Improving Analysis and Annotation of Microarray Data with Protein Interactions

Author(s):  
Max Kotlyar ◽  
Serene W. H. Wong ◽  
Chiara Pastrello ◽  
Igor Jurisica
2012 ◽  
Vol 367 (1586) ◽  
pp. 279-290 ◽  
Author(s):  
Allan Tucker ◽  
Daniel Duplisea

There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.


2009 ◽  
Vol 3 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Mathur Sachin ◽  
Visvanathan Mahesh ◽  
Svojanovsky Stan ◽  
Yoo Byunggil ◽  
Srinivas Adagarla B. ◽  
...  

We have developed the web based tool GOAPhAR (Gene Ontology, Annotations and Pathways for Array Research), that integrates information from disparate sources regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, Gene Ontology, publicly available microarray datasets and tools for statistically validating clusters in microarray data. Genes of interest can be input as Affymetrix probe identifiers, Genbank, or Unigene identifiers for human, mouse or rat genomes. Results are provided in a user friendly interface with hyperlinks to the sources of information. The tool is freely available at http://bioinformatics.kumc.edu/goaphar/.


2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxia Liu ◽  
Lin Wang ◽  
Bingping Wang ◽  
Meng Yue ◽  
Yufeng Cheng

Colon cancer is the third and second most common cancer form in men and women worldwide. It is generally accepted that colon cancer mainly results from diet. The aim of this study was to identify core pathways which elucidated the molecular mechanisms in colon cancer. The microarray data of E-GEOD-44861 was downloaded from ArrayExpress database. All human pathways were obtained from Kyoto Encyclopedia of Genes and Genomes database. In total, 135 differential expressed genes (DEG) were identified using Linear Models for Microarray Data package. Differential pathways were identified with the method of attractor after overlapping with DEG. Pathway cross talk network (PCN) was constructed by combining protein-protein interactions and differential pathways. Cross talks of all pathways were obtained in PCN. There were 65 pathways with RankProd (RP) values < 0.05 and 16 pathways with Impact Factors (IF) values > 100. Five pathways were satisfied withPvalue < 0.05, RP values < 0.05, and IF values > 100, which were considered to be the most important pathways in colon cancer. In conclusion, the five pathways were identified in the center status of colon cancer, which may contribute to understanding the mechanism and development of colon cancer.


Author(s):  
S.B. Andrews ◽  
R.D. Leapman ◽  
P.E. Gallant ◽  
T.S. Reese

As part of a study on protein interactions involved in microtubule (MT)-based transport, we used the VG HB501 field-emission STEM to obtain low-dose dark-field mass maps of isolated, taxol-stabilized MTs and correlated these micrographs with detailed stereo images from replicas of the same MTs. This approach promises to be useful for determining how protein motors interact with MTs. MTs prepared from bovine and squid brain tubulin were purified and free from microtubule-associated proteins (MAPs). These MTs (0.1-1 mg/ml tubulin) were adsorbed to 3-nm evaporated carbon films supported over Formvar nets on 600-m copper grids. Following adsorption, the grids were washed twice in buffer and then in either distilled water or in isotonic or hypotonic ammonium acetate, blotted, and plunge-frozen in ethane/propane cryogen (ca. -185 C). After cryotransfer into the STEM, specimens were freeze-dried and recooled to ca.-160 C for low-dose (<3000 e/nm2) dark-field mapping. The molecular weights per unit length of MT were determined relative to tobacco mosaic virus standards from elastic scattering intensities. Parallel grids were freeze-dried and rotary shadowed with Pt/C at 14°.


2013 ◽  
Vol 54 ◽  
pp. 79-90 ◽  
Author(s):  
Saba Valadkhan ◽  
Lalith S. Gunawardane

Eukaryotic cells contain small, highly abundant, nuclear-localized non-coding RNAs [snRNAs (small nuclear RNAs)] which play important roles in splicing of introns from primary genomic transcripts. Through a combination of RNA–RNA and RNA–protein interactions, two of the snRNPs, U1 and U2, recognize the splice sites and the branch site of introns. A complex remodelling of RNA–RNA and protein-based interactions follows, resulting in the assembly of catalytically competent spliceosomes, in which the snRNAs and their bound proteins play central roles. This process involves formation of extensive base-pairing interactions between U2 and U6, U6 and the 5′ splice site, and U5 and the exonic sequences immediately adjacent to the 5′ and 3′ splice sites. Thus RNA–RNA interactions involving U2, U5 and U6 help position the reacting groups of the first and second steps of splicing. In addition, U6 is also thought to participate in formation of the spliceosomal active site. Furthermore, emerging evidence suggests additional roles for snRNAs in regulation of various aspects of RNA biogenesis, from transcription to polyadenylation and RNA stability. These snRNP-mediated regulatory roles probably serve to ensure the co-ordination of the different processes involved in biogenesis of RNAs and point to the central importance of snRNAs in eukaryotic gene expression.


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