Annotation of nuclear lncRNAs based on chromatin interactions

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

BACKGROUND

Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.

RESULTS

Here, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.

CONCLUSIONS

Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

Human papillomavirus integration transforms chromatin to drive oncogenesis

BACKGROUND

Human papillomavirus (HPV) drives almost all cervical cancers and up to 70% of head and neck cancers. Frequent integration into the host genome occurs predominantly in tumorigenic types of HPV. We hypothesize that changes in chromatin state at the location of integration can result in changes in gene expression that contribute to the tumorigenicity of HPV.

RESULTS

We find that viral integration events often occur along with changes in chromatin state and expression of genes near the integration site. We investigate whether introduction of new transcription factor binding sites due to HPV integration could invoke these changes. Some regions within the HPV genome, particularly the position of a conserved CTCF binding site, show enriched chromatin accessibility signal. ChIP-seq reveals that the conserved CTCF binding site within the HPV genome binds CTCF in 4 HPV^+ cancer cell lines. Significant changes in CTCF binding pattern and increases in chromatin accessibility occur exclusively within 100 kbp of HPV integration sites. The chromatin changes co-occur with out-sized changes in transcription and alternative splicing of local genes. Analysis of The Cancer Genome Atlas (TCGA) HPV⁺ tumors indicates that HPV integration upregulates genes which have significantly higher essentiality scores compared to randomly selected upregulated genes from the same tumors.

CONCLUSIONS

Our results suggest that introduction of a new CTCF binding site due to HPV integration reorganizes chromatin state and upregulates genes essential for tumor viability in some HPV⁺ tumors. These findings emphasize a newly recognized role of HPV integration in oncogenesis.

Exploring the merits of research performance measures that comply with the San Francisco Declaration on Research Assessment and strategies to overcome barriers of adoption: qualitative interviews with administrators and researchers

BACKGROUND

In prior research, we identified and prioritized ten measures to assess research performance that comply with the San Francisco Declaration on Research Assessment, a principle adopted worldwide that discourages metrics-based assessment. Given the shift away from assessment based on Journal Impact Factor, we explored potential barriers to implementing and adopting the prioritized measures.

METHODS

We identified administrators and researchers across six research institutes, conducted telephone interviews with consenting participants, and used qualitative description and inductive content analysis to derive themes.

RESULTS

We interviewed 18 participants: 6 administrators (research institute business managers and directors) and 12 researchers (7 on appointment committees) who varied by career stage (2 early, 5 mid, 5 late). Participants appreciated that the measures were similar to those currently in use, comprehensive, relevant across disciplines, and generated using a rigorous process. They also said the reporting template was easy to understand and use. In contrast, a few administrators thought the measures were not relevant across disciplines. A few participants said it would be time-consuming and difficult to prepare narratives when reporting the measures, and several thought that it would be difficult to objectively evaluate researchers from a different discipline without considerable effort to read their work. Strategies viewed as necessary to overcome barriers and support implementation of the measures included high-level endorsement of the measures, an official launch accompanied by a multi-pronged communication strategy, training for both researchers and evaluators, administrative support or automated reporting for researchers, guidance for evaluators, and sharing of approaches across research institutes.

CONCLUSIONS

While participants identified many strengths of the measures, they also identified a few limitations and offered corresponding strategies to address the barriers that we will apply at our organization. Ongoing work is needed to develop a framework to help evaluators translate the measures into an overall assessment. Given little prior research that identified research assessment measures and strategies to support adoption of those measures, this research may be of interest to other organizations that assess the quality and impact of research.

Motif elucidation in ChIP-seq datasets with a knockout control

Chromatin immunoprecipitation-sequencing (ChIP-seq) is widely used to find transcription factor binding sites, but suffers from various sources of noise. Knocking out the target factor mitigates noise by acting as a negative control. Paired wild-type and knockout experiments can generate improved motifs but require optimal differential analysis. We introduce peaKO—a computational method to automatically optimize motif analyses with knockout controls, which we compare to two other methods. PeaKO often improves elucidation of the target factor and highlights the benefits of knockout controls, which far outperform input controls. It is freely available at https://peako.hoffmanlab.org.

