scholarly journals lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Fayaz Seifuddin ◽  
Komudi Singh ◽  
Abhilash Suresh ◽  
Jennifer T. Judy ◽  
Yun-Ching Chen ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Tissue Specificity ◽  
Expression Patterns ◽  
Open Science ◽  
Whole Genome Sequence ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Link Type ◽  
Science Framework ◽  
User Friendly

Abstract Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci (cis-eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/, and the data are freely available through Open Science Framework (10.17605/OSF.IO/RU4D2).

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Tissue-specificity of gene expression diverges slowly between orthologs, and rapidly between paralogs

2016 ◽  
Author(s):  
Nadezda Kryuchkova-Mostacci ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Tissue Specificity ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Divergence Time ◽  
Gene Copy ◽  
Strong Increase ◽  
Small Scale ◽  
Ontology Term ◽  
Tissue Specific

AbstractThe ortholog conjecture implies that functional similarity between orthologous genes is higher than between paralogs. It has been supported using levels of expression and Gene Ontology term analysis, although the evidence was rather weak and there were also conflicting reports. In this study on 12 species we provide strong evidence of high conservation in tissue-specificity between orthologs, in contrast to low conservation between within-species paralogs. This allows us to shed a new light on the evolution of gene expression patterns. While there have been several studies of the correlation of expression between species, little is known about the evolution of tissue-specificity itself. Ortholog tissue-specificity is strongly conserved between all tetrapod species, with the lowest Pearson correlation between mouse and frog at r = 0.66. Tissue-specificity correlation decreases strongly with divergence time. Paralogs in human show much lower conservation, even for recent Primate-specific paralogs. When both paralogs from ancient whole genome duplication tissue-specific paralogs are tissue-specific, it is often to different tissues, while other tissue-specific paralogs are mostly specific to the same tissue. The same patterns are observed using human or mouse as focal species, and are robust to choices of datasets and of thresholds. Our results support the following model of evolution: in the absence of duplication, tissue-specificity evolves slowly, and tissue-specific genes do not change their main tissue of expression; after small-scale duplication the less expressed paralog loses the ancestral specificity, leading to an immediate difference between paralogs; over time, both paralogs become more broadly expressed, but remain poorly correlated. Finally, there is a small number of paralog pairs which stay tissue-specific with the same main tissue of expression, for at least 300 million years.Author summaryFrom specific examples, it has been assumed by comparative biologists that the same gene in different species has the same function, whereas duplication of a gene inside one species to create several copies allows them to acquire different functions. Yet this model was little tested until recently, and then has proven harder than expected to confirm. One of the problems is defining “function” in a way which can be easily studied. We introduce a new way of considering function: how specific is the activity (“expression”) of a gene? Genes which are specific to certain tissues have functions related to these tissues, whereas genes which are broadly active over many or all tissues have more general functions for the organism. We find that this “tissue-specificity” evolves very slowly in the absence of duplication, while immediately after duplication the new gene copy differs. This shows that indeed duplication leads to a strong increase in the evolution of new functions.

scholarly journals The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words

2019 ◽  
Vol 52 (3) ◽  
pp. 1271-1291 ◽  
Author(s):  
Dermot Lynott ◽  
Louise Connell ◽  
Marc Brysbaert ◽  
James Brand ◽  
James Carney
Keyword(s):  
Web Application ◽  
Open Science ◽  
Grounded Cognition ◽  
Word Meanings ◽  
Link Type ◽  
Semantic Norms ◽  
Perceptual Modalities ◽  
Sensorimotor Information ◽  
Science Framework ◽  
Amazon's Mechanical Turk

AbstractSensorimotor information plays a fundamental role in cognition. However, the existing materials that measure the sensorimotor basis of word meanings and concepts have been restricted in terms of their sample size and breadth of sensorimotor experience. Here we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon’s Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: They represent the largest-ever set of semantic norms for English, at 40,000 words × 11 dimensions (plus several informative cross-dimensional variables), they extend perceptual strength norming to the new modality of interoception, and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies, in which we (1) extracted an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength) and (2) demonstrated the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas, including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big-data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).

scholarly journals Restoring invisible and abandoned trials of gabapentin for neuropathic pain: a clinical and methodological investigation

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e047785
Author(s):  
Evan Mayo-Wilson ◽  
Xiwei Chen ◽  
Riaz Qureshi ◽  
Stephanie Dickinson ◽  
Lilian Golzarri-Arroyo ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Secondary Data ◽  
Open Science ◽  
Journal Articles ◽  
Link Type ◽  
Institutional Review ◽  
Individual Trial ◽  
Individual Participant ◽  
Review Protocol ◽  
Science Framework

