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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.

BackgroundGermline variants in fumarate hydratase (FH) are associated with autosomal dominant (AD) hereditary leiomyomatosis and renal cell cancer (HLRCC) and autosomal recessive (AR) fumarase deficiency (FMRD). The prevalence and cancer penetrance across different FH variants remain unclear.MethodsA database containing 120,061 records from individuals undergoing cancer germline testing was obtained. FH variants were classified into 3 categories: AD HLRCC variants, AR FMRD variants, and variants of unknown significance (VUSs). Individuals with variants from these categories were compared with those with negative genetic testing.ResultsFH variants were detected in 1.3% of individuals (AD HLRCC, 0.3%; AR FMRD, 0.4%; VUS, 0.6%). The rate of AD HLRCC variants discovered among reportedly asymptomatic individuals without a clear indication for HLRCC testing was 1 in 2668 (0.04%). In comparison with those with negative genetic testing, the renal cell carcinoma (RCC) prevalence was elevated with AD HLRCC variants (17.0% vs 4.5%; P&nbsp

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.

BACKGROUND: Hereditary leiomyomatosis and renal cancer (HLRCC) is a cancer syndrome associated with a germline mutation in fumarate hydratase (FH). The syndrome is associated with cutaneous and uterine leiomyomas, and some patients develop a lethal form of kidney cancer. This study provides estimates for the FH carrier frequency and kidney cancer penetrance. METHODS: Data sets containing sequencing data for the FH gene were used: the 1000 Genomes Project (1000GP) and the Exome Aggregation Consortium (ExAC). Alterations in the FH gene were characterized on the basis of different variant risk tiers: 1) ClinVar annotated variants, 2) loss-of-function alterations, and 3) highly impactful missense alterations. The cumulative incidence of FH alterations overall and by different world populations was evaluated in 1000GP and ExAC. A lifetime penetrance of HLRCC kidney cancer risk was generated with 3 estimates of the annual incidence. RESULTS: The overall allele frequencies of tier 1 to 3 FH alterations in the ExAC and 1000GP data sets were 2.54 × 10(–3) (1 in 393) and 1.20 × 10(–3) (1 in 835), respectively. There were differences in the allele frequencies of FH alterations between world populations. Based on various estimates of the percentage of kidney cancers with FH alterations, the lifetime kidney cancer penetrance for carrier estimate 3 in ExAC was 1.7% to 5.8%. CONCLUSIONS: FH alterations are common and are carried by approximately 1 in 1000 individuals according to the more conservative estimates. The lifetime kidney cancer penetrance appears lower than previously estimated. Although databases are not population cohorts, they provide a useful quantitative estimate of rare variants with low penetrance.

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 personal epigenomes, for ∼25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Purpose Molecular profiling of cancer is increasingly common as part of routine care in oncology, and germline and somatic profiling may provide insights and actionable targets for men with metastatic prostate cancer. However, all reported cases are of deidentified individuals without full medical and genomic data available in the public domain. Patient and methods We present a case of whole-genome tumor and germline sequencing in a patient with advanced prostate cancer, who has agreed to make his genomic and clinical data publicly available. Results We describe an 84-year-old Caucasian male with a Gleason 10 oligometastastic hormone-sensitive prostate cancer. Whole-genome sequencing provided insights into his tumor’s underlying mutational processes and the development of an SPOP mutation. It also revealed an androgen-receptor dependency of his cancer which was reflected in his durable response to radiation and hormonal therapy. Potentially actionable genomic lesions in the tumor were identified through a personalized medicine approach for potential future therapy, but at the moment, he remains in remission, illustrating the hormonal sensitivity of his SPOP-driven prostate cancer. We also placed this patient in the context of a large prostate-cancer cohort from the PCAWG (Pan-cancer Analysis of Whole Genomes) group. In this comparison, the patient’s cancer appears typical in terms of the number and type of somatic mutations, but it has a somewhat larger contribution from the mutational process associated with aging. Conclusion We combined the expertise of medical oncology and genomics approaches to develop a molecular tumor board to integrate the care and study of this patient, who continues to have an outstanding response to his combined modality treatment. This identifiable case potentially helps overcome barriers to clinical and genomic data sharing.

