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          Identification of changed expression of mRNAs and lncRNAs in osteoarthritic synovium by RNA-sequencing.      Cache   Translate Page      
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Identification of changed expression of mRNAs and lncRNAs in osteoarthritic synovium by RNA-sequencing.

Gene. 2018 Oct 27;685:55-61

Authors: Xiang S, Li Z, Bian Y, Weng X

Long non-coding RNAs (lncRNAs) are recently reported to regulate the homeostasis of cartilage in osteoarthritis (OA), but their regulatory roles in OA synovium remain elusive. This study aimed to identify the differentially expressed mRNAs and lncRNAs in OA synovium and further explore the function of differentially expressed lncRNAs in OA progression through bioinformatics analysis. We started with RNA-sequencing in 5 OA synovium samples and 5 healthy controls to compare the expression of mRNAs and lncRNAs. GO analysis and KEGG pathway analysis were performed to annotate the function of differentially expressed mRNAs. Then real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was admitted in 17 osteoarthritic synovium and 18 healthy controls to confirm the changes of the expression of the selected lncRNAs. LncRNA-mRNA co-expression network was constructed to predict the potential molecular regulatory mechanisms of specifically expressed lncRNAs in OA synovium. 384 mRNAs and 17 lncRNAs were detected to be differentially expressed in OA synovium. The expressions of 4 lncRNAs were confirmed by qRT-PCR. We found the differentially expression lncRNAs could potentially regulate OA progression through pathways regarding to immune response through the lncRNA-mRNA network and further annotation for co-expression mRNAs. Our results indicated that lncRNAs may play key roles in OA synovitis and may have potential value in OA diagnosis.

PMID: 30393192 [PubMed - as supplied by publisher]

          miRNA-223 targets the GPAM gene and regulates the differentiation of intramuscular adipocytes.      Cache   Translate Page      
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miRNA-223 targets the GPAM gene and regulates the differentiation of intramuscular adipocytes.

Gene. 2018 Oct 30;:

Authors: Li F, Li D, Zhang M, Sun J, Li W, Jiang R, Han R, Wang Y, Tian Y, Kang X, Sun G

Intramuscular fat (IMF) has significant effects on the tenderness, juiciness, and flavor of chicken, which are important determinants of poultry meat quality. Although many studies have focused on microRNAs (miRNAs) involved in adipogenesis, little is known about miRNAs associated with poultry IMF deposition or intramuscular adipocyte differentiation. Bioinformatic analysis identified mitochondrial glycerol‑3‑phosphate acyltransferase (GPAM) as a putative target of miR-223. To explore the role of miR-223 in the process of chicken IMF deposition, loss and gain of function experiments were performed in primary intramuscular preadipocytes using miR-223 mimics, miR-223 inhibitor, and si-GPAM. Our results showed that miR-223 is significantly down-regulated in the breast muscle tissues of Gushi hens at the later-laying period in comparison with hens at the pre-laying period. Using qRT-PCR, we found that miR-223 expression in chicken tissues and intramuscular adipocytes correlates negatively with GPAM expression. Cell transfection experiments suggest that miR-223 inhibits intramuscular adipocyte differentiation via targeting GPAM. Experiments using a dual luciferase reporter system show that GPAM is a direct target of miR-223. Taken together, our results support the hypothesis that miR-223 regulates intramuscular fat deposition in chickens.

PMID: 30389563 [PubMed - as supplied by publisher]

          Co-expression analysis reveals key gene modules and pathways of oral squamous cell carcinoma.      Cache   Translate Page      
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Co-expression analysis reveals key gene modules and pathways of oral squamous cell carcinoma.

Cancer Biomark. 2018;22(4):763-771

Authors: Li X, Hu WW, Wang L, Yang XH

BACKGROUND: Oral squamous cell carcinoma is a malignant tumor which is particularly common in the developing world, mostly in older males.
OBJECTIVE: Although gene expression analyses had been performed previously, to our best knowledge, systemic co-expression analysis for this disease is still lacking to date.
METHODS: In this study, we built the co-expression modules with the help of Weighted Correlation Network Analysis (WGCNA) and investigated the function enrichment of co-expression genes from important modules by bioinformatics analysis.
RESULTS: A total of 10 co-expression modules were conducted for 4500 genes from 167 oral squamous cell carcinoma samples. Number of genes for each module ranged from 52 to 1112, with the mean of 450. Interaction relationships of hub-genes between pairwise modules showed great differences, suggesting the high confidence of modules. Functional enrichments of the co-expression modules exhibited great differences. Furthermore, genes in the module ME blue and module ME magenta significantly enriched in hsa05332 (Graft-versus-host disease) and hsa05330 (Allograft rejection), and the two pathways were associated with the oral squamous cell carcinoma.
CONCLUSION: Together, our findings provided the framework of co-expression gene modules of oral squamous cell carcinoma and further understanding of these modules at functional aspect.

