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Journal articles on the topic 'Bioinformatics analyses'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Chen, Yian A., Jonas S. Almeida, and Lien‐siang Chou. "Whale song analyses using bioinformatics sequence analysis approaches." Journal of the Acoustical Society of America 117, no. 4 (April 2005): 2470. http://dx.doi.org/10.1121/1.4787459.

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Esteban, David J., Melissa Da Silva, and Chris Upton. "New bioinformatics tools for viral genome analyses at Viral Bioinformatics – Canada." Pharmacogenomics 6, no. 3 (April 2005): 271–80. http://dx.doi.org/10.1517/14622416.6.3.271.

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3

Mshvidobadze, Tinatin. "Bioinformatics as Emerging Tool and Pipeline Frameworks." Science Progress and Research 1, no. 4 (October 23, 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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Zhou, Yi, Cheng Zhan, Yingyu Huang, and Hongchun Liu. "Comprehensive bioinformatics analyses of Crohn's disease." Molecular Medicine Reports 15, no. 4 (February 28, 2017): 2267–72. http://dx.doi.org/10.3892/mmr.2017.6250.

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Liu, Wei, Dong Li, YunPing Zhu, and FuChu He. "Bioinformatics analyses for signal transduction networks." Science in China Series C: Life Sciences 51, no. 11 (November 2008): 994–1002. http://dx.doi.org/10.1007/s11427-008-0134-5.

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6

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 (June 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 the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics .

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Fuchs, Maximilian, Fabian Philipp Kreutzer, Lorenz A. Kapsner, Saskia Mitzka, Annette Just, Filippo Perbellini, Cesare M. Terracciano, 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 (July 2, 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 without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research.

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Malkaram, Sridhar A., Yousef I. Hassan, and Janos Zempleni. "Online Tools for Bioinformatics Analyses in Nutrition Sciences." Advances in Nutrition 3, no. 5 (September 1, 2012): 654–65. http://dx.doi.org/10.3945/an.112.002477.

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Zhang, Feng, Xia Yang, and Zhijun Bao. "Bioinformatics network analyses of growth differentiation factor 11." Open Life Sciences 17, no. 1 (January 1, 2022): 426–37. http://dx.doi.org/10.1515/biol-2022-0044.

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Abstract Growth differentiation factor 11 (GDF11) has been implicated in rejuvenating functions in age-related diseases. The molecular mechanisms connecting GDF11 with these anti-aging phenomena, including reverse age-related cardiac hypertrophy and vascular and neurogenic rejuvenation, remain unclear. In this study, we sought to uncover the molecular functions of GDF11 using bioinformatics and network-driven analyses at the human gene and transcription levels using the gene co-expression network analysis, the protein–protein interaction network analysis, and the transcription factor network analysis. Our findings suggested that GDF11 is involved in a variety of functions, such as apoptosis, DNA repair, telomere maintenance, and interaction with key transcription factors, such as MYC proto-oncogene, specificity protein 1, and ETS proto-oncogene 2. The human skin fibroblast premature senescence model was established by UVB. The treatment with 10 ng/mL GDF11 in this cell model could reduce cell damage, reduce the apoptosis rate and the expression of caspase-3, and increase the length of telomeres. Therefore, our findings shed light on the functions of GDF11 and provide insights into the roles of GDF11 in aging.

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Penchovsky, Robert, Nikolet Pavlova, and Dimitrios Kaloudas. "ExBWS: extended bioinformatics web services for sequence analyses." International Journal of Bioinformatics Research and Applications 17, no. 4 (2021): 291. http://dx.doi.org/10.1504/ijbra.2021.117928.

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Peng, Fang, Xianquan Zhan, Mao-Yu Li, Fan Fang, Guoqing Li, Cui Li, Peng-Fei Zhang, and Zhuchu Chen. "Proteomic and Bioinformatics Analyses of Mouse Liver Microsomes." International Journal of Proteomics 2012 (March 20, 2012): 1–24. http://dx.doi.org/10.1155/2012/832569.

