Bioinformatics and Computational Biology - Science topic

By the above code, irrespective of protein size the grid box size will be considered as 20x20x20. End of the vina execution, most of the complex shows binding affinity "0" or much less, as the active site will be out of the grid box range. Better increase the grid box size (SIZE_X,Y,Z) up to 60 or 120 each, depending on the maximum proteins (chains in PDB code) size of each complex, and try to run VINA again. Then you may get binding energy values of maximum protein-ligand complexes (sometimes for all).

However, this will not mimic the experimental structure correctly, since you handling bulk protein-ligand (separately) complexes docking with the common configuration file same time.

Avenues you can explore to find such opportunities:

1. Academic research institutions: Many universities and research institutions offer remote research positions or internships in bioinformatics and computational biology. Check their websites, job boards, and reach out to individual researchers or research groups who align with your interests.

2. Online job portals and platforms: Websites and platforms dedicated to remote work, such as LinkedIn, Indeed, and Upwork, often have listings for bioinformatics and computational biology projects. You can search for specific keywords like "remote bioinformatics," "computational biology," or "bioinformatics internships" to find relevant opportunities.

3. Open-source projects: Contributing to open-source bioinformatics projects can provide valuable hands-on experience. Explore bioinformatics software and libraries like Biopython, Bioconductor (for R), or other popular tools on platforms like GitHub. Contribute to their development, report issues, or collaborate with the community.

4. Online communities and forums: Engage with online communities and forums focused on bioinformatics and computational biology. These platforms, such as Bioinformatics Stack Exchange, BioStars, or community forums associated with specific software packages, often have job boards or project collaboration opportunities shared by researchers or organizations.

5. Networking: Attend virtual conferences, webinars, and workshops related to bioinformatics and computational biology. Connect with researchers, presenters, and fellow attendees to express your interest in remote research opportunities. Networking can often lead to potential collaborations or recommendations for available positions.

When searching for opportunities, it's important to tailor your search keywords to include relevant terms like "remote," "internship," "volunteer," or "project-based." Additionally, clearly communicate your enthusiasm, willingness to contribute, and desire for hands-on experience in your application materials or when reaching out to potential mentors or supervisors.

Hope it helps:credit AI.

Dear Yahaya,

Thank you for your insightful suggestion regarding the active site prediction tool from the Supercomputing Facility for Bioinformatics & Computational Biology at IIT Delhi. I appreciate your thoroughness in exploring the potential utility of this tool.

I'm glad to make your attention to the paper by Tanya Singh, D. Biswas, and B. Jayaram titled "AADS - An automated active site identification, docking and scoring protocol for protein targets based on physico-chemical descriptors." The fact that this paper has been cited by 181 researchers highlights its significance in the field. While I haven't personally used the server yet. Though a humble suggestion to leverage it for gaining a preliminary understanding of the active site of proteins of interest is intriguing.

I would love to suggest an approach of cross-checking the tool's predictions with known active sites of a minimum of 10 proteins from the same organism is a thoughtful idea. This comparative validation would indeed provide valuable insights into the reliability and accuracy of the tool's predictions. If the results align well with established knowledge, it would lend substantial credibility to the tool's effectiveness.

Considering the extensive citations of the paper and the potential of the tool, I believe that exploring its feasibility is a worthwhile endeavor. The ability to gain a preliminary idea about the active site of proteins of interest could greatly inform and streamline any research efforts.

Additionally, there are several other active site prediction tools available that you might find useful for your research. Here are a few notable options:

Hope this answer will be helpful for you.

Abhijeet Singh Thank you for your response and mentioning my earlier post! My belief is that researchers would know tools that are missing based on the fact that they would run into such problem often during their research. If there is some manual analysis task that researchers can automate, I believe that PeptiCloud can be the perfect platform to develop and make those tools publicly available. (For instance, PeptiCloud has a unique feature that allows users to further alter codon sequence of each amino acid after codon optimization with respect to a specific bacterial strain). With that being said, if you could check out PeptiCloud for yourself and see if anything could be added or improved, that would be greatly appreciated!

For batch docking, I have used the vLifeMDS tool.

In the Seurat R package for analyzing single-cell RNA-seq data, "power" and "myAUC" are both functions used for selecting the most informative features or genes in the dataset. However, they employ different approaches and criteria to achieve this.