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls

Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). We developed a set of synthetic spike-in DNA controls for cfMeDIP-seq to provide a simple and inexpensive reference for quantitative normalization. We designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G + C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). Using 0.01 ng of spike-in controls enables training a generalized linear model that absolutely quantifies methylated cfDNA in MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases.

Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).

Assessing and assuring interoperability of a genomics file format

Motivation

Bioinformatics software tools operate largely through the use of specialized genomics file formats. Often these formats lack formal specification, making it difficult or impossible for the creators of these tools to robustly test them for correct handling of input and output. This causes problems in interoperability between different tools that, at best, wastes time and frustrates users. At worst, interoperability issues could lead to undetected errors in scientific results.

Results

We developed a new verification system, Acidbio, which tests for correct behavior in bioinformatics software packages. We crafted tests to unify correct behavior when tools encounter various edge cases—potentially unexpected inputs that exemplify the limits of the format. To analyze the performance of existing software, we tested the input validation of 80 Bioconda packages that parsed the Browser Extensible Data (BED) format. We also used a fuzzing approach to automatically perform additional testing. Of 80 software packages examined, 75 achieved less than 70% correctness on our test suite. We categorized multiple root causes for the poor performance of different types of software. Fuzzing detected other errors that the manually designed test suite could not. We also created a badge system that developers can use to indicate more precisely which BED variants their software accepts and to advertise the software’s performance on the test suite.

Availability and implementation

Acidbio is available at https://github.com/hoffmangroup/acidbio.

Supplementary information

Supplementary data are available at Bioinformatics online.

GA4GH: International policies and standards for data sharing across genomic research and healthcare

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Reproducibility standards for machine learning in the life sciences

To make machine learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model, and code publication, programming best practices, and workflow automation. By meeting these standards, the community of researchers applying machine learning methods in the life sciences can ensure that their analyses are worthy of trust.

this article has been peer reviewed.

Segmentation and genome annotation algorithms for identifying chromatin state and other genomic patterns

Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as chromatin immunoprecipitation-sequencing (ChIP-seq) measurements of histone modifications or transcription factor binding. They partition the genome and assign a label to each segment such that positions with the same label exhibit similar patterns of input data. SAGA algorithms discover categories of activity such as promoters, enhancers, or parts of genes without prior knowledge of known genomic elements. In this sense, they generally act in an unsupervised fashion like clustering algorithms, but with the additional simultaneous function of segmenting the genome. Here, we review the common methodological framework that underlies these methods, review variants of and improvements upon this basic framework, and discuss the outlook for future work. This review is intended for those interested in applying SAGA methods and for computational researchers interested in improving upon them.

Tumor-Naïve Multimodal Profiling of Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma

PURPOSE

Circulating tumor DNA (ctDNA) enables personalized treatment strategies in oncology by providing a noninvasive source of clinical biomarkers. In patients with low ctDNA abundance, tumor-naïve methods are needed to facilitate clinical implementation. Here, using locoregionally confined head and neck squamous cell carcinoma (HNSCC) as an example, we demonstrate tumor-naïve detection of ctDNA by simultaneous profiling of mutations and methylation.

EXPERIMENTAL DESIGN

We conducted CAncer Personalized Profiling by deep Sequencing (CAPP-seq) and cell-free Methylated DNA ImmunoPrecipitation and high-throughput sequencing (cfMeDIP-seq) for detection of ctDNA-derived somatic mutations and aberrant methylation, respectively. We analyzed 77 plasma samples from 30 patients with stage I-IVA human papillomavirus-negative HNSCC as well as plasma samples from 20 risk-matched healthy controls. In addition, we analyzed leukocytes from patients and controls.

RESULTS

CAPP-seq identified mutations in 20 of 30 patients at frequencies similar to that of The Tumor Genome Atlas (TCGA). Differential methylation analysis of cfMeDIP-seq profiles identified 941 ctDNA-derived hypermethylated regions enriched for CpG islands and HNSCC-specific methylation patterns. Both methods demonstrated an association between ctDNA abundance and shorter fragment lengths. In addition, mutation- and methylation-based ctDNA abundance was highly correlated (r > 0.85). Patients with detectable pretreatment ctDNA by both methods demonstrated significantly worse overall survival (HR = 7.5; P = 0.025) independent of clinical stage, with lack of ctDNA clearance post-treatment strongly correlating with recurrence. We further leveraged cfMeDIP-seq profiles to validate a prognostic signature identified from TCGA samples.

