The Ensembl computing architecture

Ensembl is a software project to automatically annotate large eukaryotic genomes and release them freely into the public domain. The project currently automatically annotates 10 complete genomes. This makes very large demands on compute resources, due to the vast number of sequence comparisons that need to be executed. To circumvent the financial outlay often associated with classical supercomputing environments, farms of multiple, lower-cost machines have now become the norm and have been deployed successfully with this project. The architecture and design of farms containing hundreds of compute nodes is complex and nontrivial to implement. This study will define and explain some of the essential elements to consider when designing such systems. Server architecture and network infrastructure are discussed with a particular emphasis on solutions that worked and those that did not (often with fairly spectacular consequences). The aim of the study is to give the reader, who may be implementing a large-scale biocompute project, an insight into some of the pitfalls that may be waiting ahead.     

The Ensembl analysis pipeline

The Ensembl pipeline is an extension to the Ensembl system which allows automated annotation of genomic sequence. The software comprises two parts. First, there is a set of Perl modules ("Runnables" and "RunnableDBs") which are 'wrappers' for a variety of commonly used analysis tools. These retrieve sequence data from a relational database, run the analysis, and write the results back to the database. They inherit from a common interface, which simplifies the writing of new wrapper modules. On top of this sits a job submission system (the "RuleManager") which allows efficient and reliable submission of large numbers of jobs to a compute farm. Here we describe the fundamental software components of the pipeline, and we also highlight some features of the Sanger installation which were necessary to enable the pipeline to scale to whole-genome analysis.     

The otter annotation system

With the completion of the human genome sequence and genome sequence available for other vertebrate genomes, the task of manual annotation at the large genome scale has become a priority. Possibly even more important, is the requirement to curate and improve this annotation in the light of future data. For this to be possible, there is a need for tools to access and manage the annotation. Ensembl provides an excellent means for storing gene structures, genome features, and sequence, but it does not support the extra textual data necessary for manual annotation. We have extended Ensembl to create the Otter manual annotation system. This comprises a relational database schema for storing the manual annotation data, an application-programming interface (API) to access it, an extensible markup language (XML) format to allow transfer of the data, and a server to allow multiuser/multimachine access to the data. We have also written a data-adaptor plugin for the Apollo Browser/Editor to enable it to utilize an Otter server. The otter database is currently used by the Vertebrate Genome Annotation (VEGA) site (http://vega.sanger.ac.uk), which provides access to manually curated human chromosomes. Support is also being developed for using the AceDB annotation editor, FMap, via a perl wrapper called Lace. The Human and Vertebrate Annotation (HAVANA) group annotators at the Sanger center are using this to annotate human chromosomes 1 and 20.     

The Ensembl core software libraries

Systems for managing genomic data must store a vast quantity of information. Ensembl stores these data in several MySQL databases. The core software libraries provide a practical and effective means for programmers to access these data. By encapsulating the underlying database structure, the libraries present end users with a simple, abstract interface to a complex data model. Programs that use the libraries rather than SQL to access the data are unaffected by most schema changes. The architecture of the core software libraries, the schema, and the factors influencing their design are described. All code and data are freely available.     

An overview of Ensembl

Ensembl (http://www.ensembl.org/) is a bioinformatics project to organize biological information around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of individual genomes, and of the synteny and orthology relationships between them. It is also a framework for integration of any biological data that can be mapped onto features derived from the genomic sequence. Ensembl is available as an interactive Web site, a set of flat files, and as a complete, portable open source software system for handling genomes. All data are provided without restriction, and code is freely available. Ensembl's aims are to continue to "widen" this biological integration to include other model organisms relevant to understanding human biology as they become available; to "deepen" this integration to provide an ever more seamless linkage between equivalent components in different species; and to provide further classification of functional elements in the genome that have been previously elusive.     

The Ensembl Web site: mechanics of a genome browser

The Ensembl Web site (http://www.ensembl.org/) is the principal user interface to the data of the Ensembl project, and currently serves >500,000 pages (approximately 2.5 million hits) per week, providing access to >80 GB (gigabyte) of data to users in more than 80 countries. Built atop an open-source platform comprising Apache/mod_perl and the MySQL relational database management system, it is modular, extensible, and freely available. It is being actively reused and extended in several different projects, and has been downloaded and installed in companies and academic institutions worldwide. Here, we describe some of the technical features of the site, with particular reference to its dynamic configuration that enables it to handle disparate data from multiple species.     

