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The article examines the novel transcribed regions in the human genome. It discusses the results of mapping transcribed regions using genomic tiling arrays which enable the detection of transcribed regions unbiased by genome annotation. It compares the transcriptional changes that occur upon cell differentiation based on analysis of NB4 cells treated with different agents. It analyzes the frequency of trinucleotide sequences of novel transcribed regions in intergenic and intronic regions.

Abstract Background Tiling array data is hard to interpret due to noise. The wavelet transformation is a widely used technique in signal processing for elucidating the true signal from noisy data. Consequently, we attempted to denoise representative tiling array datasets for ChIP-chip experiments using wavelets. In doing this, we used specific wavelet basis functions, Coiflets, since their triangular shape closely resembles the expected profiles of true ChIP-chip peaks. Results In our wavelet-transformed data, we observed that noise tends to be confined to small scales while the useful signal-of-interest spans multiple large scales. We were also able to show that wavelet coefficients due to non-specific cross-hybridization follow a log-normal distribution, and we used this fact in developing a thresholding procedure. In particular, wavelets allow one to set an unambiguous, absolute threshold, which has been hard to define in ChIP-chip experiments. One can set this threshold by requiring a similar confidence level at different length-scales of the transformed signal. We applied our algorithm to a number of representative ChIP-chip data sets, including those of Pol II and histone modifications, which have a diverse distribution of length-scales of biochemical activity, including some broad peaks. Conclusions Finally, we benchmarked our method in comparison to other approaches for scoring ChIP-chip data using spike-ins on the ENCODE Nimblegen tiling array. This comparison demonstrated excellent performance, with wavelets getting the best overall score.

Abstract Background Tiling arrays have been the tool of choice for probing an organism's transcriptome without prior assumptions about the transcribed regions, but RNA-Seq is becoming a viable alternative as the costs of sequencing continue to decrease. Understanding the relative merits of these technologies will help researchers select the appropriate technology for their needs. Results Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of C. elegans. We find that the raw signals from these two technologies are reasonably well correlated but that RNA-Seq outperforms tiling arrays in several respects, notably in exon boundary detection and dynamic range of expression. By exploring the accuracy of sequencing as a function of depth of coverage, we found that about 4 million reads are required to match the sensitivity of two tiling array replicates. The effects of cross-hybridization were analyzed using a "nearest neighbor" classifier applied to array probes; we describe a method for determining potential "black list" regions whose signals are unreliable. Finally, we propose a strategy for using RNA-Seq data as a gold standard set to calibrate tiling array data. All tiling array and RNA-Seq data sets have been submitted to the modENCODE Data Coordinating Center. Conclusions Tiling arrays effectively detect transcript expression levels at a low cost for many species while RNA-Seq provides greater accuracy in several regards. Researchers will need to carefully select the technology appropriate to the biological investigations they are undertaking. It will also be important to reconsider a comparison such as ours as sequencing technologies continue to evolve.

Corrected abstract We are correcting the abstract of our published article (1). The sentence that starts "We observe that 4.5% of MPSS tags...." was not scientifically complete in the original abstract, having only two of the four numbers required to describe a comparison of two technologies in two different organisms. The abstract below more accurately describes our findings, as documented in Figure 1 of the manuscript.

Abstract Background There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure.

