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Compareads: comparing huge metagenomic experiments.

Maillet N, Lemaitre C, Chikhi R, Lavenier D, Peterlongo P - BMC Bioinformatics (2012)

Bottom Line: We show that Compareads enables to retrieve biological information while being able to scale to huge datasets.Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples.

View Article: PubMed Central - HTML - PubMed

Affiliation: INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr

ABSTRACT

Background: Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.

Methods: This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.

Results: We show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.

Conclusion: Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/compareads/.

Show MeSH
The Compareads pipeline. Representation of the three steps while comparing symmetrically read sets A and B. In each set, reads are represented by horizontal lines. On each read one or two shared k-mers are represented by rectangles.
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Figure 1: The Compareads pipeline. Representation of the three steps while comparing symmetrically read sets A and B. In each set, reads are represented by horizontal lines. On each read one or two shared k-mers are represented by rectangles.

Mentions: The example presented in Figure 1 illustrates this issue for the case t = 2. The two first reads of sets A and B are similar. They are classically output by Compareads respectively in and . The two next reads contain only one shared k-mer (yellow) with reads of set B, they are discarded. The next read of set A contains two (red) shared k-mers with two distinct reads in set B. After a first comparison, contains this false positive read. However, in step 2, while computing , these two reads are not conserved in . Thus, during step 3, the two red k-mers are not present anymore in set and thus are not present in . They are thus correctly absent from the final results . However, the last read from set A is a case of false positive. It contains k-mers spread over distinct reads from B, the latter belonging to . Thus, even during step 3, these two k-mers remain shared with reads from set and are output in .


Compareads: comparing huge metagenomic experiments.

Maillet N, Lemaitre C, Chikhi R, Lavenier D, Peterlongo P - BMC Bioinformatics (2012)

The Compareads pipeline. Representation of the three steps while comparing symmetrically read sets A and B. In each set, reads are represented by horizontal lines. On each read one or two shared k-mers are represented by rectangles.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3526429&req=5

Figure 1: The Compareads pipeline. Representation of the three steps while comparing symmetrically read sets A and B. In each set, reads are represented by horizontal lines. On each read one or two shared k-mers are represented by rectangles.
Mentions: The example presented in Figure 1 illustrates this issue for the case t = 2. The two first reads of sets A and B are similar. They are classically output by Compareads respectively in and . The two next reads contain only one shared k-mer (yellow) with reads of set B, they are discarded. The next read of set A contains two (red) shared k-mers with two distinct reads in set B. After a first comparison, contains this false positive read. However, in step 2, while computing , these two reads are not conserved in . Thus, during step 3, the two red k-mers are not present anymore in set and thus are not present in . They are thus correctly absent from the final results . However, the last read from set A is a case of false positive. It contains k-mers spread over distinct reads from B, the latter belonging to . Thus, even during step 3, these two k-mers remain shared with reads from set and are output in .

Bottom Line: We show that Compareads enables to retrieve biological information while being able to scale to huge datasets.Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples.

View Article: PubMed Central - HTML - PubMed

Affiliation: INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr

ABSTRACT

Background: Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.

Methods: This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.

Results: We show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.

Conclusion: Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/compareads/.

Show MeSH