EBI Molecular Biology Databases
http://www.ebi.ac.uk/2can/databases/index.html
bioinformatics.ca
http://bioinformatics.ca/links_directory/index.php
Sunday, September 12, 2010
CRF and HMM probabilistiv models
A conditional random field (CRF) is a type of discriminative probabilistic model most often used for the labeling or parsing of sequential data, such as natural language text or biological sequences. Specifically, CRFs find applications in shallow parsing, named entity recognition and gene finding, among other tasks, being an alternative to the related hidden Markov models.
http://en.wikipedia.org/wiki/Conditional_random_field
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved state. A HMM can be considered as the simplest dynamic Bayesian network.
In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states. Note that the adjective 'hidden' refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still 'hidden'.
Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.
http://en.wikipedia.org/wiki/Hidden_Markov_model
Consider two friends, Alice and Bob, who live far apart from each other and who talk together daily over the telephone about what they did that day. Bob is only interested in three activities: walking in the park, shopping, and cleaning his apartment. The choice of what to do is determined exclusively by the weather on a given day. Alice has no definite information about the weather where Bob lives, but she knows general trends. Based on what Bob tells her he did each day, Alice tries to guess what the weather must have been like.
A Markov chain is a random process with the property that the next state depends only on the current state.
A finite state machine can be used as a representation of a Markov chain
http://en.wikipedia.org/wiki/Conditional_random_field
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved state. A HMM can be considered as the simplest dynamic Bayesian network.
In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states. Note that the adjective 'hidden' refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still 'hidden'.
Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.
http://en.wikipedia.org/wiki/Hidden_Markov_model
Consider two friends, Alice and Bob, who live far apart from each other and who talk together daily over the telephone about what they did that day. Bob is only interested in three activities: walking in the park, shopping, and cleaning his apartment. The choice of what to do is determined exclusively by the weather on a given day. Alice has no definite information about the weather where Bob lives, but she knows general trends. Based on what Bob tells her he did each day, Alice tries to guess what the weather must have been like.
A Markov chain is a random process with the property that the next state depends only on the current state.
A finite state machine can be used as a representation of a Markov chain
T cells
T helper cells (also known as Th cells) are a sub-group of lymphocytes (a type of white blood cell or leukocyte) that play an important role in establishing and maximizing the capabilities of the immune system. These cells are unusual in that they have no cytotoxic or phagocytic activity; they cannot kill infected host cells (also known as somatic cells) or pathogens, and without other immune cells they would usually be considered useless against an infection. Th cells are involved in activating and directing other immune cells, and are particularly important in the immune system. They are essential in determining B cell antibody class switching, in the activation and growth of cytotoxic T cells, and in maximizing bactericidal activity of phagocytes such as macrophages. It is this diversity in function and their role in influencing other cells that gives T helper cells their name.
Color-coding
In computer science and graph theory, the method of color-coding[1][2] efficiently finds k-vertex simple paths, k-vertex cycles, and other small subgraphs within a given graph using probabilistic algorithms, which can then be derandomized and turned into deterministic algorithms. This method shows that many subcases of the subgraph isomorphism problem (an NP-complete problem) can in fact be solved in polynomial time.
An example would be finding a simple cycle of length k in graph G = (V,E).
By applying random coloring method, each simple cycle has a probability of k!/k^k > \tfrac{1}{e^k} to become colorful, since there are kk ways of coloring the k vertices on the path, among which there are k! colorful occurrences.
An example would be finding a simple cycle of length k in graph G = (V,E).
By applying random coloring method, each simple cycle has a probability of k!/k^k > \tfrac{1}{e^k} to become colorful, since there are kk ways of coloring the k vertices on the path, among which there are k! colorful occurrences.
Saturday, September 11, 2010
MeSH - Controlled vocabulary
controlled vocabulary (such as MeSH, http://www.nlm.nih.gov/mesh/),
Medical Subject Headings (MeSH)
Medical Subject Headings (MeSH)
Dev tools
• SVN source code version control system.
