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Journal articles on the topic 'Bioinformatic analyses'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Mshvidobadze, Tinatin. "Bioinformatics as Emerging Tool and Pipeline Frameworks." Science Progress and Research 1, no. 4 (2021): 411–15. http://dx.doi.org/10.52152/spr/2021.162.

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In this article, we will discuss the areas of origin of bioinformatics in the human health care system. Due to the growing network of biological information databases such as human genomes, transcriptomics and proteomics, bioinformatics has become the approach of choosing forensic sciences. High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Here we survey and compare the design philosophies of several current pipeline frameworks.

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Grisham, William, Natalie A. Schottler, Joanne Valli-Marill, Lisa Beck, and Jackson Beatty. "Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools." CBE—Life Sciences Education 9, no. 2 (2010): 98–107. http://dx.doi.org/10.1187/cbe.09-11-0079.

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This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and th

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Sun, Huanhuan, Huasheng Wang, Hongfeng Zhan, et al. "Bioinformatic analyses and enzymatic properties of microcystinase." Algal Research 55 (May 2021): 102244. http://dx.doi.org/10.1016/j.algal.2021.102244.

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Chu, Ching Yan, Xin Xiao, Xiao Guang Zhou, et al. "Metabolomic and bioinformatic analyses in asphyxiated neonates." Clinical Biochemistry 39, no. 3 (2006): 203–9. http://dx.doi.org/10.1016/j.clinbiochem.2006.01.006.

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Linke, Burkhard, Robert Giegerich, and Alexander Goesmann. "Conveyor: a workflow engine for bioinformatic analyses." Bioinformatics 27, no. 7 (2011): 903–11. http://dx.doi.org/10.1093/bioinformatics/btr040.

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Meducator, The, Samantha Visva, John-Paul Oliveria, and Ruby Zheng. "Understanding regulatory B cell development with single cell analyses." Meducator 1, no. 35 (2019): 19–22. http://dx.doi.org/10.15173/m.v1i35.2206.

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There is very little research concerning human regulatory B cells and this may in part be due to their inconsistent responsesto immunosuppressive cytokines such as IL-10. The purpose of this critical review is to examine our current understandingof regulatory B cell development, such as time points of differentiation, and how in silico computer modelling can improvethis understanding. Specifically, bioinformatic analysis of the changes in cell surface markers and signalling moleculescan help guide our understanding of the timing of cell-fate decisions and regulatory B cell differentiation. Tra

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Jain, Vipul V., David L. Perkins, and Patricia W. Finn. "Costimulation and allergic responses: Immune and bioinformatic analyses." Pharmacology & Therapeutics 117, no. 3 (2008): 385–92. http://dx.doi.org/10.1016/j.pharmthera.2007.12.002.

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Stonestrom, Aaron, Ravi D. Barabote, Claudio F. Gonzalez, and Milton H. Saier. "Bioinformatic analyses of bacterial HPr kinase/phosphorylase homologues." Research in Microbiology 156, no. 3 (2005): 443–51. http://dx.doi.org/10.1016/j.resmic.2004.10.010.

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Mok, Timothy, Jonathan S. Chen, Maksim A. Shlykov, and Milton H. Saier. "Bioinformatic Analyses of Bacterial Mercury Ion (Hg2+) Transporters." Water, Air, & Soil Pollution 223, no. 7 (2012): 4443–57. http://dx.doi.org/10.1007/s11270-012-1208-3.

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Zhao, Yilu, Kang Ge, Yan Cheng, and Ru-zhi Zhang. "Bioinformatic Analysis of Genes Associated with Autophagy in Vitiligo." Indian Journal of Dermatology 69, no. 2 (2024): 123–31. http://dx.doi.org/10.4103/ijd.ijd_655_23.

