Computational approaches to cell cycle analysis: Data and databases

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    Computational approaches to cell cycle analysis: Data and databases - Presentation Transcript

    1. Data and databases Lars Juhl Jensen EMBL Heidelberg
    2. a lot of data
    3.  
    4. a lot of databases
    5. Duncan Hull, nodalpoint.org
    6. genes and proteins
    7. GenBank
    8.  
    9.  
    10. UniProt
    11.  
    12.  
    13. PDB
    14.  
    15.  
    16. genomes
    17. SGD Saccharomyces Genome Database
    18.  
    19.  
    20. TAIR The Arabidopsis Information Resource
    21.  
    22.  
    23. GeneDB
    24.  
    25.  
    26. Ensembl
    27.  
    28.  
    29.  
    30. and many others
    31. homology
    32. orthology and paralogy
    33.  
    34. InParanoid
    35. model organism databases
    36.  
    37.  
    38.  
    39. HomoloGene
    40. Entrez
    41.  
    42.  
    43. COG Clusters of Orthologous Groups
    44. deep orthology
    45. functional classes
    46.  
    47. OrthoMCL
    48. high resolution
    49.  
    50.  
    51.  
    52. YOGY
    53. meta-database
    54.  
    55.  
    56. eggNOG
    57. multiple resolutions
    58.  
    59.  
    60.  
    61.  
    62. TreeFam
    63. phylogenetic trees
    64.  
    65.  
    66.  
    67. protein domains
    68. CDD Conserved Domain Database
    69. NCBI BLAST
    70.  
    71.  
    72. Pfam
    73. high coverage
    74.  
    75.  
    76.  
    77. SMART
    78. signal transduction
    79.  
    80.  
    81. InterPro
    82. meta-database
    83.  
    84.  
    85.  
    86. structural classification
    87. SCOP
    88. manual assignments
    89.  
    90. CATH
    91. automatic assignments
    92.  
    93. cell cycle
    94. evolution of expression
    95.  
    96. gene expression
    97. microarrays
    98.  
    99. dynamics
    100. GEO Gene Expression Omnibus
    101.  
    102.  
    103. ArrayExpress
    104.  
    105.  
    106. cell cycle
    107.  
    108.  
    109. Cyclebase.org
    110.  
    111.  
    112.  
    113. SCEPTRANS
    114.  
    115.  
    116. interactions
    117. affinity purification
    118.  
    119. yeast two-hybrid
    120.  
    121. synthetic lethality
    122.  
    123. repositories
    124. BioGRID
    125.  
    126.  
    127. DIP Database of Interacting Proteins
    128.  
    129.  
    130.  
    131. IntAct
    132.  
    133.  
    134. MINT Molecular Interactions Database
    135.  
    136.  
    137.  
    138. cell cycle
    139. dynamic network
    140.  
    141. linear motifs
    142. less than 10 residues
    143.  
    144. Prosite
    145.  
    146. ELM
    147.  
    148. Domino
    149.  
    150.  
    151. PepCyber P~Pep
    152.  
    153.  
    154.  
    155. Phospho.ELM
    156.  
    157.  
    158. PhosphoSite
    159.  
    160.  
    161. PhosPhAt
    162.  
    163.  
    164.  
    165. Phosida
    166.  
    167.  
    168. cell cycle
    169. evolution of phosphorylation
    170.  
    171. complexes
    172. CORUM
    173.  
    174.  
    175. Gene Ontology
    176.  
    177.  
    178. cell cycle
    179. regulation of assembly
    180.  
    181. pathways
    182. KEGG
    183. broad coverage
    184.  
    185.  
    186. iPath
    187.  
    188.  
    189. EcoCyc
    190. MetaCyc
    191. metabolism
    192.  
    193.  
    194. Reactome
    195. elegant data model
    196.  
    197. PID Pathway Interaction Database
    198. meta-database
    199.  
    200.  
    201. models
    202. BioModels
    203.  
    204.  
    205. cell cycle
    206. Cyclonet
    207.  
    208.  
    209.  
    210.  
    211. CCDB
    212.  
    213.  
    214.  
    215.  
    216. integration
    217. STRING
    218.  
    219. benchmarking
    220.  
    221. transfer by orthology
    222.  
    223.  
    224. Bayesian framework
    225.  
    226. signaling networks
    227. NetworKIN
    228.  
    229.  
    230.  

    + Lars Juhl JensenLars Juhl Jensen, 2 years ago

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    PhD Program in Computational Biology, Instituto Gul more

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