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The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.

single-cell sequencing methods have enabled the profiling of multiple types of molecular readouts at cellular resolution, and recent developments in spatial barcoding, in situ hybridization, and in situ sequencing allow such molecular readouts to retain their spatial context. Since no technology can provide complete characterization across all layers of biological modalities within the same cell, there is pervasive need for computational cross-modal integration (also called diagonal integration) of single-cell and spatial omics data. For current methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori “linked” features. When such linked features are few or uninformative, a scenario that we call “weak linkage”, existing methods fail. We developed MaxFuse, a cross-modal data integration method that, through iterative co-embedding, data smoothing, and cell matching, leverages all information in each modality to obtain high-quality integration. MaxFuse is modality-agnostic and, through comprehensive benchmarks on single-cell and spatial ground-truth multiome datasets, demonstrates high robustness and accuracy in the weak linkage scenario. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, we demonstrate how MaxFuse enables the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Congenital heart diseases (CHD) are a class of birth defects affecting ∼1% of live births. These conditions are hallmarked by extreme genetic heterogeneity, and therefore, despite a strong genetic component, only a very handful of at-risk loci in CHD have been identified. We herein introduced systems analyses to uncover the hidden organization on biological networks of genomic mutations in CHD, and leveraged network analysis techniques to integrate the human interactome, large-scale patient exomes, the fetal heart spatial transcriptomes, and single-cell transcriptomes of clinical samples. We identified a highly connected network in CHD where most of the member proteins had previously uncharacterized functions in regulating fetal heart development. While genes on the network displayed strong enrichment for heart-specific functions, a sub-group, active specifically at early developmental stages, also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHD and neurodevelopmental conditions. At a small scale, we experimentally verified previously uncharacterized cardiac functions of several novel proteins employing cellular assays and gene editing techniques. At a global scale, our study revealed developmental dynamics of the identified CHD network and observed the strongest enrichment for pathogenic mutations in the network specific to hypoplastic left heart syndrome (HLHS). Our single-cell transcriptome analysis further identified pervasive dysregulation of the network in cardiac endothelial cells and the conduction system in the HLHS left ventricle. Taken together, our systems analyses identified novel factors in CHD, revealed key molecular mechanisms in HLHS, and provides a generalizable framework readily applicable to studying many other complex diseases.

In this Article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online.

In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article.

Diet management is key to managing chronic diseases such as diabetes. Automated food recommender systems may be able to assist by providing meal recommendations that conform to a user's nutrition goals and food preferences. Current recommendation systems suffer from a lack of accuracy that is in part due to a lack of knowledge of food preferences, namely foods users like to and are able to eat frequently. In this work, we propose a method for learning food preferences from food logs, a comprehensive but noisy source of information about users' dietary habits. We also introduce accompanying metrics. The method generates and compares word embeddings to identify the parent food category of each food entry and then calculates the most popular. Our proposed approach identifies 82% of a user's ten most frequently eaten foods. Our method is publicly available on (https://github.com/aametwally/LearningFoodPreferences)

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here, we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.

ObjectivesWearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.Materials and methodsComparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.ResultsAt 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult's and teen's sleeping and step counts were correlated, possibly due to coordinated behaviors.DiscussionWe end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.ConclusionWith periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.

Additional file 2: Fig. S2. Metabolite Ratio Correlations. A: Correlations between the logarithm base two of metabolite ratios.

Additional file 5: Fig. S5. Remaining F2 and Parental Distributions of Metabolite Levels. All Metabolite Levels Scaled by the Mean of the RM metabolite levels. A: Histogram of F2 segregants’ distribution of Squalene levels, with three biological replicate measurements of each of the parental strains (BY red, RM blue). B: Histogram of F2 segregants’ distribution of Lanosterol levels, with three biological replicate measurements of each of the parental strains (BY red, RM blue). C: Histogram of F2 segregants’ distribution of DCT levels, with three biological replicate measurements of each of the parental strains (BY red, RM blue). D: Histogram of F2 segregants’ distribution of CDO levels, with three biological replicate measurements of each of the parental strains (BY red, RM blue). E: Histogram of F2 segregants’ distribution of Ergosterol levels, with three biological replicate measurements of each of the parental strains (BY red, RM blue).

Additional file 1: Table S1. Benchmarking metrics on SNPs and multiple nucleotide polymorphisms (MNPs) in NA24149 (truth set NIST v3.3) for the RefSeq coding exons regions generated for both the GATK and SpeedSeq pipelines that were executed using workflows run by Loom (in-house workflow engine).