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Background: As vitiligo progresses, autophagy becomes more and more important. Objectives: To validate potential genes associated with autophagy in vitiligo through bioinformatics analysis and experimental testing. Materials and Methods: Dataset GSE75819 of mRNA expression profiles was obtained from GEO. After data normalisation, gene set enrichment analyse enrichment analysis and abundance analysis of infiltrating immune cells were performed. A list of autophagy-related differentially expressed genes (ARDEGs) associated with vitiligo was generated using R software. Protein–protein interaction

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LEUNG, Anthony K. L., Jens S. ANDERSEN, Matthias MANN, and Angus I. LAMOND. "Bioinformatic analysis of the nucleolus." Biochemical Journal 376, no. 3 (2003): 553–69. http://dx.doi.org/10.1042/bj20031169.

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The nucleolus is a plurifunctional, nuclear organelle, which is responsible for ribosome biogenesis and many other functions in eukaryotes, including RNA processing, viral replication and tumour suppression. Our knowledge of the human nucleolar proteome has been expanded dramatically by the two recent MS studies on isolated nucleoli from HeLa cells [Andersen, Lyon, Fox, Leung, Lam, Steen, Mann and Lamond (2002) Curr. Biol. 12, 1–11; Scherl, Coute, Deon, Calle, Kindbeiter, Sanchez, Greco, Hochstrasser and Diaz (2002) Mol. Biol. Cell 13, 4100–4109]. Nearly 400 proteins were identified within the

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Yan, Qing. "Bioinformatics Databases and Tools in Virology Research: An Overview." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 8, no. 2 (2008): 71–85. https://doi.org/10.3233/isb-00345.

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Viruses are major factors of human infectious diseases. Understanding of the structure-function correlation in viruses is important for the identification of potential anti-viral inhibitors and vaccine targets. In virology research, virus-related databases and bioinformatic analysis tools are essential for discerning relationships within complex datasets about viruses and host-virus interactions. Bioinformatic analyses on viruses include the identification of open reading frames, gene prediction, homology searching, sequence alignment, and motif and epitope recognition. The predictions of feat

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LEONARD WHYE KIT LIM and HUNG HUI CHUNG. "Analysis of Seven Human Respiratory Coronavirus (CoV) S Proteins from a Bioinformatics Approach." Borneo Journal of Resource Science and Technology 13, no. 2 (2023): 103–10. http://dx.doi.org/10.33736/bjrst.5853.2023.

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The coronavirus disease 2019 (COVID-19) has caused a huge pandemic repercussion across the globe and it is mainly contributed by the human severe acute respiratory syndrome coronavirus (SARS-CoV-2). There are seven human respiratory coronaviruses identified to date, namely HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, MERS-CoV, SARS-CoV and SARS-CoV-2. A recently published bioinformatic human CoV comparison only covered four human CoV. Therefore, in this study, a bioinformatics approach-based analyses route was taken to dissect the S proteins of all the available (seven) human respiratory corona

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Zhu, Junzhen. "The bioinformatic analysis of CFTR in essential hypertension." Theoretical and Natural Science 29, no. 1 (2024): 92–100. http://dx.doi.org/10.54254/2753-8818/29/20240753.

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The CFTR gene is associated with cystic fibrosis, a genetic disease primarily affecting the respiratory and digestive systems. Essential hypertension is a common cardiovascular disorder characterized by high blood pressure. In this bioinformatic analysis, various databases and tools were used to investigate the potential mechanisms underlying this association. Gene expression data and epigenetic analyses were used to identify the biological processes between CFTR and hypertension. Additionally, genetic variants within CFTR were analysed for potential effects on hypertension susceptibility. The

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Andrade, Jorge, Lisa Berglund, Mathias Uhlén, and Jacob Odeberg. "Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 6, no. 6 (2006): 495–504. https://doi.org/10.3233/isb-00261.

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For several applications and algorithms used in applied bioinformatics, a bottle neck in terms of computational time may arise when scaled up to facilitate analyses of large datasets and databases. Re-codification, algorithm modification or sacrifices in sensitivity and accuracy may be necessary to accommodate for limited computational capacity of single work stations. Grid computing offers an alternative model for solving massive computational problems by parallel execution of existing algorithms and software implementations. We present the implementation of a Grid-aware model for solving com

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Ruskovska, Tatjana, Irena Budić-Leto, Karla Fabiola Corral-Jara, et al. "Systematic Bioinformatic Analyses of Nutrigenomic Modifications by Polyphenols Associated with Cardiometabolic Health in Humans—Evidence from Targeted Nutrigenomic Studies." Nutrients 13, no. 7 (2021): 2326. http://dx.doi.org/10.3390/nu13072326.