CONCLUSIONS

Tumor-naïve detection of ctDNA by multimodal profiling may facilitate biomarker discovery and clinical use in low ctDNA abundance applications.

Transparency and reproducibility in artificial intelligence

Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field.

Functional annotation of human long noncoding RNAs via molecular phenotyping

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types

Semi-automated genome annotation methods such as Segway take as input a set of genome-wide measurements such as of histone modification or DNA accessibility and output an annotation of genomic activity in the target cell type. Here we present annotations of 164 human cell types using 1615 data sets. To produce these annotations, we automated the label interpretation step to produce a fully automated annotation strategy. Using these annotations, we developed a measure of the importance of each genomic position called the “conservation-associated activity score.” We further combined all annotations into a single, cell type-agnostic encyclopedia that catalogs all human regulatory elements.

Umap and Bismap: quantifying genome and methylome mappability

Short-read sequencing enables assessment of genetic and biochemical traits of individual genomic regions, such as the location of genetic variation, protein binding and chemical modifications. Every region in a genome assembly has a property called 'mappability', which measures the extent to which it can be uniquely mapped by sequence reads. In regions of lower mappability, estimates of genomic and epigenomic characteristics from sequencing assays are less reliable. These regions have increased susceptibility to spurious mapping from reads from other regions of the genome with sequencing errors or unexpected genetic variation. Bisulfite sequencing approaches used to identify DNA methylation exacerbate these problems by introducing large numbers of reads that map to multiple regions. Both to correct assumptions of uniformity in downstream analysis and to identify regions where the analysis is less reliable, it is necessary to know the mappability of both ordinary and bisulfite-converted genomes. We introduce the Umap software for identifying uniquely mappable regions of any genome. Its Bismap extension identifies mappability of the bisulfite-converted genome. A Umap and Bismap track hub for human genome assemblies GRCh37/hg19 and GRCh38/hg38, and mouse assemblies GRCm37/mm9 and GRCm38/mm10 is available at https://bismap.hoffmanlab.org for use with genome browsers.

DNAmod: the DNA modification database

Covalent DNA modifications, such as 5-methylcytosine (5mC), are increasingly the focus of numerous research programs. In eukaryotes, both 5mC and 5-hydroxymethylcytosine (5hmC) are now recognized as stable epigenetic marks, with diverse functions. Bacteria, archaea, and viruses contain various other modified DNA nucleobases. Numerous databases describe RNA and histone modifications, but no database specifically catalogues DNA modifications, despite their broad importance in epigenetic regulation. To address this need, we have developed DNAmod: the DNA modification database. DNAmod is an open-source database ( https://dnamod.hoffmanlab.org ) that catalogues DNA modifications and provides a single source to learn about their properties. DNAmod provides a web interface to easily browse and search through these modifications. The database annotates the chemical properties and structures of all curated modified DNA bases, and a much larger list of candidate chemical entities. DNAmod includes manual annotations of available sequencing methods, descriptions of their occurrence in nature, and provides existing and suggested nomenclature. DNAmod enables researchers to rapidly review previous work, select mapping techniques, and track recent developments concerning modified bases of interest.

Top considerations for creating bioinformatics software documentation

Investing in documenting your bioinformatics software well can increase its impact and save your time. To maximize the effectiveness of your documentation, we suggest following a few guidelines we propose here. We recommend providing multiple avenues for users to use your research software, including a navigable HTML interface with a quick start, useful help messages with detailed explanation and thorough examples for each feature of your software. By following these guidelines, you can assure that your hard work maximally benefits yourself and others.

Segway 2.0: Gaussian mixture models and minibatch training

Summary

Segway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.

Availability and implementation

Segway and its source code are freely available for download at http://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802939) and datasets (https://doi.org/10.5281/zenodo.802906) for this paper's analysis.