• JUnit test framework will be used to develop automated software unit tests. Extensive unit testing
will help the developer discover code defects early in the process. Unit tests also ensure that that
functionality, once implemented correctly, will not be accidentally be broken by other developers,
leading to a codebase of increasing quality.
• Checkstyle - Code style compliance test software. Our code will be compliant with the Sun coding
standards 65 .
• Findbugs - Code quality analysis software toolkit which will be employed to generate reports indicat-
ing potential common programming mistakes.
• JDepend - Code quality analysis software toolkit will generate code quality reports that will help
identify software design level problems.
• Bugzilla - issue tracking system will be used by the developer to ensure that all project related issue
are logged and resolved.
• JUnit test framework will be used to develop automated software unit tests. Extensive unit testing
will help the developer discover code defects early in the process. Unit tests also ensure that that
functionality, once implemented correctly, will not be accidentally be broken by other developers,
leading to a codebase of increasing quality.
• Checkstyle - Code style compliance test software. Our code will be compliant with the Sun coding
standards 65 .
• Findbugs - Code quality analysis software toolkit which will be employed to generate reports indicat-
ing potential common programming mistakes.
• JDepend - Code quality analysis software toolkit will generate code quality reports that will help
identify software design level problems.
• Bugzilla - issue tracking system will be used by the developer to ensure that all project related issue
are logged and resolved.
Teaching quote
naruto-shippuden-episode-177 Kakashi ...
"Perhaps you've forgotten the basic rule of teaching. You cannot open the mind of another unless you yourself have an open mind."
"Perhaps you've forgotten the basic rule of teaching. You cannot open the mind of another unless you yourself have an open mind."
Real Advice from Real People
“Real Advice from Real People” by Tom Loughin, Statistical
Society of Canada Liaison,Vol. 22.4
November 2008.
While universities are not the “ivory towers” that some make them
out to be, life in a university can be a somewhat sheltered existence,
quite different from the relentless drive toward profit and growth
that is typical of many businesses.
The surprise
came because the skills that they stressed were not the academic
ones, the things that the program designers hold dear as the core
curriculum of the program. Rather, they were skills that we normally
think of as peripheral, things that we don’t often emphasize within
our otherwise rigorous programs.
1. Build your communication skills
Management usually can't tell the difference between a good statistician and a great one, but they can see immediately who communicates their results well and who does so poorly.
2. Network "relationship building" like mad
most of the speakers I've talked with got their jobs because they knew somebody at the place where they were hired
- Talk to strangers
3. Branch out.
Companies would rather hire a student with good technical competence and a wide range of experiences outside the classroom, than a student with a 4.0 who has done nothing but schoolwork.
- volunteer
- whatever it is that you do, just make sure that you do excellent work!
University employers focuses more on the technical competence but when you look for a job somewhere else, these skills and experience will surely serve you then.
Society of Canada Liaison,Vol. 22.4
November 2008.
While universities are not the “ivory towers” that some make them
out to be, life in a university can be a somewhat sheltered existence,
quite different from the relentless drive toward profit and growth
that is typical of many businesses.
The surprise
came because the skills that they stressed were not the academic
ones, the things that the program designers hold dear as the core
curriculum of the program. Rather, they were skills that we normally
think of as peripheral, things that we don’t often emphasize within
our otherwise rigorous programs.
1. Build your communication skills
Management usually can't tell the difference between a good statistician and a great one, but they can see immediately who communicates their results well and who does so poorly.
2. Network "relationship building" like mad
most of the speakers I've talked with got their jobs because they knew somebody at the place where they were hired
- Talk to strangers
3. Branch out.
Companies would rather hire a student with good technical competence and a wide range of experiences outside the classroom, than a student with a 4.0 who has done nothing but schoolwork.
