bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of cryopreservation and long-term culture on biological characteristics and proteomic profiles of human umbilical cord-derived mesenchymal stem cells
Authors: Fu Xufeng , Xu Bo , Jiang Jiang , Du Xing , Yu Xiaoli , Yan Yaping , Li Shanshan , Inglis Briauna Marie , Ma Huiming , Wang Hongyan , Pei Xiuying , Si Wei 
Published: May 2020

AbstractBackgroundHuman umbilical cord-derived MSCs (hUC-MSCs) have been identified as promising seeding cells in tissue engineering and clinical applications of regenerative medicine due to their advantages of simple acquisition procedure and the capability to come from a young tissue donor over the other MSCs sources. In clinical applications, large scale production is required and optimal cryopreservation and culture conditions are essential to autologous and allogeneic transplantation in the future. However, the influence of cryopreserved post-thaw and long-term culture on hUC-MSCs remains unknown, especially in terms of specific protein expression. Therefore, biological characteristics and proteomic profiles of hUC-MSCs after cryopreserving and long-term culturing were investigated.MethodsFirstly, hUC-MSCs were isolated from human umbilical cord tissues and identified through morphology, surface markers and tri-lineage differentiation potential at passage 3, and then the biological characteristics and proteomic profiles were detected and compared after cryopreserving and long-term culturing at passage 4 and continuously cultured to passage 10 with detection occurring here as well. The proteomic profiles were tested by using the isobaric tags for relative and absolute quantification (iTRAQ) labeling technique and differential protein were confirmed by mass spectrometry.ResultsThe results showed no significant differences in phenotypes including morphology, surface marker and tri-lineage differentiation potential but have obvious changes in translation level, which is involved in metabolism, cell cycle and other pathways.ConclusionThis suggests that protein expression may be used as an indicator of hUC-MSCs security testing before applying in clinical settings, and it is also expected to provide the foundation or standardization guide of hUC-MSCs applications in regenerative medicine.
Identification of TUBB2A by quantitative proteomic analysis as a novel biomarker for the prediction of distant metastatic breast cancer
Authors: Shin Dongyoon , Park Joonho , Han Dohyun , Moon Ji Hye , Ryu Han Suk , Kim Youngsoo 
Published: May 2020

AbstractBackgroundMetastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates.MethodsAn integrated proteomics approach that combined filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data from FFPE distant metastatic breast cancer tissues. A bioinformatics analysis was performed with regard to gene ontology and signaling pathways using differentially expressed proteins (DEPs) to examine the molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and function of our protein targets.ResultsA total of 9441 and 8746 proteins were identified from the pooled and individual sample sets, respectively. Based on our criteria, TUBB2A was selected as a novel biomarker candidate. The metastatic activities of TUBB2A were subsequently validated. In our bioinformatics analysis using DEPs, we characterized the overall molecular features of distant metastasis and measured differences in the molecular functions of distant metastatic breast cancer between breast cancer subtypes.ConclusionsOur report is the first study to examine the distant metastatic breast cancer proteome using FFPE tissues. The depth of our dataset allowed us to discover a novel biomarker candidate and a proteomic characteristics of distant metastatic breast cancer. Distinct molecular features of various breast cancer subtypes were also established. Our proteomic data constitute a valuable resource for research on distant metastatic breast cancer.
Data-based RNA-seq simulations by binomial thinning
Authors: Gerard David 
Published: May 2020

AbstractBackgroundWith the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method’s performance.ResultsRather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets.ConclusionsUsing data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the R package on the Comprehensive R Archive Network:https://cran.r-project.org/package=seqgendiff.
PACVr: plastome assembly coverage visualization in R
Authors: Gruenstaeudl Michael , Jenke Nils 
Published: May 2020

Abstract
Background
Plastid genomes typically display a circular, quadripartite structure with two inverted repeat regions, which challenges automatic assembly procedures. The correct assembly of plastid genomes is a prerequisite for the validity of subsequent analyses on genome structure and evolution. The average coverage depth of a genome assembly is often used as an indicator of assembly quality. Visualizing coverage depth across a draft genome is a critical step, which allows users to inspect the quality of the assembly and, where applicable, identify regions of reduced assembly confidence. Despite the interplay between genome structure and assembly quality, no contemporary, user-friendly software tool can visualize the coverage depth of a plastid genome assembly while taking its quadripartite genome structure into account. A software tool is needed that fills this void.

Results
We introduce ’PACVr’, an R package that visualizes the coverage depth of a plastid genome assembly in relation to the circular, quadripartite structure of the genome as well as the individual plastome genes. By using a variable window approach, the tool allows visualizations on different calculation scales. It also confirms sequence equality of, as well as visualizes gene synteny between, the inverted repeat regions of the input genome. As a tool for plastid genomics, PACVr provides the functionality to identify regions of coverage depth above or below user-defined threshold values and helps to identify non-identical IR regions. To allow easy integration into bioinformatic workflows, PACVr can be invoked from a Unix shell, facilitating its use in automated quality control. We illustrate the application of PACVr on four empirical datasets and compare visualizations generated by PACVr with those of alternative software tools.

