Nature Biotechnology

John D. Roback, Erika A. Tyburski, David Alter, Saja Asakrah, Ann Chahroudi, Annette Esper, Sarah Farmer, Janet Figueroa, Jennifer K. Frediani, Mark D. Gonzalez, David S. Gottfried, Jeannette Guarner, Nitika A. Gupta, Stacy S. Heilman, Charles E. Hill, Robert Jerris, Russell R. Kempker, Jessica Ingersoll, Joshua M. Levy, Maud Mavigner, Carlos S. Moreno, Claudia R. Morris, Eric J. Nehl, Andrew S. Neish, Deniz Peker, Natia Saakadze, Paulina A. Rebolledo, Christina A. Rostad, Nils Schoof, Allie Suessmith, Julie Sullivan, Yun F. (Wayne) Wang, Anna Wood, Miriam B. Vos, Oliver Brand, Greg S. Martin & Wilbur A. Lam

doi : 10.1038/s41587-021-01047-7

Nature Biotechnology volume 39, pages1060–1062 (2021)

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A Python-based programming language for high-performance computational genomics

Ariya Shajii, Ibrahim Numanagi?, Alexander T. Leighton, Haley Greenyer, Saman Amarasinghe & Bonnie Berger

doi : 10.1038/s41587-021-00985-6

Nature Biotechnology volume 39, pages1062–1064 (2021)

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Utilize existing genetic diversity before genetic modification in indigenous crops

James S. Borrell, Zelalem Gebremariam & Wendawek M. Abebe

doi : 10.1038/s41587-021-01048-6

Nature Biotechnology volume 39, pages1064–1065 (2021)

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MAQC and the era of genomic medicine

Marc Salit & Janet Woodcock

doi : 10.1038/s41587-021-01050-y

Nature Biotechnology volume 39, pages1066–1067 (2021)

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Government targets, end-of-year patenting rush and innovative performance in China

Zhen Sun, Zhen Lei, Brian D. Wright, Mark Cohen & Taoxiong Liu

doi : 10.1038/s41587-021-01035-x

Nature Biotechnology volume 39, pages1068–1075 (2021)

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Organoids

doi : 10.1038/s41587-021-01055-7

Nature Biotechnology volume 39, page1076 (2021)

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First rounders: Anthony Atala

doi : 10.1038/s41587-021-01064-6

Nature Biotechnology volume 39, page1077 (2021)

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Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease

Ro’ee Gilron, Simon Little, Randy Perrone, Robert Wilt, Coralie de Hemptinne, Maria S. Yaroshinsky, Caroline A. Racine, Sarah S. Wang, Jill L. Ostrem, Paul S. Larson, Doris D. Wang, Nick B. Galifianakis, Ian O. Bledsoe, Marta San Luciano, Heather E. Dawes, Gregory A. Worrell, Vaclav Kremen, David A. Borton, Timothy Denison & Philip A. Starr

doi : 10.1038/s41587-021-00897-5

Nature Biotechnology volume 39, pages1078–1085 (2021)

Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson’s disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600?h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.

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Extended-representation bisulfite sequencing of gene regulatory elements in multiplexed samples and single cells

Sarah J. Shareef, Samantha M. Bevill, Ayush T. Raman, Martin J. Aryee, Peter van Galen, Volker Hovestadt & Bradley E. Bernstein

doi : 10.1038/s41587-021-00910-x

Nature Biotechnology volume 39, pages1086–1094 (2021)

The biological roles of DNA methylation have been elucidated by profiling methods based on whole-genome or reduced-representation bisulfite sequencing, but these approaches do not efficiently survey the vast numbers of non-coding regulatory elements in mammalian genomes. Here we present an extended-representation bisulfite sequencing (XRBS) method for targeted profiling of DNA methylation. Our design strikes a balance between expanding coverage of regulatory elements and reproducibly enriching informative CpG dinucleotides in promoters, enhancers and CTCF binding sites. Barcoded DNA fragments are pooled before bisulfite conversion, allowing multiplex processing and technical consistency in low-input samples. Application of XRBS to single leukemia cells enabled us to evaluate genetic copy number variations and methylation variability across individual cells. Our analysis highlights heterochromatic H3K9me3 regions as having the highest cell-to-cell variability in their methylation, likely reflecting inherent epigenetic instability of these late-replicating regions, compounded by differences in cell cycle stages among sampled cells.

