Identification of NovelL-Amino Acid α-Ligases through Hidden Markov Model-Based Profile Analysis

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

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Hidden Markov model-based approach as the first screening of binding peptides that interact with MHC class II molecules

Enzyme and Microbial Technology ◽

10.1016/s0141-0229(03)00150-9 ◽

2003 ◽

Vol 33 (4) ◽

pp. 472-481 ◽

Cited By ~ 14

Author(s):

Ryuji Kato ◽

Hideki Noguchi ◽

Hiroyuki Honda ◽

Takeshi Kobayashi

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Mhc Class Ii ◽

Hidden Markov ◽

Class Ii ◽

Model Based ◽

Binding Peptides ◽

Mhc Class Ii Molecules

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A Bayesian Hidden Markov Model-based approach for anomaly detection in electronic systems

2013 IEEE Aerospace Conference ◽

10.1109/aero.2013.6497204 ◽

2013 ◽

Cited By ~ 7

Author(s):

E. Dorj ◽

C. Chen ◽

M. Pecht

Keyword(s):

Anomaly Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Electronic Systems ◽

Model Based

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Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and metabarcoding datasets

BMC Bioinformatics ◽

10.1186/s12859-021-04180-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

T. M. Porter ◽

M. Hajibabaei

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Dna Barcoding ◽

Profile Analysis ◽

Hidden Markov ◽

Open Reading Frame ◽

Protein Coding ◽

Reading Frame ◽

Frame Length ◽

Open Reading Frame Length

Abstract Background Pseudogenes are non-functional copies of protein coding genes that typically follow a different molecular evolutionary path as compared to functional genes. The inclusion of pseudogene sequences in DNA barcoding and metabarcoding analysis can lead to misleading results. None of the most widely used bioinformatic pipelines used to process marker gene (metabarcode) high throughput sequencing data specifically accounts for the presence of pseudogenes in protein-coding marker genes. The purpose of this study is to develop a method to screen for nuclear mitochondrial DNA segments (nuMTs) in large COI datasets. We do this by: (1) describing gene and nuMT characteristics from an artificial COI barcode dataset, (2) show the impact of two different pseudogene removal methods on perturbed community datasets with simulated nuMTs, and (3) incorporate a pseudogene filtering step in a bioinformatic pipeline that can be used to process Illumina paired-end COI metabarcode sequences. Open reading frame length and sequence bit scores from hidden Markov model (HMM) profile analysis were used to detect pseudogenes. Results Our simulations showed that it was more difficult to identify nuMTs from shorter amplicon sequences such as those typically used in metabarcoding compared with full length DNA barcodes that are used in the construction of barcode libraries. It was also more difficult to identify nuMTs in datasets where there is a high percentage of nuMTs. Existing bioinformatic pipelines used to process metabarcode sequences already remove some nuMTs, especially in the rare sequence removal step, but the addition of a pseudogene filtering step can remove up to 5% of sequences even when other filtering steps are in place. Conclusions Open reading frame length filtering alone or combined with hidden Markov model profile analysis can be used to effectively screen out apparent pseudogenes from large datasets. There is more to learn from COI nuMTs such as their frequency in DNA barcoding and metabarcoding studies, their taxonomic distribution, and evolution. Thus, we encourage the submission of verified COI nuMTs to public databases to facilitate future studies.

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