Improving gene annotation using peptide mass spectrometry

Annotation of protein-coding genes is a key goal of genome sequencing projects. In spite of tremendous recent advances in computational gene finding, comprehensive annotation remains a challenge. Peptide mass spectrometry is a powerful tool for researching the dynamic proteome and suggests an attractive approach to discover and validate protein-coding genes. We present algorithms to construct and efficiently search spectra against a genomic database, with no prior knowledge of encoded proteins. By searching a corpus of 18.5 million tandem mass spectra (MS/MS) from human proteomic samples, we validate 39,000 exons and 11,000 introns at the level of translation. We present translation-level evidence for novel or extended exons in 16 genes, confirm translation of 224 hypothetical proteins, and discover or confirm over 40 alternative splicing events. Polymorphisms are efficiently encoded in our database, allowing us to observe variant alleles for 308 coding SNPs. Finally, we demonstrate the use of mass spectrometry to improve automated gene prediction, adding 800 correct exons to our predictions using a simple rescoring strategy. Our results demonstrate that proteomic profiling should play a role in any genome sequencing project.     

Predicting gene function from patterns of annotation

The Gene Ontology (GO) Consortium has produced a controlled vocabulary for annotation of gene function that is used in many organism-specific gene annotation databases. This allows the prediction of gene function based on patterns of annotation. For example, if annotations for two attributes tend to occur together in a database, then a gene holding one attribute is likely to hold the other as well. We modeled the relationships among GO attributes with decision trees and Bayesian networks, using the annotations in the Saccharomyces Genome Database (SGD) and in FlyBase as training data. We tested the models using cross-validation, and we manually assessed 100 gene-attribute associations that were predicted by the models but that were not present in the SGD or FlyBase databases. Of the 100 manually assessed associations, 41 were judged to be true, and another 42 were judged to be plausible.     

Judging the quality of gene expression-based clustering methods using gene annotation

We compare several commonly used expression-based gene clustering algorithms using a figure of merit based on the mutual information between cluster membership and known gene attributes. By studying various publicly available expression data sets we conclude that enrichment of clusters for biological function is, in general, highest at rather low cluster numbers. As a measure of dissimilarity between the expression patterns of two genes, no method outperforms Euclidean distance for ratio-based measurements, or Pearson distance for non-ratio-based measurements at the optimal choice of cluster number. We show the self-organized-map approach to be best for both measurement types at higher numbers of clusters. Clusters of genes derived from single- and average-linkage hierarchical clustering tend to produce worse-than-random results.     

Iterative gene prediction and pseudogene removal improves genome annotation

Correct gene prediction is impaired by the presence of processed pseudogenes: nonfunctional, intronless copies of real genes found elsewhere in the genome. Gene prediction programs frequently mistake processed pseudogenes for real genes or exons, leading to biologically irrelevant gene predictions. While methods exist to identify processed pseudogenes in genomes, no attempt has been made to integrate pseudogene removal with gene prediction, or even to provide a freestanding tool that identifies such erroneous gene predictions. We have created PPFINDER (for Processed Pseudogene finder), a program that integrates several methods of processed pseudogene finding in mammalian gene annotations. We used PPFINDER to remove pseudogenes from N-SCAN gene predictions, and show that gene prediction improves substantially when gene prediction and pseudogene masking are interleaved. In addition, we used PPFINDER with gene predictions as a parent database, eliminating the need for libraries of known genes. This allows us to run the gene prediction/PPFINDER procedure on newly sequenced genomes for which few genes are known.     

Automatic gene annotation using GO terms from cellular component domain

Background

The Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). GO annotation is a process which assigns gene functional information using GO terms to relevant genes in the literature. It is a common task among the Model Organism Database (MOD) groups. Manual GO annotation relies on human curators assigning gene functional information using GO terms by reading the biomedical literature. This process is very time-consuming and labor-intensive. As a result, many MODs can afford to curate only a fraction of relevant articles.

