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      Genome-wide mapping of human DNA-replication origins: Levels of transcription at ORC1 sites regulate origin selection and replication timing

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          Abstract

          We report the genome-wide mapping of ORC1 binding sites in mammals, by chromatin immunoprecipitation and parallel sequencing (ChIP-seq). ORC1 binding sites in HeLa cells were validated as active DNA replication origins (ORIs) using Repli-seq, a method that allows identification of ORI-containing regions by parallel sequencing of temporally ordered replicating DNA. ORC1 sites were universally associated with transcription start sites (TSSs) of coding or noncoding RNAs (ncRNAs). Transcription levels at the ORC1 sites directly correlated with replication timing, suggesting the existence of two classes of ORIs: those associated with moderate/high transcription levels (≥1 RNA copy/cell), firing in early S and mapping to the TSSs of coding RNAs; and those associated with low transcription levels (<1 RNA copy/cell), firing throughout the entire S and mapping to TSSs of ncRNAs. These findings are compatible with a scenario whereby TSS expression levels influence the efficiency of ORC1 recruitment at G 1 and the probability of firing during S.

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            Fast and accurate long-read alignment with Burrows–Wheeler transform

            Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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              Sequencing newly replicated DNA reveals widespread plasticity in human replication timing.

              Faithful transmission of genetic material to daughter cells involves a characteristic temporal order of DNA replication, which may play a significant role in the inheritance of epigenetic states. We developed a genome-scale approach--Repli Seq--to map temporally ordered replicating DNA using massively parallel sequencing and applied it to study regional variation in human DNA replication time across multiple human cell types. The method requires as few as 8,000 cytometry-fractionated cells for a single analysis, and provides high-resolution DNA replication patterns with respect to both cell-cycle time and genomic position. We find that different cell types exhibit characteristic replication signatures that reveal striking plasticity in regional replication time patterns covering at least 50% of the human genome. We also identified autosomal regions with marked biphasic replication timing that include known regions of monoallelic expression as well as many previously uncharacterized domains. Comparison with high-resolution genome-wide profiles of DNaseI sensitivity revealed that DNA replication typically initiates within foci of accessible chromatin comprising clustered DNaseI hypersensitive sites, and that replication time is better correlated with chromatin accessibility than with gene expression. The data collectively provide a unique, genome-wide picture of the epigenetic compartmentalization of the human genome and suggest that cell-lineage specification involves extensive reprogramming of replication timing patterns.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                January 2013
                : 23
                : 1
                : 1-11
                Affiliations
                [1 ]Department of Experimental Oncology, European Institute of Oncology, 20141 Milan, Italy;
                [2 ]Dipartimento di Scienze della Salute, University of Milano, 20122 Milan, Italy;
                [3 ]IIT@SEMM, IFOM-IEO Campus, 20141 Milan, Italy;
                [4 ]IFOM-FIRC Institute of Molecular Oncology, 20139 Milan, Italy;
                [5 ]ICGEB International Centre for Genetic Engineering and Biotechnology, 34134 Trieste, Italy
                Author notes

                Present addresses: 6Centre for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, 20132 Milan, Italy;

                [7]

                Humanitas Clinical and Research Center, 20089 Rozzano (MI), Italy;

                [8]

                Department of Oncology, San Raffaele Scientific Institute, 20132 Milan, Italy;

                [9]

                The Wistar Institute, Philadelphia, PA 19104, USA;

                [10]

                National Laboratory CIB, Area Science Park, 34149 Trieste, Italy.

                Article
                9518021
                10.1101/gr.142331.112
                3530669
                23187890
                0641c4d2-eed2-40df-85d8-eca466c97e1f
                © 2013, Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.

                History
                : 26 April 2012
                : 25 September 2012
                Categories
                Research

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