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Microsomes are derived mostly from endoplasmic reticulum and are an ideal target to investigate compound metabolism, membrane-bound enzyme functions, lipid-protein interactions, and drug-drug interactions. To better understand the molecular mechanisms of the liver and its diseases, mouse liver microsomes were isolated and enriched with differential centrifugation and sucrose gradient centrifugation, and microsome membrane proteins were further extracted from isolated microsomal fractions by the carbonate method. The enriched microsome proteins were arrayed with two-dimensional gel electrophoresis (2DE) and carbonate-extracted microsome membrane proteins with one-dimensional gel electrophoresis (1DE). A total of 183 2DE-arrayed proteins and 99 1DE-separated proteins were identified with tandem mass spectrometry. A total of 259 nonredundant microsomal proteins were obtained and represent the proteomic profile of mouse liver microsomes, including 62 definite microsome membrane proteins. The comprehensive bioinformatics analyses revealed the functional categories of those microsome proteins and provided clues into biological functions of the liver. The systematic analyses of the proteomic profile of mouse liver microsomes not only reveal essential, valuable information about the biological function of the liver, but they also provide important reference data to analyze liver disease-related microsome proteins for biomarker discovery and mechanism clarification of liver disease.

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Kaloudas, Dimitrios, Nikolet Pavlova, and Robert Penchovsky. "EBWS: Essential Bioinformatics Web Services for Sequence Analyses." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 3 (May 1, 2019): 942–53. http://dx.doi.org/10.1109/tcbb.2018.2816645.

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Penchovsky, Robert. "ExBWS: Extended Bioinformatics Web Services for Sequence Analyses." International Journal of Bioinformatics Research and Applications 17, no. 4 (2021): 1. http://dx.doi.org/10.1504/ijbra.2021.10024936.

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14

Janes, R. W. "Bioinformatics analyses of circular dichroism protein reference databases." Bioinformatics 21, no. 23 (September 27, 2005): 4230–38. http://dx.doi.org/10.1093/bioinformatics/bti690.

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15

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 (September 8, 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 covariates (variables) must be examined, and increasingly sophisticated statistical tests must be conducted, all by using bioinformatics tools. Knowing which analysis to conduct and which tools to apply remains confusing for bioaerosol scientists, as a litany of tools and data resources are now available for characterizing microbial communities. The goal of this review paper is to offer a guided tour through the bioinformatics tools that are useful in studying the microbial ecology of bioaerosols. This work explains microbial ecology features like alpha and beta diversity, multivariate analyses, differential abundances, taxonomic analyses, visualization tools and statistical tests using bioinformatics tools for bioaerosol scientists new to the field. It illustrates and promotes the use of selected bioinformatic tools in the study of bioaerosols and serves as a good source for learning the “dos and don’ts” involved in conducting a precise microbial ecology study.

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Zhang, K., and L. Jin. "HaploBlockFinder: haplotype block analyses." Bioinformatics 19, no. 10 (July 1, 2003): 1300–1301. http://dx.doi.org/10.1093/bioinformatics/btg142.

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17

Gruskin, Karen D., and Temple F. Smith. "Molecular genetics and computer analyses." Bioinformatics 3, no. 3 (1987): 167–70. http://dx.doi.org/10.1093/bioinformatics/3.3.167.

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18

Berger, S. I., and R. Iyengar. "Network analyses in systems pharmacology." Bioinformatics 25, no. 19 (July 30, 2009): 2466–72. http://dx.doi.org/10.1093/bioinformatics/btp465.

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Bayzid, Md Shamsuzzoha, and Tandy Warnow. "Naive binning improves phylogenomic analyses." Bioinformatics 29, no. 18 (July 9, 2013): 2277–84. http://dx.doi.org/10.1093/bioinformatics/btt394.

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Linke, Burkhard, Robert Giegerich, and Alexander Goesmann. "Conveyor: a workflow engine for bioinformatic analyses." Bioinformatics 27, no. 7 (January 28, 2011): 903–11. http://dx.doi.org/10.1093/bioinformatics/btr040.

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Mathé, Ewy, Ben Busby, and Helen Piontkivska. "Matchmaking in Bioinformatics." F1000Research 7 (February 9, 2018): 171. http://dx.doi.org/10.12688/f1000research.13705.1.

