1,080 research outputs found
SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data
No full textAbstract Motivation: Autism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to study it, one that is gaining popularity is the incorporation of transcriptomic data with known mutations associated to the disorder, often using the SFARI Gene list to characterise the latter. Results: SFARI genes were found not to be significantly associated to differential gene expression patterns, nor enriched in co-expression modules with strong module-diagnosis correlation, however, it was confirmed that they do provide useful insights when using network analysis and machine learning models that are able to incorporate information from the whole gene co-expression network. A statistically significant bias related to level of expression was found in the SFARI genes and SFARI scores, which was found to influence transcriptomic results at gene, module and whole-network levels, as well as other ASD gene-scoring systems. This dataset contains all of the data used in our paper cited in this data submission for reproducibility and re-use purposes. The files are described in detail in the source and file structure description section of this entry.These data accompany our paper submission, currently on bioRxiv 10.1101/2021.01.29.428754. The following describes the accompanying files:- All data that is updated by external providers (i.e. not fixed) has the date of download in the filename for version control and reproducibility purposes. FOLDER "NCBI" - contains snapshots of the gene_info and gene2ensembl files from NCBI used in the paper alongside Gene Ontology mappings for human genes. FOLDER "RNASeq" - contains the processed RNA-seq data objects used in this paper in CSV format for re-use by others. FOLDER "SFARI" - contains the snapshots of the SFARIgene rankings for Autism Spectrum Disorder Genes used in this paper. FOLDER "ASDGeneScores" - contains the snapshots from other studies that have calculated association scores between genes and Autism that we have used in this paper. - krishnan_probability_score.xlsx - contains scores from the Krishnan paper cited with this data submission. - sanders_TADA_score.xlsx - contains the scores from the Sanders paper cited with this data submission. - disgenet_21_01_25.csv - contains scores from the DisGenet database cited with this data submission
Pasture management clearly affects soil microbial community structure and N-cycling bacteria
No full textSteven A. Wakelin, Adrienne L. Gregg, Richard J. Simpson, Guandgi D. Li, Ian T. Riley and Alan C. McKa
Semi-automated framework for the analytical use of gene-centric data with biological ontologies
No full textMotivation Translational bioinformatics(TBI) has been defined as ‘the development
and application of informatics methods that connect molecular entities to clinical entities’
[1], which has emerged as a systems theory approach to bridge the huge wealth of
biomedical data into clinical actions using a combination of innovations and resources
across the entire spectrum of biomedical informatics approaches [2]. The challenge
for TBI is the availability of both comprehensive knowledge based on genes and the
corresponding tools that allow their analysis and exploitation.
Traditionally, biological researchers usually study one or only a few genes at a
time, but in recent years high throughput technologies such as gene expression microarrays,
protein mass-spectrometry and next-generation DNA and RNA sequencing
have emerged that allow the simultaneous measurement of changes on a genome-wide
scale. These technologies usually result in large lists of interesting genes, but meaningful
biological interpretation remains a major challenge. Over the last decade, enrichment
analysis has become standard practice in the analysis of such gene lists, enabling
systematic assessment of the likelihood of differential representation of defined groups
of genes compared to suitably annotated background knowledge. The success of such
analyses are highly dependent on the availability and quality of the gene annotation
data.
For many years, genes were annotated by different experts using inconsistent, non-standard
terminologies. Large amounts of variation and duplication in these unstructured
annotation sets, made them unsuitable for principled quantitative analysis. More
recently, a lot of effort has been put into the development and use of structured, domain
specific vocabularies to annotate genes. The Gene Ontology is one of the most successful
examples of this where genes are annotated with terms from three main clades;
biological process, molecular function and cellular component. However, there are
many other established and emerging ontologies to aid biological data interpretation,
but are rarely used. For the same reason, many bioinformatic tools only support analysis
analysis using the Gene Ontology.
