Current Genomics最新文献

{"title":"Draft Genome Sequence of the Earthworm <i>Eudrilus eugeniae</i>.","authors":"Arun Arumugaperumal,&nbsp;Dinesh Kumar Sudalaimani,&nbsp;Vaithilingaraja Arumugaswami,&nbsp;Sudhakar Sivasubramaniam","doi":"10.2174/1389202923666220401095626","DOIUrl":"https://doi.org/10.2174/1389202923666220401095626","url":null,"abstract":"<p><p><b><i>Background</i>:</b> Earthworms are annelids. They play a major role in agriculture and soil fertility. Vermicompost is the best organic manure for plant crops. <i>Eudrilus eugeniae</i> is an earthworm well suited for efficient vermicompost production. The worm is also used to study the cell and molecular biology of regeneration, molecular toxicology, developmental biology, <i>etc</i>., because of its abilities like high growth rate, rapid reproduction, tolerability toward wide temperature range, and less cost of maintenance. <b><i>Objective</i>:</b> The whole genome has been revealed only for <i>Eisenia andrei</i> and <i>Eisenia fetida.</i> <b><i>Methods</i>:</b> In the present work, we sequenced the genome of <i>E. eugeniae</i> using the Illumina platform and generated 160,684,383 paired-end reads. <b><i>Results</i>:</b> The reads were assembled into a draft genome of size 488 Mb with 743,870 contigs and successfully annotated 24,599 genes. Further, 208 stem cell-specific genes and 3,432 non-coding genes were identified. <b><i>Conclusion</i>:</b> The sequence and annotation details were hosted in a web application available at https://sudhakar-sivasubramaniam-labs.shinyapps.io/eudrilus_genome/.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"118-125"},"PeriodicalIF":2.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/32/CG-23-118.PMC9878837.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10705108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering.","authors":"Dipro Sinha, Anu Sharma, Dwijesh Chandra Mishra, Anil Rai, Shashi Bhushan Lal, Sanjeev Kumar, Moh Samir Farooqi, Krishna Kumar Chaturvedi","doi":"10.2174/1389202923666220413114659","DOIUrl":"10.2174/1389202923666220413114659","url":null,"abstract":"<p><p><b><i>Background</i>:</b> Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. <b><i>Objective</i>:</b> It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. <b><i>Methods</i>:</b> Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. <b><i>Results</i>:</b> Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. <b><i>Conclusion</i>:</b> This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"137-146"},"PeriodicalIF":1.8,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8c/3c/CG-23-137.PMC9878838.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10705109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAAP: A GUI-based Genome Assembly and Annotation Package.","authors":"Deepak Singla,&nbsp;Inderjit Singh Yadav","doi":"10.2174/1389202923666220128155537","DOIUrl":"https://doi.org/10.2174/1389202923666220128155537","url":null,"abstract":"<p><p><b><i>Background</i>:</b> Next-generation sequencing (NGS) technologies are being continuously used for high-throughput sequencing data generation that requires easy-to-use GUI-based data analysis software. These kinds of software could be used in-parallel with sequencing for the automatic data analysis. At present, very few software are available for use and most of them are commercial, thus creating a gap between data generation and data analysis. <b><i>Methods</i>:</b> GAAP is developed on the NodeJS platform that uses HTML, JavaScript as the front-end for communication with users. We have implemented FastQC and trimmomatic tool for quality checking and control. Velvet and Prodigal are integrated for genome assembly and gene prediction. The annotation will be done with the help of remote NCBI Blast and IPR-Scan. In the back- end, we have used PERL and JavaScript for the processing of data. To evaluate the performance of GAAP, we have assembled a viral (SRR11621811), bacterial (SRR17153353) and human genome (SRR16845439). <b><i>Results</i>:</b> We have used GAAP software to assemble, and annotate a COVID-19 genome on a desktop computer that resulted in a single contig of 27994bp with 99.57% reference genome coverage. This assembly predicted 11 genes, of which 10 were annotated using annotation module of GAAP. We have also assembled a bacterial and human genome 138 and 194281 contigs with N50 value 100399 and 610, respectively. <b><i>Conclusion</i>:</b> In this study, we have developed freely available, platform-independent genome assembly and annotation (GAAP) software (www.deepaklab.com/gaap). The software itself acts as a complete data analysis package with quality check, quality control, <i>de-novo</i> genome assembly, gene prediction and annotation (Blast, PFAM, GO-Term, pathway and enzyme mapping) modules.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"77-82"},"PeriodicalIF":2.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/4f/CG-23-77.PMC9878834.