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Cardiometabolic disorders are among the leading causes of mortality in the human population. Dietary polyphenols exert beneficial effects on cardiometabolic health in humans. Molecular mechanisms, however, are not completely understood. Aiming to conduct in-depth integrative bioinformatic analyses to elucidate molecular mechanisms underlying the protective effects of polyphenols on cardiometabolic health, we first conducted a systematic literature search to identify human intervention studies with polyphenols that demonstrate improvement of cardiometabolic risk factors in parallel with signifi

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Zhuang, Qi-Shuai, Lei Meng, Zhe Wang, Liang Shen, and Hong-Fang Ji. "Associations Between Obesity and Alzheimer’s Disease: Multiple Bioinformatic Analyses." Journal of Alzheimer's Disease 80, no. 1 (2021): 271–81. http://dx.doi.org/10.3233/jad-201235.

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Background: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer’s disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. Objective: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. Methods: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PP

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Liu, Yao-Yuan, and SallyAnn Harbison. "A review of bioinformatic methods for forensic DNA analyses." Forensic Science International: Genetics 33 (March 2018): 117–28. http://dx.doi.org/10.1016/j.fsigen.2017.12.005.

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Grahame, Douglas S. A., John H. Dupuis, Brian C. Bryksa, Takuji Tanaka, and Rickey Y. Yada. "Comparative bioinformatic and structural analyses of pepsin and renin." Enzyme and Microbial Technology 141 (November 2020): 109632. http://dx.doi.org/10.1016/j.enzmictec.2020.109632.

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Mróz, Jakub, Magdalena Pelc, Karolina Mitusińska, Joanna Chorostowska-Wynimko, and Aleksandra Jezela-Stanek. "Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)." Genes 15, no. 3 (2024): 340. http://dx.doi.org/10.3390/genes15030340.

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In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of

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Chae, H., M. Kim, and E. Lee. "P014 Bioinformatic analysis of gene expression profiling data on shift work sleep disorder." Sleep Advances 5, Supplement_1 (2024): A34. https://doi.org/10.1093/sleepadvances/zpae070.096.

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Abstract Introduction Shift work sleep disorder (SWSD) is a circadian rhythm sleep disorder affecting individuals who work nontraditional hours, such as night shifts. The complexity of SWSD arises from the altered interactions between thousands of genes, making it challenging to prioritize disease mechanism, biomarkers, and drug targets. This study aims to identify the patterns of pathways and upstream regulators involved in SWSD using a transcriptome from isolated peripheral blood mononuclear cells (PBMCs) of hospital nurses. Method We conducted bioinformatic analyses on the GSE122541 dataset

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Fuchs, Maximilian, Fabian Philipp Kreutzer, Lorenz A. Kapsner, et al. "Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies." International Journal of Molecular Sciences 21, no. 13 (2020): 4727. http://dx.doi.org/10.3390/ijms21134727.

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Integrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis

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Ben Ali, Ferid, Denis Mustafov, Maria Braoudaki, Sola Adeleke, and Iosif Mporas. "Identification of a New Lung Cancer Biomarker Signature Using Data Mining and Preliminary In Vitro Validation." BioMedInformatics 5, no. 2 (2025): 32. https://doi.org/10.3390/biomedinformatics5020032.

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Background: Lung adenocarcinoma is one of the major subtype of non-Small Cell Lung Cancer and biomarkers are essential to be identified for early diagnosis. The study aims to find in silico and preliminary in vitro analysis of potential biomarkers for lung adenocarcinoma. Methods: Bioinformatics analysis in parallel to data mining analysis was performed on microarray data with lung adenocarcinoma samples to identify potent gene biomarkers associated with lung cancer type. Afterwards, these genes were then validated in vitro using RT-qPCR analysis in cancerous (Calu-3) and non-cancerous (MRC-5)

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Chen, Ting, Haiying Wu, Chenxi Zhang, et al. "Clinical, Genetics, and Bioinformatic Characterization of Mutations Affecting an Essential Region of PLS3 in Patients with BMND18." International Journal of Endocrinology 2018 (October 14, 2018): 1–9. http://dx.doi.org/10.1155/2018/8953217.

