Set Of Annotations Research Articles

Genomic resources for the study of echinoderm development and evolution.

Echinoderms are important research models for a wide range of biological questions. In particular, echinoderm embryos are exemplary models for dissecting the molecular and cellular processes that drive development and testing how these processes can be modified through evolution to produce the extensive morphological diversity observed in the phylum. Modern attempts to characterize these processes depend on some level of genomic analysis; from querying annotated gene sets to functional genomics experiments to identify candidate cis-regulatory sequences. Given how essential these data have become, it is important that researchers using available datasets or performing their own genome-scale experiments understand the nature and limitations of echinoderm genomic analyses. In this chapter we highlight the current state of echinoderm genomic data and provide methodological considerations for common approaches, including analysis of transcriptome and functional genomics datasets.

TempCourt: evaluation of temporal taggers on a new corpus of court decisions

AbstractThe extraction and processing of temporal expressions (TEs) in textual documents have been extensively studied in several domains; however, for the legal domain it remains an open challenge. This is possibly due to the scarcity of corpora in the domain and the particularities found in legal documents that are highlighted in this paper. Considering the pivotal role played by temporal information when it comes to analyzing legal cases, this paper presents TempCourt, a corpus of 30 legal documents from the European Court of Human Rights, the European Court of Justice, and the United States Supreme Court with manually annotated TEs. The corpus contains two different temporal annotation sets that adhere to the TimeML standard, the first one capturing all TEs and the second dedicated to TEs that are relevant for the case under judgment (thus excluding dates of previous court decisions). The proposed gold standards are subsequently used to compare ten state-of-the-art cross-domain temporal taggers, and to identify not only the limitations of cross-domain temporal taggers but also limitations of the TimeML standard when applied to legal documents. Finally, the paper identifies the need for dedicated resources and the adaptation of existing tools, and specific annotation guidelines that can be adapted to different types of legal documents.

Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence — effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5% on an annotated test set, compared to human performance at 85.0%, leaving ample room for improvement.

Semantic composition of optimal process service plans in manufacturing with ODERU

PurposeThe need to flexibly react to changing demands and to cost-efficiently manage customized production even for lot size of one requires a dynamic and holistic integration of service-based processes within and across enterprises of the value chain. In this context, this paper aims at presenting ODERU, the authors’ novel pragmatic approach for automatically implementing service-based manufacturing processes at design and runtime within a cloud-based elastic manufacturing platform.Design/methodology/approachODERU relies on a set of semantic annotations of business process models encoded into an extension of the business process model and notation (BPMN) 2.0 standard. Leveraging the paradigms of semantic SOA and XaaS, ODERU integrates pattern-based semantic composition of process service plans with QoS-based optimization based on multi-objective constraint optimization problem solving.FindingsThe successful validation of ODERU in two industrial use cases for maintenance process optimization and automotive production in the European project CREMA revealed its usefulness for service-based process optimization in general and for significant cost reductions in maintenance in particular.Originality/valueODERU provides a pragmatic and flexible solution to optimal service composition with the following three main advantages: full integration of semantic service selection and composition with QoS-based optimization; executability of the generated optimal process service plans by an execution environment as they include service assignments, data flow (variable bindings) and optimal variable assignments; and support of fast replanning in a single model and plan.

Computational discovery of direct associations between GO terms and protein domains

BackgroundFamilies of related proteins and their different functions may be described systematically using common classifications and ontologies such as Pfam and GO (Gene Ontology), for example. However, many proteins consist of multiple domains, and each domain, or some combination of domains, can be responsible for a particular molecular function. Therefore, identifying which domains should be associated with a specific function is a non-trivial task.ResultsWe describe a general approach for the computational discovery of associations between different sets of annotations by formalising the problem as a bipartite graph enrichment problem in the setting of a tripartite graph. We call this approach “CODAC” (for COmputational Discovery of Direct Associations using Common Neighbours). As one application of this approach, we describe “GODomainMiner” for associating GO terms with protein domains. We used GODomainMiner to predict GO-domain associations between each of the 3 GO ontology namespaces (MF, BP, and CC) and the Pfam, CATH, and SCOP domain classifications. Overall, GODomainMiner yields average enrichments of 15-, 41- and 25-fold GO-domain associations compared to the existing GO annotations in these 3 domain classifications, respectively.ConclusionsThese associations could potentially be used to annotate many of the protein chains in the Protein Databank and protein sequences in UniProt whose domain composition is known but which currently lack GO annotation.