Abstract Background Short-read high-throughput DNA sequencing technologies provide new tools to answer biological questions. However, high cost and low throughput limit their widespread use, particularly in organisms with smaller genomes such as S. cerevisiae. Although ChIP-Seq in mammalian cell lines is replacing array-based ChIP-chip as the standard for transcription factor binding studies, ChIP-Seq in yeast is still underutilized compared to ChIP-chip. We developed a multiplex barcoding system that allows simultaneous sequencing and analysis of multiple samples using Illumina's platform. We applied this method to analyze the chromosomal distributions of three yeast DNA binding proteins (Ste12, Cse4 and RNA PolII) and a reference sample (input DNA) in a single experiment and demonstrate its utility for rapid and accurate results at reduced costs. Results We developed a barcoding ChIP-Seq method for the concurrent analysis of transcription factor binding sites in yeast. Our multiplex strategy generated high quality data that was indistinguishable from data obtained with non-barcoded libraries. None of the barcoded adapters induced differences relative to a non-barcoded adapter when applied to the same DNA sample. We used this method to map the binding sites for Cse4, Ste12 and Pol II throughout the yeast genome and we found 148 binding targets for Cse4, 823 targets for Ste12 and 2508 targets for PolII. Cse4 was strongly bound to all yeast centromeres as expected and the remaining non-centromeric targets correspond to highly expressed genes in rich media. The presence of Cse4 non-centromeric binding sites was not reported previously. Conclusion We designed a multiplex short-read DNA sequencing method to perform efficient ChIP-Seq in yeast and other small genome model organisms. This method produces accurate results with higher throughput and reduced cost. Given constant improvements in high-throughput sequencing technologies, increasing multiplexing will be possible to further decrease costs per sample and to accelerate the completion of large consortium projects such as modENCODE.

Abstract Background Mismatched oligonucleotides are widely used on microarrays to differentiate specific from nonspecific hybridization. While many experiments rely on such oligos, the hybridization behavior of various degrees of mismatch (MM) structure has not been extensively studied. Here, we present the results of two large-scale microarray experiments on S. cerevisiae and H. sapiens genomic DNA, to explore MM oligonucleotide behavior with real sample mixtures under tiling-array conditions. Results We examined all possible nucleotide substitutions at the central position of 36-nucleotide probes, and found that nonspecific binding by MM oligos depends upon the individual nucleotide substitutions they incorporate: C→A, C→G and T→A (yielding purine-purine mispairs) are most disruptive, whereas A→X were least disruptive. We also quantify a marked GC skew effect: substitutions raising probe GC content exhibit higher intensity (and vice versa). This skew is small in highly-expressed regions (± 0.5% of total intensity range) and large (± 2% or more) elsewhere. Multiple mismatches per oligo are largely additive in effect: each MM added in a distributed fashion causes an additional 21% intensity drop relative to PM, three-fold more disruptive than adding adjacent mispairs (7% drop per MM). Conclusion We investigate several parameters for oligonucleotide design, including the effects of each central nucleotide substitution on array signal intensity and of multiple MM per oligo. To avoid GC skew, individual substitutions should not alter probe GC content. RNA sample mixture complexity may increase the amount of nonspecific hybridization, magnify GC skew and boost the intensity of MM oligos at all levels.

Diffuse hemispheric gliomas, H3G34R/V-mutant (DHG-H3G34), are lethal brain tumors lacking targeted therapies. They originate from interneuronal precursors; however, leveraging this origin for therapeutic insights remains unexplored. Here, we delineate a cellular hierarchy along the interneuron lineage development continuum, revealing that DHG-H3G34 mirror spatial patterns of progenitor streams surrounding interneuron nests, as seen during human brain development. Integrating these findings with genome-wide CRISPR-Cas9 screens identifies genes upregulated in interneuron lineage progenitors as major dependencies. Among these, CDK6 emerges as a targetable vulnerability: DHG-H3G34 tumor cells show enhanced sensitivity to CDK4/6 inhibitors and a CDK6-specific degrader, promoting a shift toward more mature interneuron-like states, reducing tumor growth, and prolonging xenograft survival. Notably, a patient with progressive DHG-H3G34 treated with a CDK4/6 inhibitor achieved 17 months of stable disease. This study underscores interneuronal progenitor-like states, organized in characteristic niches, as a distinct vulnerability in DHG-H3G34, highlighting CDK6 as a promising clinically actionable target., Competing Interests: Declaration of interests M.G.F. is a consultant for Twentyeight-Seven Therapeutics and Blueprint Medicines. M.M. is an SAB member for Cygnal Therapeutics. K.L.L. is founder and equity holder of Travera Inc. and receives consulting fees from BMS, Integragen, and Rarecyte, and research support from Lilly, BMS, and Amgen. D.H. has acted as an advisor for Novartis in relation to ribociclib., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Aging is a leading risk factor for cancer. While it is proposed that age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. We show that aging and cancer share a common epigenetic replication signature, which we modeled using DNA methylation from extensively passaged immortalized human cells in vitro and tested on clinical tissues. This signature, termed CellDRIFT, increased with age across multiple tissues, distinguished tumor from normal tissue, was escalated in normal breast tissue from cancer patients, and was transiently reset upon reprogramming. In addition, within-person tissue differences were correlated with predicted lifetime tissue-specific stem cell divisions and tissue-specific cancer risk. Our findings suggest that age-related replication may drive epigenetic changes in cells and could push them toward a more tumorigenic state.

Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community., Competing Interests: E.V.N. is co-founder, member of the Board of Directors, on the SAB, equity holder, and paid consultant for Eclipse BioInnovations. E.V.N.’s interests have been reviewed and approved by the Baylor College of Medicine in accordance with its conflict of interest policies., (© 2023 The Authors.)

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics., Competing Interests: Declaration of interests Z.W. co-founded and serves as a scientific advisor for Rgenta Inc. B.E.B. declares outside interests in Fulcrum Therapeutics, HiFiBio, Arsenal Biosciences, Cell Signaling Technologies, Chroma Medicine, and Design Pharmaceuticals. M.G. is on the advisory board for HypaHub, Inc. and Elysium Health., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Loftus, Shickel, Balch, Tighe, Abbott, Fazzone, Anderson, Rozowsky, Ozrazgat-Baslanti, Ren, Berceli, Hogan, Efron, Moorman, Rashidi, Upchurch and Bihorac.)

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays., Competing Interests: Crislyn D’Souza-Schorey, Satyajyoti Senapati and Hsueh-Chia Chang hold equity in AgenDx Biosciences. Hsueh-Chia Chang is a cofounder of Aopia Biosciences and holds equity. Daniel T. Chiu has financial interest and is a scientific founder and/or board member of the following companies and their respective affiliates: Micareo, Inc, Lamprogen, Inc, Cellectricon AB, and Fluicell AB. Saumya Das is a founding member of Dyrnamix Inc. that did not play any role in this study. Kendal Van Keuren-Jensen is a board member of Dyrnamix Inc. and HTG that did not play any role in this study. Aleksandar Milosavljevic is a founder of IP Genesis, Inc. that did not play any role in this study. Louise C. Laurent has stock in Illumina, Inc. Anil K. Sood has the following disclosures: Consultant (Merck, Kiyatec), shareholder (BioPath), research funding (MTrap). John Nolan holds equity in Cellarcus Biosciences Inc. L. James Lee holds equity at Spot Biosystems. David A. Routenberg, Sigal Shachar and Alexander K. Tucker-Schwartz are employees of Meso Scale Diagnostics, LLC. Andrey Turchinovich is founder of SciBerg e.Kfm, Mannheim, Germany. Angela M. Zivkovic is cofounder and board member at Innate Biology, Inc., (© 2022 The Author(s).)

