ALBERT

Description: Genomes are organized into high-level three-dimensional structures, and DNA elements separated by long genomic distances can in principle interact functionally. Many transcription factors bind to regulatory DNA elements distant from gene promoters. Although distal binding sites have been shown to regulate transcription by long-range chromatin interactions at a few loci, chromatin interactions and their impact on transcription regulation have not been investigated in a genome-wide manner. Here we describe the development of a new strategy, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) for the de novo detection of global chromatin interactions, with which we have comprehensively mapped the chromatin interaction network bound by oestrogen receptor alpha (ER-alpha) in the human genome. We found that most high-confidence remote ER-alpha-binding sites are anchored at gene promoters through long-range chromatin interactions, suggesting that ER-alpha functions by extensive chromatin looping to bring genes together for coordinated transcriptional regulation. We propose that chromatin interactions constitute a primary mechanism for regulating transcription in mammalian genomes.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2774924/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2774924/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Fullwood, Melissa J -- Liu, Mei Hui -- Pan, You Fu -- Liu, Jun -- Xu, Han -- Mohamed, Yusoff Bin -- Orlov, Yuriy L -- Velkov, Stoyan -- Ho, Andrea -- Mei, Poh Huay -- Chew, Elaine G Y -- Huang, Phillips Yao Hui -- Welboren, Willem-Jan -- Han, Yuyuan -- Ooi, Hong Sain -- Ariyaratne, Pramila N -- Vega, Vinsensius B -- Luo, Yanquan -- Tan, Peck Yean -- Choy, Pei Ye -- Wansa, K D Senali Abayratna -- Zhao, Bing -- Lim, Kar Sian -- Leow, Shi Chi -- Yow, Jit Sin -- Joseph, Roy -- Li, Haixia -- Desai, Kartiki V -- Thomsen, Jane S -- Lee, Yew Kok -- Karuturi, R Krishna Murthy -- Herve, Thoreau -- Bourque, Guillaume -- Stunnenberg, Hendrik G -- Ruan, Xiaoan -- Cacheux-Rataboul, Valere -- Sung, Wing-Kin -- Liu, Edison T -- Wei, Chia-Lin -- Cheung, Edwin -- Ruan, Yijun -- 1U54HG004557-01/HG/NHGRI NIH HHS/ -- R01 HG004456/HG/NHGRI NIH HHS/ -- R01 HG004456-01/HG/NHGRI NIH HHS/ -- R01 HG004456-02/HG/NHGRI NIH HHS/ -- R01 HG004456-03/HG/NHGRI NIH HHS/ -- R01HG003521-01/HG/NHGRI NIH HHS/ -- R01HG004456-01/HG/NHGRI NIH HHS/ -- U54 HG004557/HG/NHGRI NIH HHS/ -- U54 HG004557-01/HG/NHGRI NIH HHS/ -- U54 HG004557-02/HG/NHGRI NIH HHS/ -- U54 HG004557-03/HG/NHGRI NIH HHS/ -- U54 HG004557-04/HG/NHGRI NIH HHS/ -- England -- Nature. 2009 Nov 5;462(7269):58-64. doi: 10.1038/nature08497.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19890323" target="_blank"〉PubMed〈/a〉

