Genome Biology最新文献

{"title":"ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics.","authors":"Helena García-Castro, Nathan J Kenny, Marta Iglesias, Patricia Álvarez-Campos, Vincent Mason, Anamaria Elek, Anna Schönauer, Victoria A Sleight, Jakke Neiro, Aziz Aboobaker, Jon Permanyer, Manuel Irimia, Arnau Sebé-Pedrós, Jordi Solana","doi":"10.1186/s13059-021-02302-5","DOIUrl":"10.1186/s13059-021-02302-5","url":null,"abstract":"<p><p>Single-cell sequencing technologies are revolutionizing biology, but they are limited by the need to dissociate live samples. Here, we present ACME (ACetic-MEthanol), a dissociation approach for single-cell transcriptomics that simultaneously fixes cells. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, and are sortable and permeable. As a proof of principle, we provide single-cell transcriptomic data of different species, using both droplet-based and combinatorial barcoding single-cell methods. ACME uses affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"89"},"PeriodicalIF":12.3,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25578997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to \"Reproducibility of CRISPR-Cas9 methods for generation of conditional mouse alleles: a multi-center evaluation\".","authors":"Hui Yang,&nbsp;Haoyi Wang,&nbsp;Rudolf Jaenisch","doi":"10.1186/s13059-021-02312-3","DOIUrl":"https://doi.org/10.1186/s13059-021-02312-3","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"98"},"PeriodicalIF":12.3,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02312-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25584562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PCIP-seq: simultaneous sequencing of integrated viral genomes and their insertion sites with long reads.","authors":"Maria Artesi,&nbsp;Vincent Hahaut,&nbsp;Basiel Cole,&nbsp;Laurens Lambrechts,&nbsp;Fereshteh Ashrafi,&nbsp;Ambroise Marçais,&nbsp;Olivier Hermine,&nbsp;Philip Griebel,&nbsp;Natasa Arsic,&nbsp;Frank van der Meer,&nbsp;Arsène Burny,&nbsp;Dominique Bron,&nbsp;Elettra Bianchi,&nbsp;Philippe Delvenne,&nbsp;Vincent Bours,&nbsp;Carole Charlier,&nbsp;Michel Georges,&nbsp;Linos Vandekerckhove,&nbsp;Anne Van den Broeke,&nbsp;Keith Durkin","doi":"10.1186/s13059-021-02307-0","DOIUrl":"10.1186/s13059-021-02307-0","url":null,"abstract":"<p><p>The integration of a viral genome into the host genome has a major impact on the trajectory of the infected cell. Integration location and variation within the associated viral genome can influence both clonal expansion and persistence of infected cells. Methods based on short-read sequencing can identify viral insertion sites, but the sequence of the viral genomes within remains unobserved. We develop PCIP-seq, a method that leverages long reads to identify insertion sites and sequence their associated viral genome. We apply the technique to exogenous retroviruses HTLV-1, BLV, and HIV-1, endogenous retroviruses, and human papillomavirus.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"97"},"PeriodicalIF":12.3,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02307-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25565205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MTSplice predicts effects of genetic variants on tissue-specific splicing.","authors":"Jun Cheng, Muhammed Hasan Çelik, Anshul Kundaje, Julien Gagneur","doi":"10.1186/s13059-021-02273-7","DOIUrl":"10.1186/s13059-021-02273-7","url":null,"abstract":"<p><p>We develop the free and open-source model Multi-tissue Splicing (MTSplice) to predict the effects of genetic variants on splicing of cassette exons in 56 human tissues. MTSplice combines MMSplice, which models constitutive regulatory sequences, with a new neural network that models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting tissue-specific variations associated with genetic variants in most tissues of the GTEx dataset, with largest improvements on brain tissues. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. We foresee that MTSplice will aid interpreting variants associated with tissue-specific disorders.