One promising goal for utilizing the molecular information circulating in biofluids is the discovery of clinically useful biomarkers. Extracellular RNAs (exRNAs) are one of the most diverse classes of molecular cargo, easily assayed by sequencing and with expressions that rapidly change in response to subject status. Despite diverse exRNA cargo, most evaluations from biofluids have focused on small RNA sequencing and analysis, specifically on microRNAs (miRNAs). Another goal of characterizing circulating molecular information, is to correlate expression to injuries associated with specific tissues of origin. Biomarker candidates are often described as being specific, enriched in a particular tissue or associated with a disease process. Likewise, miRNA data is often reported to be specific, enriched for a tissue, without rigorous testing to support the claim. Here we provide a tissue atlas of small RNAs from 30 different tissues and three different blood cell types. We analyzed the tissues for enrichment of small RNA sequences and assessed their expression in biofluids: plasma, cerebrospinal fluid, urine, and saliva. We employed published data sets representing physiological (resting vs. acute exercise) and pathologic states (early- vs. late-stage liver fibrosis, and differential subtypes of stroke) to determine differential tissue-enriched small RNAs. We also developed an online tool that provides information about exRNA sequences found in different biofluids and tissues. The data can be used to better understand the various types of small RNA sequences in different tissues as well as their potential release into biofluids, which should help in the validation or design of biomarker studies., Competing Interests: KV and SD are members of the scientific advisory board at Dyrnamix. Their involvement in this company had no bearing on the work performed in this manuscript. KV is on the scientific advisory board of HTG. Her involvement has no bearing on this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alsop, Meechoovet, Kitchen, Sweeney, Beach, Serrano, Hutchins, Ghiran, Reiman, Syring, Hsieh, Courtright-Lim, Valkov, Whitsett, Rakela, Pockros, Rozowsky, Gallego, Huentelman, Shah, Nakaji, Kalani, Laurent, Das and Van Keuren-Jensen.)

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems., Competing Interests: EV is co-founder, member of the Board of Directors, on the SAB, equity holder, and paid consultant for Eclipse BioInnovations. EV’s interests have been reviewed and approved by the Baylor College of Medicine in accordance with its conflict of interest policies. RS is a paid consultant for Lacerta Therapeutics. Author RY was employed by the company Aginome Scientific, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alexander, Kitchen, Tosar, Roth, Mestdagh, Max, Rozowsky, Kaczor-Urbanowicz, Chang, Balaj, Losic, Van Nostrand, LaPlante, Mateescu, White, Yu, Milosavljevic, Stolovitzky and Spengler.)

Introduction: Acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) often leads to mortality. Outcomes of patients with COVID-19-related ARDS compared to ARDS unrelated to COVID-19 is not well characterized., Areas Covered: We performed a systematic review of PubMed, Scopus, and MedRxiv 11/1/2019 to 3/1/2021, including studies comparing outcomes in COVID-19-related ARDS (COVID-19 group) and ARDS unrelated to COVID-19 (ARDS group). Outcomes investigated were duration of mechanical ventilation-free days, intensive care unit (ICU) length-of-stay (LOS), hospital LOS, and mortality. Random effects models were fit for each outcome measure. Effect sizes were reported as pooled median differences of medians (MDMs), mean differences (MDs), or odds ratios (ORs)., Expert Opinion: Ten studies with 2,281 patients met inclusion criteria (COVID-19: 861 [37.7%], ARDS: 1420 [62.3%]). There were no significant differences between the COVID-19 and ARDS groups for median number of mechanical ventilator-free days (MDM: -7.0 [95% CI: -14.8; 0.7], p = 0.075), ICU LOS (MD: 3.1 [95% CI: -5.9; 12.1], p = 0.501), hospital LOS (MD: 2.5 [95% CI: -5.6; 10.7], p = 0.542), or all-cause mortality (OR: 1.25 [95% CI: 0.78; 1.99], p = 0.361). Compared to the general ARDS population, results did not suggest worse outcomes in COVID-19-related ARDS.

Background: Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases., Methods: We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin)., Results: Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages., Conclusions: Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. We demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

