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Journal articles on the topic 'Statistics|Biology, Bioinformatics'

Author: Grafiati

Published: 9 July 2021

Last updated: 14 February 2022

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1

Rajpal, Deepak K. "Understanding Biology Through Bioinformatics." International Journal of Toxicology 24, no. 3 (2005): 147–52. http://dx.doi.org/10.1080/10915810590948325.

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During the journey from the discovery of DNA to be the source of genetic information and elucidation of double-helical nature of DNA molecule to the assembly of human genome sequence and there after, bioinformatics has become an integral part of modern biology. Bioinformatics relies substantially on significant contributions made by scientists in various fields, including but not limited to, linguistics, biology, mathematics, computer science, and statistics. There is an ever increasing amount of data to elucidate toxic mechanisms and/or adverse effects of xenobiotics in the field of toxicogenomics. Annotation in combination with various bioinformatics analytical tools can play a crucial role in the understanding of genes and proteins, and can potentially help draw meaningful conclusions from various data sources. This article attempts to present a simple overview of bioinformatics, and an effort is made to discuss annotation.

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Chen, Yi-Ping Phoebe, and Geoff McLachlan. "Bioinformatics Research in Australia." Asia-Pacific Biotech News 07, no. 03 (2003): 82–84. http://dx.doi.org/10.1142/s0219030303000211.

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Bioinformatics is the intersection of computer science, statistics, molecular biology and genetics. It is one of the most important emerging research areas of the 21st century and has already attracted worldwide interest. It is clear that major initiatives are being undertaken which will establish Australia both as a vital link in the international bioinformatics community for research and development and also as an Asia-Pacific service for bioinformatics. This article briefly notes some groups carrying out bioinformatics research in Australia.

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Perrière, Guy, and Jean Thioulouse. "On-line tools for sequence retrieval and multivariate statistics in molecular biology." Bioinformatics 12, no. 1 (1996): 63–69. http://dx.doi.org/10.1093/bioinformatics/12.1.63.

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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 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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Duan, Yibing. "Present Situation and Forecast of Bioinformatics in the Field of New Medicine Research and Development." E3S Web of Conferences 213 (2020): 03027. http://dx.doi.org/10.1051/e3sconf/202021303027.

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In the last several centuries, biology has accumulated a large number of data, which are disorganized and hard to be used repeatedly. Bioinformatics, synthesized informatics, statistics and some other subjects, makes them orderly and much more valuable. In drug discovery, Bioinformatics takes the place of some conventional ways because of low cast and high throughput. This article introduces the current situation and application of Bioinformatics in drug discovery and looks forward to the future, hoping to provide a Reference for the development of new drugs.

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Fogg, Christiana N. "ISMB 2016 offers outstanding science, networking, and celebration." F1000Research 5 (June 14, 2016): 1371. http://dx.doi.org/10.12688/f1000research.8640.1.

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The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community. ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas.

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Krilowicz, Beverly, Wendie Johnston, Sandra B. Sharp, Nancy Warter-Perez, and Jamil Momand. "A Summer Program Designed to Educate College Students for Careers in Bioinformatics." CBE—Life Sciences Education 6, no. 1 (2007): 74–83. http://dx.doi.org/10.1187/cbe.06-03-0150.

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A summer program was created for undergraduates and graduate students that teaches bioinformatics concepts, offers skills in professional development, and provides research opportunities in academic and industrial institutions. We estimate that 34 of 38 graduates (89%) are in a career trajectory that will use bioinformatics. Evidence from open-ended research mentor and student survey responses, student exit interview responses, and research mentor exit interview/survey responses identified skills and knowledge from the fields of computer science, biology, and mathematics that are critical for students considering bioinformatics research. Programming knowledge and general computer skills were essential to success on bioinformatics research projects. General mathematics skills obtained through current undergraduate natural sciences programs were adequate for the research projects, although knowledge of probability and statistics should be strengthened. Biology knowledge obtained through the didactic phase of the program and prior undergraduate education was adequate, but advanced or specific knowledge could help students progress on research projects. The curriculum and assessment instruments developed for this program are available for adoption by other bioinformatics programs at http://www.calstatela.edu/SoCalBSI .

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Luscombe, N. M., D. Greenbaum, and M. Gerstein. "What is Bioinformatics? A Proposed Definition and Overview of the Field." Methods of Information in Medicine 40, no. 04 (2001): 346–58. http://dx.doi.org/10.1055/s-0038-1634431.

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Summary Background: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Objectives: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Methods: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying “informatics” techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Results and Conclusions: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (eg expression data). Additional information includes the text of scientific papers and “relationship data” from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

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Fu, Wenjiang J., Arnold J. Stromberg, Kert Viele, Raymond J. Carroll, and Guoyao Wu. "Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology☆." Journal of Nutritional Biochemistry 21, no. 7 (2010): 561–72. http://dx.doi.org/10.1016/j.jnutbio.2009.11.007.

