Relevant bibliographies by topics / Bioinformatics pipeline

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Bioinformatics pipeline'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Bioinformatics pipeline"

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Ewels, Philip, Felix Krueger, Max Käller, and Simon Andrews. "Cluster Flow: A user-friendly bioinformatics workflow tool." F1000Research 5 (December 6, 2016): 2824. http://dx.doi.org/10.12688/f1000research.10335.1.

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Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the box. Pipelines are assembled using these modules with a simple syntax that can be easily modified as required. Core helper functions automate many common NGS procedures, making running pipelines simple. Cluster Flow is available with an GNU GPLv3 license on GitHub. Documentation, examples and an online demo are available at http://clusterflow.io.
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Ewels, Philip, Felix Krueger, Max Käller, and Simon Andrews. "Cluster Flow: A user-friendly bioinformatics workflow tool." F1000Research 5 (May 2, 2017): 2824. http://dx.doi.org/10.12688/f1000research.10335.2.

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Pipeline tools are becoming increasingly important within the field of bioinformatics. Using a pipeline manager to manage and run workflows comprised of multiple tools reduces workload and makes analysis results more reproducible. Existing tools require significant work to install and get running, typically needing pipeline scripts to be written from scratch before running any analysis. We present Cluster Flow, a simple and flexible bioinformatics pipeline tool designed to be quick and easy to install. Cluster Flow comes with 40 modules for common NGS processing steps, ready to work out of the box. Pipelines are assembled using these modules with a simple syntax that can be easily modified as required. Core helper functions automate many common NGS procedures, making running pipelines simple. Cluster Flow is available with an GNU GPLv3 license on GitHub. Documentation, examples and an online demo are available at http://clusterflow.io.
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SoRelle, Jeffrey A., Megan Wachsmann, and Brandi L. Cantarel. "Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays." Archives of Pathology & Laboratory Medicine 144, no. 9 (2020): 1118–30. http://dx.doi.org/10.5858/arpa.2019-0476-ra.

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Context.— Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories. Objective.— To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay. Data Sources.— This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation. Conclusions.— This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.
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Pal, Soumitra, and Teresa M. Przytycka. "Bioinformatics pipeline using JUDI: Just Do It!" Bioinformatics 36, no. 8 (2019): 2572–74. http://dx.doi.org/10.1093/bioinformatics/btz956.

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Abstract Summary Large-scale data analysis in bioinformatics requires pipelined execution of multiple software. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g. Snakemake, Nextflow, Common Workflow Language, Galaxy, etc. have been developed to ensure optimum execution of the stages across two invocations of the pipeline. However, when the pipeline needs to be executed with different settings of parameters, e.g. thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution. We developed JUDI on top of DoIt, a Python based WMS, to systematically handle parameter settings based on the principles of database management systems. Using a novel modular approach that encapsulates a parameter database in each task and file associated with a pipeline stage, JUDI simplifies plug-and-play of the pipeline stages. For a typical pipeline with n parameters, JUDI reduces the number of lines of scripting required by a factor of O(n). With properly designed parameter databases, JUDI not only enables reproducing research under published values of parameters but also facilitates exploring newer results under novel parameter settings. Availability and implementation https://github.com/ncbi/JUDI Supplementary information Supplementary data are available at Bioinformatics online.
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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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Afiahayati, Stefanus Bernard, Gunadi, et al. "A Comparison of Bioinformatics Pipelines for Enrichment Illumina Next Generation Sequencing Systems in Detecting SARS-CoV-2 Virus Strains." Genes 13, no. 8 (2022): 1330. http://dx.doi.org/10.3390/genes13081330.