PMID: 29914011 [PubMed - indexed for MEDLINE]

          Targeted regulationof STAT3 by miR-29a in mediating Taxol resistance of nasopharyngeal carcinoma cell line CNE-1.      Cache   Translate Page      
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Targeted regulationof STAT3 by miR-29a in mediating Taxol resistance of nasopharyngeal carcinoma cell line CNE-1.

Cancer Biomark. 2018;22(4):641-648

Authors: Gao J, Shao Z, Yan M, Fu T, Zhang L, Yan Y

STAT3 is an important molecule in Janus kinase (JAK) signal transducer and activator of transcription (STAT) signal pathway, and facilitates expression of various oncogenic genes such as Bcl-2, thus is correlated with tumor onset, progression and drug resistance. MiR-29a down-regulation is associated with the pathogenesis of nasopharyngeal carcinoma. Bioinformatics analysis demonstrated a complementary binding between miR-29a and 3'-UTR of STAT3. This study aims to investigate the role of miR-29a in regulating STAT3, as well as in Taxol resistance of nasopharyngeal carcinoma CNE-1 cells. Dual luciferase reporter gene assay showed a regulatory relationship between miR-29a and STAT3. Rhodamine 123 repository in CNE-1 and CNE1/Taxol drug resistant cells was measured together with the expression of miR-29a, STAT3, and p-STAT3. Flow cytometry was used to measure cell apoptosis and PCNA expression under Taxol treatment. CNE-1/Taxol cells were treated with miR-29a mimic and or si-STAT3, followed by measuring the expression of miR-29a, STAT3, and p-STAT3 and cell apoptosis. CCK-8 assay was performed to evaluate cell proliferation. MiR-29a inhibited STAT3 expression. Significantly lower Rhodamine 123 repository, miR-29a expression and apoptosis and higher expression of STAT3, p-STAT3 and PCNA were observed in CNE-2/ Taxol cells than those in CNE-1 cells. Transfection of miR-29a mimic and/or si-STAT3 decreased STAT3, p-STAT3 and PCNA expression, inhibited proliferation and promoted cell apoptosis. MiR-29a down-regulation is correlated with drug resistance of nasopharyngeal carcinoma cell line CNE-1 and MiR-29a up-regulation decreases Taxol resistance of nasopharyngeal carcinoma CNE-1 cells possibly via inhibiting STAT3 and Bcl-2 expression.

PMID: 29914005 [PubMed - indexed for MEDLINE]

          Screening and bioinformatics analysis of circular RNA expression profiles in hepatitis B-related hepatocellular carcinoma.      Cache   Translate Page      
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Screening and bioinformatics analysis of circular RNA expression profiles in hepatitis B-related hepatocellular carcinoma.

