The only conserved microsatellite in coding regions of ebolavirus is the editing site

2021 ◽  
Vol 565 ◽  
pp. 79-84
Author(s):  
Douyue Li ◽  
Ruixue Shi ◽  
Hongxi Zhang ◽  
Hanrou Huang ◽  
Saichao Pan ◽  
...  
Keyword(s):  
2018 ◽  
Author(s):  
Xiaodan Wang ◽  
Zhenshuo Zhu ◽  
Xiaolong Wu ◽  
Hao Li ◽  
Tongtong Li ◽  
...  

ABSTRACTSpermatogenesis is an important physiological process associated with male infertility. But whether there are RNA editings (REs) and what’s the role of REs during the process are still unclear. In this study, we integrated published RNA-Seq datasets and established a landscape of REs during the development of mouse spermatogenesis. 7530 editing sites among all types of male germ cells were found, which enrich on some regions of chromosome, including chromosome 17 and both ends of chromosome Y. Totally, REs occur in 2012 genes during spermatogenesis, more than half of which harbor at two different sites of the same gene at least. We also found REs mainly occur in introns, coding regions (CDSs) and intergenic regions. Moreover, about half of the REs in CDSs can cause amino acids changes. Finally, based on our adult male Kunming mice, we verified that there is a non-synonymous A-to-I RNA editing site inCog3during spermatogenesis, which is conserved not only between species but also across tissues. In short, based on the power of integrating RNA-Seq datasets, we provided the landscape of REs and found their dynamic changes during mouse spermatogenesis. This research strategy is general for other types of sequencing datasets and biological problems.


2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.


Genetics ◽  
1997 ◽  
Vol 147 (3) ◽  
pp. 1213-1224
Author(s):  
Jean-Philippe Charles ◽  
Carol Chihara ◽  
Shamim Nejad ◽  
Lynn M Riddiford

A 36-kb genomic DNA segment of the Drosophila melanogaster genome containing 12 clustered cuticle genes has been mapped and partially sequenced. The cluster maps at 65A 5-6 on the left arm of the third chromosome, in agreement with the previously determined location of a putative cluster encompassing the genes for the third instar larval cuticle proteins LCP5, LCP6 and LCP8. This cluster is the largest cuticle gene cluster discovered to date and shows a number of surprising features that explain in part the genetic complexity of the LCP5, LCP6 and LCP8 loci. The genes encoding LCP5 and LCP8 are multiple copy genes and the presence of extensive similarity in their coding regions gives the first evidence for gene conversion in cuticle genes. In addition, five genes in the cluster are intronless. Four of these five have arisen by retroposition. The other genes in the cluster have a single intron located at an unusual location for insect cuticle genes.


2021 ◽  
Vol 7 (3) ◽  
pp. 47
Author(s):  
Marios Lange ◽  
Rodiola Begolli ◽  
Antonis Giakountis

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.


Genetics ◽  
1999 ◽  
Vol 153 (1) ◽  
pp. 445-452
Author(s):  
Wei Jin ◽  
Harry T Horner ◽  
Reid G Palmer ◽  
Randy C Shoemaker

Abstract Oligonucleotide primers designed for conserved sequences from coding regions of β-1,3-glucanase genes from different species were used to amplify related sequences from soybean [Glycine max (L.) Merr.]. Sequencing and cross-hybridization of amplification products indicated that at least 12 classes of β-1,3-glucanase genes exist in the soybean. Members of classes mapped to 34 loci on five different linkage groups using an F2 population of 56 individuals. β-1,3-Glucanase genes are clustered onto regions of five linkage groups. Data suggest that more closely related genes are clustered together on one linkage group or on duplicated regions of linkage groups. Northern blot analyses performed on total RNA from root, stem, leaf, pod, flower bud, and hypocotyl using DNA probes for the different classes of β-1,3-glucanase genes revealed that the mRNA levels of all classes were low in young leaves. SGlu2, SGlu4, SGlu7, and SGlu12 mRNA were highly accumulated in young roots and hypocotyls. SGlu7 mRNA also accumulated in pods and flower buds.


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