Encyclopedia Of DNA Elements Project Research Articles

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects.

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Epidemiological evidence for associations between variants in microRNA and cancer risk.

Numerous papers have reported variants in microRNAs (miRNAs) associated with cancer risk; these results, however, are controversial. We seek to offer an updated, comprehensive synopsis of genetic associations between single nucleotide polymorphisms (SNPs) in miRNAs and cancer risk. We did a systematic field synopsis and meta-analysis to investigate 29 SNPs in 24 miRNAs associated with risk of 18 different kinds of cancer based on data from 247 eligible articles. We graded levels of cumulative epidemiological evidence of significant association using Venice criteria and a false-positive report probability (FPRP) test. We constructed functional annotations for these variants using data from the Encyclopedia of DNA Elements Project. We used FPRP to find additional noteworthy associations between 278 SNPs in 117 miRNAs and risk of 26 cancers based on each SNP with one data source. Sixteen SNPs were statistically associated with risk of 17 cancers. We graded the cumulative epidemiological evidence as strong for statistical associations between 10 SNPs in 8 miRNAs and risk of 11 cancers, moderate for 9 SNPs with 12 cancers and weak for 11 SNPs with 11 cancers. Bioinformatics analysis suggested that the SNPs with strong evidence might fall in putative functional regions. In addition, 38 significant associations were observed in 38 SNPs and risk of 13 cancers. This study offered a comprehensive research on miRNA gene variants and cancer risk and provided referenced information for the genetic predisposition to cancer risk in future research.

Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research

Gene regulatory elements are central drivers of phenotypic variation and thus of critical importance towards understanding the genetics of complex traits. The Functional Annotation of Animal Genomes consortium was formed to collaboratively annotate the functional elements in animal genomes, starting with domesticated animals. Here we present an expansive collection of datasets from eight diverse tissues in three important agricultural species: chicken (Gallus gallus), pig (Sus scrofa), and cattle (Bos taurus). Comparative analysis of these datasets and those from the human and mouse Encyclopedia of DNA Elements projects reveal that a core set of regulatory elements are functionally conserved independent of divergence between species, and that tissue-specific transcription factor occupancy at regulatory elements and their predicted target genes are also conserved. These datasets represent a unique opportunity for the emerging field of comparative epigenomics, as well as the agricultural research community, including species that are globally important food resources.

Revealing Epigenetic Factors of circRNA Expression by Machine Learning in Various Cellular Contexts.

SummaryCircular RNAs (circRNAs) have been identified as naturally occurring RNAs that are highly represented in the eukaryotic transcriptome. Although a large number of circRNAs have been reported, the underlying regulatory mechanism of circRNAs biogenesis remains largely unknown. Here, we integrated in-depth multi-omics data including epigenome, transcriptome, and non-coding RNA and identified candidate circRNAs in six cellular contexts. Next, circRNAs were divided into two classes (high versus low) with different expression levels. Machine learning models were constructed that predicted circRNA expression levels based on 11 different histone modifications and host gene expression. We found that the models achieve great accuracy in predicting high versus low expressed circRNAs. Furthermore, the expression levels of host genes of circRNAs, H3k36me3, H3k79me2, and H4k20me1 contributed greatly to the classification models in six cellular contexts. In summary, all these results suggest that epigenetic modifications, particularly histone modifications, can effectively predict expression levels of circRNAs.

Cumulative Evidence for Associations between Genetic Variants and Risk of Esophageal Cancer.

A large number of studies have been conducted to investigate associations between genetic variants and esophageal cancer risk in the past several decades. However, findings from these studies have been generally inconsistent. We aimed to provide a summary of the current understanding of the genetic architecture of esophageal cancer susceptibility. We performed a comprehensive field synopsis and meta-analysis to evaluate associations between 95 variants in 70 genes or loci and esophageal cancer risk using data from 304 eligible publications, including 104,904 cases and 159,797 controls, through screening a total of 21,328 citations. We graded levels of cumulative epidemiologic evidence of a significant association with esophageal cancer using the Venice criteria and false-positive report probability tests. We constructed functional annotations for these variants using data from the Encyclopedia of DNA Elements Project and other databases. Thirty variants were nominally significantly associated with esophageal cancer risk. Cumulative epidemiologic evidence of a significant association with overall esophageal cancer, esophageal squamous cell carcinoma, or esophageal adenocarcinoma was strong for 13 variants in or near 13 genes (ADH1B, BARX1, CDKN1A, CHEK2, CLPTM1L, CRTC1, CYP1A1, EGF, LTA, MIR34BC, PLCE1, PTEN, and PTGS2). Bioinformatics analysis suggested that these variants and others correlated with them might fall in putative functional regions. Our study summarizes the current literature on the genetic architecture of esophageal cancer susceptibility and identifies several potential polymorphisms that could be involved in esophageal cancer susceptibility. These findings provide direction for future studies to identify new genetic factors for esophageal cancer.

