2019年度生信类SCI调查报告出炉！还能愉快地短平快吗？

生信大数据挖掘可以发表SCI，近2年几乎成了一种科研圈的常识，但是拒稿率越来越高、越来越难发也已经成了共识！

首先来说一说昨天一个粉丝给我发的一段Spandidos Publications 杂志社主编的审稿意见，如下：

“Editor: Please note that we have recently revised our internal guidelines for bioinformatics-based papers at peer reviewstage, so that for manuscripts in which data/figures are generated mainly using web-based, publicly-available tools tobe accepted, we require either（或者）: i) validationexperiments to be performed; ii) original samples to be used to generate thedatasets; or iii) for a systematic review of multiple databases to be performedto generate the datasets for the study.”

什么意思呢？就是说如果你用的公共数据库挖掘的数据写文章投稿的，要么①做实验验证结果；要么②拿自己样本去测序联合分析一下；要么③全面地使用多数据集、多数据库的联合分析，较少数据集的简单分析不加实验的，将直接拒稿。

Spandidos Publications 出版社创立于 1992 年，位于希腊和英国，聚焦生物医学和基础科学领域，旗下有 6 本 SCI 收录杂志，文章的投中率高，所以深受中国人喜爱，但同时口碑也不是非常好，多个已入选了坊间的杂志黑名单（非官方）。

有钱谁不愿意多做点实验啊？有钱谁不愿意自己去测序啊？有钱的话也没时间去做啊！

作为过来人，猫头鹰博士想告诉大家：难发是必然的趋势，因为短平快的发表模式，哪个科研人不喜欢，尤其在现如今时间和精力越来越有限的时代，但是并不意味不能发，因为生信分析套路的开发模式也越来越快，越来越多，越来越高级，大家一定要稳住！听猫博给大家一一道来。

在这里咱们一起来看看近几个月见刊的生信数据SCI文章，质量如何。

猫博在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9月20日的2019年度的生信类SCI。

对2019年的“挖掘GEO、TCGA二手高通量数据”的SCI进行统计如下。

去年的一整年是1682篇，今年已经1928篇（其中有307篇做了较多实验）了，可怕，吓人，厉害了我的哥！！！

2019生信类SCI清单的下载链接如下：https://pan.baidu.com/s/1q6hgteHkAB3QpPdlncmZrA

密码：转发此贴到朋友圈或者其他科研群，截屏图片发到公众号后台即可领取。

那么我们统计一下哪些杂志接收量最大：

生信友好杂志	2019接受量	IF因子
J Cell Biochem	71	3.40
Oncol Lett	70	1.87
Mol Med Rep	62	1.85
J Cell Physiol	58	4.50
Oncol Rep	43	3.04
Sci Rep	42	4.01
Cancer Cell Int	38	3.44
Med Sci Monit	36	1.98
Front Oncol	35	4.13
BMC Cancer	34	2.93
PeerJ	34	2.00
J Cancer	32	3.18
J Comput Biol	31	0.88
Bioinformatics	30	4.53
Onco Targets Ther	30	3.00
Cancer Med	27	3.36
Biomed Res Int	26	2.20
Front Genet	26	3.52
Gene	26	2.64
Cancer Manag Res	25	2.24
Pathol Res Pract	23	1.79
Biosci Rep	22	2.54
Cancers (Basel)	22	6.16
Aging (Albany NY)	21	5.52
Medicine (Baltimore)	21	1.80
J Transl Med	20	4.10
EBioMedicine	19	6.68
Int J Mol Sci	17	4.18
PLoS One	17	2.78
Exp Ther Med	15	1.45
J Exp Clin Cancer Res	15	5.65
BMC Med Genomics	14	2.57
J Cell Mol Med	14	4.66
Biomed Pharmacother	13	3.74
Int J Mol Med	13	2.93
Biochem Biophys Res Commun	12	2.71
BMC Bioinformatics	12	2.51
Int J Oncol	12	3.57
Am J Transl Res	11	3.27
Clin Cancer Res	11	8.91
FEBS Open Bio	11	1.96
Int J Cancer	11	4.98
Oncogene	11	6.63
Breast Cancer Res Treat	10	3.47

