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Jalili V, Ghiasvand A, Ebrahimzadeh H, Vahabi M, Zendehdel R. Corrigendum to "Comparative study of molecularly imprinted polymer surface modified magnetic silica aerogel, zeolite Y, and MIL-101(Cr) for dispersive solid phase extraction of fuel ether oxygenates in drinking water" [Food Chem. 442 (2024) 138455]. Food Chem 2024; 444:138664. [PMID: 38350839 DOI: 10.1016/j.foodchem.2024.138664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Affiliation(s)
- Vahid Jalili
- Department of Occupational Health Engineering, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Ghiasvand
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Tasmania 7001, Australia; Department of Analytical Chemistry, Faculty of Chemistry, Lorestan University, Khoramabad, Iran
| | - Homeira Ebrahimzadeh
- Department of Analytical Chemistry and Pollutants, Faculty of Chemistry and Petroleum Sciences, Shahid Beheshti University, Tehran, Iran
| | - Masoomeh Vahabi
- Department of Occupational Health Engineering, School of health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Rezvan Zendehdel
- Department of Occupational Health Engineering, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Jalili V, Ghiasvand A, Ebrahimzadeh H, Vahabi M, Zendehdel R. Comparative study of molecularly imprinted polymer surface modified magnetic silica aerogel, zeolite Y, and MIL-101(Cr) for dispersive solid phase extraction of fuel ether oxygenates in drinking water. Food Chem 2024; 442:138455. [PMID: 38271905 DOI: 10.1016/j.foodchem.2024.138455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/12/2024] [Accepted: 01/14/2024] [Indexed: 01/27/2024]
Abstract
The study was performed in two phases. First, the polymerization was carried out upon three magnetized surfaces of silica aerogel, zeolite Y, and MIL-101(Cr). Then, optimal molecularly imprinted polymer and optimal extraction conditions were determined by the central composite design-response surface method. Subsequently, the validation parameters of dispersive solid-phase extraction based optimal molecularly imprinted polymer were examined for the extraction of the fuel ether oxygenates. The optimal conditions include the type of adsorbent: Zeolite-magnetic molecularly imprinted polymer, the amount of adsorbent: 40 mg, pH: 7.7, and absorption time: 24.8 min which was selected with desirability equal to 0.996. The calibration graphs were linear between 1 and 100 μg L-1, with good correlation coefficients. The limits of detection were found to be 0.64, 0. 4, and 0.34 μg L-1 for methyl tert-butyl ether, ethyl tert-butyl ether, and tert butyl formate, respectively. The method proved reliable for analyzing fuel ether oxygenates in drinking water.
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Affiliation(s)
- Vahid Jalili
- Student Research Committee, Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Ghiasvand
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Tasmania 7001, Australia; Department of Analytical Chemistry, Faculty of Chemistry, Lorestan University, Khoramabad, Iran
| | - Homeira Ebrahimzadeh
- Department of Analytical Chemistry and Pollutants, Faculty of Chemistry and Petroleum Sciences, Shahid Beheshti University, Tehran, Iran
| | - Masoomeh Vahabi
- Department of Occupational Health Engineering, School of health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Rezvan Zendehdel
- Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Jalili V, Ghiasvand A, Ebrahimzadeh H, Zendehdel R. Urinary biomonitoring of fuel ether oxygenates using a needle trap device packed with a novel molecularly imprinted polymer surface modified Zeolite Y. J Chromatogr A 2024; 1725:464949. [PMID: 38688054 DOI: 10.1016/j.chroma.2024.464949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/15/2024] [Accepted: 04/26/2024] [Indexed: 05/02/2024]
Abstract
This study introduces an innovative needle trap device (NTD) featuring a molecularly imprinted polymer (MIP) surface-modified Zeolite Y. The developed NTD was integrated with gas chromatography-flame ionization detector (GC-FID) and employed for analysis of fuel ether oxygenates (methyl tert‑butyl ether, MTBE, ethyl tert‑butyl ether, ETBE, and tert‑butyl formate, TBF) in urine samples. To optimize the key experimental variables including extraction temperature, extraction time, salt concentration, and stirring speed, a central composite design-response surface methodology (CCD-RSM) was employed. The optimal values for extraction in the study were found to be 51.2 °C extraction temperature, 46.2 min extraction time, 27 % salt concentration, and 620 rpm stirring speed. Under the optimized conditions, the calibration curves demonstrated excellent linearity within the range of 0.1-100 μg L-1, with correlation coefficients (R2) exceeding 0.99. The limits of detection (LODs) for MTBE, ETBE, and TBF were obtained 0.06, 0.08, and 0.09 μg L-1, respectively. Moreover, the limits of quantification (LOQs) for MTBE, ETBE, and TBF were obtained 0.18, 0.24, and 0.27 μg L-1, respectively. The enrichment factor was also found to be in the range of 98-129.The NTD-GC-FID procedure demonstrated a high extraction efficiency, making it a promising tool for urinary biomonitoring of fuel ether oxygenates with improved sensitivity and selectivity compared to current methods.