A Highly Robust Model for Predicting Outcome of Multiple Myeloma Patients By Inferring Patient-Specific Transcription Factor Activity

Objective: Multiple myeloma (MM) is the second most common blood cancer in the United States. Clinical and pathological indicators are inadequate for accurate assessing prognosis of MM patients1. The objective of this study was to develop a robust computational model for predicting prognosis of MM patients by inferring transcription factor (TF) activities based on global gene expression measurements. Methods: The workflow is illustrated in Figure 1. Gene expression data from MM patients were retrieved from the Multiple Myeloma Research Foundation CoMMpass study2 (MMRF) and Gene Expression Omnibus (GEO). MM-specific TF-target gene relationships were derived from the ENCODE database3 and co-expression analysis was based on MMRF data. The activity for each TF was inferred using a rank-based enrichment approach based on the target gene expression measurements in individual patients4. A prognostic model was developed using Cox regression analysis based on the inferred TF activities that demonstrated the highest association with patient survival. The TF model performance was further evaluated using data from four independent MM cohorts, in which gene expression levels were measured using different platforms. Results: Based on MMRF data (767 patients), the inferred activity of four TFs, BATF, E2F1, MYBL2, and RAD21, showed the strongest association with patient survival. The derived prognostic model was validated in four independent MM data sets with a total of 1501 patients. Importantly, gene expression levels in the validation data sets were measured using microarray, which is different from the RNAseq used in training data sets, and supports the robustness of the TF activity model across different platforms (Figure 2). In addition, the TF activity model was able to predict clinical outcomes of patients that received bortezomib treatment, and it also revealed an association with metastasis in myeloma cell line xenograft experiments5 (Figure 3). Conclusion: We developed a highly robust prognostic model for predicting MM clinical outcomes. The inferred TF activity further improves our understanding of the underlying regulatory basis of MM progression. Reference 1. Rajkumar SV. Multiple myeloma: 2012 update on diagnosis, risk-stratification, and management. Am J Hematol. 2012;87(1):78-88. 2. l, a longitudinal study in multiple myeloma relating clinical outcomes to genomic and immunophenotypic profiles: Am Soc Hematology; 2013. 3. Consortium EP. The ENCODE (ENCyclopedia of DNA elements) project. Science 2004;306(5696):636-640. 4. Moerman T, Aibar Santos S, Bravo González-Blas C, et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics. 2018;35(12):2159-2161. 5. Shen Y, Mishima Y, Shi J, et al. Deciphering Clonal Evolution and Dissemination of Multiple Myeloma Cells In Vivo: Am Soc Hematology; 2018. Disclosures Abonour: Celgene: Consultancy, Research Funding; BMS: Consultancy; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Roodman:Amgen: Membership on an entity's Board of Directors or advisory committees.

Variants in the PSCA gene associated with risk of cancer and nonneoplastic diseases: systematic research synopsis, meta-analysis and epidemiological evidence.

Variants in the prostate stem cell antigen (PSCA) gene have been linked with risk of multiple cancers and other diseases. But results have been inconclusive and no systematic research synopsis has been available. We did a comprehensive meta-analysis to investigate associations between variants in this gene and risk of nine cancers and four nonneoplastic diseases based on data from 55 publications including 81 961 cases and 442 932 controls. We graded levels of cumulative epidemiological evidence of a significant association using the Venice criteria and false-positive report probability tests. We performed functional annotation for these variants using data from the Encyclopedia of DNA Elements Project and other public databases. We found that six variants were nominally significantly associated with an increased or reduced risk of three cancers and three nonneoplastic diseases (P < 0.05). Cumulative evidence of an association was graded as strong for rs2294008 [odds ratio (OR) = 1.32, P = 5.1 × 10-33], rs2976392 (OR = 1.29, P = 1.8 × 10-8), rs9297976 (OR = 0.75, P = 1.4 × 10-7), rs2976391 (OR = 1.38, P = 6.1 × 10-5) and rs138377917 (OR = 0.53, P = 0.008) with gastric cancer, rs2294008 with bladder cancer (OR = 1.15, P = 8.0 × 10-19), gastritis (OR = 1.35, P = 1.2 × 10-5), duodenal ulcer (OR = 0.68, P = 2.4 × 10-57) and gastric ulcer (OR = 0.88, P = 1.7 × 10-7). Data from the Encyclopedia of DNA Elements Project and other databases showed that these variants and other variants correlated with them might fall in putative functional regions. In conclusion, this study provides summary evidence that variants in the PSCA gene are associated with risk of gastric and bladder cancer, gastritis, as well as duodenal and gastric ulcer and highlights the significant role of this gene in the pathogenesis of these diseases.

