4NT-New Time, New Tide, New Try, New Top

2:11PM - My Google pages

A new Hexi's Personal Homepage at Google pages, the URL:
http://hexi24.googlepages.com/
Interestingly, is not searchable at google.

3:34PM - (MOtohiko Tanino, 2004) The Human Anatomic Gene Expression Library (H-ANGEL), the H-Inv integrative

HI HexiRPA000042
DN (MOtohiko Tanino, 2004) The Human Anatomic Gene Expression Library (H-ANGEL), the H-Inv integrative display of human gene expression across disparate technologies and platforms @NAR #20060905
DA 2006.09.05
CP Nucleic Acids Res. 2005 Jan 1;33(Database issue):D567-72.
TI The Human Anatomic Gene Expression Library (H-ANGEL), the H-Inv integrative display of human gene expression across disparate technologies and platforms.
AU Tanino M, Debily MA, Tamura T, Hishiki T, Ogasawara O, Murakawa K, Kawamoto S, Itoh K, Watanabe S, de Souza SJ, Imbeaud S, Graudens E, Eveno E, Hilton P, Sudo Y, Kelso J, Ikeo K, Imanishi T, Gojobori T, Auffray C, Hide W, Okubo K.
IN Integrated Database Group, Japan Biological Information Research Center, Japan Biological Informatics Consortium, Time24 Building 10F, 2-45 Aomi, Koto-ku, Tokyo 135-0064, Japan. mtanino@jbirc.aist.go.jp
AB The Human Anatomic Gene Expression Library (H-ANGEL) is a resource for information concerning the anatomical distribution and expression of human gene transcripts. The tool contains protein expression data from multiple platforms that has been associated with both manually annotated full-length cDNAs from H-InvDB and RefSeq sequences. Of the H-Inv predicted genes, 18 897 have associated expression data generated by at least one platform. H-ANGEL utilizes categorized mRNA expression data from both publicly available and proprietary sources. It incorporates data generated by three types of methods from seven different platforms. The data are provided to the user in the form of a web-based viewer with numerous query options. H-ANGEL is updated with each new release of cDNA and genome sequence build. In future editions, we will incorporate the capability for expression data updates from existing and new platforms. H-ANGEL is accessible at http://www.jbirc.aist.go.jp/hinv/h-angel/.
PM PMID: 15608263 [PubMed - indexed for MEDLINE]
CA MOtohiko Tanino, E-mail: mtanino@jbirc.aist.go.jp from JBIRC.
CT Contents:
NT Notes:
SP Sentence Patterns from Paper:
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3:19PM - Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA

HI HexiRPA000041
DN (Tadashi Imanishi, 2004) Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones @PLoS Biology #20060905
DA 2006.09.05
CP PLoS Biol. 2004 Jun;2(6):e162. Epub 2004 Apr 20. Links
TI Integrative annotation of 21,037 human genes validated by full-length cDNA clones.
AU Imanishi T, Itoh T, Suzuki Y, O'Donovan C, Fukuchi S, Koyanagi KO, Barrero RA, Tamura T, Yamaguchi-Kabata Y, Tanino M, Yura K, Miyazaki S, Ikeo K, Homma K, Kasprzyk A, Nishikawa T, Hirakawa M, Thierry-Mieg J, Thierry-Mieg D, Ashurst J, Jia L, Nakao M, Thomas MA, Mulder N, Karavidopoulou Y, Jin L, Kim S, Yasuda T, Lenhard B, Eveno E, Suzuki Y, Yamasaki C, Takeda J, Gough C, Hilton P, Fujii Y, Sakai H, Tanaka S, Amid C, Bellgard M, Bonaldo Mde F, Bono H, Bromberg SK, Brookes AJ, Bruford E, Carninci P, Chelala C, Couillault C, de Souza SJ, Debily MA, Devignes MD, Dubchak I, Endo T, Estreicher A, Eyras E, Fukami-Kobayashi K, Gopinath GR, Graudens E, Hahn Y, Han M, Han ZG, Hanada K, Hanaoka H, Harada E, Hashimoto K, Hinz U, Hirai M, Hishiki T, Hopkinson I, Imbeaud S, Inoko H, Kanapin A, Kaneko Y, Kasukawa T, Kelso J, Kersey P, Kikuno R, Kimura K, Korn B, Kuryshev V, Makalowska I, Makino T, Mano S, Mariage-Samson R, Mashima J, Matsuda H, Mewes HW, Minoshima S, Nagai K, Nagasaki H, Nagata N, Nigam R, Ogasawara O, Ohara O, Ohtsubo M, Okada N, Okido T, Oota S, Ota M, Ota T, Otsuki T, Piatier-Tonneau D, Poustka A, Ren SX, Saitou N, Sakai K, Sakamoto S, Sakate R, Schupp I, Servant F, Sherry S, Shiba R, Shimizu N, Shimoyama M, Simpson AJ, Soares B, Steward C, Suwa M, Suzuki M, Takahashi A, Tamiya G, Tanaka H, Taylor T, Terwilliger JD, Unneberg P, Veeramachaneni V, Watanabe S, Wilming L, Yasuda N, Yoo HS, Stodolsky M, Makalowski W, Go M, Nakai K, Takagi T, Kanehisa M, Sakaki Y, Quackenbush J, Okazaki Y, Hayashizaki Y, Hide W, Chakraborty R, Nishikawa K, Sugawara H, Tateno Y, Chen Z, Oishi M, Tonellato P, Apweiler R, Okubo K, Wagner L, Wiemann S, Strausberg RL, Isogai T, Auffray C, Nomura N, Gojobori T, Sugano S.
IN Integrated Database Group, Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
AB The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.
PM PMID: 15103394 [PubMed - indexed for MEDLINE]
CA Takashi Gojobori, E-mail: tgojobor@genes.nig.ac.jp from Integrated Database Group, Biological Information Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
CT Contents:
1. 184 diverse cell types and tissues.
2. Terminal Exons: identity of 1st, last, internal exon : 78%, 50%, and 89%
3. 1,682 cNDAs multiple mapped to the genome
a. duplication events
b. artificial duplication caused by misassembly.
4. H-Inv Cluster IDs : HIX0000000 [prefix HIX + 7 digits] corresponding to Gene / Gene Loci
H-Inv Cluster IDs : HIT000000000 [prefix HIT + 9 digits] corresponding to Transcripts
5. 978 cDNAs (847 clusters) unmapped to the genome --> the genome is incomplete (3.7~4%):
a. the cDNAs could be partially mapped to the human genome.
b. most of the cDNAs could be mpped unambigously to the mouse genome.
6. the first intron (Median: 3,152) and last exon (Median: 786) are likely to be longer.
7. AS: 3,181 loci :: 8,553 AS isoforms, 55% of ORFs containing AS isoforms.
8. Enzyme and pathway
9. protein-coding ORFs and ncRNA genes
10. 72,027 SNPs and indels
a. synonymous : 11,014
b. nonsynonymous : 13,215
NT Notes:
1.Erratum in: PLoS Biol. 2004 Jul;2(7):e256.
2. SNP and synonymous, nonsynonymous:
A Single Nucleotide Polymorphism or SNP (pronounced snip) is a DNA sequence variation occurring when a single nucleotide - A, T, C, or G - in the genome (or other shared sequence) differs between members of a species (or between paired chromosomes in an individual). For example, two sequenced DNA fragments from different individuals, AAGCCTA to AAGCTTA, contain a difference in a single nucleotide. In this case we say that there are two alleles : C and T.

Within a population, SNPs can be assigned a minor allele frequency - the ratio of chromosomes in the population carrying the less common variant to those with the more common variant. Usually one will want to refer to SNPs with a minor allele frequency of ≥ 1% (or 0.5% etc.), rather than to "all SNPs" (a set so large as to be unwieldy). It is important to note that there are variations between human populations, so a SNP that is common enough for inclusion in one geographical or ethnic group may be much rarer in another.

SNPs may fall within coding sequences of genes, noncoding regions of genes, or in the intergenic regions between genes. SNPs within a coding sequence will not necessarily change the amino acid sequence of the protein that is produced, due to redundancy in the genetic code. A SNP in which both forms lead to the same protein sequence is termed synonymous - if different proteins are produced they are non-synonymous. SNPs that are not in protein coding regions may still have consequences for gene splicing, transcription factor binding, or the sequence of non-coding RNA.