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Affiliation(s)
- Vahid Jalili
- Department of Occupational Health Engineering, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Ghiasvand
- Department of Analytical Chemistry, Faculty of Chemistry, Lorestan University, Khorramabad, Iran; Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Tasmania 7001, Australia
| | - Homeira Ebrahimzadeh
- Department of Analytical Chemistry and Pollutants, Faculty of Chemistry and Petroleum Sciences, Shahid Beheshti University, Tehran, Iran
| | - Rezvan Zendehdel
- Department of Occupational Health Engineering, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Lowther C, Valkanas E, Giordano JL, Wang HZ, Currall BB, O'Keefe K, Pierce-Hoffman E, Kurtas NE, Whelan CW, Hao SP, Weisburd B, Jalili V, Fu J, Wong I, Collins RL, Zhao X, Austin-Tse CA, Evangelista E, Lemire G, Aggarwal VS, Lucente D, Gauthier LD, Tolonen C, Sahakian N, Stevens C, An JY, Dong S, Norton ME, MacKenzie TC, Devlin B, Gilmore K, Powell BC, Brandt A, Vetrini F, DiVito M, Sanders SJ, MacArthur DG, Hodge JC, O'Donnell-Luria A, Rehm HL, Vora NL, Levy B, Brand H, Wapner RJ, Talkowski ME. Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies. Am J Hum Genet 2023; 110:1454-1469. [PMID: 37595579 PMCID: PMC10502737 DOI: 10.1016/j.ajhg.2023.07.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/20/2023] Open
Abstract
Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.
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Affiliation(s)
- Chelsea Lowther
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Elise Valkanas
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Jessica L Giordano
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Harold Z Wang
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin B Currall
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Kathryn O'Keefe
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Emma Pierce-Hoffman
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nehir E Kurtas
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christopher W Whelan
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie P Hao
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ben Weisburd
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vahid Jalili
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jack Fu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Isaac Wong
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan L Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christina A Austin-Tse
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Emily Evangelista
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabrielle Lemire
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vimla S Aggarwal
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Diane Lucente
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Laura D Gauthier
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charlotte Tolonen
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nareh Sahakian
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Data Science Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christine Stevens
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joon-Yong An
- School of Biosystem and Biomedical Science, Korea University, Seoul, South Korea
| | - Shan Dong
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mary E Norton
- Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Tippi C MacKenzie
- Center for Maternal-Fetal Precision Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kelly Gilmore
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bradford C Powell
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alicia Brandt
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Francesco Vetrini
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Michelle DiVito
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Stephan J Sanders
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel G MacArthur
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Population Genomics, Garvan Institute of Medical Research, and University of New South Wales Sydney, Sydney, NSW, Australia; Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Jennelle C Hodge
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Anne O'Donnell-Luria
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Neeta L Vora
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Brynn Levy
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Ronald J Wapner
- Department of Obstetrics & Gynecology, Columbia University Medical Center, New York, NY, USA
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA; Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
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Jalili V, Cremona MA, Palluzzi F. Rescuing biologically relevant consensus regions across replicated samples. BMC Bioinformatics 2023; 24:240. [PMID: 37286963 PMCID: PMC10246347 DOI: 10.1186/s12859-023-05340-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 05/16/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites. RESULTS We rescue weak binding sites using MSPC, which efficiently exploits replicates to lower the threshold required to identify a site while keeping a low false-positive rate, and we compare it to IDR, a widely used post-processing method for identifying highly reproducible peaks across replicates. We observe several master transcription regulators (e.g., SP1 and GATA3) and HDAC2-GATA1 regulatory networks on rescued regions in K562 cell line. CONCLUSIONS We argue the biological relevance of weak binding sites and the information they add when rescued by MSPC. An implementation of the proposed extended MSPC methodology and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/ ; MSPC is distributed as a command-line application and an R package available from Bioconductor ( https://doi.org/doi:10.18129/B9.bioc.rmspc ).