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"94"},"PeriodicalIF":12.3,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25534806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Homopolish: a method for the removal of systematic errors in nanopore sequencing by homologous polishing.","authors":"Yao-Ting Huang,&nbsp;Po-Yu Liu,&nbsp;Pei-Wen Shih","doi":"10.1186/s13059-021-02282-6","DOIUrl":"https://doi.org/10.1186/s13059-021-02282-6","url":null,"abstract":"<p><p>Nanopore sequencing has been widely used for the reconstruction of microbial genomes. Owing to higher error rates, errors on the genome are corrected via neural networks trained by Nanopore reads. However, the systematic errors usually remain uncorrected. This paper designs a model that is trained by homologous sequences for the correction of Nanopore systematic errors. The developed program, Homopolish, outperforms Medaka and HELEN in bacteria, viruses, fungi, and metagenomic datasets. When combined with Medaka/HELEN, the genome quality can exceed Q50 on R9.4 flow cells. We show that Nanopore-only sequencing can produce high-quality microbial genomes sufficient for downstream analysis.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"95"},"PeriodicalIF":12.3,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02282-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25535183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.","authors":"Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller","doi":"10.1186/s13059-021-02306-1","DOIUrl":"10.1186/s13059-021-02306-1","url":null,"abstract":"<p><p>The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"93"},"PeriodicalIF":12.3,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8008609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25531630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive identification of somatic nucleotide variants in human brain tissue.","authors":"Yifan Wang, Taejeong Bae, Jeremy Thorpe, Maxwell A Sherman, Attila G Jones, Sean Cho, Kenneth Daily, Yanmei Dou, Javier Ganz, Alon Galor, Irene Lobon, Reenal Pattni, Chaggai Rosenbluh, Simone Tomasi, Livia Tomasini, Xiaoxu Yang, Bo Zhou, Schahram Akbarian, Laurel L Ball, Sara Bizzotto, Sarah B Emery, Ryan Doan, Liana Fasching, Yeongjun Jang, David Juan, Esther Lizano, Lovelace J Luquette, John B Moldovan, Rujuta Narurkar, Matthew T Oetjens, Rachel E Rodin, Shobana Sekar, Joo Heon Shin, Eduardo Soriano, Richard E Straub, Weichen Zhou, Andrew Chess, Joseph G Gleeson, Tomas Marquès-Bonet, Peter J Park, Mette A Peters, Jonathan Pevsner, Christopher A Walsh, Daniel R Weinberger, Flora M Vaccarino, John V Moran, Alexander E Urban, Jeffrey M Kidd, Ryan E Mills, Alexej Abyzov","doi":"10.1186/s13059-021-02285-3","DOIUrl":"10.1186/s13059-021-02285-3","url":null,"abstract":"<p><strong>Background: </strong>Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells.</p><p><strong>Results: </strong>Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees.</p><p><strong>Conclusions: </strong>This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"92"},"PeriodicalIF":12.3,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25539414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing genomics to understand and respond to global climate change.","authors":"Justin Borevitz","doi":"10.1186/s13059-021-02317-y","DOIUrl":"https://doi.org/10.1186/s13059-021-02317-y","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"91"},"PeriodicalIF":12.3,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02317-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25542602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Author Correction: CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies.","authors":"Rebecca Davies,&nbsp;Ling Liu,&nbsp;Sheng Taotao,&nbsp;Natasha Tuano,&nbsp;Richa Chaturvedi,&nbsp;Kie Kyon Huang,&nbsp;Catherine Itman,&nbsp;Amit Mandoli,&nbsp;Aditi Qamra,&nbsp;Changyuan Hu,&nbsp;David Powell,&nbsp;Roger J Daly,&nbsp;Patrick Tan,&nbsp;Joseph Rosenbluh","doi":"10.1186/s13059-021-02314-1","DOIUrl":"https://doi.org/10.1186/s13059-021-02314-1","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"88"},"PeriodicalIF":12.3,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02314-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25516105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling the cartography of the cancer ecosystem.","authors":"Roy Rabbie, Doreen Lau, Richard M White, David J Adams","doi":"10.1186/s13059-021-02310-5","DOIUrl":"10.1186/s13059-021-02310-5","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"87"},"PeriodicalIF":12.3,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25513299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}