In summary, the "power" function identifies highly variable genes based on their expression dispersion relative to mean expression, while the "myAUC" function ranks genes based on their ability to discriminate between two predefined groups or conditions using the area under the ROC curve. Both functions aim to identify genes that are potentially important for distinguishing between different cell types, states, or experimental conditions, but they use different statistical and computational approaches to achieve this goal.

You can use Visual Dynamics for simulations. It is freely available at https://visualdynamics.fiocruz.br/login

I have the same question.

It's depended on the software algorithm.

The equipment listed below might be useful

AutoDock Vina, AutoDock

The Aidos-x system is an excellent intelligent system for diagnosing and classifying human diseases. The methods of using the Aidos-x system for diagnosing human diseases are disclosed in lectures with sound "Using automated system-cognitive analysis for the classification of human organ tumors", "Intelligent system for diagnosing early stages of chronic kidney disease", which can be downloaded right now from the website https ://www.patreon.com/user?u=87599532 Creator's title: «Lectures on Electronic Medicine». After subscribing to this site, you will receive databases for medical research to identify the diseases that you will read about in lectures. The acquired skills of working in the Aidos-x system will allow you to apply for grants to carry out scientific research in the field of medicine.

To subscribe to the site https://www.patreon.com/user?u=87599532 you do not need to go to the bank, but you can do it using the Pay Pal system. Send a transfer in your currencies, and the transfer to dollars will be done automatically. After subscribing on the site, you will receive the Aidos-x system with an English user interface for free.

Thank you.

Myles Joshua Toledo Tan Converting a.csv file to an AnnData file for use in Scanpy is a simple procedure. Here's an example of how it may be done:

1. You must first install the anndata package, which can be done by typing pip install anndata into your command line.

2. Following that, import the relevant libraries, such as pandas and anndata.

import pandas as pd

import anndata

3. Then, using the pd.read csv() method, read your.csv file into a pandas DataFrame.

data = pd.read_csv("your_file.csv")

4. After that, you can use the anndata.AnnData() method to convert the DataFrame to an AnnData object.

adata = anndata.AnnData(data)

5. Finally, you may use the scanpy library's different methods to conduct any extra processing or analysis on your AnnData object.

It's worth noting that when you convert a dataframe to an AnnData object, it thinks the rows are observations and the columns are variables. If you have the inverse, use.T to transpose the dataframe.

You can alternatively use scanpy.read csv() to directly import the csv file into an AnnData object to get the same result.

adata = sc.read_csv("your_file.csv")

Please let me know if there is anything else I can do for you.

HI,

where are you from? Are you willing to travel?

This web site is filled with opportunities regarding what you are looking for:

I use TASSEL software for genome analysis. You need plink format of map and pad to operate it. You can try and explore this software

Dear Dr. Ryan Gourlie

In my case it takes 5 days to generate the BUSCO result

Thanks for your reply! I modified the parameters close to the paper's parameters, but it still gives me the same error: resName = resname_prefix+residue.attrib['name']

KeyError: 'name'

First, I'd re-state the question to assure that I understood it correctly. A nucleotide sequence of length l contains a palindrom with unit of length k. The palindrom is not exact; there can be from kmin to k matches between units. The distance between palindrom units can be from smin to smax. First and last sub-sequences of length k are not exact matches of any palindrom unit.

My solution. Let's omit the last condition for now. How we search for a palindrom with unit of length k? Take any subsequence of length k and search for a 'match'. Searching for a 'match' is equal to checking (l-k-smin) subsequences, because the unit itself occupies k nucleotides and a spacer can't be shorter than smin nucleotides. In each window the probability of hit is (1/4)^(kmin), if every nucleotide has equal probability of occurrence. The probability of having 1 or more hits then is equal to binomial cdf with the number of attempts equal to n-k-smin, the probability of success equal to (0.25)^kmin and number of successes equal to 1. For example, GSL function gsl_cdf_binom_Q(n-k-smin,0.25^kmin,0) would give the answer. The last paramerter is zero, because the function computes the probability of more than x successes, i.e. 1 and more in this case.

Now, let's include the last condition. It is important to define what 'does not match' mean. I suppose that it means that we can't find the second palindrom unit at postions 1 and l-k. So, the number of windows that we check has to be decreased by 2. The final answer would be:

F(n-k-smin-2,0.25^kmin,0), where F - binomial cdf.

For varying length the answer would be a weighted sum of those propabilities, with weights equal to the probability of observing given legnth. So, if all lengths have equal probability, this is the mean.

I checked the answer on a synthetic set and it seems it is correct or close to being so.