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Ever return from a meeting feeling elated by all those exciting talks, yet unsure how all those presented glamorous and/or exciting tools can be useful in your research? Or do you have a great piece of software you want to share, yet only a handful of people visited your poster? We have all been there, and that is why we organized the Matchmaking for Computational and Experimental Biologists Session at the latest ISCB/GLBIO’2017 meeting in Chicago (May 15-17, 2017). The session exemplifies a novel approach, mimicking “matchmaking”, to encouraging communication, making connections and fostering collaborations between computational and non-computational biologists. More specifically, the session facilitates face-to-face communication between researchers with similar or differing research interests, which we feel are critical for promoting productive discussions and collaborations. To accomplish this, three short scheduled talks were delivered, focusing on RNA-seq, integration of clinical and genomic data, and chromatin accessibility analyses. Next, small-table developer-led discussions, modeled after speed-dating, enabled each developer (including the speakers) to introduce a specific tool and to engage potential users or other developers around the table. Notably, we asked the audience whether any other tool developers would want to showcase their tool and we thus added four developers as moderators of these small-table discussions. Given the positive feedback from the tool developers, we feel that this type of session is an effective approach for promoting valuable scientific discussion, and is particularly helpful in the context of conferences where the number of participants and activities could hamper such interactions.

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Wang, ZhiCheng, MingRui Jiang, ZhuZhu Yue, XiaoTong Wei, ShuangHui Shi, MengLin Wang, HuiNan Wang, et al. "Key Genes of Chronic Pain Identified Through Bioinformatics Analyses." TMR Modern Herbal Medicine 5, no. 2 (2022): 7. http://dx.doi.org/10.53388/mhm2022a0213001.

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Wang, G., W. Zhao, Y. Yang, G. Yang, Z. Wei, and J. Guo. "Identification of Biomarkers of Venous Thromboembolism by Bioinformatics Analyses." Journal of Vascular Surgery: Venous and Lymphatic Disorders 6, no. 5 (September 2018): 674–75. http://dx.doi.org/10.1016/j.jvsv.2018.07.005.

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Cao, Manjing, Sha Wang, Jing Zou, and Wanpeng Wang. "Bioinformatics analyses of retinoblastoma reveal the retinoblastoma progression subtypes." PeerJ 8 (May 21, 2020): e8873. http://dx.doi.org/10.7717/peerj.8873.

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Introduction Retinoblastoma (RB) is one common pediatric malignant tumor with dismal outcomes. Heterogeneity of RB and subtypes of RB were identified but the association between the subtypes of RB and RB progression have not been fully investigated. Methods Four public datasets were downloaded from Gene expression omnibus and normalization was performed to remove batch effect. Two public datasets were explored to obtain the RB progression gene signatures by differentially expression analysis while another two datasets were iterated for RB subtypes identification using consensus clustering. After the RB progressive subtype gene signatures were identified, we tested the diagnostic capacity of these gene signatures by receiver operation curve. Results Three hundreds and forty six genes that were enriched in cell cycle were identified as the progression signature in RB from two independent datasets. Four subtypes of RB were stratified by consensus clustering. A total of 21 genes from RB progression signature were differentially expressed between RB subtypes. One subtype with low expression cell division genes have less progression of all four subtypes. A panel of five RB subtype genes (CLUL1, CNGB1, ROM1, LRRC39 and RDH12) predict progression of RB. Conclusion Retinoblastoma is a highly heterogeneous tumor and the level of cell cycle related gene expression is associated with RB progression. A subpopulation of RB with high expression of visual perception has less progressive features. LRRC39 is potentially the RB progression subtype biomarker.

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Wang, Guiming, Wenbo Zhao, Yudong Yang, Gaochao Yang, Zhigang Wei, and Jiansheng Guo. "Identification of biomarkers of venous thromboembolism by bioinformatics analyses." Medicine 97, no. 14 (April 2018): e0152. http://dx.doi.org/10.1097/md.0000000000010152.

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Amos, Beatrice, Cristina Aurrecoechea, Matthieu Barba, Ana Barreto, Evelina Y. Basenko, Wojciech Bażant, Robert Belnap, et al. "VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center." Nucleic Acids Research 50, no. D1 (October 28, 2021): D898—D911. http://dx.doi.org/10.1093/nar/gkab929.

