Preprints

2024

Complex genetic variation in nearly complete human genomes

Glennis A. Logsdon, Peter Ebert, Peter A. Audano, Mark Loftus, David Porubsky, Jana Ebler, Feyza Yilmaz, Pille Hallast, Timofey Prodanov, DongAhn Yoo, Carolyn A. Paisie, William T. Harvey, Xuefang Zhao, Gianni V. Martino, Mir Henglin, Katherine M. Munson, Keon Rabbani, Chen-Shan Chin, Bida Gu, Hufsah Ashraf, Olanrewaju Austine-Orimoloye, Parithi Balachandran, Marc Jan Bonder, Haoyu Cheng, Zechen Chong, Jonathan Crabtree, Mark Gerstein, Lisbeth A. Guethlein, Patrick Hasenfeld, Glenn Hickey, Kendra Hoekzema, Sarah E. Hunt, Matthew Jensen, Yunzhe Jiang, Sergey Koren, Youngjun Kwon, Chong Li, Heng Li, Jiaqi Li, Paul J. Norman, Keisuke K. Oshima, Benedict Paten, Adam M. Phillippy, Nicholas R Pollock, Tobias Rausch, Mikko Rautiainen, Stephan Scholz, Yuwei Song, Arda Söylev, Arvis Sulovari, Likhitha Surapaneni, Vasiliki Tsapalou, Weichen Zhou, Ying Zhou, Qihui Zhu, Michael C. Zody, Ryan E. Mills, Scott E. Devine, Xinghua Shi, Mike E. Talkowski, Mark J.P. Chaisson, Alexander T. Dilthey, Miriam K. Konkel, Jan O. Korbel, Charles Lee, Christine R. Beck, Evan E. Eichler, and Tobias Marschall

bioRxiv, 2024

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Diverse sets of complete human genomes are required to construct a pangenome reference and to understand the extent of complex structural variation. Here, we sequence 65 diverse human genomes and build 130 haplotype-resolved assemblies (130 Mbp median continuity), closing 92% of all previous assembly gaps1,2 and reaching telomere-to-telomere (T2T) status for 39% of the chromosomes. We highlight complete sequence continuity of complex loci, including the major histocompatibility complex (MHC), SMN1/SMN2, NBPF8, and AMY1/AMY2, and fully resolve 1,852 complex structural variants (SVs). In addition, we completely assemble and validate 1,246 human centromeres. We find up to 30-fold variation in α-satellite high-order repeat (HOR) array length and characterize the pattern of mobile element insertions into α-satellite HOR arrays. While most centromeres predict a single site of kinetochore attachment, epigenetic analysis suggests the presence of two hypomethylated regions for 7% of centromeres. Combining our data with the draft pangenome reference1 significantly enhances genotyping accuracy from short-read data, enabling whole-genome inference3 to a median quality value (QV) of 45. Using this approach, 26,115 SVs per sample are detected, substantially increasing the number of SVs now amenable to downstream disease association studies.

2023

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

Fairlie Reese, Brian Williams, Gabriela Balderrama-Gutierrez, Dana Wyman, Muhammed Hasan Çelik, Elisabeth Rebboah, Narges Rezaie, Diane Trout, Milad Razavi-Mohseni, Yunzhe Jiang, Beatrice Borsari, Samuel Morabito, Heidi Yahan Liang, Cassandra J. McGill, Sorena Rahmanian, Jasmine Sakr, Shan Jiang, Weihua Zeng, Klebea Carvalho, Annika K. Weimer, Louise A. Dionne, Ariel McShane, Karan Bedi, Shaimae I. Elhajjajy, Sean Upchurch, Jennifer Jou, Ingrid Youngworth, Idan Gabdank, Paul Sud, Otto Jolanki, J. Seth Strattan, Meenakshi S. Kagda, Michael P. Snyder, Ben C. Hitz, Jill E. Moore, Zhiping Weng, David Bennett, Laura Reinholdt, Mats Ljungman, Michael A. Beer, Mark B. Gerstein, Lior Pachter, Roderic Guigó, Barbara J. Wold, and Ali Mortazavi

bioRxiv, 2023

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The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3’ end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains.To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3’ processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.Competing Interest StatementThe authors have declared no competing interest.

