Metagenomics is the analysis of the genomic sequences from a usually uncultured environmental sample. A large term goal of most metagenomics is to inventory and measure the extent and the role of microbialbiodiversity in the ecosystem due to discoveries that the diversity of microbial organisms and viral agents in the environment is far greater than previously estimated.[3] Tools that allow the investigation of very large data sets from environmental samples using shotgun sequencing techniques in particular, such as MEGAN, are designed to sample and investigate the unknown biodiversity of environmental samples where more precise techniques with smaller, better known samples, cannot be used.
Fragments of DNA from an metagenomics sample, such as ocean waters or soil, are compared against databases of known DNA sequences using BLAST or another sequence comparison tool to assemble the segments into discrete comparable sequences. MEGAN is then used to compare the resulting sequences with gene sequences from GenBank in NCBI.[4] The program was used to investigate the DNA of a mammoth recovered from the Siberianpermafrost[5] and Sargasso Sea data set.[6]
Metagenomics is the study of genomic content of samples from same habitat, which is designed to determine the role and the extent of species diversity. Targeted or random sequencing are widely used with comparisons against sequence databases.[1] Recent developments in sequencing technology increased the number of metagenomics samples. MEGAN is an easy to use tool for analysing such metagenomics data. First version of MEGAN was released in 2007 [1] and the most recent version is MEGAN6.[7] First version is capable of analysing taxonomic content of a single dataset while the latest version can analyse multiple datasets including new features (query different databases, new algorithm etc.).
MEGAN Pipelineedit
MEGAN analysis starts with collecting reads from any shotgun platform. Then, the reads are compared with sequence databases using BLAST or similar. Third, MEGAN assigns a taxon ID to processed read results based on NCBI taxonomy which creates a MEGAN file that contains required information for statistical and graphical analysis. Lastly, lowest common ancestor (LCA) algorithm can be run to inspect assignments, to analyze data and to create summaries of data based on different NCBI taxonomy levels. LCA algorithm simply finds the lowest common ancestor of different species.[1][2]
Referencesedit
^ abcdHuson, H.; A. Auch; Ji Qi; S. C. Schuster (2007). "MEGAN Analysis of Metagenomic Data". Genome Research. 17 (3): 377–386. doi:10.1101/gr.5969107. PMC1800929. PMID 17255551. Retrieved April 3, 2008.
^ abHuson, Daniel H; S. Mitra; N. Weber; H. Ruscheweyh; Stephan C. Schuster (2011). "Integrative analysis of environmental sequences using MEGAN4". Genome Research. 21 (9): 1552–1560. doi:10.1101/gr.120618.111. PMC3166839. PMID 21690186.
^Nee, S. (2004). "More than meets the eye". Nature. 429 (6994): 804–805. Bibcode:2004Natur.429..804N. doi:10.1038/429804a. PMID 15215837. S2CID 1699973.
^Frias-Lopez, Jorge; Yanmei Shi; Gene W. Tyson; Maureen L. Coleman; Stephan C. Schuster; Sallie W. Chisholm; band Edward F. DeLong (March 11, 2008). "Microbial community gene expression in ocean surface waters" (PDF). PNAS. 105 (10): 3805–3810. doi:10.1073/pnas.0708897105. PMC2268829. PMID 18316740. Retrieved April 3, 2008.
^Poinar, Hendrik N.; Carsten Schwarz; Ji Qi; Beth Shapiro; Ross D. E. MacPhee; Bernard Buigues; Alexei Tikhonov; Daniel Huson; Lynn P. Tomsho; Alexander Auch; Markus Rampp; Webb Miller; Stephan C. Schuster (2007). "Metagenomics to Paleogenomics: Large-Scale Sequencing of Mammoth DNA". Science. 331 (6016): 392–394. doi:10.1126/science.331.6016.392. PMID 21273464. Retrieved April 3, 2008.
^Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers YH, Smith HO (April 2004). "Environmental Genome Shotgun Sequencing of the Sargasso Sea". Science. 304 (5667): 66–74. Bibcode:2004Sci...304...66V. CiteSeerX10.1.1.124.1840. doi:10.1126/science.1093857. PMID 15001713. S2CID 1454587.
^"MEGAN6 — Algorithms in Bioinformatics". uni-tuebingen.de. Retrieved December 21, 2020. Huson, Daniel H; S. Beier; I. Flade; A. Gorska; M. El-Hadidi; H. Ruscheweyh; R. Tappu (2016). "MEGAN Community Edition - Interactive exploration and analysis of large-scale microbiome sequencing data". PLOS Computational Biology. 12 (6): e1004957. Bibcode:2016PLSCB..12E4957H. doi:10.1371/journal.pcbi.1004957. PMC4915700. PMID 27327495.
