The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.
{"title":"iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks.","authors":"Dongfang Wang, Siyu Hou, Lei Zhang, Xiliang Wang, Baolin Liu, Zemin Zhang","doi":"10.1186/s13059-021-02280-8","DOIUrl":"10.1186/s13059-021-02280-8","url":null,"abstract":"<p><p>The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"63"},"PeriodicalIF":12.3,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25381784","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}
Pub Date : 2021-02-18DOI: 10.1186/s13059-021-02260-y
Maša Roller, Ericca Stamper, Diego Villar, Osagie Izuogu, Fergal Martin, Aisling M Redmond, Raghavendra Ramachanderan, Louise Harewood, Duncan T Odom, Paul Flicek
Background: To investigate the mechanisms driving regulatory evolution across tissues, we experimentally mapped promoters, enhancers, and gene expression in the liver, brain, muscle, and testis from ten diverse mammals.
Results: The regulatory landscape around genes included both tissue-shared and tissue-specific regulatory regions, where tissue-specific promoters and enhancers evolved most rapidly. Genomic regions switching between promoters and enhancers were more common across species, and less common across tissues within a single species. Long Interspersed Nuclear Elements (LINEs) played recurrent evolutionary roles: LINE L1s were associated with tissue-specific regulatory regions, whereas more ancient LINE L2s were associated with tissue-shared regulatory regions and with those switching between promoter and enhancer signatures across species.
Conclusions: Our analyses of the tissue-specificity and evolutionary stability among promoters and enhancers reveal how specific LINE families have helped shape the dynamic mammalian regulome.
{"title":"LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions.","authors":"Maša Roller, Ericca Stamper, Diego Villar, Osagie Izuogu, Fergal Martin, Aisling M Redmond, Raghavendra Ramachanderan, Louise Harewood, Duncan T Odom, Paul Flicek","doi":"10.1186/s13059-021-02260-y","DOIUrl":"10.1186/s13059-021-02260-y","url":null,"abstract":"<p><strong>Background: </strong>To investigate the mechanisms driving regulatory evolution across tissues, we experimentally mapped promoters, enhancers, and gene expression in the liver, brain, muscle, and testis from ten diverse mammals.</p><p><strong>Results: </strong>The regulatory landscape around genes included both tissue-shared and tissue-specific regulatory regions, where tissue-specific promoters and enhancers evolved most rapidly. Genomic regions switching between promoters and enhancers were more common across species, and less common across tissues within a single species. Long Interspersed Nuclear Elements (LINEs) played recurrent evolutionary roles: LINE L1s were associated with tissue-specific regulatory regions, whereas more ancient LINE L2s were associated with tissue-shared regulatory regions and with those switching between promoter and enhancer signatures across species.</p><p><strong>Conclusions: </strong>Our analyses of the tissue-specificity and evolutionary stability among promoters and enhancers reveal how specific LINE families have helped shape the dynamic mammalian regulome.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"62"},"PeriodicalIF":12.3,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25381713","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}
Pub Date : 2021-02-12DOI: 10.1186/s13059-021-02284-4
Laura Glendinning, Robert D Stewart, Mark J Pallen, Kellie A Watson, Mick Watson
{"title":"Author Correction: Assembly of hundreds of novel bacterial genomes from the chicken caecum.","authors":"Laura Glendinning, Robert D Stewart, Mark J Pallen, Kellie A Watson, Mick Watson","doi":"10.1186/s13059-021-02284-4","DOIUrl":"https://doi.org/10.1186/s13059-021-02284-4","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"60"},"PeriodicalIF":12.3,"publicationDate":"2021-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02284-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25363796","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}
Pub Date : 2021-02-10DOI: 10.1186/s13059-021-02276-4
Teppo Felin, Jan Koenderink, Joachim I Krueger, Denis Noble, George F R Ellis
{"title":"The data-hypothesis relationship.","authors":"Teppo Felin, Jan Koenderink, Joachim I Krueger, Denis Noble, George F R Ellis","doi":"10.1186/s13059-021-02276-4","DOIUrl":"https://doi.org/10.1186/s13059-021-02276-4","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"57"},"PeriodicalIF":12.3,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02276-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25354471","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}
Pub Date : 2021-02-10DOI: 10.1186/s13059-021-02278-2
Teppo Felin, Jan Koenderink, Joachim I Krueger, Denis Noble, George F R Ellis
{"title":"Data bias.","authors":"Teppo Felin, Jan Koenderink, Joachim I Krueger, Denis Noble, George F R Ellis","doi":"10.1186/s13059-021-02278-2","DOIUrl":"https://doi.org/10.1186/s13059-021-02278-2","url":null,"abstract":"","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"59"},"PeriodicalIF":12.3,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02278-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25357142","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}
Pub Date : 2021-02-04DOI: 10.1186/s13059-020-02227-5
Massimo Cavallaro, Mark D Walsh, Matt Jones, James Teahan, Simone Tiberi, Bärbel Finkenstädt, Daniel Hebenstreit
Background: Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 ' and 5 ' ends of a gene that enable reinitiation of transcription upon termination.
Results: Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment.