- volunteer
- whatever it is that you do, just make sure that you do excellent work!
University employers focuses more on the technical competence but when you look for a job somewhere else, these skills and experience will surely serve you then.
Guide to writing a good research paper
---Scientific method http://en.wikipedia.org/wiki/Scientific_method
1. Use your experience / observations: Consider the problem and try to make sense of it. Look for previous explanations. If this is a new problem to you, then move to step 2.
2. Form a conjecture / hypothesis: When nothing else is yet known, try to state an explanation, to someone else, or to your notebook.
3. Deduce a prediction from that explanation: If you assume 2 is true, what consequences follow?
4. Test: Look for the opposite of each consequence in order to disprove 2. It is a logical error to seek 3 directly as proof of 2. This error is called affirming the consequent.[13]
A linearized, pragmatic scheme of the four points above is sometimes offered as a guideline for proceeding:[49]
1. Define the question
2. Gather information and resources (observe)
3. Form hypothesis
4. Perform experiment and collect data
5. Analyze data
6. Interpret data and draw conclusions that serve as a starting point for new hypothesis
7. Publish results
8. Retest (frequently done by other scientists)
---Graphical graphs preferred over tables
---Good headings and sub-headings
"A picture is worth a thousand words." -- always, always, always plot the data
Implication for statistical analysis: if two models
are equally wrong-but-compatible-with-data, the
simpler one is more useful!
Summary of main philosophical points:
•Data analysis is important.
•Simple methods are preferred.
•Visual presentations of data and results are valuable.
1. Use your experience / observations: Consider the problem and try to make sense of it. Look for previous explanations. If this is a new problem to you, then move to step 2.
2. Form a conjecture / hypothesis: When nothing else is yet known, try to state an explanation, to someone else, or to your notebook.
3. Deduce a prediction from that explanation: If you assume 2 is true, what consequences follow?
4. Test: Look for the opposite of each consequence in order to disprove 2. It is a logical error to seek 3 directly as proof of 2. This error is called affirming the consequent.[13]
A linearized, pragmatic scheme of the four points above is sometimes offered as a guideline for proceeding:[49]
1. Define the question
2. Gather information and resources (observe)
3. Form hypothesis
4. Perform experiment and collect data
5. Analyze data
6. Interpret data and draw conclusions that serve as a starting point for new hypothesis
7. Publish results
8. Retest (frequently done by other scientists)
---Graphical graphs preferred over tables
---Good headings and sub-headings
"A picture is worth a thousand words." -- always, always, always plot the data
Implication for statistical analysis: if two models
are equally wrong-but-compatible-with-data, the
simpler one is more useful!
Summary of main philosophical points:
•Data analysis is important.
•Simple methods are preferred.
•Visual presentations of data and results are valuable.
Friday, September 10, 2010
Thursday, September 9, 2010
Haeckel's evolutionary paradigm of embryonic development
Inspired by the Darwinian principle of descent with modification, many embryologist of the nineteenth century proposed the extreme view that "ontogeny recapitulates phylogeny." This notion holds that ontogeny, the development of an individual organism is a replay of the evolutionary history of the species, phylogeny. The theory of recapitulation is an overstatement (Biology, 1999, page 425).
