Wikiomics:Sequence motifs - Revision history

Darek Kedra: typos

Darek Kedra — Mon, 07 Jun 2010 21:59:13 GMT

typos

←Older revision		Revision as of 21:59, 7 June 2010
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	* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.		* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.

-	* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "~~phylgenetic~~ shadowing"	+	* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences from more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "phylogenetic shadowing"

	* not all collection of sequences are equally good for sequence motif prediction. Order of preference:		* not all collection of sequences are equally good for sequence motif prediction. Order of preference:

Darek Kedra: reformat

Darek Kedra — Wed, 07 Apr 2010 14:45:26 GMT

reformat

←Older revision		Revision as of 14:45, 7 April 2010
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	* transcriptional regulation differs in complexity from simple organisms (i.e. yeast) to human. What works in yeast (multiple algorithms on promoters of co-expressed genes) does not work on human data		* transcriptional regulation differs in complexity from simple organisms (i.e. yeast) to human. What works in yeast (multiple algorithms on promoters of co-expressed genes) does not work on human data
-

	* for any given gene, start with comparative genomics (called here phylogenetic fingerprinting). For 60-70% of promoters you can find common TF binding sites between human and mouse		* for any given gene, start with comparative genomics (called here phylogenetic fingerprinting). For 60-70% of promoters you can find common TF binding sites between human and mouse
-

	* more species, stricter motif detection, but less sensitivity (gene can be regulated differently in some species, TF binding site outside of fragment used for prediction etc.)		* more species, stricter motif detection, but less sensitivity (gene can be regulated differently in some species, TF binding site outside of fragment used for prediction etc.)
-

	* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.		* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.
-

	* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "phylgenetic shadowing"		* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "phylgenetic shadowing"
-

	* not all collection of sequences are equally good for sequence motif prediction. Order of preference:		* not all collection of sequences are equally good for sequence motif prediction. Order of preference:

Darek Kedra: forat

Darek Kedra — Thu, 18 Mar 2010 11:59:40 GMT

forat

←Older revision		Revision as of 11:59, 18 March 2010
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	* transcriptional regulation differs in complexity from simple organisms (i.e. yeast) to human. What works in yeast (multiple algorithms on promoters of co-expressed genes) does not work on human data		* transcriptional regulation differs in complexity from simple organisms (i.e. yeast) to human. What works in yeast (multiple algorithms on promoters of co-expressed genes) does not work on human data
		+

	* for any given gene, start with comparative genomics (called here phylogenetic fingerprinting). For 60-70% of promoters you can find common TF binding sites between human and mouse		* for any given gene, start with comparative genomics (called here phylogenetic fingerprinting). For 60-70% of promoters you can find common TF binding sites between human and mouse
		+

	* more species, stricter motif detection, but less sensitivity (gene can be regulated differently in some species, TF binding site outside of fragment used for prediction etc.)		* more species, stricter motif detection, but less sensitivity (gene can be regulated differently in some species, TF binding site outside of fragment used for prediction etc.)
		+

	* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.		* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.
		+

	* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "phylgenetic shadowing"		* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "phylgenetic shadowing"
		+

	* not all collection of sequences are equally good for sequence motif prediction. Order of preference:		* not all collection of sequences are equally good for sequence motif prediction. Order of preference:
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	** promoters of ortologues (see above)		** promoters of ortologues (see above)
	** co-expressed genes		** co-expressed genes
		+

	* pointless: searching for highly degenerate motifs in long stretches of DNA without any statistics (hundreds of sites of almost no value).		* pointless: searching for highly degenerate motifs in long stretches of DNA without any statistics (hundreds of sites of almost no value).
	** only by comparing scores and frequencies of a given motif in a large set of sequences one can estimate if TF site detected in promoter of interest is of any significance. See [http://159.149.109.9/pscan/ PSCAN] and "Prediction of over Represented Transcription Factor Binding Sites in Co-regulated Genes Using Whole Genome Matching Statistics" Pavesi 2007		** only by comparing scores and frequencies of a given motif in a large set of sequences one can estimate if TF site detected in promoter of interest is of any significance. See [http://159.149.109.9/pscan/ PSCAN] and "Prediction of over Represented Transcription Factor Binding Sites in Co-regulated Genes Using Whole Genome Matching Statistics" Pavesi 2007

		+

	It is important to use few complementary programs as well as allow predictions of several motifs per program. There is threshold when it comes to number of sequences used, above which there is no improvement in sensitivity.		It is important to use few complementary programs as well as allow predictions of several motifs per program. There is threshold when it comes to number of sequences used, above which there is no improvement in sensitivity.