Conclusions: Interactions between the 3 ' and 5 ' ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.
{"title":"3 <sup>'</sup>-5 <sup>'</sup> crosstalk contributes to transcriptional bursting.","authors":"Massimo Cavallaro, Mark D Walsh, Matt Jones, James Teahan, Simone Tiberi, Bärbel Finkenstädt, Daniel Hebenstreit","doi":"10.1186/s13059-020-02227-5","DOIUrl":"10.1186/s13059-020-02227-5","url":null,"abstract":"<p><strong>Background: </strong>Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 <sup>'</sup> and 5 <sup>'</sup> ends of a gene that enable reinitiation of transcription upon termination.</p><p><strong>Results: </strong>Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment.</p><p><strong>Conclusions: </strong>Interactions between the 3 <sup>'</sup> and 5 <sup>'</sup> ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"56"},"PeriodicalIF":12.3,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25332581","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}
Pub Date : 2021-02-02DOI: 10.1186/s13059-021-02264-8
Robert Ietswaart, Benjamin M Gyori, John A Bachman, Peter K Sorger, L Stirling Churchman
A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.
高通量功能基因组学实验的一个瓶颈是从基因命中列表中识别出最重要的基因及其相关功能。基因本体(GO)富集方法提供了基因组水平的洞察力。在这里,我们介绍 GeneWalk(github.com/churchmanlab/genewalk),它能识别对实验环境至关重要的单个基因及其相关功能。在自动组装特定于实验的基因调控网络之后,GeneWalk 利用表征学习量化每个基因的向量表征与其 GO 注释之间的相似性,从而得出反映实验背景的注释意义分数。通过进行特定基因和条件的功能分析,GeneWalk将基因列表转化为数据驱动的假设。
{"title":"GeneWalk identifies relevant gene functions for a biological context using network representation learning.","authors":"Robert Ietswaart, Benjamin M Gyori, John A Bachman, Peter K Sorger, L Stirling Churchman","doi":"10.1186/s13059-021-02264-8","DOIUrl":"10.1186/s13059-021-02264-8","url":null,"abstract":"<p><p>A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"55"},"PeriodicalIF":12.3,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25320919","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}
Pub Date : 2021-01-29DOI: 10.1186/s13059-021-02272-8
Li Li, Alejandro P Ugalde, Colinda L G J Scheele, Sebastian M Dieter, Remco Nagel, Jin Ma, Abhijeet Pataskar, Gozde Korkmaz, Ran Elkon, Miao-Ping Chien, Li You, Pin-Rui Su, Onno B Bleijerveld, Maarten Altelaar, Lyubomir Momchev, Zohar Manber, Ruiqi Han, Pieter C van Breugel, Rui Lopes, Peter Ten Dijke, Jacco van Rheenen, Reuven Agami
Background: Frequent activation of the co-transcriptional factor YAP is observed in a large number of solid tumors. Activated YAP associates with enhancer loci via TEAD4-DNA-binding protein and stimulates cancer aggressiveness. Although thousands of YAP/TEAD4 binding-sites are annotated, their functional importance is unknown. Here, we aim at further identification of enhancer elements that are required for YAP functions.
Results: We first apply genome-wide ChIP profiling of YAP to systematically identify enhancers that are bound by YAP/TEAD4. Next, we implement a genetic approach to uncover functions of YAP/TEAD4-associated enhancers, demonstrate its robustness, and use it to reveal a network of enhancers required for YAP-mediated proliferation. We focus on EnhancerTRAM2, as its target gene TRAM2 shows the strongest expression-correlation with YAP activity in nearly all tumor types. Interestingly, TRAM2 phenocopies the YAP-induced cell proliferation, migration, and invasion phenotypes and correlates with poor patient survival. Mechanistically, we identify FSTL-1 as a major direct client of TRAM2 that is involved in these phenotypes. Thus, TRAM2 is a key novel mediator of YAP-induced oncogenic proliferation and cellular invasiveness.
Conclusions: YAP is a transcription co-factor that binds to thousands of enhancer loci and stimulates tumor aggressiveness. Using unbiased functional approaches, we dissect YAP enhancer network and characterize TRAM2 as a novel mediator of cellular proliferation, migration, and invasion. Our findings elucidate how YAP induces cancer aggressiveness and may assist diagnosis of cancer metastasis.