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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., ENCODE is a resource comprising thousands of functional genomic datasets. Here, the authors present custom annotation within ENCODE for cancer, highlighting a workflow that can help prioritise key elements in oncogenesis.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (, Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

The rapidly growing volume of data being produced by next-generation sequencing initiatives is enabling more in-depth analyses of conservation than previously possible. Deep sequencing is uncovering disease loci and regions under selective constraint, despite the fact that intuitive biophysical reasons for such constraint are sometimes absent. Allostery may often provide the missing explanatory link. We use models of protein conformational change to identify allosteric residues by finding essential surface pockets and information-flow bottlenecks, and we develop a software tool that enables users to perform this analysis on their own proteins of interest. Though fundamentally 3D-structural in nature, our analysis is computationally fast, thereby allowing us to run it across the PDB and to evaluate general properties of predicted allosteric residues. We find that these tend to be conserved over diverse evolutionary time scales. Finally, we highlight examples of allosteric residues that help explain poorly understood disease-associated variants.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Multiple mutational processes drive carcinogenesis, leaving characteristic signatures on tumor genomes. Determining the active signatures from the full repertoire of potential ones can help elucidate mechanisms underlying cancer initiation and development. This task in-volves decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known mutational signatures. We formulate it as an optimization problem and develop sigLASSO, a software tool, to carry it out efficiently. (An R package implementation is available at github.com/gersteinlab/siglasso). sigLASSO features four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly adding multinomial sampling into the overall objective function. This is particularly important when mutation counts are low and sampling variance is high, such as in exome sequencing. (2) sigLASSO uses L1 regularization to parsimoniously assign signatures to mutation profiles, leading to sparse and more biologically interpretable solutions resembling previously well-characterized results. (3) sigLASSO fine-tunes model complexity, informed by the scale of the data and biological-knowledge based priors. In particular, instead of hard thresholding and choosing a priori a discrete subset of active signatures, sigLASSO allows continuous priors, which can be effectively learned from auxiliary information. (4) Because of this, sigLASSO can assess model uncertainty and abstain from making certain assignments in low-confidence contexts. Finally, to evaluate sigLASSO signature assignments in comparison to other approaches, we develop a set of reasonable expectations (e.g. sparsity, the ability to abstain, and robustness to noise) that we apply consistently in a variety of contexts.

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed “putative passengers”) are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent–child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (

We present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called “bridged” fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.

Retroduplications come from reverse transcription of mRNAs and their insertion back into the genome. Here, we performed comprehensive discovery and analysis of retroduplications in a large cohort of 2,535 individuals from 26 human populations, as part of 1000 Genomes Phase 3. We developed an integrated approach to discover novel retroduplications combining high-coverage exome and low-coverage whole-genome sequencing data, utilizing information from both exon-exon junctions and discordant paired-end reads. We found 503 parent genes having novel retroduplications absent from the reference genome. Based solely on retroduplication variation, we built phylogenetic trees of human populations; these represent superpopulation structure well and indicate that variable retroduplications are effective population markers. We further identified 43 retroduplication parent genes differentiating superpopulations. This group contains several interesting insertion events, including a SLMO2 retroduplication and insertion into CAV3, which has a potential disease association. We also found retroduplications to be associated with a variety of genomic features: (1) Insertion sites were correlated with regular nucleosome positioning. (2) They, predictably, tend to avoid conserved functional regions, such as exons, but, somewhat surprisingly, also avoid introns. (3) Retroduplications tend to be co-inserted with young L1 elements, indicating recent retrotranspositional activity, and (4) they have a weak tendency to originate from highly expressed parent genes. Our investigation provides insight into the functional impact and association with genomic elements of retroduplications. We anticipate our approach and analytical methodology to have application in a more clinical context, where exome sequencing data is abundant and the discovery of retroduplications can potentially improve the accuracy of SNP calling., Author summary We developed an approach and performed comprehensive discovery of retroduplications from 26 human populations, utilizing whole-exome and whole-genome sequencing data. Our high-resolution landscape of retroduplications reveals that variable retroduplications are effective markers of human populations and can track population divergence. We observed that novel retroduplications come from genes with relatively high expression level and co-inserted L1 elements belong to young L1 families, indicating recent retroduplication activity in human migration contributing to genetic diversity. We have also detected several interesting intragenic insertion events, including SLMO2 retroduplication and insertion into CAV3, which worth further investigation for disease predisposition.

