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1. Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing

Author

James W. Hicks, Timour Baslan, Nevenka Dimitrova, Junyan Song, Assaf Gordon, Ronak Shah, Jie Wu, Michael Riggs, Kalyani Chadalavada, Vinay Varadan, Hilary Cox, Linda Rodgers, Lyndsay Harris, Sean D'Italia, Rodrigo Gularte-Mérida, Gouri Nanjangud, Anthony Leotta, Michael Wigler, Alexander Krasnitz, Frank Ambrosio, Yong Mao, Christina Curtis, Jude Kendall, Konstantin Volyanskyy, and Katherine McNamara

Subjects
0301 basic medicine, Somatic cell, Gene Dosage, Genome, 0302 clinical medicine, genetics, RNA-Seq, Biology (General), Cancer Biology, copy number alterations, General Neuroscience, Genomics, General Medicine, Prognosis, Phenotype, 030220 oncology & carcinogenesis, Medicine, Female, Single-Cell Analysis, Research Article, Human, DNA Copy Number Variations, QH301-705.5, Science, Breast Neoplasms, Computational biology, single-cell sequencing, Biology, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, Genetic Heterogeneity, 03 medical and health sciences, Clinical Trials, Phase II as Topic, breast cancer, Breast cancer, Biomarkers, Tumor, medicine, Humans, cancer, Genetic Predisposition to Disease, Whole Genome Sequencing, General Immunology and Microbiology, Genetic heterogeneity, Cancer, Genetics and Genomics, medicine.disease, 030104 developmental biology, Single cell sequencing
Abstract

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse., eLife digest Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ – sometimes dramatically – between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

Published
2020
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2. Author response: Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing

Author

Gouri Nanjangud, Junyan Song, Rodrigo Gularte-Mérida, Lyndsay Harris, Vinay Varadan, Katherine McNamara, Jie Wu, Hilary Cox, James W. Hicks, Timour Baslan, Yong Mao, Jude Kendall, Linda Rodgers, Konstantin Volyanskyy, Nevenka Dimitrova, Christina Curtis, Sean D'Italia, Assaf Gordon, Ronak Shah, Michael Riggs, Kalyani Chadalavada, Anthony Leotta, Michael Wigler, Alexander Krasnitz, and Frank Ambrosio

Subjects
Breast cancer, medicine.anatomical_structure, Genetic heterogeneity, Cell, medicine, Computational biology, Biology, medicine.disease, DNA sequencing
Published
2020
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3. Rapid re-identification of human samples using portable DNA sequencing

Author

Assaf Gordon, Daniel Speyer, Robert Piccone, Simon C. Groen, Sophie Zaaijer, and Yaniv Erlich

Subjects
0301 basic medicine, Time Factors, Genotyping Techniques, cell biology, Biology (General), cell line authentication, Genetics, General Neuroscience, Single Nucleotide, General Medicine, Genomics, Tools and Resources, re-identification, DNA profiling, Genomics and Evolutionary Biology, Cell lines, Medicine, Sequence Analysis, Human, QH301-705.5, Sample (material), Science, DNA fingerprinting, Biology, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, 03 medical and health sciences, Clinical Research, genomics, Humans, human, Polymorphism, General Immunology and Microbiology, evolutionary biology, Computational Biology, DNA, Sequence Analysis, DNA, Cell Biology, Data science, Computer science, DNA sequencer, 030104 developmental biology, forensics, Minion, FOS: Biological sciences, nanopore sequencing, Human genome, Biochemistry and Cell Biology, Nanopore sequencing, Cytology
Abstract