The lack of annotation coverage and the support for them in existing analytical
tools to aid biological interpretation of data has become a major limitation to their utility
and uptake. Thus, automatic approaches are needed to facilitate the transformation
of unstructured data to unlock the potential of all ontologies, with corresponding bioinformatics
tools to support their interpretation.
Approaches In this thesis, firstly, similar to the approach in [3,4], I propose a series
of computational approaches implemented in a new tool OntoSuite-Miner to address
the ontology based gene association data integration challenge. This approach uses
NLP based text mining methods for ontology based biomedical text mining. What
differentiates my approach from other approaches is that I integrate two of the most
wildly used NLP modules into the framework, not only increasing the confidence of
the text mining results, but also providing an annotation score for each mapping, based
on the number of pieces of evidence in the literature and the number of NLP modules
that agreed with the mapping. Since heterogeneous data is important in understanding
human disease, the approach was designed to be generic, thus the ontology
based annotation generation can be applied to different sources and can be repeated
with different ontologies. Secondly, in respect of the second challenge proposed by
TBI, to increase the statistical power of the annotation enrichment analysis, I propose
OntoSuite-Analytics, which integrates a collection of enrichment analysis methods into
a unified open-source software package named topOnto, in the statistical programming
language R. The package supports enrichment analysis across multiple ontologies with
a set of implemented statistical/topological algorithms, allowing the comparison of enrichment
results across multiple ontologies and between different algorithms.
Results The methodologies described above were implemented and a Human Disease
Ontology (HDO) based gene annotation database was generated by mining three
publicly available database, OMIM, GeneRIF and Ensembl variation. With the availability
of the HDO annotation and the corresponding ontology enrichment analysis
tools in topOnto, I profiled 277 gene classes with human diseases and generated ‘disease
environments’ for 1310 human diseases. The exploration of the disease profiles
and disease environment provides an overview of known disease knowledge and provides
new insights into disease mechanisms. The integration of multiple ontologies
into a disease context demonstrates how ‘orthogonal’ ontologies can lead to biological
insight that would have been missed by more traditional single ontology analysis
Using domain-targeted text corpora to improve phenotype named entity recognition
No full textScientific corpora serve as the backbone for advancements in Natural Language Processing (NLP) tasks within the biomedical domain. However, current methods for corpus creation often rely solely on PubMed abstracts and Open Access (OA) publica- tions on PubMed Central (PMC). This approach overlooks the amount of information contained within the full text of scientific articles not available in these two services. Furthermore, existing tools for UMLS named entities recognition, such as MetaMap, can be computationally slow, hindering large-scale analysis. This work addresses these limitations by introducing a novel tools and resources specifically designed to enhance NLP tasks, especially UMLS and Phenotype NER, in the biomedical field.
First, I present Cadmus, the first fully automated pipeline for scientific corpus creation that goes beyond PubMed abstracts and leverages the full text of OA and non-OA publications. Cadmus utilizes a combination of APIs, web scraping and text processing techniques to create comprehensive scientific corpora. Our analysis demonstrates that Cadmus corpus creation provides a significant increase in the number of identified entities (representing 64.9% of the total available UMLS entities on our DDG2P dataset) compared to prior methods.
Second, I introduce ParallelPyMetaMap, a Python implementation of MetaMap. Par- allelPyMetaMap offers full access to MetaMap’s robust named entity recognition cap- abilities while incorporating a multiprocessing approach. This approach significantly accelerates processing times, allowing researchers to analyze larger datasets in a more efficient manner.
Third, I present the Autism Spectrum Disorder (ASD) Corpus, the first fully auto- mated, full-text biomedical corpus. The ASD corpus is constructed by employing Cadmus to gather full-text articles related to ASD, encompassing both OA and non- OA publications. This corpus represents a valuable resource for researchers focused on ASD, providing a comprehensive collection of full-text articles for in-depth analysis. Our ASD corpus captures a significant portion of relevant publications (82.64% out of 72,058) for ASD research.