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a newly emerging virus well known as the major cause of the worldwide pandemic due to Coronavirus Disease 2019 (COVID-19). Major breakthroughs in the Next Generation Sequencing (NGS) field were elucidated following the first release of a full-length SARS-CoV-2 genome on the 10 January 2020, with the hope of turning the table against the worsening pandemic situation. Previous studies in respiratory virus characterization require mapping of raw sequences to the human genome in the downstream bioinformatics pipeline as part of metagenomic principles. Illumina, as the major player in the NGS arena, took action by releasing guidelines for improved enrichment kits called the Respiratory Virus Oligo Panel (RVOP) based on a hybridization capture method capable of capturing targeted respiratory viruses, including SARS-CoV-2; therefore, allowing a direct map of raw sequences data to SARS-CoV-2 genome in downstream bioinformatics pipeline. Consequently, two bioinformatics pipelines emerged with no previous studies benchmarking the pipelines. This study focuses on gaining insight and understanding of target enrichment workflow by Illumina through the utilization of different bioinformatics pipelines named as ‘Fast Pipeline’ and ‘Normal Pipeline’ to SARS-CoV-2 strains isolated from Yogyakarta and Central Java, Indonesia. Overall, both pipelines work well in the characterization of SARS-CoV-2 samples, including in the identification of major studied nucleotide substitutions and amino acid mutations. A higher number of reads mapped to the SARS-CoV-2 genome in Fast Pipeline and merely were discovered as a contributing factor in a higher number of coverage depth and identified variations (SNPs, insertion, and deletion). Fast Pipeline ultimately works well in a situation where time is a critical factor. On the other hand, Normal Pipeline would require a longer time as it mapped reads to the human genome. Certain limitations were identified in terms of pipeline algorithm, whereas it is highly recommended in future studies to design a pipeline in an integrated framework, for instance, by using NextFlow, a workflow framework to combine all scripts into one fully integrated pipeline.
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Cervera, Alejandra, Ville Rantanen, Kristian Ovaska, et al. "Anduril 2: upgraded large-scale data integration framework." Bioinformatics 35, no. 19 (2019): 3815–17. http://dx.doi.org/10.1093/bioinformatics/btz133.

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Abstract Summary Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril’s bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. Availabilityand implementation Freely available at http://anduril.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Allain, Fabrice, Julien Roméjon, Philippe La Rosa, Frédéric Jarlier, Nicolas Servant, and Philippe Hupé. "Geniac: Automatic Configuration GENerator and Installer for nextflow pipelines." Open Research Europe 1 (July 2, 2021): 76. http://dx.doi.org/10.12688/openreseurope.13861.1.

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With the advent of high-throughput biotechnological platforms and their ever-growing capacity, life science has turned into a digitized, computational and data-intensive discipline. As a consequence, standard analysis with a bioinformatics pipeline in the context of routine production has become a challenge such that the data can be processed in real-time and delivered to the end-users as fast as possible. The usage of workflow management systems along with packaging systems and containerization technologies offer an opportunity to tackle this challenge. While very powerful, they can be used and combined in multiple ways thus increasing their usage complexity. Therefore, guidelines and protocols are required in order to detail how the source code of the bioinformatics pipeline should be written and organized to ensure its usability, maintainability, interoperability, sustainability, portability, reproducibility, scalability and efficiency. Capitalizing on Nextflow, Conda, Docker, Singularity and the nf-core initiative, we propose a set of best practices along the development life cycle of the bioinformatics pipeline and deployment for production operations which address different expert communities including i) the bioinformaticians and statisticians ii) the software engineers and iii) the data managers and core facility engineers. We implemented Geniac (Automatic Configuration GENerator and Installer for nextflow pipelines) which consists of a toolbox with three components: i) a technical documentation available at https://geniac.readthedocs.io to detail coding guidelines for the bioinformatics pipeline with Nextflow, ii) a linter to check that the code respects the guidelines, and iii) an add-on to generate configuration files, build the containers and deploy the pipeline. The Geniac toolbox aims at the harmonization of development practices across developers and automation of the generation of configuration files and containers by parsing the source code of the Nextflow pipeline. The Geniac toolbox and two demo pipelines are available on GitHub. This article presents the main functionalities of Geniac.
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Parmen, Adibah, MOHD NOOR MAT ISA, FARAH FADWA BENBELGACEM, Hamzah Mohd Salleh, and Ibrahim Ali Noorbatcha. "COMPARATIVE METAGENOMICS ANALYSIS OF PALM OIL MILL EFFLUENT (POME) USING THREE DIFFERENT BIOINFORMATICS PIPELINES." IIUM Engineering Journal 20, no. 1 (2019): 1–11. http://dx.doi.org/10.31436/iiumej.v20i1.909.

