年末总结！这样的纯生信思路不做实验也能发高分SCI

从GEO、TCGA找数据，做生信挖掘发表SCI，为何拒稿率越来越高？因为现在会用R等软件，画个热图、火山图、PPI网络的人越来越多了，大家一窝蜂地每3、5天一篇文章的质量去刷稿，可想而知数据量和作图都是粗糙的，审稿人自然开始什么疲劳了。

所以要求我们道高一尺魔高一丈，那么猫头鹰博士给大家分析了一下2019年高分期刊（影响因子3.5分~12分）纯生信文章的统计结果。在Pubmed中利用关键词包括“TCGA”“gene expression omnibus”+“Differentially Expressed”“dataset”“biomarker”“signature”“microarray”“RNA-seq”“prognosis”等关键词，检索了截至2019年11月末，共发现~600篇，平均50篇/月。

我们按照年接收量＞10篇的标准对杂志统计，如果按照接受数量排序，J Cell Physiol（4.5分）、Front Oncol（4.13分）、Sci Rep（4.011）、Front Genet（3.517分）、Cancers (Basel)（6.16分）位居前茅，如下：

期刊	2019接受数量	影响因子
J Cell Physiol	68	4.50
Front Oncol	51	4.13
Sci Rep（口碑不好）	50	4.01
Front Genet	41	3.52
Cancers (Basel)	35	6.16
Bioinformatics	30	4.53
Aging	29	5.52
J Transl Med	24	4.10
Biomed Pharmacother	22	3.74
Int J Mol Sci	19	4.18
J Cell Mol Med	16	4.66
EBioMedicine	15	6.68
Nucleic Acids Res	12	11.15
Int J Cancer	11	4.98
Epigenomics	10	4.40
Oncogene	9	6.63
Int J Oncol	8	3.57
Clin Cancer Res	7	8.91
J Immunother Cancer	7	8.68
Mol Oncol	7	5.96
J Exp Clin Cancer Res	7	5.65
Clin Epigenetics	7	5.50
Oncoimmunology	6	5.33
Cancer Immunol Immunother	6	4.90
Cancer Sci	6	4.75
Gynecol Oncol	6	4.39
Carcinogenesis	6	4.00
Front Pharmacol	6	3.85
Cancer Res	5	8.38
Cancer Epidemiol Biomarkers Prev	5	5.06
Cancer Gene Ther	5	4.68
Ann Transl Med	5	3.69
Nat Commun	4	11.88
Semin Cancer Biol	4	9.66
Cell Death Dis	4	5.96
Front Immunol	4	4.72
Autophagy	2	11.06
J Cell Biochem	82	3.40
Cancer Cell Int	37	3.44

但3.4分的J Cell Biochem和Cancer Cell Int的接收量也是很惊人的，而且J Cell Biochem还免版面费。

如果按照影响因子IF大小排序，如下：

期刊	影响因子	2019接受量
Nat Commun	11.88	4
Nucleic Acids Res	11.15	12
Autophagy	11.06	2
Semin Cancer Biol	9.66	4
Clin Cancer Res	8.91	7
J Immunother Cancer	8.68	7
Cancer Res	8.38	5
EBioMedicine	6.68	15
Oncogene	6.63	9
Cancers (Basel)	6.16	35
Mol Oncol	5.96	7
Cell Death Dis	5.96	4
J Exp Clin Cancer Res	5.65	7
Aging (Albany NY)	5.52	29
Clin Epigenetics	5.50	7
Oncoimmunology	5.33	6
Cancer Epidemiol Biomarkers Prev	5.06	5
Int J Cancer	4.98	11
Cancer Immunol Immunother	4.90	6
Cancer Sci	4.75	6
Front Immunol	4.72	4
Cancer Gene Ther	4.68	5
J Cell Mol Med	4.66	16
Bioinformatics	4.53	30
J Cell Physiol	4.50	68
Epigenomics	4.40	10
Gynecol Oncol	4.39	6
Int J Mol Sci	4.18	19
Front Oncol	4.13	51
J Transl Med	4.10	24
Sci Rep	4.01	50
Carcinogenesis	4.00	6
Front Pharmacol	3.85	6
Biomed Pharmacother	3.74	22
Ann Transl Med	3.69	5
Int J Oncol	3.57	8
Front Genet	3.52	41

其中大于5分的杂志里Cancers (Basel)、Aging (Albany NY)、EBioMedicine对纯生信类的文章最为友好的，好中一些。

我们举例一些高分文章：

文章名	杂志	影响因子
A comprehensive PDX gastric cancer collection captures  cancer cell intrinsic transcriptional MSI traits.	Cancer Res	8.378
Identification of Coding and Long Noncoding RNAs Differentially  Expressed in Tumors and Preferentially Expressed in Healthy Tissues.(泛癌)
Identifying and targeting cancer-specific metabolism with network-based  drug target prediction.(泛癌)	EBioMedicine	6.68
Pathway-based biomarker identification with crosstalk analysis for robust prognosis prediction in  hepatocellular carcinoma.
Increased glycolysis correlates with elevated immune activity in tumor immune  microenvironment.(泛癌)
Incorporation of long non-coding RNA expression profile in the 2017 ELN risk  classification can improve  prognostic prediction of acute myeloid leukemia patients.
Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma.
Comprehensive characterization of the rRNA metabolism-related genes in human cancer.(泛癌)	Oncogene	6.634
Histoepigenetic analysis of HPV- and tobacco-associated head and neck cancer identifies both subtype-specific and common therapeutic targets despite  divergent microenvironments.
Identification of SERPINE1 as a Regulator of Glioblastoma Cell Dispersal  with Transcriptome Profiling	Cancers (Basel)	6.16
The YAP1-NMU Axis Is Associated with Pancreatic Cancer Progression and Poor Outcome:  Identification of a Novel Diagnostic Biomarker and Therapeutic Target.
KRAS-Driven Lung Adenocarcinoma and B Cell Infiltration: Novel Insights for Immunotherapy.免疫浸润
Clinical Impact of RANK Signalling in Ovarian Cancer.
Identification of microRNAs involved in pathways which characterize the expression subtypes of NSCLC.	Mol Oncol	5.962
Identification of lncRNAs associated with  early-stage breast cancer and their prognostic implications.
Differentially expressed autophagy-related genes are potential  prognostic and diagnostic biomarkers in clear-cell renal cell carcinoma.	Aging (Albany NY)	5.515
TPM2 as a potential predictive biomarker for atherosclerosis.非肿瘤
An eight-long non-coding RNA signature as a candidate prognostic biomarker for bladder cancer.
Identification and validation of four hub genes involved in the plaque  deterioration of atherosclerosis.
Identification of potential blood biomarkers for Parkinson's disease（非肿瘤）by gene expression and DNA  methylation data integration analysis.	Clin Epigenetics	5.496

我们将高分生信SCI模式分为以下9类：

1．泛癌研究：多肿瘤组合分析，找共享基因；

2．单疾病的多组学（转录组、DNA甲基化、ATAC-seq）联合分析；

3．单细胞测序数据分析：聚类分析、PCA/t-SNE降维、细胞分群、拟时分析、多数据库验证的创新模式；

4．疾病相关免疫浸润分析价值分子；

5．表观遗传→lncRNA在疾病发生中的分析；

6．m6A、DNA甲基化表观遗传组在疾病中的大数据挖掘；

等等

总之，以此类推，分数和工作量是成正比的。

欢迎咨询，我们会给您提供个性化的分析方案，免费的哦。

扫码备注：生信博士你出来

相关阅读：

关注后获取《科研修炼手册》1、2、3、4、5、6、7、8、9、10