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Abstract The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate >1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial & Viral BRC.

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Pedersen, Brent S., and Aaron R. Quinlan. "hts-nim: scripting high-performance genomic analyses." Bioinformatics 34, no. 19 (April 30, 2018): 3387–89. http://dx.doi.org/10.1093/bioinformatics/bty358.

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Abstract Motivation Extracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets. Results We present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance. Availability and implementation hts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT license.

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Schubert, Michael. "clustermq enables efficient parallelization of genomic analyses." Bioinformatics 35, no. 21 (May 27, 2019): 4493–95. http://dx.doi.org/10.1093/bioinformatics/btz284.

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Abstract Motivation High performance computing (HPC) clusters play a pivotal role in large-scale bioinformatics analysis and modeling. For the statistical computing language R, packages exist to enable a user to submit their analyses as jobs on HPC schedulers. However, these packages do not scale well to high numbers of tasks, and their processing overhead quickly becomes a prohibitive bottleneck. Results Here we present clustermq, an R package that can process analyses up to three orders of magnitude faster than previously published alternatives. We show this for investigating genomic associations of drug sensitivity in cancer cell lines, but it can be applied to any kind of parallelizable workflow. Availability and implementation The package is available on CRAN and https://github.com/mschubert/clustermq. Code for performance testing is available at https://github.com/mschubert/clustermq-performance. Supplementary information Supplementary data are available at Bioinformatics online.

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Demissie, Meaza, Barbara Mascialino, Stefano Calza, and Yudi Pawitan. "Unequal group variances in microarray data analyses." Bioinformatics 24, no. 9 (March 14, 2008): 1168–74. http://dx.doi.org/10.1093/bioinformatics/btn100.

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Groß, Anika, Michael Hartung, Kay Prüfer, Janet Kelso, and Erhard Rahm. "Impact of ontology evolution on functional analyses." Bioinformatics 28, no. 20 (September 6, 2012): 2671–77. http://dx.doi.org/10.1093/bioinformatics/bts498.

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31

Roberts, Adam, Lorian Schaeffer, and Lior Pachter. "Updating RNA-Seq analyses after re-annotation." Bioinformatics 29, no. 13 (May 14, 2013): 1631–37. http://dx.doi.org/10.1093/bioinformatics/btt197.

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Creevey, C. J., and J. O. McInerney. "Clann: investigating phylogenetic information through supertree analyses." Bioinformatics 21, no. 3 (September 16, 2004): 390–92. http://dx.doi.org/10.1093/bioinformatics/bti020.

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Lang, E. "Section 2: Patient Records: Integrating Bioinformatics into Clinical Practice: Progress and Evaluation." Yearbook of Medical Informatics 16, no. 01 (August 2007): 106–8. http://dx.doi.org/10.1055/s-0038-1638534.

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SummaryTo summarize current excellent research in the field of bioinformatics.Synopsis of the articles selected for the IMIA Yearbook 2007.Current research in the field of bioinformatics is characterized by careful evaluation of methods and by improved integration of methods into clinical workflows. Ongoing research on genetic causes of diseases is performed on more and better sources of reference data (genome sets and respective annotations), but is still hampered by insufficient, lacking or biased patient data. The application area of bioinformatics has been broadened, leading to amendment or even replacement of traditional methods in fields like characterization of microorganisms. Researchers carry out thorough statistical analyses in order to ensure quality and methodological correctness of new methods based on bioinformatic approaches which are more and more competitive compared to well-established techniques.The best paper selection of articles on bioinformatics shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of reliable and comprehensive data sources is affirmed, while technical development draws attention to the increasing problem of comparability of data derived some years ago with weaker equipment and those that are of up-to-date quality.

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Chen, Yang, En-Min Li, and Li-Yan Xu. "Guide to Metabolomics Analysis: A Bioinformatics Workflow." Metabolites 12, no. 4 (April 15, 2022): 357. http://dx.doi.org/10.3390/metabo12040357.