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ABSTRACT: The substantial cost reduction and massive production of next-generation sequencing (NGS) data have contributed to the progress in the rapid growth of metagenomics. However, production of the massive amount of data by NGS has revealed the challenges in handling the existing bioinformatics tools related to metagenomics. Therefore, in this research we have investigated an equal set of DNA metagenomics data from palm oil mill effluent (POME) sample using three different freeware bioinformatics pipelines’ websites of metagenomics RAST server (MG-RAST), Integrated Microbial Genomes with Microbiome Samples (IMG/M) and European Bioinformatics Institute (EBI) Metagenomics, in term of the taxonomic assignment and functional analysis. We found that MG-RAST is the quickest among these three pipelines. However, in term of analysis of results, IMG/M provides more variety of phylum with wider percent identities for taxonomical assignment and IMG/M provides the highest carbohydrates, amino acids, lipids, and coenzymes transport and metabolism functional annotation beside the highest in total number of glycoside hydrolase enzymes. Next, in identifying the conserved domain and family involved, EBI Metagenomics would be much more appropriate. All the three bioinformatics pipelines have their own specialties and can be used alternately or at the same time based on the user’s functional preference.
 ABSTRAK: Pengurangan kos dalam skala besar dan pengeluaran data ‘next-generation sequencing’ (NGS) secara besar-besaran telah menyumbang kepada pertumbuhan pesat metagenomik. Walau bagaimanapun, pengeluaran data dalam skala yang besar oleh NGS telah menimbulkan cabaran dalam mengendalikan alat-alat bioinformatika yang sedia ada berkaitan dengan metagenomik. Justeru itu, dalam kajian ini, kami telah menyiasat satu set data metagenomik DNA yang sama dari sampel effluen kilang minyak sawit dengan menggunakan tiga laman web bioinformatik percuma iaitu dari laman web ‘metagenomics RAST server’ (MG-RAST), ‘Integrated Microbial Genomes with Microbiome Samples’ (IMG/M) dan ‘European Bioinformatics Institute’ (EBI) Metagenomics dari segi taksonomi dan analisis fungsi. Kami mendapati bahawa MG-RAST ialah yang paling cepat di antara ketiga-tiga ‘pipeline’, tetapi mengikut keputusan analisa, IMG/M mengeluarkan maklumat philum yang lebih pelbagai bersama peratus identiti yang lebih luas berbanding yang lain untuk pembahagian taksonomi dan IMG/M juga mempunyai bacaan tertinggi dalam hampir semua anotasi fungsional karbohidrat, amino asid, lipid, dan koenzima pengangkutan dan metabolisma malah juga paling tinggi dalam jumlah enzim hidrolase glikosida. Kemudian, untuk mengenal pasti ‘domain’ terpelihara dan keluarga yang terlibat, EBI metagenomics lebih bersesuaian. Ketiga-tiga saluran ‘bioinformatics pipeline’ mempunyai keistimewaan mereka yang tersendiri dan boleh digunakan bersilih ganti dalam masa yang sama berdasarkan pilihan fungsi penggun.
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Van Neste, Leander, James G. Herman, Kornel E. Schuebel, et al. "A Bioinformatics Pipeline for Cancer Epigenetics." Current Bioinformatics 5, no. 3 (2010): 153–63. http://dx.doi.org/10.2174/157489310792006710.

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Dissertations / Theses on the topic "Bioinformatics pipeline"

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Johansson-Åkhe, Isak. "PePIP : a Pipeline for Peptide-Protein Interaction-site Prediction." Thesis, Linköpings universitet, Institutionen för fysik, kemi och biologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138411.

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Protein-peptide interactions play a major role in several biological processes, such as cellproliferation and cancer cell life-cycles. Accurate computational methods for predictingprotein-protein interactions exist, but few of these method can be extended to predictinginteractions between a protein and a particularly small or intrinsically disordered peptide. In this thesis, PePIP is presented. PePIP is a pipeline for predicting where on a given proteina given peptide will most probably bind. The pipeline utilizes structural aligning to perusethe Protein Data Bank for possible templates for the interaction to be predicted, using thelarger chain as the query. The possible templates are then evaluated as to whether they canrepresent the query protein and peptide using a Random Forest classifier machine learningalgorithm, and the best templates are found by using the evaluation from the Random Forest in combination with hierarchical clustering. These final templates are then combined to givea prediction of binding site. PePIP is proven to be highly accurate when testing on a set of 502 experimentally determinedprotein-peptide structures, suggesting a binding site on the correct part of the protein- surfaceroughly 4 out of 5 times.
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Ishak, Helena. "Developing a ChIP-seq pipeline that analyzes the human genome and its repetitive sequences." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-335914.

