The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.
References
[1]
Proctor GN (1994) Mathematics of microbial plasmid instability and subsequent differential growth of plasmid-free and plasmid-containing cells, relevant to the analysis of experimental colony number data. Plasmid 32: 101–130. doi: 10.1006/plas.1994.1051
[2]
Jurkowski A, Reid AH, Labov JB (2007) Metagenomics: a call for bringing a new science into the classroom (while it's still new). CBE Life Sci Educ 6: 260–265. doi: 10.1187/cbe.07-09-0075
[3]
Eisen JA (2007) Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes. PLoS Biol 5: e82. doi: 10.1371/journal.pbio.0050082
[4]
Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, et al. (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304: 66–74. doi: 10.1126/science.1093857
[5]
Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, et al. (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428: 37–43. doi: 10.1038/nature02340
[6]
Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, et al. (2011) Enterotypes of the human gut microbiome. Nature 473: 174–180. doi: 10.1038/nature10187
[7]
Shah N, Tang H, Doak TG, Ye Y (2011) Comparing Bacterial Communities Inferred from 16S rRNA Gene Sequencing and Shotgun Metagenomics. Pac Symp Biocomput 165–176. doi: 10.1142/9789814335058_0018
[8]
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, et al. (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75: 7537–7541. doi: 10.1128/aem.01541-09
[9]
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7: 335–336. doi: 10.1038/nmeth.f.303
[10]
Huson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN analysis of metagenomic data. Genome Res 17: 377–386. doi: 10.1101/gr.5969107
[11]
Krause L, Diaz NN, Goesmann A, Kelley S, Nattkemper TW, et al. (2008) Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Res 36: 2230–2239. doi: 10.1093/nar/gkn038
[12]
Monzoorul Haque M, Ghosh TS, Komanduri D, Mande SS (2009) SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences. Bioinformatics 25: 1722–1730. doi: 10.1093/bioinformatics/btp317
[13]
Gnerre S, Maccallum I, Przybylski D, Ribeiro FJ, Burton JN, et al. (2011) High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc Natl Acad Sci U S A 108: 1513–1518. doi: 10.1073/pnas.1017351108
[14]
Peng Y, Leung HC, Yiu SM, Chin FY (2012) IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28: 1420–1428. doi: 10.1093/bioinformatics/bts174
[15]
Glass EM, Wilkening J, Wilke A, Antonopoulos D, Meyer F (2010) Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. Cold Spring Harb Protoc 2010: pdb prot5368. doi: 10.1101/pdb.prot5368
[16]
Seshadri R, Kravitz SA, Smarr L, Gilna P, Frazier M (2007) CAMERA: a community resource for metagenomics. PLoS Biol 5: e75. doi: 10.1371/journal.pbio.0050075
[17]
Su X, Xu J, Ning K (2012) Parallel-META: efficient metagenomic data analysis based on high-performance computation. BMC Systems Biology 6: S16. doi: 10.1186/1752-0509-6-s1-s16
[18]
Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, et al. (2012) Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 9: 811–814. doi: 10.1038/nmeth.2066
[19]
Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, et al. (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21: 3674–3676. doi: 10.1093/bioinformatics/bti610
[20]
Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, et al. (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33: 5691–5702. doi: 10.1093/nar/gki866
[21]
Johnson LS, Eddy SR, Portugaly E (2010) Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics 11: 431. doi: 10.1186/1471-2105-11-431
[22]
Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77: 257–286. doi: 10.1109/5.18626
[23]
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, et al. (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35: 7188–7196. doi: 10.1093/nar/gkm864
[24]
Walters JP, Balu V, Kompalli S, Chaudhary V (2009) Evaluating the use of GPUs in Liver Image Segmentation and HMMER Database Searches. 2009 Ieee International Symposium on Parallel & Distributed Processing 1–5: 1010–1021. doi: 10.1109/ipdps.2009.5161073
[25]
Sun YT, Li P, Gu GC, Wen Y, Liu Y, et al. (2009) Accelerating HMMer on FPGAs Using Systolic Array Based Architecture. 2009 Ieee International Symposium on Parallel & Distributed Processing 1–5: 1570–1577. doi: 10.1109/ipdps.2009.5160927
[26]
Rho M, Tang H, Ye Y (2010) FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res 38: e191. doi: 10.1093/nar/gkq747
[27]
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, et al. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72: 5069–5072. doi: 10.1128/aem.03006-05
[28]
Cole JR, Wang Q, Cardenas E, Fish J, Chai B, et al. (2009) The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 37: D141–145. doi: 10.1093/nar/gkn879
[29]
Griffen AL, Beall CJ, Firestone ND, Gross EL, Difranco JM, et al. (2011) CORE: a phylogenetically-curated 16S rDNA database of the core oral microbiome. PLoS One 6: e19051. doi: 10.1371/journal.pone.0019051
[30]
Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18: 821–829. doi: 10.1101/gr.074492.107
[31]
Song B, Su X, Xu J, Ning K (2012) MetaSee: An Interactive and Extendable Visualization Toolbox for Metagenomic Sample Analysis and Comparison. PLoS One 7: e48998. doi: 10.1371/journal.pone.0048998
[32]
Ondov BD, Bergman NH, Phillippy AM (2011) Interactive metagenomic visualization in a Web browser. BMC Bioinformatics 12: 385. doi: 10.1186/1471-2105-12-385
[33]
Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, et al. (2009) A core gut microbiome in obese and lean twins. Nature 457: 480–484. doi: 10.1038/nature07540
[34]
Chen T, Yu WH, Izard J, Baranova OV, Lakshmanan A, et al. (2010) The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information. Database (Oxford) 2010: baq013. doi: 10.1093/database/baq013
[35]
Yang F, Zeng X, Ning K, Liu KL, Lo CC, et al. (2011) Saliva microbiomes distinguish caries-active from healthy human populations. ISME J doi: 10.1038/ismej.2011.71
