Olá pessoal, acho que já escolhi o meu paper.
"The use of structure information to increase alignment accuracy does not aid homologue detection with profile HMMs"
Sam Griffiths-Jones ∗ and Alex Bateman
Abraços
Amanda
In reply to Amanda Rusiska Piovezani
Re: Textos p/ aula de segunda (18/10/2010)
by André Luiz Oliveira -
Fazendo jus a minha veia de biólogo. O título do meu paper é:
"Automatic Identification of individual Killer Whales"
Judith C. Brown, Paris Smaragdis and Anna Nousek-McGregor.
Caso alguém queira saber sobre o que se trata. Segue o abstract:
"Following the successful use of HMM and GMM models for classification of a set of 75 calls of northern resident killer whales into call types [Brown, J. C., and Smaragdis, P., J. Acoust. Soc. Am. 125, 221-224 (2009)], the use of these same methods has been explored for the identification of vocalizations from the same call type N2 of four individual killer whales. With an average of 20 vocalizations from each of the individuals the pairwise comparisons have an extremely high success rate of 80 to 100% and the identifications within the entire group yield around 78%."
Bom final de semana a todos.
"Automatic Identification of individual Killer Whales"
Judith C. Brown, Paris Smaragdis and Anna Nousek-McGregor.
Caso alguém queira saber sobre o que se trata. Segue o abstract:
"Following the successful use of HMM and GMM models for classification of a set of 75 calls of northern resident killer whales into call types [Brown, J. C., and Smaragdis, P., J. Acoust. Soc. Am. 125, 221-224 (2009)], the use of these same methods has been explored for the identification of vocalizations from the same call type N2 of four individual killer whales. With an average of 20 vocalizations from each of the individuals the pairwise comparisons have an extremely high success rate of 80 to 100% and the identifications within the entire group yield around 78%."
Bom final de semana a todos.
In reply to André Luiz Oliveira
Re: Textos p/ aula de segunda (18/10/2010)
by Luiz Thibério Rangel -
Opa,
meu texto será:
meu texto será:
Andrieu O, Fiston AS, Anxolabéhère D, Quesneville H. Detection of transposable elements by their compositional bias. BMC Bioinformatics. 2004 Jul 13;5:94. PubMed PMID: 15251040; PubMed Central PMCID: PMC497039. http://www.ncbi.nlm.nih.gov/pubmed/15251040
In reply to Amanda Rusiska Piovezani
Re: Textos p/ aula de segunda (18/10/2010)
by Alan Mitchell Durham -
Voces estao entendo errado. Quero que achem um texto GERAL e introdutório sobre HMMs E profileHMMs que fale preferencialmente sobre as áreas de aplicação em Bionformática,
O que voces mandaram são textos de UMA aplicação de HMM OU profileHMM.
O que voces mandaram são textos de UMA aplicação de HMM OU profileHMM.
In reply to Alan Mitchell Durham
Re: Textos p/ aula de segunda (18/10/2010)
by Luiz Thibério Rangel -
Olá Professor,
acho, espero, que este texto esteja no módulo que o senhor deseja.
Hidden Markov Models in Bioinformatics
Segue o abstract:
acho, espero, que este texto esteja no módulo que o senhor deseja.
Hidden Markov Models in Bioinformatics
Segue o abstract:
Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics applications, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition. We finally provide a critical appraisal of the use and perspectives of HMMs in bioinformatics.
In reply to Alan Mitchell Durham
Re: Textos p/ aula de segunda (18/10/2010)
Oi Alan,
deixa ver se eu entendi. Vc quer um texto geral sobre HMM:
deixa ver se eu entendi. Vc quer um texto geral sobre HMM:
MARKOV CHAIN AND HIDDEN MARKOV MODEL
Markov chain and hidden Markov model are probably the simplest models which can be used to model sequential data, i.e. data samples which are not independent from each other.
E um texto sobre profile HMM que fale sobre áreas de aplicação em Bioinfo:
E um texto sobre profile HMM que fale sobre áreas de aplicação em Bioinfo:
A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes
Abstract
Background: Jumping alignments have recently been proposed as a strategy to search a given
multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events.
Results: We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic
generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied our method to a large set of genome sequences from human immunodeficiency virus (HIV) and hepatitis C virus (HCV) as well as to simulated recombined genome sequences.
Conclusion: Our results demonstrate that jumps in our jumping profile HMM often correspond
to recombination breakpoints; our approach can therefore be used to detect recombinations in
genomic sequences. The recombination breakpoints identified by jpHMM were found to be
significantly more accurate than breakpoints defined by traditional methods based on comparing single representative sequences.
É isso?
Background: Jumping alignments have recently been proposed as a strategy to search a given
multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events.
Results: We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic
generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied our method to a large set of genome sequences from human immunodeficiency virus (HIV) and hepatitis C virus (HCV) as well as to simulated recombined genome sequences.
Conclusion: Our results demonstrate that jumps in our jumping profile HMM often correspond
to recombination breakpoints; our approach can therefore be used to detect recombinations in
genomic sequences. The recombination breakpoints identified by jpHMM were found to be
significantly more accurate than breakpoints defined by traditional methods based on comparing single representative sequences.