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Affiliation(s)
- Vahid Jalili
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Marzia A Cremona
- Department of Operations and Decision Systems, Université Laval, Quebec, Canada.
- CHU de Québec - Université Laval Research Center, Quebec, Canada.
| | - Fernando Palluzzi
- Department of Brain and Behavioral Sciences, Università di Pavia, Pavia, Italy.
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Jalili V, Barkhordari A, Paull B, Ghiasvand A. Microextraction and Determination of Poly- and Perfluoroalkyl Substances, Challenges, and Future Trends. Crit Rev Anal Chem 2023; 53:463-482. [PMID: 34414831 DOI: 10.1080/10408347.2021.1964345] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are fluorocarbon compounds in which hydrogen atoms have been partly or entirely replaced by fluorine. They have a very wide range of applications, while they are persistent in the environment and exhibit bioaccumulative and toxic properties. Neither chemical nor biological mechanisms can decompose PFAS due to their strong C-F bonds. PFAS have shown adverse effects on various organisms, even at trace levels. Accordingly, highly sensitive and selective analytical methods are required for their tracing in biological and environmental matrices. The physicochemical properties of PFAS like surfactant characteristics and high-water solubility are unique and different from other known pollutants. Accordingly, the number of articles on the analysis of PFAS is less than the other well-known contaminants. The routine PFAS sample preparation methods (like solvent extraction) coupled with chromatographic systems, face challenges such as high limits of detection, need for laborious derivatization, limited selectivity, and expensive instrumentation. Recent efforts to address these limitations have aroused considerable attention to the development of microextraction techniques, which are consistent with the principles of green chemistry and can be made easily portable and automated. Moreover, these methods have shown enough sensitivity and selectivity for the analysis of different analytes (including PFAS) in a wide range of samples with different matrices. This research aims to review the microextraction methods and detection techniques, applied for the sample pretreatment of PFAS in various matrices, along with a critical discussion of the challenges and potential future trends.