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 ENCODE 1 and Roadmap Epigenomics 2 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 presence of nonhuman RNAs in man has been questioned and it is unclear if food-derived miRNAs cross into the circulation. In a large population study, we found nonhuman miRNAs in plasma by RNA sequencing and validated a small number of pine-pollen miRNAs by RT-qPCR in 2,776 people. The presence of these pine-pollen miRNAs associated with hay fever and not with overt cardiovascular or pulmonary disease. Using in vivo and in vitro models, we found that transmission of pollen-miRNAs into the circulation occurs via pulmonary transfer and this transfer was mediated by platelet-pulmonary vascular cell interactions and platelet pollen-DNA uptake. These data demonstrate that pollen-derived plant miRNAs can be horizontally transferred into the circulation via the pulmonary system in humans. Although these data suggest mechanistic plausibility for pulmonary-mediated plant-derived miRNA transfer into the human circulation, our large observational cohort data do not implicate major disease or risk factor association., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Small RNA sequencing has been widely adopted to study the diversity of extracellular RNAs (exRNAs) in biofluids; however, the analysis of exRNA samples can be challenging: they are vulnerable to contamination and artifacts from different isolation techniques, present in lower concentrations than cellular RNA, and occasionally of exogenous origin. To address these challenges, we present exceRpt, the exRNA-processing toolkit of the NIH Extracellular RNA Communication Consortium (ERCC). exceRpt is structured as a cascade of filters and quantifications prioritized based on one's confidence in a given set of annotated RNAs. It generates quality control reports and abundance estimates for RNA biotypes. It is also capable of characterizing mappings to exogenous genomes, which, in turn, can be used to generate phylogenetic trees. exceRpt has been used to uniformly process all ∼3,500 exRNA-seq datasets in the public exRNA Atlas and is available from genboree.org and github.gersteinlab.org/exceRpt., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The Extracellular RNA Communication Consortium (ERCC) was launched to accelerate progress in the new field of extracellular RNA (exRNA) biology and to establish whether exRNAs and their carriers, including extracellular vesicles (EVs), can mediate intercellular communication and be utilized for clinical applications. Phase 1 of the ERCC focused on exRNA/EV biogenesis and function, discovery of exRNA biomarkers, development of exRNA/EV-based therapeutics, and construction of a robust set of reference exRNA profiles for a variety of biofluids. Here, we present progress by ERCC investigators in these areas, and we discuss collaborative projects directed at development of robust methods for EV/exRNA isolation and analysis and tools for sharing and computational analysis of exRNA profiling data., (Copyright © 2019 Elsevier Inc. All rights reserved.)

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies., (Copyright © 2019 Elsevier Inc. All rights reserved.)

To assess the impact of genetic variation in regulatory loci on human health, we constructed a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, which is defined as random transitions between fully methylated and unmethylated states of DNA. The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provided a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated genetic variation., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Background: Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms., Results: In this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome)., Conclusions: The concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies.

Motivation: Analysis of RNA sequencing (RNA-Seq) data in human saliva is challenging. Lack of standardization and unification of the bioinformatic procedures undermines saliva's diagnostic potential. Thus, it motivated us to perform this study., Results: We applied principal pipelines for bioinformatic analysis of small RNA-Seq data of saliva of 98 healthy Korean volunteers including either direct or indirect mapping of the reads to the human genome using Bowtie1. Analysis of alignments to exogenous genomes by another pipeline revealed that almost all of the reads map to bacterial genomes. Thus, salivary exRNA has fundamental properties that warrant the design of unique additional steps while performing the bioinformatic analysis. Our pipelines can serve as potential guidelines for processing of RNA-Seq data of human saliva., Availability and Implementation: Processing and analysis results of the experimental data generated by the exceRpt (v4.6.3) small RNA-seq pipeline (github.gersteinlab.org/exceRpt) are available from exRNA atlas (exrna-atlas.org). Alignment to exogenous genomes and their quantification results were used in this paper for the analyses of small RNAs of exogenous origin., Contact: dtww@ucla.edu., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Supplements are increasingly important to the scientific record, particularly in genomics. However, they are often underutilized. Optimally, supplements should make results findable, accessible, interoperable, and reusable (i.e., "FAIR"). Moreover, properly off-loading to them the data and detail in a paper could make the main text more readable. We propose a hierarchical organization for supplements, with some parts paralleling and "shadowing" the main text and other elements branching off from it, and we suggest a specific formatting to make this structure explicit. Furthermore, sections of the supplement could be presented in multiple scientific "dialects", including machine-readable and lay-friendly formats.