You can check this talk I had at ACS Fall 2022

and our project on de novo molecular design: https://www.researchgate.net/project/Development-of-software-tools-for-the-de-novo-molecular-design

Following companies have offices in Tehran

Sanofil - Roche - Novo NorDisk - Novartis - Bayer - Johnson & Johnson - Merck KGaA - Zoetis - TCI - BioHorizons Implant Systems. In US many are located in Philadelphia, Pennsylvania and Boston, Mine and San Fransisco, California. - But Iran had made working with US companies nearly impossible. For ease of working outside Iran I'd look to China. In general use caution sharing new ideas with companies you have not worked with in past..

Here is the hardware informations.

Hardware with GPGPU

Intel Xeon 4C or above

500GB or above Class 20 Solid State Drive

2TB SATA 7200 rpm

16GB or above DDR4

Nvidia Quadro RTX5000, 16GB

Ubuntu 20.04 LTS

DOI: 10.1039/b804675c

This paper explains very well how unfolding and melting of DNA hairpin works. kindly have a look.

I can not visualize the link. I tried from two different browsers without success. Is it possible that the page is not working? is any other site for viewing it?

Thanks in advance!

Hello,

I have the same problem. Did you fix it?

Bikash Ranjan Sahoo what was the reason for the negative values ?

Hi Azin

you can also go to the UCSC web server and apply the right parameters (species/genome/genename) and look on the genome browser what's up with the right options.

UCSC (http://genome.ucsc.edu) provides additional tools that could also be interesting in your research, take some times to get used.

keep safe

fred

Hi,

If it still persists, just add a new line character around the line of SOL in topol.top file. Sometimes, gmx behaves in a weird manner during reading SOL.

HTH.

I suspect it has to do with the non-canonical residues in chain C: 02J in position C1 ( 5-methyl-1,2-oxazole-3-carboxylic acid, https://www.rcsb.org/ligand/02J ), PJE in position C5 ( (E,4S)-4-azanyl-5-[(3S)-2-oxidanylidenepyrrolidin-3-yl]pent-2-enoic acid, https://www.rcsb.org/ligand/PJE ) and 010 in position C6 ( phenylmethanol, https://www.rcsb.org/ligand/010 ). Chain C is a ligand to the protein. If you are interested in just the protein, e.g. in preparation for docking, you should remove chain C. If you actually are interested in the complex, you need to supply the topology and parameter files for these hetero-compounds as you would for non-peptidomimetic ligands.

To avoid such problems, start by looking at the structure in a molecule viewer to look for non-proteinic components ( e.g. PyMOL selection "organic" ) and/or look carefully at the annotations at rcsb.org ( https://www.rcsb.org/structure/6LU7 ), listing Entity ID 2: N-[(5-METHYLISOXAZOL-3-YL)CARBONYL]ALANYL-L-VALYL-N~1~-((1R,2Z)-4-(BENZYLOXY)-4-OXO-1-{[(3R)-2-OXOPYRROLIDIN-3-YL]METHYL}BUT-2-ENYL)-L-LEUCINAMIDE

In addition, the biological unit of the protein is a homodimer. However, since the dimer symmetry coincides with crystallographic symmetry, the asymmetric unit only contains one monomer. Depending on the questions you wish to adress with your computational analysis, you might want to use the biological assembly rather than the asymmetric unit of the structure.

Hi, I think you should look into this thread for details:

From "Bowen Tang"

Of course you can do this.

thermal_mmgbsa.py is in $SCHRODINGER/mmshare-vxxxxx/python/common

run it as below:

$SCHRODINGER/run thermal_mmgbsa.py <your_trajectory.cms>

Good luck.

Dear Sam Mohel ,

luster analysis is an exploratory analysis that tries to identify structures within the data.  Cluster analysis is also called segmentation analysis or taxonomy analysis.  More specifically, it tries to identify homogenous groups of cases if the grouping is not previously known.  Because it is exploratory, it does not make any distinction between dependent and independent variables.  The different cluster analysis methods that SPSS offers can handle binary, nominal, ordinal, and scale (interval or ratio) data.

Regards,

Shafagat

Thank You, Dr Giovanni Colonna, for taking your time in answering the question. I concur with your explanation.

Sorry, I think you have already got your answer. Anyways, for the reference of the other people having similar questions, You can use Integrative Genomic Viewer (https://software.broadinstitute.org/software/igv/) to visualize your BAM file against any reference sequence. After visualization, if you click on any specific point on the coverage plot, it will show you the mutation rate of that particular point of the genome.