É isso?
In reply to Liliane Santana Oliveira
Re: Textos p/ aula de segunda (18/10/2010)
by André Luiz Oliveira -
Alan,
Extinguindo as baleias, segue o paper que tem uma base introdutória sobre HMM e algumas variações dessa técnica além de alguns aspectos relacionados com a bioinformática tais como:
(1)- Gene finding
(2)- Alternative splicing
(3)- Signal Analysis.
Abstract
Url: http://www.biomedcentral.com/1471-2105/7/S2/S14
Extinguindo as baleias, segue o paper que tem uma base introdutória sobre HMM e algumas variações dessa técnica além de alguns aspectos relacionados com a bioinformática tais como:
(1)- Gene finding
(2)- Alternative splicing
(3)- Signal Analysis.
Abstract
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way. This approach is based on mutual information, probabilistic measures, hidden Markov models, and other purely statistical inputs. This approach also provides a uniquely common ground for comparative prokaryotic genomics. The approach is an on-going effort by its nature, as a multi-pass learning process, where each round is more informed than the last, and thereby allows a shift to the more powerful methods available for supervised learning at each iteration. It is envisaged that this "bootstrap" learning process will also be useful as a knowledge discovery tool. For such an ab initio prokaryotic gene-finder to work, however, it needs a mechanism to identify critical motif structure, such as those around the start of coding or start of transcription (and then, hopefully more).
For eukaryotes, even with better start-of-coding identification, parsing of eukaryotic coding regions by the HMM is still limited by the HMM's single gene assumption, as evidenced by the poor performance in alternatively spliced regions. To address these complications an approach is described to expand the states in a eukaryotic gene-predictor HMM, to operate with two layers of DNA parsing. This extension from the single layer gene prediction parse is indicated after preliminary analysis of the C. elegans alt-splice statistics. State profiles have made use of a novel hash-interpolating MM (hIMM) method. A new implementation for an HMM-with-Duration is also described, with far-reaching application to gene-structure identification and analysis of channelUrl: http://www.biomedcentral.com/1471-2105/7/S2/S14
In reply to Liliane Santana Oliveira
Re: Textos p/ aula de segunda (18/10/2010)
by Alan Mitchell Durham -
Mais ou enos. Basta um texto apenas. Mas 2 é legal. A idéia de voces postarem é para evitar dois textos iguais.
Alan
Alan
In reply to Liliane Santana Oliveira
Re: Textos p/ aula de segunda (18/10/2010)
Gnt, estou trocando meu primeiro artigo(acho q ele estava errado...)
Agr é:
Recent Applications of Hidden Markov Models in Computational Biology
Khar Heng Choo, Joo Chuan Tong, Louxin Zhang
Agr é:
Recent Applications of Hidden Markov Models in Computational Biology
Khar Heng Choo, Joo Chuan Tong, Louxin Zhang
In reply to Alan Mitchell Durham
Re: Textos p/ aula de segunda (18/10/2010)
Ah! Só p confirmar: a aula de segunda começa as 8:00h ou as 8:30h? Já sabe em q sala vai ser?
In reply to Amanda Rusiska Piovezani
Re: Textos p/ aula de segunda (18/10/2010)
Oi Alan,
Confirma para mim se esses dois paper pode ser?
Sobre HMMs:
Título: What is a hidden Markov model?
Autor: Sean R Eddy
Comentário: "Statistical models called hidden Markov models are a recurring theme in computational biology. What are hidden Markov models, and why are they so useful for so many different problems?"
Disponível em: <http://www.nature.com/nbt/journal/v22/n10/full/nbt1004-1315.html>;
Sobre profile HMMs:
Título: Profile hidden Markov models
Autor: Sean R. Eddy
Resumo: The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
Obrigada
Abraços
Amanda
Confirma para mim se esses dois paper pode ser?
Sobre HMMs:
Título: What is a hidden Markov model?
Autor: Sean R Eddy
Comentário: "Statistical models called hidden Markov models are a recurring theme in computational biology. What are hidden Markov models, and why are they so useful for so many different problems?"
Disponível em: <http://www.nature.com/nbt/journal/v22/n10/full/nbt1004-1315.html>;
Sobre profile HMMs:
Título: Profile hidden Markov models
Autor: Sean R. Eddy
Resumo: The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
Obrigada
Abraços
Amanda
Segue abaixo o artigo que eu escolhi:
Hidden Markov Models and their Applications in Biological Sequence
Analysis
Byung-Jun Yoon
Current Genomics, 2009, 10, 402-415
Abraços,
Fernando
Hidden Markov Models and their Applications in Biological Sequence
Analysis
Byung-Jun Yoon
Current Genomics, 2009, 10, 402-415
Abraços,
Fernando
Sei que dificilmente alguem usou esses artigos, mas pra evitar qualquer transtorno, foram esses ai.
HMM: Martin, 2006 - Analysis of an optimal hidden Markov model for secondary structure prediction. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1769381/)
pHMM: Huang, 2005 - KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160232)
HMM: Martin, 2006 - Analysis of an optimal hidden Markov model for secondary structure prediction. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1769381/)
pHMM: Huang, 2005 - KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160232)