Drawing Out the Essentials: Historiographic Annotation as a Textual Network

Abstract During the Liu-Song 劉宋 dynasty (420–79), Pei Songzhi 裴松之 (372–451) compiled an elaborate set of annotations to the history of the Three Kingdoms era, Sanguozhi 三國志. Several decades later, during the Liang 梁 dynasty (502–57), Liu Xiaobiao 劉孝標 (462–521) compiled similar annotations for Shishuo xinyu 世說新語, a collection of pithy anecdotes concerning prominent figures from the Han through Jin dynasties. These annotations were products of a new era of textual production, in which fervent interest in historiography and book collecting reached new heights. Though building on earlier traditions of commentary and exegesis, the influence of this newly expanded network of textual circulation can be seen in the sheer variety of sources Pei and Liu cite, as well as in their meticulous and unprecedented attention to bibliographic detail. This has made it possible to use their annotations to trace the compilers and titles of hundreds of texts that would otherwise be completely unknown. Relying on this wealth of information, earlier studies have tabulated the titles of all cited sources to create lengthy bibliographies. But in doing so, they divorce this bibliographic information from the context in which it was originally embedded. This study uses data mined from these annotations to create a series of network diagrams, which illustrate how citations of older texts create connections among the various chapters of the two texts to which they were appended. By considering the networks of textual relationships created through annotation, this study reveals the importance of otherwise marginalized texts in the construction of historiographic knowledge and sheds new light on how scholars of the early medieval period made use of, and made sense of, the increasingly vast sea of text to which they had access.

HmtVar: a new resource for human mitochondrial variations and pathogenicity data

Interest in human mitochondrial genetic data is constantly increasing among both clinicians and researchers, due to the involvement of mitochondrial DNA (mtDNA) in a number of physiological and pathological processes. Thanks to new sequencing technologies and modern databases, the large amount of information on mtDNA variability may be exploited to gain insights into the relationship between mtDNA variants, phenotypes and diseases. To facilitate this process, we have developed the HmtVar resource, a variant-focused database that allows the exploration of a dataset of over 40 000 human mitochondrial variants. Mitochondrial variation data, initially gathered from the HmtDB platform, are integrated with in-house pathogenicity assessments based on various evaluation criteria and with a set of additional annotations from third-party resources. The result is a comprehensive collection of information of crucial importance for human mitochondrial variation studies and investigation of common and rare diseases in which the mitochondrion may be involved. HmtVar is accessible at https://www.hmtvar.uniba.it and data may be retrieved using either a web interface through the Query page or a state-of-the-art API for programmatic access.

Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders.

Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems. Conventional machine learning approaches normally depend on selected features within a human annotated training set. However, it is quite laborious to obtain a well labeled data set and its generation can easily be influenced by accumulative errors caused by human factors. To overcome this, we will present our processing pipeline for query matching realized through neural networks within the task of medical procedure classification. The pipeline is built upon convolutional neural networks (CNN) and autoencoder with logistic regression. On the task of relevance determination between query and category text, the autoencoder based method has achieved a micro F1 score of 70.29%, while the convolutional based method has reached a micro F1 score of 60.86% with high efficiency. These two algorithms are compared in experiments with different configurations and baselines (SVM, logistic regression) with respect to their suitability for the task of automatic encoding. Advantages and limitations are discussed.

Cardiac anomaly detection based on time and frequency domain features using tree-based classifiers

Objective: Recent advantages in mHealth-enabled ECG recorders boosted the demand for algorithms, which are able to automatically detect cardiac anomalies with high accuracy. Approach: We present a combined method of classical signal analysis and machine learning which has been developed during the Computing in Cardiology Challenge (CinC) 2017. Almost 400 hand-crafted features have been developed to reflect the complex physiology of cardiac arrhythmias and their appearance in single-channel ECG recordings. For the scope of this article, we performed several experiments on the publicly available challenge dataset to improve the classification accuracy. We compared the performance of two tree-based algorithms—gradient boosted trees and random forests—using different parameters for learning. We assessed the influence of five different sets of training annotations on the classifiers performance. Further, we present a new web-based ECG viewer to review and correct the training labels of a signal data set. Moreover, we analysed the feature importance and evaluated the model performance when using only a subset of the features. The primary data source used in the analysis was the dataset of the CinC 2017, consisting of 8528 signals from four classes. Our best results were achieved using a gradient boosted tree model which worked significantly better than random forests. Main results: Official results of the challenge follow-up phase provided by the Challenge organizers on the full hidden test set are 90.8% (Normal), 84.1% (AF), 74.5% (Other), resulting in a mean F1-score of 83.2%, which was only 1.6% behind the challenge winner and 0.2% ahead of the next-best algorithm. Official results were rounded to two decimal places which lead to the equal-second best F1F-score of 83% with five others. Significance: The algorithm achieved the second-best score among 80 algorithms of the Challenge follow-up phase equal with five others.

Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific noncoding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤MAF<5%) and common (MAF≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17±1% of low-frequency variant heritability ( ) versus 2.1±0.2% of common variant heritability ( ). Cell-type-specific noncoding annotations that were significantly enriched for of corresponding traits were similarly enriched for for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57±12% of vs. 12±2% of for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (MAF<0.5%).

A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms

Objective: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs are challenging tasks, especially in the presence of atrial fibrillation and other arrhythmias. Our aim is to increase the accuracy of heart rhythm estimation by the use of extreme gradient boosting trees and the development of a deep convolutional neural network for ECG segmentation.Approach: We trained a convolutional neural network with waveforms from PhysioNet databases to annotate QRS complexes, P waves, T waves, noise and interbeat ECG segments that characterize the essences of normal and irregular heart beats. We evaluated true positive rates, positive predictive values and mean absolute differences of our annotation based on reference annotations of the QT and MIT-BIH P-wave database. Moreover, we compared the results with standard QRS detectors and Ecgpuwave. Extreme gradient boosting trees were used to determine the heart rhythm based on hand-crafted features. More precisely, a noise estimation function was used in combination with heart rate and interval data. Furthermore we defined particular features based on ECG morphology, appearance of P waves and detection of irregular beats. We examined the feature importance and identified key features for normal sinus rhythm, atrial fibrillation, alternative rhythm and noisy recordings. The classification performance was evaluated externally using F1 scores by applying the algorithm to the hidden test set provided by the PhysioNet/CinC Challenge 2017.Main results: The true positive rate of the convolutional neural network in detection of manually revised R peaks in the QT database was and the positive predictive value was . The detection of P and T waves reached a true positive rate of and respectively, given a 50 ms tolerance when comparing the reference to the test annotation set. The rhythm classification performance reached an overall F1 score of 0.82 when applying the algorithm to the hidden test set.Significance: We achieved a shared rank #9 in the post-challenge phase of the PhysioNet/CinC Challenge 2017.

Pointly-Supervised Action Localization

This paper strives for spatio-temporal localization of human actions in videos. In the literature, the consensus is to achieve localization by training on bounding box annotations provided for each frame of each training video. As annotating boxes in video is expensive, cumbersome and error-prone, we propose to bypass box-supervision. Instead, we introduce action localization based on point-supervision. We start from unsupervised spatio-temporal proposals, which provide a set of candidate regions in videos. While normally used exclusively for inference, we show spatio-temporal proposals can also be leveraged during training when guided by a sparse set of point annotations. We introduce an overlap measure between points and spatio-temporal proposals and incorporate them all into a new objective of a multiple instance learning optimization. During inference, we introduce pseudo-points, visual cues from videos, that automatically guide the selection of spatio-temporal proposals. We outline five spatial and one temporal pseudo-point, as well as a measure to best leverage pseudo-points at test time. Experimental evaluation on three action localization datasets shows our pointly-supervised approach (1) is as effective as traditional box-supervision at a fraction of the annotation cost, (2) is robust to sparse and noisy point annotations, (3) benefits from pseudo-points during inference, and (4) outperforms recent weakly-supervised alternatives. This leads us to conclude that points provide a viable alternative to boxes for action localization.