Peer-reviewed Paper

2024

Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition

Yumeng Zhang, Zhikang Wang, Yunzhe Jiang, Dene R Littler, Mark Gerstein, Anthony W Purcell, Jamie Rossjohn, Hong-Yu Ou, and Jiangning Song

Nature Machine Intelligence, Oct 2024

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Understanding the mechanisms of T cell antigen recognition that underpin adaptive immune responses is critical for developing vaccines, immunotherapies and treatments against autoimmune diseases. Despite extensive research efforts, accurate prediction of T cell receptor (TCR)–antigen binding pairs remains a great challenge due to the vast diversity and cross-reactivity of TCRs. Here we propose a deep-learning-based framework termed epitope-anchored contrastive transfer learning (EPACT) tailored to paired human CD8+ TCRs. Harnessing the pretrained representations and co-embeddings of peptide–major histocompatibility complex (pMHC) and TCR, EPACT demonstrated generalizability in predicting binding specificity for unseen epitopes and distinct TCR repertoires. Contrastive learning enabled highly precise predictions for immunodominant epitopes and interpretable analysis of epitope-specific T cells. We applied EPACT to SARS-CoV-2-responsive T cells, and the predicted binding strength aligned well with the surge in spike-specific immune responses after vaccination. We further fine-tuned EPACT on structural data to decipher the residue-level interactions involved in TCR–antigen recognition. EPACT was capable of quantifying interchain distance matrices and identifying contact residues, corroborating the presence of TCR cross-reactivity across multiple tumour-associated antigens. Together, EPACT can serve as a useful artificial intelligence approach with important potential in practical applications and contribute towards the development of TCR-based immunotherapies.
Single-cell genomics and regulatory networks for 388 human brains

