Dataset Information

An optimized procedure greatly improves EST vector contamination removal.

ABSTRACT:

Background

The enormous amount of sequence data available in the public domain database has been a gold mine for researchers exploring various themes in life sciences, and hence the quality of such data is of serious concern to researchers. Removal of vector contamination is one of the most significant operations to obtain accurate sequence data containing only a cDNA insert from the basecalls output by an automatic DNA sequencer. Popular bioinformatics programs to accomplish vector trimming include LUCY, cross_match and SeqClean.

Results

In a recent study, where the program SeqClean was used to remove vector contamination from our test set of EST data compiled through various library construction systems, however, a significant number of errors remained after preliminary trimming. These errors were later almost completely corrected by simply using a re-linearized form of the cloning vector to compare against the target ESTs. The modified trimming procedure for SeqClean was also compared with the trimming efficiency of the other two popular programs, LUCY2, and cross_match. Using SeqClean with a re-linearized form of the cloning vector significantly surpassed the other two programs in all tested conditions, while the performance of the other two programs was not influenced by the modified procedure. Vector contamination in dbEST was also investigated in this study: 2203 out of the 48212 ESTs sampled from dbEST (2007-04-18 freeze) were found to match sequences in UNIVEC.

Conclusion

Vector contamination remains a serious concern to the data quality in the public sequence database nowadays. Based on the results presented here, we feel that our modified procedure with SeqClean should be recommended to all researchers for the task of vector removal from EST or genomic sequences.

SUBMITTER: Chen YA

PROVIDER: S-EPMC2194723 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature

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Publications

An optimized procedure greatly improves EST vector contamination removal.

Chen Yi-An YA Lin Chang-Chun CC Wang Chin-Di CD Wu Huan-Bin HB Hwang Pei-Ing PI

BMC genomics 20071113

<h4>Background</h4>The enormous amount of sequence data available in the public domain database has been a gold mine for researchers exploring various themes in life sciences, and hence the quality of such data is of serious concern to researchers. Removal of vector contamination is one of the most significant operations to obtain accurate sequence data containing only a cDNA insert from the basecalls output by an automatic DNA sequencer. Popular bioinformatics programs to accomplish vector trim ...[more]

PMID: 17997864

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Project description:BackgroundShotgun metagenome sequencing data obtained from a host environment will usually be contaminated with sequences from the host organism. Host sequences should be removed before further analysis to avoid biases, reduce downstream computational load, or ensure privacy in the case of a human host. The tools that we identified, as designed specifically to perform host contamination sequence removal, were either outdated, not maintained, or complicated to use. Consequently, we have developed HoCoRT, a fast and user-friendly tool that implements several methods for optimised host sequence removal. We have evaluated the speed and accuracy of these methods.ResultsHoCoRT is an open-source command-line tool for host contamination removal. It is designed to be easy to install and use, offering a one-step option for genome indexing. HoCoRT employs a variety of well-known mapping, classification, and alignment methods to classify reads. The user can select the underlying classification method and its parameters, allowing adaptation to different scenarios. Based on our investigation of various methods and parameters using synthetic human gut and oral microbiomes, and on assessment of publicly available data, we provide recommendations for typical datasets with short and long reads.ConclusionsTo decontaminate a human gut microbiome with short reads using HoCoRT, we found the optimal combination of speed and accuracy with BioBloom, Bowtie2 in end-to-end mode, and HISAT2. Kraken2 consistently demonstrated the highest speed, albeit with a trade-off in accuracy. The same applies to an oral microbiome, but here Bowtie2 was notably slower than the other tools. For long reads, the detection of human host reads is more difficult. In this case, a combination of Kraken2 and Minimap2 achieved the highest accuracy and detected 59% of human reads. In comparison to the dedicated DeconSeq tool, HoCoRT using Bowtie2 in end-to-end mode proved considerably faster and slightly more accurate. HoCoRT is available as a Bioconda package, and the source code can be accessed at https://github.com/ignasrum/hocort along with the documentation. It is released under the MIT licence and is compatible with Linux and macOS (except for the BioBloom module).

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Dataset Information

An optimized procedure greatly improves EST vector contamination removal.

Background

Results

Conclusion

Publications

An optimized procedure greatly improves EST vector contamination removal.

Similar Datasets

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