Conclusions
PACVr provides a user-friendly tool to visualize (a) the coverage depth of a plastid genome assembly on a circular, quadripartite plastome map and in relation to individual plastome genes, and (b) gene synteny across the inverted repeat regions. It contributes to optimizing plastid genome assemblies and increasing the reliability of publicly available plastome sequences. The software, example datasets, technical documentation, and a tutorial are available with the package at https://cran.r-project.org/package=PACVr.
A novel investigation of the effect of iterations in sliding semi-landmarks for 3D human facial images
Authors: Nazri Azree , Agbolade Olalekan , Yaakob Razali , Ghani Abdul Azim , Cheah Yoke Kqueen 
Published: May 2020

AbstractBackgroundLandmark-based approaches of two- or three-dimensional coordinates are the most widely used in geometric morphometrics (GM). As human face hosts the organs that act as the central interface for identification, more landmarks are needed to characterize biological shape variation. Because the use of few anatomical landmarks may not be sufficient for variability of some biological patterns and form, sliding semi-landmarks are required to quantify complex shape.ResultsThis study investigates the effect of iterations in sliding semi-landmarks and their results on the predictive ability in GM analyses of soft-tissue in 3D human face. Principal Component Analysis (PCA) is used for feature selection and the gender are predicted using Linear Discriminant Analysis (LDA) to test the effect of each relaxation state. The results show that the classification accuracy is affected by the number of iterations but not in progressive pattern. Also, there is stability at 12 relaxation state with highest accuracy of 96.43% and an unchanging decline after the 12 relaxation state.ConclusionsThe results indicate that there is a particular number of iteration or cycle where the sliding becomes optimally relaxed. This means the higher the number of iterations is not necessarily the higher the accuracy.
Substitution matrix based color schemes for sequence alignment visualization
Authors: Kunzmann Patrick , Mayer Benjamin E. , Hamacher Kay 
Published: May 2020

Abstract
Background
Visualization of multiple sequence alignments often includes colored symbols, usually characters encoding amino acids, according to some (physical) properties, such as hydrophobicity or charge. Typically, color schemes are created manually, so that equal or similar colors are assigned to amino acids that share similar properties. However, this assessment is subjective and may not represent the similarity of symbols very well.

Results
In this article we propose a different approach for color scheme creation: We leverage the similarity information of a substitution matrix to derive an appropriate color scheme. Similar colors are assigned to high scoring pairs of symbols, distant colors are assigned to low scoring pairs. In order to find these optimal points in color space a simulated annealing algorithm is employed.

Conclusions
Using the substitution matrix as basis for a color scheme is consistent with the alignment, which itself is based on the very substitution matrix. This approach allows fully automatic generation of new color schemes, even for special purposes which have not been covered, yet, including schemes for structural alphabets or schemes that are adapted for people with color vision deficiency.
RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp
Authors: Takizawa Hiroki , Iwakiri Junichi , Asai Kiyoshi 
Published: May 2020

Abstract
Background
Analysis of secondary structures is essential for understanding the functions of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distributions of their secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical errors.

Result
In this research, we reduced the computational complexity of calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with previous research on thermophilic bacteria, suggesting that our method is applicable in thermal stability analysis. Furthermore, we quantitatively assessed numerical stability using our method..

Conclusion
These results demonstrate that the proposed method is applicable to long RNAs..
VADR: validation and annotation of virus sequence submissions to GenBank
Authors: Schäffer Alejandro A. , Hatcher Eneida L. , Yankie Linda , Shonkwiler Lara , Brister J. Rodney , Karsch-Mizrachi Ilene , Nawrocki Eric P. 
Published: May 2020

Abstract
Background
GenBank contains over 3 million viral sequences. The National Center for Biotechnology Information (NCBI) previously made available a tool for validating and annotating influenza virus sequences that is used to check submissions to GenBank. Before this project, there was no analogous tool in use for non-influenza viral sequence submissions.

Results
We developed a system called VADR (Viral Annotation DefineR) that validates and annotates viral sequences in GenBank submissions. The annotation system is based on the analysis of the input nucleotide sequence using models built from curated RefSeqs. Hidden Markov models are used to classify sequences by determining the RefSeq they are most similar to, and feature annotation from the RefSeq is mapped based on a nucleotide alignment of the full sequence to a covariance model. Predicted proteins encoded by the sequence are validated with nucleotide-to-protein alignments using BLAST. The system identifies 43 types of “alerts” that (unlike the previous BLAST-based system) provide deterministic and rigorous feedback to researchers who submit sequences with unexpected characteristics. VADR has been integrated into GenBank’s submission processing pipeline allowing for viral submissions passing all tests to be accepted and annotated automatically, without the need for any human (GenBank indexer) intervention. Unlike the previous submission-checking system, VADR is freely available (https://github.com/nawrockie/vadr) for local installation and use. VADR has been used for Norovirus submissions since May 2018 and for Dengue virus submissions since January 2019. Since March 2020, VADR has also been used to check SARS-CoV-2 sequence submissions. Other viruses with high numbers of submissions will be added incrementally.