megan, metagenome, analyzer, computer, program, that, allows, optimized, analysis, large, metagenomic, datasets, developer, daniel, huson, stable, release6, 2017repositorygithub, husonlab, megan, cewritten, injavaoperating, systemwindows, unix, linux, macospla. MEGAN MEtaGenome ANalyzer is a computer program that allows optimized analysis of large metagenomic datasets 1 2 MEGANDeveloper s Daniel Huson et al Stable release6 13 1 2017Repositorygithub wbr com wbr husonlab wbr megan ceWritten inJavaOperating systemWindows Unix Linux macOSPlatformJavaTypeBioinformaticsLicenseFree open source community edition commercial Ultimate edition licensed by ComputomicsWebsiteuni tuebingen wbr de wbr fakultaeten wbr mathematisch naturwissenschaftliche fakultaet wbr fachbereiche wbr informatik wbr lehrstuehle wbr algorithms in bioinformatics wbr software wbr megan6 wbr Metagenomics is the analysis of the genomic sequences from a usually uncultured environmental sample A large term goal of most metagenomics is to inventory and measure the extent and the role of microbial biodiversity in the ecosystem due to discoveries that the diversity of microbial organisms and viral agents in the environment is far greater than previously estimated 3 Tools that allow the investigation of very large data sets from environmental samples using shotgun sequencing techniques in particular such as MEGAN are designed to sample and investigate the unknown biodiversity of environmental samples where more precise techniques with smaller better known samples cannot be used Fragments of DNA from an metagenomics sample such as ocean waters or soil are compared against databases of known DNA sequences using BLAST or another sequence comparison tool to assemble the segments into discrete comparable sequences MEGAN is then used to compare the resulting sequences with gene sequences from GenBank in NCBI 4 The program was used to investigate the DNA of a mammoth recovered from the Siberian permafrost 5 and Sargasso Sea data set 6 Contents 1 Introduction 2 MEGAN Pipeline 3 References 4 External linksIntroduction editMetagenomics is the study of genomic content of samples from same habitat which is designed to determine the role and the extent of species diversity Targeted or random sequencing are widely used with comparisons against sequence databases 1 Recent developments in sequencing technology increased the number of metagenomics samples MEGAN is an easy to use tool for analysing such metagenomics data First version of MEGAN was released in 2007 1 and the most recent version is MEGAN6 7 First version is capable of analysing taxonomic content of a single dataset while the latest version can analyse multiple datasets including new features query different databases new algorithm etc MEGAN Pipeline editMEGAN analysis starts with collecting reads from any shotgun platform Then the reads are compared with sequence databases using BLAST or similar Third MEGAN assigns a taxon ID to processed read results based on NCBI taxonomy which creates a MEGAN file that contains required information for statistical and graphical analysis Lastly lowest common ancestor LCA algorithm can be run to inspect assignments to analyze data and to create summaries of data based on different NCBI taxonomy levels LCA algorithm simply finds the lowest common ancestor of different species 1 2 References edit a b c d Huson H A Auch Ji Qi S C Schuster 2007 MEGAN Analysis of Metagenomic Data Genome Research 17 3 377 386 doi 10 1101 gr 5969107 PMC 1800929 PMID 17255551 Retrieved April 3 2008 a b Huson Daniel H S Mitra N Weber H Ruscheweyh Stephan C Schuster 2011 Integrative analysis of environmental sequences using MEGAN4 Genome Research 21 9 1552 1560 doi 10 1101 gr 120618 111 PMC 3166839 PMID 21690186 Nee S 2004 More than meets the eye Nature 429 6994 804 805 Bibcode 2004Natur 429 804N doi 10 1038 429804a PMID 15215837 S2CID 1699973 Frias Lopez Jorge Yanmei Shi Gene W Tyson Maureen L Coleman Stephan C Schuster Sallie W Chisholm band Edward F DeLong March 11 2008 Microbial community gene expression in ocean surface waters PDF PNAS 105 10 3805 3810 doi 10 1073 pnas 0708897105 PMC 2268829 PMID 18316740 Retrieved April 3 2008 Poinar Hendrik N Carsten Schwarz Ji Qi Beth Shapiro Ross D E MacPhee Bernard Buigues Alexei Tikhonov Daniel Huson Lynn P Tomsho Alexander Auch Markus Rampp Webb Miller Stephan C Schuster 2007 Metagenomics to Paleogenomics Large Scale Sequencing of Mammoth DNA Science 331 6016 392 394 doi 10 1126 science 331 6016 392 PMID 21273464 Retrieved April 3 2008 Venter JC Remington K Heidelberg JF Halpern AL Rusch D Eisen JA Wu D Paulsen I Nelson KE Nelson W Fouts DE Levy S Knap AH Lomas MW Nealson K White O Peterson J Hoffman J Parsons R Baden Tillson H Pfannkoch C Rogers YH Smith HO April 2004 Environmental Genome Shotgun Sequencing of the Sargasso Sea Science 304 5667 66 74 Bibcode 2004Sci 304 66V CiteSeerX 10 1 1 124 1840 doi 10 1126 science 1093857 PMID 15001713 S2CID 1454587 MEGAN6 Algorithms in Bioinformatics uni tuebingen de Retrieved December 21 2020 Huson Daniel H S Beier I Flade A Gorska M El Hadidi H Ruscheweyh R Tappu 2016 MEGAN Community Edition Interactive exploration and analysis of large scale microbiome sequencing data PLOS Computational Biology 12 6 e1004957 Bibcode 2016PLSCB 12E4957H doi 10 1371 journal pcbi 1004957 PMC 4915700 PMID 27327495 External links editmegan ce on GitHub Retrieved from https en wikipedia org w index php title MEGAN amp oldid 1189439803, wikipedia, wiki, book, books, library,