Prashant S. Emani, Jason J. Liu, Declan Clarke, Matthew Jensen, Jonathan Warrell, Chirag Gupta, Ran Meng, Che Yu Lee, Siwei Xu, Cagatay Dursun, Shaoke Lou, Yuhang Chen, Zhiyuan Chu, Timur Galeev, Ahyeon Hwang, Yunyang Li, Pengyu Ni, Xiao Zhou, PsychENCODE Consortium‡, Trygve E. Bakken, Jaroslav Bendl, Lucy Bicks, Tanima Chatterjee, Lijun Cheng, Yuyan Cheng, Yi Dai, Ziheng Duan, Mary Flaherty, John F. Fullard, Michael Gancz, Diego Garrido-Martín, Sophia Gaynor-Gillett, Jennifer Grundman, Natalie Hawken, Ella Henry, Gabriel E. Hoffman, Ao Huang, Yunzhe Jiang, Ting Jin, Nikolas L. Jorstad, Riki Kawaguchi, Saniya Khullar, Jianyin Liu, Junhao Liu, Shuang Liu, Shaojie Ma, Michael Margolis, Samantha Mazariegos, Jill Moore, Jennifer R. Moran, Eric Nguyen, Nishigandha Phalke, Milos Pjanic, Henry Pratt, Diana Quintero, Ananya S. Rajagopalan, Tiernon R. Riesenmy, Nicole Shedd, Manman Shi, Megan Spector, Rosemarie Terwilliger, Kyle J. Travaglini, Brie Wamsley, Gaoyuan Wang, Yan Xia, Shaohua Xiao, Andrew C. Yang, Suchen Zheng, Michael J. Gandal, Donghoon Lee, Ed S. Lein, Panos Roussos, Nenad Sestan, Zhiping Weng, Kevin P. White, Hyejung Won, Matthew J. Girgenti, Jing Zhang, Daifeng Wang, Daniel Geschwind, Mark Gerstein, Schahram Akbarian, Alexej Abyzov, Nadav Ahituv, Dhivya Arasappan, Jose Juan Almagro Armenteros, Brian J. Beliveau, Sabina Berretta, Rahul A. Bharadwaj, Arjun Bhattacharya, Kristen Brennand, Davide Capauto, Frances A. Champagne, Chris Chatzinakos, H. Isaac Chen, Lijun Cheng, Andrew Chess, Jo-fan Chien, Ashley Clement, Leonardo Collado-Torres, Gregory M. Cooper, Gregory E. Crawford, Rujia Dai, Nikolaos P. Daskalakis, Jose Davila-Velderrain, Amy Deep-Soboslay, Chengyu Deng, Christopher P. DiPietro, Stella Dracheva, Shiron Drusinsky, Duc Duong, Nicholas J. Eagles, Jonathan Edelstein, Kiki Galani, Kiran Girdhar, Fernando S. Goes, William Greenleaf, Hanmin Guo, Qiuyu Guo, Yoav Hadas, Joachim Hallmayer, Xikun Han, Vahram Haroutunian, Chuan He, Stephanie C. Hicks, Marcus Ho, Li-Lun Ho, Yiling Huang, Louise A. Huuki-Myers, Thomas M. Hyde, Artemis Iatrou, Fumitaka Inoue, Aarti Jajoo, Lihua Jiang, Peng Jin, Connor Jops, Alexandre Jourdon, Manolis Kellis, Joel E. Kleinman, Steven P. Kleopoulos, Alex Kozlenkov, Arnold Kriegstein, Anshul Kundaje, Soumya Kundu, Junhao Li, Mingfeng Li, Xiao Lin, Shuang Liu, Chunyu Liu, Jacob M. Loupe, Dan Lu, Liang Ma, Jessica Mariani, Keri Martinowich, Kristen R. Maynard, Richard M. Myers, Courtney Micallef, Tatiana Mikhailova, Guo-li Ming, Shahin Mohammadi, Emma Monte, Kelsey S. Montgomery, Eran A. Mukamel, Angus C. Nairn, Charles B. Nemeroff, Scott Norton, Tomasz Nowakowski, Larsson Omberg, Stephanie C. Page, Saejeong Park, Ashok Patowary, Reenal Pattni, Geo Pertea, Mette A. Peters, Dalila Pinto, Sirisha Pochareddy, Katherine S. Pollard, Alex Pollen, Pawel F. Przytycki, Carolin Purmann, Zhaohui S. Qin, Ping-Ping Qu, Towfique Raj, Sarah Reach, Thomas Reimonn, Kerry J. Ressler, Deanna Ross, Joel Rozowsky, Misir Ruth, W. Brad Ruzicka, Stephan J. Sanders, Juliane M. Schneider, Soraya Scuderi, Robert Sebra, Nicholas Seyfried, Zhiping Shao, Annie W. Shieh, Joo Heon Shin, Mario Skarica, Clara Snijders, Hongjun Song, Matthew W. State, Jason Stein, Marilyn Steyert, Sivan Subburaju, Thomas Sudhof, Michael Snyder, Ran Tao, Karen Therrien, Li-Huei Tsai, Alexander E. Urban, Flora M. Vaccarino, Harm Bakel, Daniel Vo, Georgios Voloudakis, Tao Wang, Sidney H. Wang, Yifan Wang, Yu Wei, Annika K. Weimer, Daniel R. Weinberger, Cindy Wen, Sean Whalen, A. Jeremy Willsey, Wing Wong, Hao Wu, Feinan Wu, Stefan Wuchty, Dennis Wylie, Chloe X. Yap, Biao Zeng, Pan Zhang, Chunling Zhang, Bin Zhang, Yanqiong Zhang, Ryan Ziffra, Zane R. Zeier, and Trisha M. Zintel

Science, May 2024

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Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type–specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized 250 disease-risk genes and drug targets with associated cell types.