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Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

Akira Cortal, Loredana Martignetti, Emmanuelle Six & Antonio Rausell

doi : 10.1038/s41587-021-00896-6

Nature Biotechnology volume 39, pages1095–1102 (2021)

Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package.

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A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples

Wanqiu Chen, Yongmei Zhao, Xin Chen, Zhaowei Yang, Xiaojiang Xu, Yingtao Bi, Vicky Chen, Jing Li, Hannah Choi, Ben Ernest, Bao Tran, Monika Mehta, Parimal Kumar, Andrew Farmer, Alain Mir, Urvashi Ann Mehra, Jian-Liang Li, Malcolm Moos Jr., Wenming Xiao & Charles Wang

doi : 10.1038/s41587-020-00748-9

Nature Biotechnology volume 39, pages1103–1114 (2021)

Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

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Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology

Ira W. Deveson, Binsheng Gong, Kevin Lai, Jennifer S. LoCoco, Todd A. Richmond, Jeoffrey Schageman, Zhihong Zhang, Natalia Novoradovskaya, James C. Willey, Wendell Jones, Rebecca Kusko, Guangchun Chen, Bindu Swapna Madala, James Blackburn, Igor Stevanovski, Ambica Bhandari, Devin Close, Jeffrey Conroy, Michael Hubank, Narasimha Marella, Piotr A. Mieczkowski, Fujun Qiu, Robert Sebra, Daniel Stetson, Lihyun Sun, Philippe Szankasi, Haowen Tan, Lin-ya Tang, Hanane Arib, Hunter Best, Blake Burgher, Pierre R. Bushel, Fergal Casey, Simon Cawley, Chia-Jung Chang, Jonathan Choi, Jorge Dinis, Daniel Duncan, Agda Karina Eterovic, Liang Feng, Abhisek Ghosal, Kristina Giorda, Sean Glenn, Scott Happe, Nathan Haseley, Kyle Horvath, Li-Yuan Hung, Mirna Jarosz, Garima Kushwaha, Dan Li, Quan-Zhen Li, Zhiguang Li, Liang-Chun Liu, Zhichao Liu, Charles Ma, Christopher E. Mason, Dalila B. Megherbi, Tom Morrison, Carlos Pabón-Peña, Mehdi Pirooznia, Paula Z. Proszek, Amelia Raymond, Paul Rindler, Rebecca Ringler, Andreas Scherer, Rita Shaknovich, Tieliu Shi, Melissa Smith, Ping Song, Maya Strahl, Venkat J. Thodima, Nikola Tom, Suman Verma, Jiashi Wang, Leihong Wu, Wenzhong Xiao, Chang Xu, Mary Yang, Guangliang Zhang, Sa Zhang, Yilin Zhang, Leming Shi, Weida Tong, Donald J. Johann Jr, Timothy R. Mercer, Joshua Xu & SEQC2 Oncopanel Sequencing Working Group-Show fewer authors

doi : 10.1038/s41587-021-00857-z

Nature Biotechnology volume 39, pages1115–1128 (2021)

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.

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Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study

Jonathan Foox, Scott W. Tighe, Charles M. Nicolet, Justin M. Zook, Marta Byrska-Bishop, Wayne E. Clarke, Michael M. Khayat, Medhat Mahmoud, Phoebe K. Laaguiby, Zachary T. Herbert, Derek Warner, George S. Grills, Jin Jen, Shawn Levy, Jenny Xiang, Alicia Alonso, Xia Zhao, Wenwei Zhang, Fei Teng, Yonggang Zhao, Haorong Lu, Gary P. Schroth, Giuseppe Narzisi, William Farmerie, Fritz J. Sedlazeck, Don A. Baldwin & Christopher E. Mason

doi : 10.1038/s41587-021-01049-5

Nature Biotechnology volume 39, pages1129–1140 (2021)

Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2?×?250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2?×?250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling.