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Lember, Geivi. "Sepsis-associated Escherichia coli whole-genome sequencing analysis using in-house developed pipeline and 1928 diagnostics tool." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19841.

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Sepsis is a life-threatening condition that is caused by a dysregulated host response to infection. Timely detection of sepsis and antibiotic treatment is important for the patient’s recovery from sepsis. Usually, when sepsis is detected, immediate antibiotic treatment is started with broad-spectrum antibiotics as it takes time to determine the correct antibiotic susceptibility. To overcome this problem, next-generation sequencing is seen as one possible development in clinical diagnostics in the future. Automated bioinformatics pipelines could be used initially for surveillance purposes but eventually for rapid clinical diagnosis. Therefore, the results of 1928 Diagnostics, an automated pipeline for whole-genome sequencing (WGS) data analysis, were compared with the results of an in-house developed pipeline for manual data processing by analyzing sepsis-associated Escherichia coli (SEPEC) WGS data. The pipelines were compared by assessing their predicted antimicrobial resistance (AMR) genes, virulence genes and epidemiological relatedness. In addition, the predicted resistance genes were compared to phenotypic antimicrobial susceptibility testing (AST) data from the clinical microbiology laboratory. All the results obtained from the 1928 Diagnostics and in-house pipeline were similar but differed in the number of virulence/predicted AMR genes, AMR gene variants, detection of species and epidemiologically related E. coli samples. Moreover, the predicted AMR genes from both pipelines did not show a good overall relation to the phenotypic AST result. More studies are needed to make predictions of genes from the WGS analysis more reliable so that WGS analysis can be used as a diagnostics tool in clinical laboratories in the future.
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Ramsay, Trevor. "A Motif Discovery and Analysis Pipeline for Heterogeneous Next-Generation Sequencing Data." Thesis, University of California, Davis, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1599520.

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<p> Bioinformatics has made great strides in understanding the regulation of gene expression, but many of the tools developed for this purpose depend on data from a limited number of species. Despite their unique genetic attributes, there remains a dearth of research into undomesticated trees. The poplar tree, <i> Populus trichocarpa</i>, has undergone multiple rounds of genome duplication during its evolution. In addition its life cycle varies from other annual crop and model plants previously studied, leading to significant technical challenges to understand the unique biology of these trees. For example, the process of secondary growth occurs as the tree stems thicken, and creates secondary xylem (wood) and phloem (inner bark) for water and products of photosynthesis transport, respectively. Because of this, the research group I work with studies the secondary growth of <i>P. trichocarpa</i> (Spicer, 2010) (Groover, et al., 2010) (Groover, et al., 2006) (Groover, 2005).</p><p> The genomic tools to investigate gene regulation in <i>P. trichocarpa </i> are readily available. Next-generation sequencing technologies such as RNA-Seq and ChIP-Seq can be used to understand gene expression and binding of transcription factors to specific locations in the genome. Similarly, a variety of specialized bioinformatic tools such as EdgeR, Cufflinks, and MACS can be used to analyze gene binding and expression from sequencing data provided by ChIP-seq and RNA-seq (Blahnik, et al., 2010) (Mortazavi, et al., 2008) (Robinson, 2010) (Robinson, 2007) (Robinson, et al., 2008) (McCarthy, 2012) (Trapnell, 2013) (Zhang, 2008). The binding and expression data these tools provide form a foundation for analyzing the gene expression regulation in <i> P. trichocarpa.</i></p><p> The goal of my project is to provide a motif discovery and analysis pipeline for analyses of <i>Populus</i> species. The motif discovery and analysis pipeline utilizes heterogeneous data collected from poplar and aspen mutants to elucidate the gene regulatory mechanisms involved in secondary growth. The experiments target transcription factors related to secondary growth, and through analysis of the variety of transcription factor binding experiments, I have identified the motifs involved in gene regulation of secondary growth within <i>P. trichocarpa.</i> (Filkov, et al., 2008).</p>
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Garcia, Krystine. "Bioinformatics Pipeline for Improving Identification of Modified Proteins by Neutral Loss Peak Filtering." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1440157843.

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Norris, Shaun W. "A Pipeline for Creation of Genome-Scale Metabolic Reconstructions." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4667.

