COMPARATIVE METAGENOMICS ANALYSIS OF PALM OIL MILL EFFLUENT (POME) USING THREE DIFFERENT BIOINFORMATICS PIPELINES
DOI:
https://doi.org/10.31436/iiumej.v20i1.909Keywords:
bioinformatics pipeline, metagenomics analysisAbstract
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.
Downloads
Metrics
References
Kumar S, Krishnani KK, Bhushan B, and Brahmane MP. (2015) Metagenomics : Retrospect and Prospects in High Throughput Age, vol. 2015. DOI: https://doi.org/10.1155/2015/121735
Mincheol K, Lee KH, Yoon SW, Kim BS, Chun J, and Hana Y. (2013) Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era, 11(3): 102-113. DOI: https://doi.org/10.5808/GI.2013.11.3.102
Abdullah N and Sulaiman F. (2013) The oil palm wastes in Malaysia, Biomass Now – Sustain. Growth Use, pp. 75-100.
Meyer F, Paarmann D, Souza MD, Olson R, Glass EM, Kubal M, Edwards RA. (2008) The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes, 8: 1-8. https://doi.org/10.1186/1471-2105-9-386. DOI: https://doi.org/10.1186/1471-2105-9-386
Tang W, Bischof J, Desai N, Mahadik K, Gerlach W, Harrison T, Meyer F. (2014) Workload characterization for MG-RAST metagenomic data analytics service in the cloud. 2014 IEEE International Conference on Big Data (Big Data), 56-63. https://doi.org/10.1109/BigData.2014.7004394. DOI: https://doi.org/10.1109/BigData.2014.7004394
Wilke A, Gerlach W, Harrison T, Paczian T, Trimble WL, & Meyer F. (2016) MG-RAST Manual for version 4, revision 1.
Markowitz VM, Chen IMA, Chu K, Szeto E, Palaniappan K, Pillay M, Kyrpides NC. (2014) IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Research, 42(D1): 568-573. https://doi.org/10.1093/nar/gkt919. DOI: https://doi.org/10.1093/nar/gkt919
Huntemann M, Ivanova NN, Mavromatis K, Tripp HJ, Paez-espino D, Tennessen K, Kyrpides NC. (2016) The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v . 4). Standards in Genomic Sciences, 1: 1-5. https://doi.org/10.1186/s40793-016-0138-x. DOI: https://doi.org/10.1186/s40793-016-0138-x
Chen IMA, Markowitz VM, Chu K, Palaniappan K, Szeto E, Pillay M, Kyrpides NC. (2017) IMG/M: Integrated genome and metagenome comparative data analysis system. Nucleic Acids Research, 45(D1): D507-D516. https://doi.org/10.1093/nar/gkw929. DOI: https://doi.org/10.1093/nar/gkw929
Hunter S, Corbett M, Denise H, Fraser M, Gonzalez-beltran A, Hunter C, Sansone S. (2014) EBI metagenomics — a new resource for the analysis and archiving of metagenomic data, 42: 600-606. https://doi.org/10.1093/nar/gkt961. DOI: https://doi.org/10.1093/nar/gkt961
Denise H, Mitchell A, Bucchini F, Cochrane G, Denise H, Hoopen P, Finn RD. (2016) EBI metagenomics in 2016 - An expanding and evolving resource for the analysis and archiving of metagenomic data EBI metagenomics in 2016 - An expanding and evolving resource for the analysis and archiving of metagenomic data, (November 2015). https://doi.org/10.1093/nar/gkv1195. DOI: https://doi.org/10.1093/nar/gkv1195
Mitchell AL, Scheremetjew M, Denise H, Potter S, Tarkowska A, Qureshi M, Finn RD. (2017) EBI Metagenomics in 2017: Enriching the analysis of microbial communities, from sequence reads to assemblies. Nucleic Acids Research, 46(D1): D726-D735. https://doi.org/10.1093/nar/gkx967. DOI: https://doi.org/10.1093/nar/gkx967