Contact

michael.hoffman@utoronto.ca.

Supplementary information

Supplementary data are available at Bioinformatics online.

Sensitive tumour detection and classification using plasma cell-free DNA methylomes

The use of liquid biopsies for cancer detection and management is rapidly gaining prominence¹. Current methods for the detection of circulating tumour DNA involve sequencing somatic mutations using cell-free DNA, but the sensitivity of these methods may be low among patients with early-stage cancer given the limited number of recurrent mutations^2-5. By contrast, large-scale epigenetic alterations-which are tissue- and cancer-type specific-are not similarly constrained⁶ and therefore potentially have greater ability to detect and classify cancers in patients with early-stage disease. Here we develop a sensitive, immunoprecipitation-based protocol to analyse the methylome of small quantities of circulating cell-free DNA, and demonstrate the ability to detect large-scale DNA methylation changes that are enriched for tumour-specific patterns. We also demonstrate robust performance in cancer detection and classification across an extensive collection of plasma samples from several tumour types. This work sets the stage to establish biomarkers for the minimally invasive detection, interception and classification of early-stage cancers based on plasma cell-free DNA methylation patterns.

Iriomoteolides: novel chemical tools to study actin dynamics

Despite its promising biological profile, the cellular targets of iriomoteolide-3a, a novel 15-membered macrolide isolated from Amphidinium sp., have remained unknown. A small library of non-natural iriomoteolide-3a analogues is presented here as a result of a novel, highly convergent, catalysis-based scaffold-diversification campaign, which revealed the suitable sites for chemical editing in the original core. We provide compelling experimental evidence for actin as one of iriomoteolides' primary cellular targets, establishing the ability of these secondary metabolites to inhibit cell migration, induce severe morphological changes in cells and cause a reversible cytoplasmic retraction and reduction of F-actin fibers in a time and dose dependent manner. These results are interpreted in light of the ability of iriomoteolides to stabilize F-actin filaments. Molecular dynamics simulations provide evidence for iriomoteolide-3a binding to the barbed end of G-actin. These results showcase iriomoteolides as novel and easily tunable chemical probes for the in vitro study of actin dynamics in the context of cell motility processes including cell invasion and division.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Genome Informatics 2016

A report on the Genome Informatics conference, held at the Wellcome Genome Campus Conference Centre, Hinxton, United Kingdom, 19-22 September 2016.

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data

A cell's epigenome arises from interactions among regulatory factors-transcription factors and histone modifications-co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC-HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu.

Stimulation of regressing subordinate follicles of wave 2 with a gonadotropin product in heifers

The recovery of regressing wave-2 subordinate follicles was studied by treating heifers with a gonadotropin product that had about 84% and 16% of follicle-stimulating hormone and luteinizing hormone activity, respectively. A treated group (n = 8) received a single dose of 50 mg (2.5 mL) of the gonadotropin product, and a control group (n = 8) received 2.5 mL of saline vehicle. The group assignment of heifers was not known to the ultrasonographer who tracked the follicles and measured follicle diameters. Follicle measurements began on the day of expected follicle deviation in wave 2 (largest follicle closest to 8.5 mm), and treatment (hour 0) was given on Day 13.4 ± 0.2 (Day 0 = ovulation) when the dominant follicles of waves 1 and 2 were 14.1 ± 0.3 mm and 10.7 ± 0.1 mm, respectively. Subordinate follicles of wave 2 that had regressed to a 3-mm category (3.0-3.9 mm) or 4-mm category by hour 0 decreased in diameter for at least 48 h before hour 0, whereas follicles that were in the 5-mm or 6-mm categories at hour 0 did not change significantly in diameter during the previous 48 h. About 55% of the follicles that had regressed to the 3-mm and 4-mm categories at hour 0% and 78% of the follicles in the 5-mm and 6-mm categories increased in diameter after gonadotropin treatment, whereas follicles in the control group continued to decrease (regress) in diameter. The follicles for each of the 4 diameter categories were greater (P < 0.05) in diameter 9 h after treatment in the treated group than in the control group. The dominant follicle of wave 1 and the largest subordinate follicle of wave 2 in the treated group also increased in diameter so that diameter was greater (P < 0.05) than in the controls at hour 9. The results demonstrated that subordinate follicles of wave 2 that had decreased in diameter (regressed) for at least 48 h retained the capability to recover as indicated by a diameter increase when exposed to a gonadotropin product.

Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression

The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term “specific expression domains.” We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data.

Extending reference assembly models

The human genome reference assembly is crucial for aligning and analyzing sequence data, and for genome annotation, among other roles. However, the models and analysis assumptions that underlie the current assembly need revising to fully represent human sequence diversity. Improved analysis tools and updated data reporting formats are also required.

Endocrinology of number of follicular waves per estrous cycle and contralateral or ipsilateral relationship between corpus luteum and preovulatory follicle in heifers

A 3-d extension of the luteal phase occurs in interovulatory intervals (IOIs) with a contralateral relationship between the corpus luteum (CL) and preovulatory follicle with 3 follicular waves (Contra-3W group). Concentrations of FSH, progesterone, LH, and estradiol-17β for the ipsilateral versus contralateral CL and/or follicle relationship and 2 versus 3 waves per IOI were studied in 14 heifers. Follicular waves and FSH surges were designated 1, 2, or 3, according to order of occurrence in the IOI. The day (day 0 = ovulation) of the FSH peak in surge 2 occurred earlier (P < 0.02) in 3-wave IOIs (day 6.3 ± 0.5) than in 2-wave IOIs (day 8.5 ± 0.5). Mean FSH was higher in 3-wave than in 2-wave IOI on 82% of the days in the IOI. Repeatability or individuality in FSH concentration was indicated by a correlation (r = 0.54, P < 0.04) in FSH concentrations between ovulations at the beginning and at the end of the IOI. Concentrations of LH and estradiol increased (P < 0.05) near the beginning of the luteolytic period in 2-wave IOI regardless of the CL and/or follicle relationship. In the Contra-3W group, LH and estradiol remained at basal concentrations concurrently with FSH surge 3 and extension of the luteal phase. The hypotheses were supported that FSH surge 2 occurs earlier in 3-wave IOIs than in 2-wave IOIs and that the development of 3-wave IOIs occurs in individuals with greater FSH concentrations. Extension of the luteal phase in the Contra-3W group was temporally associated with lower concentrations of LH and estradiol.

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals.

Integrative annotation of chromatin elements from ENCODE data

The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape.

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.

Exploratory analysis of genomic segmentations with Segtools

Background

As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale. Many functional genomics experiments involve partitioning the genome into labeled segments, such that segments sharing the same label exhibit one or more biochemical or functional traits. For example, a collection of ChlP-seq experiments yields a compendium of peaks, each labeled with one or more associated DNA-binding proteins. Similarly, manually or automatically generated annotations of functional genomic elements, including cis-regulatory modules and protein-coding or RNA genes, can also be summarized as genomic segmentations.

Results

We present a software toolkit called Segtools that simplifies and automates the exploration of genomic segmentations. The software operates as a series of interacting tools, each of which provides one mode of summarization. These various tools can be pipelined and summarized in a single HTML page. We describe the Segtools toolkit and demonstrate its use in interpreting a collection of human histone modification data sets and Plasmodium falciparum local chromatin structure data sets.

Conclusions

Segtools provides a convenient, powerful means of interpreting a genomic segmentation.

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data

Motivation: A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem.

Results: We present a dynamic Bayesian network-based approach for the identification and assignment of statistical confidence estimates to protein-binding footprints from digital genomic footprinting data. The method, DBFP, allows footprints to be identified in a probabilistic framework and outperforms our previously described algorithm in terms of precision at a fixed recall. Applied to a digital footprinting data set from Saccharomyces cerevisiae, DBFP identifies 4679 statistically significant footprints within intergenic regions. These footprints are mainly located near transcription start sites and are strongly enriched for known TFBSs. Footprints containing no known motif are preferentially located proximal to other footprints, consistent with cooperative binding of these footprints. DBFP also identifies a set of statistically significant footprints in the yeast coding regions. Many of these footprints coincide with the boundaries of antisense transcripts, and the most significant footprints are enriched for binding sites of the chromatin-associated factors Abf1 and Rap1.