ENCODE and the parts of the human genome

This paper examines a specific kind of part-whole relations that exist in the molecular genetic domain. The central question is under which conditions a particular molecule, such as a DNA sequence, is a biological part of the human genome. I address this question by analyzing how biologists in fact partition the human genome into parts. This paper thus presents a case study in the metaphysics of biological practice. I develop a metaphysical account of genomic parthood by analyzing the investigative and reasoning practices in the ENCODE (ENCyclopedia Of DNA Elements) project. My account reveals two conditions that determine whether a molecule is a part of the human genome (i.e., a genomic part). First, genomic parts must possess a causal role function in the genome as a whole, that is, their functions must contribute to the genome directing the overall functioning of the cell. Second, genomic parts must have a specific chemical structure and be actual segments of the DNA sequence of the genome.

Identification of genome-wide non-canonical spliced regions and analysis of biological functions for spliced sequences using Read-Split-Fly

BackgroundIt is generally thought that most canonical or non-canonical splicing events involving U2- and U12 spliceosomes occur within nuclear pre-mRNAs. However, the question of whether at least some U12-type splicing occurs in the cytoplasm is still unclear. In recent years next-generation sequencing technologies have revolutionized the field. The “Read-Split-Walk” (RSW) and “Read-Split-Run” (RSR) methods were developed to identify genome-wide non-canonical spliced regions including special events occurring in cytoplasm. As the significant amount of genome/transcriptome data such as, Encyclopedia of DNA Elements (ENCODE) project, have been generated, we have advanced a newer more memory-efficient version of the algorithm, “Read-Split-Fly” (RSF), which can detect non-canonical spliced regions with higher sensitivity and improved speed. The RSF algorithm also outputs the spliced sequences for further downstream biological function analysis.ResultsWe used open access ENCODE project RNA-Seq data to search spliced intron sequences against the U12-type spliced intron sequence database to examine whether some events could occur as potential signatures of U12-type splicing. The check was performed by searching spliced sequences against 5’ss and 3’ss sequences from the well-known orthologous U12-type spliceosomal intron database U12DB. Preliminary results of searching 70 ENCODE samples indicated that the presence of 5’ss with U12-type signature is more frequent than U2-type and prevalent in non-canonical junctions reported by RSF. The selected spliced sequences have also been further studied using miRBase to elucidate their functionality. Preliminary results from 70 samples of ENCODE datasets show that several miRNAs are prevalent in studied ENCODE samples. Two of these are associated with many diseases as suggested in the literature. Specifically, hsa-miR-1273 and hsa-miR-548 are associated with many diseases and cancers.ConclusionsOur RSF pipeline is able to detect many possible junctions (especially those with a high RPKM) with very high overall accuracy and relative high accuracy for novel junctions. We have incorporated useful parameter features into the pipeline such as, handling variable-length read data, and searching spliced sequences for splicing signatures and miRNA events. We suggest RSF, a tool for identifying novel splicing events, is applicable to study a range of diseases across biological systems under different experimental conditions.

NIH Extends Support for Genomics Catalog.

Earlier this month the NIH announced plans to expand the scope of its Encyclopedia of DNA Elements Project, better known as ENCODE. The new funds will support efforts to characterize gene regulatory elements in various cell types.

Genome-Wide Analysis of Chromatin Accessibility in Arabidopsis Infected with Pseudomonas syringae.