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Background. Bone mineral density quantitative trait locus 18 (BMND18, OMIM #300910) is a type of early-onset osteogenesis imperfecta (OI) caused by loss-of-function mutations in the PLS3 gene, which encodes plastin-3, a key protein in the formation of actin bundles throughout the cytoskeleton. Here, we report a patient with PLS3 mutation caused BMND18 and evaluated all the reported disease-causing mutations by bioinformatic analysis. Methods. Targeted gene sequencing was performed to find the disease-causing mutation in our patient. Bioinformatic analyses mainly including homology modelling an

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Adenaike, A. S., S. O. Peters, A. O. Fafiolu, R. A. Lawal, M. Wheto, and C. O. N. Ikeobi. "Bioinformatic analyses of kappa casein gene in mammalian livestock species." Nigerian Journal of Animal Production 40, no. 2 (2020): 7–12. http://dx.doi.org/10.51791/njap.v40i2.1126.

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Kappa casein (CSN3) gene is a variant of the milk protein highly conserved in mammalian species. Genetic variations in CSN3 gene of six mammalian livestock species were investigated using bioinformatics approach. A total of twenty-seven CSN3 gene sequences with corresponding amino acids belonging to the six species studied were retrieved from GenBank. Using a comparative genomic approach, we obtained 1797 bp of the CSN3 sequences from cattle, goat, horse, pig, rabbit and sheep. Alignment of twenty-seven sequences within the region of 1713 bp and containing gaps was carried out using Clustal W.

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Bonavida, Benjamin, Silvia Vivarelli, Luca Falzone, and Massimo Libra. "Immunosuppressive Mechanisms of Tumor-Associated Macrophages: Bioinformatic Analyses and Targeting." Onco Therapeutics 8, no. 1 (2021): 27–46. http://dx.doi.org/10.1615/forumimmundisther.2021041300.

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VanDrisse, Chelsey M., Kristy L. Hentchel, and Jorge C. Escalante-Semerena. "Phosphinothricin Acetyltransferases Identified UsingIn Vivo,In Vitro, and Bioinformatic Analyses." Applied and Environmental Microbiology 82, no. 24 (2016): 7041–51. http://dx.doi.org/10.1128/aem.02604-16.

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ABSTRACTAcetylation of small molecules is widespread in nature, and in some cases, cells use this process to detoxify harmful chemicals.Streptomycesspecies utilize aGcn5N-acetyltransferase (GNAT), known as Bar, to acetylate and detoxify a self-produced toxin,phosphinothricin (PPT), a glutamate analogue. Bar homologues, such as MddA fromSalmonella enterica, acetylate methionine analogues such as methionine sulfoximine (MSX) and methionine sulfone (MSO), but not PPT, even though Bar homologues are annotated as PPT acetyltransferases.S. entericawas used as a heterologous host to determine whether

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Arancio, Walter, Giuseppe Pizzolanti, Swonild Ilenia Genovese, Concetta Baiamonte, and Carla Giordano. "Competing Endogenous RNA and Interactome Bioinformatic Analyses on Human Telomerase." Rejuvenation Research 17, no. 2 (2014): 161–67. http://dx.doi.org/10.1089/rej.2013.1486.

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Norton, Carter, Chad Pollard, Kelaney Stalker, Kenneth Aston, and Timothy Jenkins. "Novel bioinformatic analyses of somatic cell contamination in sperm samples." Systems Biology in Reproductive Medicine 70, no. 1 (2024): 174–82. http://dx.doi.org/10.1080/19396368.2024.2368716.

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Kamer Çolak, Dilara, Ufuk Ünal, and Sema Bolkent. "In silico Evaluation of WWC1 in Melanoma Using Bioinformatic Analyses." European Journal of Biology 81, no. 2 (2022): 257–66. http://dx.doi.org/10.26650/eurjbiol.2022.1168881.