Cancer Biomark. 2018;22(4):631-640

Authors: Wang S, Cui S, Zhao W, Qian Z, Liu H, Chen Y, Lv F, Ding HG

BACKGROUND: Circular RNAs (circRNAs) play an important role in pathogenesis and development of hepatocellular carcinoma (HCC). However, circRNA expression profiles in hepatitis B Virus (HBV)-related HCC remain to be studied.
METHODS: Total 13 HBV-related HCC patients were enrolled for study. Three HCC and 3 paired adjacent non-tumorous (NT) tissues from 3 patients were performed for microarray. Ten pairs of HCC tissues were used to verify the identified up-regulated and down-regulated circRNAs obtained from the microarray data by quantitative real-time reverse transcription PCR (qRT-PCR). Total RNA was isolated and treated with Rnase R to remove linear RNA, then hybridized to the array to screen for circRNAs. Bioinformatics analyses including clustering, differential expression, annotation of circRNA/microRNA (miRNA) interactions, Go analysis and KEGG pathway analysis, were performed.
RESULTS: Based on the microarray data, we found significantly up-regulation of 24 circRNAs and down-regulation of 23 circRNAs in the HCC samples compared to NT samples (fold change ⩾ 2.0 and P< 0.05). Of them, 6 candidate circRNAs (hsa_circRNA_102814, 100381, 103489, 101764, 100327, and 103361) were verified by qRT-PCR. Of them, hsa_circRNA 100381, 103489 up-regulation and 101764 down-regulation were found to be significantly different in the 10 validation HCC tissue. Clusters of circRNAs were aberrantly expressed in HCC compared with NT samples. CircRNA_101764 was the largest nodes in circRNA/microRNA co-expression network, especially co-expression with hsa-miR-181 family, which plays an important role in cell network. Annotation of circRNA/miRNA interactions indicated that the biological effects of circRNA may be achieved by binding of miRNAs. GO analysis revealed that numerous target genes were involved in the biological processes, cellular component and molecular function. There was nearly 30 target genes enrichment on KEGG pathways analysis, PI3K-Akt signaling pathway which the most number of genes involved.
CONCLUSION: In this study, we comprehensively explored the expression of differentially expressed circRNAs in HBV-related HCC, and our results indicate that circRNA_101764 may play an important role in the development of HCC.

PMID: 29914004 [PubMed - indexed for MEDLINE]

          Less bar charts with error bars please      Cache   Translate Page      

Computer scientists, psychologists, and statisticians have all studied how to create visualizations that communicate data effectively. Graphical perception experiments showed that the human visual system is pretty good at decoding numerical data mapped to the spatial position of a graphical feature (Heer et al. 2010). This may be why bar charts are so popular and widely-used.

Less bar charts with error bars please
CCO image

To show counts or frequencies of observations for different groups, we can make the length of the bars represent the corresponding values. The problems start when bar charts are also used to show multiple observations for each group. What usually happens is that the length of the bars now represent the group means, and then silly-looking error bars are drawn on top to show standard errors or standard deviations for the means. These plots are also known as ‘dynamite plunger plots’ because they look like the detonator boxes from cartoons.

Less bar charts with error bars please
CCO image

Bar charts for multiple observations are not good because:

they conceal the underlying data, so we cannot see the its distribution or the sample size they look pretty lame

I’m writing this quick post after teaching a ggplot workshop last month, followed by attending two biology conferences where I saw these plots used by students, plenary speakers, bioinformaticians, and various other researchers. In 2016, Helena Jambor also noted how prevalent these charts are, even in top journals.

Alternatives to the bar chart

I’m not the first to call for less of these plots, but I want to share some alernatives that I haven’t seen implemented too often.

First of all, why draw bars if we can show the underlying data points, or at least show more information about the distribution of the data, or some nice parametric summaries (e.g. box plots, violins, etc.).

This tweet speaks for itself, and all those options can be made in ggplot with the right packages and extensions (e.g. ggforce and ggbeeswarm ).

Comparison of sina plot with other styles. This is a great suggestion.

― Tim Triche, Jr. (@timtriche) October 29, 2018

If for some reason we absolutely need to plot bars for multiple observations per group, the approach below may be an option. At this point it’s pretty hacky, but I think something like this could be made into a custom geom by anyone who is good at ggproto.

Here’s how it works:

Generate random values within the bounds of the mean plus or minus the standard error. Overlay semi-transparent bars to show the range of the standard error. Draw a bar with an outline but no fill to show the point estimate (the mean).

This is not meant to show the sample size or underlying distribution of the data, but it shows the estimates and standard errors without looking as lame. I was mostly interested in learning the data manipulation steps needed to generate the random values. I have to admit that I could not figure out how to add the layers iteratively, so there is a lot of copying and pasting.

Let’s work through the typical bar charts first:

These are the means for one of the variables (total sleeping time) in the built-in mammalian sleep dataset, by dietary guild.

library(ggplot2) library(dplyr) library(tidyr) library(ggthemes) # summarize by guild msleep_summary <- msleep %>% filter(! %>% group_by(vore) %>% summarise(total_sleep=mean(sleep_total),sleep_se=sd(sleep_total)/sqrt(n())) %>% mutate(diet=paste0(vore,"vore")) # plot ggplot(msleep_summary,aes(diet,total_sleep))+ geom_col(fill="#8ad2f2",color="#0db5c1")+theme_base()

Less bar charts with error bars please

Now with error bars.