Changes in chromatin accessibility are an important aspect of the molecular changes that occur in eukaryotic cells responding to stress, and they appear to play a critical role in stress-induced transcriptional activation/reprogramming and epigenetic changes. In plants, pathogen infection has been shown to induce rapid and drastic transcriptional reprogramming; growing evidence suggests that chromatin remodeling plays an essential role in this phenomenon. The recent development of genomic tools to assess chromatin accessibility presents a significant opportunity to investigate the relationship between chromatin dynamicity and gene expression. In this protocol, we have adopted a popular chromatin accessibility assay, DNase-seq, to measure chromatin accessibility in Arabidopsis infected with the bacterial pathogen Pseudomonas syringae pv. tomato (Pst). DNase-seq provides information on chromatin accessibility through the sequencing of DNA fragments generated by DNase I digestion of open chromatin, followed by mapping these sequences on a reference genome. Of the two popular DNase-seq approaches, we based our method on the Stamatoyannopoulos protocol, which involves two DNase cleavages rather than a single cleavage, followed by size fractionation. Please note that this two-cleavage approach is widely accepted and has been used extensively by ENCODE (Encyclopedia of DNA Elements) project, a public research consortium investigating cis- and trans-elements in the transcriptional regulation in animal cells. To enhance the quality of the chromatin accessibility assay, we modified this protocol by including two centrifugation steps for nuclear enrichment and size fractionation and an extra washing step for removal of chloroplasts and Pst. The outcomes obtained by this approach are also discussed.

Establishment of Enhancer Elements during Erythro-Megakaryopoiesis

Enhancers are cis acting regulatory modules associated with lineage-specific gene expression. The Encyclopedia of DNA Elements project (ENCODE) showed that enhancers are in open chromatin regions identified by the Assay for Transposable-Accessible Chromatin (ATAC) and bound with histone H3 is mono-methylated at lysine 4 (H3K4me1). Chromatin regions marked by H3K4me1 alone identifies "poised" enhancers (not active), while the additional presence and histone H3 acetylated at lysine 27 (H3K27ac) identifies "active" enhancers. To establish a genome-wide enhancer map in the erythro-megakaryocytic lineage, we performed ChIPSeq of H3K4me1 and H3K27ac in primary erythroblasts (EB) and megakaryocytes (MEG) isolated from mouse bone marrow. We also assayed primary mouse EB, MEG, hematopoietic stem and progenitor cells (LSK), and common myeloid progenitor cells (CMP) for open chromatin regions with ATAC and transcriptome profiling by RNASeq. Finally, we compared histone-defined enhancers in mature cells with the corresponding ATAC regions in progenitor cells to identify the preservation of poised and active enhancers through hematopoiesis.We identified 6565 and 3543 active enhancers in EB and MEG respectively; approximately 10% (434) were shared. We further refined our enhancer set to the ~90% of EB and MEG active enhancers that overlap with ATAC regions (AER, histone-marked active enhancer within an ATAC region). To identify enhancers in the open chromatin of progenitor cells, we overlaid EB and MEG AER with CMP ATAC sites. This revealed that 82% (5226/6399) of EB AER and 87% (1437/3302) of MEG AER were present in CMP. Overlaying the EB and MEG AER onto LSK ATAC showed that 67% (4278/6399) of EB-specific AER and 79% (2594/3302) of MEG-specific AER overlapped with LSK ATAC sites.To identify the EB and MEG AER in LSK-accessible chromatin that are active (not poised), we compared our LSK enhancer set with the indexing-first ChIP (iChIP) histone marks identified by Lara-Astiaso et al., (Science, 2014). 1840 of the 4278 (43%) LSK-accessible EB AER overlapped with LSK iChIP H3K4me1 marks; 632 of these (15% overall) also had the active H3K27ac mark. 1083 of the 2594 (42%) MEG AER that were present in LSK overlapped with LSK iChIP H3K4me1 marks; 241 of these (9% overall) had the H3K27ac mark. For both EB and MEG, AER not marked by iChIP K4me1 were within gene bodies.To further characterize enhancer roles in lineage commitment, we profiled super enhancers (SE), which have highly lineage-specific activity. We defined SE as the 2% of AER with the highest H3K27ac levels (Hnisz et al., Cell, 2013) and identified 101 EB and 98 MEG SE; all of these were cell-specific. We found that 65% (66/101) of EB SE and 87% (85/98) of MEG SE overlapped with LSK ATAC sites. 30 of the 66 (45%) LSK-accessible EB SE overlapped with LSK iChIP H3K4me1 marks; 9 of these (14% overall) also had the active H3K27ac mark. In comparison, 15 of the 85 (18%) LSK-accessible MEG SE overlapped with LSK iChIP H3K4me1 marks; 4 of these (5% overall) also had the active H3K27ac mark.We correlated our LSK-accessible, iChIP-marked active AER with gene expression by assigning each AER to the nearest gene. We then used RNASeq data to perform gene set enrichment analysis via Ingenuity Pathway Analysis. We found that the LSK-accessible EB-specific AER gene set included erythropoietin-regulated genes (p £ 9x10-5) and genes associated with Fanconi anemia (6x10-4). Conversely, LSK-accessible and iChIP-active MEG AER were associated an increase of progenitor cell populations and proliferation activities for several hematopoietic lineages (p £ 2x10-5). However, the genes in the non-megakaryocyte pathways were significantly down-regulated as LSK committed to the megakaryocyte lineage.In summary, our results demonstrate the establishment of poised and active enhancers in hematopoietic progenitors and their preservation through erythro-megakaryopoiesis. We show that >40% of EB and MEG enhancers were also enhancers in LSK and CMP; the EB and MEG enhancers that were not LSK enhancers were primarily within gene bodies. We also found that MEG, but not EB, super enhancers were less likely than conventional enhancers to be established in LSK. Finally, our data show that, while LSK-established EB enhancers target EB-specific functions, LSK-established MEG enhancers have more universal hematopoietic functions that are down-regulated during megakaryocytic lineage commitment. DisclosuresNo relevant conflicts of interest to declare.