2019年的统计↑（按文章接收量排序）

生信友好杂志	2019接受量	IF因子
Clin Cancer Res	11	8.91
EBioMedicine	19	6.68
Oncogene	11	6.63
Cancers (Basel)	22	6.16
J Exp Clin Cancer Res	15	5.65
Aging (Albany NY)	21	5.52
Int J Cancer	11	4.98
J Cell Mol Med	14	4.66
Bioinformatics	30	4.53
J Cell Physiol	58	4.50
Int J Mol Sci	17	4.18
Front Oncol	35	4.13
J Transl Med	20	4.10
Sci Rep	42	4.01
Biomed Pharmacother	13	3.74
Int J Oncol	12	3.57
Front Genet	26	3.52
Breast Cancer Res Treat	10	3.47
Cancer Cell Int	38	3.44
J Cell Biochem	71	3.40
Cancer Med	27	3.36
Am J Transl Res	11	3.27
J Cancer	32	3.18
Oncol Rep	43	3.04
Onco Targets Ther	30	3.00
BMC Cancer	34	2.93
Int J Mol Med	13	2.93
PLoS One	17	2.78
Biochem Biophys Res Commun	12	2.71
Gene	26	2.64
BMC Med Genomics	14	2.57
Biosci Rep	22	2.54
BMC Bioinformatics	12	2.51
Cancer Manag Res	25	2.24
Biomed Res Int	26	2.20
PeerJ	34	2.00
Med Sci Monit	36	1.98
FEBS Open Bio	11	1.96
Oncol Lett	70	1.87
Mol Med Rep	62	1.85
Medicine (Baltimore)	21	1.80
Pathol Res Pract	23	1.79
Exp Ther Med	15	1.45
J Comput Biol	31	0.88

2019年的统计↑（按IF大小排序）

其中J Cell Biochem、J Cell Physiol、Oncol Lett、Mol Med Rep、Cancer Cell Int、Oncol Reports、Med Sci Monit、Front Oncol对纯生信类的文章最为友好的。

所以我们在这里抽样调查的方式对不同IF因子档次的文章质量和分析策略进行展示，看看是否真的越来越难发了。

首先来看看Clin Cancer Res（IF=8.9）

以下就是发表在上面的纯生信类文章：

发表在CCR上的生信文章名称	杂志
Transcriptomic analysis reveals prognostic molecular signatures of stage  I melanoma.	Clin Cancer Res
Transcriptomic heterogeneity of androgen receptor (AR) activity defines a  de novo low AR-active subclass in treatment naïve primary prostate cancer.
Altered gene expression along the glycolysis-cholesterol synthesis axis  is associated with outcome in pancreatic cancer.
Tumor Mutation Burden and Prognosis in Patients with Colorectal Cancer  Treated with Adjuvant Fluoropyrimidine and Oxaliplatin.
Development and Validation of a Combined Hypoxia and Immune Prognostic  Classifier for Head and Neck Cancer.
Novel RB1-Loss Transcriptomic Signature Is Associated with Poor Clinical  Outcomes across Cancer Types.
The Immune Subtypes and Landscape of Squamous Cell Carcinoma.
Genomic Landscape of Pancreatic Adenocarcinoma in Younger versus Older  Patients: Does Age Matter?
Characterization of Alternative Splicing Events in HPV-Negative Head and  Neck Squamous Cell Carcinoma Identifies an Oncogenic DOCK5 Variant.