Finally, I introduce a novel Phenotype Named Entity Recognition (NER) model spe- cifically optimized for identifying phenotypic entities within biomedical text. Our Phenotype NER model is trained on a large-scale silver standard dataset and incorpor- ates optimized pre-processing strategies. When compared to current state-of-the-art methods on three Human expert annotated datasets, our model outperforms existing approaches on two out of three datasets, demonstrating its effectiveness in identifying phenotypic entities.
In conclusion, this work presents a comprehensive suite of tools and resources that significantly enhance NLP capabilities in the biomedical domain. Cadmus with its corpus creation and the Phenotype NER model demonstrably improve the identifica- tion of entities and phenotypes, while ParallelPyMetaMap accelerates UMLS named entity recognition. The ASD Corpus offers a valuable collection of full-text articles for researchers focused on Autism Spectrum Disorder. These advancements offer an alternative to existing methods that have been used and reused over the years
Independent effects of the -219 G>T and ?2/ ?3/ ?4 polymorphisms in the apolipoprotein E gene on coronary artery disease: the Southampton Atherosclerosis Study
No full textA number of studies have shown that coronary artery disease severity is associated with the ?2/ ?3/ ?4 polymorphism in the coding region of the apolipoprotein E gene. In this study, we investigated whether the severity of the disease was also influenced by a functional polymorphism (-219 G>T) in the promoter of the gene, and if so, whether the effects of the two polymorphisms were independent. A cohort of 1170 patients with angiographically documented coronary artery disease were genotyped for the two polymorphisms. The frequency of the 4 allele of the ?2/ ?3/ ?4 polymorphism increased linearly with increasing number of diseased vessels, so did the -219T allele of the -219 G>T polymorphism. In the sample as a whole, logistic regression analyses indicated that compared with the G/G genotype, the T/T genotype conferred an odds ratio of 1.598 (95% CI=1.161-2.201, P=0.004) in favor of increased disease severity, and the relationship remained significant after adjustment for ?2/? 3/ ?4 polymorphism genotypes, plasma cholesterol and triglyceride levels, and other risk factors. The effect of the T/T genotype on disease severity was more significant in patients who did not carry the 4 allele (OR=1.510, 95% CI=1.028-2.221) than in 4 allele carriers (OR=1.303, 95% CI=0.619-2.742). There was considerable linkage disequilibrium between the two polymorphisms (=0.9, P<0.001). Logistic regression analysis showed that the -219T-4 haplotype conferred an odds ratio of 1.488 (95% CI=1.133-1.954). These findings suggest that the -219 G>T and 2/3/4 polymorphisms, which may affect respectively the quantity and quality of apoE, have independent and possibly additive effects on coronary artery disease severity
Understanding the groundwater system of a heavily drained coastal catchment and the implications for salinity management
No full textThe Thurne catchment in north-east Norfolk, UK, is an extremely important part of the Broads National Park,
an internationally important wetland environment. Extensive engineered land drainage of the marshes of this
low-lying coastal catchment over the past two centuries has led to land subsidence and the need for drainage
pumps to control water levels sufficiently below sea-level to maintain agricultural productivity.
Consequently, seawater from the North Sea has intruded into the underlying Pleistocene Crag (sand) aquifer
and brackish groundwater enters into land drainage channels, thereby raising their salinity. Powerful pumps
discharge these brackish drainage waters into a Special Area of Conservation (SAC) and RAMSAR site,
leading to adverse ecological impacts on salt-sensitive species.
Chloride concentrations within drainage channels throughout the network have been found to significantly
vary, with several influential factors affecting channel salinity such as proximity to the sea and connectivity to
the underlying aquifer. A thorough understanding of the surface-water/groundwater system and a subsequent
quantification of the various processes has been necessary for the development for the drain/aquifer
interactions and a numerical groundwater model. These models are used to estimate the long-term
distribution of the salinity within the drainage system under current conditions. The model credibility is
justified by comparable aquifer-drain water balance, a comparable coast water inflow/ total groundwater ratio
and the particle tracking from the coastal reaches trace to previously-measured saline-vulnerable locations.