Darek Kedra: notes after Pavesi lecture

Darek Kedra — Fri, 12 Mar 2010 13:13:08 GMT

notes after Pavesi lecture

←Older revision		Revision as of 13:13, 12 March 2010
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	'''Sequence motifs''' - for more general background see Wikipedias [http://en.wikipedia.org/wiki/Sequence_motif Sequence_motif].		'''Sequence motifs''' - for more general background see Wikipedias [http://en.wikipedia.org/wiki/Sequence_motif Sequence_motif].

		+	Sequence motif finding is used often in contexts of transcription binding sites.
		+	Before starting it is important to keep few things in mind:
		+
		+	* transcriptional regulation differs in complexity from simple organisms (i.e. yeast) to human. What works in yeast (multiple algorithms on promoters of co-expressed genes) does not work on human data
		+
		+	* for any given gene, start with comparative genomics (called here phylogenetic fingerprinting). For 60-70% of promoters you can find common TF binding sites between human and mouse
		+
		+	* more species, stricter motif detection, but less sensitivity (gene can be regulated differently in some species, TF binding site outside of fragment used for prediction etc.)
		+
		+	* conservation of promoters varies between genes. For some highly conserved ones (i.e. some SOX genes) there is too little of a difference between human and mouse to detect anything. You have to go down the tree to fish species to find island of conservation.
		+
		+	* for some more human specific genes there may be no conservation human-mouse, or the orthologue gene may not even exist in one of the species. Use sequences more closely related species. This goes up to studying multiple sequences from very closely related species (i.e. 12 Drosophila's). Search the term "phylgenetic shadowing"
		+
		+	* not all collection of sequences are equally good for sequence motif prediction. Order of preference:
		+	** chromatin immunoprecipitation followed by next generation sequencing
		+	** promoters of ortologues (see above)
		+	** co-expressed genes
		+
		+	* pointless: searching for highly degenerate motifs in long stretches of DNA without any statistics (hundreds of sites of almost no value).
		+	** only by comparing scores and frequencies of a given motif in a large set of sequences one can estimate if TF site detected in promoter of interest is of any significance. See [http://159.149.109.9/pscan/ PSCAN] and "Prediction of over Represented Transcription Factor Binding Sites in Co-regulated Genes Using Whole Genome Matching Statistics" Pavesi 2007
		+

	It is important to use few complementary programs as well as allow predictions of several motifs per program. There is threshold when it comes to number of sequences used, above which there is no improvement in sensitivity.		It is important to use few complementary programs as well as allow predictions of several motifs per program. There is threshold when it comes to number of sequences used, above which there is no improvement in sensitivity.
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	==Motif finding programs==		==Motif finding programs==
	There are obvious trade offs between speed and accuracy/sensitivity when running these programs. Few rules based on review by Hu et al.:		There are obvious trade offs between speed and accuracy/sensitivity when running these programs. Few rules based on review by Hu et al.:
-	* avoid using longer ~~sequencess~~ than necessary. It does increase noise signal and increases running time.	+	* avoid using longer sequences than necessary. It does increase noise signal and increases running time.
-	* for programs like MEME there is a ~~plateu~~ of motifs found after about 10 input sequences, so one can randomly select 10 sequences out of a larger set and	+	* for programs like MEME there is a plateau of motifs found after about 10 input sequences, so one can randomly select 10 sequences out of a larger set and
-	then check for ~~occurence~~ of detected motifs in the remaining ones.	+	then check for occurrence of detected motifs in the remaining ones.
	* scores between different programs are incompatible		* scores between different programs are incompatible

	==Tested set==		==Tested set==
		+	This section is few years old and requires retesting.
		+
	===AlignACE===		===AlignACE===
	Gibbs sampling algorithm (stochastic), requires multiple runs to get all top hits.		Gibbs sampling algorithm (stochastic), requires multiple runs to get all top hits.
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	* [http://bioinformatics.caltech.edu/cis-analysis.txt Erich Schwarz's list from 2002]		* [http://bioinformatics.caltech.edu/cis-analysis.txt Erich Schwarz's list from 2002]
	* [http://biobase.ist.unomaha.edu/mediawiki/index.php/Main_Page Motif Tool Assessment Platform (MTAP)] wiki from Omaha		* [http://biobase.ist.unomaha.edu/mediawiki/index.php/Main_Page Motif Tool Assessment Platform (MTAP)] wiki from Omaha
		+	* Giulio Pavesi and Federico Zambelli, “Prediction of over Represented Transcription Factor Binding Sites in Co-regulated Genes Using Whole Genome Matching Statistics,” in Applications of Fuzzy Sets Theory, 2007, 651-658, http://dx.doi.org/10.1007/978-3-540-73400-0_83.
		+

	See also: http://www.connotea.org/user/darked89/tag/motif		See also: http://www.connotea.org/user/darked89/tag/motif