Background and Purpose: There is increasing interest in extracellular RNAs (ex-RNAs), with numerous reports of associations between selected microRNAs (miRNAs) and a variety of cardiovascular disease phenotypes. Previous studies of ex-RNAs in relation to risk for cardiovascular disease have investigated small numbers of patients and assayed only candidate miRNAs. No human studies have investigated links between novel ex-RNAs and stroke., Methods: We conducted unbiased next-generation sequencing using plasma from 40 participants of the FHS (Framingham Heart Study; Offspring Cohort Exam 8) followed by high-throughput polymerase chain reaction of 471 ex-RNAs. The reverse transcription quantitative polymerase chain reaction included 331 of the most abundant miRNAs, 43 small nucleolar RNAs, and 97 piwi-interacting RNAs in 2763 additional FHS participants and explored the relations of ex-RNAs and prevalent (n=63) and incident (n=51) stroke and coronary heart disease (prevalent=286, incident=69)., Results: After adjustment for multiple cardiovascular disease risk factors, 7 ex-RNAs were associated with stroke prevalence or incidence; there were no ex-RNA associated with prevalent or incident coronary heart disease. Statistically significant ex-RNA associations with stroke were specific, with no overlap between prevalent and incident events., Conclusions: This is the largest study of ex-RNAs in relation to stroke using an unbiased approach in an observational cohort and the first large study to examine human small noncoding RNAs beyond miRNAs. These results demonstrate that when studied in a large observational cohort, extracellular miRNAs are associated with stroke risk., (© 2017 The Authors.)

Interest in circulating RNAs for monitoring and diagnosing human health has grown significantly. There are few datasets describing baseline expression levels for total cell-free circulating RNA from healthy control subjects. In this study, total extracellular RNA (exRNA) was isolated and sequenced from 183 plasma samples, 204 urine samples and 46 saliva samples from 55 male college athletes ages 18-25 years. Many participants provided more than one sample, allowing us to investigate variability in an individual's exRNA expression levels over time. Here we provide a systematic analysis of small exRNAs present in each biofluid, as well as an analysis of exogenous RNAs. The small RNA profile of each biofluid is distinct. We find that a large number of RNA fragments in plasma (63%) and urine (54%) have sequences that are assigned to YRNA and tRNA fragments respectively. Surprisingly, while many miRNAs can be detected, there are few miRNAs that are consistently detected in all samples from a single biofluid, and profiles of miRNA are different for each biofluid. Not unexpectedly, saliva samples have high levels of exogenous sequence that can be traced to bacteria. These data significantly contribute to the current number of sequenced exRNA samples from normal healthy individuals.

The emergence of collective creative enterprise such as large scientific consortia is a unique feature in modern scientific research. We analyzed the temporal co-authorship network structures of ENCODE and modENCODE consortia. Our analysis revealed that the consortium members work closely as a community whereas non-members collaborate in the scale of a few laboratories. We also identified a few brokers playing an important role to facilitate collaborations with outside researchers., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

MicroRNA (miRNA) expression has rapidly grown into one of the largest fields for disease characterization and development of clinical biomarkers. Consensus is lacking in regards to the optimal sample source or if different circulating sources are concordant. Here, using miRNA measurements from contemporaneously obtained whole blood- and plasma-derived RNA from 2391 individuals, we demonstrate that plasma and blood miRNA levels are divergent and may reflect different biological processes and disease associations.

There is growing appreciation for the importance of non-protein-coding genes in development and disease. Although much is known about microRNAs, limitations in bioinformatic analyses of RNA sequencing have precluded broad assessment of other forms of small-RNAs in humans. By analysing sequencing data from plasma-derived RNA from 40 individuals, here we identified over a thousand human extracellular RNAs including microRNAs, piwi-interacting RNA (piRNA), and small nucleolar RNAs. Using a targeted quantitative PCR with reverse transcription approach in an additional 2,763 individuals, we characterized almost 500 of the most abundant extracellular transcripts including microRNAs, piRNAs and small nucleolar RNAs. The presence in plasma of many non-microRNA small-RNAs was confirmed in an independent cohort. We present comprehensive data to demonstrate the broad and consistent detection of diverse classes of circulating non-cellular small-RNAs from a large population.

