Dataset Information

CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.

ABSTRACT:

Summary

Sequence comparison of genetic material between known and unknown organisms plays a crucial role in genomics, metagenomics and phylogenetic analysis. The emerging long-read sequencing technologies can now produce reads of tens of kilobases in length that promise a more accurate assessment of their origin. To facilitate the classification of long and short DNA sequences, we have developed a Python package that implements a new sequence classification model that we have demonstrated to improve the classification accuracy when compared with other state of the art classification methods. For the purpose of validation, and to demonstrate its usefulness, we test the combined sequence similarity score classifier (CSSSCL) using three different datasets, including a metagenomic dataset composed of short reads.

Availability and implementation

Package's source code and test datasets are available under the GPLv3 license at https://github.com/oicr-ibc/cssscl.

Contact

ivan.borozan@oicr.on.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Borozan I

PROVIDER: S-EPMC4734043 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Publications

CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.

Borozan Ivan I Ferretti Vincent V

Bioinformatics (Oxford, England) 20151009 3

<h4>Summary</h4>Sequence comparison of genetic material between known and unknown organisms plays a crucial role in genomics, metagenomics and phylogenetic analysis. The emerging long-read sequencing technologies can now produce reads of tens of kilobases in length that promise a more accurate assessment of their origin. To facilitate the classification of long and short DNA sequences, we have developed a Python package that implements a new sequence classification model that we have demonstrate ...[more]

PMID: 26454281

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Project description:In recent years several different fields, such as ecology, medicine and microbiology, have experienced an unprecedented development due to the possibility of direct sequencing of microbioimic samples. Among problems that researchers in the field have to deal with, taxonomic classification of metagenomic reads is one of the most challenging. State of the art methods classify single reads with almost 100% precision. However, very often, the performance in terms of recall falls at about 50%. As a consequence, state-of-the-art methods are indeed capable of correctly classify only half of the reads in the sample. How to achieve better performances in terms of overall quality of classification remains a largely unsolved problem.In this paper we propose a method for metagenomics CLassification Improvement with Overlapping Reads (CLIOR), that exploits the information carried by the overlapping reads graph of the input read dataset to improve recall, f-measure, and the estimated abundance of species. In this work, we applied CLIOR on top of the classification produced by the classifier Clark-l. Experiments on simulated and synthetic metagenomes show that CLIOR can lead to substantial improvement of the recall rate, sometimes doubling it. On average, on simulated datasets, the increase of recall is paired with an higher precision too, while on synthetic datasets it comes at expenses of a small loss of precision. On experiments on real metagenomes CLIOR is able to assign many more reads while keeping the abundance ratios in line with previous studies.Our results showed that with CLIOR is possible to boost the recall of a state-of-the-art metagenomic classifier by inferring and/or correcting the assignment of reads with missing or erroneous labeling. CLIOR is not restricted to the reads classification algorithm used in our experiments, but it may be applied to other methods too. Finally, CLIOR does not need large computational resources, and it can be run on a laptop.

Dataset Information

CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.

Summary

Availability and implementation

Contact

Supplementary information

Publications

CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.

Similar Datasets

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