D’Argenio V, Casaburi G, Precone V, & Salvatore F. (2014) Comparative metagenomic analysis of human gut microbiome composition using two different bioinformatic pipelines. Biomed Res Int, 325340. https://doi.org/10.1155/2014/325340. DOI: https://doi.org/10.1155/2014/325340
Plummer E, Twin J. (2015) A Comparison of Three Bioinformatics Pipelines for the Analysis of Preterm Gut Microbiota using 16S rRNA Gene Sequencing Data. J. Proteomics & Bioinformatics, 8(12): 283–291. https://doi.org/10.4172/jpb.1000381. DOI: https://doi.org/10.4172/jpb.1000381
Oulas A, Pavloudi C, Polymenakou P, Pavlopoulos GA, Papanikolaou N, Kotoulas G, Iliopoulos I. (2015) Metagenomics: Tools and Insights for Analyzing Next-Generation Sequencing Data Derived from Biodiversity Studies. Bioinformatics and Biology Insights, 9: 75-88. https://doi.org/10.4137/BBi.s12462. DOI: https://doi.org/10.4137/BBI.S12462
Dudhagara P, Bhavsar S, Bhagat C, Ghelani A, Bhatt S, & Patel R. (2015) Web Resources for Metagenomics Studies, 13: 296–30. DOI: https://doi.org/10.1016/j.gpb.2015.10.003
Malo N, Hanley JA, Cerquozzi S, Pelletier J, & Nadon R. (2006) Statistical practice in high-throughput screening data analysis. Nature Biotechnology, 24(2): 167-175. https://doi.org/10.1038/nbt1186. DOI: https://doi.org/10.1038/nbt1186
Birmingham A, Selfors LM, Forster T, Wrobel D, Kennedy CJ, Shanks E, Shamu CE. (2009) Statistical methods for analysis of high-throughput RNA interference screens. Nat Methods, 6(8): 569-575. https://doi.org/10.1038/nmeth.1351. DOI: https://doi.org/10.1038/nmeth.1351
Cox MP, Peterson DA, & Biggs PJ. (2010) SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics, 11: 485. https://doi.org/10.1186/1471-2105-11-485. DOI: https://doi.org/10.1186/1471-2105-11-485
Langmead B, & Salzberg SL. (2013) Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4): 357-359. https://doi.org/10.1038/nmeth.1923.Fast.
Zerbino DR, Birney E. (2008) Velvet Manual Version 1.1. Genome Research, 18(5): 821-829. https://doi.org/10.1101/gr.074492.107. DOI: https://doi.org/10.1101/gr.074492.107
Soleimaninanadegani M, Manshad S. (2014) Enhancement of Biodegradation of Palm Oil Mill Effluents by Local Isolated Microorganisms. International Scholarly Research Notices, 1-8. https://doi.org/10.1155/2014/727049. DOI: https://doi.org/10.1155/2014/727049
Habib MAB, Yusoff FM, Phang SM., Ang KJ, & Mohamed S. (1997) Nutritional values of chironomid larvae grown in palm oil mill effluent and algal culture. Aquaculture, 158(1-2): 95-105. https://doi.org/http://dx.doi.org/10.1016/S0044-8486(97)00176-2. DOI: https://doi.org/10.1016/S0044-8486(97)00176-2
Finn RD, Attwood TK, Babbitt PC, Bateman A, Bork P, Bridge AJ, Mitchell AL. (2017) InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Research, 45(D1): D190-D199. https://doi.org/10.1093/nar/gkw1107. DOI: https://doi.org/10.1093/nar/gkw1107
Kent WJ. (2002) BLAT — The BLAST -Like Alignment Tool. Genome Research, 12: 656-664. https://doi.org/10.1101/gr.229202. DOI: https://doi.org/10.1101/gr.229202
Downloads
Published
How to Cite
Issue
Section
