Drawing enhances item information but undermines sequence information in memory.

Although correlations between personality and health are consistently observed, often the causal pathway, or even the direction of effect, is unknown. Genes provide an additional node of information which may be included to help clarify the relationship between personality and health. Genetically informative studies, whether focused on family-identified relationships or specific genotypes, provide clear benefits to disentangling causal processes. Genetic measures approach near universal reliability and validity: processes of inheritance are consistent across cultures, geography, and time, such that similar models and instruments may be applied to incredibly diverse populations. Although frequency and intercorrelations differ by ancestry background (Novembre et al., 2008) and cultural context (Tucker-Drob & Bates, 2016) may exert powerful moderating effects, fundamental form and function is consistent across all members of our species, and even many other species. Genetic sequence information is also of course highly temporally stable, and possesses temporal precedence. That is, the literal genetic sequence is lifetime-stable and comes before all other experiences. Human behavior genetic research, like most personality research, faces limitations in terms of causal inferences that may be made in the absence of experimental manipulation. But behavior genetics takes advantage of natural experiments: populations that differ in terms of genetic similarity (either inferred – such as twins – or measured – such as genotyping methods) to begin to unravel the complex influences on individual differences in personality and health outcomes.

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Faculty Opinions recommendation of Diversity arrays: a solid state technology for sequence information independent genotyping.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1000471.11353 ◽

2001 ◽

Author(s):

Peter Langridge

Keyword(s):

Solid State ◽

Sequence Information ◽

State Technology

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Faculty Opinions recommendation of A study of archaeal enzymes involved in polar lipid synthesis linking amino acid sequence information, genomic contexts and lipid composition.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1028632.342399 ◽

2005 ◽

Author(s):

Robert Michell

Keyword(s):

Amino Acid ◽

Amino Acid Sequence ◽

Polar Lipid ◽

Lipid Composition ◽

Lipid Synthesis ◽

Sequence Information ◽

Amino Acid Sequence Information

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Faculty Opinions recommendation of A new platform linking chromosomal and sequence information.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1087056.540024 ◽

2007 ◽

Author(s):

Pat Heslop-Harrison

Keyword(s):

Sequence Information

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AFLP-derived, Codominant Markers for Locus-specific Applications

HortScience ◽

10.21273/hortsci.33.3.514e ◽

1998 ◽

Vol 33 (3) ◽

pp. 514e-514

Author(s):

James M. Bradeen ◽

Philipp W. Simon

Keyword(s):

Linkage Mapping ◽

Large Scale ◽

Pcr Primers ◽

Inverse Pcr ◽

Sequence Information ◽

Pcr Assay ◽

Specific Primers ◽

Simultaneous Evaluation ◽

Feral Populations ◽

Diversity Assessment

The amplified fragment length polymorphism (AFLP) is a powerful marker, allowing rapid and simultaneous evaluation of multiple potentially polymorphic sites. Although well-adapted to linkage mapping and diversity assessment, AFLPs are primarily dominant in nature. Dominance, relatively high cost, and technological difficulty limit use of AFLPs for marker-aided selection and other locus-specific applications. In carrot the Y2 locus conditions carotene accumulation in the root xylem. We identified AFLP fragments linked to the dominant Y2 allele and pursued conversion of those fragments to codominant, PCR-based forms useful for locus-specific applications. The short length of AFLPs (≈60 to 500 bp) precludes development of longer, more specific primers as in SCAR development. Instead, using sequence information from cloned AFLP fragments for primer design, regions outside of the original fragment were amplified by inverse PCR or ligation-mediated PCR, cloned, and sequenced. Differences in sequences associated with Y2 vs. y2 allowed development of simple PCR assays differentiating those alleles. PCR primers flanking an insertion associated with the recessive allele amplified differently sized products for the two Y2 alleles in one assay. This assay is rapid, technologically simple (requiring no radioactivity and little advanced training or equipment), reliable, inexpensive, and codominant. Our PCR assay has a variety of large scale, locus-specific applications including genotyping diverse carrot cultivars and wild and feral populations. Efforts are underway to improve upon conversion technology and to more extensively test the techniques we have developed.