Cyber-physical systems (CPS) are often characterized as smart systems, which intelligently interact with other systems across information and physical interfaces. An increased dependence on CPS led to the collection of a vast amount of human-centric data, which brings the information overload problem across multiple domains. Recommender systems in CPS, which always provide information recommendations for users based on historical ratings collected from a single domain only, suffer from the data sparsity problem. Recently, several recommendation models have been proposed to transfer knowledge across multiple domains to alleviate the sparsity problem, which typically assumes that multiple domains share a latent common rating pattern. However, real-world related domains do not necessarily share such a rating pattern, and diversity across domains might outweigh the advantages of such common pattern, which results in performance degradations. In this paper, we propose a novel cross-domain recommendation model, which not only learn the common rating pattern across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns in each domain involving discriminative information propitious to performance improvement. Extensive experiments on real world data sets suggest that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task in CPS.

After the publication of this work [1], the authors noticed an error in Fig. 4. The error occurred on the y-axis, instead of running from 0 to 20 it just has 20 at each point. The publisher apologises for the error and any inconvenience caused. The corrected figure is given below: Fig. 4 The number of faculty position hires at 51 US universities in 3-year bins. The recent increase in hiring coincides with the explosion in sequencing data. Data were obtained from http://jeffhuang.com/computer_science_professors.html

Cancer progression is an evolutionary process during which thousands of somatic variants are accumulated within an individual. In the classic view of cancer progression, a handful of driver variants are thought to give a positive selection advantage to the cancer cell. The remaining variants, termed passengers, represent the overwhelming majority of the variants in cancer genomes, and their functional consequences are poorly understood. Furthermore, the bulk of these passengers fall within noncoding regions of the genome, making these the main product of whole-genome sequencing of tumors. In this work, we explore the functional landscape of passenger variants in various cancer cohorts by leveraging extensive pan-cancer variant calls from ~2500 uniformly processed whole cancer genomes. More specifically, we integrate their annotations and predicted functional impact scores to quantify the overall burdening of various elements in cancer genomes. We show that disruption of genetic regulatory elements in the noncoding genome correlates with altered gene expression. Furthermore, we also show how overall functional burdening of various genomic elements correlate with patient survival time, and tumor clonality. Finally, we observe statistical signals consistent with the notion that aggregated subsets of passenger variants - particularly those we predict to be functionally impactful- might confer weak selective effects. Citation Format: Sushant Kumar, Jonathan Warrel, Patrick Mcgillivray, William Meyerson, Shantao Li, Leonidas Salichos, Arif Harmanci, Alexander Fundichely, Calvin Chan, Carl Herrmann, Morten Nielsen, Lucas Lochovsky, Yan Zhang, Xiaotong Li, Ekta Khurana, Gad Getz, Mark Gerstein. Passenger mutation landscape in cancer genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1279.

Investigating genomic structural variants at basepair resolution is crucial for understanding their formation mechanisms. We identify and analyze 8,943 deletion breakpoints in 1,092 samples from the 1000 Genomes Project. We find breakpoints have more nearby SNPs and indels than the genomic average, likely a consequence of relaxed selection. By investigating the correlation of breakpoints with DNA methylation, Hi-C interactions, and histone marks and the substitution patterns of nucleotides near them, we find that breakpoints with the signature of non-allelic homologous recombination (NAHR) are associated with open chromatin. We hypothesize that some NAHR deletions occur without DNA replication and cell division, in embryonic and germline cells. In contrast, breakpoints associated with non-homologous (NH) mechanisms often have sequence micro-insertions, templated from later replicating genomic sites, spaced at two characteristic distances from the breakpoint. These micro-insertions are consistent with template-switching events and suggest a particular spatiotemporal configuration for DNA during the events.

