scholarly journals BASiNET—BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification

2018 ◽  
Vol 46 (16) ◽  
pp. e96-e96 ◽  
Author(s):  
Eric Augusto Ito ◽  
Isaque Katahira ◽  
Fábio Fernandes da Rocha Vicente ◽  
Luiz Filipe Protasio Pereira ◽  
Fabrício Martins Lopes
2017 ◽  
Vol 14 (4) ◽  
Author(s):  
Gökhan Karakülah

AbstractNovel transcript discovery through RNA sequencing has substantially improved our understanding of the transcriptome dynamics of biological systems. Endogenous target mimicry (eTM) transcripts, a novel class of regulatory molecules, bind to their target microRNAs (miRNAs) by base pairing and block their biological activity. The objective of this study was to provide a computational analysis framework for the prediction of putative eTM sequences in plants, and as an example, to discover previously un-annotated eTMs in Prunus persica (peach) transcriptome. Therefore, two public peach transcriptome libraries downloaded from Sequence Read Archive (SRA) and a previously published set of long non-coding RNAs (lncRNAs) were investigated with multi-step analysis pipeline, and 44 putative eTMs were found. Additionally, an eTM-miRNA-mRNA regulatory network module associated with peach fruit organ development was built via integration of the miRNA target information and predicted eTM-miRNA interactions. My findings suggest that one of the most widely expressed miRNA families among diverse plant species, miR156, might be potentially sponged by seven putative eTMs. Besides, the study indicates eTMs potentially play roles in the regulation of development processes in peach fruit via targeting specific miRNAs. In conclusion, by following the step-by step instructions provided in this study, novel eTMs can be identified and annotated effectively in public plant transcriptome libraries.


Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Rongrong Wu ◽  
Yuxin Lin ◽  
Xingyun Liu ◽  
Chaoying Zhan ◽  
Hongxin He ◽  
...  

Abstract The phenotype–genotype relationship is a key for personalized and precision medicine for complex diseases. To unravel the complexity of the clinical phenotype–genotype network, we used cardiovascular diseases (CVDs) and associated non-coding RNAs (ncRNAs) (i.e. miRNAs, long ncRNAs, etc.) as the case for the study of CVDs at a systems or network level. We first integrated a database of CVDs and ncRNAs (CVDncR, http://sysbio.org.cn/cvdncr/) to construct CVD–ncRNA networks and annotate their clinical associations. To characterize the networks, we then separated the miRNAs into two groups, i.e. universal miRNAs associated with at least two types of CVDs and specific miRNAs related only to one type of CVD. Our analyses indicated two interesting patterns in these CVD–ncRNA networks. First, scale-free features were present within both CVD–miRNA and CVD–lncRNA networks; second, universal miRNAs were more likely to be CVDs biomarkers. These results were confirmed by computational functional analyses. The findings offer theoretical guidance for decoding CVD–ncRNA associations and will facilitate the screening of CVD ncRNA biomarkers. Database URL: http://sysbio.org.cn/cvdncr/


2021 ◽  
Vol 12 ◽  
Author(s):  
Xibo Sun ◽  
Leiming Cheng ◽  
Jinyang Liu ◽  
Cuinan Xie ◽  
Jiasheng Yang ◽  
...  

Long non-coding RNAs (lncRNAs) are widely concerned because of their close associations with many key biological activities. Though precise functions of most lncRNAs are unknown, research works show that lncRNAs usually exert biological function by interacting with the corresponding proteins. The experimental validation of interactions between lncRNAs and proteins is costly and time-consuming. In this study, we developed a weighted graph-regularized matrix factorization (LPI-WGRMF) method to find unobserved lncRNA–protein interactions (LPIs) based on lncRNA similarity matrix, protein similarity matrix, and known LPIs. We compared our proposed LPI-WGRMF method with five classical LPI prediction methods, that is, LPBNI, LPI-IBNRA, LPIHN, RWR, and collaborative filtering (CF). The results demonstrate that the LPI-WGRMF method can produce high-accuracy performance, obtaining an AUC score of 0.9012 and AUPR of 0.7324. The case study showed that SFPQ, SNHG3, and PRPF31 may associate with Q9NUL5, Q9NUL5, and Q9UKV8 with the highest linking probabilities and need to further experimental validation.


2014 ◽  
Vol 38 (01) ◽  
pp. 102-129
Author(s):  
ALBERTO MARTÍN ÁLVAREZ ◽  
EUDALD CORTINA ORERO

AbstractUsing interviews with former militants and previously unpublished documents, this article traces the genesis and internal dynamics of the Ejército Revolucionario del Pueblo (People's Revolutionary Army, ERP) in El Salvador during the early years of its existence (1970–6). This period was marked by the inability of the ERP to maintain internal coherence or any consensus on revolutionary strategy, which led to a series of splits and internal fights over control of the organisation. The evidence marshalled in this case study sheds new light on the origins of the armed Salvadorean Left and thus contributes to a wider understanding of the processes of formation and internal dynamics of armed left-wing groups that emerged from the 1960s onwards in Latin America.


2020 ◽  
Vol 43 ◽  
Author(s):  
Michael Lifshitz ◽  
T. M. Luhrmann

Abstract Culture shapes our basic sensory experience of the world. This is particularly striking in the study of religion and psychosis, where we and others have shown that cultural context determines both the structure and content of hallucination-like events. The cultural shaping of hallucinations may provide a rich case-study for linking cultural learning with emerging prediction-based models of perception.


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