The numerical groundwater model has demonstrated that the average daily inflow of saline groundwater into
the Crag aquifer of the Thurne catchment is 3,081 m3/day, of which the HempsteadMarshes main drain is one
of the main conduits for saline inflow into the Brograve system, which discharges directly into the SAC.
Various changes to the engineering design or operation of the drainage system have been proposed to
minimise the saline inflow to the SAC, but the implementation of any proposals must be considered in
conjunction with the current dynamics of the system. Three separate management or engineering remedial
measures have been modelled: (i) raising the water levels in the drains of the Hempstead Marshes in the north
east of the catchment (ii) lining the main drain of the HempsteadMarshes with low permeability material, and
(iii) The construction of a new coastal open ditch drain which is intended to ‘intercept’ the saline intrusion
and prevent ingress into inland drains of the Brograve system. The results suggest that raising the water
levels in the Hempstead Marshes will reduce the saline inflow into the Brograve sub-catchment substantially,
and decrease the overall saline inflow into the Thurne catchment from 3081 m3/day to 2822 m3/day). The
lining of the main drain in Hempstead produces a less than 10% decrease in saline inflow into the catchment
from 3,081 m3/day to 2,958 m3/day. The simulated coastal interceptor drain could in theory through
maintaining a low groundwater head near the coast, prevent the inflow of saline groundwater into the
Brograve system. However, such a drain would increase the saline inflow across the coastal boundary by
around six times (from 3,081 m3/day to 19,750 m3/day), remove large quantities of fresh groundwater from
the Pleistocene Crag aquifer and lead to high energy and pumping costs.
The research has shown that there are partial solutions to reducing the saline inflow into the drainage systems
in this lowland coastal catchment. However, any intended alterations must first consider other potential
impacts, such as changes to flood risk, land management restrictions or hydrodynamic effects on the receiving
watercourse through changed discharge volumes
Book reviews
No full textThere has been a bumper crop of books dedicated to social pedagogy this year – a fortuitous situation for providing a series of relevant reviews for this special issue of the Journal. This offers a nice opportunity to compare, in this case, three newly-published books on social pedagogy. Each of our reviewers gives a nice sense of the tone and slant of their books, giving an indication of some of the differences between them. I hope this will be helpful in deciding about further reading. Another of our reviews in this issue is on a book dedicated to storytelling and while it does not explicitly address social pedagogy, the reviewer clearly and adeptly provides strong connections between the book's content and the practice and philosophy of social pedagogy. Finally, it was no mean feat to find a seminal text or author from the social pedagogic tradition written in English, but we have managed to do so and thus provide a review of a book originally written by Janusz Korczak. Thanks to Ian Milligan, Robyn Kemp, Kiaras Gharabaghi, Dawn Simpson and Evelyn Vrouwenfelder for their generous contributions to our book review section
Developing a framework for semi-automated rule-based modelling for neuroscience research
No full textDynamic modelling has significantly improved our understanding of the complex
molecular mechanisms underpinning neurobiological processes. The detailed
mechanistic insights these models offer depend on the availability of
a diverse range of experimental observations. Despite the huge increase in
biomolecular data generation from novel high-throughput technologies and
extensive research in bioinformatics and dynamical modelling, efficient creation
of accurate dynamical models remains highly challenging. To study this
problem, three perspectives are considered: comparison of modelling methods,
prioritisation of results and analysis of primary data sets. Firstly, I compare two
models of the DARPP-32 signalling network: a classically defined model with
ordinary differential equations (ODE) and its equivalent, defined using a novel
rule-based (RB) paradigm. The RB model recapitulates the results of the ODE
model, but offers a more expressive and flexible syntax that can efficiently handle
the “combinatorial complexity” commonly found in signalling networks,
and allows ready access to fine-grain details of the emerging system. RB modelling
is particularly well suited to encoding protein-centred features such as
domain information and post-translational modification sites. Secondly, I propose
a new pipeline for prioritisation of molecular species that arise during
model simulation using a recently developed algorithm based on multivariate
mutual information (CorEx) coupled with global sensitivity analysis (GSA) using
the RKappa package. To efficiently evaluate the importance of parameters,
Hilber-Schmidt Independence Criterion (HSIC)-based indices are aggregated
into a weighted network that allows compact analysis of the model across conditions.