Additional file 17: Fig S1. InDel size distribution histograms for NA24385 as generated by the benchmarking workflow for the coding exons of ~7000 clinically relevant genes and whole exome regions (as specified in Methods).

Additional file 11: Table S11. Benchmarking metrics on the number of InDels of different size ranges in NA24143 (truth set NIST v3.3, total bases = 65657646) for the whole exome regions including non-coding exons, splice sites (+/- 20 bp) and clinically relevant deep intronic regions.

Additional file 1: Fig. S1. Technical Replicate Correlations for Remaining Metabolites, Scaled By All Segregants’ Mean Metabolite Levels. A Between technical replicate pearson correlation of squalene level for 73 F2 segregants with technical replicates. B: Between technical replicate pearson correlation of epoxysqualene level for 73 F2 segregants with technical replicates. C: Between technical replicate pearson correlation of lanosterol level for 73 F2 segregants with technical replicates. D: Between technical replicate pearson correlation of DCT level for 73 F2 segregants with technical replicates. E: Between technical replicate pearson correlation of DCD level for 73 F2 segregants with technical replicates. F: Between technical replicate pearson correlation of CDO level for 73 F2 segregants with technical replicates. G: Between technical replicate pearson correlation of Zymosterol level for 73 F2 segregants with technical replicates.

Additional file 10: Table S10. Benchmarking metrics on the number of InDels of different size ranges in NA12878 (truth set NIST v3.3, total bases = 71152019) for the whole exome regions including non–coding exons, splice sites (+/- 20 bp) and clinically relevant deep intronic regions.

Additional file 13: Table S13. Benchmarking metrics on the number of InDels of different size ranges in NA24631 (truth set NIST v3.3, total bases = 65657646) for the whole exome regions including non-coding exons, splice sites (+/- 20 bp) and clinically relevant deep intronic regions.

Additional file 3: Table S3. Benchmarking metrics on SNPs and multiple nucleotide polymorphisms (MNPs) in NA24143 (truth set NIST v3.3) for the RefSeq coding exons regions generated for both the GATK and SpeedSeq pipelines that were executed using workflows run by Loom (in-house workflow engine).

Additional file 2: Table S2. Benchmarking metrics on InDels in NA24149 (truth set NIST v3.3) for the RefSeq coding exons regions generated for both the GATK and SpeedSeq pipelines that were executed using workflows run by Loom (in-house workflow engine).

Additional file 3: Fig. S3. Overlap Between eQTLs and metabolite QTLs. A: Venn Diagram showing the overlap between eQTLs mapped for genes within the ergosterol bionsynthesis pathway and metabolite QTLs. B: Distribution of the number of permuted metabolite QTLs out of nineteen possible, overlapping ergosterol pathway eQTLs from 1000 permutations. Values greater than or equal to the true overlap from the data are in orange.

Additional file 3: Figure S3. Transcript and UTR lengths of the host (C. aethiops) (a) Length of transcripts in the uninfected and each p.i. samples. (b, c) Length of the 5’ and 3’ UTRs were calculated using ti.py in uninfected and each p.i. samples. Letter ‘M’ following the sample name indicates MinION sequencing, and the letter ‘S’ indicates Sequel sequencing. Horizontal lines in the box plots represent median transcript length of the given samples. Transcripts were annotated using the LoRTIA software suit.

Additional file 4: Table S4. Benchmarking metrics on InDels in NA24143 (truth set NIST v3.3) for the RefSeq coding exons regions generated for both the GATK and SpeedSeq pipelines that were executed using workflows run by Loom (in-house workflow engine).

Additional file 12: Table S12. Benchmarking metrics on the number of InDels of different size ranges in NA24149 (truth set NIST v3.3, total bases = 65597266) for the whole exome regions including non-coding exons, splice sites (+/- 20 bp) and clinically relevant deep intronic regions.

Additional file 4: Fig. S4. The transcript isoform categories used in this study and their abbreviations.

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space., Comment: 28 pages, 4 figures

Additional file 2: Figure S2. The vicinity of the host TESs. (a) Nucleotide distribution surrounding the TESs of Chlorocebus aethiops were visualized using WebLogo showing canonical sequences signaling RNA cleavage and polyadenylation. (b) The distance of the host’s polyadenylation signals from the TESs in the uninfected and the p.i. sample. The letter ‘M’ following the sample name indicates MinION sequencing, while letter ‘S’ Sequel sequencing. The horizontal lines in the box plots represent the median distance for the given sample. No significant change in the distance of TESs were observed during the viral infection. The TES positions were determined using the LoRTIA toolkit.

