Sommer DD, Delcher AL, Salzberg SL et al (2007) Minimus: a fast, lightweight genome assembler.Mason OU, Hazen TC, Borglin S et al (2012) Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill.Luo R, Liu B, Xie Y et al (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler.Miller CS, Baker BJ, Thomas BC et al (2011) EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data.Fan L, McElroy K, Thomas T (2012) Reconstruction of ribosomal RNA genes from metagenomic data.Radax R, Rattei T, Lanzen A et al (2012) Metatranscriptomics of the marine sponge Geodia barretti: tackling phylogeny and function of its microbial community.Goecks J, Nekrutenko A, Taylor J, The Galaxy Team (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences.Salmela L, Schroder J (2011) Correcting errors in short reads by multiple alignments.Morgulis A, Gertz EM, Schäffer AA et al (2006) A fast and symmetric DUST implementation to mask low-complexity DNA sequences.Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets.Schmieder R, Lim YW, Rohwer F et al (2010) TagCleaner: identification and removal of tag sequences from genomic and metagenomic datasets.Brown CT, Howe A, Zhang Q et al (2013) A reference-free algorithm for computational normalization of shotgun sequencing data.Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST.
#CLC GENOMICS WORKBENCH PHYLOGENY TOOLS SOFTWARE#
#CLC GENOMICS WORKBENCH PHYLOGENY TOOLS SERIAL#
Velculescu VE et al (1995) Serial analysis of gene expression.Kapranov P et al (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription.Additionally, we propose a post-processing pipeline using the latest software tools to conduct further studies on the filtered data, including the reconstruction of mRNA transcripts for functional analyses and phylogenetic classification of a community using the ribosomal RNA. In this chapter, we demonstrate a computational technique for filtering rRNA from total RNA using the software SortMeRNA. Numerous chemical and computational methods exist to separate families of RNA prior to conducting further downstream analyses, primarily suitable for isolating mRNA or rRNA from a total RNA sample. Species identification is mainly established using the ribosomal RNA genes, whereas the behavior and functionality of a community is revealed by the messenger RNA of the expressed genes. Software tools designed for deciphering metatranscriptomic data fall under two main categories: the first is to reassemble millions of short nucleotide fragments produced by high-throughput sequencing technologies into the original full-length transcriptomes for all organisms within a sample, and the second is to taxonomically classify the organisms and determine their individual functional roles within a community. High-quality total RNA is a bountiful mixture of ribosomal, transfer, messenger and other noncoding RNAs, where each family of RNA is vital to answering questions concerning the hidden microbial world. Metatranscriptomic data contributes another piece of the puzzle to understanding the phylogenetic structure and function of a community of organisms.
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