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牛血液转录组的计算机分析，以鉴定lncRNAs及其在牛结核病中的作用

In-silico analysis of cattle blood transcriptome to identify lncRNAs and their role during bovine tuberculosis

Nature 等信源发布 2024-07-17 23:28

可切换为仅中文

AbstractLong noncoding RNAs (lncRNAs) are RNA molecules with a length greater than 200 nucleotides that do not code for functional proteins. Although, genes play a vital role in immune response against a disease, it is less known that lncRNAs also contribute through gene regulation. Bovine tuberculosis is a significant zoonotic disease caused by Mycobacterium bovis (M.

摘要长非编码RNA（lncRNA）是长度大于200个核苷酸的RNA分子，不编码功能蛋白。尽管基因在针对疾病的免疫应答中起着至关重要的作用，但尚不清楚lncRNA也通过基因调控起作用。牛结核病是由牛分枝杆菌（M。

bovis) in cattle. Here, we report the in-silico analysis of the publicly available transcriptomic data of calves infected with M. bovis. A total of 51,812 lncRNAs were extracted across all the samples. A total of 216 genes and 260 lncRNAs were found to be differentially expressed across all the 4 conditions—infected vs uninfected at 8- and 20-week post-infection (WPI), 8 vs 20-WPI of both infected and uninfected.

牛）在牛身上。在这里，我们报告了对感染牛分枝杆菌的犊牛的公开可用转录组数据的计算机分析。在所有样品中共提取了51812个lncRNA。发现总共216个基因和260个lncRNA在感染后8周和20周（WPI）感染与未感染的所有4种条件下差异表达，感染和未感染的8对20-WPI。

Gene Ontology and Functional annotation showed that 8 DEGs were annotated with immune system GOs and 2 DEGs with REACTOME immune system pathways. Co-expression analysis of DElncRNAs with DEGs revealed the involvement of lncRNAs with the genes annotated with Immune related GOs and pathways. Overall, our study sheds light on the dynamic transcriptomic changes in response to M.

基因本体论和功能注释显示，8个DEG用免疫系统GOs注释，2个DEG用REACTOME免疫系统途径注释。DELncRNA与DEG的共表达分析揭示了lncRNA与免疫相关GOs和途径注释的基因的参与。总体而言，我们的研究揭示了响应M的动态转录组变化。

bovis infection, particularly highlighting the involvement of lncRNAs with immune-related genes. The identified immune pathways and gene–lncRNA interactions offer valuable insights for further research in understanding host–pathogen interactions and potential avenues for genetic improvement strategies in cattle..

牛感染，特别突出了lncRNA与免疫相关基因的参与。鉴定出的免疫途径和基因-lncRNA相互作用为进一步研究宿主-病原体相互作用以及牛遗传改良策略的潜在途径提供了有价值的见解。。

IntroductionOne of the most significant zoonotic infectious diseases, Bovine tuberculosis (BTB) is caused by Mycobacterium bovis (M. bovis)1. BTB severely threatens livestock, human health, and economic well-being2. M. tuberculosis is the primary cause of tuberculosis (TB) in humans, while M. bovis is responsible for the disease mainly in animals.

引言牛结核病（BTB）是最重要的人畜共患传染病之一，由牛分枝杆菌（M.bovis）1引起。BTB严重威胁牲畜，人类健康和经济福祉2。M、 结核病是人类结核病（TB）的主要原因，而牛分枝杆菌主要在动物中引起该疾病。

TB caused by both pathogens can be transmitted from animals to humans and vice versa3.The respiratory system is the primary site of infection of M. bovis4. The primary routes of M. bovis transmission involve inhaling aerosols containing the bacteria, particularly in settings where infected animals, such as cattle, release respiratory secretions.

由这两种病原体引起的结核病都可以从动物传播给人类，反之亦然。呼吸系统是牛分枝杆菌感染的主要部位4。牛分枝杆菌传播的主要途径包括吸入含有细菌的气溶胶，特别是在感染动物（如牛）释放呼吸道分泌物的环境中。

Additionally, infection can occur through the consumption of raw milk obtained from diseased animals5. Consequently, bovine TB is primarily a respiratory infection, and most infections are believed to be spread between animals nearby through 'direct' aerosol transmission6.Analysing the transcriptome can offer valuable insights into complex diseases, shedding light on the genes responsible for immune responses7.