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidate cis-regulatory elements (cCREs) that may serve functional roles in regulating gene expression1. The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.

Exercise provides a robust physiological stimulus that evokes cross-talk among multiple tissues that when repeated regularly (i.e., training) improves physiological capacity, benefits numerous organ systems, and decreases the risk for premature mortality. However, a gap remains in identifying the detailed molecular signals induced by exercise that benefits health and prevents disease. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this gap and generate a molecular map of exercise. Preclinical and clinical studies will examine the systemic effects of endurance and resistance exercise across a range of ages and fitness levels by molecular probing of multiple tissues before and after acute and chronic exercise. From this multi-omic and bioinformatic analysis, a molecular map of exercise will be established. Altogether, MoTrPAC will provide a public database that is expected to enhance our understanding of the health benefits of exercise and to provide insight into how physical activity mitigates disease.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

The disease burden associated with air pollution continues to grow. The World Health Organization (WHO) estimates ≈7million people worldwide die yearly from exposure to polluted air, half of which-3.3million-are attributable to cardiovascular disease (CVD), greater than from major modifiable CVD risks including smoking, hypertension, hyperlipidemia, and diabetes mellitus. This serious and growing health threat is attributed to increasing urbanization of the world's populations with consequent exposure to polluted air. Especially vulnerable are the elderly, patients with pre-existing CVD, and children. The cumulative lifetime burden in children is particularly of concern because their rapidly developing cardiopulmonary systems are more susceptible to damage and they spend more time outdoors and therefore inhale more pollutants. World Health Organization estimates that 93% of the world's children aged

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020–01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the “missing proteins” (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019–01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020–01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases. G.S.O. acknowledges support from National Institutes of Health grants P30ES017885-01A1 and U24CA210967; E.W.D. from National Institutes of Health grants R01GM087221, R24GM127667, U19AG023122, and from National Science Foundation grant DBI-1933311; L.L. and neXtProt from the SIB Swiss Institute of Bioinformatics; C.M.O. by Canadian Institutes of Health Research Foundation Grant 148408 and a Canada Research Chair in Protease Proteomics and Systems Biology; M.S.B. by NHMRC Project GrantAPP1010303; C.L. by the Knut and Alice Wallenberg Foundation for the Human Protein Atlas; and Y.-K.P. by grants from the Korean Ministry of Health and Welfare HI13C22098 and HI16C0257.

Presentation of poster 1150A at TAGC 2020 Online. Files include a PDF of the poster: TAGC_2020_Rajaei.PDF

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.

Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.

During infection, Legionella pneumophila translocates over 300 effector proteins into the host cytosol, allowing the pathogen to establish an endoplasmic reticulum (ER)-like Legionella-containing vacuole (LCV) that supports bacterial replication. Here, we perform a genome-wide CRISPR-Cas9 screen and secondary targeted screens in U937 human monocyte/macrophage-like cells to systematically identify host factorsthat regulate killing by L.pneumophila. The screens reveal known host factors hijacked by L.pneumophila, as well as genes spanning diverse trafficking and signaling pathways previously not linked to L.pneumophila pathogenesis. We further characterize C1orf43 and KIAA1109 as regulators ofphagocytosis and show that RAB10 and its chaperone RABIF are required for optimal L.pneumophila replication and ER recruitment to the LCV. Finally, we show that Rab10 protein is recruited to the LCV and ubiquitinated by the effectors SidC/SdcA. Collectively, our results provide a wealth of previously undescribed insights into L.pneumophila pathogenesis and mammalian cell function.

SUMMARY Human neural stem cell cultures provide progenitor cells that are potential cells of origin for brain cancers. However, the extent to which genetic predisposition to tumor formation can be faithfully captured in stem cell lines is uncertain. Here, we evaluated neuroepithelial stem (NES) cells, representative of cerebellar progenitors. We transduced NES cells with MYCN, observing medulloblastoma upon orthotopic implantation in mice. Significantly, transcriptomes and patterns of DNA methylation from xenograft tumors were globally more representative of human medulloblastoma compared to a MYCN-driven genetically engineered mouse model. Orthotopic transplantation of NES cells generated from Gorlin syndrome patients, who are predisposed to medulloblastoma due to germline-mutated PTCH1, also generated medulloblastoma. We engineered candidate cooperating mutations in Gorlin NES cells, with mutation of DDX3X or loss of GSE1 both accelerating tumorigenesis. These findings demonstrate that human NES cells provide a potent experimental resource for dissecting genetic causation in medulloblastoma., Graphical Abstract, In Brief Huang, Tailor, et al. show that neuroepithelial stem (NES) cells derived from normal induced pluripotent stem cells (iPSCs) provide a renewable humancell-based resource to evaluate genetic mutations in medulloblastoma. Misexpression of MYCN in both otherwise-normal NES cells and PTCH1+/− NES cells derived from patients with Gorlin syndrome each generated medulloblastoma in mice.

