This Wiki is intended to collectively make the point on methodologies employed in research papers we analyze during the course. "Writers" are students who wish to contribute to a specific subject. Before contributing, please add your name in the "Writers group choice". When initiating a contribution, please indicate your name in brackets.

PLEASE:  DO NOT change the INDEX page !!!
This page contains the links to the nine official subjects, which are the same in the Choice.

To contribute, go to the correct page by clicking on the description here in the index, then click EDIT and contribute. At the end, please save.

 IMPORTANT !!!

Please do not make extensive cut-and-paste: it s useless, anybody can go to the source you use and read it.  Read the texts, digest, and make a short résumé. If you wih you can include link(s) to the source(s).

Other contributors can revise, add, erase, modify...   Please do not repeat the same text as well. 

Back to index

(Karen Odin)

EMSA (electrophoretic mobility shift assay) or gel shift: technique used for studying gene regulation and determining protein–DNA interactions. EMSA can be used qualitatively to identify sequence-specific, DNA-binding proteins (such as transcription factors) in crude lysates and, in conjunction with mutagenesis, to identify the important binding sequences within the upstream regulatory region of a given gene. It can also be used quantitatively to measure thermodynamic and kinetic parameters.

Advantages:

• simple to perform
• robust enough to accommodate a wide range of binding conditions
• highly sensitive, allowing assays to be performed with small protein and nucleic acid concentrations and small sample volumes
• a wide range of nucleic acid sizes and structures are compatible with the assay
• the distribution of proteins between several nucleic acid molecules can be monitored within a single solution

Disadvantages:

• samples are not at chemical equilibrium during the electrophoresis step
• the electrophoretic mobility of a protein-nucleic acid complex depends on many factors other than the size of the protein
• the electrophoretic mobility of a complex provides little direct information about the location of the nucleic acid sequences that are occupied by protein
• the time resolution of the current assay is defined by the interval required for manual solution handling

EMSA uses native PolyAcrylamide Gel Electrophoresis (PAGE) to resolve a mixture of a protein of interest and a labeled DNA probe containing potential target sites of the protein. A DNA probe bound with protein will migrate slower compared with a free DNA probe and is therefore retarded in its migration through a polyacrylamide matrix. Radiolabeling of DNA by 32P has been the predominant method for detection in EMSAs.

Protocol:

1. Gel preparation
2. Preparation of infrared fluorescent dye-labeled probes
3. Preparation of unlabeled/cold probes (competitors)
4. Binding reaction and electrophoresis
5. Imaging

For further informations, watch this video  and read this https://www.thermofisher.com/it/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/gel-shift-assays-emsa.html

(Francisco Soto)

NetProphet 2.0: New bioinformatic algorithm that integrate diverse biological databases to predict targets for Transcription Factors.  [edit]

Developing effective methods for mapping  transcription factor (TF) networks genome-wide is a long-standing goal in genomics and computational biology. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. TF network maps encode basic knowledge about the biochemical functions of molecules, much like metabolic network maps. They are thus a key part the encyclopedic knowledge that enables research and development. 

Aim of NetProphet 2.0 Algorithm

Previous work has described network mapping algorithms that rely exclusively on gene expression data and ‘integrative’ algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types.

This new generation predictor algorithm, compared to previous versions, relies on three fundamental ideas. First, combining several expression-based network algorithms that use different types of models can yield better results than using either one alone. Second, TFs with similar DNA binding domains tend to bind similar sets of target genes. Third, even an imperfect network map can be used to infer models of each TF’s DNA binding preferences from the promoter sequences of its putative targets and these models can be used to further refine the network.

Fig.1. Overview of the NetProphet 2.0 pipeline. Database icons: input data sources. Rectangles: computational modules. Circles: network maps. (Image taken from Kang et al, 2018.)

For additional information how use this tool, view this links:

Paper

Yiming Kang, Hien-Haw Liow, Ezekiel J Maier, Michael R Brent, NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources, Bioinformatics, Volume 34, Issue 2, 15 January 2018, Pages 249–257.

https://doi.org/10.1093/bioinformatics/btx563

Availability and implementation

Source code and comprehensive documentation are freely available at:

https://github.com/yiming-kang/NetProphet_2.0