of key downstream applications, including allele frequency estimation, linkage disequilibrium analysis, and read mapping. By addressing privacy concerns, PanMixer enables the inclusion of individuals, particularly those from underrepresented populations, who might otherwise be reluctant to contribute but seek representation in future genomic studies. Our results provide both a practical tool and a generalizable framework for balancing privacy and utility in future large-scale pangenome references.

bioinformatics↗

bioRxiv2026

Private Information Leakage from Polygenic Risk Scores

Polygenic Risk Scores (PRSs) estimate the likelihood of individuals to develop complex diseases based on their genetic variations. While their use in clinical practice and direct-to-consumer genetic testing is growing, the privacy implications of publicly sharing PRS values are often underestimated. In this work, we demonstrate that PRSs can be exploited to recover genotypes and to de-anonymize individuals. We describe how to reconstruct a portion of an individuals genome from a single PRS value by using dynamic programming and population-based likelihood estimation, which we experimentally demonstrate on PRS panels of up 50 variants. We highlight the risks of combining multiple, even larger-panel PRSs to improve genotype-recovery accuracy, which can lead to the re-identification of individuals or their relatives in genomic databases or to the prediction of additional health risks, not originally associated with the disclosed PRSs. We then develop an analytical frame-work to assess the privacy risk of releasing individual PRS values and provide a potential solution for sharing PRS models without decreasing their utility. Our tool and instructions to reproduce our calculations can be found at https://github.com/G2Lab/prs-privacy.

bioinformatics↗

bioRxiv2025

African Green Monkeys Respond to Synthetic AB Oligomers with Persistent Alzheimers-like Activation

Wild African green monkeys (AGMs) provide a promising alternative to congenic rodent models because of their closer evolutionary relationship to humans and natural genetic variation. They share key physiological and biochemical traits with humans, including lifespan, neuroanatomy, vascular structure, and inflammatory responses. Unlike rodents, AGMs naturally develop Alzheimers-like amyloid-{beta} (A{beta}) plaques and tau tangles with age. Immunohistochemical studies further show that AGMs inoculated with synthetic A{beta} oligomers (A{beta}O) exhibit hyperphosphorylated tau and neuroinflammation one year later, in the absence of overt neurodegeneration. The AGM body size permits collection of cerebrospinal fluid (CSF) and CSF derived extracellular vesicles (EV) from living individuals, which are key sources of Alzheimers disease biomarkers that can be monitored during disease progression. Here, we evaluate A{beta}O treated AGMs at the systems level using proteomics of CSF and phosphatidylserine affinity isolated EVs (EVps). We optimized a workflow to obtain paired CSF and EVps proteomics from <1 mL volumes, i.e. comparable to human liquid biopsy. Our measurements reveal robust, persistent AD-like responses at the biochemical level without overt loss of cognitive function. As such, these findings in AGMs suggest potential alternatives for disease tracking or point to protective mechanisms for limiting disease progression in AD. HighlightsO_LIDual proteomics of African green monkeys transiently challenged with synthetic A{beta} oligomers (A{beta}O) C_LIO_LIPhosphotidylserine (TIM4) based workflow enables CSF and EV profiling using clinical volumes C_LIO_LIOne year post-A{beta}O: vascular-inflammatory pathways rise; neuronal-axonal pathways fall C_LIO_LIA{beta}Os drive human-AD-like proteome shifts on time scales shorter than cognitive decline C_LI In briefWild African green monkeys (AGMs) offer a translational model for early Alzheimers biology, with physiology more similar to humans. We transiently exposed AGMs to synthetic A{beta} oligomers and, 12 months later, profiled paired proteomes from whole CSF and a CSF subcompartment enriched for extracellular vesicles. Despite no overt cognitive decline, AGM proteomes showed persistent Alzheimers-like remodeling, particularly in vascular, inflammatory, and neuronal systems. Parallel analysis of the CSF subcompartment revealed proteins and pathways under-represented in bulk CSF, sharpening disease-relevant signals and candidate biomarkers. This systems-level, longitudinal study establishes AGMs as a powerful platform for liquid biopsy discovery and illuminates basic biology of molecular responses to soluble A{beta} oligomers accompany and potentially protect against neurodegeneration.