Haeckel's evolutionary paradigm of embryonic development, where an intermediate in the assembly of a complex represents a form that appeared in its own evolutionary history
http://www.doesgodexist.org/JanFeb01/ErnstHaeckelsSleightOfHandRevealed.html
Haeckel's evolutionary paradigm of embryonic development, where an intermediate in the assembly of a complex represents a form that appeared in its own evolutionary history
http://www.doesgodexist.org/JanFeb01/ErnstHaeckelsSleightOfHandRevealed.html
Bioinformatic experimental methods in DNA expression
Complexity of poly(A+) and poly(A-) polysomal RNA in mouse liver and cultured mouse fibroblasts
- polyadenylated mRNA (poly(A+))
- 30-50% of HeLa cells mRNA is not polyadenylated (poly(A-))
- poly(A+) and poly(A-) show little or no overlap in sequence
RNA-seq, - poly-A tail binds to poly-T beads
RNA-Seq, also called "Whole Transcriptome Shotgun Sequencing" [1] ("WTSS") and dubbed "a revolutionary tool for transcriptomics" [2], refers to the use of High-throughput sequencing technologies to sequence cDNA in order to get information about a sample's RNA content, a technique that is quickly becoming invaluable in the study of diseases like cancer [3]. Thanks to the deep coverage and base level resolution provided by next-generation sequencing instruments, RNA-Seq provides researchers with efficient ways to measure transcriptome data experimentally, allowing them to get information such as how different alleles of a gene are expressed, detect post-transcriptional mutations or identifying gene fusions [3].
Cistrome This term http://cistrome.pbwiki.com was coined by investigators at the Dana-Farber Cancer Institute and Harvard Medical School to define the set of cis-acting targets (DNA binding sites) of a trans-acting factor (transcription factor, pioneer factor, restriction enzyme, etc) on a genome scale.
ChIP-chip - ChIP-on-chip - chromatin immunoprecipitation coupled with microarray
Horak, C. E. et al. GATA-1 binding sites mapped in the beta-globin locus by using mammalian chIp–chip analysis. Proc. Natl Acad. Sci. USA 99, 2924–2929 (2002).
The goal of ChIP-on-chip is to localize protein binding sites that may help identify functional elements in the genome. For example, in the case of a transcription factor as a protein of interest, one can determine its transcription factor binding sites throughout the genome. Other proteins allow the identification of promoter regions, enhancers, repressors and silencing elements, insulators, boundary elements, and sequences that control DNA replication[2].
ChIP-seq
Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods 4, 651–657 (2007).
Refs 115 and 116 are the first uses of chromatin immunoprecipitation and ultra-high-throughput sequencing to determine genome-wide binding sites of mammalian TFs
Chip-Sequencing is a recently developed technology that still uses chromatin immunoprecipitation to crosslink the proteins of interest to the DNA but then instead of using a micro-array, it uses the more accurate, higher throughput method of sequencing to localize interaction points.
Determining how proteins interact with DNA to regulate gene expression is essential for fully understanding many biological processes and disease states. This epigenetic information is complementary to genotype and expression analysis. ChIP-Seq technology is currently seen primarily as an alternative to ChIP-chip which requires a hybridization array. This necessarily introduces some bias, as an array is restricted to a fixed number of probes. Sequencing, by contrast, is thought to have less bias, although the sequencing bias of different sequencing technologies is not yet fully understood.
SELEX
A procedure to identify protein ligands. For DNA-binding proteins, the protein is mixed with a pool of double-stranded oligonucleotides that contain a random core of nucleotides flanked by specific sequences. The protein–DNA complex is recovered, the oligonucleotides amplified by PCR and sequenced to reveal the binding specificity of the protein.
Hallikas, O. et al. Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell 124, 47–59 (2006).
A study combining SELEX and motif-finding methods to identify the DNA-binding specificities and target regions for five mammalian TFs.
In situ hybridization (ISH)
- is a type of hybridization that uses a labeled complementary DNA or RNA strand (i.e., probe) to localize a specific DNA or RNA sequence in a portion or section of tissue (in situ), or, if the tissue is small enough (e.g. plant seeds, Drosophila embryos), in the entire tissue (whole mount ISH). This is distinct from immunohistochemistry, which localizes proteins in tissue sections. DNA ISH can be used to determine the structure of chromosomes. Fluorescent DNA ISH (FISH) can, for example, be used in medical diagnostics to assess chromosomal integrity. RNA ISH (hybridization histochemistry) is used to measure and localize mRNAs and other transcripts within tissue sections or whole mounts.
