scholarly journals TFutils: Data structures for transcription factor bioinformatics

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 152
Author(s):  
Benjamin J. Stubbs ◽  
Shweta Gopaulakrishnan ◽  
Kimberly Glass ◽  
Nathalie Pochet ◽  
Celine Everaert ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Data Structures ◽  
Binding Sites ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Bioconductor Package ◽  
Genome Wide ◽  
Study Results ◽  
Integrative Analyses

DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.

scholarly journals TFutils: Data structures for transcription factor bioinformatics

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 152
Author(s):  
Benjamin J. Stubbs ◽  
Shweta Gopaulakrishnan ◽  
Kimberly Glass ◽  
Nathalie Pochet ◽  
Celine Everaert ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Data Structures ◽  
Binding Sites ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Bioconductor Package ◽  
Genome Wide ◽  
Study Results ◽  
Integrative Analyses

DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites, including an interface to results of 690 ENCODE experiments. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.

scholarly journals Genome-wide association study of susceptibility to hospitalised respiratory infections

2021 ◽  
Vol 6 ◽  
pp. 290
Author(s):  
Alexander T. Williams ◽  
Nick Shrine ◽  
Hardeep Naghra-van Gijzel ◽  
Joanna C. Betts ◽  
Edith M. Hessel ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Association Study ◽  
Respiratory Infection ◽  
Genome Wide Association Study ◽  
Respiratory Infections ◽  
Genome Wide Association ◽  
Uk Biobank ◽  
Biological Mechanisms ◽  
Genome Wide ◽  
Infection Susceptibility

Background: Globally, respiratory infections contribute to significant morbidity and mortality. However, genetic determinants of respiratory infections are understudied and remain poorly understood. Methods: We conducted a genome-wide association study in 19,459 hospitalised respiratory infection cases and 101,438 controls from UK Biobank. We followed-up well-imputed top signals from the UK Biobank discovery analysis in 50,912 respiratory infection cases and 150,442 controls from 11 cohorts. We aggregated effect estimates across studies using inverse variance-weighted meta-analyses. Additionally, we investigated the function of the top signals in order to gain understanding of the underlying biological mechanisms. Results: In the discovery analysis, we report 56 signals at P<5×10-6, one of which was genome-wide significant (P<5×10-8). The genome-wide significant signal was in an intron of PBX3, a gene that encodes pre-B-cell leukaemia transcription factor 3, a homeodomain-containing transcription factor. Further, the genome-wide significant signal was found to colocalise with gene-specific expression quantitative trait loci (eQTLs) affecting expression of PBX3 in lung tissue, where the respiratory infection risk alleles were associated with decreased PBX3 expression in lung tissue, highlighting a possible biological mechanism. Of the 56 signals, 40 were well-imputed in UK Biobank and were investigated in the 11 follow-up cohorts. None of the 40 signals replicated, with effect estimates attenuated. Conclusions: Our discovery analysis implicated PBX3 as a candidate causal gene and suggests a possible role of transcription factor binding activity in respiratory infection susceptibility. However, the PBX3 signal, and the other well-imputed signals, did not replicate when aggregating effect estimates across 11 independent cohorts. Significant phenotypic heterogeneity and differences in study ascertainment may have contributed to this lack of statistical replication. Overall, our study highlighted putative associations and possible biological mechanisms that may provide insight into respiratory infection susceptibility.

scholarly journals Genome-Wide Association Study (GWAS) to Identify Salt-Tolerance QTLs Carrying Novel Candidate Genes in Rice During Early Vegetative Stage

2020 ◽  
Author(s):  
Leila Nayyeripasand ◽  
Ghasem Ali Garoosi ◽  
Asadollah Ahmadikhah
Keyword(s):  
Transcription Factor ◽  
Association Study ◽  
Candidate Gene ◽  
Candidate Genes ◽  
Salinity Tolerance ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Vegetative Stage ◽  
Genome Wide ◽  
Genomic Regions

Abstract Background Rice is considered as a salt-sensitive plant, particularly at early vegetative stage, and its production is suffered from salinity due to expansion of salt affected land in areas under cultivation. Hence, significant increase of rice productivity on salinized lands is really necessary. Today genome-wide association study (GWAS) is a method of choice for fine mapping of QTLs involved in plant responses to abiotic stresses including salinity stress at early vegetative stage. In this study using > 33,000 SNP markers we identified rice genomic regions associated to early stage salinity tolerance. Eight salinity-related traits including SL, RL, RDW, RFW, SFW, SDW, RWC and TW in a diverse panel of rice consisted of 202 varieties were evaluated under salinity (100 mM NaCl) and normal conditions in growth chamber. Genome-wide association study (GWAS) was applied based on MLM(+ Q + K) model.Results Under stress conditions 151 trait-marker associations were identified that were scattered on 10 chromosomes of rice that arranged in 29 genomic regions. A genomic region on chromosome 1 (11.26 Mbp) was identified which co-located with a known QTL region SalTol1 for salinity tolerance at vegetative stage. A candidate gene (Os01g0304100) was identified in this region which encodes a cation chloride cotransporter. Furthermore, on this chromosome two other candidate genes, Os01g0624700 (24.95 Mbp) and Os01g0812000 (34.51 Mbp), were identified that encode a WRKY transcription factor (WRKY 12) and a transcriptional activator of gibberellin-dependent alpha-amylase expression (GAMyb), respectively. Also, a narrow interval on the same chromosome (40.79–42.98 Mbp) carries 12 candidate genes, some of them were not so far reported for salinity tolerance at seedling stage. Two of more interesting genes are Os01g0966000 and Os01g0963000, encoding a plasma membrane (PM) H+-ATPase and a peroxidase BP1 protein. On chromosome 6 a DnaJ-encoding gene and pseudouridine synthase gene were identified. Two novel genes on chromosome 8 including the ABI/VP1 transcription factor and retinoblastoma-related protein (RBR), and 3 novel genes on chromosome 11 including a Lox, F-box and Na+/H+ antiporter, were also identified.Conclusion The results for RDW and RFW were found more important than other traits, and known or novel candidate genes in this research can be used for improvement of salinity tolerance in molecular breeding programmes. Further study and identification of effective genes on salinity tolerance by the use of candidate gene-association analysis can help to precisely uncover the mechanisms of salinity tolerance at molecular level.

scholarly journals target: an R package to predict combined function of transcription factors

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 344
Author(s):  
Mahmoud Ahmed ◽  
Deok Ryong Kim
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
R Package ◽  
Expression Data ◽  
Bioconductor Package ◽  
Chip Experiment ◽  
Binding Data ◽  
Two Factors

Researchers use ChIP binding data to identify potential transcription factor binding sites. Similarly, they use gene expression data from sequencing or microarrays to quantify the effect of the factor overexpression or knockdown on its targets. Therefore, the integration of the binding and expression data can be used to improve the understanding of a transcription factor function. Here, we implemented the binding and expression target analysis (BETA) in an R/Bioconductor package. This algorithm ranks the targets based on the distances of their assigned peaks from the factor ChIP experiment and the signed statistics from gene expression profiling with factor perturbation. We further extend BETA to integrate two sets of data from two factors to predict their targets and their combined functions. In this article, we briefly describe the workings of the algorithm and provide a workflow with a real dataset for using it. The gene targets and the aggregate functions of transcription factors YY1 and YY2 in HeLa cells were identified. Using the same datasets, we identified the shared targets of the two factors, which were found to be, on average, more cooperatively regulated.

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