Rational Design of Small Molecules Targeting Oncogenic Noncoding RNAs from Sequence.

The discovery of RNA catalysis in the 1980s and the dissemination of the human genome sequence at the start of this century inspired investigations of the regulatory roles of noncoding RNAs in biology. In fact, the Encyclopedia of DNA Elements (ENCODE) project has shown that only 1-2% of the human genome encodes protein, yet 75% is transcribed into RNA. Functional studies both preceding and following the ENCODE project have shown that these noncoding RNAs have important roles in regulating gene expression, developmental timing, and other critical functions. RNA's diverse roles are often a consequence of the various folds that it adopts. The single-stranded nature of the biopolymer enables it to adopt intramolecular folds with noncanonical pairings to lower its free energy. These folds can be scaffolds to bind proteins or to form frameworks to interact with other RNAs. Not surprisingly, dysregulation of certain noncoding RNAs has been shown to be causative of disease. Given this as the background, it is easy to see why it would be useful to develop methods that target RNA and manipulate its biology in rational and predictable ways. The antisense approach has afforded strategies to target RNAs via Watson-Crick base pairing and has typically focused on targeting partially unstructured regions of RNA. Small molecule strategies to target RNA would be desirable not only because compounds could be lead optimized via medicinal chemistry but also because structured regions within an RNA of interest could be targeted to directly interfere with RNA folds that contribute to disease. Additionally, small molecules have historically been the most successful drug candidates. Until recently, the ability to design small molecules that target non-ribosomal RNAs has been elusive, creating the perception that they are "undruggable". In this Account, approaches to demystify targeting RNA with small molecules are described. Rather than bulk screening for compounds that bind to singular targets, which is the purview of the pharmaceutical industry and academic institutions with high throughput screening facilities, we focus on methods that allow for the rational design of small molecules toward biological RNAs. One enabling and foundational technology that has been developed is two-dimensional combinatorial screening (2DCS), a library-versus-library selection approach that allows the identification of the RNA motif binding preferences of small molecules from millions of combinations. A landscape map of the 2DCS-defined and annotated RNA motif-small molecule interactions is then placed into Inforna, a computational tool that allows one to mine these interactions against an RNA of interest or an entire transcriptome. Indeed, this approach has been enabled by tools to annotate RNA structure from sequence, an invaluable asset to the RNA community and this work, and has allowed for the rational identification of "druggable" RNAs in a target agnostic fashion.

Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus.

BackgroundMultiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.MethodWe performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation.ResultsAnalysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06–1.12; P = 3 × 10-9), rs805510 (OR = 1.08, 95 % CI = 1.04–1.12, P = 2 × 10-5), and rs1871152 (OR = 1.04, 95 % CI = 1.02–1.06; P = 2 × 10-4) identified in the general populations, and rs113824616 (P = 7 × 10-5) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P < 0.05 in East Asians, but none of the associations were statistically significant in African descendants. Multiple candidate functional variants are located in putative enhancer sequences. Chromatin interaction data suggested that PTHLH was the likely target gene of these enhancers. Of the six variants with the strongest evidence of potential functionality, rs11049453 was statistically significantly associated with the expression of PTHLH and its nearby gene CCDC91 at P < 0.05.ConclusionThis study identified four independent association signals at 12p11 and revealed potentially functional variants, providing additional insights into the underlying biological mechanism(s) for the association observed between variants at 12p11 and breast cancer risk.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0718-0) contains supplementary material, which is available to authorized users.