# with error bars ggplot(msleep_summary,aes(diet,total_sleep))+ geom_col(fill="#8ad2f2",color="#0db5c1")+ geom_errorbar(aes(ymin=total_sleep-sleep_se,ymax=total_sleep+sleep_se),width=0.4)+ theme_base()

Less bar charts with error bars please

Now bars for the means, and the lower and upper bounds of the estimates.

ggplot(msleep_summary,aes(diet,total_sleep))+ geom_col(aes(y=total_sleep+sleep_se),alpha=0.6,fill="#8ad2f2")+ geom_col(alpha=0,color="#0db5c1")+ geom_col(aes(y=total_sleep-sleep_se),alpha=0.6,fill="#8ad2f2")+theme_base()

Less bar charts with error bars please

We need several steps to create a separate wide-form dataset of random values within the range of the standard errors for each group.

# generate data within the se bounds msleep_summary_ser <- msleep_summary %>% group_by(diet) %>% do(data.frame(se_range=runif(20,min=.$total_sleep-.$sleep_se,max=.$total_sleep+.$sleep_se))) %>% left_join(msleep_summary) %>% arrange(diet,se_range) %>% mutate(gid=row_number()) %>% mutate(serID=paste0("se",gid)) # reshape msleep_ser_wide <- msleep_summary_ser %>% select(diet,se_range,serID) %>% tibble::rowid_to_column() %>% spread(serID,-diet)

The plotting code is a mess but bear with me.

ggplot(msleep_summary,aes(diet,total_sleep))+ geom_col(color="black",alpha=0)+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se1),alpha=0.2,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se2),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se3),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se4),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se5),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se6),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se7),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se8),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se9),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se10),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se11),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se12),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se13),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se14),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se15),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se16),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se17),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se18),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se19),alpha=0.03,fill="#71B9D9")+ geom_col(data=msleep_ser_wide,aes(x=diet,y=se20),alpha=0.03,fill="#71B9D9")+ theme_base()

Less bar charts with error bars please

It looks OK, but I would personally use a sina plot instead (and I did in my last publication on bats ).

library(ggforce) msleep %>% filter(! %>% mutate(diet=paste0(vore,"vore")) %>% ggplot(aes(diet,sleep_total))+ geom_sina(shape=21,fill="#71B9D9",binwidth=0.6)+theme_base()+labs(y="total_sleep")

Less bar charts with error bars please

Contact me if you have any feedback or questions.


Heer, Jeffrey, Michael Bostock, and Vadim Ogievetsky. “A tour through the visualization zoo.” Commun. Acm 53.6 (2010): 59-67.

          Nutrition & Dietetics/Dietitian/Food & Nutritionist Freshers Wanted For Medical Coding Jobs      Cache   Translate Page      
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          Θέσεις εργασίας στο Ινστιτούτο Εφαρμοσμένων Βιοεπιστημών του ΕΚΕΤΑ      Cache   Translate Page      
2 άτομα με Σύμβαση Μίσθωσης Έργου στο Ινστιτούτο Εφαρμοσμένων Βιοεπιστημών του ΕΚΕΤΑ: Μία (1) θέση στα πλαίσια υλοποίησης του ερευνητικού έργου «Bioinformatic approaches for antigen receptor gene repertoire analysis in Lymphoid malignancies» – «Euro-NGS»  Μία (1) θέση στα πλαίσια υλοποίησης του ερευνητικού έργου «Bioinformatic approaches for antigen receptor gene repertoire analysis in Lymphoid malignancies» –
          Paramount Recruitment: Developer Team Lead (UI) - Cambridge      Cache   Translate Page      
Negotiable: Paramount Recruitment: Developer Team Lead (UI) - Cambridge A new opportunity is now available for a talented Lead Developer to join a fantastic Biotech company, working in the field of Genomics and Bioinformatics! Using your background in UI Development and management, the Cambridge, England
          Bioinformatics Analyst/Genomic Data Scientist - Frederick National Laboratory - Fort Detrick, MD      Cache   Translate Page      
Bachelor’s degree in biomedical science/bioinformatics/math/computer science related field from an accredited college or university according to the Council for...
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          miR-539-5p inhibits experimental choroidal neovascularization by targeting CXCR7.      Cache   Translate Page      
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miR-539-5p inhibits experimental choroidal neovascularization by targeting CXCR7.