Finally, I describe an approach for the development of disease-specific
dynamical models using genes known to be associated with Attention Deficit
Hyperactivity Disorder (ADHD) as an exemplar. Candidate disease genes are
mapped to a selection of datasets that are potentially relevant to the modelling
process (e.g. interactions between proteins and domains, protein-domain and
kinase-substrates mappings) and these are jointly analysed using network clustering
and pathway enrichment analyses to evaluate their coverage and utility
in developing rule-based models
Book Reviews Vol12 no2
No full textThere has been a bumper crop of books dedicated to social pedagogy this year – a fortuitous situation for providing a series of relevant reviews for this special issue of the Journal. This offers a nice opportunity to compare, in this case, three newly-published books on social pedagogy. Each of our reviewers gives a nice sense of the tone and slant of their books, giving an indication of some of the differences between them. I hope this will be helpful in deciding about further reading. Another of our reviews in this issue is on a book dedicated to storytelling and while it does not explicitly address social pedagogy, the reviewer clearly and adeptly provides strong connections between the book's content and the practice and philosophy of social pedagogy. Finally, it was no mean feat to find a seminal text or author from the social pedagogic tradition written in English, but we have managed to do so and thus provide a review of a book originally written by Janusz Korczak. Thanks to Ian Milligan, Robyn Kemp, Kiaras Gharabaghi, Dawn Simpson and Evelyn Vrouwenfelder for their generous contributions to our book review section
An investigation into the role of methylation in mammalian X-chromosome inactivation
No full textX-chromosome inactivation achieves dosage compensation of X-linked genes between male (XY) and female (XX) mammals. This process involves the down-regulation of most, but not all genes on one of the two X-chromosomes in the nucleus of each female somatic cell. The mechanism of X-inactivation has yet to be elucidated in full, but is known to involve the noncoding transcript of theXist gene, DNA methylation, histone hypo-acetylation and the condensation of higher order chromatin. Recent studies have established mechanisms linking methylation to repressive chromatin structures through methyl-binding proteins and histone deacetylase complexes.In order to better understand the role of methylation in X-inactivation, the promoters of the human Pyruvate dehydrogenase El a (PDHA1) and the human and murine Norrie disease protein (NDP/Ndp) genes were subjected to direct methylation sequencing, allowing the definition of methylation profiles at nucleotide resolution. The promoter of the PDHA1 gene was found to be hyper-methylated on the inactive X-chromosome and hypo-methylated on the active X-chromosome in agreement with studies at the promoters of other X-linked housekeeping genes. Methylation at the promoters of the NDP/Ndp genes was extensively investigated in a range of primary tissues and cell lines. The Ndp promoter was found to be methylated on both active and inactive X-chromosomes, but hypo-methylated in the proximal promoter exclusively in tissues that expressed the Ndp gene. The NDP promoter was found to be unmethylated on the active X-chromosome and hyper-methylated across the proximal promoter on the inactive X-chromosome in expressing cell lines and human retinal tissues. The novel promoter sequences of the human and murine SMCX/Smcx genes were isolated for comparative analysis and to provide a future resource for studying methylation at the promoters of genes which escape the X-inactivation process.Promoter sequences of the PDHA1, NDPI Ndp and SMCX/Smcx genes were screened for putative transcription factor binding sites and for conserved CpG-dinucleotide content. Promoter-reporter gene constructs for these genes were transfected into mammalian cells establishing that the sequences studied were functional promoters. Artificial methylation of these constructs was shown to repress their promoter activities
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