2023

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Joel Rozowsky, Jiahao Gao, Beatrice Borsari, Yucheng T. Yang, Timur Galeev, Gamze Gürsoy, Charles B. Epstein, Kun Xiong, Jinrui Xu, Tianxiao Li, Jason Liu, Keyang Yu, Ana Berthel, Zhanlin Chen, Fabio Navarro, Maxwell S. Sun, James Wright, Justin Chang, Christopher J.F. Cameron, Noam Shoresh, Elizabeth Gaskell, Jorg Drenkow, Jessika Adrian, Sergey Aganezov, François Aguet, Gabriela Balderrama-Gutierrez, Samridhi Banskota, Guillermo Barreto Corona, Sora Chee, Surya B. Chhetri, Gabriel Conte Cortez Martins, Cassidy Danyko, Carrie A. Davis, Daniel Farid, Nina P. Farrell, Idan Gabdank, Yoel Gofin, David U. Gorkin, Mengting Gu, Vivian Hecht, Benjamin C. Hitz, Robbyn Issner, Yunzhe Jiang, Melanie Kirsche, Xiangmeng Kong, Bonita R. Lam, Shantao Li, Bian Li, Xiqi Li, Khine Zin Lin, Ruibang Luo, Mark Mackiewicz, Ran Meng, Jill E. Moore, Jonathan Mudge, Nicholas Nelson, Chad Nusbaum, Ioann Popov, Henry E. Pratt, Yunjiang Qiu, Srividya Ramakrishnan, Joe Raymond, Leonidas Salichos, Alexandra Scavelli, Jacob M. Schreiber, Fritz J. Sedlazeck, Lei Hoon See, Rachel M. Sherman, Xu Shi, Minyi Shi, Cricket Alicia Sloan, J Seth Strattan, Zhen Tan, Forrest Y. Tanaka, Anna Vlasova, Jun Wang, Jonathan Werner, Brian Williams, Min Xu, Chengfei Yan, Lu Yu, Christopher Zaleski, Jing Zhang, Kristin Ardlie, J Michael Cherry, Eric M. Mendenhall, William S. Noble, Zhiping Weng, Morgan E. Levine, Alexander Dobin, Barbara Wold, Ali Mortazavi, Bing Ren, Jesse Gillis, Richard M. Myers, Michael P. Snyder, Jyoti Choudhary, Aleksandar Milosavljevic, Michael C. Schatz, Bradley E. Bernstein, Roderic Guigó, Thomas R. Gingeras, and Mark Gerstein

Cell, Mar 2023

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Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

2022

GENCODE: reference annotation for the human and mouse genomes in 2023

Adam Frankish, Sílvia Carbonell-Sala, Mark Diekhans, Irwin Jungreis, Jane E Loveland, Jonathan M Mudge, Cristina Sisu, James C Wright, Carme Arnan, If Barnes, Abhimanyu Banerjee, Ruth Bennett, Andrew Berry, Alexandra Bignell, Carles Boix, Ferriol Calvet, Daniel Cerdán-Vélez, Fiona Cunningham, Claire Davidson, Sarah Donaldson, Cagatay Dursun, Reham Fatima, Stefano Giorgetti, Carlos Garcıa Giron, Jose Manuel Gonzalez, Matthew Hardy, Peter W Harrison, Thibaut Hourlier, Zoe Hollis, Toby Hunt, Benjamin James, Yunzhe Jiang, Rory Johnson, Mike Kay, Julien Lagarde, Fergal J Martin, Laura Martínez Gómez, Surag Nair, Pengyu Ni, Fernando Pozo, Vivek Ramalingam, Magali Ruffier, Bianca M Schmitt, Jacob M Schreiber, Emily Steed, Marie-Marthe Suner, Dulika Sumathipala, Irina Sycheva, Barbara Uszczynska-Ratajczak, Elizabeth Wass, Yucheng T Yang, Andrew Yates, Zahoor Zafrulla, Jyoti S Choudhary, Mark Gerstein, Roderic Guigo, Tim J P Hubbard, Manolis Kellis, Anshul Kundaje, Benedict Paten, Michael L Tress, and Paul Flicek

Nucleic Acids Research, Nov 2022

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GENCODE produces high quality gene and transcript annotation for the human and mouse genomes. All GENCODE annotation is supported by experimental data and serves as a reference for genome biology and clinical genomics. The GENCODE consortium generates targeted experimental data, develops bioinformatic tools and carries out analyses that, along with externally produced data and methods, support the identification and annotation of transcript structures and the determination of their function. Here, we present an update on the annotation of human and mouse genes, including developments in the tools, data, analyses and major collaborations which underpin this progress. For example, we report the creation of a set of non-canonical ORFs identified in GENCODE transcripts, the LRGASP collaboration to assess the use of long transcriptomic data to build transcript models, the progress in collaborations with RefSeq and UniProt to increase convergence in the annotation of human and mouse protein-coding genes, the propagation of GENCODE across the human pan-genome and the development of new tools to support annotation of regulatory features by GENCODE. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org.