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Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing

Wenming Xiao, Luyao Ren, Zhong Chen, Li Tai Fang, Yongmei Zhao, Justin Lack, Meijian Guan, Bin Zhu, Erich Jaeger, Liz Kerrigan, Thomas M. Blomquist, Tiffany Hung, Marc Sultan, Kenneth Idler, Charles Lu, Andreas Scherer, Rebecca Kusko, Malcolm Moos, Chunlin Xiao, Stephen T. Sherry, Ogan D. Abaan, Wanqiu Chen, Xin Chen, Jessica Nordlund, Ulrika Liljedahl, Roberta Maestro, Maurizio Polano, Jiri Drabek, Petr Vojta, Sulev Kõks, Ene Reimann, Bindu Swapna Madala, Timothy Mercer, Chris Miller, Howard Jacob, Tiffany Truong, Ali Moshrefi, Aparna Natarajan, Ana Granat, Gary P. Schroth, Rasika Kalamegham, Eric Peters, Virginie Petitjean, Ashley Walton, Tsai-Wei Shen, Keyur Talsania, Cristobal Juan Vera, Kurt Langenbach, Maryellen de Mars, Jennifer A. Hipp, James C. Willey, Jing Wang, Jyoti Shetty, Yuliya Kriga, Arati Raziuddin, Bao Tran, Yuanting Zheng, Ying Yu, Margaret Cam, Parthav Jailwala, Cu Nguyen, Daoud Meerzaman, Qingrong Chen, Chunhua Yan, Ben Ernest, Urvashi Mehra, Roderick V. Jensen, Wendell Jones, Jian-Liang Li, Brian N. Papas, Mehdi Pirooznia, Yun-Ching Chen, Fayaz Seifuddin, Zhipan Li, Xuelu Liu, Wolfgang Resch, Jingya Wang, Leihong Wu, Gokhan Yavas, Corey Miles, Baitang Ning, Weida Tong, Christopher E. Mason, Eric Donaldson, Samir Lababidi, Louis M. Staudt, Zivana Tezak, Huixiao Hong, Charles Wang & Leming Shi

doi : 10.1038/s41587-021-00994-5

Nature Biotechnology volume 39, pages1141–1150 (2021)

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor–normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G?>?T/C?>?A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

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Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

Li Tai Fang, Bin Zhu, Yongmei Zhao, Wanqiu Chen, Zhaowei Yang, Liz Kerrigan, Kurt Langenbach, Maryellen de Mars, Charles Lu, Kenneth Idler, Howard Jacob, Yuanting Zheng, Luyao Ren, Ying Yu, Erich Jaeger, Gary P. Schroth, Ogan D. Abaan, Keyur Talsania, Justin Lack, Tsai-Wei Shen, Zhong Chen, Seta Stanbouly, Bao Tran, Jyoti Shetty, Yuliya Kriga, Daoud Meerzaman, Cu Nguyen, Virginie Petitjean, Marc Sultan, Margaret Cam, Monika Mehta, Tiffany Hung, Eric Peters, Rasika Kalamegham, Sayed Mohammad Ebrahim Sahraeian, Marghoob Mohiyuddin, Yunfei Guo, Lijing Yao, Lei Song, Hugo Y. K. Lam, Jiri Drabek, Petr Vojta, Roberta Maestro, Daniela Gasparotto, Sulev Kõks, Ene Reimann, Andreas Scherer, Jessica Nordlund, Ulrika Liljedahl, Roderick V. Jensen, Mehdi Pirooznia, Zhipan Li, Chunlin Xiao, Stephen T. Sherry, Rebecca Kusko, Malcolm Moos, Eric Donaldson, Zivana Tezak, Baitang Ning, Weida Tong, Jing Li, Penelope Duerken-Hughes, Claudia Catalanotti, Shamoni Maheshwari, Joe Shuga, Winnie S. Liang, Jonathan Keats, Jonathan Adkins, Erica Tassone, Victoria Zismann, Timothy McDaniel, Jeffrey Trent, Jonathan Foox, Daniel Butler, Christopher E. Mason, Huixiao Hong, Leming Shi, Charles Wang, Wenming Xiao & The Somatic Mutation Working Group of Sequencing Quality Control Phase II Consortium

doi : 10.1038/s41587-021-00993-6

Nature Biotechnology volume 39, pages1151–1160 (2021)

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor–normal genomic DNA (gDNA) samples derived from a breast cancer cell line—which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations—and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking ‘tumor-only’ or ‘matched tumor–normal’ analyses.

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People

doi : 10.1038/s41587-021-01054-8

Nature Biotechnology volume 39, page1166 (2021)

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