An efficient algorithm to explore liquid association on a genome-wide scale

Genes of the F-box family play specific roles in protein degradation by post-translational modification in several biological processes, including flowering, the regulation of circadian rhythms, photomorphogenesis, seed development, leaf senescence, and hormone signaling. F-box genes have not been previously investigated on a genome-wide scale; however, the establishment of the wheat (Triticum aestivum L.) reference genome sequence enabled a genome-based examination of the F-box genes to be conducted in the present study. In total, 1796 F-box genes were detected in the wheat genome and classified into various subgroups based on their functional C-terminal domain. The F-box genes were distributed among 21 chromosomes and most showed high sequence homology with F-box genes located on the homoeologous chromosomes because of allohexaploidy in the wheat genome. Additionally, a synteny analysis of wheat F-box genes was conducted in rice and Brachypodium distachyon. Transcriptome analysis during various wheat developmental stages and expression analysis by quantitative real-time PCR revealed that some F-box genes were specifically expressed in the vegetative and/or seed developmental stages. A genome-based examination and classification of F-box genes provide an opportunity to elucidate the biological functions of F-box genes in wheat.

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Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

Nucleic Acids Research ◽

10.1093/nar/gku576 ◽

2014 ◽

Vol 42 (15) ◽

pp. 9838-9853 ◽

Cited By ~ 6

Author(s):

Saeed Kaboli ◽

Takuya Yamakawa ◽

Keisuke Sunada ◽

Tao Takagaki ◽

Yu Sasano ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Genetic Interaction ◽

Interaction Network ◽

Essential Genes ◽

Genetic Interactions ◽

Lethal Gene ◽

Synthetic Lethal ◽

Genome Wide ◽

A Genome ◽

Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

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Machine-learning annotation of human splicing branchpoints

10.1101/094003 ◽

2016 ◽

Cited By ~ 3

Author(s):

Bethany Signal ◽

Brian S Gloss ◽

Marcel E Dinger ◽

Timothy R Mercer

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Gene Splicing ◽

Genetic Encoding ◽

Genome Wide ◽

Common Genetic Variants ◽

A Genome ◽

Wide Scale ◽

The Impact ◽

Splicing Patterns

ABSTRACTBackgroundThe branchpoint element is required for the first lariat-forming reaction in splicing. However due to difficulty in experimentally mapping at a genome-wide scale, current catalogues are incomplete.ResultsWe have developed a machine-learning algorithm trained with empirical human branchpoint annotations to identify branchpoint elements from primary genome sequence alone. Using this approach, we can accurately locate branchpoints elements in 85% of introns in current gene annotations. Consistent with branchpoints as basal genetic elements, we find our annotation is unbiased towards gene type and expression levels. A major fraction of introns was found to encode multiple branchpoints raising the prospect that mutational redundancy is encoded in key genes. We also confirmed all deleterious branchpoint mutations annotated in clinical variant databases, and further identified thousands of clinical and common genetic variants with similar predicted effects.ConclusionsWe propose the broad annotation of branchpoints constitutes a valuable resource for further investigations into the genetic encoding of splicing patterns, and interpreting the impact of common- and disease-causing human genetic variation on gene splicing.

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Emerging Technologies for Genome-Wide Profiling of DNA Breakage

Frontiers in Genetics ◽

10.3389/fgene.2020.610386 ◽

2021 ◽

Vol 11 ◽

Author(s):

Matthew J. Rybin ◽

Melina Ramic ◽

Natalie R. Ricciardi ◽

Philipp Kapranov ◽

Claes Wahlestedt ◽

...

Keyword(s):

Genome Instability ◽

Dna Double Strand Breaks ◽

Single Nucleotide ◽

Strand Breaks ◽

Single Strand Breaks ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Nucleotide Resolution ◽

Genomic Regions

Genome instability is associated with myriad human diseases and is a well-known feature of both cancer and neurodegenerative disease. Until recently, the ability to assess DNA damage—the principal driver of genome instability—was limited to relatively imprecise methods or restricted to studying predefined genomic regions. Recently, new techniques for detecting DNA double strand breaks (DSBs) and single strand breaks (SSBs) with next-generation sequencing on a genome-wide scale with single nucleotide resolution have emerged. With these new tools, efforts are underway to define the “breakome” in normal aging and disease. Here, we compare the relative strengths and weaknesses of these technologies and their potential application to studying neurodegenerative diseases.

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The Genetics and Genomics of Asthma

Annual Review of Genomics and Human Genetics ◽

10.1146/annurev-genom-083117-021651 ◽

2018 ◽

Vol 19 (1) ◽

pp. 223-246 ◽

Cited By ~ 14

Author(s):

Saffron A.G. Willis-Owen ◽

William O.C. Cookson ◽

Miriam F. Moffatt

Keyword(s):

Sequence Variation ◽

Human Microbiome ◽

Technological Advances ◽

Genome Wide ◽

A Genome ◽

Wide Range ◽

Health And Disease ◽

Wide Scale ◽

Genetics And Genomics ◽

The Individual

Asthma is a common, clinically heterogeneous disease with strong evidence of heritability. Progress in defining the genetic underpinnings of asthma, however, has been slow and hampered by issues of inconsistency. Recent advances in the tools available for analysis—assaying transcription, sequence variation, and epigenetic marks on a genome-wide scale—have substantially altered this landscape. Applications of such approaches are consistent with heterogeneity at the level of causation and specify patterns of commonality with a wide range of alternative disease traits. Looking beyond the individual as the unit of study, advances in technology have also fostered comprehensive analysis of the human microbiome and its varied roles in health and disease. In this article, we consider the implications of these technological advances for our current understanding of the genetics and genomics of asthma.

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A Clustering Approach for Motif Discovery in ChIP-Seq Dataset

Entropy ◽

10.3390/e21080802 ◽

2019 ◽

Vol 21 (8) ◽

pp. 802

Author(s):

Chun-xiao Sun ◽

Yu Yang ◽

Hua Wang ◽

Wen-hu Wang

Keyword(s):

Dna Sequences ◽

Motif Discovery ◽

Simulated Data ◽

Data Set ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Clustering Approach ◽

Ap Clustering ◽

Generation Sequencing

Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-Seq) technology has enabled the identification of transcription factor binding sites (TFBSs) on a genome-wide scale. To effectively and efficiently discover TFBSs in the thousand or more DNA sequences generated by a ChIP-Seq data set, we propose a new algorithm named AP-ChIP. First, we set two thresholds based on probabilistic analysis to construct and further filter the cluster subsets. Then, we use Affinity Propagation (AP) clustering on the candidate cluster subsets to find the potential motifs. Experimental results on simulated data show that the AP-ChIP algorithm is able to make an almost accurate prediction of TFBSs in a reasonable time. Also, the validity of the AP-ChIP algorithm is tested on a real ChIP-Seq data set.

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