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Affiliation(s)
- Vahid Jalili
- Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abdullah Barkhordari
- Environmental and Occupational Health Research Center, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Brett Paull
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - Alireza Ghiasvand
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
- Department of Chemistry, Lorestan University, Khoramabad, Iran
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Jalili V, Ghanbari Kakavandi M, Ghiasvand A, Barkhordari A. Microextraction techniques for sampling and determination of polychlorinated biphenyls: A comprehensive review. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Afgan E, Nekrutenko A, Grüning BA, Blankenberg D, Goecks J, Schatz MC, Ostrovsky AE, Mahmoud A, Lonie AJ, Syme A, Fouilloux A, Bretaudeau A, Nekrutenko A, Kumar A, Eschenlauer AC, DeSanto AD, Guerler A, Serrano-Solano B, Batut B, Grüning BA, Langhorst BW, Carr B, Raubenolt BA, Hyde CJ, Bromhead CJ, Barnett CB, Royaux C, Gallardo C, Blankenberg D, Fornika DJ, Baker D, Bouvier D, Clements D, de Lima Morais DA, Tabernero DL, Lariviere D, Nasr E, Afgan E, Zambelli F, Heyl F, Psomopoulos F, Coppens F, Price GR, Cuccuru G, Corguillé GL, Von Kuster G, Akbulut GG, Rasche H, Hotz HR, Eguinoa I, Makunin I, Ranawaka IJ, Taylor JP, Joshi J, Hillman-Jackson J, Goecks J, Chilton JM, Kamali K, Suderman K, Poterlowicz K, Yvan LB, Lopez-Delisle L, Sargent L, Bassetti ME, Tangaro MA, van den Beek M, Čech M, Bernt M, Fahrner M, Tekman M, Föll MC, Schatz MC, Crusoe MR, Roncoroni M, Kucher N, Coraor N, Stoler N, Rhodes N, Soranzo N, Pinter N, Goonasekera NA, Moreno PA, Videm P, Melanie P, Mandreoli P, Jagtap PD, Gu Q, Weber RJM, Lazarus R, Vorderman RHP, Hiltemann S, Golitsynskiy S, Garg S, Bray SA, Gladman SL, Leo S, Mehta SP, Griffin TJ, Jalili V, Yves V, Wen V, Nagampalli VK, Bacon WA, de Koning W, Maier W, Briggs PJ. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Res 2022; 50:W345-W351. [PMID: 35446428 PMCID: PMC9252830 DOI: 10.1093/nar/gkac247] [Citation(s) in RCA: 235] [Impact Index Per Article: 117.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/17/2022] [Accepted: 03/30/2022] [Indexed: 01/19/2023] Open
Abstract
Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.
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Moradpour Z, Helmi Kohnehshahri M, Vahabi Shekarloo M, Jalili V, Zendehdel R. Peroxidase-like reaction by a synergistic inorganic catalyst colloid: a new method for hydrogen peroxide detecting in air samples. Colloid Polym Sci 2021. [DOI: 10.1007/s00396-021-04887-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Gu Q, Kumar A, Bray S, Creason A, Khanteymoori A, Jalili V, Grüning B, Goecks J. Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine. PLoS Comput Biol 2021; 17:e1009014. [PMID: 34061826 PMCID: PMC8213174 DOI: 10.1371/journal.pcbi.1009014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/18/2021] [Accepted: 04/27/2021] [Indexed: 11/25/2022] Open
Abstract
Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.
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Affiliation(s)
- Qiang Gu
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America
- The Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Anup Kumar
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Simon Bray
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Allison Creason
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America
- The Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Alireza Khanteymoori
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Vahid Jalili
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America
- The Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America
- The Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, United States of America
- * E-mail:
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Jalili V, Barkhordari A, Ghiasvand A. Solid-phase microextraction technique for sampling and preconcentration of polycyclic aromatic hydrocarbons: A review. Microchem J 2020. [DOI: 10.1016/j.microc.2020.104967] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Heidari N, Ghiasvand A, Abdolhosseini S, Ghaedrahmati L, Barkhordari A, Jalili V. Magnetic field-assisted solid-phase extraction of nucleoside drugs using Fe3O4@PANI core/shell nanocomposite. J LIQ CHROMATOGR R T 2020. [DOI: 10.1080/10826076.2020.1798249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Nahid Heidari
- Department of Chemistry, Lorestan University, Khoramabad, Iran
| | - Alireza Ghiasvand
- Department of Chemistry, Lorestan University, Khoramabad, Iran
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Australia
| | | | | | - Abdullah Barkhordari
- Department of Occupational Health Engineering, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran
| | - Vahid Jalili
- Student Research Committee, Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Jalili V, Afgan E, Gu Q, Clements D, Blankenberg D, Goecks J, Taylor J, Nekrutenko A. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res 2020; 48:W395-W402. [PMID: 32479607 PMCID: PMC7319590 DOI: 10.1093/nar/gkaa434] [Citation(s) in RCA: 238] [Impact Index Per Article: 59.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/24/2020] [Accepted: 05/11/2020] [Indexed: 12/18/2022] Open
Abstract
Galaxy (https://galaxyproject.org) is a web-based computational workbench used by tens of thousands of scientists across the world to analyze large biomedical datasets. Since 2005, the Galaxy project has fostered a global community focused on achieving accessible, reproducible, and collaborative research. Together, this community develops the Galaxy software framework, integrates analysis tools and visualizations into the framework, runs public servers that make Galaxy available via a web browser, performs and publishes analyses using Galaxy, leads bioinformatics workshops that introduce and use Galaxy, and develops interactive training materials for Galaxy. Over the last two years, all aspects of the Galaxy project have grown: code contributions, tools integrated, users, and training materials. Key advances in Galaxy's user interface include enhancements for analyzing large dataset collections as well as interactive tools for exploratory data analysis. Extensions to Galaxy's framework include support for federated identity and access management and increased ability to distribute analysis jobs to remote resources. New community resources include large public servers in Europe and Australia, an increasing number of regional and local Galaxy communities, and substantial growth in the Galaxy Training Network.