SNPs make up 90% of all human genetic variations, and SNPs with a minor allele frequency of ≥ 1% occur every 100 to 300 bases along the human genome, on average, where two of every three SNPs substitute cytosine with thymine.

Variations in the DNA sequences of humans can affect how humans develop diseases, respond to pathogens, chemicals, drugs, etc. As a consequence SNPs are of great value to biomedical research and in developing pharmacy products. Because SNPs are inherited and do not change much from generation to generation, following them during population studies is straightforward. They are also used in some forms of genealogical DNA testing.

3. Nonsense mutation: From Wikipedia, the free encyclopedia
In genetics, a nonsense mutation is a point mutation in a sequence of DNA that results in a premature stop codon, or a nonsense codon in the transcribed mRNA, and possibly a truncated, and often nonfunctional protein product.

Simple example
For example, given the following coding strand of a DNA sequence, the corresponding mRNA transcript, and the translated protein product:
DNA : ATG ACT CAC CGA GCG CGA AGC TGA
mRNA : AUG ACU CAC CGA GCG CGU AGC UGA
Protein: Met Thr His Arg Ala Arg Ser Stop
Suppose that a nonsense mutation were introduced at the fourth triplet in the DNA sequence (CGA) causing the cytosine to be replaced with thymine, yielding TGA in the DNA sequence. Since TGA is transcribed as UGA, the resulting transcript would be:
mRNA: AUG ACU CAC UGA CGC CGU AGC UGA
Furthermore, the resulting protein product would be prematurely stopped since UGA is a stop codon:
Protein: Met Thr His Stop
The remaining codons of the mRNA are not translated into amino acids because the stop codon is prematurely reached during translation. This can yield a truncated abbreviated protein product, which quite often lacks the functionality of the normal, non-mutant protein.

Nonsense-mediated mRNA decay
Despite an expected tendency for premature termination codons to yield shortened polypeptide products, in fact the formation of truncated proteins does not occur often in vivo. Many organisms -- including humans and lower species, such as yeast -- employ a nonsense-mediated mRNA decay pathway, which degrades mRNAs containing nonsense mutations before they are translated into nonfunctional polypeptides.

Pathology associated with nonsense mutations
Cystic fibrosis - though rare, a nonsense mutation in the cystic fibrosis transmembrane conductance regulator gene can cause the disease
SP Sentence Patterns from Paper:
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4:08PM - (Velculescu VE, 1995) Serial analysis of gene expression @Science #20060726

HI HexiRPA000006
DN (Velculescu VE, 1995) Serial analysis of gene expression @Science #20060726
DA 2006.07.26
CP Science. 1995 Oct 20;270(5235):484-7.
TI Serial analysis of gene expression
AU Velculescu VE, Zhang L, Vogelstein B, Kinzler KW.
IN Oncology Center, Johns Hopkins University, Baltimore, MD 21231, USA.
AB The characteristics of an organism are determined by the genes expressed within it. A method was developed, called serial analysis of gene expression (SAGE), that allows the quantitative and simultaneous analysis of a large number of transcripts. To demonstrate this strategy, short diagnostic sequence tags were isolated from pancreas, concatenated, and cloned. Manual sequencing of 1000 tags revealed a gene expression pattern characteristic of pancreatic function. New pancreatic transcripts corresponding to novel tags were identified. SAGE should provide a broadly applicable means for the quantitative cataloging and comparison of expressed genes in a variety of normal, developmental, and disease states.
PM PMID: 7570003 [PubMed - indexed for MEDLINE]
CT Contents:
Limitation of other techoniques:
a. cDNA : Partial picture with no direct information about abundance
b. EST / RNase / RT-PCR : a limited number of genes at a time.
Principles of SAGE:
a. Short Tags (9-10 bp) contains sufficient information to uniquely indentify a transcript.
4 exp 9 = 262,144 ;
Human whole transcripts : 80, 000 estimated
b. concatenation of short tags allows the efficient analysis of transcripts in a serial maner by the sequencing of multiple tags within a signle clone.
Schema / Steps of SAGE (see Fig. 1):
1. Cleave with Anchoring Enzyme (AE) - Nla III
2. Bind to streptavidin beads
3. Dividein half
4. Ligate to Linkers (A +B)
5. Cleave with Tagging Enzyme (TE) - Bsm FI (type IIS Restiction Endonulease [Blunt end])
6. Ligate and amplify with Primer A and B
7. Cleave with AE
8. Isolate Ditags ( 4 bp of punctuation per ditag)
9. Concatenate and Clone
NT Notes:
RT-PCR:
SP Sentence Patterns from Paper:
1. A method was developed, called XX, that allows --.
2. Table 1 shows the analysis of XX.
3. Obviously, XX could also be applied to the analysis of YY other than ZZ.
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3:40PM - Readme for HexiRPA Document - my personal notes to reading papers