Automated dendritic spine detection using convolutional neural networks on maximum intensity projected microscopic volumes

BackgroundDendritic spines are structural correlates of excitatory synapses in the brain. Their density and structure are shaped by experience, pointing to their role in memory encoding. Dendritic spine imaging, followed by manual analysis, is a primary way to study spines. However, an approach that analyses dendritic spines images in an automated and unbiased manner is needed to fully capture how spines change with normal experience, as well as in disease. New methodWe propose an approach based on fully convolutional neural networks (FCNs) to detect dendritic spines in two-dimensional maximum-intensity projected images from confocal fluorescent micrographs. We experiment on both fractionally strided convolution and efficient sub-pixel convolutions. Dendritic spines far from the dendritic shaft are pruned by extraction of the shaft to reduce false positives. Performance of the proposed method is evaluated by comparing predicted spine positions to those manually marked by experts. ResultsThe averaged distance between predicted and manually annotated spines is 2.81 ± 2.63 pixels (0.082 ± 0.076 microns) and 2.87 ± 2.33 pixels (0.084 ± 0.068 microns) based on two different experts. FCN-based detection achieves F scores > 0.80 for both sets of expert annotations. Comparison with existing methodsOur method significantly outperforms two well-known software, NeuronStudio and Neurolucida (p-value < 0.02). ConclusionsFCN architectures used in this work allow for automated dendritic spine detection. Superior outcomes are possible even with small training data-sets. The proposed method may generalize to other datasets on larger scales.

High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability.

Interest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. Here we introduce a powerful new method, ASMC, that can estimate coalescence times using only SNP array data, and is orders of magnitude faster than previous approaches. We applied ASMC to detect recent positive selection in 113,851 phased British samples from the UK Biobank, and detected 12 genome-wide significant signals, including 6 novel loci. We also applied ASMC to sequencing data from 498 Dutch individuals to detect background selection at deeper time scales. We detected strong heritability enrichment in regions of high background selection in an analysis of 20 independent diseases and complex traits using stratified LD score regression, conditioned on a broad set of functional annotations (including other background selection annotations). These results underscore the widespread effects of background selection on the genetic architecture of complex traits.

HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences.

Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provides researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein—ontology term—disease relations. As an application of the proposed approach, HPO term—protein associations (i.e., HPO2protein) were predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO performance was among the best (Fmax = 0.35). The automated cross ontology mapping approach developed in this work may be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

Identification of lead anti-human cytomegalovirus compounds targeting MAP4K4 via machine learning analysis of kinase inhibitor screening data.

Chemogenomic approaches involving highly annotated compound sets and cell based high throughput screening are emerging as a means to identify novel drug targets. We have previously screened a collection of highly characterized kinase inhibitors (Khan et al., Journal of General Virology, 2016) to identify compounds that increase or decrease expression of a human cytomegalovirus (HCMV) protein in infected cells. To identify potential novel anti-HCMV drug targets we used a machine learning approach to relate our phenotypic data from the aforementioned screen to kinase inhibition profiling of compounds used in this screen. Several of the potential targets had no previously reported role in HCMV replication. We focused on one potential anti-HCMV target, MAPK4K, and identified lead compounds inhibiting MAP4K4 that have anti-HCMV activity with little cellular cytotoxicity. We found that treatment of HCMV infected cells with inhibitors of MAP4K4, or an siRNA that inhibited MAP4K4 production, reduced HCMV replication and impaired detection of IE2-60, a viral protein necessary for efficient HCMV replication. Our findings demonstrate the potential of this machine learning approach to identify novel anti-viral drug targets, which can inform the discovery of novel anti-viral lead compounds.

Abstract 5295: Toward high throughput immune infiltrate analysis from H&amp;E stained images

Abstract Our appreciation of the immune system in breast cancer is rapidly evolving. New methods to quantify the composition and spatial distribution of the immune infiltrate from pathology images are urgently needed to develop better strategies to activate the anti-tumor immune response. Existing methods to quantify the immune infiltrate are tedious (manual counting) or rely on immunohistochemistry (IHC) staining to identify immune cells. While significant efforts are underway to develop machine learning tools to categorize cells based on morphologic features in hematoxylin and eosin (H&amp;E) stained histopathology images, all approaches are limited by the scarcity of large, annotated ground truth training sets. In this work, we propose a new approach, leveraging “style-transfer” algorithms from the computer vision community to generate large quantities of training data from IHC stains. Using style transfer, we generate synthetic H&amp;E images from IHC stains for immune markers, such as CD45, CD8 and CD4. The generated images have pixel-perfect ground truth provided by IHC, and do not need manual labeling. We investigate the impact of training on increasing numbers of synthesized images and validate our algorithms on an independent test set annotated by a pathologist (n=100 patients, 3064 identified lymphocytes). Our baseline classifier achieves a 83% accuracy, compared to the pathologist ground truth. This work provides a novel, tractable, and efficient way to train data-hungry algorithms to identify multiple cell types from H&amp;E stained images. This work is supported by a grant from the Breast Cancer Research Foundation (BCRF-17-002) Citation Format: Rishi R. Rawat, Daniel Ruderman, David B. Agus. Toward high throughput immune infiltrate analysis from H&amp;E stained images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5295.

