scholarly journals Machine Boss: Rapid Prototyping of Bioinformatic Automata

2020 ◽  
Author(s):  
J. Silvestre-Ryan ◽  
Y. Wang ◽  
M. Sharma ◽  
S. Lin ◽  
Y. Shen ◽  
...  
Keyword(s):  
Data Storage ◽  
Markov Models ◽  
Hidden Markov ◽  
Software Tool ◽  
Regular Expressions ◽  
Time Saving ◽  
Parameter Fitting ◽  
Calculation Parameter ◽  
Report Data ◽  
Dna Alignment

ABSTRACTMotivationMany C++ libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting, and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone.ResultsWe present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line “recipes” for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage, and nanopore sequence analysis.Availability and ImplementationMachine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/.ContactIan Holmes, [email protected]

scholarly journals Machine Boss: rapid prototyping of bioinformatic automata

2020 ◽  
Author(s):  
Jordi Silvestre-Ryan ◽  
Yujie Wang ◽  
Mehak Sharma ◽  
Stephen Lin ◽  
Yolanda Shen ◽  
...  
Keyword(s):  
Data Storage ◽  
Markov Models ◽  
Software Tool ◽  
Supplementary Information ◽  
Time Saving ◽  
Parameter Fitting ◽  
Software Libraries ◽  
Calculation Parameter ◽  
Report Data ◽  
Dna Alignment

Abstract Motivation Many software libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. Results We present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line ‘recipes’ for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage and nanopore sequence analysis. Availability and implementation Machine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals digIS: towards detecting distant and putative novel insertion sequence elements in prokaryotic genomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janka Puterová ◽  
Tomáš Martínek
Keyword(s):  
Hidden Markov Models ◽  
Insertion Sequence ◽  
Markov Models ◽  
Hidden Markov ◽  
Software Tool ◽  
Is Elements ◽  
Profile Hidden Markov Models ◽  
Prokaryotic Genomes ◽  
Sequence Elements ◽  
Insertion Sequence Elements

Abstract Background The insertion sequence elements (IS elements) represent the smallest and the most abundant mobile elements in prokaryotic genomes. It has been shown that they play a significant role in genome organization and evolution. To better understand their function in the host genome, it is desirable to have an effective detection and annotation tool. This need becomes even more crucial when considering rapid-growing genomic and metagenomic data. The existing tools for IS elements detection and annotation are usually based on comparing sequence similarity with a database of known IS families. Thus, they have limited ability to discover distant and putative novel IS elements. Results In this paper, we present digIS, a software tool based on profile hidden Markov models assembled from catalytic domains of transposases. It shows a very good performance in detecting known IS elements when tested on datasets with manually curated annotation. The main contribution of digIS is in its ability to detect distant and putative novel IS elements while maintaining a moderate level of false positives. In this category it outperforms existing tools, especially when tested on large datasets of archaeal and bacterial genomes. Conclusion We provide digIS, a software tool using a novel approach based on manually curated profile hidden Markov models, which is able to detect distant and putative novel IS elements. Although digIS can find known IS elements as well, we expect it to be used primarily by scientists interested in finding novel IS elements. The tool is available at https://github.com/janka2012/digIS.

scholarly journals Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

Biology ◽  
2013 ◽  
Vol 2 (4) ◽  
pp. 1282-1295 ◽  
Author(s):  
Paula Tataru ◽  
Andreas Sand ◽  
Asger Hobolth ◽  
Thomas Mailund ◽  
Christian Pedersen
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Regular Expressions

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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