DNA re-identification is used for a broad suite of applications, ranging from cell line authentication to forensics. However, current re-identification schemes suffer from high latency and limited access. Here, we describe a rapid, inexpensive, and portable strategy to robustly re-identify human DNA called 'MinION sketching'. MinION sketching requires as few as 3 min of sequencing and 60-300 random SNPs to re-identify a sample enabling near real-time applications of DNA re-identification. Our method capitalizes on the rapidly growing availability of genomic reference data for cell lines, tissues in biobanks, and individuals. This empowers the application of MinION sketching in research and clinical settings for periodic cell line and tissue authentication. Importantly, our method enables considerably faster and more robust cell line authentication relative to current practices and could help to minimize the amount of irreproducible research caused by mix-ups and contamination in human cell and tissue cultures., eLife digest The human genome represents the complete set of genetic information needed to make a person. DNA sequencing technologies used to study genomes have become much faster, cheaper and more accessible over recent years. This has enabled them to be used more regularly in various fields like precision medicine, in research laboratories and forensics. Even so, there are still fields where optimization is critical. Reproducibility is an important issue in biomedical research; one group of scientists working with human cells may report results that other scientists cannot reproduce. Sometimes this is because the original work was done in the wrong type of cells by mistake. Human cells used in biomedical research are very hard to discriminate from each other using microscopes; however, DNA analysis can be used to ensure the origin of the cells. The MinION device, a USB compatible handheld DNA sequencer, has become available in the last few years. Its size, speed and portability could enable many new uses for DNA sequencing. Technology like this could be used to confirm which cells the scientists are working with before they publish their results. Yet, currently DNA readings from the MinION are not accurate enough to be used to reliably confirm the identity of human cells used in research. Zaaijer et al. have now developed an approach that can accurately identify human cells using the MinION device. The approach involves “DNA re-identification”, which works by comparing an unknown DNA sample to a collection of known DNA profiles. Using their new method, Zaaijer et al. report that, with three minutes of DNA sequencing, they can correctly identify a DNA sample, with 99.9% confidence. This is a high enough level of accuracy for the system to tell the difference between one person and another, using only their DNA. This new technology is much faster than current rapid DNA sequencing approaches. Previously, processing DNA samples could take hours or even days and was not particularly portable. The new technology has many applications from finding criminals to diagnosing illnesses and tracking epidemics. It is also an affordable way for laboratories to confirm the identity of cells they are working with. This has the potential to save billions in research funding each year and speed up scientific progress.

Published
2017

4. Genome-wide profiling of heritable and de novo STR variations

Author

Yaniv Erlich, Assaf Gordon, Dina Zielinski, Melissa Gymrek, and Thomas Willems

Subjects
Whole genome sequencing, Genotype, Haplotype, Genome wide profiling, Population genetics, Microsatellite, Genomics, Computational biology, Biology, Genotyping, humanities
Abstract

Short tandem repeats (STRs) are highly variable elements that play a pivotal role in multiple genetic diseases, population genetics applications, and forensic casework. However, STRs have proven problematic to genotype from high-throughput sequencing data. Here, we describe HipSTR, a novel haplotype-based method for robustly genotyping, haplotyping, and phasing STRs from whole genome sequencing data and report a genome-wide analysis and validation of de novo STR mutations.

Published
2016
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5. Democratizing DNA Fingerprinting

Author

Robert Piccone, Assaf Gordon, Daniel Speyer, Sophie Zaaijer, and Yaniv Erlich

Subjects
Genetics, chemistry.chemical_compound, DNA profiling, chemistry, Human dna, Minion, Low input, Genomics, Computational biology, Nanopore sequencing, Biology, DNA
Abstract

We report a rapid, inexpensive, and portable strategy to re-identify human DNA using the MinION, a miniature sequencing sensor by Oxford Nanopore Technologies. Our strategy requires only 10-30 minutes of MinION sequencing, works with low input DNA, and enables familial searches. We also show that it can re-identify individuals from Direct-to-Consumer genomic datasets that are publicly available. We discuss potential forensic applications as well as the legal and ethical implications of a democratized DNA fingerprinting strategy available to the public.

Published
2016
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6. DNA Sudoku—harnessing high-throughput sequencing for multiplexed specimen analysis

Author

Gregory J. Hannon, Yaniv Erlich, Roy Ronen, Michelle Rooks, Oron Navon, Assaf Gordon, and Kenneth Chang

Subjects
DNA, Complementary, Cystic Fibrosis, Sequence analysis, Population, Pooling, Arabidopsis, Cystic Fibrosis Transmembrane Conductance Regulator, Computational biology, Biology, Polymerase Chain Reaction, DNA sequencing, Species Specificity, Methods, Escherichia coli, Genetics, Humans, Computer Simulation, Genomic library, RNA, Small Interfering, education, Genetics (clinical), Gene Library, Oligonucleotide Array Sequence Analysis, Sequence (medicine), Expressed Sequence Tags, education.field_of_study, Expressed sequence tag, Gene Expression Profiling, Sequence Analysis, DNA, Mutation, Human genome
Abstract

