Megraft
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Application data |
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| Created by | Bengtsson J, Hartmann M, Eriksson KM, Nilsson RH |
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| Biological application domain(s) | Metagenomics, Phylogenetics, Sequence analysis, Community analysis, Rarefaction |
| Principal bioinformatics method(s) | Sequence analysis |
| Technology | 454, ABI SOLiD, Illumina, Ion Torrent, Sanger |
| Created at | University of Gothenburg |
| Maintained? | Yes |
| Input format(s) | FASTA |
| Output format(s) | FASTA |
| Programming language(s) | Perl |
| Licence | GPLv3 |
| Operating system(s) | Linux, UNIX, Mac OS X |
Summary: Megraft is a software tool to graft ribosomal small subunit (16S/18S) fragments from metagenomes onto full-length SSU sequences, enabling accurate diversity estimates from fragmentary and non-overlapping sequence data.
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Description
Megraft is a software tool to graft ribosomal small subunit (16S/18S) fragments onto full-length SSU sequences. Megraft is built upon the notion that when examining the depth of a community sequencing effort, researchers often use rarefaction analysis of the ribosomal small subunit (SSU/16S/18S) gene in a metagenome. However, the SSU sequences in metagenomic libraries generally are present as fragmentary, non-overlapping entries, which poses a great problem for this kind of analysis. Megraft aims to remedy this problem by grafting the input SSU fragments from the metagenome (obtained by e.g. Metaxa) onto full-length SSU sequences. The software also uses a variability model which accounts for observed and unobserved variability. This way, Megraft enables accurate assessment of species richness and sequencing depth in metagenomic datasets.
The algorithm, efficiency and accuracy of Megraft is thoroughly described in a recent paper. Megraft is ideally used with SSU sequence fragments extracted from a metagenome as input, and will output a set of artificial full-length SSU sequences in FASTA format, accounting for observed and unobserved variability between the input fragments and the closest reference sequence. The output file can then be used for rarefaction estimates and other diversity analyses. It should be noted that this is not a panacea for species richness estimates in metagenomics, but it is a huge step forward over existing approaches.
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