Dataset Information

CREST--classification resources for environmental sequence tags.

ABSTRACT: Sequencing of taxonomic or phylogenetic markers is becoming a fast and efficient method for studying environmental microbial communities. This has resulted in a steadily growing collection of marker sequences, most notably of the small-subunit (SSU) ribosomal RNA gene, and an increased understanding of microbial phylogeny, diversity and community composition patterns. However, to utilize these large datasets together with new sequencing technologies, a reliable and flexible system for taxonomic classification is critical. We developed CREST (Classification Resources for Environmental Sequence Tags), a set of resources and tools for generating and utilizing custom taxonomies and reference datasets for classification of environmental sequences. CREST uses an alignment-based classification method with the lowest common ancestor algorithm. It also uses explicit rank similarity criteria to reduce false positives and identify novel taxa. We implemented this method in a web server, a command line tool and the graphical user interfaced program MEGAN. Further, we provide the SSU rRNA reference database and taxonomy SilvaMod, derived from the publicly available SILVA SSURef, for classification of sequences from bacteria, archaea and eukaryotes. Using cross-validation and environmental datasets, we compared the performance of CREST and SilvaMod to the RDP Classifier. We also utilized Greengenes as a reference database, both with CREST and the RDP Classifier. These analyses indicate that CREST performs better than alignment-free methods with higher recall rate (sensitivity) as well as precision, and with the ability to accurately identify most sequences from novel taxa. Classification using SilvaMod performed better than with Greengenes, particularly when applied to environmental sequences. CREST is freely available under a GNU General Public License (v3) from http://apps.cbu.uib.no/crest and http://lcaclassifier.googlecode.com.

SUBMITTER: Lanzen A

PROVIDER: S-EPMC3493522 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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CREST--classification resources for environmental sequence tags.

Lanzén Anders A Jørgensen Steffen L SL Huson Daniel H DH Gorfer Markus M Grindhaug Svenn Helge SH Jonassen Inge I Øvreås Lise L Urich Tim T

PloS one 20121108 11

Sequencing of taxonomic or phylogenetic markers is becoming a fast and efficient method for studying environmental microbial communities. This has resulted in a steadily growing collection of marker sequences, most notably of the small-subunit (SSU) ribosomal RNA gene, and an increased understanding of microbial phylogeny, diversity and community composition patterns. However, to utilize these large datasets together with new sequencing technologies, a reliable and flexible system for taxonomic ...[more]

PMID: 23145153

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Project description:Ligating adapters with unique synthetic oligonucleotide sequences (sequence tags) onto individual DNA samples before massively parallel sequencing is a popular and efficient way to obtain sequence data from many individual samples. Tag sequences should be numerous and sufficiently different to ensure sequencing, replication, and oligonucleotide synthesis errors do not cause tags to be unrecoverable or confused. However, many design approaches only protect against substitution errors during sequencing and extant tag sets contain too few tag sequences. We developed an open-source software package to validate sequence tags for conformance to two distance metrics and design sequence tags robust to indel and substitution errors. We use this software package to evaluate several commercial and non-commercial sequence tag sets, design several large sets (max(count) = 7,198) of edit metric sequence tags having different lengths and degrees of error correction, and integrate a subset of these edit metric tags to polymerase chain reaction (PCR) primers and sequencing adapters. We validate a subset of these edit metric tagged PCR primers and sequencing adapters by sequencing on several platforms and subsequent comparison to commercially available alternatives. We find that several commonly used sets of sequence tags or design methodologies used to produce sequence tags do not meet the minimum expectations of their underlying distance metric, and we find that PCR primers and sequencing adapters incorporating edit metric sequence tags designed by our software package perform as well as their commercial counterparts. We suggest that researchers evaluate sequence tags prior to use or evaluate tags that they have been using. The sequence tag sets we design improve on extant sets because they are large, valid across the set, and robust to the suite of substitution, insertion, and deletion errors affecting massively parallel sequencing workflows on all currently used platforms.

Dataset Information

CREST--classification resources for environmental sequence tags.

Publications

CREST--classification resources for environmental sequence tags.

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