systems biology↗

bioRxiv2025

ScatTR: Estimating the Size of Long Tandem Repeat Expansions from Short-Reads

Tandem repeats (TRs) are sequences of DNA where two or more base pairs are repeated back-to-back at specific locations in the genome. The expansions of TRs are implicated in over 50 conditions, including Friedreichs ataxia, autism, and cancer. However, accurately measuring the copy number of TRs is challenging, especially when their expansions are larger than the fragment sizes used in standard short-read genome sequencing. Here we introduce ScatTR, a novel computational method that leverages a maximum likelihood framework to estimate the copy number of large TR expansions from short-read sequencing data. ScatTR calculates the likelihood of different alignments between sequencing reads and reference sequences that represent various TR lengths and employs a Monte Carlo technique to find the best match. In simulated data, ScatTR outperforms state-of-the-art methods, particularly for TRs with longer motifs and those with lengths that greatly exceed typical sequencing fragment sizes. When applied to data from the 1000 Genomes Project, ScatTR detected potential large TR expansions that other methods missed, highlighting its ability to better identify genome-wide characterization of TR variation. ScatTR can be accessed via: https://github.com/g2lab/scattr.

bioinformatics↗

bioRxiv2022

A genome-wide atlas of recurrent repeat expansions in human cancer

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs (STRs), a phenomenon termed microsatellite instability (MSI); however larger repeat expansions have not been systematically analyzed in cancer. Here, we identified TR expansions in 2,622 cancer genomes, spanning 29 cancer types. In 7 cancer types, we found 160 recurrent repeat expansions (rREs); most of these (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with an enrichment near candidate cis-regulatory elements, suggesting a role in gene regulation. One rRE located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, targeting cells harboring this rRE with a rationally designed, sequence-specific DNA binder led to a dose-dependent decrease in cell proliferation. Overall, our results demonstrate that rREs are an important but unexplored source of genetic variation in human cancers, and we provide a comprehensive catalog for further study.

genomics↗

bioRxiv2022

Predicting A/B compartments from histone modifications using deep learning

Genomes fold into organizational units in the 3D space that can influence critical biological functions. In particular, the organization of chromatin into A and B compartments segregates its active regions from inactive regions. Compartments, evident in Hi-C contact matrices, have been used to describe cell-type specific changes in the A/B organization. However, obtaining Hi-C data for all cell and tissue types of interest is prohibitively expensive, which has limited the widespread consideration of compartment status. We present a prediction tool called Compartment prediction using Recurrent Neural Network (CoRNN) that models the relationship between the compartmental organization of the genome and histone modification enrichment. Our model predicts A/B compartments, in a cross-cell type setting, with an average area under the ROC curve of 90.9%. Our cell type-specific compartment predictions show high overlap with known functional elements. We investigate our predictions by systematically removing combinations of histone marks and find that H3K27ac and H3K36me3 are the most predictive marks. We then perform a detailed analysis of loci where compartment status cannot be accurately predicted from these marks. These regions represent chromatin with ambiguous compartmental status, likely due to variations in status within the population of cells. These ambiguous loci also show highly variable compartmental status between biological replicates in the same GM12878 cell type. Finally, we demonstrate the generalizability of our model by predicting compartments in independent tissue samples. Our software and trained model are publicly available at https://github.com/rsinghlab/CoRNN.

bioinformatics↗

bioRxiv2021

PLIGHT: A tool to assess privacy risk by inferring identifying characteristics from sparse, noisy genotypes