Next-generation sequencers have sufficient power to analyze simultaneously DNAs from many different specimens, a practice known as multiplexing. Such schemes rely on the ability to associate each sequence read with the specimen from which it was derived. The current practice of appending molecular barcodes prior to pooling is practical for parallel analysis of up to many dozen samples. Here, we report a strategy that permits simultaneous analysis of tens of thousands of specimens. Our approach relies on the use of combinatorial pooling strategies in which pools rather than individual specimens are assigned barcodes. Thus, the identity of each specimen is encoded within the pooling pattern rather than by its association with a particular sequence tag. Decoding the pattern allows the sequence of an original specimen to be inferred with high confidence. We verified the ability of our encoding and decoding strategies to accurately report the sequence of individual samples within a large number of mixed specimens in two ways. First, we simulated data both from a clone library and from a human population in which a sequence variant associated with cystic fibrosis was present. Second, we actually pooled, sequenced, and decoded identities within two sets of 40,000 bacterial clones comprising approximately 20,000 different artificial microRNAs targeting Arabidopsis or human genes. We achieved greater than 97% accuracy in these trials. The strategies reported here can be applied to a wide variety of biological problems, including the determination of genotypic variation within large populations of individuals.

Published
2009
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7. A computational algorithm to predict shRNA potency

Author

Sun Y. Kim, Nicolas Erard, Xin Zhou, Osama El Demerdash, Christof Fellmann, Gregory J. Hannon, Simon R.V. Knott, Ashley R. Maceli, Kenneth Chang, Elvin Wagenblast, Krista Marran, and Assaf Gordon

Subjects
Small interfering RNA, Molecular Sequence Data, Computational algorithm, Computational biology, Biology, Bioinformatics, Article, Small hairpin RNA, RNA interference, Cell Line, Tumor, Consensus Sequence, Gene Knockdown Techniques, Potency, Humans, Base sequence, Computer Simulation, RNA, Small Interfering, Molecular Biology, Gene knockdown, Base Sequence, Models, Genetic, Cell Biology, MicroRNAs, Algorithms, Software
Abstract

The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and have used this method to generate ~250,000 shRNA efficacy data-points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that target, specifically, the majority of each gene’s multiple transcripts. We have validated the performance of our shRNA designs using several orthogonal strategies and have constructed genome-wide collections of shRNAs for humans and mice based upon our approach.

Published
2014

8. Landscape of transcription in human cells

Author

Tyler Alioto, Colin Kingswood, Lorian Schaffer, Sylvain Foissac, Kimberly Bell, Diane Trout, Wei Lin, Nathalie Walters, Thomas R. Gingeras, Oscar Junhong Luo, Pedro G. Ferreira, Atif Shahab, Assaf Gordon, Kata Fejes-Toth, Joao Curado, Jacqueline Chrast, Thomas Derrien, Georgi K. Marinov, Hazuki Takahashi, Alexander Dobin, Nadav Bar, Lei-Hoon See, Morgan C. Giddings, Erica Dumais, Michael Sammeth, Philipp Kapranov, Jainab Khatun, Meagan Fastuca, Jørgen Skancke, Ian Bell, Emilie Falconnet, Roderic Guigó, Huaien Wang, Kimberly Persaud, Alexandre Reymond, Carrie A. Davis, Xiaoan Ruan, Rehab F. Abdelhamid, Daniel Robyr, Joel Rozowsky, Ana Maria Suzuki, Sonali Jha, Marion S. Röder, Sudipto K. Chakrabortty, Brian A. Risk, Hagen Tilgner, Eddie Park, Stylianos E. Antonarakis, Piero Carninci, Hui Gao, Sarah Djebali, Timo Lassmann, Andrea Tanzer, Philippe Batut, Cédric Howald, Yanbao Yu, Ali Mortazavi, Felix Kokocinski, Jennifer Harrow, Mark Gerstein, Melissa J. Fullwood, Chenghai Xue, Gregory J. Hannon, Xian Chen, Igor Antoshechkin, Yoshihide Hayashizaki, Brian A. Williams, Chris Zaleski, John A. Wrobel, Radha Duttagupta, Barbara J. Wold, Julien Lagarde, David Gonzalez, Yijun Ruan, Tim Hubbard, Angelika Merkel, Rory Johnson, Jonathan B. Preall, Jorg Drenkow, Brandon King, Paolo Ribeca, Harsha P. Gunawardena, Jacqueline Dumais, Felix Schlesinger, Michael T. Baer, Centre for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra [Barcelona] (UPF), Cold Spring Harbor Laboratory, Riken Yokohama Institute, California Institute of Technology (CALTECH), University of California [Irvine] (UCI), University of California, Boise State University, Yale University [New Haven], The Wellcome Trust Sanger Institute [Cambridge], Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Affymetrix Inc., University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Université de Lausanne (UNIL), Laboratoire de Génétique Cellulaire (LGC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Geneva [Switzerland], (IGE3 Inst Genet & Genom Geneva), University of Geneva Medical School, Genome Institute of Singapore (GIS), St Laurent Institut, Falconnet, Emilie, Robyr, Daniel, Reymond, Alexandre, Antonarakis, Stylianos, Universitat Pompeu Fabra [Barcelona], Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)