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Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach’s ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

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Memon, Abdulrezzak, and Nuriye Meraklı. "Comparative Structural Analysis of Heavy Metal ATPases in Arabidopsis thaliana, Arabidopsis halleri, Brassica rapa, and Brassica juncea." Turkish Journal of Agriculture - Food Science and Technology 10, sp2 (December 30, 2022): 2988–95. http://dx.doi.org/10.24925/turjaf.v10isp2.2988-2995.5692.

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Arabidopsis thaliana has eight genes encoding members of the type P1B heavy metal–transporting ATPase, subfamily of the P-type ATPases. We focused our study on four ATPases, mainly HMA1, HMA2, HMA3, and HMA4, which are closely related and most similar in their sequences. We carried out the bioinformatics analysis of these metal ATPases and obtained their structure in A. thaliana, A. halleri, and the other heavy metal accumulators in Brassica spp. A. thaliana is a model plant for research because of the duplications and other evolutionary events. These evolutionary events provided a chance to elucidate their regulation and function in the cell. All previous bioinformatics analyses have given some information about their structure, but not much work has been done on their structural components and interactome analysis. Experimental determination of 3D structures is essential to understand better these proteins’ function, which is crucial for the proper functioning of all plant cellular processes. Especially, docking sites and domains need to be worked out to understand the role of these transporter proteins and their interaction in plant cells. These bioinformatic analyses will help the researcher understand these ATPases’ role in detoxifying the toxic metals from the cells of accumulator plants. Further research on gene cloning, gene expression, and generating new accumulator plants for phytoremediation is needed to reclamation polluted soils from toxic heavy metals.

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Elworth, R. A. Leo, Chabrielle Allen, Travis Benedict, Peter Dulworth, and Luay Nakhleh. "ALPHA: a toolkit for Automated Local PHylogenomic Analyses." Bioinformatics 34, no. 16 (March 19, 2018): 2848–50. http://dx.doi.org/10.1093/bioinformatics/bty173.

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Petr, Martin, Benjamin Vernot, and Janet Kelso. "admixr—R package for reproducible analyses using ADMIXTOOLS." Bioinformatics 35, no. 17 (January 22, 2019): 3194–95. http://dx.doi.org/10.1093/bioinformatics/btz030.

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Abstract Summary We present a new R package admixr, which provides a convenient interface for performing reproducible population genetic analyses (f3, D, f4, f4-ratio, qpWave and qpAdm), as implemented by command-line programs in the ADMIXTOOLS software suite. In a traditional ADMIXTOOLS workflow, the user must first generate a set of text configuration files tailored to each individual analysis, often using a combination of shell scripting and manual text editing. The non-tabular output files then need to be parsed to extract values of interest prior to further analyses. Our package simplifies this process by automating all low-level configuration and parsing steps, making analyses as simple as running a single R command. Furthermore, we provide a set of R functions for processing, filtering and manipulating datasets in the EIGENSTRAT format. By unifying all steps of the workflow under a single R framework, this package enables the automation of analytic pipelines, significantly improving the reproducibility of population genetic studies. Availability and implementation The source code of the R package is available under the MIT license. Installation instructions, reference manual and a tutorial can be found on the package website at https://bioinf.eva.mpg.de/admixr. Supplementary information Supplementary data are available at Bioinformatics online.

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Bhasi, K., L. Zhang, D. Brazeau, A. Zhang, and M. Ramanathan. "VizStruct for visualization of genome-wide SNP analyses." Bioinformatics 22, no. 13 (April 13, 2006): 1569–76. http://dx.doi.org/10.1093/bioinformatics/btl144.

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Denis, Marie, and Mahlet G. Tadesse. "Evaluation of hierarchical models for integrative genomic analyses." Bioinformatics 32, no. 5 (November 5, 2015): 738–46. http://dx.doi.org/10.1093/bioinformatics/btv653.

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Sloggett, C., N. Goonasekera, and E. Afgan. "BioBlend: automating pipeline analyses within Galaxy and CloudMan." Bioinformatics 29, no. 13 (April 28, 2013): 1685–86. http://dx.doi.org/10.1093/bioinformatics/btt199.