Fine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer.

Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2) = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.

Long noncoding RNAs in gastric cancer carcinogenesis and metastasis.

Recent studies of the human transcriptome, most prominently by the ENCyclopedia Of DNA Elements project, have revealed an unexpected number of noncoding RNAs (ncRNAs). Long noncoding RNAs (lncRNAs) are typically referred to a heterogeneous group of polyadenylated long ncRNAs, with a length of > 200 nt. LncRNAs constitute an integral part of tumor biology, with many lncRNAs discovered to be aberrantly expressed in various cancer types. They are involved in many aspects of cancer pathogenesis from its initiation to progression, metastasis and treatment response. Gastric cancer (GC) is the third leading cause of cancer death worldwide. Despite the current improvements of life expectancy and survival rate, most of the patients are diagnosed when their cancer has been progressed to advanced stages. Therefore, unraveling the molecular mechanisms of GC to find early-stage biomarkers is urgent. As the list of lncRNAs with deregulated expression in GC is steadily expanding, these molecules offer a source for developing GC-specific biomarkers. In this review, we will present and discuss those lncRNAs whose expression has been shown to be deregulated in GC.

Empirical estimation of sequencing error rates using smoothing splines.

BackgroundNext-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows.ResultsWe performed simulation studies using a frequency-based approach to generate the read and shadow counts directly, which can mimic the real sequence counts data structure. Using simulation, we investigated the performance of the proposed approach and compared it to that of shadow linear regression. The proposed approach provided more accurate error rate estimations than the shadow linear regression approach for all the scenarios tested. We also applied the proposed approach to assess the error rates for the sequence data from the MicroArray Quality Control project, a mutation screening study, the Encyclopedia of DNA Elements project, and bacteriophage PhiX DNA samples.ConclusionsThe proposed empirical error rate estimation approach does not assume a linear relationship between the error-free read and shadow counts and provides more accurate estimations of error rates for next-generation, short-read sequencing data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1052-3) contains supplementary material, which is available to authorized users.

A decade of human genome project conclusion: Scientific diffusion about our genome knowledge.

The Human Genome Project (HGP) was initiated in 1990 and completed in 2003. It aimed to sequence the whole human genome. Although it represented an advance in understanding the human genome and its complexity, many questions remained unanswered. Other projects were launched in order to unravel the mysteries of our genome, including the ENCyclopedia of DNA Elements (ENCODE). This review aims to analyze the evolution of scientific knowledge related to both the HGP and ENCODE projects. Data were retrieved from scientific articles published in 1990-2014, a period comprising the development and the 10 years following the HGP completion. The fact that only 20,000 genes are protein and RNA-coding is one of the most striking HGP results. A new concept about the organization of genome arose. The ENCODE project was initiated in 2003 and targeted to map the functional elements of the human genome. This project revealed that the human genome is pervasively transcribed. Therefore, it was determined that a large part of the non-protein coding regions are functional. Finally, a more sophisticated view of chromatin structure emerged. The mechanistic functioning of the genome has been redrafted, revealing a much more complex picture. Besides, a gene-centric conception of the organism has to be reviewed. A number of criticisms have emerged against the ENCODE project approaches, raising the question of whether non-conserved but biochemically active regions are truly functional. Thus, HGP and ENCODE projects accomplished a great map of the human genome, but the data generated still requires further in depth analysis. © 2016 by The International Union of Biochemistry and Molecular Biology, 44:215-223, 2016.

Principles of metadata organization at the ENCODE data coordination center.

The Encyclopedia of DNA Elements (ENCODE) Data Coordinating Center (DCC) is responsible for organizing, describing and providing access to the diverse data generated by the ENCODE project. The description of these data, known as metadata, includes the biological sample used as input, the protocols and assays performed on these samples, the data files generated from the results and the computational methods used to analyze the data. Here, we outline the principles and philosophy used to define the ENCODE metadata in order to create a metadata standard that can be applied to diverse assays and multiple genomic projects. In addition, we present how the data are validated and used by the ENCODE DCC in creating the ENCODE Portal (https://www.encodeproject.org/).Database URL: www.encodeproject.org