IntroductionGabapentin (Neurontin) is prescribed widely for conditions for which it has not been approved by regulators, including certain neuropathic pain conditions. There is limited evidence that gabapentin is safe and effective for the treatment of neuropathic pain. Published trial reports, and systematic reviews based on published trial reports, mislead patients and providers because information about gabapentin’s harms has been published only partly. We confirmed that trials conducted by the drug developer have been abandoned, and we plan to conduct a restoration with support from the Restoring Invisible and Abandoned Trials Support Centre (https://restoringtrials.org/).Methods and analysisIn this study, we will analyse and report the harms that were observed in six trials of gabapentin, which have not been reported publicly (eg, in journal articles). We will use clinical study reports and individual participant data to identify and report the harms observed in each individual trial and to summarise the harms observed across all six trials. We will report all adverse events observed in the included trials by sharing deidentified data and summary tables on the Open Science Framework (https://osf.io/w8puv/). Additionally, we will produce a summary report that describes differences between the randomised groups in each trial and across trials for prespecified harms outcomes.Ethics and disseminationWe will use secondary data. This study was determined to be exempt from Institutional Review Board (IRB) review (protocol #1910607198).

scholarly journals Atlas of tissue-specific and tissue-preferential gene expression in ecologically and economically significant conifer Pinus sylvestris

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11781
Author(s):  
Sandra Cervantes ◽  
Jaana Vuosku ◽  
Tanja Pyhäjärvi
Keyword(s):  
Gene Expression ◽  
Pinus Sylvestris ◽  
Tissue Specificity ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Biological Databases ◽  
Ploidy Levels ◽  
Tissue Specific ◽  
Genomic Resources ◽  
Wide Range

Despite their ecological and economical importance, conifers genomic resources are limited, mainly due to the large size and complexity of their genomes. Additionally, the available genomic resources lack complete structural and functional annotation. Transcriptomic resources have been commonly used to compensate for these deficiencies, though for most conifer species they are limited to a small number of tissues, or capture only a fraction of the genes present in the genome. Here we provide an atlas of gene expression patterns for conifer Pinus sylvestris across five tissues: embryo, megagametophyte, needle, phloem and vegetative bud. We used a wide range of tissues and focused our analyses on the expression profiles of genes at tissue level. We provide comprehensive information of the per-tissue normalized expression level, indication of tissue preferential upregulation and tissue-specificity of expression. We identified a total of 48,001 tissue preferentially upregulated and tissue specifically expressed genes, of which 28% have annotation in the Swiss-Prot database. Even though most of the putative genes identified do not have functional information in current biological databases, the tissue-specific patterns discovered provide valuable information about their potential functions for further studies, as for example in the areas of plant physiology, population genetics and genomics in general. As we provide information on tissue specificity at both diploid and haploid life stages, our data will also contribute to the understanding of evolutionary rates of different tissue types and ploidy levels.

scholarly journals Atlas of tissue-specific and tissue-preferential gene expression in ecologically and economically significant conifer Pinus sylvestris

2020 ◽  
Author(s):  
Sandra Cervantes ◽  
Jaana Vuosku ◽  
Dorota Paczesniak ◽  
Tanja Pyhäjärvi
Keyword(s):  
Gene Expression ◽  
Pinus Sylvestris ◽  
Tissue Specificity ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Biological Databases ◽  
Ploidy Levels ◽  
Tissue Specific ◽  
Genomic Resources ◽  
Wide Range

AbstractDespite their ecological and economical importance, conifers genomic resources are limited, mainly due to the large size and complexity of their genomes. Additionally, the available genomic resources lack complete structural and functional annotation. Transcriptomic resources have been commonly used to compensate for these deficiencies, though for most conifer species they are limited to a small number of tissues, or capture only a fraction of the genes present in the genome.Here we provide an atlas of gene expression patterns for conifer Pinus sylvestris across five tissues: embryo, megagametophyte, needle, phloem, and vegetative bud. We used a wide range of tissues and focused our analyses on the expression profiles of genes at tissue level. We provide comprehensive information of the per-tissue normalized expression level, indication of tissue preferential upregulation and tissue-specificity of expression. We identified a total of 48,001 tissue preferentially upregulated and tissue specifically expressed genes, of which 28% have annotation in the Swiss-Prot database. Even though most of the putative genes identified do not have functional information in current biological databases, the tissue-specific patterns discovered provide valuable information about their potential functions for further studies, as for example in the areas of plant physiology, population genetics, and genomics in general. As we provide information on tissue specificity at both diploid and haploid life stages, our data will also contribute to the understanding of evolutionary rates of different tissue types and ploidy levels.