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Klahan, Sukhontip, Chi-Cheng Huang, Shu-Chen Chien, et al. "Bioinformatic analyses revealed underlying biological functions correlated with oxaliplatin responsiveness." Tumor Biology 37, no. 1 (2015): 583–90. http://dx.doi.org/10.1007/s13277-015-3807-2.

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Troshin, Konstantin, Nina Sykilinda, Sofia Shuraleva, et al. "Pseudomonas Phage Lydia and the Evolution of the Mesyanzhinovviridae Family." Viruses 17, no. 3 (2025): 369. https://doi.org/10.3390/v17030369.

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Phage Lydia, a newly isolated siphovirus infecting Pseudomonas aeruginosa, was characterized with respect to its basic kinetic properties and subjected to comparative bioinformatic analysis with related phages. The phage exhibited a restricted host range, with lytic activity observed against 7 of 30 tested isolates. The genome of phage Lydia consists of a 61,986 bp dsDNA molecule and contains 89 predicted genes. Bioinformatic analysis suggests the presence of a DNA modification system, but no apparent genes associated with lysogeny or antibiotic resistance were identified. Taxonomic classifica

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Zhuang, Hongqin, Ziyi Gan, Weiwei Jiang, Xiangyu Zhang, and Zi-Chun Hua. "Functional specific roles of FADD: comparative proteomic analyses from knockout cell lines." Molecular BioSystems 9, no. 8 (2013): 2063–78. http://dx.doi.org/10.1039/c3mb70023b.

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Lyu, Tianyi, Haonan Qian, and Sung-Pil Chung. "Impact of Physical Activity, Sedentary Behavior, and Basal Metabolic Rate on PTSD, Depression, and Emotional Instability." Brain Sciences 14, no. 11 (2024): 1071. http://dx.doi.org/10.3390/brainsci14111071.

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Objective: This study aimed to investigate the potential associations between physical activity, sedentary behavior, and the basal metabolic rate (BMR) with post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and emotional instability (EI) using bidirectional Mendelian randomization (MR). Additionally, it sought to identify key molecular mechanisms underlying emotional instability through a comprehensive bioinformatic analysis. Methods: MR analyses utilizing genome-wide association study (GWAS) data were conducted to estimate the effects of physical activity, sedentary beha

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dos Santos, Romulo A., and Petr Blabolil. "Comparison of Bioinformatic Pipelines for eDNA Metabarcoding Data Analysis of Fish Populations." Fishes 10, no. 5 (2025): 214. https://doi.org/10.3390/fishes10050214.

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Environmental DNA (eDNA) metabarcoding has gained popularity as a biomonitoring tool, leading to the emergence of various bioinformatic pipelines. However, comparisons are essential to assessing the reliability and similarity of results. In this study, we compared five bioinformatic pipelines (Anacapa, Barque, metaBEAT, MiFish, and SEQme) using samples collected from three reservoirs in the Czech Republic during the autumn and summer seasons. Negative and positive controls were used to monitor potential contamination during sample processing. eDNA was amplified, targeting the 12S fish rRNA gen

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Li, P. Jonathan, Jeroen P. Roose, David M. Jablons, and Johannes R. Kratz. "Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models." Cancers 13, no. 4 (2021): 701. http://dx.doi.org/10.3390/cancers13040701.

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3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these mod

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Novitasari, Dhania, Laeli Muntafiah, Nur Fitra Sari, Edy Meiyanto, and Adam Hermawan. "Ethanolic extract of sappan wood (Caesalpinia sappan L.) inhibits MCF-7 and MCF-7/HER2 mammospheres' formation: an in vitro and bioinformatic study." Indonesian Journal of Biotechnology 26, no. 3 (2021): 133. http://dx.doi.org/10.22146/ijbiotech.63510.