Darek Kedra: /* Multiple algorithms / metaservers */

Darek Kedra — Wed, 03 Mar 2010 00:53:50 GMT

Multiple algorithms / metaservers

←Older revision		Revision as of 00:53, 3 March 2010
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	==Multiple algorithms / metaservers==		==Multiple algorithms / metaservers==

-	* [http://~~www~~.comp.nus.edu.sg/~~~bioinfo~~/~~MotifVoter~~/ MotifVoter] [http://precedings.nature.com/documents/1251/version/1/files/npre20071251-1.pdf PDF] 10! algorithms from National University of Singapore. Web server, result by email.	+	* [http://compbio.ddns.comp.nus.edu.sg/~edward/MotifVoter2/ MotifVoter] [http://precedings.nature.com/documents/1251/version/1/files/npre20071251-1.pdf PDF] 11! algorithms from National University of Singapore. Web server, result by email.

Darek Kedra: /* Multiple algorithms / metaservers */

Darek Kedra — Wed, 03 Mar 2010 00:49:45 GMT

Multiple algorithms / metaservers

←Older revision		Revision as of 00:49, 3 March 2010
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-	* Credo [http://mips.gsf.de/proj/regulomips/credo.htm]	+	* Credo [http://mips.gsf.de/proj/regulomips/credo.htm] (broken link)
	Visualisation of AlignACE, DIALIGN, FootPrinter, MEME and MotifSampler results.		Visualisation of AlignACE, DIALIGN, FootPrinter, MEME and MotifSampler results.
	Paper: [http://bioinformatics.oxfordjournals.org/cgi/content/full/21/23/4304]		Paper: [http://bioinformatics.oxfordjournals.org/cgi/content/full/21/23/4304]

Darek Kedra: /* Novel algorithms */

Darek Kedra — Wed, 03 Mar 2010 00:18:54 GMT

Novel algorithms

←Older revision		Revision as of 00:18, 3 March 2010
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	* The AMADEUS Motif Discovery Tool (whole platform)		* The AMADEUS Motif Discovery Tool (whole platform)
-	http://~~www~~.cs.tau.ac.il~~/~rshamir~~/amadeus/~~Amadeus.htm~~	+	http://acgt.cs.tau.ac.il/amadeus/

-	* GAME (java, genetic algorithm) [http://~~mail~~.~~med~~.upenn.edu/~~~zhiwei/GAME~~/]	+	* GAME (java, genetic algorithm) [http://www-stat.wharton.upenn.edu/~stjensen/doc.html] (authors page)
	paper http://bioinformatics.oxfordjournals.org/cgi/content/abstract/22/13/1577		paper http://bioinformatics.oxfordjournals.org/cgi/content/abstract/22/13/1577

Darek Kedra: /* NestedMica */

Darek Kedra — Wed, 03 Mar 2010 00:05:32 GMT

NestedMica

←Older revision		Revision as of 00:05, 3 March 2010
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	===NestedMica===		===NestedMica===
-	(java + C program, requires compilation ) [http://www.sanger.ac.uk/~~Software~~/~~analysis~~/~~nmica~~/]	+	(java + C program, requires compilation ) [http://www.sanger.ac.uk/resources/software/nestedmica/]

	* Calculate background:		* Calculate background:

Perry Evans at 19:03, 17 September 2008

Perry Evans — Wed, 17 Sep 2008 19:03:31 GMT

←Older revision		Revision as of 19:03, 17 September 2008
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	* Command line		* Command line
	<pre>		<pre>
-	meme -nmotifs numberof_motifs_2_find -~~model~~ oops -protein -sequence your_fasta_file -outfile your_fasta_file.meme	+	meme -nmotifs numberof_motifs_2_find -mod oops -protein -sequence your_fasta_file -outfile your_fasta_file.meme
	</pre>		</pre>

Darek Kedra: /* Multiple algorithms / metaservers */ +MotifVoter]

Darek Kedra — Tue, 01 Apr 2008 15:21:54 GMT

Multiple algorithms / metaservers: +MotifVoter]

←Older revision		Revision as of 15:21, 1 April 2008
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	==Multiple algorithms / metaservers==		==Multiple algorithms / metaservers==
		+
		+	* [http://www.comp.nus.edu.sg/~bioinfo/MotifVoter/ MotifVoter] [http://precedings.nature.com/documents/1251/version/1/files/npre20071251-1.pdf PDF] 10! algorithms from National University of Singapore. Web server, result by email.
		+
		+
	* Credo [http://mips.gsf.de/proj/regulomips/credo.htm]		* Credo [http://mips.gsf.de/proj/regulomips/credo.htm]
	Visualisation of AlignACE, DIALIGN, FootPrinter, MEME and MotifSampler results.		Visualisation of AlignACE, DIALIGN, FootPrinter, MEME and MotifSampler results.