-- from wikipedia
- polyadenylated mRNA (poly(A+))
- 30-50% of HeLa cells mRNA is not polyadenylated (poly(A-))
- poly(A+) and poly(A-) show little or no overlap in sequence
RNA-seq, - poly-A tail binds to poly-T beads
RNA-Seq, also called "Whole Transcriptome Shotgun Sequencing" [1] ("WTSS") and dubbed "a revolutionary tool for transcriptomics" [2], refers to the use of High-throughput sequencing technologies to sequence cDNA in order to get information about a sample's RNA content, a technique that is quickly becoming invaluable in the study of diseases like cancer [3]. Thanks to the deep coverage and base level resolution provided by next-generation sequencing instruments, RNA-Seq provides researchers with efficient ways to measure transcriptome data experimentally, allowing them to get information such as how different alleles of a gene are expressed, detect post-transcriptional mutations or identifying gene fusions [3].
Cistrome This term http://cistrome.pbwiki.com was coined by investigators at the Dana-Farber Cancer Institute and Harvard Medical School to define the set of cis-acting targets (DNA binding sites) of a trans-acting factor (transcription factor, pioneer factor, restriction enzyme, etc) on a genome scale.
ChIP-chip - ChIP-on-chip - chromatin immunoprecipitation coupled with microarray
Horak, C. E. et al. GATA-1 binding sites mapped in the beta-globin locus by using mammalian chIp–chip analysis. Proc. Natl Acad. Sci. USA 99, 2924–2929 (2002).
The goal of ChIP-on-chip is to localize protein binding sites that may help identify functional elements in the genome. For example, in the case of a transcription factor as a protein of interest, one can determine its transcription factor binding sites throughout the genome. Other proteins allow the identification of promoter regions, enhancers, repressors and silencing elements, insulators, boundary elements, and sequences that control DNA replication[2].
ChIP-seq
Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods 4, 651–657 (2007).
Refs 115 and 116 are the first uses of chromatin immunoprecipitation and ultra-high-throughput sequencing to determine genome-wide binding sites of mammalian TFs
Chip-Sequencing is a recently developed technology that still uses chromatin immunoprecipitation to crosslink the proteins of interest to the DNA but then instead of using a micro-array, it uses the more accurate, higher throughput method of sequencing to localize interaction points.
Determining how proteins interact with DNA to regulate gene expression is essential for fully understanding many biological processes and disease states. This epigenetic information is complementary to genotype and expression analysis. ChIP-Seq technology is currently seen primarily as an alternative to ChIP-chip which requires a hybridization array. This necessarily introduces some bias, as an array is restricted to a fixed number of probes. Sequencing, by contrast, is thought to have less bias, although the sequencing bias of different sequencing technologies is not yet fully understood.
SELEX
A procedure to identify protein ligands. For DNA-binding proteins, the protein is mixed with a pool of double-stranded oligonucleotides that contain a random core of nucleotides flanked by specific sequences. The protein–DNA complex is recovered, the oligonucleotides amplified by PCR and sequenced to reveal the binding specificity of the protein.
Hallikas, O. et al. Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell 124, 47–59 (2006).
A study combining SELEX and motif-finding methods to identify the DNA-binding specificities and target regions for five mammalian TFs.
In situ hybridization (ISH)
- is a type of hybridization that uses a labeled complementary DNA or RNA strand (i.e., probe) to localize a specific DNA or RNA sequence in a portion or section of tissue (in situ), or, if the tissue is small enough (e.g. plant seeds, Drosophila embryos), in the entire tissue (whole mount ISH). This is distinct from immunohistochemistry, which localizes proteins in tissue sections. DNA ISH can be used to determine the structure of chromosomes. Fluorescent DNA ISH (FISH) can, for example, be used in medical diagnostics to assess chromosomal integrity. RNA ISH (hybridization histochemistry) is used to measure and localize mRNAs and other transcripts within tissue sections or whole mounts.
-- from wikipedia
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