Subjects
Encyclopedias as Topic, Repetitive Sequences, Nucleic Acid/genetics, Polyadenylation, [SDV]Life Sciences [q-bio], Genoma humà, Genome, Repetitive Sequences, RNA Editing/genetics, 0302 clinical medicine, Exons/genetics, Protein Isoforms, RNA/biosynthesis/genetics, ddc:576.5, RNA Splicing/genetics, Genetics, 0303 health sciences, Multidisciplinary, Exons, Genomics, Transcriptome/genetics, Long non-coding RNA, Enhancer Elements, Genetic, 030220 oncology & carcinogenesis, Sequence Analysis, Transcription, Human, Enhancer Elements, General Science & Technology, 1.1 Normal biological development and functioning, RNA Splicing, DNA, Intergenic/genetics, Computational biology, DNA/genetics, Biology, Article, Cell Line, 03 medical and health sciences, Genetic, Underpinning research, Protein Isoforms/genetics, map, expression, Humans, Genes/genetics, genome, Gene, Alleles, Transcription, Genetic/genetics, 030304 developmental biology, Intergenic, Nucleic Acid, small rnas, Sequence Analysis, RNA, Gene Expression Profiling, Human Genome, RNA, Molecular Sequence Annotation, DNA, Regulatory Sequences, Nucleic Acid/genetics, Gene expression profiling, Genes, Genome, Human/genetics, Polyadenylation/genetics, RNA/biosynthesis, RNA/genetics, enhancer, identification, Human genome, Generic health relevance, RNA Editing, Transcriptome, Regulatory Sequences, Genètica
Abstract

Eukaryotic cells make many types of primary and processed RNAs that are found either in specific subcellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic subcellular localizations are also poorly understood. Because RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell's regulatory capabilities are focused on its synthesis, processing, transport, modification and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three-quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations, taken together, prompt a redefinition of the concept of a gene. This work was supported by the National Human Genome Research Institute (NHGRI) production grants U54HG004557, U54HG004555, U54HG004576 and U54HG004558, and by the NHGRI pilot grant R01HG003700. It was also supported by the NHGRI ARRA stimulus grant 1RC2HG005591, the National Science Foundation (SNF) grant 127375, the European Research Council (ERC) grant/n249968, a research grant for the RIKEN Omics Science Center from the Japanese Ministry of Education, Culture, Sports, Science and Technology, and grants BIO2011-26205, CSD2007-00050 and INB GNV-1 from the Spanish Ministry of Science

Published
2012
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9. A User's Guide to the Encyclopedia of DNA Elements (ENCODE)

