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Article ; Online: Pan-Cancer Analysis Reveals Common and Specific Relationships between Intragenic miRNAs and Their Host Genes

Baohong Liu / Yu Shyr / Qi Liu

Biomedicines, Vol 9, Iss 1263, p

2021 Volume 1263

Abstract: MicroRNAs (miRNAs) are small endogenous non-coding RNAs that play important roles in regulating gene expression. Most miRNAs are located within or close to genes (host). miRNAs and their host genes have either coordinated or independent transcription. We ...

Abstract	MicroRNAs (miRNAs) are small endogenous non-coding RNAs that play important roles in regulating gene expression. Most miRNAs are located within or close to genes (host). miRNAs and their host genes have either coordinated or independent transcription. We performed a comprehensive investigation on co-transcriptional patterns of miRNAs and host genes based on 4707 patients across 21 cancer types. We found that only 11.6% of miRNA-host pairs were co-transcribed consistently and strongly across cancer types. Most miRNA-host pairs showed a strong coexpression only in some specific cancer types, demonstrating a high heterogenous pattern. For two particular types of intergenic miRNAs, readthrough and divergent miRNAs, readthrough miRNAs showed higher coexpression with their host genes than divergent ones. miRNAs located within non-coding genes had tighter co-transcription with their hosts than those located within protein-coding genes, especially exonic and junction miRNAs. A few precursor miRNAs changed their dominate form between 5′ and 3′ strands in different cancer types, including miR-486, miR-99b, let-7e, miR-125a, let-7g, miR-339, miR-26a, miR-16, and miR-218, whereas only two miRNAs with multiple host genes switched their co-transcriptional partner in different cancer types (miR-219a-1 with SLC39A7/HSD17B8 and miR-3615 with RAB37/SLC9A3R1 ). miRNAs generated from distinct precursors (such as miR-125b from miR-125b-1 or miR-125b-2) were more likely to have cancer-dependent main contributors. miRNAs and hosts were less co-expressed in KIRC than other cancer types, possibly due to its frequent VHL mutations. Our findings shed new light on miRNA biogenesis and cancer diagnosis and treatments.
Keywords	miRNAs ; host genes ; coexpression ; Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Distance Metric Based Oversampling Method for Bioinformatics and Performance Evaluation.

Tsai, Meng-Fong / Yu, Shyr-Shen

Journal of medical systems

2016 Volume 40, Issue 7, Page(s) 159

Abstract: An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority ... ...

Abstract	An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority class but significantly less accurate for the minority class. To overcome this problem, this study took several imbalanced datasets from the famed UCI datasets and designed and implemented an efficient algorithm which couples Top-N Reverse k-Nearest Neighbor (TRkNN) with the Synthetic Minority Oversampling TEchnique (SMOTE). The proposed algorithm was investigated by applying it to classification methods such as logistic regression (LR), C4.5, Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN). This research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrate that the Euclidean and Manhattan distances are not only more accurate, but also show greater computational efficiency when compared to the Chebyshev and Cosine distances. Therefore, the proposed algorithm based on TRkNN and SMOTE can be widely used to handle imbalanced datasets. Our recommendations on choosing suitable distance metrics can also serve as a reference for future studies.
MeSH term(s)	Algorithms ; Cluster Analysis ; Computational Biology/methods ; Data Accuracy ; Humans ; Logistic Models ; Neural Networks (Computer)
Language	English
Publishing date	2016-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	423488-1
ISSN	1573-689X ; 0148-5598
ISSN (online)	1573-689X
ISSN	0148-5598
DOI	10.1007/s10916-016-0516-3
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Article ; Online: MetaGSCA

Yan Guo / Hui Yu / Haocan Song / Jiapeng He / Olufunmilola Oyebamiji / Huining Kang / Jie Ping / Scott Ness / Yu Shyr / Fei Ye

PLoS Computational Biology, Vol 17, Iss 5, p e

A tool for meta-analysis of gene set differential coexpression.

2021 Volume 1008976

Abstract: Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to ... ...