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10705111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infestation of Rice by Gall Midge Influences Density and Diversity of <i>Pseudomonas</i> and <i>Wolbachia</i> in the Host Plant Microbiome.","authors":"Deepak K Sinha,&nbsp;Ayushi Gupta,&nbsp;Ayyagari P Padmakumari,&nbsp;Jagadish S Bentur,&nbsp;Suresh Nair","doi":"10.2174/1389202923666220401101604","DOIUrl":"https://doi.org/10.2174/1389202923666220401101604","url":null,"abstract":"<p><p><b><i>Background</i>:</b> The virulence of phytophagous insects is predominantly determined by their ability to evade or suppress host defense for their survival. The rice gall midge (GM, <i>Orseolia oryzae</i>), a monophagous pest of rice, elicits a host defense similar to the one elicited upon pathogen attack. This could be due to the GM feeding behaviour, wherein the GM endosymbionts are transferred to the host plant <i>via</i> oral secretions, and as a result, the host mounts an appropriate defense response(s) (<i>i.e</i>., up-regulation of the salicylic acid pathway) against these endosymbionts. <b><i>Methods</i>:</b> The current study aimed to analyze the microbiome present at the feeding site of GM maggots to determine the exchange of bacterial species between GM and its host and to elucidate their role in rice-GM interaction using a next-generation sequencing approach. <b><i>Results</i>:</b> Our results revealed differential representation of the phylum Proteobacteria in the GM-infested and -uninfested rice tissues. Furthermore, analysis of the species diversity of <i>Pseudomonas</i> and <i>Wolbachia</i> supergroups at the feeding sites indicated the exchange of bacterial species between GM and its host upon infestation. <b><i>Conclusion</i>:</b> As rice-GM microbial associations remain relatively unstudied, these findings not only add to our current understanding of microbe-assisted insect-plant interactions but also provide valuable insights into how these bacteria drive insect-plant coevolution. Moreover, to the best of our knowledge, this is the first report analyzing the microbiome of a host plant (rice) at the feeding site of its insect pest (GM).</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"126-136"},"PeriodicalIF":2.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/87/87/CG-23-126.PMC9878839.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10698033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective Analysis of Proteins Carried in Extracellular Vesicles with Clinical Outcome in Hepatocellular Carcinoma.","authors":"Wenbiao Chen,&nbsp;Feng Zhang,&nbsp;Huixuan Xu,&nbsp;Xianliang Hou,&nbsp;Donge Tang","doi":"10.2174/1389202923666220304125458","DOIUrl":"https://doi.org/10.2174/1389202923666220304125458","url":null,"abstract":"<p><p><b><i>Background</i>:</b> Extracellular vehicles (EVs) contain different proteins that relay information between tumor cells, thus promoting tumorigenesis. Therefore, EVs can serve as an ideal marker for tumor pathogenesis and clinical application. <b><i>Objective</i>:</b> Here, we characterised EV-specific proteins in hepatocellular carcinoma (HCC) samples and established their potential protein-protein interaction (PPI) networks. <b><i>Materials and Methods</i>:</b> We used multi-dimensional bioinformatics methods to mine a network module to use as a prognostic signature and validated the model's prediction using additional datasets. The relationship between the prognostic model and tumor immune cells or the tumor microenvironment status was also examined. <b><i>Results</i>:</b> 1134 proteins from 316 HCC samples were mapped to the exoRBase database. HCC-specific EVs specifically expressed a total of 437 proteins. The PPI network revealed 321 proteins and 938 interaction pathways, which were mined to identify a three network module (3NM) with significant prognostic prediction ability. Validation of the 3NM in two more datasets demonstrated that the model outperformed the other signatures in prognostic prediction ability. Functional analysis revealed that the network proteins were involved in various tumor-related pathways. Additionally, these findings demonstrated a favorable association between the 3NM signature and macrophages, dendritic, and mast cells. Besides, the 3NM revealed the tumor microenvironment status, including hypoxia and inflammation. <b><i>Conclusion</i>:</b> These findings demonstrate that the 3NM signature reliably predicts HCC pathogenesis. Therefore, the model may be used as an effective prognostic biomarker in managing patients with HCC.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"109-117"},"PeriodicalIF":2.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a3/d7/CG-23-109.PMC9878836.