THE PURITY MEASURE FOR GENOMIC REGIONS LEADS TO HORIZONTALLY TRANSFERRED GENES

2013 ◽  
Vol 11 (06) ◽  
pp. 1343002 ◽  
Author(s):  
YUTA TANIGUCHI ◽  
YASUHIRO YAMADA ◽  
OSAMU MARUYAMA ◽  
SATORU KUHARA ◽  
DAISUKE IKEDA
Keyword(s):  
Sequence Analysis ◽  
High Purity ◽  
Domain Knowledge ◽  
Markov Models ◽  
Hidden Markov ◽  
Bacterial Genome ◽  
Genome Sequences ◽  
Sequence Alignments ◽  
A Genome ◽  
Genomic Regions

Sequence analysis is important to understand a genome, and a number of approaches such as sequence alignments and hidden Markov models have been employed. In the field of text mining, the purity measure is developed to detect unusual regions of a string without any domain knowledge. It is reported in that work that only RNAs and transposons are shown to have high purity values. In this work, the purity values of regions of various bacterial genome sequences are computed, and those regions are analyzed extensively. It is found that mobile elements and phages as well as RNAs and transposons have high purity values. It is interesting that they are all classified into a group of horizontally transferred genes. This means that the purity measure is useful to predict horizontally transferred genes.

scholarly journals THE NET-HMM APPROACH: PHYLOGENETIC NETWORK INFERENCE BY COMBINING MAXIMUM LIKELIHOOD AND HIDDEN MARKOV MODELS

2009 ◽  
Vol 07 (04) ◽  
pp. 625-644 ◽  
Author(s):  
SAGI SNIR ◽  
TAMIR TULLER
Keyword(s):  
Network Inference ◽  
Markov Models ◽  
Hidden Markov ◽  
Bacterial Genome ◽  
Genetic Material ◽  
Phylogenetic Network ◽  
Significance Test ◽  
Amino Acid Sequences ◽  
Phylogenetic Networks ◽  
Significant Mechanism

Horizontal gene transfer (HGT) is the event of transferring genetic material from one lineage in the evolutionary tree to a different lineage. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Although the prevailing assumption is of complete HGT, cases of partial HGT (which are also named chimeric HGT) where only part of a gene is horizontally transferred, have also been reported, albeit less frequently. In this work we suggest a new probabilistic model, the NET-HMM, for analyzing and modeling phylogenetic networks. This new model captures the biologically realistic assumption that neighboring sites of DNA or amino acid sequences are not independent, which increases the accuracy of the inference. The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. One of the advantages of the NET-HMM is its ability to infer partial HGT as well as complete HGT. We describe the properties of the NET-HMM, devise efficient algorithms for solving a set of problems related to it, and implement them in software. We also provide a novel complementary significance test for evaluating the fitness of a model (NET-HMM) to a given dataset. Using NET-HMM, we are able to answer interesting biological questions, such as inferring the length of partial HGT's and the affected nucleotides in the genomic sequences, as well as inferring the exact location of HGT events along the tree branches. These advantages are demonstrated through the analysis of synthetical inputs and three different biological inputs.

scholarly journals RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses

2020 ◽  
Author(s):  
Ramya Rangan ◽  
Ivan N. Zheludev ◽  
Rhiju Das
Keyword(s):  
Secondary Structure ◽  
Viral Genome ◽  
Rna Secondary Structure ◽  
Therapeutic Strategies ◽  
Genome Sequences ◽  
Sequence Alignments ◽  
Secondary Structure Models ◽  
Prior Literature ◽  
Rna Genome ◽  
Genomic Regions

AbstractAs the COVID-19 outbreak spreads, there is a growing need for a compilation of conserved RNA genome regions in the SARS-CoV-2 virus along with their structural propensities to guide development of antivirals and diagnostics. Using sequence alignments spanning a range of betacoronaviruses, we rank genomic regions by RNA sequence conservation, identifying 79 regions of length at least 15 nucleotides as exactly conserved over SARS-related complete genome sequences available near the beginning of the COVID-19 outbreak. We then confirm the conservation of the majority of these genome regions across 739 SARS-CoV-2 sequences reported to date from the current COVID-19 outbreak, and we present a curated list of 30 ‘SARS-related-conserved’ regions. We find that known RNA structured elements curated as Rfam families and in prior literature are enriched in these conserved genome regions, and we predict additional conserved, stable secondary structures across the viral genome. We provide 106 ‘SARS-CoV-2-conserved-structured’ regions as potential targets for antivirals that bind to structured RNA. We further provide detailed secondary structure models for the 5’ UTR, frame-shifting element, and 3’ UTR. Last, we predict regions of the SARS-CoV-2 viral genome have low propensity for RNA secondary structure and are conserved within SARS-CoV-2 strains. These 59 ‘SARS-CoV-2-conserved-unstructured’ genomic regions may be most easily targeted in primer-based diagnostic and oligonucleotide-based therapeutic strategies.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis

2018 ◽  
Vol 35 (13) ◽  
pp. 2208-2215 ◽  
Author(s):  
Ioannis A Tamposis ◽  
Konstantinos D Tsirigos ◽  
Margarita C Theodoropoulou ◽  
Panagiota I Kontou ◽  
Pantelis G Bagos
Keyword(s):  
Sequence Analysis ◽  
Supervised Learning ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Transmembrane Protein ◽  
Training Data ◽  
Supplementary Information ◽  
Training Procedure ◽  
Partially Labeled Data

Abstract Motivation Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. Results We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Draft Genome Sequences of Five Enterococcus Species Isolated from the Gut of Patients with Suspected Clostridium difficile Infection

2017 ◽  
Vol 5 (20) ◽  
Author(s):  
Eduardo Castro-Nallar ◽  
Sandro L. Valenzuela ◽  
Sebastián Baquedano ◽  
Carolina Sánchez ◽  
Fabiola Fernández ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Clostridium Difficile ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Draft Genome ◽  
Enterococcus Species ◽  
Beta Lactams ◽  
Genome Sequences ◽  
Content Type

ABSTRACT We present draft genome sequences of five Enterococcus species from patients suspected of Clostridium difficile infection. Genome completeness was confirmed by presence of bacterial orthologs (97%). Gene searches using Hidden-Markov models revealed that the isolates harbor between seven and 11 genes involved in antibiotic resistance to tetracyclines, beta-lactams, and vancomycin.

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