Biology subjects

Yardimci, G. G.

Publications and source records attributed to Yardimci, G. G..

5 recordsLinked to original sources

bioRxiv2017

Dynamic reorganization of nuclear architecture during human cardiogenesis

While chromosomal architecture varies among cell types, little is known about how this organization is established or its role in development. We integrated Hi-C, RNA-seq and ATAC-seq during cardiac differentiation from human pluripotent stem cells to generate a comprehensive profile of chromosomal architecture. We identified active and repressive domains that are dynamic during cardiogenesis and recapitulate in vivo cardiomyocytes. During differentiation, heterochromatic regions condense in cis. In contrast, many cardiac-specific genes, such as TTN (titin), decompact and transition to an active compartment coincident with upregulation. Moreover, we identify a network of genes, including TTN, that share the heart-specific splicing factor, RBM20, and become associated in trans during differentiation, suggesting the existence of a 3D nuclear splicing factory. Our results demonstrate both the dynamic nature in nuclear architecture and provide insights into how developmental genes are coordinately regulated.\n\nOne Sentence SummaryThe three-dimensional structure of the human genome is dynamically regulated both globally and locally during cardiogenesis.

genomics↗

bioRxiv2017

GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

MotivationThe three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of 3D chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts.\n\nResultsWe introduce a concordance measure called GenomeDISCO (DIfferences between Smoothed COntact maps) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCOs sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP.\n\nAvailabilitySoftware implementing GenomeDISCO is available at https://github.com/kundajelab/genomedisco.\n\nContactakundaje@stanford.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

An Integrative Framework For Detecting Structural Variations In Cancer Genomes

Structural variants can contribute to oncogenesis through a variety of mechanisms, yet, despite their importance, the identification of structural variants in cancer genomes remains challenging. Here, we present an integrative framework for comprehensively identifying structural variation in cancer genomes. For the first time, we apply next-generation optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole genome sequencing to systematically detect SVs in a variety of cancer cells.\n\nUsing this approach, we identify and characterize structural variants in up to 29 commonly used normal and cancer cell lines. We find that each method has unique strengths in identifying different classes of structural variants and at different scales, suggesting that integrative approaches are likely the only way to comprehensively identify structural variants in the genome. Studying the impact of the structural variants in cancer cell lines, we identify widespread structural variation events affecting the functions of non-coding sequences in the genome, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel 3D chromatin structural domains.\n\nThese results underscore the importance of comprehensive structural variant identification and indicate that non-coding structural variation may be an underappreciated mutational process in cancer genomes.

genomics↗

bioRxiv2017

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

bioinformatics↗

bioRxiv2016

Software tools for visualizing Hi-C data

Recently developed, high-throughput assays for measuring the three-dimensional configuration of DNA in the nucleus have provided unprecedented insights into the relationship between DNA 3D configuration and function. However, accurate interpretation of data from assays such as ChIA-PET and Hi-C is challenging because the data is large and cannot be easily rendered using a standard genome browser. In particular, an effective Hi-C visualization tool must provide a variety of visualization modes and be capable of viewing the data in conjunction with existing, complementary data. We review a number of such software tools that have been described recently in the literature, focusing on tools that do not require programming expertise on the part of the user. In particular, we describe HiBrowse, Juicebox, my5C, the 3D Genome Browser, and the Epigenome Browser, outlining their complementary functionalities and highlighting which types of visualization tasks each tool is best designed to handle.

genomics↗