Large-scale sequencing in the 1000 Genomes Project has revealed multitudes of single nucleotide variants (SNVs). Here, we provide insights into the functional effect of these variants using allele-specific behaviour. This can be assessed for an individual by mapping ChIP-seq and RNA-seq reads to a personal genome, and then measuring 'allelic imbalances' between the numbers of reads mapped to the paternal and maternal chromosomes. We annotate variants associated with allele-specific binding and expression in 382 individuals by uniformly processing 1,263 functional genomics data sets, developing approaches to reduce the heterogeneity between data sets due to overdispersion and mapping bias. Since many allelic variants are rare, aggregation across multiple individuals is necessary to identify broadly applicable 'allelic elements'. We also found SNVs for which we can anticipate allelic imbalance from the disruption of a binding motif. Our results serve as an allele-specific annotation for the 1000 Genomes variant catalogue and are distributed as an online resource (alleledb.gersteinlab.org).

Background: To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain. By extending GO in this manner, the exRNA data/metadata will be more easily annotated and queried because it will be based on a shared set of terms and relationships relevant to extracellular research., Methods: By following a consensus-building process, we have worked with several academic societies/consortia, including ERCC, ISEV, and ASEMV, to identify and approve a set of exRNA and extracellular vesicle-related terms and relationships that have been incorporated into GO. In addition, we have initiated an ongoing process of extractions of gene product annotations associated with these terms from Vesiclepedia and ExoCarta, conversion of the extracted annotations to Gene Association File (GAF) format for batch submission to GO, and curation of the submitted annotations by the GO Consortium. As a use case, we have incorporated some of the GO terms into annotations of samples from the exRNA Atlas and implemented a faceted search interface based on such annotations., Results: We have added 7 new terms and modified 9 existing terms (along with their synonyms and relationships) to GO. Additionally, 18,695 unique coding gene products (mRNAs and proteins) and 963 unique non-coding gene products (ncRNAs) which are associated with the terms: "extracellular vesicle", "extracellular exosome", "apoptotic body", and "microvesicle" were extracted from ExoCarta and Vesiclepedia. These annotations are currently being processed for submission to GO., Conclusions: As an inter-community effort, we have made a substantial update to GO in the exRNA context. We have also demonstrated the utility of some of the new GO terms for sample annotation and metadata search.

As the cost of sequencing continues to decrease and the amount of sequence data generated grows, new paradigms for data storage and analysis are increasingly important. The relative scaling behavior of these evolving technologies will impact genomics research moving forward.

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet's observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

Pseudogenes are degraded fossil copies of genes. Here, we report a comparison of pseudogenes spanning three phyla, leveraging the completed annotations of the human, worm, and fly genomes, which we make available as an online resource. We find that pseudogenes are lineage specific, much more so than protein-coding genes, reflecting the different remodeling processes marking each organism's genome evolution. The majority of human pseudogenes are processed, resulting from a retrotranspositional burst at the dawn of the primate lineage. This burst can be seen in the largely uniform distribution of pseudogenes across the genome, their preservation in areas with low recombination rates, and their preponderance in highly expressed gene families. In contrast, worm and fly pseudogenes tell a story of numerous duplication events. In worm, these duplications have been preserved through selective sweeps, so we see a large number of pseudogenes associated with highly duplicated families such as chemoreceptors. However, in fly, the large effective population size and high deletion rate resulted in a depletion of the pseudogene complement. Despite large variations between these species, we also find notable similarities. Overall, we identify a broad spectrum of biochemical activity for pseudogenes, with the majority in each organism exhibiting varying degrees of partial activity. In particular, we identify a consistent amount of transcription (∼15%) across all species, suggesting a uniform degradation process. Also, we see a uniform decay of pseudogene promoter activity relative to their coding counterparts and identify a number of pseudogenes with conserved upstream sequences and activity, hinting at potential regulatory roles.

Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.