Contact:jay.hesselberth@ucdenver.edu; william-noble@u.washington.edu

Supplementary information:Supplementary material is available at Bioinformatics online.

The Genomedata format for storing large-scale functional genomics data

Summary: We present a format for efficient storage of multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. We have also developed utilities to load data into this format. We show that retrieving data from this format is more than 2900 times faster than a naive approach using wiggle files.

Availability and Implementation: Reference implementation in Python and C components available at http://noble.gs.washington.edu/proj/genomedata/ under the GNU General Public License.

Contact:william-noble@uw.edu

An effective model for natural selection in promoters

We have produced an evolutionary model for promoters, analogous to the commonly used synonymous/nonsynonymous mutation models for protein-coding sequences. Although our model, called Sunflower, relies on some simple assumptions, it captures enough of the biology of transcription factor action to show clear correlation with other biological features. Sunflower predicts a binding profile of transcription factors to DNA sequences, in which different factors compete for the same potential binding sites. The parametrized model simultaneously estimates a continuous measurement of binding occupancy across the genomic sequence for each factor. We can then introduce a localized mutation, rerun the binding model, and record the difference in binding profiles. A single mutation can alter interactions both upstream and downstream of its position due to potential overlapping binding sites, and our statistic captures this domino effect. Over evolutionary time, we observe a clear excess of low-scoring mutations fixed in promoters, consistent with most changes being neutral. However, this is not consistent across all promoters, and some promoters show more rapid divergence. This divergence often occurs in the presence of relatively constant protein-coding divergence. Interestingly, different classes of promoters show different sensitivity to mutations, with phosphorylation-related genes having promoters inherently more sensitive to mutations than immune genes. Although there have previously been a number of models attempting to handle transcription factor binding, Sunflower provides a richer biological model, incorporating weak binding sites and the possibility of competition. The results show the first clear correlations between such a model and evolutionary processes.

Estimating the neutral rate of nucleotide substitution using introns

Evolutionary biologists frequently rely on estimates of the neutral rate of evolution when characterizing the selective pressure on protein-coding genes. We introduce a new method to estimate this value based on intron nucleotide substitutions. The new method uses a metascript model that considers alternative splicing forms and an algorithm to pair orthologous introns, which we call Introndeuce. We compare the intron method with a widely used method that uses observed substitutions in synonymous coding nucleotides, by using both methods to estimate the neutral rate for human-dog and mouse-rat comparisons. The estimates of the 2 methods correlate strongly (r(S) = 0.75), but cannot be considered directly equivalent. We also investigate the effect of alignment error and G + C content on the variance in the intron method: in both cases there is an effect, and it is species-pair specific. Although the intron method may be more useful for shorter evolutionary distances, it is less useful at longer distances due to the poor alignment of less-conserved positions.

Volume-dependent Glutamate Permeation Depends on Transmembrane Ionic Strength and Extracellular Cl -

Many mammalian cells regulate their volume by the osmotic movement of water directed by anion and cation flux. Ubiquitous volume-dependent anion currents permit cells to recover volume after swelling in response to a hypotonic environment. This study addressed competition between glutamate (Glu) and Cl(-) permeation in volume-activated anion currents in order to provide insight into the ionic requirements for volume regulation, volume-dependent anion channel activity and to the architecture of the channel pore. The effect of changing the intracellular molar fraction (MF) of Glu and Cl(-) on conductance and relative anion permeability was evaluated as a function of the extracellular permeant anion and/or the ionic strength. Relative permeability of Glu to Cl(-) was determined by measuring reversal potentials under defined ionic conditions. Under conditions with high (150 m M) or low (50 m M) ionic strength solutions on both sides of the membrane, Cl(-) was always more permeable than Glu. When a transmembrane ionic strength gradient (150 m M extracellular: 50 m M intracellular) was set to drive water into the cell, and in the presence of extracellular Cl(-), Glu became up to 16-fold more permeable than Cl(-). Replacement of extracellular Cl(-) with Glu abolished this effect. These results indicate that it is possible for Glu to move into the extracellular environment during volume-regulatory events and they support the emerging role of glutamate as a modulator of anion channel activity.