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Affiliation(s)
- Vahid Jalili
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Enis Afgan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Qiang Gu
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Dave Clements
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - James Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Anton Nekrutenko
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
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Jalili V, Afgan E, Gu Q, Clements D, Blankenberg D, Goecks J, Taylor J, Nekrutenko A. Corrigendum: The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res 2020; 48:8205-8207. [PMID: 32585001 PMCID: PMC7641327 DOI: 10.1093/nar/gkaa554] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Vahid Jalili
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Enis Afgan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Qiang Gu
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Dave Clements
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - James Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Anton Nekrutenko
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
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Jalili V, Barkhordari A, Ghiasvand A. Correction to: Bioanalytical Applications of Microextraction Techniques: A Review of Reviews. Chromatographia 2020. [DOI: 10.1007/s10337-020-03891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Abstract
This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.
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Affiliation(s)
- Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
| | - Vahid Jalili
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joe W Gray
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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Jalili V, Afgan E, Taylor J, Goecks J. Cloud bursting galaxy: federated identity and access management. Bioinformatics 2020; 36:1-9. [PMID: 31197310 PMCID: PMC6956780 DOI: 10.1093/bioinformatics/btz472] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 05/29/2019] [Accepted: 06/05/2019] [Indexed: 12/02/2022] Open
Abstract
MOTIVATION Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to high-speed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users. RESULTS We have developed a generic and extensible approach for securely accessing biomedical datasets distributed across cloud computing platforms. Our approach combines OpenID Connect and OAuth2, best-practice Web protocols for authentication and authorization, together with Galaxy (https://galaxyproject.org), a web-based computational workbench used by thousands of scientists across the world. With our enhanced version of Galaxy, users can access and analyze data distributed across multiple cloud computing providers without any special knowledge of access/authorization protocols. Our approach does not require users to share permanent credentials (e.g. username, password, API key), instead relying on automatically generated temporary tokens that refresh as needed. Our approach is generalizable to most identity providers and cloud computing platforms. To the best of our knowledge, Galaxy is the only computational workbench where users can access biomedical datasets across multiple cloud computing platforms using best-practice Web security approaches and thereby minimize risks of unauthorized data access and credential use. AVAILABILITY AND IMPLEMENTATION Freely available for academic and commercial use under the open-source Academic Free License (https://opensource.org/licenses/AFL-3.0) from the following Github repositories: https://github.com/galaxyproject/galaxy and https://github.com/galaxyproject/cloudauthz.
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Affiliation(s)
- Vahid Jalili
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Enis Afgan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - James Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
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Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Grüning BA, Guerler A, Hillman-Jackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 2018; 46:W537-W544. [PMID: 29790989 PMCID: PMC6030816 DOI: 10.1093/nar/gky379] [Citation(s) in RCA: 2148] [Impact Index Per Article: 358.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 04/25/2018] [Accepted: 05/02/2018] [Indexed: 02/06/2023] Open
Abstract
Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.