Abstract	Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network ...
Keywords	Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Podocyte-Related Mechanisms Underlying Survival Benefit of Long-Term Angiotensin Receptor Blocker

Xuejing Zhu / Dan Gao / Vittorio Albertazzi / Jianyong Zhong / Li-Jun Ma / Liping Du / Yu Shyr / Valentina Kon / Hai-Chun Yang / Agnes B. Fogo

International Journal of Molecular Sciences, Vol 23, Iss 6018, p

2022 Volume 6018

Abstract: We previously found that short-term treatment (week 8 to 12 after injury) with high-dose angiotensin receptor blocker (ARB) induced the regression of existing glomerulosclerosis in 5/6 nephrectomy rats. We therefore assessed the effects of long-term ... ...

Abstract	We previously found that short-term treatment (week 8 to 12 after injury) with high-dose angiotensin receptor blocker (ARB) induced the regression of existing glomerulosclerosis in 5/6 nephrectomy rats. We therefore assessed the effects of long-term intervention with ARB vs. nonspecific antihypertensives in this study. Adult rats underwent 5/6 nephrectomy and renal biopsy 8 weeks later. The rats were then divided into three groups with equivalent renal function and glomerular sclerosis and treated with high-dose losartan (ARB), nonspecific antihypertensive triple-therapy (TRX), or left untreated (Control) until week 30. We found that blood pressure, serum creatinine levels, and glomerulosclerosis were lower at sacrifice in ARB and TRX vs. Control. Only ARB reduced proteinuria and maintained the density of WT-1-positive podocytes. Glomerular tufts showed more double-positive cells for CD44, a marker of activated parietal epithelial cells, and synaptopodin after ARB vs. TRX or Control. ARB treatment reduced aldosterone levels. ARB-treated rats had significantly improved survival when compared with TRX or Control. We conclude that both long-term ARB and triple-therapy ameliorate progression, but do not sustain the regression of glomerulosclerosis. ARB resulted in the superior preservation of podocyte integrity and decreased proteinuria and aldosterone, linked to increased survival in the uremic environment.
Keywords	ARB ; glomerulosclerosis ; survival ; podocyte ; proteinuria ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	610
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Associations of influenza vaccination with severity of immune-related adverse events in patients with advanced thoracic cancers on immune checkpoint inhibitors

Emily Pei-Ying Lin / Li-Ching Huang / Jennifer Whisenant / Sally York / Travis Osterman / Jennifer Lewis / Wade Iams / Emily Skotte / Amanda Cass / Chih-Yuan Hsu / Yu Shyr / Leora Horn

ERJ Open Research, Vol 8, Iss

2022 Volume 4

Abstract: Background Whether influenza vaccination (FV) is associated with the severity of immune-related adverse events (IRAEs) in patients with advanced thoracic cancer on immune checkpoint inhibitors (ICIs) is not fully understood. Methods Patients enrolled in ... ...

Abstract	Background Whether influenza vaccination (FV) is associated with the severity of immune-related adverse events (IRAEs) in patients with advanced thoracic cancer on immune checkpoint inhibitors (ICIs) is not fully understood. Methods Patients enrolled in this retrospective cohort study were identified from the Vanderbilt BioVU database and their medical records were reviewed. Patients with advanced thoracic cancer who received FV within 3 months prior to or during their ICI treatment period were enrolled in the FV-positive cohort and those who did not were enrolled in the FV-negative cohort. The primary objective was to detect whether FV is associated with decreased IRAE severity. The secondary objectives were to evaluate whether FV is associated with a decreased risk for grade 3–5 IRAEs and better survival times. Multivariable ordinal logistic regression was used for the primary analysis. Results A total of 142 and 105 patients were enrolled in the FV-positive and FV-negative cohorts, respectively. There was no statistically significant difference in patient demographics or cumulative incidences of IRAEs between the two cohorts. In the primary analysis, FV was inversely associated with the severity of IRAEs (OR 0.63; p=0.046). In the secondary analysis, FV was associated with a decreased risk for grade 3–5 IRAEs (OR 0.42; p=0.005). Multivariable Cox regression showed that FV was not associated with survival times. Conclusions Our study showed that FV does not increase toxicity for patients with advanced thoracic cancer on ICIs and is associated with a decreased risk for grade 3–5 IRAEs. No statistically significant survival differences were found between patients with and without FV.
Keywords	Medicine ; R
Subject code	616
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	European Respiratory Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: RnaSeqSampleSize