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One of the mechanisms of cancer cell resistance toward chemotherapy is through cancer stem cells (CSCs), which are characterized by excessive activation of regulator proteins such as human epidermal receptor 2 (HER2). Sappan wood (Caesalpinia sappan L.) contains brazilin and brazilein that exhibit cytotoxic effects on several cancer cell lines. We aimed to explore the potency of the ethanolic extract of sappan (EES) in CSCs through bioinformatic analyses and by using a three-dimensional (3D) breast cancer stem cells (BCSCs) for in vitro assay with two different models (i.e., BCSCs and HER2-BCS

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Gaafar, Yahya Z. A., Marcel Westenberg, Marleen Botermans, et al. "Interlaboratory Comparison Study on Ribodepleted Total RNA High-Throughput Sequencing for Plant Virus Diagnostics and Bioinformatic Competence." Pathogens 10, no. 9 (2021): 1174. http://dx.doi.org/10.3390/pathogens10091174.

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High-throughput sequencing (HTS) technologies and bioinformatic analyses are of growing interest to be used as a routine diagnostic tool in the field of plant viruses. The reliability of HTS workflows from sample preparation to data analysis and results interpretation for plant virus detection and identification must be evaluated (verified and validated) to approve this tool for diagnostics. Many different extraction methods, library preparation protocols, and sequence and bioinformatic pipelines are available for virus sequence detection. To assess the performance of plant virology diagnostic

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Yoshioka, Hiroki, Yin-Ying Wang, Akiko Suzuki, et al. "Overexpression of miR-1306-5p, miR-3195, and miR-3914 Inhibits Ameloblast Differentiation through Suppression of Genes Associated with Human Amelogenesis Imperfecta." International Journal of Molecular Sciences 22, no. 4 (2021): 2202. http://dx.doi.org/10.3390/ijms22042202.

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Amelogenesis imperfecta is a congenital form of enamel hypoplasia. Although a number of genetic mutations have been reported in humans, the regulatory network of these genes remains mostly unclear. To identify signatures of biological pathways in amelogenesis imperfecta, we conducted bioinformatic analyses on genes associated with the condition in humans. Through an extensive search of the main biomedical databases, we found 56 genes in which mutations and/or association/linkage were reported in individuals with amelogenesis imperfecta. These candidate genes were further grouped by function, p

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Storf, Stefanie, Friedhelm Pfeiffer, Kieran Dilks, Zhong Qiang Chen, Saheed Imam, and Mechthild Pohlschröder. "Mutational and Bioinformatic Analysis of Haloarchaeal Lipobox-Containing Proteins." Archaea 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/410975.

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A conserved lipid-modified cysteine found in a protein motif commonly referred to as a lipobox mediates the membrane anchoring of a subset of proteins transported across the bacterial cytoplasmic membrane via the Sec pathway. Sequenced haloarchaeal genomes encode many putative lipoproteins and recent studies have confirmed the importance of the conserved lipobox cysteine for signal peptide processing of three lipobox-containing proteins in the model archaeonHaloferax volcanii. We have extended thesein vivoanalyses to additionalHfx. volcaniisubstrates, supporting our previousin silicoprediction

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Chen, Weining, Nicholas Woods, Fangfang Qiao, et al. "Abstract 2028: Using bioinformatic analysis of proteomic data to probe novel drug mechanism of action." Cancer Research 83, no. 7_Supplement (2023): 2028. http://dx.doi.org/10.1158/1538-7445.am2023-2028.

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Abstract Introduction: Metastasis is a major cause of death for patients with solid tumors. Our group has discovered a selective inhibitor of cell motility, KBU2046, that blocks the activation of Raf1 by inhibiting the phosphorylation of ser338 on its activation motif (Nature Communications 2018). To better understand the effect of KBU2046 on human prostate cancer (PCa) cells, we undertook a proteomic analysis. Experimental Procedures: After treatment of PC3 cells with KBU2046 or vehicle (N=3/group), protein expression of whole cell lysate was quantified by Liquid Chromatography/Tandem Mass Sp

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Chen, Luting, Yuchang Fei, Yurong Zhao, Quan Chen, Peifeng Chen, and Lei Pan. "Expression and prognostic analyses of HDACs in human gastric cancer based on bioinformatic analysis." Medicine 100, no. 27 (2021): e26554. http://dx.doi.org/10.1097/md.0000000000026554.