Single nucleotide polymorphisms (SNPs) from omics data carry a high risk of reidentification for individuals and their relatives. While the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly demonstrated, the ready availability of small sets of noisy genotypes - such as from environmental DNA samples or functional genomics data - motivated us to quantify their informativeness. Here, we present a computational tool suite, PLIGHT ("Privacy Leakage by Inference across Genotypic HMM Trajectories"), that employs population-genetics-based Hidden Markov Models of recombination and mutation to find piecewise alignment of small, noisy query SNP sets to a reference haplotype database. We explore cases where query individuals are either known to be in a database, or not, and consider a variety of queries, including simulated genotype "mosaics" (composites from 2 source individuals) and genotypes from swabs of coffee cups from a known individual. Using PLIGHT on a database with ~5,000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ~20 can identify both components in two-individual simulated mosaics, and 20-30 can identify first-order relatives (parents, children, and siblings). Using noisy coffee-cup-derived SNPs, PLIGHT identifies an individual (within the database) using ~30 SNPs. Moreover, even when the individual is not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained GWAS SNP imputation and polygenic risk scores. Overall, PLIGHT maximizes the identifying information content of sparse SNP sets through exact or partial matches to databases. Finally, by quantifying such privacy attacks, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about underlying population membership or allele frequencies. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from a query set.

bioinformatics↗

bioRxiv2021

Multi-tissue integrative analysis of personal epigenomes

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for [~]25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

genomics↗

bioRxiv2020

Recovering genomes and phenotypes using allele-specific gene expression

With the recent increase in RNA sequencing efforts using large cohorts of individuals, studying allele-specific gene expression is becoming increasingly important. Here, we report that, despite not containing explicit variant information, a list of allele-specific gene names of an individual is enough to recover key variants and link the individual back to their genome or phenotype. This creates a privacy conundrum.

bioinformatics↗

bioRxiv2020

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

BackgroundDuring transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. MethodsTo address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. ResultsWe assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. ConclusionsOur method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

bioinformatics↗

bioRxiv2020

Privacy-preserving genotype imputation with fully homomorphic encryption

Genotype imputation is the statistical inference of unknown genotypes using known population haplotype structures observed in large genomic datasets, such as HapMap and 1000 genomes project. Genotype imputation can help further our understanding of the relationships between genotypes and traits, and is extremely useful for analyses such as genome-wide association studies and expression quantitative loci inference. Increasing the number of genotyped genomes will increase the statistical power for inferring genotype-phenotype relationships, but the amount of data required and the compute-intense nature of the genotype imputation problem overwhelms servers. Hence, many institutions are moving towards outsourcing cloud services to scale up research in a cost effective manner. This raises privacy concerns, which we propose to address via homomorphic encryption. Homomorphic encryption is a type of encryption that allows data analysis on cipher texts, and would thereby avoid the decryption of private genotypes in the cloud. Here we develop an efficient, privacy-preserving genotype imputation algorithm, p-Impute, using homomorphic encryption. Our results showed that the performance of p-Impute is equivalent to the state-of-the-art plaintext solutions, achieving up to 99% micro area under curve score, and requiring a scalable amount of memory and computational time.

bioinformatics↗

bioRxiv2020

Storing and analyzing a genome on a blockchain

The genomic characterization of individuals promises to be immensely useful for biomedical research and healthcare. However, a critical barrier to expanding personal genome sequencing is achieving secure, high-integrity storage of raw data. While cloud storage offers solutions to access such data from any place and device, the vulnerabilities of centralized storage in relation to security, data integrity, and robustness, such as single points of failure, have not yet been addressed. Blockchain is a potential alternative to these storage modes. However, storing large-scale data on blockchain can be challenging due to slow transaction speeds, the potential for chains to reach large sizes, and limitations on querying data stored on-chain. Currently, several genomic storage applications incorporate blockchain, but likely because of these challenges, many use blockchain only to facilitate and log data-access transactions, rather than to store raw genomic data on-chain. While this secures the process of data access, it does not secure the data itself, which is often stored off-chain (i.e. in a cloud or file-hosting services). Here, we developed a novel method of storing reference-aligned reads on-chain in a private blockchain network. We also developed tools for accessing and analyzing the on-chain data. We addressed the challenges of on-chain data storage by minimizing the data inserted to the chain using reference-based data compression techniques and by binning the on-chain data by genomic location to reduce retrieval times. Our tools provide open-source blockchain-based storage and access for advanced genomic analyses such as variant calling.