The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019–01–11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the “missing proteins” (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research. G.S.O. acknowledges grant support from National Institutes of Health grants P30ES017885-01A1 and U24CA210967; E.W.D. from National Institutes of Health grants, R01GM087221, R24GM127667, U54EB020406, and the U19AG023122; L.L. and neXtProt from the SIB Swiss Institute of Bioinformatics; C.M.O. by a Canadian Institutes of Health Research Foundation Grant and a Canada Research Chair in Protease Proteomics and Systems Biology; M.S.B. by NHMRC Project Grant APP1010303; J.M.S. by the Knut and Alice Wallenberg Foundation for the Human Protein Atlas; and Y.-K.P. by grants from the Korean Ministry of Health and Welfare HI13C22098 and HI16C0257.

Additional file 2: Figure S2. Overview of the analysis. (a) A summary of the pipeline. Specific steps are explained in detail in boxes. (b) Identifying potential poly(A) sites. The criteria used by the LoRTIA toolkit to detect poly(A) sites are listed. The number of polyadenylated reads ending at a given nucleotide had to be at least two and at least 0.1% of the overlapping reads for the site to be considered a potential poly(A) site. If multiple sites fulfilled these criteria in a 21-nt window, the site with the highest number of supporting reads was accepted. (c) The algorithm for determining the adenine content of a region. The counting started from the poly(A) site, and each A increased, while every different letter decreased the counter by one. The counting continued until maximum 20 nucleotides or until the counter reached − 1. The highest value of the counter was regarded as the adenine content. (d) Criteria for characterizing a potential poly(A) site as a TES. One criterion was that the number of reads ending in not A-rich regions (less than three As) in a 21-nt window, had to be higher than the number of reads ending in A-rich regions (three or more As). The second criterion was that the number of supporting reads had to be higher than the percentage of overlapping reads calculated by a logistic function that takes into consideration the adenine content of the region. A potential poly(A) site was classified as a TES if any of the two criteria was fulfilled.

Figure S1. Plasticity across multiple biological levels in Astatotilapia burtoni. Figure S2. Descriptive statistics across all DMRs. Figure S3. Methylation levels and variability by genome scaffold in D versus ND fish. Figure S4. Comparisons of descriptive statistics in D-DMRs versus ND-DMRs. Figure S5. Distances between DMRs and genes across different genome scaffold sizes. Figure S6. DMR statistics as a function of the number of genes within different distances. Figure S7. DMR counts in different combinations of genomic features. Figure S8. Expression fold-difference and isoforms of DMR genes depending on location and sign of DMR. Figure S9. DMR gene properties compared to other genes. Figure S10. Enriched biological functions in subsets of DMR genes. Figure S11. DMR genes in glutamatergic and GABAergic synapses. Figure S12. DMR genes involved in axon guidance and oligodendrocyte progenitor cell development. Figure S13. Properties of DMR genes in development (Dev) versus neural activity (Act) GO term clusters. Figure S14. Correlations between gene properties and number of TEs they contain. Figure S15. M-bias plots before and after trimming BS-Seq read pairs. Figure S16. No relationship between genome scaffold length and DMR statistical properties. (PDF 4380 kb)

Additional file 1: Table S1. Electronic search strategy. Table S2. Summary of key themes and associated key evidence. Table S3. Overlap of scoping review themes with Joint External Evaluation indicators.

Additional file 3: Figure S1. Selection of Sources of Evidence.

Additional file 3: Table S1. Negative predictive value in A-rich regions in the human dataset. The percentage of putative TS artifacts not supported by the dRNA sequencing experiment (i.e. negative predictive value) is presented in A-rich regions of different adenine content calculated by two algorithms. If artifacts were mainly caused by internal priming, it would be expected that true negative calls accumulated in regions which have a high number of adenines despite having few consecutive adenines close to the artifactual pA site. The cells are colored according to their negative predictive value.