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Affiliation(s)
- Enis Afgan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Dannon Baker
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Bérénice Batut
- Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany
| | | | - Dave Bouvier
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - Martin Čech
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - John Chilton
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - Dave Clements
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Nate Coraor
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - Björn A Grüning
- Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany
- Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany
| | - Aysam Guerler
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer Hillman-Jackson
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - Saskia Hiltemann
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Vahid Jalili
- Department of Biomedical Engineering, Oregon Health and Science University, OR, USA
| | - Helena Rasche
- Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany
| | | | - Jeremy Goecks
- Department of Biomedical Engineering, Oregon Health and Science University, OR, USA
| | - James Taylor
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Anton Nekrutenko
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA, USA
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
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Afgan E, Jalili V, Goonasekera N, Taylor J, Goecks J. Federated Galaxy: Biomedical Computing at the Frontier. IEEE Int Conf Cloud Comput 2018; 2018:10.1109/cloud.2018.00124. [PMID: 34386295 PMCID: PMC8356149 DOI: 10.1109/cloud.2018.00124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Biomedical data exploration requires integrative analyses of large datasets using a diverse ecosystem of tools. For more than a decade, the Galaxy project (https://galaxyproject.org) has provided researchers with a web-based, user-friendly, scalable data analysis framework complemented by a rich ecosystem of tools (https://usegalaxy.org/toolshed) used to perform genomic, proteomic, metabolomic, and imaging experiments. Galaxy can be deployed on the cloud (https://launch.usegalaxy.org), institutional computing clusters, and personal computers, or readily used on a number of public servers (e.g., https://usegalaxy.org). In this paper, we present our plan and progress towards creating Galaxy-as-a-Service-a federation of distributed data and computing resources into a panoptic analysis platform. Users can leverage a pool of public and institutional resources, in addition to plugging-in their private resources, helping answer the challenge of resource divergence across various Galaxy instances and enabling seamless analysis of biomedical data.
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Affiliation(s)
- Enis Afgan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Vahid Jalili
- Department of Biomedical Engineering Oregon Health and Science University Portland, OR, USA
| | - Nuwan Goonasekera
- Melbourne Bioinformatics University of Melbourne Melbourne, VIC, Australia
| | - James Taylor
- Departments of Biology amd Computer Science Johns Hopkins University Baltimore, MD, USA
| | - Jeremy Goecks
- Department of Biomedical Engineering Oregon Health and Science University Portland, OR, USA
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Abstract
Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser. We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www.bioinformatics.deib.polimi.it/MuSERA/.
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Jalili V, Matteucci M, Masseroli M, Ceri S. Explorative visual analytics on interval-based genomic data and their metadata. BMC Bioinformatics 2017; 18:536. [PMID: 29202689 PMCID: PMC5715631 DOI: 10.1186/s12859-017-1945-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Accepted: 11/19/2017] [Indexed: 02/07/2023] Open
Abstract
Background With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless “sense-making” of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/, and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.
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Affiliation(s)
- Vahid Jalili
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy.
| | - Matteo Matteucci
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy
| | - Marco Masseroli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy
| | - Stefano Ceri
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy
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Jalili V, Matteucci M, Masseroli M, Morelli MJ. Using combined evidence from replicates to evaluate ChIP-seq peaks. Bioinformatics 2015; 31:2761-9. [DOI: 10.1093/bioinformatics/btv293] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Accepted: 05/04/2015] [Indexed: 11/14/2022] Open
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Masseroli M, Pinoli P, Venco F, Kaitoua A, Jalili V, Palluzzi F, Muller H, Ceri S. GenoMetric Query Language: a novel approach to large-scale genomic data management. Bioinformatics 2015; 31:1881-8. [DOI: 10.1093/bioinformatics/btv048] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 01/21/2015] [Indexed: 01/15/2023] Open
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