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Paraskevopoulou-Kollia, Efrosyni-Alkisti, and Pantelis G. Bagos. "Bioinformatics Education in Greece: A Survey." Biosaintifika: Journal of Biology & Biology Education 9, no. 1 (2017): 1. http://dx.doi.org/10.15294/biosaintifika.v9i1.7257.

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<p>Bioinformatics is an interdisciplinary field, placed at the interface of Biology, Mathematics and Computer Science. In this work, we tried for the first time to investigate the current situation of Bioinformatics education in Greece. We searched the online resources of all relevant University Departments for Bioinformatics or relevant courses. We found that all the Departments of Biological Sciences include in their curricula courses dedicated to Bioinformatics, but this is not the case for Departments of Computer Science, Computer Engineering, or Medical Schools. Despite the fact that large Universities played a crucial role in establishing Bioinformatics research and education in Greece, we observe that Universities of the periphery invest in the field, by including more relevant courses in the curricula and appointing faculty members trained in the field. In order for us to “triangulate” we didn’t confine ourselves to online resources and descriptive statistics but we also included interviews so as to have a more spherical view of the subject under discussion. The interviews provided useful insights regarding the teaching methods used by bioinformatics tutors, their attitudes and the difficulties they encounter. The tutors mentioned also the material that they choose, the audience’s attraction techniques and the feedback they receive.</p>

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Bayır, Mehtap, and Gökhan Arslan. "Balon Balığı (Fugu rubripes)’nda Katalaz Geninin Biyoinformatik Analizleri." Turkish Journal of Agriculture - Food Science and Technology 8, no. 6 (2020): 1413–17. http://dx.doi.org/10.24925/turjaf.v8i6.1413-1417.3353.

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In this study, bioinformatics analysis of fugu (Fugu rubripes) catalase (cat) gene was performed. Molecular biology science is developing rapidly in parallel with the increasing importance of bioinformatics, thanks to the developed techniques in recent years. In this bioinformatics-based study wich enables the effective identification and characterization of genes in living organisms using online genome databases and statistics and storage, organization and sharing of the ever-increasing genetic data we designed the conserved gene synteny and gene structure and detected the identiy-similarity ratios between fugu and the other telosts and tetrapods. NCBI-GeneBank, EMBL, ENSEML and UNIPROT databases have been used for all these bioinformatics studies. Bioedit and Mega programs were used to perform the analysis and evaluate the data obtained from all these databases. In silico analysis such as the identification and characterization of fugu cat gene, exons-introns organization, phylogenetic tree and gene synteny were completed in this study and presented with tables and figures.

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Jamison, D. C. "Open Bioinformatics." Bioinformatics 19, no. 6 (2003): 679–80. http://dx.doi.org/10.1093/bioinformatics/btg214.

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Aldinucci, Marco, Cristina Calcagno, Mario Coppo, et al. "On Designing Multicore-Aware Simulators for Systems Biology Endowed with OnLine Statistics." BioMed Research International 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/207041.

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The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists .In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed.

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Valencia, A. "BIOINFORMATICS: BIOLOGY BY OTHER MEANS." Bioinformatics 18, no. 12 (2002): 1551–52. http://dx.doi.org/10.1093/bioinformatics/18.12.1551.

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Saska, Ales, David Tichy, Robert Moore, et al. "ccNetViz: a WebGL-based JavaScript library for visualization of large networks." Bioinformatics 36, no. 16 (2020): 4527–29. http://dx.doi.org/10.1093/bioinformatics/btaa559.

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Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

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Zok, Tomasz. "BioCommons: a robust java library for RNA structural bioinformatics." Bioinformatics 37, no. 17 (2021): 2766–67. http://dx.doi.org/10.1093/bioinformatics/btab069.

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Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

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17

Good, B. M., and A. I. Su. "Crowdsourcing for bioinformatics." Bioinformatics 29, no. 16 (2013): 1925–33. http://dx.doi.org/10.1093/bioinformatics/btt333.

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18

Sander, C. "Growth in Bioinformatics." Bioinformatics 19, no. 1 (2003): 1. http://dx.doi.org/10.1093/bioinformatics/19.1.1.

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19

Gumpinger, Anja C., Kasper Lage, Heiko Horn, and Karsten Borgwardt. "Prediction of cancer driver genes through network-based moment propagation of mutation scores." Bioinformatics 36, Supplement_1 (2020): i508—i515. http://dx.doi.org/10.1093/bioinformatics/btaa452.

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Abstract Motivation Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein–protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers. Results We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node’s local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation. Availability and implementation Code available at https://github.com/BorgwardtLab/MoProEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.

