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HMM-FRAME: accurate protein domain classification for metagenomic sequences containing frameshift errors.
1Computer Science and Engineering Department, Michigan State University, East Lansing, USA.
HMM-FRAME corrects sequencing errors in protein domain classification, improving metagenomic annotation accuracy. This new tool enhances the ability to classify gene fragments previously unidentifiable due to frameshifts.
Area of Science:
- Bioinformatics
- Computational Biology
- Metagenomics
Background:
- Protein domain classification is crucial for metagenomic annotation.
- Profile Hidden Markov Model (HMM)-based alignment is the current standard.
- Pyrosequencing errors, like frameshifts, hinder accurate classification by conventional tools.
Purpose of the Study:
- To develop a novel protein domain classification tool capable of detecting and correcting sequencing errors.
- To improve the classification of gene fragments affected by frameshifts.
Main Methods:
- Introduction of HMM-FRAME, a tool utilizing an augmented Viterbi algorithm.
- Incorporation of error models specific to different sequencing platforms.
- Application of HMM-FRAME to targeted and published metagenomic datasets.
Main Results:
- HMM-FRAME demonstrated high sensitivity and specificity in detecting errors.
- The tool successfully corrected frameshifts in error-containing sequences.
- Generated significantly longer alignments with improved E-values, leading to more accurate family classifications.
Conclusions:
- HMM-FRAME serves as a valuable complement to existing profile HMM methods for datasets with frameshifts.
- The tool is particularly effective for small-scale metagenomic analyses.
- Source code is publicly available for download.
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