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10705110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics.","authors":"Liying Mo,&nbsp;Yuangang Su,&nbsp;Jianhui Yuan,&nbsp;Zhiwei Xiao,&nbsp;Ziyan Zhang,&nbsp;Xiuwan Lan,&nbsp;Daizheng Huang","doi":"10.2174/1389202923666220204153744","DOIUrl":"https://doi.org/10.2174/1389202923666220204153744","url":null,"abstract":"<p><p><b><i>Background</i>:</b> Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. <b><i>Methods</i>:</b> The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. <i>In vitro</i> qPCR was performed to verify core genes predicted by the random forest algorithm. <b><i>Results</i>:</b> For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of <i>in vitro</i> qPCR were consistent with the RF algorithm. <b><i>Conclusion</i>:</b> Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"94-108"},"PeriodicalIF":2.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a7/b3/CG-23-94.PMC9878835.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10698037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating LASSO Feature Selection and Soft Voting Classifier to Identify Origins of Replication Sites.","authors":"Yingying Yao,&nbsp;Shengli Zhang,&nbsp;Tian Xue","doi":"10.2174/1389202923666220214122506","DOIUrl":"https://doi.org/10.2174/1389202923666220214122506","url":null,"abstract":"<p><p><b><i>Background</i>:</b> DNA replication plays an indispensable role in the transmission of genetic information. It is considered to be the basis of biological inheritance and the most fundamental process in all biological life. Considering that DNA replication initiates with a special location, namely the origin of replication, a better and accurate prediction of the origins of replication sites (ORIs) is essential to gain insight into the relationship with gene expression. <b><i>Objective</i>:</b> In this study, we have developed an efficient predictor called iORI-LAVT for ORIs identification. <b><i>Methods</i>:</b> This work focuses on extracting feature information from three aspects, including mono-nucleotide encoding, <i>k</i>-mer and ring-function-hydrogen-chemical properties. Subsequently, least absolute shrinkage and selection operator (LASSO) as a feature selection is applied to select the optimal features. Comparing the different combined soft voting classifiers results, the soft voting classifier based on GaussianNB and Logistic Regression is employed as the final classifier. <b><i>Results</i>:</b> Based on 10-fold cross-validation test, the prediction accuracies of two benchmark datasets are 90.39% and 95.96%, respectively. As for the independent dataset, our method achieves high accuracy of 91.3%. <b><i>Conclusion</i>:</b> Compared with previous predictors, iORI-LAVT outperforms the existing methods. It is believed that iORI-LAVT predictor is a promising alternative for further research on identifying ORIs.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 2","pages":"83-93"},"PeriodicalIF":2.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2b/70/CG-23-83.PMC9878833.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10698035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Transcriptome Analysis of Flower Senescence of <i>Camellia lutchuensis</i>.","authors":"Weixin Liu,&nbsp;Hengfu Yin,&nbsp;Yi Feng,&nbsp;Suhang Yu,&nbsp;Zhengqi Fan,&nbsp;Xinlei Li,&nbsp;Jiyuan Li","doi":"10.2174/1389202923666220203104340","DOIUrl":"https://doi.org/10.2174/1389202923666220203104340","url":null,"abstract":"<p><strong>Background: </strong>Flower senescence is the last stage of flower development and affects the ornamental and economic value of flower plants. There is still less known on flower senescence of the ornamental plant <i>Camellia lutchuensis</i>, a precious species of <i>Camellia</i> with significant commercial application value.</p><p><strong>Methods: </strong>Transcriptome sequencing was used to investigate the flower senescence in five developmental stages of <i>C. lutchuensis</i>.</p><p><strong>Results: </strong>By Illumina HiSeq sequencing, we generated approximately 101.16 Gb clean data and 46649 differentially expressed unigenes. Based on the different expression pattern, differentially expressed unigenes were classified into 10 Sub Class. And Sub Class 9 including 8252 unigenes, was highly expressed in the flower senescent stage, suggesting it had a potential regulatory relationship of flower senescence. First, we found that ethylene biosynthesis genes <i>ACSs</i>, <i>ACOs</i>, receptor <i>ETR</i> genes and signaling genes <i>EINs</i>, <i>ERFs</i> all upregulated during flower senescence, suggesting ethylene might play a key role in the flower senescence of <i>C. lutchuensis</i>. Furthermore, reactive oxygen species (ROS) production related genes <i>peroxidase</i> (<i>POD</i>), <i>lipase</i> (<i>LIP</i>), <i>polyphenoloxidase</i> (<i>PPO</i>), and ROS scavenging related genes <i>glutathione S-transferase</i> (<i>GST</i>), <i>glutathione reductase</i> (<i>GR</i>) and <i>superoxide dismutase</i> (<i>SOD</i>) were induced in senescent stage, suggesting ROS might be involved in the flower senescence. Besides, the expression of monoterpenoid and isoflavonoid biosynthesis genes, transcription factors (<i>WRKY</i>, <i>NAC</i>, <i>MYB</i> and <i>C₂H₂</i> ), <i>senescence-associated gene SAG20</i> also were increased during flower senescence.