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."
Saturday, September 11, 2010
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
SymAtlas - Gene annotation for protein and function
http://symatlas.gnf.org/SymAtlas/
http://biogps.gnf.org/#goto=welcome
http://biogps.gnf.org/#goto=welcome
Bioinformatics videos
Walter + Eliza Hall Institute of Medical Research
http://www.wehi.edu.au/education/wehi-tv/?page=2
http://www.wehi.edu.au/education/wehi-tv/?page=2
Tuesday, September 7, 2010
Flow cytometry - Day 1
Sept 7
--------------------------------------------------------------
FCS - forward scatter
- increasing in size of cell == increasing FSC
- increasing in refractive index of cell == increasing FSC
- bright == large refractive index
SSC - side scatter
- increasing granularity (contents in cell) == increasing SSC
Cell types:
platelets (smallest) - erythrocytes (RBC) - lymphocytes (WBC, T cells, B cells, Natural killer cells, neutrophil)
CD3 and CD4 T cell receptors
FCS - Flow Cytometry Standard
Parameters:
- FSC
- SSC
- FITC - fluorescein isothicyanate fluorochrome
- PE fluorescence - phycoerythin fluorochrome
Gating:
- restricts analysis to one population eg. snap-to-gate, quadrant, polygon
Event:
- cell passing through a laser
Compensation correction for multicolor experiments
http://www.bdbiosciences.com/support/training/itf_launch.jsp
--------------------------------------------------------------
FCS - forward scatter
- increasing in size of cell == increasing FSC
- increasing in refractive index of cell == increasing FSC
- bright == large refractive index
SSC - side scatter
- increasing granularity (contents in cell) == increasing SSC
Cell types:
platelets (smallest) - erythrocytes (RBC) - lymphocytes (WBC, T cells, B cells, Natural killer cells, neutrophil)
CD3 and CD4 T cell receptors
FCS - Flow Cytometry Standard
Parameters:
- FSC
- SSC
- FITC - fluorescein isothicyanate fluorochrome
- PE fluorescence - phycoerythin fluorochrome
Gating:
- restricts analysis to one population eg. snap-to-gate, quadrant, polygon
Event:
- cell passing through a laser
Compensation correction for multicolor experiments
http://www.bdbiosciences.com/support/training/itf_launch.jsp
Monday, September 6, 2010
Grad school requirements
Your program may require any or all of the following:
•
– Coursework
– Comprehensive or „qualifying‟ exams
– A research thesis or major project
– Public presentation and/or defense of thesis or project
•
– Masters students must complete all degree requirements
within 5 years of enrolment
Doctoral students:
Phase I: Pre-Candidacy
– Take all required courses
“Advance
– Pass a comprehensive exam
to
– Gain approval for a research proposal Candidacy”
Phase II: Candidacy
– Complete a research project (dissertation)
– Defend the thesis at a Doctoral Oral Examination
Doctoral Students:
– Should advance to candidacy within 2 years, and must within 3
– Should complete all degree requirements within 5 years of
enrollment and must within 6 years
http://www.grad.ubc.ca/current-students/newly-admitted/grad-orientation
GPS graduate pathways success
http://www.grad.ubc.ca/current-students/gps-graduate-pathways-success/gps-workshops-events
Find experts
http://webservices.publicaffairs.ubc.ca/clients/pa/apps/experts/public/index.php
•
– Coursework
– Comprehensive or „qualifying‟ exams
– A research thesis or major project
– Public presentation and/or defense of thesis or project
•
– Masters students must complete all degree requirements
within 5 years of enrolment
Doctoral students:
Phase I: Pre-Candidacy
– Take all required courses
“Advance
– Pass a comprehensive exam
to
– Gain approval for a research proposal Candidacy”
Phase II: Candidacy
– Complete a research project (dissertation)
– Defend the thesis at a Doctoral Oral Examination
Doctoral Students:
– Should advance to candidacy within 2 years, and must within 3
– Should complete all degree requirements within 5 years of
enrollment and must within 6 years
http://www.grad.ubc.ca/current-students/newly-admitted/grad-orientation
GPS graduate pathways success
http://www.grad.ubc.ca/current-students/gps-graduate-pathways-success/gps-workshops-events
Find experts
http://webservices.publicaffairs.ubc.ca/clients/pa/apps/experts/public/index.php
Saturday, September 4, 2010
GPU
http://www.biomedcentral.com/1471-2105/11/446/abstract
Fast and accurate protein substructure searching with simulated annealing and GPUs
Alex D Stivala email, Peter J Stuckey email and Anthony I Wirth email
BMC Bioinformatics 2010, 11:446doi:10.1186/1471-2105-11-446
Published: 3 September 2010
http://www.biomedcentral.com/1471-2105/11/447/abstract
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms
Guo Y, Graber A, McBurney RN, Balasubramanian R
BMC Bioinformatics 2010, 11:447 (3 September 2010)
Fast and accurate protein substructure searching with simulated annealing and GPUs
Alex D Stivala email, Peter J Stuckey email and Anthony I Wirth email
BMC Bioinformatics 2010, 11:446doi:10.1186/1471-2105-11-446
Published: 3 September 2010
http://www.biomedcentral.com/1471-2105/11/447/abstract
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms
Guo Y, Graber A, McBurney RN, Balasubramanian R
BMC Bioinformatics 2010, 11:447 (3 September 2010)
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