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20

Pevzner, P. A. "Educating biologists in the 21st century: bioinformatics scientists versus bioinformatics technicians." Bioinformatics 20, no. 14 (2004): 2159–61. http://dx.doi.org/10.1093/bioinformatics/bth217.

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Valencia, A., and A. Bateman. "Software patents in Bioinformatics." Bioinformatics 22, no. 12 (2006): 1415. http://dx.doi.org/10.1093/bioinformatics/btl166.

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Bateman, A., and A. Valencia. "New Leadership for Bioinformatics." Bioinformatics 20, no. 12 (2004): 1821. http://dx.doi.org/10.1093/bioinformatics/bth403.

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Kibby, Michael R. "Spreadsheet statistics." Bioinformatics 2, no. 3 (1986): 151–57. http://dx.doi.org/10.1093/bioinformatics/2.3.151.

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Crass, T., I. Antes, R. Basekow, et al. "The Helmholtz Network for Bioinformatics: an integrative web portal for bioinformatics resources." Bioinformatics 20, no. 2 (2004): 268–70. http://dx.doi.org/10.1093/bioinformatics/btg398.

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Fogel, Gary B., and David W. Corne. "Computational intelligence in bioinformatics." Biosystems 72, no. 1-2 (2003): 1–4. http://dx.doi.org/10.1016/s0303-2647(03)00129-1.

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Baldi, P., and R. W. Benz. "BLASTing small molecules--statistics and extreme statistics of chemical similarity scores." Bioinformatics 24, no. 13 (2008): i357—i365. http://dx.doi.org/10.1093/bioinformatics/btn187.

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Saxby, C. "The Bioinformatics Open Access option." Bioinformatics 21, no. 22 (2005): 4071–72. http://dx.doi.org/10.1093/bioinformatics/bti707.

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Clare, A., A. Karwath, H. Ougham, and R. D. King. "Functional bioinformatics for Arabidopsis thaliana." Bioinformatics 22, no. 9 (2006): 1130–36. http://dx.doi.org/10.1093/bioinformatics/btl051.

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Clare, A., A. Karwath, H. Ougham, and R. D. King. "Functional bioinformatics for Arabidopsis thaliana." Bioinformatics 22, no. 13 (2006): 1674. http://dx.doi.org/10.1093/bioinformatics/btl169.

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Bolchini, Davide, Anthony Finkelstein, Vito Perrone, and Sylvia Nagl. "Better bioinformatics through usability analysis." Bioinformatics 25, no. 3 (2008): 406–12. http://dx.doi.org/10.1093/bioinformatics/btn633.

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Novella, Jon Ander, Payam Emami Khoonsari, Stephanie Herman, et al. "Container-based bioinformatics with Pachyderm." Bioinformatics 35, no. 5 (2018): 839–46. http://dx.doi.org/10.1093/bioinformatics/bty699.

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Boulesteix, A. L. "Over-optimism in bioinformatics research." Bioinformatics 26, no. 3 (2009): 437–39. http://dx.doi.org/10.1093/bioinformatics/btp648.

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Chasapi, Anastasia, Vasilis J. Promponas, and Christos A. Ouzounis. "The bioinformatics wealth of nations." Bioinformatics 36, no. 9 (2020): 2963–65. http://dx.doi.org/10.1093/bioinformatics/btaa132.

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Finak, G., N. Godin, M. Hallett, et al. "BIAS: Bioinformatics Integrated Application Software." Bioinformatics 21, no. 8 (2004): 1745–46. http://dx.doi.org/10.1093/bioinformatics/bti170.

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Valencia, A., and A. Bateman. "INCREASING THE IMPACT OF BIOINFORMATICS." Bioinformatics 21, no. 1 (2005): 1. http://dx.doi.org/10.1093/bioinformatics/bti185.

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Marz, Manja, Niko Beerenwinkel, Christian Drosten, et al. "Challenges in RNA virus bioinformatics." Bioinformatics 30, no. 13 (2014): 1793–99. http://dx.doi.org/10.1093/bioinformatics/btu105.

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Firth, A. E., and W. M. Patrick. "Statistics of protein library construction." Bioinformatics 21, no. 15 (2005): 3314–15. http://dx.doi.org/10.1093/bioinformatics/bti516.

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Prakash, A., and M. Tompa. "Statistics of local multiple alignments." Bioinformatics 21, Suppl 1 (2005): i344—i350. http://dx.doi.org/10.1093/bioinformatics/bti1042.

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Lood, Cédric, Hans Gerstmans, Yves Briers, Vera van Noort, and Rob Lavigne. "Quality control and statistical evaluation of combinatorial DNA libraries using nanopore sequencing." BioTechniques 69, no. 5 (2020): 379–83. http://dx.doi.org/10.2144/btn-2020-0060.