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The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the FBA results to Model SEED models. We found that our gene annotations were larger and highly similar in terms of function and gene types to the gene annotations downloaded from NCBI. Further, we found that our FBA models required a minimal addition of transport reactions, sources, and escapes indicating that our draft pathway models were very complete. We also found that on average our solutions contained more reactions than the models obtained from Model SEED due to a large amount of baseline reactions and gene products found in ASGARD.
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Kuntala, Prashant Kumar. "Optimizing Biomarkers From an Ensemble Learning Pipeline." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1503592057943043.

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Milani, Renato 1985. "Desenvolvimento de um pipeline para analise em larga escala de um chip de proteinas quinases." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/314742.

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Orientadores: Eduardo Galembeck, Carmen Verissima Ferreira<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia<br>Made available in DSpace on 2018-08-15T15:43:32Z (GMT). No. of bitstreams: 1 Milani_Renato_M.pdf: 4434069 bytes, checksum: f6dd6ae86e6b3ac6b79f8c4b28828a91 (MD5) Previous issue date: 2010<br>Resumo: A atividade de proteínas quinases é responsável pela regulação de muitos processos biológicos através de cascatas de sinalização que levam a diferentes efeitos celulares. No entanto, a análise da reação de fosforilação reversível catalisada por estas enzimas e prejudicada pela complexidade inerente as interações entre proteínas neste sistema de sinalização. Consequentemente, o foco em uma única proteína quinase pode não ser suficiente para revelar completamente os mecanismos por trás dos fenótipos observados. Nesse sentido, em conjunto com outras técnicas de análise em larga-escala como chips de expressão de mRNA, arranjos de peptídios contendo substratos de proteínas quinases vem sendo cada vez mais utilizados por pesquisadores. No entanto, a falta de uniformidade na análise estatística desses chips tem sido um grande empecilho à obtenção de dados relevantes com o uso dessa técnica. Por conta disso, o objetivo desse trabalho foi desenvolver uma metodologia, chamada de PepMatrix, capaz de aplicar estatística básica de forma automatizada visando a seleção de replicações com baixa variabilidade e a obtenção da anotação das proteínas envolvidas nos eventos de fosforilação ocorridos no chip. Esse novo método foi aplicado em vários conjuntos de dados de diferentes experimentos biológicos e seus resultados revelaram atividades quinásicas significativamente alteradas, muitas das quais tiveram confirmação por Western blot. Alem disso, os resultados ressaltaram a importância da análise sistêmica dos eventos de sinalização celular em conjunto com uma análise crítica das replicações. O alto grau de uniformidade analítica obtido por esse método faz com que ele seja uma poderosa e confiável ferramenta na análise quinômica em larga-escala<br>Abstract: The activity of protein kinases governs many biological processes through signaling cascades that lead to distinct outputs, from homeostasis to disease. However, analysis of the reversible phosphorylation perpetrated by these enzymes is hindered by the inherent complexity of interactions in this signaling system. Consequently, focusing on a single kinase may not be enough to completely unfold the mechanisms behind observed phenomena. In this sense, together with other omics approaches like mRNA expression analysis, peptide arrays have shown increasing popularity, particularly ones containing kinase substrate sequences. The lack of uniformity in statistical analysis of these chips, though, has been a major issue for the field. In this paper, we propose PepMatrix, a fast and accurate method for selecting so called "reliable replicate spots" and automatically retrieving differential activity and annotation information about phosphorylation events identified in a peptide array of kinase substrates. Here, we present several cases where this new methodology was applied to biological datasets. We successfully identified putative up and down-regulated kinases, many of which were confirmed to have altered activity by Western blot. Moreover, the results emphasized the need for a true systems biology approach to the cellular signaling events alongside a critical replicate selection method. The high degree of analysis uniformity we achieved with this method provides a powerful and reliable addition for high-throughput kinome analysis<br>Mestrado<br>Bioquimica<br>Mestre em Biologia Funcional e Molecular
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Isak, Sylvin. "Increasing bioinformatics in third world countries : Studies of S.digitata and P.Polymyxa to further bioinformatics in east Africa." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-293636.

