Biology subjects

Keles, S.

Publications and source records attributed to Keles, S..

24 records · Page 2Linked to original sources

bioRxiv2020

SURF: integrative analysis of a compendium of RNA-seq and CLIP-seq datasets highlights complex governing of alternative transcriptional regulation by RNA-binding proteins

Advances in high-throughput profiling of RNA binding proteins (RBPs) have resulted in CLIP-seq datasets coupled with transcriptome profiling by RNA-seq. However, analysis methods that integrate both types of data are lacking. We describe SURF, Statistical Utility for RBP Functions, for integrative analysis of large collections of CLIP-seq and RNA-seq data. We demonstrate SURFs ability to accurately detect differential alternative transcriptional regulation events and associate them to local protein-RNA interactions. We apply SURF to ENCODE RBP compendium and carry out downstream analysis with additional reference datasets. The results of this application are browsable at http://www.statlab.wisc.edu/shiny/surf/.

genomics↗

bioRxiv2019

Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder

Recent advances in consortium-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in autism spectrum disorder (ASD), but our understanding of their etiologic roles, especially the interplay with rare variants, is incomplete. In this work, we introduce an analytical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offspring. We applied this framework to conduct a transcriptome-wide association study (TWAS) on 7,805 ASD proband-parent trios, and replicated our findings using 35,740 independent samples. We identified 31 associations at the transcriptome-wide significance level. In particular, we identified POU3F2 (p=2.1e-7), a transcription factor (TF) mainly expressed in developmental brain. TF targets regulated by POU3F2 showed a 2.1-fold enrichment for known ASD genes (p=4.6e-5) and a 2.7-fold enrichment for loss-of-function de novo mutations in ASD probands (p=7.1e-5). These results provide a clear example of the connection between ASD genes affected by very rare mutations and an unlinked key regulator affected by common genetic variations.

genetics↗

bioRxiv2019

FreeHi-C: high fidelity Hi-C data simulation for benchmarking and data augmentation

Ability to simulate realistic high-throughput chromatin conformation (Hi-C) data is foundational for developing and benchmarking statistical and computational methods for Hi-C data analysis. We propose FreeHi-C, a data-driven Hi-C simulator for simulating and augmenting Hi-C datasets. FreeHi-C employs a non-parametric strategy for estimating interaction distribution of genome fragments from a given sample and simulates Hi-C reads from interacting fragments. Data from FreeHi-C exhibit higher fidelity to the biological Hi-C data compared with other tools in its class. FreeHi-C not only enables benchmarking a wide range of Hi-C analysis methods but also boosts the precision and power of differential chromatin interaction detection methods while preserving false discovery rate control through data augmentation.

bioinformatics↗

bioRxiv2019

PRAM: a novel pooling approach for discovering intergenic transcripts from large-scale RNA sequencing experiments

Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by joint examination of large collections of RNA-seq datasets has emerged as one such analysis. Current methods for transcript discovery rely on a 2-Step approach where the first step encompasses building transcripts from individual datasets, followed by the second step that merges predicted transcripts across datasets. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a novel 1-Step approach named Pooling RNA-seq and Assembling Models (PRAM) that builds transcript models from pooled RNA-seq datasets. We demonstrate in a computational benchmark that 1-Step outperforms 2-Step approaches in predicting overall transcript structures and individual splice junctions, while performing competitively in detecting exonic nucleotides. Applying PRAM to 30 human ENCODE RNA-seq datasets identified unannotated transcripts with epigenetic and RAMPAGE signatures similar to those of recently annotated transcripts. In a case study, we discovered and experimentally validated new transcripts through the application of PRAM to mouse hematopoietic RNA-seq datasets. Notably, we uncovered new transcripts that share a differential expression pattern with a neighboring gene Pik3cg implicated in human hematopoietic phenotypes, and we provided evidence for the conservation of this relationship in human. PRAM is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/pram.

bioinformatics↗

bioRxiv2018

Unbiased screen of RNA tailing enzymes at single-nucleotide resolution reveals a poly(UG) polymerase required for genome integrity and RNA silencing

Ribonucleotidyl transferases (rNTases) add non-templated ribonucleotides to diverse RNAs. We developed a screening strategy in S. cerevisiae to identify sequences added by candidate enzymes from different organisms at single-nucleotide resolution. The rNTase activities of 19 previously unexplored enzymes were determined. In addition to poly(A)- and poly(U)-adding enzymes, we identified a C-adding enzyme that is likely part of a two-enzyme system that adds CCA to tRNAs in a eukaryote; a nucleotidyl transferase that adds nucleotides to RNA without apparent nucleotide preference; and a poly(UG) polymerase, C. elegans MUT-2, which adds alternating U and G nucleotides to form poly(UG) tails. MUT-2 is known to be required for certain forms of RNA silencing, and mutations in the enzyme that are defective in silencing also fail to add poly(UG) tails in our assay. We propose that MUT-2 poly(UG) polymerase activity is required to promote genome integrity and RNA silencing.

molecular biology↗

bioRxiv2018

mHi-C: robust leveraging of multi-mapping reads in Hi-C analysis

Current Hi-C analysis approaches are unable to account for reads that align to multiple locations, and hence underestimate biological signal from repetitive regions of genomes. We developed mHi-C, a multi-read mapping strategy to probabilistically allocate Hi-C multi-reads. mHi-C exhibited superior performance over utilizing only uni-reads and heuristic approaches aimed at rescuing multi-reads on benchmarks. Specifically, mHi-C increased the sequencing depth by an average of 20% leading to higher reproducibility of contact matrices and larger number of significant interactions across biological replicates. The impact of the multi-reads on the identification of novel significant interactions is influenced marginally by relative contribution of multi-reads to the sequencing depth compared to uni-reads, cis-to-trans ratio of contacts, and the broad data quality as reflected by the proportion of mappable reads of datasets. Computational experiments highlighted that in Hi-C studies with short read lengths, mHi-C rescued multi-reads can emulate the effect of longer reads. mHi-C also revealed biologically supported bona fide promoter-enhancer interactions and topologically associating domains involving repetitive genomic regions, thereby unlocking a previously masked portion of the genome for conformation capture studies.

genomics↗