The data set SPD3 was uploaded on 2019-09-17 at 10:12:30 and contains 8,160,732 sequences totaling 1,185,564,231 basepairs with an average length of 145 bps.

Of the sequences tested, 1,146,798 sequences (14.05%) failed to pass the QC pipeline. Of those, dereplication identified 798,030 sequences as artificial duplicate reads.

Of the sequences that passed QC, 32,424 sequences (1%) contain ribosomal RNA genes, 1,295,924 sequences (23.51%) contain predicted proteins with known functions, and 4,183,397 sequences (75.90%) 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.

For assessing the quality of the data set, the nucleotide position histogram is a good starting point.

Duplicate Read Inferred Sequencing Error Estimation (Keegan et al., PLoS Computational Biology, 2012)

The above image shows the range of total DRISEE percent errors in all of MG-RAST. The min, max, and mean values are shown, with the standard deviation ranges (σ and 2σ) in different shades. The total DRISEE percent error of this metagenome is shown in red.

DRISEE is a tool that utilizes artificial duplicate reads (ADRs) to provide a platform-independent assessment of sequencing error in metagenomic (or genomic) sequencing data. DRISEE is designed to consider shotgun data. Currently, it is not appropriate for amplicon data.

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

This graph shows the fraction of base pairs of each type (A, C, G, T, or ambiguous base N) at each position starting from the beginning of each read. Shotgun data sets should have roughly equal proportions of basecalls.

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.