Yes, AI has already been heavily deployed for biological diagnosis through predictions and classifications.

For a List of Venn diagram tools, their features, and references, you may check out the link below.

Try my own implementation, namely Bio-Jupiter:

Bio-Jupiter - Open source project:

How it works:

Donato Calabrese Split the .sdf file into individual file using openBabel,something like:

babel -i sdf bigfile.sdf -o sdf file.sdf -m

(-m option should force output of files file1.sdf, file2.sdf, file3.sdf etc.)

For parallel installation of Amber, you can visit my web page. There I summarized our experiences on Amber installation on a local Linux machine.

For a successful run of MPI, add ‘mpirun -bind-to core -np 4’ and ‘.MPI’ before and after the module name. The number before the module name is the number of cores you want to allocate for parallel calculation and can be changed accordingly. For example, to run the sander module of Amber, we use the following command in our local system.

$ mpirun -bind-to core -np 4 sander.MPI -O -i sander.in1 -o sander.out1 -r mdrest1 -c prmcrd -ref prmcrd

You need to remove lipid before MD simulation. You can not delete or add any atom/residue/molecule during and after MD simulation, as it will destroy your trajectory data.

Check out the following tools

MAFFT allows you to do that https://mafft.cbrc.jp/alignment/software/closelyrelatedviralgenomes.html

import urllib

Please have look on our(Eminent Biosciences (EMBS)) collaborations.. and let me know if interested to associate with us

Here is our lab page: https://www.eminentbio.com/home/members

Here is our publication: https://www.eminentbio.com/home/Publication

Our recent publications In collaborations with industries and academia in India and world wide.

Our Lab EMBS's Publication In collaboration with Universidad Tecnológica Metropolitana, Santiago, Chile. Publication Link: https://pubmed.ncbi.nlm.nih.gov/33397265/

Our Lab EMBS's Publication In collaboration with Moscow State University , Russia. Publication Link: https://pubmed.ncbi.nlm.nih.gov/32967475/

Our Lab EMBS's Publication In collaboration with Icahn Institute of Genomics and Multiscale Biology,, Mount Sinai Health System, Manhattan, NY, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918

Our Lab EMBS's Publication In collaboration with University of Missouri, St. Louis, MO, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30457050

Our Lab EMBS's Publication In collaboration with Virginia Commonwealth University, Richmond, Virginia, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211

Our Lab EMBS's Publication In collaboration with ICMR- NIN(National Institute of Nutrition), Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611

Our Lab EMBS's Publication In collaboration with University of Minnesota Duluth, Duluth MN 55811 USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211

Our Lab EMBS's Publication In collaboration with University of Yaounde I, PO Box 812, Yaoundé, Cameroon. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335

Our Lab EMBS's Publication In collaboration with Federal University of Paraíba, João Pessoa, PB, Brazil. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30693065

Our Lab EMBS's Publication In collaboration with collaboration with University of Yaoundé I, Yaoundé, Cameroon. Publication Link: https://pubmed.ncbi.nlm.nih.gov/31210847/

Our Lab EMBS's Publication In collaboration with University of the Basque Country UPV/EHU, 48080, Leioa, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852204

Our Lab EMBS's Publication In collaboration with King Saud University, Riyadh, Saudi Arabia. Publication Link: http://www.eurekaselect.com/135585

Our Lab EMBS's Publication In collaboration with NIPER , Hyderabad, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759

Our Lab EMBS's Publication In collaboration with Alagappa University, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335

Our Lab EMBS's Publication In collaboration with Jawaharlal Nehru Technological University, Hyderabad , India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910

Our Lab EMBS's Publication In collaboration with C.S.I.R – CRISAT, Karaikudi, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237676

Our Lab EMBS's Publication In collaboration with Karpagam academy of higher education, Eachinary, Coimbatore , Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672

Our Lab EMBS's Publication In collaboration with Ballets Olaeta Kalea, 4, 48014 Bilbao, Bizkaia, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918

Our Lab EMBS's Publication In collaboration with Hospital for Genetic Diseases, Osmania University, Hyderabad - 500 016, Telangana, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910

Our Lab EMBS's Publication In collaboration with School of Ocean Science and Technology, Kerala University of Fisheries and Ocean Studies, Panangad-682 506, Cochin, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27964704

Our Lab EMBS's Publication In collaboration with CODEWEL Nireekshana-ACET, Hyderabad, Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26770024