Additional file 1: Figure S1. Comparison of the cDNA and dRNA sequencing results of all potential poly(A) sites. In addition to Fig. 2, Additional file 1 Figure S1 contains the results of potential poly(A) sites that were supported by 10 or less cDNA sequencing reads as well. (a) The proportion of potential poly(A) sites that were supported by dRNA sequencing for the HCMV (purple, n = 403) and the human (orange, n = 87,980) datasets. (b) The performance of the different filtering methods. The left side shows the true positive predictive value of the internal-priming (red) and template-switching (blue) filters based on the dRNA sequencing results (positive predictive value ~ kept sites which are also detected in dRNA Seq). The human potential poly(A) sites were filtered using SQANTI (yellow) and also based on whether or not they were found in the PolyA_DB (green). The right side of the panel shows the proportion of potential poly(A) sites filtered out by the different filtering options, which were not supported by dRNA sequencing (~ positive predictive value). (c) Barplot of the number of potential poly(A) sites in regions with different adenine-content in the HCMV (left) and (right) human dataset. The features that the filtering algorithm characterized as TES are marked in blue, the putative artefacts are marked in red. (d) The positive predictive value of the different filtering methods is shown as a function of the adenine content. The HCMV results are not detailed because the low number of TESs contained in that dataset would not provide for a meaningful analysis.

Table S1. Number of high-quality biallelic variants in GENOME, EXOME, BEADCHIP, and HUN-EXOM datasets before and after LD pruning. Table S2. f3-statistics of Hungarian and selected South Asian populations indicate admixture which could be attributed to the Gypsies living as an ethnic minority in Hungary. Figure S1. The f4 (TSI, X; CHB; YRI) statistics estimates the East Asian and African components for each dataset. Negative values indicates East Asian gene flow, while positive values indicate dominant African genetic components. Figure S2. Calculated CV error for different admixture models (K=3 to 10). The best fitting model of admixture indicated was K=6 in the GENOME, EXOME and HUN-EXOME datasets, however, it was K=9 in case of the BEADCHIP dataset. Figure S3. Admixture analysis of 25 populations with the K=6 admixture model. The major topology and admixture components were very similar in each dataset for all analyzed populations but the minor admix components were systematically overestimated using the BEADCHIP dataset. Figure S4. Admixture analysis of 25 populations with the K=9 admixture model. Besides the minor admix components overestimation using BEADCHIP this admixture model suggested that the European super-population was split into two and the African super-population was split into three admixing components. Figure S5. PCA of the HUN-EXOME dataset confirms that genetically, modern Hungarians belong to the European super-population and within the Europeans, two genetic components exist with a North-South European gradient. Figure S6. F4(TSI, X; CHB, YRI) values of the HUN-EXOME dataset indicates that Hungarians have higher East Asian admixture component than British but lower than Finnish population. Figure S7. Admixture analysis of 25 populations using the HUN-EXOME dataset for the A) K=6 and B) K=9 admixture models. Besides the major Europian admixture component in few Hungarian individuals minor East Asian admixture components also detected. Data Supplementary Data was deposited to: https://figshare.com/s/e91794c7141a7eb16255 . (PDF 2255 kb)

Domain and cluster-level organization of tDNA transcription (related to Fig. 1). Figure S2. tDNA copy number estimation and transcription in THP-1 cells (related to Fig. 2). Figure S3. Dynamic chromatin and tRNA transcription profiles during differentiation (related to Fig. 3). Figure S4. Dynamic chromatin and tDNA transcription profiles during differentiation (related to Fig. 4). Figure S5. Concurrent downregulation of tDNA looping and transcription (related to Fig. 4). Figure S6. MAF1 binding and differential TF footprinting during THP-1 differentiation (related to Fig. 5). Figure S7. Dynamic range of codon usage and aggregate tDNA expression levels during THP-1 differentiation. (PDF 8237 kb)

Percentage of bases covered for HSPA1L, HSPA1A, and HSPA1B in the Agilent SureSelect V4 and V5 kits. (DOCX 42 kb)

Sanger traces of each of the four variants of interest found across six pedigrees. (DOCX 1252 kb)

Results of SKAT-O within HSPA1L using non-synonymous and non-frameshift variants, excluding common variants (MAFâ >â 0.05). (DOCX 51 kb)

Domain and cluster-level organization of tDNA transcription (related to Fig. 1). Figure S2. tDNA copy number estimation and transcription in THP-1 cells (related to Fig. 2). Figure S3. Dynamic chromatin and tRNA transcription profiles during differentiation (related to Fig. 3). Figure S4. Dynamic chromatin and tDNA transcription profiles during differentiation (related to Fig. 4). Figure S5. Concurrent downregulation of tDNA looping and transcription (related to Fig. 4). Figure S6. MAF1 binding and differential TF footprinting during THP-1 differentiation (related to Fig. 5). Figure S7. Dynamic range of codon usage and aggregate tDNA expression levels during THP-1 differentiation. (PDF 8237 kb)