CCR的生信分析文章在探索的科学问题是：年龄、某一个代谢通路相关基因群、基因组不稳定、基因可变剪接活动、免疫微环境在和某1种肿瘤或者多种肿瘤的不同分期的预后相关性。

有人说，CCR的分数太高了，达不到，好吧，那咱们就挑一些3-5分之间的例子来看看。

序号	2019文章名称	杂志	IF因子
1	Measurement of  tumor mutational burden (TMB) in routine molecular diagnostics: in silico and  real-life analysis of three larger gene panels.	Int J Cancer	4.98
2	Systematic  identification of lincRNA-based prognostic biomarkers by integrating lincRNA  expression and copy number variation in lung adenocarcinoma.
3	Identification of  prognosis-related alternative splicing events in kidney renal clear cell  carcinoma.	J Cell Mol Med	4.66
4	Prognostic power of  a lipid metabolism gene panel for diffuse gliomas.
5	Identification of  potential prognostic TF-associated lncRNAs for predicting survival in ovarian  cancer.
6	A large-scale  transcriptome analysis identified ELANE and PRTN3 as novel methylation  prognostic signatures for clear cell renal cell carcinoma.
7	Comprehensive  analysis of prognostic immune-related genes in the tumor microenvironment of  cutaneous melanoma.
8	The underlying  pathophysiology association between the Type 2-diabetic and hepatocellular  carcinoma.
9	Triple-Negative  Breast Cancer with High Levels of Annexin A1 Expression Is Associated with  Mast Cell Infiltration, Inflammation, and Angiogenesis.	Int J Mol Sci	4.18
10	Analysis of  Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning  Algorithms.
11	LINC00957 Acted as  Prognostic Marker Was Associated With Fluorouracil Resistance in Human  Colorectal Cancer.	Front Oncol	4.13
12	Construction and  Investigation of a lncRNA-Associated ceRNA Regulatory Network in Cholangiocarcinoma.
13	Identification and  Validation of Tumor Stromal Immunotype in Patients With Hepatocellular  Carcinoma.
14	Prognosis of clear  cell renal cell carcinoma (ccRCC) based on a six-lncRNA-based risk score: an  investigation based on RNA-sequencing data.	J Transl Med	4.10
15	Identification of  an immune signature predicting prognosis risk of patients in lung  adenocarcinoma.
16	A two-circular RNA  signature as a noninvasive diagnostic biomarker for lung adenocarcinoma.
17	LMX1B mRNA  expression and its gene body CpG methylation are valuable prognostic  biomarkers for laryngeal squamous cell carcinoma.	Biomed Pharmacother	3.74
18	A long non-coding  RNA signature to improve prognostic prediction in clear cell renal cell  carcinoma.
19	Profiles of immune  cell infiltration and immune-related genes in the tumor microenvironment of  colorectal cancer.
20	Polycystic Ovary  Syndrome: Novel and Hub lncRNAs in the Insulin Resistance-Associated  lncRNA-mRNA Network.	Front Genet	3.52
21	Survival Analysis  of Multi-Omics Data Identifies Potential Prognostic Markers of Pancreatic  Ductal Adenocarcinoma.
22	A six-CpG-based  methylation markers for the diagnosis of ovarian cancer in blood.	J Cell Biochem	3.40
23	Identification of  aberrantly expressed lncRNA and the associated TF-mRNA network in  hepatocellular carcinoma.
24	Integrative  prognostic subtype discovery in high-grade serous ovarian cancer.

我们将3-5分的公共数据生信SCI模式分为以下9类：

1．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、TCGA数据验证的创新模式；

4．肿瘤类免疫浸润分析价值分子；

5．Biomarker类：lncRNA、甲基化印记、miRNA作为预后标志物；

6．WGCNA基因共表达模块：疾病相关的共表达基因；

7．转录因子-lncRNA在肿瘤发生中的分析：

8．基因突变、拷贝数变化对肿瘤的影响和功能分析；

9．m6A表观遗传组在肿瘤发病中的大数据挖掘；

总之，以此类推，分数和工作量是成正比的，不过还是建议大家尽量做点验证实验，不做实验的生信文章拒稿率还是不低的。

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你+我=高质量分析数据

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