AANT: the Amino Acid-Nucleotide Interaction Database

We have created an Amino Acid-Nucleotide Interaction Database (AANT; http://aant.icmb.utexas. edu/) that categorizes all amino acid-nucleotide interactions from experimentally determined protein-nucleic acid structures, and provides users with a graphic interface for visualizing these interactions in aggregate. AANT accomplishes this by extracting individual amino acid-nucleotide interactions from structures in the Protein Data Bank, combining and superimposing these interactions into multiple structure files (e.g. 20 amino acids x 5 nucleotides) and grouping structurally similar interactions into more readily identifiable clusters. Using the Chime web browser plug-in, users can view 3D representations of the superimpositions and clusters. The unique collection and representation of data on amino acid-nucleotide interactions facilitates understanding the specificity of protein-nucleic acid interactions at a more fundamental level, and allows comparison of otherwise extremely disparate sets of structures. Moreover, by modularly representing the fundamental interactions that govern binding specificity it may prove possible to better engineer nucleic acid binding proteins.

Modulation of volume regulated anion current by I(Cln)

I(Cln), a cytosolic protein associated with a nucleotide-sensitive chloride current, may be involved in the regulation of a volume-regulated anion current (VRAC) associated with hypotonic cell swelling. We have determined the nucleic acid sequences of I(Cln) from human tsA201a, colonic (T84) and myeloma (RPMI 8826) cell lines. The amino acid sequences are highly homologous (>/=99%) to each other but less homologous to I(Cln) protein from other species. Using the whole-cell patch clamp technique, we examined the effect of I(Cln) protein expression levels on VRAC properties during a hyposmotic challenge. Overexpression of T84 or RPMI 8226-derived I(Cln) protein in tsA201a cells results in a more than 9-fold increase in the rate of VRAC activation over control values, while having no effect on VRAC inactivation properties. Underexpression of endogenous I(Cln) protein in tsA201a cells using antisense oligonucleotides results in a more than 180-fold decrease in VRAC activation rate as compared to control values. These results indicate that I(Cln) protein expression modulates VRAC activation but not inactivation.

Evidence for altered ion transport in Saccharomyces cerevisiae overexpressing human MDR 1 protein

Recently [Hoffman, M. M., and Roepe, P. D. (1997) Biochemistry 36, 11153-11168] we presented evidence for a novel Na+- and Cl--dependent H+ transport process in LR73/hu MDR 1 CHO transfectants that likely explains pHi, volume, and membrane potential changes in eukaryotic cells overexpressing the hu MDR 1 protein. To further explore this process, we have overexpressed human MDR 1 protein in yeast strain 9.3 following a combination of approaches used previously [Kuchler, K., and Thorner, J. (1992) Proc. Natl. Acad. Sci. U.S.A. 89, 2302-2306; Ruetz, S., et al. (1993) Proc. Natl. Acad. Sci. U.S.A. 90, 11588-11592]. Thus, a truncated hu MDR 1 cDNA was cloned behind a tandem array of sterile 6 (Ste6) and alchohol dehydrogenase (Adh) promoters to create the yeast expression vector pFF1. Valinomycin resistance of intact cells and Western blot analysis with purified yeast plasma membranes confirmed the overexpression of full length, functional, and properly localized hu MDR 1 protein in independently isolated 9.3/pFF1 colonies. Interestingly, relative valinomycin resistance and growth of the 9.3/hu MDR 1 strains are found to strongly depend on the ionic composition of the growth medium. Atomic absorption reveals significant differences in intracellular K+ for 9.3/hu MDR 1 versus control yeast. Transport assays using [3H]tetraphenylphosphonium ([3H]TPP+) reveal perturbations in membrane potential for 9.3/hu MDR 1 yeast that are stimulated by KCl and alkaline pHex. ATPase activity of purified plasma membrane fractions from yeast strains and LR73/hu MDR 1 CHO transfectants constructed previously [Hoffman, M. M., et al. (1996) J. Gen. Physiol. 108, 295-313] was compared. MDR 1 ATPase activity exhibits a higher pH optimum and different salt dependencies, relative to yeast H+ ATPase. Inside-out plasma membrane vesicles (ISOV) fabricated from 9.3/hu MDR 1 and control strains were analyzed for formation of H+ gradients +/- verapamil. Similar pharmacologic profiles are found for verapamil stimulation of MDR 1 ATPase activity and H+ pumping in 9.3/hu MDR 1 ISOV. In sum, these experiments strongly support the notion that hu MDR 1 catalyzes H+ transport in some fashion and lowers membrane potential in yeast when K+ contributes strongly to that potential. In the accompanying paper [Santai, C. T., Fritz, F., and Roepe, P. D. (1999) Biochemistry 38, XXXX-XXXX] the effects of ion gradients on H+ transport by hu MDR 1 are examined.

