The data set SPD3.k51.ForMGRAST was uploaded on 2019-09-30 at 14:19:02 and contains 67,077 sequences totaling 10,342,990 basepairs with an average length of 154 bps.

Of the sequences tested, 10,773 sequences (16.06%) failed to pass the QC pipeline. Of those, dereplication identified 6,655 sequences as artificial duplicate reads.

Of the sequences that passed QC, 707 sequences (2%) contain ribosomal RNA genes, 12,083 sequences (27.68%) contain predicted proteins with known functions, and 30,868 sequences (70.70%) contain predicted proteins with unknown function.

Data shown here is displayed as a quick way to assess the quality and contents of the data set. We note that the submitting authors may have performed their own analysis. The analysis page provides the best way to perform in-depth analyses of this data set.

The data on this page represents the automated analysis generated by the MG-RAST automated processing pipeline. Details on the processing of this data set are in the processing receipt.

The kmer rank abundance graph plots the kmer coverage as a function of abundance rank, with the most abundant sequences at the left.

The graph below displays the number of hits in the different databases listed. These include protein databases, protein databases with functional hierarchy information, and ribosomal RNA databases. The bars representing annotated reads are colored by e-value range. Different databases have different numbers of hits but can also have different types of annotation data.

The pie charts below illustrate the distribution of functional categories for COGs, KOs, NOGs, and Subsystems at the highest level supported by these functional hierarchies. Each slice indicates the percentage of reads with predicted protein functions annotated to the category for the given source.

The charts below represent the distribution of taxa using a contigLCA algorithm finding a single consensus taxonomic entity for all features on each individual sequence.

The plots below show the taxonomic abundances ordered from the most abundant to least abundant. Only the top 50 most abundant are shown. The y-axis plots the abundances of annotations on a log scale.

The rank abundance chart is a tool for visually representing taxonomic richness and evenness.

The plot below shows the rarefaction curve of annotated species richness. This curve is a plot of the total number of distinct species annotations as a function of the number of sequences sampled. On the left, a steep slope indicates that a large fraction of the species diversity remains to be discovered. If the curve becomes flatter to the right, a reasonable number of individuals is sampled: more intensive sampling is likely to yield only few additional species.

Sampling curves generally rise very quickly at first and then level off toward an asymptote as fewer new species are found per unit of individuals collected. These rarefaction curves are calculated from the table of species abundance. The curves represent the average number of different species annotations for subsamples of the complete data set.

The above image shows the range of α-diversity values in the study 'SPlab-run431-SPD3-asm'. The min, max, and mean values are shown, with the standard deviation ranges (σ and 2σ) in different shades. The α-diversity of this metagenome is shown in red.

Alpha diversity summarizes the diversity of organisms in a sample with a single number. The α-diversity of annotated samples can be estimated from the distribution of the species-level annotations.

Annotated species richness is the number of distinct species annotations in the combined MG-RAST data set. Shannon diversity is an abundance-weighted average of the logarithm of the relative abundances of annotated species. The species-level annotations are from all the annotation source databases used by MG-RAST.

The histograms below show the distribution of sequence lengths in basepairs for this metagenome. Each position represents the number of sequences within a length bp range.

The data used in these graphs is based on raw upload and post-QC sequences.

The histograms below show the distribution of the GC percentage for this metagenome. Each position represents the number of sequences within a GC percentage range. The data used in these graphs is based on raw upload and post-QC sequences.