</p><p><strong>Conclusion: </strong>In <i>C. lutchuensis</i>, ethylene pathway might be the key to regulate flower senescence, and ROS signal might play a role in the flower senescence.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 1","pages":"66-76"},"PeriodicalIF":2.6,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d1/ca/CG-23-66.PMC9199534.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40582298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptomic Analysis Reveals Key Candidate Genes Related to Seed Abortion in Chinese Jujube (<i>Ziziphus jujuba</i> Mill).","authors":"Fengxia Shao,&nbsp;Hengfu Yin,&nbsp;Sen Wang,&nbsp;Saiyang Zhang,&nbsp;Juan Chen,&nbsp;Can Feng","doi":"10.2174/1389202922666211110100017","DOIUrl":"https://doi.org/10.2174/1389202922666211110100017","url":null,"abstract":"<p><strong>Background: </strong>Seed abortion is a common phenomenon in Chinese jujube that seriously hinders the process of cross-breeding. However, the molecular mechanisms of seed abortion remain unclear in jujube.</p><p><strong>Methods: </strong>Here, we performed transcriptome sequencing using eight flower and fruit tissues at different developmental stages in <i>Ziziphus jujuba</i> Mill. 'Zhongqiusucui' to identify key genes related to seed abortion. Histological analysis revealed a critical developmental process of embryo abortion after fertilization.</p><p><strong>Results: </strong>Comparisons of gene expression revealed a total of 14,012 differentially expressed genes. Functional enrichment analyses of differentially expressed genes between various sample types uncovered several important biological processes, such as embryo development, cellular metabolism, and stress response, that were potentially involved in the regulation of seed abortion. Furthermore, gene co-expression network analysis revealed a suite of potential key genes related to ovule and seed development. We focused on three types of candidate genes, agamous subfamily genes, plant ATP-binding cassette subfamily G transporters, and metacaspase enzymes, and showed that the expression profiles of some members were associated with embryo abortion.</p><p><strong>Conclusion: </strong>This work generates a comprehensive gene expression data source for unraveling the molecular mechanisms of seed abortion and aids future cross-breeding efforts in jujube.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 1","pages":"26-40"},"PeriodicalIF":2.6,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d6/46/CG-23-26.PMC9199538.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40605791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel MicroRNAs and their Functional Targets from <i>Phytophthora infestans</i> and <i>Phytophthora cinnamomi</i>.","authors":"Binta Varghese,&nbsp;Ravisankar Valsalan,&nbsp;Deepu Mathew","doi":"10.2174/1389202923666211223122305","DOIUrl":"https://doi.org/10.2174/1389202923666211223122305","url":null,"abstract":"<p><strong>Background: </strong>Even though miRNAs play vital roles in developmental biology by regulating the translation of mRNAs, they are poorly studied in oomycetes, especially in the plant pathogen <i>Phytophthora</i>.</p><p><strong>Objective: </strong>The study aimed to predict and identify the putative miRNAs and their targets in <i>Phytophthora infestans</i> and <i>Phytophthora cinnamomi</i>.</p><p><strong>Methods: </strong>The homology-based comparative method was used to identify the unique miRNA sequences in <i>P. infestans</i> and <i>P. cinnamomi</i> with 148,689 EST and TSA sequences of these species. Secondary structure prediction of sRNAs for the 76 resultant sequences has been performed with the MFOLD tool, and their targets were predicted using psRNATarget.</p><p><strong>Results: </strong>Novel miRNAs, miR-8210 and miR-4968, were predicted from <i>P. infestans</i> and <i>P. cinnamomi</i>, respectively, along with their structural features. The newly identified miRNAs were identified to play important roles in gene regulation, with few of their target genes predicted as transcription factors, tumor suppressor genes, stress-responsive genes, DNA repair genes, <i>etc</i>.</p><p><strong>Conclusion: </strong>The miRNAs and their targets identified have opened new interference and editing targets for the development of <i>Phytophthora</i> resistant crop varieties.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"23 1","pages":"41-49"},"PeriodicalIF":2.6,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5a/a7/CG-23-41.PMC9199537.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40582301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}