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Protein engineering and synthetic biology applications increasingly rely on the assembly of modular libraries composed of thousands of different combinations of DNA building blocks. At present, the validation of such libraries is performed by Sanger sequencing analysis on a small subset of clones on an ad hoc basis. Here, we implement a systematic procedure for the comprehensive evaluation of combinatorial libraries, immediately after their creation in vitro, using long reads sequencing technology. After an initial step of nanopore sequencing, we use straightforward bioinformatics tools to tabulate the composition and synteny of the building blocks in each read. We subsequently use exploratory statistics to assess the library and validate its diversity before carrying downstream cloning and screening assays.

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Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: interactive visualization of scRNA-seq data." Bioinformatics 36, no. 7 (2019): 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.

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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

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Sykacek, P., R. A. Furlong, and G. Micklem. "A friendly statistics package for microarray analysis." Bioinformatics 21, no. 21 (2005): 4069–70. http://dx.doi.org/10.1093/bioinformatics/bti663.

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Barta, E., L. Kajan, and S. Pongor. "IS: a web-site for intron statistics." Bioinformatics 19, no. 4 (2003): 543. http://dx.doi.org/10.1093/bioinformatics/btg019.

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Mukherjee, S., S. J. Roberts, and M. J. van der Laan. "Data-adaptive test statistics for microarray data." Bioinformatics 21, Suppl 2 (2005): ii108—ii114. http://dx.doi.org/10.1093/bioinformatics/bti1119.

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Sturm, Gregor, Tamas Szabo, Georgios Fotakis, et al. "Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data." Bioinformatics 36, no. 18 (2020): 4817–18. http://dx.doi.org/10.1093/bioinformatics/btaa611.

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Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

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Briane, Vincent, Myriam Vimond, Cesar Augusto Valades-Cruz, Antoine Salomon, Christian Wunder, and Charles Kervrann. "A sequential algorithm to detect diffusion switching along intracellular particle trajectories." Bioinformatics 36, no. 1 (2019): 317–29. http://dx.doi.org/10.1093/bioinformatics/btz489.

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Abstract Motivation Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of single molecules in living cells. Changes of dynamics can occur along a trajectory. Then, an issue is to estimate the temporal change-points that is the times at which a change of dynamics occurs. The number of points in the trajectory required to detect such changes will depend on both the magnitude and type of the motion changes. Here, the number of points per trajectory is of the order of 102, even if in practice dramatic motion changes can be detected with less points. Results We propose a non-parametric procedure based on test statistics computed on local windows along the trajectory to detect the change-points. This algorithm controls the number of false change-point detections in the case where the trajectory is fully Brownian. We also develop a strategy for aggregating the detections obtained with different window sizes so that the window size is no longer a parameter to optimize. A Monte Carlo study is proposed to demonstrate the performances of the method and also to compare the procedure to two competitive algorithms. At the end, we illustrate the efficacy of the method on real data in 2D and 3D, depicting the motion of mRNA complexes—called mRNA-binding proteins—in neuronal dendrites, Galectin-3 endocytosis and trafficking within the cell. Availability and implementation A user-friendly Matlab package containing examples and the code of the simulations used in the paper is available at http://serpico.rennes.inria.fr/doku.php? id=software:cpanalysis:index. Supplementary information Supplementary data are available at Bioinformatics online.

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Svishcheva, Gulnara R., Nadezhda M. Belonogova, Irina V. Zorkoltseva, Anatoly V. Kirichenko, and Tatiana I. Axenovich. "Gene-based association tests using GWAS summary statistics." Bioinformatics 35, no. 19 (2019): 3701–8. http://dx.doi.org/10.1093/bioinformatics/btz172.

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Abstract Motivation A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. Results We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. Availability and implementation The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. Supplementary information Supplementary data are available at Bioinformatics online.

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Silva, Israel T., Rafael A. Rosales, Adriano J. Holanda, Michel C. Nussenzweig, and Mila Jankovic. "Identification of chromosomal translocation hotspots via scan statistics." Bioinformatics 30, no. 18 (2014): 2551–58. http://dx.doi.org/10.1093/bioinformatics/btu351.

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Wei, Changshuai, and Qing Lu. "A generalized association test based on U statistics." Bioinformatics 33, no. 13 (2017): 1963–71. http://dx.doi.org/10.1093/bioinformatics/btx103.

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Alterovitz, G., A. Jiwaji, and M. F. Ramoni. "Automated programming for bioinformatics algorithm deployment." Bioinformatics 24, no. 3 (2008): 450–51. http://dx.doi.org/10.1093/bioinformatics/btm602.

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Sun, Y., S. Zhao, H. Yu, G. Gao, and J. Luo. "ABCGrid: Application for Bioinformatics Computing Grid." Bioinformatics 23, no. 9 (2007): 1175–77. http://dx.doi.org/10.1093/bioinformatics/btm086.

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