Author

Zhi Lu, Giltae Song, Troy W. Whitfield, Vishwanath R. Iyer, Teresa Vales, Angelika Merkel, Max Libbrecht, David Haussler, Ting Wang, Kristen Lee, Lingyun Song, Richard M. Myers, Alfonso Valencia, Rachel A. Harte, Xiaoqin Xu, Lucas D. Ward, Hazuki Takahashi, Nathan C. Sheffield, Thomas Derrien, Georgi K. Marinov, Eric D. Nguyen, Bernard B. Suh, Brian J. Raney, Richard Sandstrom, Thomas D. Tullius, Benoit Miotto, Alexander Dobin, Youhan Xu, Lukas Habegger, Ian Dunham, Brian A. Risk, Paul G. Giresi, Morgan C. Giddings, Hualin Xi, Anshul Kundaje, Robert S. Harris, Devin Absher, Peter J. Bickel, Yanbao Yu, Browen Aken, Colin Kingswood, Bryan R. Lajoie, Peter J. Good, Katrina Learned, Laura Elnitski, Shirley Pepke, Brandon King, Piero Carninci, Xinqiong Yang, Ghia Euskirchen, Kathryn Beal, Christelle Borel, Michael Muratet, Robert L. Grossman, David G. Knowles, Zarmik Moqtaderi, Veronika Boychenko, Steven P. Wilder, Michael L. Tress, Florencia Pauli, Alan P. Boyle, Andrea Tanzer, Philipp Kapranov, Serafim Batzoglou, Audra K. Johnson, Jun Neri, Nitin Bhardwaj, Elise A. Feingold, Venkat S. Malladi, Michael M. Hoffman, William Stafford Noble, Andrea Sboner, Mark Gerstein, Stephanie L. Parker, Jacqueline Dumais, Felix Schlesinger, Deborah R. Winter, Randall H. Brown, Thanh Truong, Rebecca F. Lowdon, Paolo Ribeca, Brooke Rhead, Peggy J. Farnham, Krista Thibeault, Terrence S. Furey, Donna Karolchik, Alec Victorsen, Xiaoan Ruan, Rehab F. Abdelhamid, Amy S. Nesmith, Jing Wang, Nicholas M. Luscombe, Alina R. Cao, Diane Trout, Teri Slifer, Peter E. Newburger, Cricket A. Sloan, Dimitra Lotakis, Stephen M. J. Searle, Ali Mortazavi, Alexandra Bignell, Alex Reynolds, Orion J. Buske, Chris Zaleski, Theresa K. Canfield, Ian Bell, Jin Lian, Vanessa K. Swing, Katalin Toth Fejes, Catherine Ucla, Robert E. Thurman, Jacqueline Chrast, Wei Lin, Tim Hubbard, Gary Saunders, Minyi Shi, Vihra Sotirova, Sherman M. Weissman, Jason D. Lieb, Richard Humbert, Kevin M. Bowling, Assaf Gordon, Tarjei S. Mikkelsen, Jing Leng, Thomas R. Gingeras, Fabian Grubert, Nader Jameel, Jost Vielmetter, Hannah Monahan, Preti Jain, Lindsay L. Waite, Tony Shafer, Joel Rozowsky, Michael Coyne, Brian Reed, M. Kay, Harsha P. Gunawardena, Ross C. Hardison, Gavin Sherlock, Alexandra Charos, Joseph D. Fleming, Ann S. Zweig, Jason Gertz, Rajinder Kaul, Xianjun Dong, Alexandre Reymond, Carrie A. Davis, Haiyan Huang, Chao Cheng, Marco Mariotti, Phil Lacroute, Jason A. Dilocker, Kenneth McCue, R. Robilotto, Stylianos E. Antonarakis, Sridar V. Chittur, Justin Jee, Barbara J. Wold, Sudipto K. Chakrabortty, Erica Dumais, Amartya Sanyal, Nathan Boley, Tianyuan Wang, Julien Lagarde, Anthony Kirilusha, Jonathan B. Preall, Kevin Roberts, Erika Giste, Hugo Y. K. Lam, Alvis Brazma, Gregory J. Hannon, Eric Rynes, Philippe Batut, Kevin Struhl, Margus Lukk, Manching Ku, Suganthi Balasubramanian, Sonali Jha, Jorg Drenkow, W. James Kent, Michael Snyder, Jie Wang, Anna Battenhouse, Charles B. Epstein, Rami Rauch, Christopher Shestak, John A. Stamatoyannopoulos, Gaurab Mukherjee, Cédric Howald, Tanya Kutyavin, Huaien Wang, Scott A. Tenenbaum, Wan Ting Poh, Kate R. Rosenbloom, Manolis Kellis, Pauline A. Fujita, Linfeng Wu, Anita Bansal, Molly Weaver, Linda L. Grasfeder, Peter J. Sabo, Qiang Li, Melissa S. Cline, Robert M. Kuhn, Darin London, Seth Frietze, Atif Shahab, Shane Neph, Damian Keefe, James B. Brown, Mark Diekhans, Webb Miller, Katherine Aylor Fisher, Jiang Du, Hadar H. Sheffer, Sarah Djebali, Frank Doyle, Nathan Lamarre-Vincent, Chia-Lin Wei, Laura A.L. Dillon, Jennifer Harrow, Robert C. Altshuler, Tyler Alioto, Raymond K. Auerbach, Adam Frankish, Rebekka O. Sprouse, Patrick J. Collins, E. Christopher Partridge, Zheng Liu, Yoichiro Shibata, Elliott H. Margulies, Abigail K. Ebersol, Kimberly A. Showers, Eric D. Green, Krishna M. Roskin, Job Dekker, Barbara N. Pusey, Ekta Khurana, Gilberto DeSalvo, Yijun Ruan, Hao Wang, Jainab Khatun, Henriette O'Geen, Alexej Abyzov, Brian Williams, Ryan M. McDaniell, Maya Kasowski, Manoj Hariharan, Felix Kokocinski, Gloria Despacio-Reyes, Zhancheng Zhang, Subhradip Karmakar, Ewan Birney, Koon-Kiu Yan, Xian Chen, Shinny Vong, Daniel Sobral, Nick Bild, Seul K.C. Kim, Timo Lassmann, Li Wang, Minerva E. Sanchez, Vaughan Roach, Theodore Gibson, Stephen C. J. Parker, Michael F. Lin, Patrick A. Navas, Laurence R. Meyer, Luiz O. F. Penalva, Bradley E. Bernstein, Kevin P. White, Emilie Aït Yahya Graison, Juan M. Vaquerizas, Sushma Iyengar, Kimberly M. Newberry, Akshay Bhinge, Xiaolan Zhang, Kim Bell, Yoshihide Hayashizaki, Lucas Lochovsky, Noam Shoresh, Hagen Tilgner, Philip Cayting, Dorrelyn Patacsil, Timothy E. Reddy, Eric Haugen, Katherine E. Varley, M. van Baren, Nathan D. Trinklein, Bum Kyu Lee, Tristan Frum, Marianne Lindahl-Allen, Timothy Durham, Roderic Guigó, Christopher W. Maier, Micha Sammeth, Debasish Raha, Timothy R. Dreszer, Benedict Paten, Robbyn Issner, Michael R. Brent, Kevin Y. Yip, Kim Blahnik, Jason Ernst, Zhiping Weng, Henry Amrhein, Arend Sidow, Javier Herrero, Hui Gao, Stephen G. Landt, Pouya Kheradpour, Galt P. Barber, Gregory E. 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Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Department of Physics, Kellis, Manolis, Epstein, Charles B., Bernstein, Bradley E., Shoresh, Noam, Ernst, Jason, Mikkelsen, Tarjei Sigurd, Kheradpour, Pouya, Zhang, Xiaolan, Wang, Li, Issner, Robbyn, Coyne, Michael J., Durham, Timothy, Ku, Manching, Truong, Thanh, Ward, Lucas D., Altshuler, Robert Charles, Lin, Michael F., ENCODE Project Consortium, Antonarakis, Stylianos, and Miotto, Benoit