Genome structural variations (SVs) in the human genome are defined as DNA sequence polymorphisms of at least a few dozen or few hundred bases in length and include deletions, duplications, inversions, translocation, retroelement insertions, and more complex rearrangements that could be thought of as consisting of multiple fragments from the just listed categories. More bases in a personal genome are affected by SVs than by single nucleotide polymorphisms (SNPs), suggesting that SV have a larger or comparable effect on personal phenotype than SNPs. SVs frequently occur in tumor genomes, with several tumor types (e.g., ovarian) having SVs as the dominant type of genomic alteration. Numerous de novo SVs have been linked to various diseases. Because of their size and enrichment in repeat regions, these are the most challenging variants to discover and analyze. Even more challenging is the precise identification of SV breakpoints at a single base pair resolution. But reward is huge, as precise breakpoints hold invaluable information about the origin of each SV; i.e., about the mutational process that created it. The main mechanisms of SV mutagenesis are largely known or hypothesized based on existing evidence [1]: Non-Allele Homologous Recombination (NAHR), Non-Homologous End Joining (NHEJ), Microhomology-Mediated End Joining (MMEJ), errors during replication (replicative mechanisms), and retroelement insertions. However, the details of how they generate SVs are still to be uncovered. The 1,000 Genomes Project, specifically aimed at the analysis of genomic variants across 2,504 individuals from 26 diverse human sub-populations, provided one of the largest data resources to date. Analysis of the data allowed precise reconstruction of breakpoints for over 30 thousand germline SVs, while, in turn, analyses of their breakpoints revealed details of mutation mechanisms generating SVs [2-4]. The classical NAHR mechanism postulates meiotic cell division as a requirement for generating a germline SV, which happens during chromosomal crossover. Interestingly, breakpoints with a signature of NAHR (i.e., with long sequence homologies) found by the project were associated with open chromatin, higher DNA accessibility, and active histone marks in mitotically dividing cells [2]. Such associations were specific to NAHR breakpoints and could not be fully explained by recombination rate, segmental duplication, or repeat content. Therefore, besides recombination during meiosis there could be other circumstances when such SVs are generated. In particular, these associations imply that NAHR-like mutagenesis can happen in non-dividing cells during the repair of double stranded DNA breaks [2], and thus support the proposed intramolecular NAHR. It was also noted that such mutagenesis could also explain extrachromosomal circular DNA (eccDNA), while distribution of eccDNA origins across genome was consistent with the mutagenesis [2]. Breakpoints of SVs generated by NHEJ and replicative mechanisms were known to often be non-blunt; i.e., to include a few extra bases at sequence junctions and sometimes being rather complex. Analysis of 1,651 complex deletions, which are thought to be exclusively created by replicative mechanisms, allowed classifying patterns of rearrangements around breakpoints into few but inclusive classes [3]: deletion with insertion, deletion with insertion and duplication, deletion with insertion and multiple duplications, multiple deletions separated by a forward or inverted spacer, and deletion with inversion. Analysis of origin for the duplicated sequence revealed two characteristic locations relative to breakpoints - 20 to 60 bps and 2 to 6 kbps - and their generally later replication than the locations of breakpoints [2]. While these observations are likely to be related to the way SVs are generated during replication (e.g., they may suggest the coiling of DNA around the replication bubble), their exact meaning is yet to be deciphered. Mechanism for insertions of retrotransposable elements through reverse transcription of their mRNA is well characterized. Still, large-scale analysis revealed peculiar preference of transposon integration complexes for hypomethylated DNA [2]. Additionally, analysis of rare cases - when reverse transcriptase mistakenly integrates the mRNA of regular genes, thereby creating a processed pseudogene - exposed the association of gene expression during a cycle with pseudogene generation rate [4]. The closer maximum gene expression was to the end of metaphase the more pseudogenes it had on average, suggesting the coupling of retrotransposition to cell division [4].

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.