此外，通过食用从患病动物获得的原料奶可能会发生感染5。因此，牛结核病主要是呼吸道感染，大多数感染被认为是通过“直接”气溶胶传播在附近的动物之间传播6。分析转录组可以为复杂疾病提供有价值的见解，揭示负责免疫反应的基因7。

Genes play a crucial role in determining the host immune response, but it's essential to recognize that long noncoding RNAs (lncRNAs) also contribute significantly by influencing gene expression through various mechanisms. These include participation in chromatin remodelling processes, acting as coactivators or corepressors with transcription factors, inhibiting translation, modulating splicing events, and influencing mRNA degradation by interacting with microRNAs.

基因在决定宿主免疫反应中起着至关重要的作用，但必须认识到，长的非编码RNA（lncRNA）也通过各种机制影响基因表达而发挥重要作用。这些包括参与染色质重塑过程，充当转录因子的共激活因子或共抑制因子，抑制翻译，调节剪接事件，以及通过与microRNA相互作用影响mRNA降解。

Importantly, despite not encoding proteins themselves, lncRNAs play a pivotal role in shaping cellular processes, exerting regulatory effects at both the RNA and protein levels8,9.Previous studies have analysed transc.

重要的是，尽管不编码蛋白质本身，但lncRNA在塑造细胞过程中起着关键作用，在RNA和蛋白质水平上发挥调节作用8,9。以前的研究已经分析了transc。

Table 2 Table showing the number of differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) obtained in (I) between infected and uninfected and (II) between different time points.Full size tableThe Circos plots, illustrated in Fig. 2A and B, provide a visual representation of the chromosomal localization of differentially expressed genes and lncRNAs under various conditions.

表2表显示了在（I）感染和未感染之间以及（II）不同时间点之间获得的差异表达基因（DEG）和差异表达lncRNA（DElncRNA）的数量。全尺寸表图2A和B所示的Circos图提供了在各种条件下差异表达基因和lncRNA的染色体定位的视觉表示。

In the visualization, the outermost black circle signifies the chromosomes of cow. The inner concentric circles depict the positions of differentially expressed genes and lncRNAs on chromosomes in the order infected 8 vs 20 WPI, uninfected 8 vs 20 WPI, infected vs uninfected 8 WPI and infected vs uninfected 20 WPI.

在可视化中，最外层的黑色圆圈表示奶牛的染色体。内部同心圆描绘了染色体上差异表达基因和lncRNA的位置，顺序为感染8对20 WPI，未感染8对20 WPI，感染与未感染8 WPI和感染与未感染20 WPI。

Lines extending outward from the central line signify up-regulation, while lines moving inward denote down-regulation of the genes and lncRNAs. The figure shows that in comparison to genes, more lncRNAs were differentially expressed in two analysis conditions. Time-based analysis showed more differentially expressed genes and lncRNAs, while infected and uninfected based analysis showed fewer genes and lncRNAs.

从中心线向外延伸的线表示上调，而向内移动的线表示基因和lncRNA的下调。该图显示，与基因相比，更多的lncRNA在两种分析条件下差异表达。基于时间的分析显示差异表达的基因和lncRNA更多，而基于感染和未感染的分析显示基因和lncRNA更少。

In the infected and uninfected-based analysis, the number of genes remained constant between the two time points, while the count of differentially expressed lncRNAs was higher at 8 WPI compared to 20 WPI. These visualizations provide a clear overview of the genomic distribution and expression patterns of genes and lncRNAs under different conditions and timepoints.Figure 2Figure showing the chromosomal localisation of (A) differentially expressed genes and (B) long noncoding RNAs.

在基于感染和未感染的分析中，两个时间点之间的基因数量保持不变，而差异表达的lncRNA的数量在8 WPI时高于20 WPI。这些可视化提供了在不同条件和时间点下基因和lncRNA的基因组分布和表达模式的清晰概述。图2显示了（A）差异表达基因和（B）长非编码RNA的染色体定位。

The outermost black circle signifies the chromosomes of cow. The inner concentric circles depict the positions of DEGs and DElncRNAs identified in different conditions in the.