Subjects
RNA, Messenger/genetics, [SDV]Life Sciences [q-bio], Messenger, Genoma humà, Genome, Medical and Health Sciences, 0302 clinical medicine, Models, ddc:576.5, Biology (General), Conserved Sequence, Genetics, 0303 health sciences, General Neuroscience, RNA-Binding Proteins, Genomics, Biological Sciences, Chromatin, 3. Good health, [SDV] Life Sciences [q-bio], DNA-Binding Proteins, Gene Components, 030220 oncology & carcinogenesis, DNA methylation, Encyclopedia, HIV/AIDS, Proteïnes de la sang -- Aspectes genètics, General Agricultural and Biological Sciences, Databases, Nucleic Acid, Human, Research Article, Quality Control, Process (engineering), QH301-705.5, 1.1 Normal biological development and functioning, Computational biology, Biology, ENCODE, General Biochemistry, Genetics and Molecular Biology, Chromatin/metabolism, Vaccine Related, 03 medical and health sciences, Databases, Genetic, Underpinning research, Humans, RNA, Messenger, RNA-Binding Proteins/genetics/metabolism, Vaccine Related (AIDS), Gene, 030304 developmental biology, Internet, General Immunology and Microbiology, Nucleic Acid, Agricultural and Veterinary Sciences, Base Sequence, Models, Genetic, Genome, Human, Prevention, Human Genome, Computational Biology, DNA Methylation, ENCODE Project Consortium, Gene Expression Regulation, DNA-Binding Proteins/genetics/metabolism, RNA, Human genome, Immunization, Generic health relevance, Developmental Biology
Abstract

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome., National Human Genome Research Institute (U.S.), National Institutes of Health (U.S.)

Published
2011
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