Biology subjects

Tsirigos, A.

Publications and source records attributed to Tsirigos, A..

6 recordsLinked to original sources

bioRxiv2018

KLF4 binding during reprogramming is involved in 3D architectural rewiring and transcriptional regulation of enhancer hubs

Cell fate transitions are accompanied by global transcriptional, epigenetic and topological changes driven by transcription factors (TFs), as is strikingly exemplified by reprogramming somatic cells to pluripotent stem cells (PSCs) via expression of OCT4, KLF4, SOX2 and cMYC. How TFs orchestrate the complex molecular changes around their target gene loci in a temporal manner remains incompletely understood. Here, using KLF4 as a paradigm, we provide the first TF-centric view of chromatin reorganization and its association to 3D enhancer rewiring and transcriptional changes of linked genes during reprogramming of mouse embryonic fibroblasts (MEFs) to PSCs. Inducible depletion of KLF factors in PSCs caused a genome-wide decrease in the connectivity of enhancers, while disruption of individual KLF4 binding sites from PSC-specific enhancers was sufficient to impair enhancer-promoter contacts and reduce expression of associated genes. Our study provides an integrative view of the complex activities of a lineage-specifying TF during a controlled cell fate transition and offers novel insights into the order and nature of molecular events that follow TF binding.

molecular biology↗

bioRxiv2018

ChIPSeqSpike: A R/Bioconductor package for ChIP-Seq data scaling according to spike-in control

MotivationChromatin Immuno-Precipitation followed by Sequencing (ChlP-Seq) is used to determine the binding sites of any protein of interest. ChIP-Seq data suffer from being more qualitative than quantitative. The recent use of Spike-in controls along with the standard protocol tackled this problem. However, no dedicated tool is available for a robust evaluation of this new ChIP-seq approach\n\nResultsWe developed ChIPSeqSpike, an R/Bioconductor package that enables ChIP-Seq spike-in normalization, assessment and analysis. Ready to use scaled bigwig files and scaling factors values are obtained as output. ChIPSeqSpike also provides tools for ChIP-Seq spike-in assessment and analysis through a versatile collection of graphical functions.\n\nAvailabilityThe package is implemented in R (as of version 3.4) and is available from Bioconductor at the URL: https://www.bioconductor.org/packages/3.7/bioc/html/ChIPSeqSpike.html, where installation and usage instructions can be found.\n\nContactnicolas.descostes@nyumc.org

bioinformatics↗

bioRxiv2017

Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning

Visual analysis of histopathology slides of lung cell tissues is one of the main methods used by pathologists to assess the stage, types and sub-types of lung cancers. Adenocarcinoma and squamous cell carcinoma are two most prevalent sub-types of lung cancer, but their distinction can be challenging and time-consuming even for the expert eye. In this study, we trained a deep learning convolutional neural network (CNN) model (inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately classify whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. Our method slightly outperforms a human pathologist, achieving better sensitivity and specificity, with [~]0.97 average Area Under the Curve (AUC) on a held-out population of whole-slide scans. Furthermore, we trained the neural network to predict the ten most commonly mutated genes in lung adenocarcinoma. We found that six of these genes - STK11, EGFR, FAT1, SETBP1, KRAS and TP53 - can be predicted from pathology images with an accuracy ranging from 0.733 to 0.856, as measured by the AUC on the held-out population. These findings suggest that deep learning models can offer both specialists and patients a fast, accurate and inexpensive detection of cancer types or gene mutations, and thus have a significant impact on cancer treatment.

cancer biology↗

bioRxiv2017

Robust Estimation Of Hi-C Contact Matrices Using Fused Lasso Reveals Preferential Insulation Of Super-Enhancers By Strong TAD Boundaries