Analysis of ion transport perturbations caused by hu MDR 1 protein overexpression

In previous work [Luz et al. (1994) Biochemistry 33, 7239-7249; Roepe et al. (1994) Biochemistry 33, 11008-11015] we measured changes in Cl- and HCO3--dependent pHi regulation for LR73 Chinese hamster ovary fibroblasts overexpressing mu MDR 1 protein. However, only one clonal cell line overexpressing the protein but not previously exposed to chemotherapeutic drug (i.e., a "true" transfectant) was examined, since very few MDR cell lines of this nature have been constructed. Recently [Hoffman et al. (1996) J. Gen. Physiol. 108, 295-313] we derived a series of true LR73/hu MDR 1 transfectants that are valuable for defining the MDR phenotype mediated by MDR protein alone, without the additional complexities introduced by exposing cells to chemotherapeutic drugs. Several independently derived clones from these and additional transfection experiments exhibit expression of MDR protein that is higher than that found in other true transfectants, and that is similar to the highest level of overexpression yet recorded for drug selected MDR cells. We examined altered Cl--dependent pHi regulation for these clones using improved single-cell photometry (SCP) techniques. Short-term isotonic Cl- substitution experiments performed in the presence of CO2/HCO3- reveal that mild overexpression of hu MDR 1 protein alters anion exchange (Cl-/HCO3- exchange or AE) for LR73 cells, as expected on the basis of previous work [Luz et al. (1994) Biochemistry 33, 7239-7249]. Interestingly, we now find that several independently selected high-level MDR 1 overexpressing clones acidify quite extensively upon isotonic exchange of Cl- and then rapidly alkalinize upon restoring normal [Cl-]. These data suggest that MDR protein may effectively compete against AE. The MDR protein effect is not dependent on HCO3-/CO2 or K+, is partially inhibited by verapamil, is completely inhibited by substituting K+ or N-methylglucamine (NMG+) for Na+ in the SCP perfusate but is not affected by 100 microM levels of amiloride, bumetanide, chlorothiazide, or stilbene. ATP depletion inhibits the MDR 1 effect. We are unable to restore normal AE activity for the MDR clones via manipulation of Cl- or HCO3- gradients. We thus suggest that MDR protein overexpression provides a novel Na+- and Cl--dependent pathway for transmembrane H+ transport. From analysis of ion dependency and inhibitor sensitivities, we conclude the transport is not via altered regulation of any known K+/H+, Na+/H+, or Cl-/HCO3- antiporters, Na+:K+:2Cl-, Na+:K+:2HCO3-, K+:HCO3-, or Na+:HCO3- co-transporters, or any combination of these. Thus, it appears to represent a novel ATP and Na+-dependent Cl-/H+ antiport process that (1) may be directly mediated by the MDR protein, (2) may represent the modulation of one or more currently unidentified ion transport proteins by MDR protein, (3) may be due to some combination of direct ion transport and regulation of ion transport, or (4) may represent unusual passive H+ movement in response to a novel Cl--dependent electrical perturbation that occurs during our Cl- substitution protocol. The results have important implications for understanding drug resistance mediated by MDR 1 overexpression, as well as the physiologic function of endogenously expressed MDR protein.