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A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

Protein and Peptide Letters ◽

10.2174/0929866526666191014144618 ◽

2020 ◽

Vol 27 (4) ◽

pp. 313-320 ◽

Cited By ~ 1

Author(s):

Xuan Xiao ◽

Wei-Jie Chen ◽

Wang-Ren Qiu

Keyword(s):

Gene Ontology ◽

Feature Extraction ◽

Extraction Method ◽

Quaternary Structure ◽

Structural Type ◽

Sequence Information ◽

Prediction System ◽

Data Set ◽

Feature Extraction Method ◽

Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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Acanthamoeba Keratitis: Current Status and Urgent Research Priorities

Current Medicinal Chemistry ◽

10.2174/0929867325666180510125633 ◽

2019 ◽

Vol 26 (30) ◽

pp. 5711-5726 ◽

Cited By ~ 4

Author(s):

Naveed Ahmed Khan ◽

Ayaz Anwar ◽

Ruqaiyyah Siddiqui

Keyword(s):

Rational Design ◽

Research Priorities ◽

Acanthamoeba Keratitis ◽

Current Treatment ◽

Research Literature ◽

Current Status ◽

Bibliographic Databases ◽

Sequence Information ◽

Therapeutic Measures ◽

Genome Sequence Information

Background:First discovered in the early 1970s, Acanthamoeba keratitis has remained a major eye infection and presents a significant threat to the public health, especially in developing countries. The aim is to present a timely review of our current understanding of the advances made in this field in a comprehensible manner and includes novel concepts and provides clear directions for immediate research priorities.Methods:We undertook a search of bibliographic databases for peer-reviewed research literature and also summarized our published results in this field.Results:The present review focuses on novel diagnostic and therapeutic strategies in details which can provide access to management and treatment of Acanthamoeba keratitis. This coupled with the recently available genome sequence information together with high throughput genomics technology and innovative approaches should stimulate interest in the rational design of preventative and therapeutic measures. Current treatment of Acanthamoeba keratitis is problematic and often leads to infection recurrence. Better understanding of diagnosis, pathogenesis, pathophysiology and therapeutic regimens, would lead to novel strategies in treatment and prophylaxis.

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pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset

Current Pharmaceutical Design ◽

10.2174/1381612824666181119145030 ◽

2019 ◽

Vol 24 (34) ◽

pp. 4013-4022 ◽

Cited By ~ 28

Author(s):

Xiang Cheng ◽

Xuan Xiao ◽

Kuo-Chen Chou

Keyword(s):

Subcellular Localization ◽

Basic Research ◽

Training Dataset ◽

Sequence Information ◽

Plant Proteins ◽

Protein Subcellular Localization ◽

Computational Tools ◽

Validation Tests ◽

User Friendly ◽

Better Than

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mPlant” was developed for identifying the subcellular localization of plant proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mPlant was trained by an extremely skewed dataset in which some subsets (i.e., the protein numbers for some subcellular locations) were more than 10 times larger than the others. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To overcome such biased consequence, we have developed a new and bias-free predictor called pLoc_bal-mPlant by balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mPlant, the existing state-of-the-art predictor in identifying the subcellular localization of plant proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mPlant/, by which users can easily get their desired results without the need to go through the detailed mathematics.

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pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

Medicinal Chemistry ◽

10.2174/1573406415666181218102517 ◽

2019 ◽

Vol 15 (5) ◽

pp. 472-485 ◽

Cited By ~ 21

Author(s):

Kuo-Chen Chou ◽

Xiang Cheng ◽

Xuan Xiao

Keyword(s):

Drug Development ◽

Subcellular Localization ◽

Basic Research ◽

The Other ◽

Training Dataset ◽

Sequence Information ◽

Eukaryotic Proteins ◽

Validation Tests ◽

User Friendly ◽

Better Than

Background/Objective: Information of protein subcellular localization is crucially important for both basic research and drug development. With the explosive growth of protein sequences discovered in the post-genomic age, it is highly demanded to develop powerful bioinformatics tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called “pLoc-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems where many proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mEuk was trained by an extremely skewed dataset where some subset was about 200 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. Methods: To alleviate such bias, we have developed a new predictor called pLoc_bal-mEuk by quasi-balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLocmEuk, the existing state-of-the-art predictor in identifying the subcellular localization of eukaryotic proteins. It has not escaped our notice that the quasi-balancing treatment can also be used to deal with many other biological systems. Results: To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/. Conclusion: It is anticipated that the pLoc_bal-Euk predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly for finding multi-target drugs that is currently a very hot trend trend in drug development.

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