Wednesday, November 23, 2011

Folder list to text file, text file to folders

How to make folder with name from test file ?

You could do this:
1. Make sure all your entries are in column A of your spreadsheet.
2. Edit/copy column A
3. Click Start / Run / notepad c:\folders.txt {OK}
4. Click Edit / paste. You now have a text file with all the folder names
inside.
5. Click Start / run / cmd {OK}
6. Type this test command:
for /F "tokens=*" %* in (c:\folders.txt) do @echo md "D:\My Folders\%*"
{Enter}

If you're happy with the result, make it happen by typing this command:
for /F "tokens=*" %* in (c:\folders.txt) do @md "D:\My Folders\%*"
{Enter}



How do I print a listing of files in a directory?

    Get to the MS-DOS prompt or the Windows command line.
    Navigate to the directory you wish to print the contents of. If you're new to the command line, familiarize yourself with the cd command and the dir command.
    Once in the directory you wish to print the contents of, type one of the below commands.

    dir > print.txt

    The above command will take the list of all the files and all of the information about the files, including size, modified date, etc., and send that output to the print.txt file in the current directory.

    dir /b > print.txt

    This command would print only the file names and not the file information of the files in the current directory.

    dir /s /b > print.txt

    This command would print only the file names of the files in the current directory and any other files in the directories in the current directory.

    After doing any of the above steps the print.txt file is created. Open this file in any text editor (e.g. Notepad) and print the file. You can also do this from the command prompt by typing notepad print.txt.

Saturday, November 5, 2011

In-silico characterization of proteins

BLAST : In bioinformatics, Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid sequences of different proteins or the nucleotides of DNA sequences. A BLAST search enables a researcher to compare a query sequence with a library or database of sequences, and identify library sequences that resemble the query sequence above a certain threshold. Different types of BLASTs are available according to the query sequences. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. The BLAST program was designed by Eugene Myers, Stephen Altschul, Warren Gish, David J. Lipman, and Webb Miller at the NIH and was published in the Journal of Molecular Biology in 1990


CDD search: Conserved Domain Database (CDD) is a protein annotation resource that consists of a collection of well-annotated multiple sequence alignment models for ancient domains and full-length proteins. These are available as position-specific score matrices (PSSMs) for fast identification of conserved domains in protein sequences via RPS-BLAST. CDD content includes NCBI-curated domains, which use 3D-structure information to explicitly to define domain boundaries and provide insights into sequence/structure/function relationships, as well as domain models imported from a number of external source databases (Pfam, SMART, COG, PRK, TIGRFAM).


PFAM: The Pfam database is a large collection of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Proteins are generally composed of one or more functional regions, commonly termed domains. Different combinations of domains give rise to the diverse range of proteins found in nature. The identification of domains that occur within proteins can therefore provide insights into their function. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families. Although these Pfam-A entries cover a large proportion of the sequences in the underlying sequence database, in order to give a more comprehensive coverage of known proteins we also generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans. A clan is a collection of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM.


TMHMM: A variety of tools are available to predict the topology of transmembrane proteins. To date no independent evaluation of the performance of these tools has been published. A better understanding of the strengths and weaknesses of the different tools would guide both the biologist and the bioinformatician to make better predictions of membrane protein topology.

SignalP: SignalP 4.0 server predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks. 


STRING: STRING is a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources i.e. Genomic context, high throughput experiments, coexpression, previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5'214'234 proteins from 1133 organisms.


PROTPARAM: ProtParam (References / Documentation) is a tool which allows the computation of various physical and chemical parameters for a given protein stored in Swiss-Prot or TrEMBL or for a user entered sequence. The computed parameters include the molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (GRAVY)


PROSITE: Search your query sequence for protein motifs, rapidly compare your query protein sequence against all patterns stored in the PROSITE pattern database and determine what the function of an uncharacterised protein is. This tool requires a protein sequence as input, but DNA/RNA may be translated into a protein sequence using transeq and then queried.


InterPro: InterPro is an integrated database of predictive protein "signatures" used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures.

GlobPlot Webservice:

Prediction of disorder:

  • DisEMBL - DisEMBL is our neural network based predictor.
  • DISOPRED - Predictor from David Jones' lab.

Function prediction in non-globular protein space:

  • ELM - The Eukaryotic Linear Motif Resource.
  • NetworKIN - Systematic Discovery of In Vivo Phosphorylation Networks.

Thesis on disorder and linear motifs

Function prediction in globular protein space:

  • SMART - SMART/Pfam domains

Domain boundaries:

  • DomCut - A domain boundary detector
  • DomPred - Domain predictor from David Jones' lab.

Synthetic Biology

Synthetic Biology Project @ SLRI - Applying GlobPlot.

 

Resources
Subcellular localization predictors: Subcellular localization databases:

 

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