Eu-Detect: an algorithm for detecting eukaryotic sequences in metagenomic data sets.

Physical partitioning techniques are routinely employed (during sample preparation stage) for segregating the prokaryotic and eukaryotic fractions of metagenomic samples. In spite of these efforts, several metagenomic studies focusing on bacterial and archaeal populations have reported the presence of contaminating eukaryotic sequences in metagenomic data sets. Contaminating sequences originate not only from genomes of micro-eukaryotic species but also from genomes of (higher) eukaryotic host cells.

ProViDE: A software tool for accurate estimation of viral diversity in metagenomic samples.

Given the absence of universal marker genes in the viral kingdom, researchers typically use BLAST (with stringent E-values) for taxonomic classification of viral metagenomic sequences. Since majority of metagenomic sequences originate from hitherto unknown viral groups, using stringent e-values results in most sequences remaining unclassified. Furthermore, using less stringent e-values results in a high number of incorrect taxonomic assignments.

SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences.

Motivation: One of the first steps in metagenomic analysis is the assignment of reads/contigs obtained from various sequencing technologies to their correct taxonomic bins. Similarity-based binning methods assign a read to a taxon/clade, based on the pattern of significant BLAST hits generated against sequence databases. Existing methods, which use bit-score as the sole parameter to ascertain the significance of BLAST hits, have limited specificity and accuracy of binning.