Readme for HexiRPA Document - my personal notes to reading papers
view edit
Submitted by HexiToFor on Thu, 2006-07-27 11:34.
Hexi Reading Professional Articles (HexiRPA):

Readme for the docment:

for Each entry, there are 5 sections:Paper Tag (Line 1-2), Read Date (Line 3), PubMed Citation (Line 4-9), MyNotes (Line 10-12) and End Tag (Line 13)
Line 1 : Paper No. --(HI:HexiRRA Idendifier)
Line 2 : Document Name on myPC --(DN: Document Name)
Line 3 : Date read the paper --(DA:Date)
LIne 4 : Citation of PubMed Format (CP: Citation of PubMed)
Line 5 : Title (TI: Title)
LIne 6 : Authors (AU : Author)
Line 7 : Institutes [one or more lines] (IN : INstitutes)
Line 8 : Abstract (AB : ABstract)
Line 9 : PubMed ID (PM : PubMed ID)
Line 10 : Contents [summary, lists and questions] (CT :CoTents)
Line 11 : Notes [Explanation or Defination to Vocabulory, Jargons] (NT: NoTes)
LIne 12 : Sentence Patterns from Paper (SP :Sentence Patterns)
Line 13 : //// --End Tag

Notes:
1. Format of Paper NO. : HexiRPA000000
Identifier = Prefix (HexiRPA) + Six Digits (Order of Reading Since 2006.07.18)
e.g. HexiRPA000001
2. Format of Document Name on myPC
Name = (First Author Name, Publication Year) Title @Journal Name #Date of Reading Paper
e.g. (Shabalina SA, 2004) The mammalian transcriptome and the function of non-coding DNA sequences @Genome Biology #20060719

Template:
HI HexiRPA000000
DN (First Authour, 2004) Title @Journal #20060719
DA YYYY.MM.DD
CP XX
TI XX
AU XX
IN XX
AB XX
PM XX
CT Contents:
NONE
NT Notes:
NONE
SP Sentence Patterns from Paper:
1. XX
////

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9:39AM - A sequence-oriented comparison of gene expression measuremens, from Nature Biotech

A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies, Nat Biotech
view edit
Submitted by HexiToFor on Tue, 2006-07-25 10:31.
The paper described a framework for comparisons across gene expression microarray platforms and laboratories, which including: 1) Affymetrix; 2) Agilent; 3) Applied Biosystems (ABI); 4) Amersham (now GE Healthcare); 5) cDNA arrays provided by the Cepko laboratory (academic cDNA); 6) Compugen (now Sigma-Genosys); 7) Mergen; 8) long oligonuceotide arrays from the Microarray Core facility at Massachusetts General Hospital (MGH long oligo); 9) MWG BioTech (now Ocimum Biosolutions); 10) Operon. As a result, the commercial platform ABI has the best performace, where the academic cDNA from Harvard poorest.

Nat Biotechnol. 2006 Jul;24(7):832-840. Epub 2006 Jul 2.

A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies.

Kuo WP, Liu F, Trimarchi J, Punzo C, Lombardi M, Sarang J, Whipple ME, Maysuria M, Serikawa K, Lee SY, McCrann D, Kang J, Shearstone JR, Burke J, Park DJ, Wang X, Rector TL, Ricciardi-Castagnoli P, Perrin S, Choi S, Bumgarner R, Kim JH, Short GF 3rd, Freeman MW, Seed B, Jensen R, Church GM, Hovig E, Cepko CL, Park P, Ohno-Machado L, Jenssen TK.