scholarly journals PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs

2020 ◽  
Vol 49 (D1) ◽  
pp. D1489-D1495 ◽  
Author(s):  
Jingjing Jin ◽  
Peng Lu ◽  
Yalong Xu ◽  
Zefeng Li ◽  
Shizhou Yu ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Data Driven ◽  
Rna Seq ◽  
Protein Coding ◽  
User Friendly ◽  
Coding Potential

Abstract Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein coding potential. The expanding list of lncRNAs and accumulating evidence of their functions in plants have necessitated the creation of a comprehensive database for lncRNA research. However, currently available plant lncRNA databases have some deficiencies, including the lack of lncRNA data from some model plants, uneven annotation standards, a lack of visualization for expression patterns, and the absence of epigenetic information. To overcome these problems, we upgraded our Plant Long noncoding RNA Database (PLncDB, http://plncdb.tobaccodb.org/), which was based on a uniform annotation pipeline. PLncDB V2.0 currently contains 1 246 372 lncRNAs for 80 plant species based on 13 834 RNA-Seq datasets, integrating lncRNA information from four other resources including EVLncRNAs, RNAcentral and etc. Expression patterns and epigenetic signals can be visualized using multiple tools (JBrowse, eFP Browser and EPexplorer). Targets and regulatory networks for lncRNAs are also provided for function exploration. In addition, PLncDB V2.0 is hierarchical and user-friendly and has five built-in search engines. We believe PLncDB V2.0 is useful for the plant lncRNA community and data mining studies and provides a comprehensive resource for data-driven lncRNA research in plants.

scholarly journals SPECS: A non-parameteric method to identify tissue-specific molecular features for unbalanced sample groups

2019 ◽  
Author(s):  
Celine Everaert ◽  
Pieter-Jan Volders ◽  
Annelien Morlion ◽  
Olivier Thas ◽  
Pieter Mestdagh
Keyword(s):  
Cell Types ◽  
Tissue Specific ◽  
Sample Group ◽  
Link Type ◽  
Molecular Features ◽  
Abundance Patterns ◽  
User Friendly ◽  
Specificity Score ◽  
Non Parametric

AbstractTo understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all. We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.ugent.be/ceeverae/SPECs. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Vol 17 (6) ◽  
pp. e1009085
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Does pre-notification increase questionnaire response rates: a randomised controlled trial nested within a systematic review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Benjamin Woolf ◽  
Phil Edwards
Keyword(s):  
Systematic Review ◽  
Controlled Trial ◽  
Response Rates ◽  
Open Science ◽  
Outcome Data ◽  
Absolute Difference ◽  
Questionnaire Response ◽  
Link Type ◽  
Science Framework ◽  
Randomised Controlled

Abstract Background Missing outcome data can lead to bias in the results of systematic reviews. One way to address missing outcome data is by requesting the data from the trial authors, but non-response is common. One way to potentially improve response rates is by sending study participants advance communication. During the update of a systematic review examining the effect of pre-notification on response rates, study authors needed to be contacted for further information. This study was nested within the systematic review by randomising authors to receive a notification of the upcoming request for information. The objective was to test if pre-notification increased response rates. Methods The participants were study authors included in the systematic review, whose studies were at unclear risk of bias. The intervention was a pre-notification of the request for further information, sent 1 day before the request. The outcome was defined as the proportion of authors who responded to the request for information. Authors were randomised by simple randomisation. Thirty three authors were randomised to the pre-notification arm, and 42 were randomised to the control arm. Authors were blinded to the possibility of an alternative condition. Results All authors randomised were analysed. 14/33 (42.4%) authors in the pre-notification arm had returned responses to the questionnaire, and 18/42 (42.9%) in the control arm. There was no evidence of a difference between these groups (absolute difference = − 0.5, 95% CI (− 23.4 to 22.5%), p = 1). We received no complaints about receiving the pre-notification. Conclusions This study’s results do not support the hypothesis that pre-notification increases response from study authors being contacted for a request for more information. However, the study has a low power, and the results may not generalise to other contexts, methods of administering a pre-notification, or study populations. Trial registration Registration and protocol: This trial is not registered with any trial registry. However, the protocol was posted in advance on the Open Science Framework website and is available on the Open Science Framework website: DOI: 10.17605/OSF.IO/MSV2W or https://osf.io/msv2w/

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