The metazoan genome is compartmentalized in megabase-scale areas of highly interacting chromatin known as topologically associating domains (TADs), typically identified by computational analyses of Hi-C sequencing data. TADs are demarcated by boundaries that are largely conserved across cell types and even across species, although, increasing evidence suggests that the seemingly invariant TAD boundaries may exhibit plasticity and their insulating strength can vary. However, a genome-wide characterization of TAD boundary strength in mammals is still lacking. A systematic classification and characterization of TAD boundaries may generate new insights into their function. In this study, we first use fused two-dimensional lasso as a machine learning method to improve Hi-C contact matrix reproducibility, and, subsequently, we categorize TAD boundaries based on their insulation score. We demonstrate that higher TAD boundary insulation scores are associated with elevated CTCF levels and that they may differ across cell types. Intriguingly, we observe that super-enhancer elements are preferentially insulated by strong boundaries, i.e. boundaries of higher insulation score. Furthermore, we perform a pan-cancer analysis to demonstrate that strong TAD boundaries and super-enhancer elements are frequently co-duplicated in cancer patients. Taken together, our findings suggest that super-enhancers insulated by strong TAD boundaries may be exploited, as a functional unit, by cancer cells to promote oncogenesis.

bioinformatics↗

bioRxiv2016

lncRNA-screen: an interactive platform for computationally screening long non-coding RNAs in large genomics datasets

Long non-coding RNAs (lncRNAs) have emerged as a class of factors that are important for regulating development and cancer. Computational prediction of lncRNAs from ultra-deep RNA sequencing has been successful in identifying candidate lncRNAs. However, the complexity of handling and integrating different types of genomics data poses significant challenges to experimental laboratories that lack extensive genomics expertise. To address this issue, we have developed lncRNA-screen, a comprehensive pipeline for computationally screening putative lncRNA transcripts over large multimodal datasets. The main objective of this work is to facilitate the computational discovery of lncRNA candidates to be further examined by functional experiments. lncRNA-screen provides a fully automated easy-to-run pipeline which performs data download, RNA-seq alignment, assembly, quality assessment, transcript filtration, novel lncRNA identification, coding potential estimation, expression level quantification, histone mark enrichment profile integration, differential expression analysis, annotation with other type of segmented data (CNVs, SNPs, Hi-C, etc.) and visualization. Importantly, lncRNA-screen generates an interactive report summarizing all interesting lncRNA features including genome browser snapshots and lncRNA-mRNA interactions based on Hi-C data. In summary, our pipeline provides a comprehensive solution for lncRNA discovery and an intuitive interactive report for identifying promising lncRNA candidates. lncRNA-screen is available as free open-source software on GitHub.

bioinformatics↗

bioRxiv2016

HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

BackgroundChromatin conformation capture techniques have evolved rapidly over the last few years and have provided new insights into genome organization at an unprecedented resolution. Analysis of Hi-C data is complex and computationally intensive involving multiple tasks and requiring robust quality assessment. This has led to the development of several tools and methods for processing Hi-C data. However, most of the existing tools do not cover all aspects of the analysis and only offer few quality assessment options. Additionally, availability of a multitude of tools makes scientists wonder how these tools and associated parameters can be optimally used, and how potential discrepancies can be interpreted and resolved. Most importantly, investigators need to be ensured that slight changes in parameters and/or methods do not affect the conclusions of their studies.\n\nResultsTo address these issues (compare, explore and reproduce), we introduce HiC-bench, a configurable computational platform for comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs all common Hi-C analysis tasks, such as alignment, filtering, contact matrix generation and normalization, identification of topological domains, scoring and annotation of specific interactions using both published tools and our own. We have also embedded various tasks that perform quality assessment and visualization. HiC-bench is implemented as a data flow platform with an emphasis on analysis reproducibility. Additionally, the user can readily perform parameter exploration and comparison of different tools in a combinatorial manner that takes into account all desired parameter settings in each pipeline task. This unique feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple tool/parameter choices in each step of the analysis. To demonstrate the usefulness of our platform, we performed a comprehensive benchmark of existing and new TAD callers exploring different matrix correction methods, parameter settings and sequencing depths. Users can extend our pipeline by adding more tools as they become available.\n\nConclusionsHiC-bench consists an easy-to-use and extensible platform for comprehensive analysis of Hi-C datasets. We expect that it will facilitate current analyses and help scientists formulate and test new hypotheses in the field of three-dimensional genome organization.

bioinformatics↗