Our Lab EMBS's Publication In collaboration with Bharathiyar University, Coimbatore-641046, Tamilnadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27919211

Our Lab EMBS's Publication In collaboration with LPU University, Phagwara, Punjab, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/31030499

Our Lab EMBS's Publication In collaboration with Department of Bioinformatics, Kerala University, Kerala. Publication Link: http://www.eurekaselect.com/135585

Our Lab EMBS's Publication In collaboration with Gandhi Medical College and Osmania Medical College, Hyderabad 500 038, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27450915

Our Lab EMBS's Publication In collaboration with National College (Affiliated to Bharathidasan University), Tiruchirapalli, 620 001 Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27266485

Our Lab EMBS's Publication In collaboration with University of Calicut - 673635, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611

Our Lab EMBS's Publication In collaboration with NIPER, Hyderabad, India. ) Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759

Our Lab EMBS's Publication In collaboration with King George's Medical University, (Erstwhile C.S.M. Medical University), Lucknow-226 003, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579575

Our Lab EMBS's Publication In collaboration with School of Chemical & Biotechnology, SASTRA University, Thanjavur, India Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579569

Our Lab EMBS's Publication In collaboration with Safi center for scientific research, Malappuram, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672

Our Lab EMBS's Publication In collaboration with Dept of Genetics, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25248957

Our Lab EMBS's Publication In collaboration with Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26229292

Sincerely,

Dr. Anuraj Nayarisseri

Principal Scientist & Director,

Eminent Biosciences.

Mob :+91 97522 95342

Please have look on our(Eminent Biosciences (EMBS)) collaborations.. and let me know if interested to associate with us

Here is our lab page: https://www.eminentbio.com/home/members

Here is our publication: https://www.eminentbio.com/home/Publication

Our recent publications In collaborations with industries and academia in India and world wide.

Our Lab EMBS's Publication In collaboration with Universidad Tecnológica Metropolitana, Santiago, Chile. Publication Link: https://pubmed.ncbi.nlm.nih.gov/33397265/

Our Lab EMBS's Publication In collaboration with Moscow State University , Russia. Publication Link: https://pubmed.ncbi.nlm.nih.gov/32967475/

Our Lab EMBS's Publication In collaboration with Icahn Institute of Genomics and Multiscale Biology,, Mount Sinai Health System, Manhattan, NY, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918

Our Lab EMBS's Publication In collaboration with University of Missouri, St. Louis, MO, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30457050

Our Lab EMBS's Publication In collaboration with Virginia Commonwealth University, Richmond, Virginia, USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211

Our Lab EMBS's Publication In collaboration with ICMR- NIN(National Institute of Nutrition), Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611

Our Lab EMBS's Publication In collaboration with University of Minnesota Duluth, Duluth MN 55811 USA. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852211

Our Lab EMBS's Publication In collaboration with University of Yaounde I, PO Box 812, Yaoundé, Cameroon. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335

Our Lab EMBS's Publication In collaboration with Federal University of Paraíba, João Pessoa, PB, Brazil. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30693065

Our Lab EMBS's Publication In collaboration with collaboration with University of Yaoundé I, Yaoundé, Cameroon. Publication Link: https://pubmed.ncbi.nlm.nih.gov/31210847/

Our Lab EMBS's Publication In collaboration with University of the Basque Country UPV/EHU, 48080, Leioa, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27852204

Our Lab EMBS's Publication In collaboration with King Saud University, Riyadh, Saudi Arabia. Publication Link: http://www.eurekaselect.com/135585

Our Lab EMBS's Publication In collaboration with NIPER , Hyderabad, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759

Our Lab EMBS's Publication In collaboration with Alagappa University, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30950335

Our Lab EMBS's Publication In collaboration with Jawaharlal Nehru Technological University, Hyderabad , India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910

Our Lab EMBS's Publication In collaboration with C.S.I.R – CRISAT, Karaikudi, Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237676

Our Lab EMBS's Publication In collaboration with Karpagam academy of higher education, Eachinary, Coimbatore , Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672

Our Lab EMBS's Publication In collaboration with Ballets Olaeta Kalea, 4, 48014 Bilbao, Bizkaia, Spain. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29199918

Our Lab EMBS's Publication In collaboration with Hospital for Genetic Diseases, Osmania University, Hyderabad - 500 016, Telangana, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/28472910

Our Lab EMBS's Publication In collaboration with School of Ocean Science and Technology, Kerala University of Fisheries and Ocean Studies, Panangad-682 506, Cochin, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27964704