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Zhang, Lingang, Bo Li, Jing Liu, Yan feng Bian, Guo xing Lin, and Ying Zhou. "Unveiling hub genes and biological pathways: A bioinformatics analysis of Trauma-Induced Coagulopathy (TIC)." PLOS One 20, no. 4 (2025): e0322043. https://doi.org/10.1371/journal.pone.0322043.

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Background Trauma-Induced Coagulopathy is a severe condition that rapidly manifests following traumatic injury and is characterized by shock, hypoperfusion, and vascular damage. This study employed bioinformatics methods to identify crucial hub genes and pathways associated with TIC. Methods Microarray datasets (accession number GSE223245) were obtained from the Gene Expression Omnibus (GEO) database. The data were subjected analyses to identify the Differentially Expressed Genes (DEGs), which were further subjected to GO and KEGG pathway analyses. Subsequently, a Protein-Protein Interaction (

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Luo, Jiao, Dongdong Li, Lili Jiang, Chunhua Shi, and Lihua Duan. "Identification of Tregs-Related Genes with Molecular Patterns in Patients with Systemic Sclerosis Related to ILD." Biomolecules 13, no. 3 (2023): 535. http://dx.doi.org/10.3390/biom13030535.

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Background: Systemic Sclerosis (SSc) is an autoimmune disease that is characterized by vasculopathy, digital ulcers, Raynaud’s phenomenon, renal failure, pulmonary arterial hypertension, and fibrosis. Regulatory T (Treg) cell subsets have recently been found to play crucial roles in SSc with interstitial lung disease (ILD) pathogenesis. This study investigates the molecular mechanism of Treg-related genes in SSc patients through bioinformatic analyses. Methods: The GSE181228 dataset of SSc was used in this study. CIBERSORT was used for assessing the category and proportions of immune cells in

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Mbareche, Hamza, Nathan Dumont-Leblond, Guillaume J. Bilodeau, and Caroline Duchaine. "An Overview of Bioinformatics Tools for DNA Meta-Barcoding Analysis of Microbial Communities of Bioaerosols: Digest for Microbiologists." Life 10, no. 9 (2020): 185. http://dx.doi.org/10.3390/life10090185.

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High-throughput DNA sequencing (HTS) has changed our understanding of the microbial composition present in a wide range of environments. Applying HTS methods to air samples from different environments allows the identification and quantification (relative abundance) of the microorganisms present and gives a better understanding of human exposure to indoor and outdoor bioaerosols. To make full use of the avalanche of information made available by these sequences, repeated measurements must be taken, community composition described, error estimates made, correlations of microbiota with covariate

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Hu, Kuang-Yu, and Milton Saier. "Bioinformatic Analyses of Gram-Negative Bacterial OstA Outer Membrane Assembly Homologues." Current Genomics 7, no. 7 (2006): 447–61. http://dx.doi.org/10.2174/138920206779116765.

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Hvorup, Rikki, Abraham B. Chang, and Milton H. Saier Jr. "Bioinformatic Analyses of the Bacterial L-Ascorbate Phosphotransferase System Permease Family." Journal of Molecular Microbiology and Biotechnology 6, no. 3-4 (2003): 191–205. http://dx.doi.org/10.1159/000077250.

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Kareta, Michael S. "Bioinformatic and Genomic Analyses of Cellular Reprogramming and Direct Lineage Conversion." Current Pharmacology Reports 2, no. 3 (2016): 103–12. http://dx.doi.org/10.1007/s40495-016-0054-1.

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Romanova, Elena V., Yurij S. Bukin, and Dmitry Yu Sherbakov. "Bioinformatic tools for tRNA gene analyses in mitochondrial DNA sequence data." Data in Brief 29 (April 2020): 105284. http://dx.doi.org/10.1016/j.dib.2020.105284.

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O'Rourke, Devon R., Nicholas A. Bokulich, Michelle A. Jusino, Matthew D. MacManes, and Jeffrey T. Foster. "A total crapshoot? Evaluating bioinformatic decisions in animal diet metabarcoding analyses." Ecology and Evolution 10, no. 18 (2020): 9721–39. http://dx.doi.org/10.1002/ece3.6594.

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