Methods

GO terms from the CC domain can be essentially divided into two sub-hierarchies: subcellular location terms, and protein complex terms. We cast the task of gene annotation using GO terms from the CC domain as relation extraction between gene and other entities: (1) extract cases where a protein is found to be in a subcellular location, and (2) extract cases where a protein is a subunit of a protein complex. For each relation extraction task, we use an approach based on triggers and syntactic dependencies to extract the desired relations among entities.

Results

We tested our approach on the BC4GO test set, a publicly available corpus for GO annotation. Our approach obtains a F1-score of 71%, a precision of 91% and a recall of 58% for predicting GO terms from CC Domain for given genes.

Conclusions

We have described a novel approach of treating gene annotation with GO terms from CC domain as two relation extraction subtasks. Evaluation results show that our approach achieves a F1-score of 71% for predicting GO terms for given genes. Thereby our approach can be used to accelerate the process of GO annotation for the bio-annotators.

     

心电自动分析系统

心电图的自动分析对于心血管疾病的分析和诊断有着十分重要的意义.随着心电图在临床中日益广泛的应用,如何对海量的心电图数据记录进行更加精确和快速的计算机自动分类就越来越受到广大研究者的重视.心电自动分析系统实现了对心电数据的自动分类和智能诊断,并具备对心电数据、病人信息、医生诊断分析等数据管理功能.本文介绍了系统所涉及到的算法,系统的框架、功能与实现,并对系统的技术特点进行了分析,最后预测了系统的应用前景.     

Literature-based concept profiles for gene annotation: the issue of weighting

BACKGROUND: Text-mining has been used to link biomedical concepts, such as genes or biological processes, to each other for annotation purposes or the generation of new hypotheses. To relate two concepts to each other several authors have used the vector space model, as vectors can be compared efficiently and transparently. Using this model, a concept is characterized by a list of associated concepts, together with weights that indicate the strength of the association. The associated concepts in the vectors and their weights are derived from a set of documents linked to the concept of interest. An important issue with this approach is the determination of the weights of the associated concepts. Various schemes have been proposed to determine these weights, but no comparative studies of the different approaches are available. Here we compare several weighting approaches in a large scale classification experiment. METHODS: Three different techniques were evaluated: (1) weighting based on averaging, an empirical approach; (2) the log likelihood ratio, a test-based measure; (3) the uncertainty coefficient, an information-theory based measure. The weighting schemes were applied in a system that annotates genes with Gene Ontology codes. As the gold standard for our study we used the annotations provided by the Gene Ontology Annotation project. Classification performance was evaluated by means of the receiver operating characteristics (ROC) curve using the area under the curve (AUC) as the measure of performance. RESULTS AND DISCUSSION: All methods performed well with median AUC scores greater than 0.84, and scored considerably higher than a binary approach without any weighting. Especially for the more specific Gene Ontology codes excellent performance was observed. The differences between the methods were small when considering the whole experiment. However, the number of documents that were linked to a concept proved to be an important variable. When larger amounts of texts were available for the generation of the concepts' vectors, the performance of the methods diverged considerably, with the uncertainty coefficient then outperforming the two other methods.     

Systematic genome-wide annotation of spliceosomal proteins reveals differential gene family expansion

Although more than 200 human spliceosomal and splicing-associated proteins are known, the evolution of the splicing machinery has not been studied extensively. The recent near-complete sequencing and annotation of distant vertebrate and chordate genomes provides the opportunity for an exhaustive comparative analysis of splicing factors across eukaryotes. We describe here our semiautomated computational pipeline to identify and annotate splicing factors in representative species of eukaryotes. We focused on protein families whose role in splicing is confirmed by experimental evidence. We visually inspected 1894 proteins and manually curated 224 of them. Our analysis shows a general conservation of the core spliceosomal proteins across the eukaryotic lineage, contrasting with selective expansions of protein families known to play a role in the regulation of splicing, most notably of SR proteins in metazoans and of heterogeneous nuclear ribonucleoproteins (hnRNP) in vertebrates. We also observed vertebrate-specific expansion of the CLK and SRPK kinases (which phosphorylate SR proteins), and the CUG-BP/CELF family of splicing regulators. Furthermore, we report several intronless genes amongst splicing proteins in mammals, suggesting that retrotransposition contributed to the complexity of the mammalian splicing apparatus.     

An active learning-enabled annotation system for clinical named entity recognition

Background

Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.

Methods

In this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models.

Results

The simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users.

Conclusions

We found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.