Description: In multicellular organisms, transcription regulation is one of the central mechanisms modelling lineage differentiation and cell-fate determination. Transcription requires dynamic chromatin configurations between promoters and their corresponding distal regulatory elements. It is believed that their communication occurs within large discrete foci of aggregated RNA polymerases termed transcription factories in three-dimensional nuclear space. However, the dynamic nature of chromatin connectivity has not been characterized at the genome-wide level. Here, through a chromatin interaction analysis with paired-end tagging approach using an antibody that primarily recognizes the pre-initiation complexes of RNA polymerase II, we explore the transcriptional interactomes of three mouse cells of progressive lineage commitment, including pluripotent embryonic stem cells, neural stem cells and neurosphere stem/progenitor cells. Our global chromatin connectivity maps reveal approximately 40,000 long-range interactions, suggest precise enhancer-promoter associations and delineate cell-type-specific chromatin structures. Analysis of the complex regulatory repertoire shows that there are extensive colocalizations among promoters and distal-acting enhancers. Most of the enhancers associate with promoters located beyond their nearest active genes, indicating that the linear juxtaposition is not the only guiding principle driving enhancer target selection. Although promoter-enhancer interactions exhibit high cell-type specificity, promoters involved in interactions are found to be generally common and mostly active among different cells. Chromatin connectivity networks reveal that the pivotal genes of reprogramming functions are transcribed within physical proximity to each other in embryonic stem cells, linking chromatin architecture to coordinated gene expression. Our study sets the stage for the full-scale dissection of spatial and temporal genome structures and their roles in orchestrating development.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954713/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954713/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Zhang, Yubo -- Wong, Chee-Hong -- Birnbaum, Ramon Y -- Li, Guoliang -- Favaro, Rebecca -- Ngan, Chew Yee -- Lim, Joanne -- Tai, Eunice -- Poh, Huay Mei -- Wong, Eleanor -- Mulawadi, Fabianus Hendriyan -- Sung, Wing-Kin -- Nicolis, Silvia -- Ahituv, Nadav -- Ruan, Yijun -- Wei, Chia-Lin -- 1U54HG004557-01/HG/NHGRI NIH HHS/ -- GGP12152/Telethon/Italy -- GM61390/GM/NIGMS NIH HHS/ -- R01 DK090382/DK/NIDDK NIH HHS/ -- R01 HD059862/HD/NICHD NIH HHS/ -- R01 HG004456-01/HG/NHGRI NIH HHS/ -- R01 NS079231/NS/NINDS NIH HHS/ -- R01DK090382/DK/NIDDK NIH HHS/ -- R01HD059862/HD/NICHD NIH HHS/ -- R01HG003521-01/HG/NHGRI NIH HHS/ -- R01HG005058/HG/NHGRI NIH HHS/ -- R01HG006768/HG/NHGRI NIH HHS/ -- R01NS079231/NS/NINDS NIH HHS/ -- U01 GM061390/GM/NIGMS NIH HHS/ -- U19 GM061390/GM/NIGMS NIH HHS/ -- England -- Nature. 2013 Dec 12;504(7479):306-10. doi: 10.1038/nature12716. Epub 2013 Nov 10.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Sequencing Technology Group, Joint Genome Institute, Lawrence Berkeley National Laboratory, Walnut Creek, California 94598, USA [2] [3] Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel (R.Y.B.); National Heart, Lung, and Blood Institute, National Institutes of Health, Systems Biology Center, 9000 Rockville Pike, Bethesda, Maryland 20892, USA (Y.Z.). ; 1] Sequencing Technology Group, Joint Genome Institute, Lawrence Berkeley National Laboratory, Walnut Creek, California 94598, USA [2]. ; 1] Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, UCSF, San Francisco, California 94158, USA [2] [3] Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel (R.Y.B.); National Heart, Lung, and Blood Institute, National Institutes of Health, Systems Biology Center, 9000 Rockville Pike, Bethesda, Maryland 20892, USA (Y.Z.). ; 1] The Jackson Laboratory for Genomic Medicine, and Department of Genetic and Development Biology, University of Connecticut, 400 Farmington, Connecticut 06030, USA [2] Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore. ; Department of Biological Sciences and Biotechnology, University of Milano-Bicocca, 20126 Milano, Italy. ; Sequencing Technology Group, Joint Genome Institute, Lawrence Berkeley National Laboratory, Walnut Creek, California 94598, USA. ; Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore. ; Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, UCSF, San Francisco, California 94158, USA. ; The Jackson Laboratory for Genomic Medicine, and Department of Genetic and Development Biology, University of Connecticut, 400 Farmington, Connecticut 06030, USA. ; 1] Sequencing Technology Group, Joint Genome Institute, Lawrence Berkeley National Laboratory, Walnut Creek, California 94598, USA [2] Genome Institute of Singapore, 60 Biopolis Street, 138672 Singapore.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24213634" target="_blank"〉PubMed〈/a〉

Description: Structural variations (SVs) play a crucial role in genetic diversity. However, the alignments of reads near/across SVs are made inaccurate by the presence of polymorphisms. BatAlign is an algorithm that integrated two strategies called ‘Reverse-Alignment’ and ‘Deep-Scan’ to improve the accuracy of read-alignment. In our experiments, BatAlign was able to obtain the highest F-measures in read-alignments on mismatch-aberrant, indel-aberrant, concordantly/discordantly paired and SV-spanning data sets. On real data, the alignments of BatAlign were able to recover 4.3% more PCR-validated SVs with 73.3% less callings. These suggest BatAlign to be effective in detecting SVs and other polymorphic-variants accurately using high-throughput data. BatAlign is publicly available at https://goo.gl/a6phxB .

Description: Motivation: BigWig, a format to represent read density data, is one of the most popular data types. They can represent the peak intensity in ChIP-seq, the transcript expression in RNA-seq, the copy number variation in whole genome sequencing, etc. UCSC Encode project uses the bigWig format heavily for storage and visualization. Of 5.2 TB Encode hg19 database, 1.6 TB (31% of the total space) is used to store bigWig files. BigWig format not only saves a lot of space but also supports fast queries that are crucial for interactive analysis and browsing. In our benchmark, bigWig often has similar size to the gzipped raw data, while is still able to support ~5000 random queries per second. Results: Although bigWig is good enough at the moment, both storage space and query time are expected to become limited when sequencing gets cheaper. This article describes a new method to store density data named CWig. The format uses on average one-third of the size of existing bigWig files and improves random query speed up to 100 times. Availability and implementation: http://genome.ddns.comp.nus.edu.sg/~cwig Contact: ksung@comp.nus.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Second-generation sequencing (SGS) generates millions of reads that need to be aligned to a reference genome allowing errors. Although current aligners can efficiently map reads allowing a small number of mismatches, they are not well suited for handling a large number of mismatches. The efficiency of aligners can be improved using various heuristics, but the sensitivity and accuracy of the alignments are sacrificed. In this article, we introduce Basic Alignment tool for Mismatches (BatMis)—an efficient method to align short reads to a reference allowing k mismatches. BatMis is a Burrows–Wheeler transformation based aligner that uses a seed and extend approach, and it is an exact method. Results: Benchmark tests show that BatMis performs better than competing aligners in solving the k -mismatch problem. Furthermore, it can compete favorably even when compared with the heuristic modes of the other aligners. BatMis is a useful alternative for applications where fast k -mismatch mappings, unique mappings or multiple mappings of SGS data are required. Availability and implementation: BatMis is written in C/C++ and is freely available from http://code.google.com/p/batmis/ Contact: ksung@comp.nus.edu.sg Supplementary Information: Supplementary information is available from Bioinformatics online.