Biology subjects

Cicek, A. E.

Publications and source records attributed to Cicek, A. E..

4 recordsLinked to original sources

bioRxiv2018

SPADIS: An Algorithm for Selecting Predictive and Diverse SNPs in GWAS

Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants. Algorithms that select SNPs which are close and connected on a biological network have been successful in finding biologically-interpretable and predictive loci. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that selects SNPs that cover diverse regions in the underlying SNP-SNP network. SPADIS favors the selection of remotely located SNPs in order to account for the complementary additive effects of SNPs that are associated with the phenotype. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor (1-1/e) approximation. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana genotype and continuous flowering time phenotypes. SPADIS has better regression performance in 12 out of 17 phenotypes on average, it identifies more candidate genes and runs faster. We also investigate the use of Hi-C data to construct SNP-SNP network in the context of SNP selection problem for the first time, which yields slight but consistent improvements in regression performance. SPADIS is available at http://ciceklab.cs.bilkent.edu.tr/spadis

bioinformatics↗

bioRxiv2018

Spatio-Temporal Gene Discovery For Autism Spectrum Disorder

Whole exome sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited in power. Here, we present a spatio-temporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a variant of prize-collecting Steiner forest-based problem on coexpression networks to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of earlyand mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the prediction power: Predicted clusters are hit more and show higher enrichment in ASD-related functions compared to the state-of-the-art. Code is available at http://ciceklab.cs.bilkent.edu.tr/ST-Steiner/.

bioinformatics↗

bioRxiv2017

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

MotivationChoosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time-efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies.\n\nResultsWe designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platforms error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads.\n\nAvailabilityHercules source code is available at https://github.com/BilkentCompGen/Hercules

bioinformatics↗

bioRxiv2017

Re-Identification of Individuals in Genomic Data-Sharing Beacons via Allele Inference

Genomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his/her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95% confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5% of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model.

bioinformatics↗