Conclusion
VADR improves the speed with which non-flu virus submissions to GenBank can be checked and improves the content and quality of the GenBank annotations. The availability and portability of the software allow researchers to run the GenBank checks prior to submitting their viral sequences, and thereby gain confidence that their submissions will be accepted immediately without the need to correspond with GenBank staff. Reciprocally, the adoption of VADR frees GenBank staff to spend more time on services other than checking routine viral sequence submissions.
Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA
Authors: Du Lei , Meng Qingfang , Chen Yuehui , Wu Peng 
Published: May 2020

AbstractBackgroundApoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins.ResultsIn this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively.ConclusionsThe experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.
Bio-semantic relation extraction with attention-based external knowledge reinforcement
Authors: Li Zhijing , Lian Yuchen , Ma Xiaoyong , Zhang Xiangrong , Li Chen 
Published: May 2020

AbstractBackgroundSemantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge.ResultsThe paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining.ConclusionThe experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.
Aligning functional network constraint to evolutionary outcomes
Authors: Wollenberg Valero Katharina C. 
Published: May 2020

AbstractBackgroundFunctional constraint through genomic architecture is suggested to be an important dimension of genome evolution, but quantitative evidence for this idea is rare. In this contribution, existing evidence and discussions on genomic architecture as constraint for convergent evolution, rapid adaptation, and genic adaptation are summarized into alternative, testable hypotheses. Network architecture statistics from protein-protein interaction networks are then used to calculate differences in evolutionary outcomes on the example of genomic evolution in yeast, and the results are used to evaluate statistical support for these longstanding hypotheses.ResultsA discriminant function analysis lent statistical support to classifying the yeast interactome into hub, intermediate and peripheral nodes based on network neighborhood connectivity, betweenness centrality, and average shortest path length. Quantitative support for the existence of genomic architecture as a mechanistic basis for evolutionary constraint is then revealed through utilizing these statistical parameters of the protein-protein interaction network in combination with estimators of protein evolution.ConclusionsAs functional genetic networks are becoming increasingly available, it will now be possible to evaluate functional genetic network constraint against variables describing complex phenotypes and environments, for better understanding of commonly observed deterministic patterns of evolution in non-model organisms. The hypothesis framework and methodological approach outlined herein may help to quantify the extrinsic versus intrinsic dimensions of evolutionary constraint, and result in a better understanding of how fast, effectively, or deterministically organisms adapt.
The features of polyglutamine regions depend on their evolutionary stability
Authors: Mier Pablo , Andrade-Navarro Miguel A. 
Published: May 2020

Abstract
Background
Polyglutamine regions (polyQ) are one of the most studied and prevalent homorepeats in eukaryotes. They have a particular length-dependent codon usage, which relates to a characteristic CAG-slippage mechanism. Pathologically expanded tracts of polyQ are known to form aggregates and are involved in the development of several human neurodegenerative diseases. The non-pathogenic function of polyQ is to mediate protein-protein interactions via a coiled-coil pairing with an interactor. They are usually located in a helical context.

Results
Here we study the stability of polyQ regions in evolution, using a set of 60 proteomes from four distinct taxonomic groups (Insecta, Teleostei, Sauria and Mammalia). The polyQ regions can be distinctly grouped in three categories based on their evolutionary stability: stable, unstable by length variation (inserted), and unstable by mutations (mutated). PolyQ regions in these categories can be significantly distinguished by their glutamine codon usage, and we show that the CAG-slippage mechanism is predominant in inserted polyQ of Sauria and Mammalia. The polyQ amino acid context is also influenced by the polyQ stability, with a higher proportion of proline residues around inserted polyQ. By studying the secondary structure of the sequences surrounding polyQ regions, we found that regarding the structural conformation around a polyQ, its stability category is more relevant than its taxonomic information. The protein-protein interaction capacity of a polyQ is also affected by its stability, as stable polyQ have more interactors than unstable polyQ.

Conclusions
Our results show that apart from the sequence of a polyQ, information about its orthologous sequences is needed to assess its function. Codon usage, amino acid context, structural conformation and the protein-protein interaction capacity of polyQ from all studied taxa critically depend on the region stability. There are however some taxa-specific polyQ features that override this importance. We conclude that a taxa-driven evolutionary analysis is of the highest importance for the comprehensive study of any feature of polyglutamine regions.