bioinformatics↗

bioRxiv2019

Using Ethereum blockchain to store and query pharmacogenomics data via smart contracts

BackgroundWith the advent of precision medicine, pharmacogenomics data is becoming increasingly critical to patient care. These data describe the relationship between a particular variant in the genome and the response to a drug by the patient. As utilizing this kind of data becomes more integral to medical treatment decisions, appropriate storage and sharing of this data will be critical. A potential way of securely storing and sharing pharmacogenomics data is a smart contract with the Ethereum blockchain. This is an open-source blockchain platform for decentralized applications. A transaction-based, state machine, the "world" of Ethereum maintains user accounts and storage in a network state. Immutable pieces of code called "smart contracts" may be deployed to the Ethereum network and run on the Ethereum Virtual Machine when called by a user or other contract. The 2019 iDASH (Integrating Data for Analysis, Anonymization, and Sharing) competition for Secure Genome Analysis challenged participants to develop time- and space-efficient smart contracts to log and query gene-drug relationship data on the Ethereum blockchain. MethodsWe designed a smart contract to store and query pharmacogenomics data (gene-drug interaction data) in Ethereum using an index-based, multi-mapping approach allowing for time and space efficient storage and query. Our solution to the IDASH competition ranked in the top three at a workshop held in Bloomington, IN in October 2019. Although our solution performed well in the challenge, we wanted to improve its scalability and query efficiency. To that end, we developed an alternate "fastQuery" solution that stores pooled rather than raw data, allowing for significantly improved query time for 0-AND queries, and constant query time for 1- and 2-AND queries. ResultsWe tested the performance of both of our solutions in Truffle (v5.0.31) using datasets ranging from 100 to 1000 entries, and inserting data at 25, 50, 100, and 200 observations at a time. On a private, proof-of-authority test network, our challenge solution requires approximately 70 seconds, 500 MB of memory, and 80 MB of disk space to insert 1000 entries (200 at a time); and 400 ms and 5 MB of memory to query a two-AND query from 1000 entries. This solution exhibits constant memory for insertion and querying, and linear query time. Our alternate fastQuery solution requires approximately 60 seconds, 500 MB of memory, and 80 MB of disk space to insert 1000 entries (200 at a time); and 83 ms and 5 MB of memory to query a two-AND query from 1000 entries. This solution exhibits constant memory for insertion and querying, linear query time for 0-AND queries, and constant query time for 1- and 2-AND queries in a database of up to 1000 entries. ConclusionIn this study we showed that pharmacogenomics data can be stored and queried efficiently on the Ethereum blockchain. Our approach has the potential to be useful for a wide range of datasets in biomedical research; while we focused on gene-drug interaction data, our solution designs could be used to store a range of clinical trial data. Moreover, our solutions could be adapted to store and query data in any field where high-integrity data storage and efficient access is required.

bioinformatics↗

bioRxiv2019

FANCY: Fast Estimation of Privacy Risk in Functional Genomics Data

Functional genomics data is becoming clinically actionable, raising privacy concerns. However, quantifying the privacy leakage by genotyping is difficult due to the heterogeneous nature of sequencing techniques. Thus, we present FANCY, a tool that rapidly estimates number of leaking variants from raw RNA-Seq, ATAC-Seq and ChIP-Seq reads, without explicit genotyping. FANCY employs supervised regression using overall sequencing statistics as features and provides an estimate of the overall privacy risk before data release. FANCY can predict the cumulative number of leaking SNVs with a 0.95 average R2 for all independent test sets. We acknowledged the importance of accurate prediction even when the number of leaked variants is low, so we developed a special version of model, which can make predictions with higher accuracy for only a few leaking variants. A python and MATLAB implementation of FANCY, as well as custom scripts to generate the features can be found at https://github.com/gersteinlab/FANCY. We also provide jupyter notebooks so that users can optimize the parameters in the regression model based on their own data. An easy-to-use webserver that takes inputs and displays results can be found at fancy.gersteinlab.org.

bioinformatics↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