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41

Dittrich, Marcus, Ingvild Birschmann, Christiane Stuhlfelder, Albert Sickmann, Sabine Herterich, Bernhard Nieswandt, Ulrich Walter, and Thomas Dandekar. "Understanding platelets." Thrombosis and Haemostasis 94, no. 11 (2005): 916–25. http://dx.doi.org/10.1160/th05-02-0121.

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SummaryNew large-scale analysis techniques such as bioinformatics, mass spectrometry and SAGE data analysis will allow a new framework for understanding platelets. This review analyses some important options and tasks for these tools and examines an outline of the new, refined picture of the platelet outlined by these new techniques. Looking at the platelet-specific building blocks of genome, (active) transcriptome and proteome (notably secretome and phospho-proteome), we summarize current bioinformatical and biochemical approaches, tasks as well as their limitations. Understanding the surprisingly complex platelet regarding compartmentalization, key cascades, and pathways including clinical implications will remain an exciting and hopefully fruitful challenge for the future.

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Oliver, Gavin R., Steven N. Hart, and Eric W. Klee. "Bioinformatics for Clinical Next Generation Sequencing." Clinical Chemistry 61, no. 1 (January 1, 2015): 124–35. http://dx.doi.org/10.1373/clinchem.2014.224360.

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Abstract BACKGROUND Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. CONTENT The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. SUMMARY Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.

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Morcillo-Suarez, C., J. Alegre, R. Sangros, E. Gazave, R. de Cid, R. Milne, J. Amigo, et al. "SNP analysis to results (SNPator): a web-based environment oriented to statistical genomics analyses upon SNP data." Bioinformatics 24, no. 14 (May 30, 2008): 1643–44. http://dx.doi.org/10.1093/bioinformatics/btn241.

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Zhang, Chuan, Mandy Berndt-Paetz, and Jochen Neuhaus. "Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer." Data 5, no. 2 (April 16, 2020): 38. http://dx.doi.org/10.3390/data5020038.

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Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.

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Gao, Yuan, Guo-Xian Qi, Zhi-Mei Jia, and Ying-Xian Sun. "Prediction of marker genes associated with hypertension by bioinformatics analyses." International Journal of Molecular Medicine 40, no. 1 (May 25, 2017): 137–45. http://dx.doi.org/10.3892/ijmm.2017.3000.

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Peri, Suraj, Nieves Ibarrola, Blagoy Blagoev, Matthias Mann, and Akhilesh Pandey. "Common pitfalls in bioinformatics-based analyses: look before you leap." Trends in Genetics 17, no. 9 (September 2001): 541–45. http://dx.doi.org/10.1016/s0168-9525(01)02443-x.

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Hu, Zhang-Zhi, Julio C. Valencia, Hongzhan Huang, An Chi, Jeffrey Shabanowitz, Vincent J. Hearing, Ettore Appella, and Cathy Wu. "Comparative bioinformatics analyses and profiling of lysosome-related organelle proteomes." International Journal of Mass Spectrometry 259, no. 1-3 (January 2007): 147–60. http://dx.doi.org/10.1016/j.ijms.2006.09.024.

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Huddleston, John, James Hadfield, Thomas Sibley, Jover Lee, Kairsten Fay, Misja Ilcisin, Elias Harkins, Trevor Bedford, Richard Neher, and Emma Hodcroft. "Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens." Journal of Open Source Software 6, no. 57 (January 7, 2021): 2906. http://dx.doi.org/10.21105/joss.02906.

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Courcelles, Mathieu, Sébastien Lemieux, Laure Voisin, Sylvain Meloche, and Pierre Thibault. "ProteoConnections: A bioinformatics platform to facilitate proteome and phosphoproteome analyses." PROTEOMICS 11, no. 13 (May 31, 2011): 2654–71. http://dx.doi.org/10.1002/pmic.201000776.

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Barbera, R., G. Andronico, G. Donvito, A. Falzone, J. J. Keijser, G. La Rocca, L. Milanesi, G. P. Maggi, and S. Vicario. "A grid portal with robot certificates for bioinformatics phylogenetic analyses." Concurrency and Computation: Practice and Experience 23, no. 3 (November 22, 2010): 246–55. http://dx.doi.org/10.1002/cpe.1682.

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