Abstract 524: Impact of age, sex, smoking, body mass and physical activity on circulating small non-coding RNA expression profiles

Abstract Non-coding RNAs (ncRNA) are regulators of cell functions and circulating ncRNAs from the majority of the RNA classes, such as miRNA, tRNA, piRNAs, lncRNA, snoRNA, snRNA and miscRNAs, are potential non-invasive cancer biomarkers. Understanding how non-disease traits influence ncRNA expression is essential for assessing their biomarker potential. The aim of the study was to investigate associations between sex, age, smoking, body mass, physical activity, technical factors such as sample storage and processing, and serum ncRNA expression profiles. Serum samples from 526 healthy individuals in the Janus Serum Bank Cohort were included in the study. The samples were collected in the time-period 1972-2004 with varying collection procedures, and stored at - 25º C. Information on smoking habits, body mass and physical activity was linked from health examination survey data. RNA was extracted from 400 µl serum using phenol-chloroform separation and the miRNA Neasy Serum/Plasma kit (Qiagen). Small RNAseq was performed using NEBNext Small RNA Library Prep Set for Illumina with an average sequencing depth of 18 million reads per sample. The RNAseq reads were initially trimmed for adapters using Adapter Removal (v2.1.7). We then mapped the collapsed reads (generated by FASTX v0.14) to the human genome (hg38) using Bowtie2 (10 alignments per read were allowed). We compiled a comprehensive annotation set from miRBase (v21) for miRNAs, pirBAse (v1.0) for piRNAs, GENCODE (v26) for other RNAs and tRNAs. We used SeqBuster (v3.1) to get isomiR and miRNA profiles. To count the mapped reads, HTSeq (v0.7.2) was used. Differential gene expression analyses based on the negative binomial distribution and Wald significance tests were performed for each trait using the R package DESeq2 version 1.14.1. We identified associations between all RNA classes and traits. Ageing showed the strongest association with ncRNA expression, both in terms of statistical significance and number of RNAs, regardless of RNA class. Serum processing modifications and storage times significantly altered expression levels of a number of ncRNAs. Smoking cessation generally restored RNA expression to non-smoking levels, although for some isomiRs, mRNA fragments and tRNAs smoking-related expression levels persisted. sncRNA expression levels in serum are considerably age-dependent and age should be adjusted for in studies of circulating sncRNA expression. Certain biomarkers are also influenced by body mass, smoking, physical activity, serum processing and storage conditions. Citation Format: Trine Ballestad Rounge, Sinan Ugur Umu, Andreas Keller, Eckart Meese, Giske Ursin, Steinar Tretli, Robert Lyle, Hilde Langseth. Impact of age, sex, smoking, body mass and physical activity on circulating small non-coding RNA expression profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 524.

Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits.

There is increasing evidence that many GWAS risk loci are molecular QTL (eQTL, hQTL, sQTL, and/or meQTL). Here, we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are far more strongly enriched for heritability (e.g. 5.84x for eQTL; P=1.19×10−31) across 41 independent diseases and complex traits than annotations containing all significant molecular QTL (1.80x for eQTL). eQTL annotations that were obtained by meta-analyzing all GTEx tissues generally performed best, but tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for heritability (17.06x; P=1.20×10−35). All molecular QTL except sQTL remained significantly enriched in a joint analysis, implying that each of these annotations is uniquely informative for disease and complex trait architectures.

Improved Genome Assembly and Annotation for the Rock Pigeon (Columba livia).

The domestic rock pigeon (Columba livia) is among the most widely distributed and phenotypically diverse avian species. C. livia is broadly studied in ecology, genetics, physiology, behavior, and evolutionary biology, and has recently emerged as a model for understanding the molecular basis of anatomical diversity, the magnetic sense, and other key aspects of avian biology. Here we report an update to the C. livia genome reference assembly and gene annotation dataset. Greatly increased scaffold lengths in the updated reference assembly, along with an updated annotation set, provide improved tools for evolutionary and functional genetic studies of the pigeon, and for comparative avian genomics in general.