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Despite an increase of biotechnical studies in third world countries, the bioinformatical side is largely lacking. In this paper we attempt to further the bioinformatical capabilities of east Af-rica. The project consisted of two teaching segments for east African doctorates, one as part of an academic workshop at ILRI, Kenya, and one in a small class at SLU, Sweden. The project also included the generation of two simple to use bioinformatical pipelines with the explicit aim to be reused by novice bioinformaticians from the very same region. The viability of the piplines were verified by generating transcriptional expression level differences for Paeni-bacillus polymyxa strain A26 and whole genome annotations for Setaria digitata. Both pipe-lines may have some merit for the collaborative effort between ILRI and SLU to annotate Eleusine coracana, a draught resilient crop, the annotation of which may save lives. The teaching material, source code for the pipelines and overall teaching impression have been included in this paper.
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Andersson, Christoffer. "PELICAN : a PipELIne, including a novel redundancy-eliminating algorithm, to Create and maintain a topicAl family-specific Non-redundant protein database." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-960.

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<p>The increasing number of biological databases today requires that users are able to search more efficiently among as well as in individual databases. One of the most widespread problems is redundancy, i.e. the problem of duplicated information in sets of data. This thesis aims at implementing an algorithm that distinguishes from other related attempts by using the genomic positions of sequences, instead of similarity based sequence comparisons, when making a sequence data set non-redundant. In an automatic updating procedure the algorithm drastically increases the possibility to update and to maintain the topicality of a non-redundant database. The procedure creates a biologically sound non-redundant data set with accuracy comparable to other algorithms focusing on making data sets non-redundant</p>
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Book chapters on the topic "Bioinformatics pipeline"

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Petereit, Jakob. "Pipeline Automation via Snakemake." In Plant Bioinformatics. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_9.

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Djebali, Sarah, Valentin Wucher, Sylvain Foissac, Christophe Hitte, Erwan Corre, and Thomas Derrien. "Bioinformatics Pipeline for Transcriptome Sequencing Analysis." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4035-6_14.

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Todoerti, Katia, Domenica Ronchetti, Martina Manzoni, Elisa Taiana, Antonino Neri, and Luca Agnelli. "Bioinformatics Pipeline to Analyze lncRNA Arrays." In Long Non-Coding RNAs in Cancer. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1581-2_3.

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Urbanova, Pavla, Vladyslav Bozhynov, Dinara Bekkozhayeva, Petr Císař, and Miloš Železný. "Pipeline for Electron Microscopy Images Processing." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17938-0_14.

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Missirian, Victor, Isabelle Henry, Luca Comai, and Vladimir Filkov. "POPE: Pipeline of Parentally-Biased Expression." In Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30191-9_17.

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Ma, Terry, and Xin Xing. "A Scalable Reference-Free Metagenomic Binning Pipeline." In Bioinformatics Research and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94968-0_7.

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Oliveira, Renato R. M., Raíssa Silva, Gisele L. Nunes, and Guilherme Oliveira. "PIMBA: A PIpeline for MetaBarcoding Analysis." In Advances in Bioinformatics and Computational Biology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91814-9_10.

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Kodysh, Julia, and Alex Rubinsteyn. "OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction." In Bioinformatics for Cancer Immunotherapy. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0327-7_10.

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Djebali, Sarah, Valentin Wucher, Sylvain Foissac, Christophe Hitte, Erwan Corre, and Thomas Derrien. "Erratum to: Bioinformatics Pipeline for Transcriptome Sequencing Analysis." In Methods in Molecular Biology. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4035-6_17.

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Huacarpuma, Ruben Cruz, Maristela Holanda, and Maria Emilia Walter. "A Conceptual Model for Transcriptome High-Throughput Sequencing Pipeline." In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22825-4_10.

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Conference papers on the topic "Bioinformatics pipeline"

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Yao, Shunyu David, Muhammad Ali Gulzar, Liqing Zhang, and Ali R. Butt. "Towards a Serverless Bioinformatics Cyberinfrastructure Pipeline." In HPDC '21: The 30th International Symposium on High-Performance Parallel and Distributed Computing. ACM, 2020. http://dx.doi.org/10.1145/3452413.3464787.

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Duitama, Jorge, Eun-Kyung Suk, Sabrina Schulz, Gayle McEwen, Thomas Huebsch, and Margret Hoehe. "Workshop: Bioinformatics pipeline for fosmid based molecular haplotype sequencing." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729923.

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Skarzyńska, Agnieszka, Magdalena Pawełkowicz, Tomasz Krzywkowski, Katarzyna Świerkula, Wojciech Pląder, and Zbigniew Przybecki. "Bioinformatics pipeline for functional identification and characterization of proteins." In XXXVI Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (Wilga 2015), edited by Ryszard S. Romaniuk. SPIE, 2015. http://dx.doi.org/10.1117/12.2205559.