{"title":"A comprehensive enhancer screen identifies TRAM2 as a key and novel mediator of YAP oncogenesis.","authors":"Li Li, Alejandro P Ugalde, Colinda L G J Scheele, Sebastian M Dieter, Remco Nagel, Jin Ma, Abhijeet Pataskar, Gozde Korkmaz, Ran Elkon, Miao-Ping Chien, Li You, Pin-Rui Su, Onno B Bleijerveld, Maarten Altelaar, Lyubomir Momchev, Zohar Manber, Ruiqi Han, Pieter C van Breugel, Rui Lopes, Peter Ten Dijke, Jacco van Rheenen, Reuven Agami","doi":"10.1186/s13059-021-02272-8","DOIUrl":"10.1186/s13059-021-02272-8","url":null,"abstract":"<p><strong>Background: </strong>Frequent activation of the co-transcriptional factor YAP is observed in a large number of solid tumors. Activated YAP associates with enhancer loci via TEAD4-DNA-binding protein and stimulates cancer aggressiveness. Although thousands of YAP/TEAD4 binding-sites are annotated, their functional importance is unknown. Here, we aim at further identification of enhancer elements that are required for YAP functions.</p><p><strong>Results: </strong>We first apply genome-wide ChIP profiling of YAP to systematically identify enhancers that are bound by YAP/TEAD4. Next, we implement a genetic approach to uncover functions of YAP/TEAD4-associated enhancers, demonstrate its robustness, and use it to reveal a network of enhancers required for YAP-mediated proliferation. We focus on Enhancer<sup>TRAM2</sup>, as its target gene TRAM2 shows the strongest expression-correlation with YAP activity in nearly all tumor types. Interestingly, TRAM2 phenocopies the YAP-induced cell proliferation, migration, and invasion phenotypes and correlates with poor patient survival. Mechanistically, we identify FSTL-1 as a major direct client of TRAM2 that is involved in these phenotypes. Thus, TRAM2 is a key novel mediator of YAP-induced oncogenic proliferation and cellular invasiveness.</p><p><strong>Conclusions: </strong>YAP is a transcription co-factor that binds to thousands of enhancer loci and stimulates tumor aggressiveness. Using unbiased functional approaches, we dissect YAP enhancer network and characterize TRAM2 as a novel mediator of cellular proliferation, migration, and invasion. Our findings elucidate how YAP induces cancer aggressiveness and may assist diagnosis of cancer metastasis.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"54"},"PeriodicalIF":12.3,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-021-02272-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25310983","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}
Pub Date : 2021-01-29DOI: 10.1186/s13059-020-02257-z
William G Pembroke, Christopher L Hartl, Daniel H Geschwind
Background: Mouse models have allowed for the direct interrogation of genetic effects on molecular, physiological, and behavioral brain phenotypes. However, it is unknown to what extent neurological or psychiatric traits may be human- or primate-specific and therefore which components can be faithfully recapitulated in mouse models.
Results: We compare conservation of co-expression in 116 independent data sets derived from human, mouse, and non-human primate representing more than 15,000 total samples. We observe greater changes occurring on the human lineage than mouse, and substantial regional variation that highlights cerebral cortex as the most diverged region. Glia, notably microglia, astrocytes, and oligodendrocytes are the most divergent cell type, three times more on average than neurons. We show that cis-regulatory sequence divergence explains a significant fraction of co-expression divergence. Moreover, protein coding sequence constraint parallels co-expression conservation, such that genes with loss of function intolerance are enriched in neuronal, rather than glial modules. We identify dozens of human neuropsychiatric and neurodegenerative disease risk genes, such as COMT, PSEN-1, LRRK2, SHANK3, and SNCA, with highly divergent co-expression between mouse and human and show that 3D human brain organoids recapitulate in vivo co-expression modules representing several human cell types.
Conclusions: We identify robust co-expression modules reflecting whole-brain and regional patterns of gene expression. Compared with those that represent basic metabolic processes, cell-type-specific modules, most prominently glial modules, are the most divergent between species. These data and analyses serve as a foundational resource to guide human disease modeling and its interpretation.
{"title":"Evolutionary conservation and divergence of the human brain transcriptome.","authors":"William G Pembroke, Christopher L Hartl, Daniel H Geschwind","doi":"10.1186/s13059-020-02257-z","DOIUrl":"https://doi.org/10.1186/s13059-020-02257-z","url":null,"abstract":"<p><strong>Background: </strong>Mouse models have allowed for the direct interrogation of genetic effects on molecular, physiological, and behavioral brain phenotypes. However, it is unknown to what extent neurological or psychiatric traits may be human- or primate-specific and therefore which components can be faithfully recapitulated in mouse models.</p><p><strong>Results: </strong>We compare conservation of co-expression in 116 independent data sets derived from human, mouse, and non-human primate representing more than 15,000 total samples. We observe greater changes occurring on the human lineage than mouse, and substantial regional variation that highlights cerebral cortex as the most diverged region. Glia, notably microglia, astrocytes, and oligodendrocytes are the most divergent cell type, three times more on average than neurons. We show that cis-regulatory sequence divergence explains a significant fraction of co-expression divergence. Moreover, protein coding sequence constraint parallels co-expression conservation, such that genes with loss of function intolerance are enriched in neuronal, rather than glial modules. We identify dozens of human neuropsychiatric and neurodegenerative disease risk genes, such as COMT, PSEN-1, LRRK2, SHANK3, and SNCA, with highly divergent co-expression between mouse and human and show that 3D human brain organoids recapitulate in vivo co-expression modules representing several human cell types.</p><p><strong>Conclusions: </strong>We identify robust co-expression modules reflecting whole-brain and regional patterns of gene expression. Compared with those that represent basic metabolic processes, cell-type-specific modules, most prominently glial modules, are the most divergent between species. These data and analyses serve as a foundational resource to guide human disease modeling and its interpretation.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"22 1","pages":"52"},"PeriodicalIF":12.3,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-020-02257-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25311054","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}