Shilin Zhao / Chung-I Li / Yan Guo / Quanhu Sheng / Yu Shyr

BMC Bioinformatics, Vol 19, Iss 1, Pp 1-

real data based sample size estimation for RNA sequencing

2018 Volume 8

Abstract: Abstract Background One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on ... ...

Abstract	Abstract Background One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.
Keywords	RNA-Seq ; Sample size ; Power analysis ; Simulation ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	310
Language	English
Publishing date	2018-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Advanced Heat Map and Clustering Analysis Using Heatmap3

Shilin Zhao / Yan Guo / Quanhu Sheng / Yu Shyr

BioMed Research International, Vol

2014 Volume 2014

Abstract: Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat maps can be produced from the “heatmap” function in R. However, the “heatmap” function lacks certain ... ...

Abstract	Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat maps can be produced from the “heatmap” function in R. However, the “heatmap” function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of the “heatmap” function, we have developed an R package “heatmap3” which significantly improves the original “heatmap” function by adding several more powerful and convenient features. The “heatmap3” package allows users to produce highly customizable state of the art heat maps and dendrograms. The “heatmap3” package is developed based on the “heatmap” function in R, and it is completely compatible with it. The new features of “heatmap3” include highly customizable legends and side annotation, a wider range of color selections, new labeling features which allow users to define multiple layers of phenotype variables, and automatically conducted association tests based on the phenotypes provided. Additional features such as different agglomeration methods for estimating distance between two samples are also added for clustering.
Keywords	Medicine ; R
Language	English
Publishing date	2014-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification

Tu, Chengjian / Jun Qu / Quanhu Sheng / Shichen Shen / Yu Shyr

Journal of proteomics. 2017 Jan. 30, v. 152

2017

Abstract: Reliable quantification of low-abundance proteins in complex proteomes is challenging largely owing to the limited number of spectra/peptides identified. In this study we developed a straightforward method to improve the quantitative accuracy and ... ...

Abstract	Reliable quantification of low-abundance proteins in complex proteomes is challenging largely owing to the limited number of spectra/peptides identified. In this study we developed a straightforward method to improve the quantitative accuracy and precision of proteins by strategically retrieving the less confident peptides that were previously filtered out using the standard target-decoy search strategy. The filtered-out MS/MS spectra matched to confidently-identified proteins were recovered, and the peptide-spectrum-match FDR were re-calculated and controlled at a confident level of FDR≤1%, while protein FDR maintained at ~1%. We evaluated the performance of this strategy in both spectral count- and ion current-based methods. >60% increase of total quantified spectra/peptides was respectively achieved for analyzing a spike-in sample set and a public dataset from CPTAC. Incorporating the peptide retrieval strategy significantly improved the quantitative accuracy and precision, especially for low-abundance proteins (e.g. one-hit proteins). Moreover, the capacity of confidently discovering significantly-altered proteins was also enhanced substantially, as demonstrated with two spike-in datasets. In summary, improved quantitative performance was achieved by this peptide recovery strategy without compromising confidence of protein identification, which can be readily implemented in a broad range of quantitative proteomics techniques including label-free or labeling approaches.We hypothesize that more quantifiable spectra and peptides in a protein, even including less confident peptides, could help reduce variations and improve protein quantification. Hence the peptide retrieval strategy was developed and evaluated in two spike-in sample sets with different LC-MS/MS variations using both MS1- and MS2-based quantitative approach. The list of confidently identified proteins using the standard target-decoy search strategy was fixed and more spectra/peptides with less confidence matched to confident proteins were retrieved. However, the total peptide-spectrum-match false discovery rate (PSM FDR) after retrieval analysis was still controlled at a confident level of FDR≤1%. As expected, the penalty for occasionally incorporating incorrect peptide identifications is negligible by comparison with the improvements in quantitative performance. More quantifiable peptides, lower missing value rate, better quantitative accuracy and precision were significantly achieved for the same protein identifications by this simple strategy. This strategy is theoretically applicable for any quantitative approaches in proteomics and thereby provides more quantitative information, especially on low-abundance proteins.
Keywords	data collection ; peptides ; proteins ; proteomics
Language	English
Dates of publication	2017-0130
Size	p. 276-282.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	2400835-7
ISSN	1876-7737 ; 1874-3919
ISSN (online)	1876-7737
ISSN	1874-3919
DOI	10.1016/j.jprot.2016.11.020
Database	NAL-Catalogue (AGRICOLA)