FASEB J. 2018 03;32(3):1626-1639

Authors: Feng Y, Wang J, Yuan Y, Zhang X, Shen M, Yuan F

Stromal cell-derived factor-1 (SDF-1) has been previously confirmed to participate in the formation of choroidal neovascularization (CNV) via its receptor, CXC chemokine receptor (CXCR) 4; CXCR7 is a recently identified receptor for SDF-1. The molecular mechanisms and therapeutic value of CXCR7 in CNV remain undefined. In this study, experimental CNV was induced by laser photocoagulation in Brown-Norway pigmented rats, and aberrant CXCR7 overexpression was detected in the retinal pigment epithelial/choroid/sclera tissues of laser-injured eyes. Blockade of CXCR7 activation via CXCR7 knockdown or neutralizing Ab administration inhibited SDF-1-induced cell survival and the tubular formation of human retinal microvascular endothelial cells (HRMECs) in vitro and reduced CNV leakage and lesion size in vivo. By using microRNA array screening and bioinformatic analyses, we identified miR-539-5p as a regulator of CXCR7. Transfection of HRMECs and choroid-retinal endothelial (RF/6A) cells with the miR-539-5p mimic inhibited their survival and tube formation, whereas CXCR7 overexpression rescued the suppressive effect of miR-539-5p. The antiangiogenic activities of the miR-539-5p mimic were additionally demonstrated in vivo by intravitreal injection. ERK1/2 and AKT signaling downstream of CXCR7 is involved in the miR-539-5p regulation of endothelial cell behaviors. These findings suggest that the manipulation of miR-539-5p/CXCR7 levels may have important therapeutic implications in CNV-associated diseases.-Feng, Y., Wang, J., Yuan, Y., Zhang, X., Shen, M., Yuan, F. miR-539-5p inhibits experimental choroidal neovascularization by targeting CXCR7.

PMID: 29146732 [PubMed - indexed for MEDLINE]

           Bioinformatics in Malaysia: hope, initiative, effort, reality, and challenges       Cache   Translate Page      
Merican, A.F. and Zeti, A.M.H. and Tajul-Arifin, K. and Shamsir, M.S. and Nathan, S. and Mahadi, N.M. and Napis, S. and Tan, T.W. and Mohamed, R. (2009) Bioinformatics in Malaysia: hope, initiative, effort, reality, and challenges. Plos Computational Biology, 5 (8). e1000457. ISSN 1553-734X
          BioInformatics Inc. Honors The “Rock Stars” of Life Science      Cache   Translate Page      

On Tuesday, November 6, winners of the 2018 Life Science Industry Awards were formally announced at the Hard Rock Hotel in San Diego. The Life Science Industry Awards recognize the life science...

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          Algorithms In Bioinformatics: 15th International Workshop, Wabi 2015, Atlanta, Ga, Usa      Cache   Translate Page      
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          Systems Engineer-Bioinformatics - Sage Bionetworks - Seattle, WA      Cache   Translate Page      
Sage was founded in 2009 as a non-profit spinoff from Rosetta Inpharmatics (Merck) to pioneer open approaches to advancing biomedical research....
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          Bioinformatics Data Analyst (m/f) in Epigenetics and Chronic Obstructive Pulmonary Disease (COPD)      Cache   Translate Page      


BioMed X Innovation Center, Im Neuenheimer Feld 583, 69120 Heidelberg, Germany

About BioMed X

The BioMed X Innovation Center is an exciting new collaboration model at the interface between academia and industry. At our center, distinguished early career scientists recruited from all over the world are working jointly on novel pre-clinical research projects in the fields of biomedicine, molecular biology, cell biology, diagnostics, and consumer care. These interdisc…

          Bioinformatics Data Analyst (m/w)      Cache   Translate Page      
Bioinformatics Data Analyst (m/w) Scionics Computer Innovation GmbH Dresden Entwicklung von Software, um repetitive Arbeitsschritte in der bioinformatischen Analyse zu automatisieren und neuartige biologische Fragestellungen von zu beantworten; Anbieten eines Beratungs-Services für kleinere Analysen von biologischen Daten;... bioinformatics data analyst job description seeks to recruit a highly

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