最外面的黑色圆圈表示奶牛的染色体。内部同心圆描绘了在不同条件下鉴定的DEG和DElncRNA的位置。

Table 3 Table showing the number of trans, cis and different categories of cis co-expressing interactions between differentially expressed genes and long noncoding RNAs—(I) between infected and uninfected and (II) between different time points.Full size tableGene–transcription factor interaction analysisIn the analysis of transcription factors, 10 motifs were identified within the differentially expressed genes in both conditions.

表3表格显示了差异表达基因和长非编码RNA之间的反式，顺式和不同类别的顺式共表达相互作用的数量-（I）感染和未感染之间，以及（II）不同时间点之间。全尺寸表基因-转录因子相互作用分析在转录因子分析中，在两种条件下的差异表达基因中鉴定出10个基序。

Three motifs (Motif 1, 2 and 9) with a p-value cut-off of 0.05 were related to the transcription factors. Six different transcription factors were identified to match the three motifs; among them, two belong to the ZNF263 family, and the other four transcription factors were identified as SP2, SP1, NFKB1, and NFATC2.

p值截止值为0.05的三个基序（基序1、2和9）与转录因子有关。鉴定出六种不同的转录因子以匹配三个基序；其中，两个属于ZNF263家族，另外四个转录因子被鉴定为SP2，SP1，NFKB1和NFATC2。

The transcription factors SP2 and SP1 were found to match Motif-1, NFKB1 and NFATC2 were found to be matching to Motif-9 and ZNF263 was found to be matching to both Motif-1 and Motif-2. Supplementary Table 6 provides detailed information about the identified transcription factors, including their associations with specific motifs.Gene miRNA analysisIn the miRNA analysis, 125 microRNAs were found to target approximately 48 differentially expressed genes in both analysis conditions.

发现转录因子SP2和SP1与Motif-1匹配，NFKB1和NFATC2与Motif-9匹配，ZNF263与Motif-1和Motif-2匹配。补充表6提供了有关已鉴定转录因子的详细信息，包括它们与特定基序的关联。基因miRNA分析在miRNA分析中，发现125个microRNA在两种分析条件下靶向大约48个差异表达的基因。

In infected and uninfected based analysis, miRNA bta-mir-326, bta-mir-328, bta-mir-1291, bta-mir-2364, bta-mir-2447, bta-mir-2455, bta-mir-2897, and bta-mir-3431 were found to be targeting one differentially expressed gene at 8 WPI. No miRNAs were found to target differentially expressed genes at 20 WPI in this specific analysis.Network visualisationUsing the Cytoscape tool with the yfiles organic layout, the interaction network of differentially expressed lncRNAs, co-expressing genes with transcription factors, and associated pathways or biological proc.

在基于感染和未感染的分析中，发现miRNA bta-mir-326，bta-mir-328，bta-mir-1291，bta-mir-2364，bta-mir-2447，bta-mir-2455，bta-mir-2897和bta-mir-3431以8 WPI靶向一个差异表达的基因。在该特定分析中，未发现miRNA靶向20 WPI的差异表达基因。网络可视化使用具有yfiles有机布局的Cytoscape工具，差异表达的lncRNA的相互作用网络，与转录因子共表达的基因以及相关途径或生物学过程。

Data availability

数据可用性

Publicly available datasets were analysed in this study. This data can be found here: EBI-ENA database with project ID PRJNA791899. The pipeline used in the study—FHSpipe can be found here: https://github.com/Venky2804/FHSpipe.

本研究分析了公开可用的数据集。这些数据可以在这里找到：项目ID为PRJNA791899的EBI-ENA数据库。FHSpipe研究中使用的管道可以在这里找到：https://github.com/Venky2804/FHSpipe.

ReferencesMcLoughlin, K. E. et al. RNA-seq transcriptome analysis of peripheral blood from cattle infected with Mycobacterium bovis across an experimental time course. Front. Vet. Sci. 8, 662002 (2021).Article

参考文献McLoughlin，K.E.等人在实验时间过程中感染牛分枝杆菌的牛的外周血的RNA-seq转录组分析。正面。兽医。科学。8662002（2021）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Nol, P. et al. The potential for transmission of BCG from orally vaccinated white-tailed deer (Odocoileus virginianus) to cattle (Bos taurus) through a contaminated environment: experimental findings. PLoS ONE 8(4), e60257 (2013).Article

Nol，P.等人。卡介苗通过污染环境从口服疫苗接种的白尾鹿（Odocoileus virginianus）传播给牛（Bos taurus）的可能性：实验结果。PLoS ONE 8（4），e60257（2013）。文章

ADS

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hlokwe, T. M., Said, H. & Gcebe, N. Mycobacterium tuberculosis infection in cattle from the Eastern Cape Province of South Africa. BMC Vet. Res. 13(1), 299 (2017).Article

Hlokwe，T.M.，Said，H.＆Gcebe，南非东开普省牛的结核分枝杆菌感染。BMC兽医。第13（1）号决议，第299（2017）号决议。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Abdelaal, H. F. M., Thacker, T. C., Wadie, B., Palmer, M. V. & Talaat, A. M. Transcriptional profiling of early and late phases of bovine tuberculosis. Infect. Immunity 90(2), e0031321 (2022).Article

Abdelaal，H.F.M.，Thacker，T.C.，Wadie，B.，Palmer，M.V。＆Talaat，A.M。牛结核病早期和晚期的转录谱分析。感染。抗扰度90（2），e0031321（2022）。文章

Google Scholar

谷歌学者

Ameni, G. et al. Transmission of Mycobacterium tuberculosis between farmers and cattle in central Ethiopia. PLoS ONE 8(10), e76891 (2013).Article

Ameni，G。等人。埃塞俄比亚中部农民和牛之间结核分枝杆菌的传播。PLoS ONE 8（10），e76891（2013）。文章

ADS

广告

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Skuce, R. A., Allen, A. R. & McDowell, S. W. Herd-level risk factors for bovine tuberculosis: a literature review. Vet. Med. Int. 1, 621210 (2012).

Skuce，R.A.，Allen，A.R。＆McDowell，S.W。牛结核病的群体水平危险因素：文献综述。兽医。医学国际1621210（2012）。

Google Scholar

谷歌学者

Wiarda, J. E. et al. Severity of bovine tuberculosis is associated with innate immune-biased transcriptional signatures of whole blood in early weeks after experimental Mycobacterium bovis infection. PLoS ONE 15(11), e0239938 (2020).Article

。PLoS ONE 15（11），e0239938（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Statello, L., Guo, C.-J., Chen, L.-L. & Huarte, M. Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell Biol. 22, 96–118 (2021).Article

Statello，L.，Guo，C.-J.，Chen，L.-L.＆Huarte，M。长非编码RNA的基因调控及其生物学功能。Nat。Rev。Mol。Cell Biol。22，96-118（2021）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Li, Z., Zhao, W., Wang, M. & Zhou, X. The role of long noncoding RNAs in gene expression regulation. in Gene Expression Profiling in Cancer (IntechOpen, 2019).Vanamamalai, V. et al. Transcriptomic analysis to infer key molecular players involved during host response to NDV challenge in Gallus gallus (Leghorn & Fayoumi).

Li，Z.，Zhao，W.，Wang，M。＆Zhou，X。长非编码RNA在基因表达调控中的作用。在癌症的基因表达谱中（IntechOpen，2019）。Vanamamalai，V。等人。转录组学分析，以推断宿主对鸡NDV攻击的反应过程中涉及的关键分子参与者（Leghorn＆Fayoumi）。

Sci. Rep. 11, 8486 (2021).Article .

科学。代表118486（2021）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Vanamamalai, V. K., Priyanka, E., Kannaki, T. R. & Sharma, S. Integrated analysis of genes and long non-coding RNAs in trachea transcriptome to decipher the host response during Newcastle disease challenge in different breeds of chicken. Int. J. Biol. Macromol. 253, 127183 (2023).Article .

Vanamamalai，V.K.，Priyanka，E.，Kannaki，T.R。＆Sharma，S。气管转录组中基因和长非编码RNA的综合分析，以破译不同品种鸡在新城疫攻击期间的宿主反应。。大分子。253127183（2023）。文章。

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Vanamamalai, V. K., Priyanka, E., Kannaki, T. R. & Sharma, S. Breed and timepoint-based analysis of chicken harderian gland transcriptome during Newcastle Disease Virus challenge. Front. Mol. Biosci. 11, 1365888 (2024).Article

Vanamamalai，V.K.，Priyanka，E.，Kannaki，T.R。＆Sharma，S。新城疫病毒攻击期间鸡哈德氏腺转录组的品种和基于时间点的分析。正面。摩尔生物科学。11365888（2024）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pooja, G. et al. Analysis of long non-coding RNA and mRNA expression in bovine macrophages brings up novel aspects of Mycobacterium avium subspecies paratuberculosis infections. Sci. Rep. 9, 1571 (2019).Article

Pooja，G。等人对牛巨噬细胞中长非编码RNA和mRNA表达的分析提出了鸟分枝杆菌亚种副结核感染的新方面。科学。代表91571（2019）。文章

Google Scholar

谷歌学者

Shifu, C., Yanqing, Z., Yaru, C. & Jia, G. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17), i884–i890 (2018).Article

Shifu，C.，Yanqing，Z.，Yaru，C。＆Jia，G。Fastp：一种超快速的一体化FASTQ预处理器。生物信息学34（17），i884-i890（2018）。文章

Google Scholar

谷歌学者

Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37(8), 907–915 (2019).Article

Kim，D.，Paggi，J.M.，Park，C.，Bennett，C。＆Salzberg，S.L。基于图形的基因组比对和HISAT2和HISAT基因型的基因分型。美国国家生物技术公司。37（8），907-915（2019）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10(2), 008 (2021).Article

Danecek，P.等人，《SAMtools和BCFtools的十二年》。GigaScience 10（2），008（2021）。文章

Google Scholar

谷歌学者

Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33(3), 290–295 (2015).Article

Pertea，M。等人StringTie能够从RNA-seq读数改进转录组的重建。美国国家生物技术公司。33（3），290-295（2015）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pertea, G. & Pertea, M. GFF Utilities: GffRead and GffCompare. F1000 Res. 9, 304 (2020).Article

Pertea G.和Pertea M.GFF实用程序：GffRead和GffCompare。F1000第9304号决议（2020年）。文章

Google Scholar

谷歌学者

Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26(6), 841–842 (2010).Article

Quinlan，A.R。＆Hall，I.M。BEDTools：用于比较基因组特征的灵活实用程序套件。生物信息学26（6），841-842（2010）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Xiang Jia, M., Gregory, B., Reginald, S. & Adrian, T. OrfPredictor: Predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 33(2), W677–W680 (2005).

Xiang Jia，M.，Gregory，B.，Reginald，S。＆Adrian，T。OrfPredictor：预测EST衍生序列中的蛋白质编码区。核酸研究33（2），W677–W680（2005）。

Google Scholar

谷歌学者

Mistry, J. et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 49(D1), D412–D419 (2021).Article

。核酸研究49（D1），D412-D419（2021）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Christiam, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).Article

Christiam，C.等人，《BLAST+：体系结构和应用》。BMC生物信息。10421（2009）。文章

Google Scholar

谷歌学者

Kang, Y. J. et al. CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 45(W1), W12–W16 (2017).Article

Kang，Y。J。等人。CPC2：一种基于序列内在特征的快速准确的编码电位计算器。核酸研究45（W1），W12-W16（2017）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Chan, P. P. & Lowe, T. M. GtRNAdb: A database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93–D97 (2009).Article

Chan，P。P。＆Lowe，T。M。GtRNAdb：在基因组序列中检测到的转移RNA基因的数据库。核酸研究37，D93–D97（2009）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. MiRBase: From microRNA sequences to function. Nucleic Acids Res. 47(D1), D155–D162 (2019).Article

Kozomara，A.，Birgoanu，M。和Griffiths-Jones，S。MiRBase：从microRNA序列到功能。核酸研究47（D1），D155-D162（2019）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Zhao, L. et al. NONCODEV6: An updated database dedicated to long non-coding RNA annotation in both animals and plants. Nucleic Acids Res. 49(D1), D165–D171 (2021).Article

Zhao，L。等人。NONCODEV6：一个更新的数据库，专门用于动物和植物中的长非编码RNA注释。核酸研究49（D1），D165-D171（2021）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139–140 (2010).Article

Robinson，M.D.，McCarthy，D.J。＆Smyth，G.K.edgeR：用于数字基因表达数据差异表达分析的生物导体软件包。生物信息学26（1），139-140（2010）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Krzywinski, M. et al. Circos: An information aesthetic for comparative genomics. Genome Res. 19(9), 1639–1645 (2009).Article

Krzywinski，M.等人，《Circos：比较基因组学的信息美学》。基因组研究19（9），1639-1645（2009）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

BioBam Bioinformatics. OmicsBox: Bioinformatics Made Easy (2019).Altschul, S., Gish, W., Miller, W., Myers, E. & Lipman, D. Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990).Article

BioBam生物信息学。OmicsBox：生物信息学变得容易（2019）。Altschul，S.，Gish，W.，Miller，W.，Myers，E。＆Lipman，D。基本局部对齐搜索工具。J、 分子生物学。215（3），403-410（1990）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Philip, J. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30(9), 1236–1240 (2014).Article

Philip，J.等人，《InterProScan 5：基因组规模的蛋白质功能分类》。生物信息学30（9），1236-1240（2014）。文章

Google Scholar

谷歌学者

Gotz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36(10), 3420–3435 (2008).Article

Gotz，S.等人。Blast2GO套件的高通量功能注释和数据挖掘。核酸研究36（10），3420-3435（2008）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Huerta-Cepas, J. et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47(D1), D309–D314 (2019).Article

Huerta-Cepas，J。等人，《eggNOG 5.0：基于5090种生物体和2502种病毒的分层，功能和系统发育注释的直系同源资源》。核酸研究47（D1），D309-D314（2019）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000).Article

Kanehisa，M。＆Goto，S。KEGG：京都基因与基因组百科全书。核酸研究28（1），27-30（2000）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46(D1), D649–D655 (2018).Article

Fabregat，A。等人。reactome pathway知识库。核酸研究46（D1），D649-D655（2018）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).Article

Langfelder，P。＆Horvath，S。WGCNA：用于加权相关网络分析的R包。BMC生物信息。9559（2008）。文章

Google Scholar

谷歌学者

Zhao, R. et al. Transcriptomic analysis reveals the involvement of lncRNA–miRNA–mRNA networks in hair follicle induction in Aohan fine wool sheep skin. Front. Genet. 11, 590 (2020).Article

转录组学分析揭示了lncRNA–miRNA–mRNA网络在敖汉细毛羊皮肤毛囊诱导中的作用。正面。基因。11590（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Guttman, M. & Rinn, J. Modular regulatory principles of large non-coding RNAs. Nature 482(7385), 339–346 (2012).Article

Guttman，M。＆Rinn，J。大型非编码RNA的模块化调控原理。《自然》482（7385），339–346（2012）。文章

ADS

广告

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Bailey, T. L. et al. MEME SUITE: Tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).Article

Bailey，T.L.等人，《MEME套件：主题发现和搜索工具》。核酸研究37，W202–W208（2009）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Shen, W. K. et al. AnimalTFDB 4.0: A comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic acids Res. 51(D1), D39–D45 (2023).Article

Shen，W.K.等人。AnimalTFDB 4.0：用变异和表达注释更新的综合动物转录因子数据库。核酸研究51（D1），D39-D45（2023）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Chang, L., Zhou, G., Soufan, O. & Xia, J. miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 48(W1), W244–W251 (2020).Article

Chang，L.，Zhou，G.，Soufan，O。＆Xia，J。miRNet 2.0：基于网络的miRNA功能分析和系统生物学可视化分析。核酸研究48（W1），W244–W251（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003).Article

Shannon，P。等。Cytoscape：用于生物分子相互作用网络集成模型的软件环境。。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wolfe, D., Dudek, S., Ritchie, M. D. & Pendergrass, S. A. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. 6(1), 18 (2013).Article

Wolfe，D.，Dudek，S.，Ritchie，M.D。和Pendergrass，S.A。用表型图可视化染色体上的基因组信息。BioData Min.6（1），18（2013）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Download referencesAcknowledgementsWe are thankful to DBT—Department of Biotechnology, India and CSIR—Council of Scientific and Industrial Research, India for providing Fellowship; National Institute of Animal Biotechnology, India and DBT—Department of Biotechnology, India for providing necessary infrastructure and funds, and the Regional centre for Biotechnology for PhD registration.Author informationAuthor notesThese authors contributed equally: Priyanka Garg and Venkata Krishna Vanamamalai.Authors and AffiliationsNational Institute of Animal Biotechnology (NIAB), Opp.

下载参考文献致谢我们感谢印度DBT生物技术部和印度CSIR科学与工业研究理事会提供奖学金；印度国家动物生物技术研究所和印度DBT生物技术部提供必要的基础设施和资金，以及生物技术区域中心进行博士注册。作者信息作者注意到这些作者做出了同样的贡献：Priyanka Garg和Venkata Krishna Vanamamalai。作者和附属机构国家动物生物技术研究所（NIAB），Opp。

Journalist Colony, Near Gowlidoddi, Extended Q City Road, Gachibowli, Hyderabad, Telangana, 500032, IndiaPriyanka Garg, Venkata Krishna Vanamamalai & Shailesh SharmaRegional Centre for Biotechnology, Faridabad-Gurgaon Expressway, Faridabad Rd, Faridabad, Haryana, 121001, IndiaVenkata Krishna Vanamamalai & Shailesh SharmaAuthorsPriyanka GargView author publicationsYou can also search for this author in.

记者殖民地，靠近Gowlidoddi，扩展Q City Road，Gachibowli，Hyderabad，Telangana，500032，IndiaPriyanka Garg，Venkata Krishna Vanamamalai＆Shailesh SharmaRegional Centre for Biotechnology，Faridabad Gurgaon Expression，Faridabad Rd，Faridabad，Haryana，121001，IndiaVenkata Krishna Vanamamalai＆Shailesh SharmaAuthorsPriyanka GargView作者出版物您也可以在中搜索这位作者。

PubMed Google ScholarVenkata Krishna VanamamalaiView author publicationsYou can also search for this author in

PubMed Google ScholarVenkata Krishna VanamamalaiView作者出版物您也可以在

PubMed Google ScholarShailesh SharmaView author publicationsYou can also search for this author in

PubMed Google ScholarShailesh SharmaView作者出版物您也可以在

PubMed Google ScholarContributionsPG: methodology, formal analysis, data curation, visualisation, writing—original draft. VKV: methodology, formal analysis, data curation, visualisation, writing—original draft. SS: conceptualisation, project administration, writing—review and editing, and supervision.

PubMed谷歌学术贡献SPG：方法论，形式分析，数据管理，可视化，撰写原稿。VKV：方法论，形式分析，数据管理，可视化，撰写原稿。SS：概念化，项目管理，写作审查和编辑，以及监督。

PG and VKV contributed equally to this work and share first authorship.Corresponding authorCorrespondence to.

PG和VKV对这项工作做出了同样的贡献，并拥有第一作者身份。对应作者对应。

Shailesh Sharma.Ethics declarations

Shailesh Sharma。道德宣言

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Reprints and permissionsAbout this articleCite this articleGarg, P., Vanamamalai, V.K. & Sharma, S. In-silico analysis of cattle blood transcriptome to identify lncRNAs and their role during bovine tuberculosis.

转载和许可本文引用本文Garg，P.，Vanamamalai，V.K。＆Sharma，S。对牛血转录组进行计算机分析，以鉴定lncRNA及其在牛结核病中的作用。

Sci Rep 14, 16537 (2024). https://doi.org/10.1038/s41598-024-67001-0Download citationReceived: 01 March 2024Accepted: 08 July 2024Published: 17 July 2024DOI: https://doi.org/10.1038/s41598-024-67001-0Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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KeywordsLong noncoding RNABovine tuberculosis (BTB)In-silico transcriptome analysisDifferential expressed analysisFunctional annotationCo-expression analysisPublic data analysis

关键词长非编码RNA牛结核病（BTB）计算机转录组分析差异表达分析功能注释共表达分析公共数据分析

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