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Article: Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Guo, Yan / David C. Samuels / Hui Yu / Shilin Zhao / Yu Shyr / Yulin Dai

Genomics. 2017 Mar., v. 109, no. 2

2017

Abstract: Analyses of high throughput sequencing data starts with alignment against a reference genome, which is the foundation for all re-sequencing data analyses. Each new release of the human reference genome has been augmented with improved accuracy and ... ...

Abstract	Analyses of high throughput sequencing data starts with alignment against a reference genome, which is the foundation for all re-sequencing data analyses. Each new release of the human reference genome has been augmented with improved accuracy and completeness. It is presumed that the latest release of human reference genome, GRCh38 will contribute more to high throughput sequencing data analysis by providing more accuracy. But the amount of improvement has not yet been quantified. We conducted a study to compare the genomic analysis results between the GRCh38 reference and its predecessor GRCh37. Through analyses of alignment, single nucleotide polymorphisms, small insertion/deletions, copy number and structural variants, we show that GRCh38 offers overall more accurate analysis of human sequencing data. More importantly, GRCh38 produced fewer false positive structural variants. In conclusion, GRCh38 is an improvement over GRCh37 not only from the genome assembly aspect, but also yields more reliable genomic analysis results.
Keywords	genome ; genome assembly ; genomics ; high-throughput nucleotide sequencing ; humans ; single nucleotide polymorphism
Language	English
Dates of publication	2017-03
Size	p. 83-90.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	356334-0
ISSN	1089-8646 ; 0888-7543
ISSN (online)	1089-8646
ISSN	0888-7543
DOI	10.1016/j.ygeno.2017.01.005
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: MultiRankSeq

Yan Guo / Shilin Zhao / Fei Ye / Quanhu Sheng / Yu Shyr

BioMed Research International, Vol

Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control

2014 Volume 2014

Abstract: Background. After a decade of microarray technology dominating the field of high-throughput gene expression profiling, the introduction of RNAseq has revolutionized gene expression research. While RNAseq provides more abundant information than microarray, ...

Abstract	Background. After a decade of microarray technology dominating the field of high-throughput gene expression profiling, the introduction of RNAseq has revolutionized gene expression research. While RNAseq provides more abundant information than microarray, its analysis has proved considerably more complicated. To date, no consensus has been reached on the best approach for RNAseq-based differential expression analysis. Not surprisingly, different studies have drawn different conclusions as to the best approach to identify differentially expressed genes based upon their own criteria and scenarios considered. Furthermore, the lack of effective quality control may lead to misleading results interpretation and erroneous conclusions. To solve these aforementioned problems, we propose a simple yet safe and practical rank-sum approach for RNAseq-based differential gene expression analysis named MultiRankSeq. MultiRankSeq first performs quality control assessment. For data meeting the quality control criteria, MultiRankSeq compares the study groups using several of the most commonly applied analytical methods and combines their results to generate a new rank-sum interpretation. MultiRankSeq provides a unique analysis approach to RNAseq differential expression analysis. MultiRankSeq is written in R, and it is easily applicable. Detailed graphical and tabular analysis reports can be generated with a single command line.
Keywords	Medicine ; R
Language	English
Publishing date	2014-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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