[1] Department of Developmental Biology, Harvard School of Dental Medicine, 188 Longwood Ave., Boston, Massachusetts 02115, USA.
[2] Department of Genetics, Harvard Medical School, Howard Hughes Medical Institute, Boston, Massachusetts, USA.
[3] Decision Systems Group, Brigham and Women's Hospital, Boston, Massachusetts, USA.
[4] These authors contributed equally to this work.

Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

PMID: 16823376 [PubMed - as supplied by publisher]

Protocols for microarray:
1. the Minimum Information about a Microarray Experiment (MIAME)
http://www.mged.org/Workgroups/MIAME/miame.html
2. the External RNA Control Consortium (ERCC)
http://www.cstl.nist.gov/biotech/Cell&TissueMeasurements/GeneExpression/ERCC.htm
3. the MicroArray Quality Control (MAQC) Project:
http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc

Microarray Data:
1. Gene Expression Omnibus (GEO)
2. ArrayExpress

Bias-induced Factors:
1. nonidentical samples on different platforms
2. samples not sufficiently distinct
3. samples processed using different protocols
4. lack of technical replicates
5. data prepocessin steps not standardized
6. only a few types of platforms directly compared
7. measurements matched using probe annotations
8. 'agreement' not unambigously quantified
9. insufficient biological validation

Comparisons on ten different microarray platforms:
1. Affymetrix
2. Agilent
3. Applied Biosystems (ABI)
4. Amersham (now GE Healthcare)
5. cDNA arrays provided by the Cepko laboratory (academic cDNA)
6. Compugen (now Sigma-Genosys)
7. Mergen
8. long oligonuceotide arrays from the Microarray Core facility at Massachusetts General Hospital (MGH long oligo)
9. MWG BioTech (now Ocimum Biosolutions)
10. Operon

5 replicate assays for each sample

Intra-Platform Comparsions:
1. see Table 1
Correlation (Pearson + Spearman)
Highest: ABI
Lowest: academic cDNA
2. Coefficents of Viration (CVs)
Best: ABI, Affymetrix, Amersham, Agilent
Poorest: academic cDNA

Inter-platform Comparsions:
1. Probe measurements were mapped to the following gene identifiers:
a. UniGene (UG)
b. LocusLink (LL)
c. RefSeq (RS)
d. RefSeq exon (RSEXON)
See Figure 1
e.g. NM_008086 (Gas1)
2. Assessment of measurement deviation from pseudo-nomival values
? what is outlier (statistics)
Principal Component Analysis (PCA) Plot
See Figure 2 ?

Inter-laboratory Comparison:
Data from 3 platforms: a)Affymetrix, b)Amersham, c)Mergent

Difference of one-dye platforms and two-dye platforms ?

Discussion:
1. Why Cortex and Retina:
Cortex: brain tissues are generally considered to have broad expression profiles
Retina: has some well-known tissue specific transcripts
2. One-dye platforms: ABI, Affymetrix, Amersham, Mergen
Two-dye platforms: The other six ones.

Methods:
1. Preprocessing of Microarray data:
a. Normalization
b. Transformation
c. Filtering
2. Mapping of Genes across platforms:
a. annotation-based approaches: MatchMiner (UG, LL)
b. sequence-based approaches: UCSC, BLAT
3. Analyses tools/softwares:
a. R software environment (http://www.R-project.org)
b. BioConductor package
c. MATLAB
4. Biological validations:
a. genes should be present in at least six platforms (4+2): One-dye Platforms + Other 2 platforms
b. genes should span the dynamic range
c. genes should include pairs with measurements that were in disagreement.

7:51PM - It does not matter where U start

It does not matter where U start, the Interest is more impotant, and where there U enjoy it, U would follow up and learn What U need, then work hard.
--P. Roy Vagelos
from the lecture at GUCAS

About P. Roy Vagelos:
former CEO of Merck & Co., Inc. and Author of Medicine, Science, and Merck as the latest keynote to join the Pharmaceutical Marketing Congress Speaking Faculty.

3 examples he took in the lec.
a. Cholesterol and Heart attack
b. Hep B Virus and the Vaccine
c. River Blind and the parasite

His breif life history from doctor to chemist.

5:18PM - Kaifu Lee and Google China

Lee starts job as boss of Google in China

11:55PM - Happy Mid-Autumn Festival!

Just as the title.
and the test for the livejournal！