Dataset Information

Quantitative measures for the management and comparison of annotated genomes.

ABSTRACT: The ever-increasing number of sequenced and annotated genomes has made management of their annotations a significant undertaking, especially for large eukaryotic genomes containing many thousands of genes. Typically, changes in gene and transcript numbers are used to summarize changes from release to release, but these measures say nothing about changes to individual annotations, nor do they provide any means to identify annotations in need of manual review.In response, we have developed a suite of quantitative measures to better characterize changes to a genome's annotations between releases, and to prioritize problematic annotations for manual review. We have applied these measures to the annotations of five eukaryotic genomes over multiple releases -- H. sapiens, M. musculus, D. melanogaster, A. gambiae, and C. elegans.Our results provide the first detailed, historical overview of how these genomes' annotations have changed over the years, and demonstrate the usefulness of these measures for genome annotation management.

SUBMITTER: Eilbeck K

PROVIDER: S-EPMC2653490 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative measures for the management and comparison of annotated genomes.

Eilbeck Karen K Moore Barry B Holt Carson C Yandell Mark M

BMC bioinformatics 20090223

<h4>Background</h4>The ever-increasing number of sequenced and annotated genomes has made management of their annotations a significant undertaking, especially for large eukaryotic genomes containing many thousands of genes. Typically, changes in gene and transcript numbers are used to summarize changes from release to release, but these measures say nothing about changes to individual annotations, nor do they provide any means to identify annotations in need of manual review.<h4>Results</h4>In ...[more]

PMID: 19236712

Similar Datasets

Project description:BackgroundStudies investigating the prevalence and risk factors for postpartum depression (PPD) have used different definitions. Some studies have used a high score on the Edinburgh Postnatal Depression Scale (EPDS) to define PPD, whereas others have used information on antidepressant medication use and/or diagnostic information on treatment for depression at a psychiatric hospital. We wanted to compare results using these two approaches to evaluate to what degree results can be compared. Moreover we wanted to evaluate, whether use of EPDS or PPAT (defined below) leads to identification of different risk factor profiles.MethodsWe identified women who delivered a child between 1 January 2014 and 31 December 2016 in Copenhagen or in one of the municipalities that were part of the Danish Health Visitors' Child Health Database. The potential risk factors were demographic factors and pregnancy- and obstetrical events. Outcomes of interest were an EPDS score ≥ 13, use of antidepressants (ATC: N06A) and/or a diagnosis of depression (F32) within six months after birth. Use of antidepressants and/or diagnosis of depression will be referred to as postpartum antidepressant treatment (PPAT). Agreement between EPDS ≥ 13 and PPAT was evaluated by the kappa coefficient. Associations between risk factors and the two outcomes (EPDS ≥ 13 and PPAT) were estimated by risk ratios (RR) using log-linear binomial regression. Presence of a systematic difference between RRs based on EPDS ≥ 13 (RREPDS≥13) and PPAT (RRPPAT) was evaluated in a meta-regression approach weighted by inverse-variance and with logarithm of the RRs as outcome.ResultsThe estimated PPD prevalence using EPDS ≥ 13 was 3.2% and of PPAT 0.4%. The agreement between the two measures was small (Kappa = 0.08), but their risk factor profile was very similar with no systematic difference between them.ConclusionsUsing the two different methods of case identification produced different prevalence estimates, but a similar risk factor profile. The differences in estimated prevalence and low agreement suggest that the two measures identify different potential PPD cases and using only one of the methods in defining PPD would underestimate PPD prevalence. The similar risk factor profile suggests that the considered risk factors are involved in the general development of PPD.

Project description:IntroductionMagnetic resonance imaging (MRI) is gold-standard for investigating Degenerative Cervical Myelopathy (DCM), a disabling disease triggered by compression of the spinal cord following degenerative changes of adjacent structures. Quantifiable compression correlates poorly with disease and language describing compression in radiological reports is un-standardised.Study designRetrospective chart review.Objectives1) Identify terminology in radiological reporting of cord compression and elucidate relationships between language and quantitative measures 2) Evaluate language's ability to distinguish myelopathic from asymptomatic compression 3) Explore correlations between quantitative or qualitative features and symptom severity 4) Investigate the influence of quantitative and qualitative measures on surgical referrals.MethodsFrom all cervical spine MRIs conducted during one year at a tertiary centre (N = 1123), 166 patients had reported cord compression. For each spinal level deemed compressed by radiologists (N = 218), four quantitative measurements were calculated: 'Maximum Canal Compromise (MCC); 'Maximum Spinal Cord Compression' (MSCC); 'Spinal Canal Occupation Ratio' (SCOR) and 'Compression Ratio' (CR). These were compared to associated radiological reporting terminology.Results1) Terminology in radiological reports was varied. Objective measures of compromise correlated poorly with language. "Compressed" was used for more severe cord compromise as measured by MCC (p<0.001), MSCC (p<0.001), and CR (p = 0.002). 2) Greater compromise was seen in cords with a myelopathy diagnosis across MCC (p<0.001); MSCC (p = 0.002) and CR (p<0.001). "Compress" (p<0.001) and "Flatten" (p<0.001) were used more commonly for myelopathy-diagnosis levels. 3) Measurements of cord compromise (MCC: p = 0.304; MSCC: p = 0.217; SCOR: p = 0.503; CR: p = 0.256) and descriptive terms (p = 0.591) did not correlate with i-mJOA score. 4) The only variables affecting spinal surgery referral were increased MSCC (p = 0.001) and use of 'Compressed' (p = 0.045).ConclusionsRadiological reporting in DCM is variable and language is not fully predictive of the degree of quantitative cord compression. Additionally, terminology may influence surgical referrals.

Project description:Background Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently. Results Cogito “COmpare annotated Genomic Intervals TOol” provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation. Conclusions Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html, a comprehensive documentation with detailed descriptions and reproducible examples is included. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04853-1.

Dataset Information

Quantitative measures for the management and comparison of annotated genomes.

Publications

Quantitative measures for the management and comparison of annotated genomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets