2020年生信SCI大赏-最全生信文章、最友好生信期刊、最详情发文趋势都在这里！

i生信

专注生物分析最前沿定期解读生信文章提供生信分析思路和套路方便大家短平快发SCI

时光如水，即便是如此艰辛坎坷的2020也已悄然离去。生活在继续，舞会从来不曾停止！在这不平凡的2020年，生信SCI发文量如何？发文趋势又如何？有哪些生信友好期刊？今天我们就来整体回顾一下2020年生信SCI文章进展！2020年生信SCI发文量统计小编在Pubmed中，通过“gene expression omnibus”、“TCGA”、“bioinformatics”、“biomarker”、“differentially expressed”、“protein protein interaction”、“ROC analysis”“signature”等关键词进行组合检索，再经过筛选、去重复，最终检索到2020年发表的所有生信SCI共4165篇，其中纯生信（或仅含少量表达检测）文章3194篇，剩下的971篇生信实验类文章，其包含实验占比＞30%。看到这个数据你是不是有疑惑“听说生信文章很难发了，怎么还有这么多的文章呢，而且还不缺乏高分文章”。那别人的生信文章都是怎么设计的呢？纯生信文章投哪些期刊比较容易接收呢？下面我们来一探究竟！生信友好期刊推荐对2020年所有生信文章的发表期刊进行统计，以下列举出接收生信文章量大于30篇的期刊，根据接收量排序如下：2020生信友好期刊接收量2020-IF因子Front Oncol1814.85Biomed Res Int1542.28Front Genet1523.26Aging (Albany NY)1294.83Oncol Lett1012.31PeerJ932.38Cancer Cell Int874.18Cancers (Basel)846.13Sci Rep774.00J Cancer713.57Biosci Rep692.94BMC Cancer673.15Med Sci Monit651.92Medicine (Baltimore)621.55Cancer Manag Res612.89Cancer Med583.49J Cell Mol Med574.49Ann Transl Med513.30PLoS One442.74J Cell Physiol395.55J Comput Biol371.05Mol Med Rep362.10J Cell Biochem354.24Int J Mol Sci344.56J Transl Med344.12Am J Transl Res333.38DNA Cell Biol333.19Technol Cancer Res Treat332.07Front Mol Biosci304.19猫头鹰博士（微信：ipaper360）根据接收量>20篇，影响因子>3筛选条件，按照期刊影响因子排序如下：2020生信友好期刊2020-IF因子接收量Theranostics8.5821Cancers (Basel)6.1384Bioinformatics5.6120J Cell Physiol5.5539Front Cell Dev Biol5.2023Front Immunol5.0922Front Oncol4.85181Aging (Albany NY)4.83129Int J Mol Sci4.5634J Cell Mol Med4.4957J Cell Biochem4.2435Front Mol Biosci4.1930Cancer Cell Int4.1887J Transl Med4.1234Sci Rep4.0077J Cancer3.5771Cancer Med3.4958Cancer Biomark3.4426Oncol Rep3.4227Am J Transl Res3.3833Ann Transl Med3.3051Front Genet3.26152DNA Cell Biol3.1933BMC Cancer3.1567猫头鹰博士（微信：ipaper360）以上期刊列表绝对都能称作“生信友好期刊”了，大家有需要投稿的生信文章，可以根据影响因子在列表中选择合适的期刊哦。生信SCI发文趋势分析2020年年度生信SCI大赏评选正式开始！本次评选，192篇(IF>7)从4165篇生信SCI中脱颖而出。下面让我们一同欣赏这些文章亮点！2020年生信发文趋势影响因子（IF）发文量ALL4165IF>32552IF>5613IF>71921.2020年最香生信SCI提名文献标题期刊IFMicrobiome analyses of blood and tissues suggest cancer diagnostic approach.Nature42.77Genomic basis for RNA alterations in cancer.Nature42.77The repertoire of mutational signatures in human cancer.Nature42.77Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.Nature42.77The evolutionary history of 2,658 cancers.Nature42.77Patterns of somatic structural variation in human cancer genomes.Nature42.77Pan-cancer analysis of whole genomes.Nature42.77Single-Cell Analyses Identify Brain Mural Cells Expressing CD19 as Potential Off-Tumor Targets for CAR-T ImmunotherapiesCell38.63Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA SequencingCell38.63Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon CancerCell38.63Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.Cell38.63能发表在Nature、Cell国际订刊上的文章能不香吗？从研究内容来看这几篇文章都是泛癌研究或者是单细胞测序研究，果然不是一般人能够驾驭的。2.2020年最佳生信工具文章提名文献标题分类期刊IFXenbase: deep integration of GEO & SRA RNA-seq and ChIP-seq data in a model organism database.数据库NucleicAcidsRes11.50LncTarD: a manually-curated database of experimentally-supported functional lncRNA-target regulations in human diseases.数据库NucleicAcidsRes11.50ChimerDB 4.0: an updated and expanded database of fusion genes.数据库NucleicAcidsRes11.50LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation.数据库NucleicAcidsRes11.50DNMIVD: DNA methylation interactive visualization database.数据库NucleicAcidsRes11.50SNP2APA: a database for evaluating effects of genetic variants on alternative polyadenylation in human cancers.数据库NucleicAcidsRes11.50ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types.数据库NucleicAcidsRes11.50TIMER2.0 for analysis of tumor-infiltrating immune cells.数据库NucleicAcidsRes11.50CVCDAP: an integrated platform for molecular and clinical analysis of cancer virtual cohorts.数据库NucleicAcidsRes11.50miRactDB characterizes miRNA-gene relation switch between normal and cancer tissues across pan-cancer.数据库BriefBioinform8.99Network control principles for identifying personalized driver genes in cancer.生信方法BriefBioinform8.99TOD-CUP: a gene expression rank-based majority vote algorithm for tissue origin diagnosis of cancers of unknown primary.生信方法BriefBioinform8.99DiSNEP: a Disease-Specific gene Network Enhancement to improve Prioritizing candidate disease genes.生信方法BriefBioinform8.99DeepHPV: a deep learning model to predict human papillomavirus integration sites.生信方法BriefBioinform8.99Extended application of genomic selection to screen multiomics data for prognostic signatures of prostate cancer.生信方法BriefBioinform8.99Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.生信方法BriefBioinform8.99CNApp, a tool for the quantification of copy number alterations and integrative analysis revealing clinical implications.生信方法Elife7.08sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers.生信方法MolTherNucleicAcids7.03Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz.生信方法NatProtoc10.41A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles.生信方法GenomeMed10.67Characterization of the dual functional effects of heat shock proteins (HSPs) in cancer hallmarks to aid development of HSP inhibitors.生信方法Genome Med10.67CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data.生信方法Genome Biol10.80Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.生信方法IEEETransCybern11.07Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.生信方法Gut19.812020年发表数据库高分文章10篇，生信方法开发高分文章14篇。这些数据库及生信方法都是生信数据挖掘，文章写作的有力工具，有了这些神器加持，有木有觉得生信分析未来可期呀！（友情提示：想找不错的数据库或生信方法的文章推荐在Nucleic Acids Res、Brief Bioinform期刊查找哦！）3.2020年单细胞生信sci提名文献标题期刊IFSingle-cell transcriptome analysis reveals tumor immune microenvironment heterogenicity and granulocytes enrichment in colorectal cancer liver metastases.CancerLett7.36Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system.PLoSBiol7.07Single-Cell Transcriptome Analysis Reveals Intratumoral Heterogeneity in ccRCC, which Results in Different Clinical Outcomes.MolTher8.98Single-Cell Transcriptome Analysis Reveals Intratumoral Heterogeneity in ccRCC, which Results in Different Clinical Outcomes.MolTher8.98Stromal cell diversity associated with immune evasion in human triple-negative breast cancerEMBO J9.88Malignant cell-specific CXCL14 promotes tumor lymphocyte infiltration in oral cavity squamous cell carcinoma.JImmunotherCancer9.91Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples.NucleicAcidsRes11.50A gene expression signature of TREM2 macrophages and γδ T cells predicts immunotherapy responseNatCommun12.12Single-cell RNA sequencing highlights the role ofinflammatory cancer-associatedfibroblasts inbladder urothelial carcinomaNatCommun12.12Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levelsNatCellBiol20.04Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic TissueCirculation23.60Single-Cell Analyses Identify Brain Mural Cells Expressing CD19 as Potential Off-Tumor Targets for CAR-T ImmunotherapiesCell38.63Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA SequencingCell38.63Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon CancerCell38.632020年发表的单细胞生信高分文章14篇，相信随着单细胞测序成本的降低及单细胞数据集的增加，单细胞生信发文势必会是下一个爆点。4.2020年最热生信套路文章提名（1）预后模型构建/biomarker筛选生信SCI列29篇：延续了2019年的生信研究热点，2020年诊断/预后模型、biomarker筛选的生信文章有增无减。且高分文章的生信套路得到了升级，比如糅合多个研究热点的生信套路（m6A相关的lncRNA构建肿瘤预后模型），又比如增加多个数据库的数据集及临床信息进行多角度模型验证的生信套路。总之，预后模型相关，尤其是肿瘤预后相关生信文章还是大有可为的！（2）非编码RNA研究高分生信SCI共22篇：从这些高分文章可以看出，纯生信的ceRNA机制研究目前已经很难再发5分以上的文章了，想要发表非编码RNA研究的高分文章，生信分析加上表达量验证实验（Real-Time qPCR、Westorn Blot）体外细胞表型实验（细胞增殖/细胞凋亡/细胞迁移等实验）已成为标配。（3）免疫治疗/免疫浸润相关生信SCI共14篇：随着肿瘤异质性、肿瘤微环境的深入研究，肿瘤治疗已进入肿瘤免疫治疗新时代。预测肿瘤复发的可能性，分析患者免疫状态，筛选诊断生物标志物等研究，利用高通量生信分析无疑是行之有效的有力手段。