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Chen, Ethan W. "A General Framework and Maturity Model for Bioinformatics Pipeline Development." In 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, 2018. http://dx.doi.org/10.1109/sera.2018.8477200.

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Jensen, Tanner D., Kristi A. Bresciano, Emma Dallon, et al. "The PepSeq Pipeline." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2018. http://dx.doi.org/10.1145/3233547.3233599.

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Ram, Jeffrey L., and Yi Lu. "Bioinformatics Pipeline for Identification of Binding Motifs of Flexible Protein Tethers." In 2009 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2009. http://dx.doi.org/10.1109/bibm.2009.20.

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Troup, Michael, Andrian Yang, Amir Hossein Kamali, Eleni Giannoulatou, Tsong Yueh Chen, and Joshua W. K. Ho. "A cloud-based framework for applying metamorphic testing to a bioinformatics pipeline." In ICSE '16: 38th International Conference on Software Engineering. ACM, 2016. http://dx.doi.org/10.1145/2896971.2896975.

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Deshpande, Sumukh, Anne James, Chris H. Franklin, Lindsey J. Leach, Sandy Taramonli, and Jianhua Yang. "An RNA-Seq Bioinformatics Pipeline for Data Processing of Arabidopsis Thaliana Datasets." In the International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3175587.3175592.

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Zengin, Talip, and Tugba Onal-Suzek. "TCGA Lung Cancer Analysis Pipeline." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2018. http://dx.doi.org/10.1145/3233547.3233615.

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Agapito, Giuseppe, and Mario Cannataro. "A software pipeline for multiple microarray data analysis." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217956.

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Reports on the topic "Bioinformatics pipeline"

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Cytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598174.bard.

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Abstract:
Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed at detecting BCAs and associated biologically-active compounds, which included: (A) isolation of multidrug-resistant desert soil bacteria and root-associated bacteria from medicinal plants; (B) invitro screening of bacterial isolates against known plant, animal and human pathogens; (C) nextgeneration sequencing of isolates that displayed antagonistic activity against at least one of the model pathogens and (D) in-planta screening of promising BCAs in a model bean-Sclerotiumrolfsii system. The BCA genome data were examined for presence of: i) secondary metabolite encoding genes potentially linked to the anti-pathogenic activity of the isolates; and ii) rhizosphere competence-associated genes, associated with the capacity of microorganisms to successfully inhabit plant roots, and a prerequisite for the success of a soil amended BCA. Altogether, 56 phylogenetically-diverse isolates with bioactivity against bacterial, oomycete and fungal plant pathogens were identified. These strains were sent to Auburn University where bioassays against a panel of animal and human pathogens (including multi-drug resistant pathogenic strains such as A. baumannii 3806) were conducted. Nineteen isolates that showed substantial antagonistic activity against at least one of the screened pathogens were sequenced, assembled and subjected to bioinformatics analyses aimed at identifying secondary metabolite-encoding and rhizosphere competence-associated genes. The genome size of the bacteria ranged from 3.77 to 9.85 Mbp. All of the genomes were characterized by a plethora of secondary metabolite encoding genes including non-ribosomal peptide synthase, polyketidesynthases, lantipeptides, bacteriocins, terpenes and siderophores. While some of these genes were highly similar to documented genes, many were unique and therefore may encode for novel antagonistic compounds. Comparative genomic analysis of root-associated isolates with similar strains not isolated from root environments revealed genes encoding for several rhizospherecompetence- associated traits including urea utilization, chitin degradation, plant cell polymerdegradation, biofilm formation, mechanisms for iron, phosphorus and sulfur acquisition and antibiotic resistance. Our labs are currently writing a continuation of this feasibility study that proposes a unique pipeline for the detection of BCAs and biopesticides that can be used against phytopathogens. It will combine i) metabolomic screening of strains from our collection that contain unique secondary metabolite-encoding genes, in order to isolate novel antimicrobial compounds; ii) model plant-based experiments to assess the antagonistic capacities of selected BCAs toward selected phytopathogens; and iii) an innovative next-generation-sequencing based method to monitor the relative abundance and distribution of selected BCAs in field experiments in order to assess their persistence in natural agro-environments. We believe that this integrated approach will enable development of novel strains and compounds that can be used in large-scale operations.
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