Our Lab EMBS's Publication In collaboration with CODEWEL Nireekshana-ACET, Hyderabad, Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26770024

Our Lab EMBS's Publication In collaboration with Bharathiyar University, Coimbatore-641046, Tamilnadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27919211

Our Lab EMBS's Publication In collaboration with LPU University, Phagwara, Punjab, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/31030499

Our Lab EMBS's Publication In collaboration with Department of Bioinformatics, Kerala University, Kerala. Publication Link: http://www.eurekaselect.com/135585

Our Lab EMBS's Publication In collaboration with Gandhi Medical College and Osmania Medical College, Hyderabad 500 038, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27450915

Our Lab EMBS's Publication In collaboration with National College (Affiliated to Bharathidasan University), Tiruchirapalli, 620 001 Tamil Nadu, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/27266485

Our Lab EMBS's Publication In collaboration with University of Calicut - 673635, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/23030611

Our Lab EMBS's Publication In collaboration with NIPER, Hyderabad, India. ) Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/29053759

Our Lab EMBS's Publication In collaboration with King George's Medical University, (Erstwhile C.S.M. Medical University), Lucknow-226 003, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579575

Our Lab EMBS's Publication In collaboration with School of Chemical & Biotechnology, SASTRA University, Thanjavur, India Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25579569

Our Lab EMBS's Publication In collaboration with Safi center for scientific research, Malappuram, Kerala, India. Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/30237672

Our Lab EMBS's Publication In collaboration with Dept of Genetics, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/25248957

Our Lab EMBS's Publication In collaboration with Institute of Genetics and Hospital for Genetic Diseases, Osmania University, Hyderabad Publication Link: https://www.ncbi.nlm.nih.gov/pubmed/26229292

Sincerely,

Dr. Anuraj Nayarisseri

Principal Scientist & Director,

Eminent Biosciences.

Mob :+91 97522 95342

you can install CASTp plugin to pymol.

follow this pipeline

You can use perl scripts provide by VTST tools (https://theory.cm.utexas.edu/vtsttools/scripts.html) to generate linearly interpolated images between initial and final states. It is always better to use chemical intuition even with generated images and that would ease the convergence.

Hello Neetika Nath

Thank you for your post !!

I'm in a similar state and was searching for the right option for a week !! The above-mentioned online tool is no more available for the public.

Can you suggest any possible method which I could follow to search approximately 400 unknown sequences?

Thank you very much.

Hi.,

Kindly Check this link:

Best Wishes..

Dear Fiorenza Sella ,

Look the link, maybe useful.

Regards,

Shafagat

DAVID adopts Fisher's Exact test to measure the gene-enrichment in annotation terms. It is just a matter of making a 2x2 contingency table, as in the example here: https://david.ncifcrf.gov/content.jsp?file=functional_annotation.html (section 2.2).

---------------------------------------

A Hypothetical Example In the human genome background (30,000 genes total; Population Total (PT)), 40 genes are involved in the p53 signaling pathway (Population Hits (PH)). A given gene list has found that three genes (List Hits (LH)) out of 300 total genes in the list (List Total (LT)) belong to the p53 signaling pathway. Then we ask if 3/300 is more than a random chance compared to the human background of 40/30000. A 2 x 2 contingency table is built based on the above numbers: List Hits (LH) = 3 List Total (LT) = 300 Population Hits (PH) = 40 Population Total (PT) = 30,000

Exact p-value = 0.007. Since p-value < 0.05, this user's gene list is specifically associated (enriched) in the p53 signaling pathway by more than random chance.

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Hence, quantitative data are not considered in such enrichment analyses unless you don't want to calculate an additional activation/inhibition score, as computed by Ingenuity Pathway Analysis, for example.

Deformation energy measures the rigidity of protein residues. Higher the Deformation energy, higher the rigidity residues have. Also, it gives idea about protein's local flexibility.

Dear Abdullah Al Nahid

I think my suggestion still holds. If your tool is usefull for let's say people working on viruses journals that are focussed on this topic might be interested.

Perhaps a good way might be to 'just' search for "bioinformatics tool" in Google Scholar to see which type of journals 'pop up' (besides the ones you already know).

Again good luck.

Best regards.

Thank you Rahul sir.

From the page:

Possible causes include:

There is a troubleshooting list here on how to diagnose the problem. I suggest trying the list first.

I'm sorry I don't have a better answer. I'm having similar issues and if I solve them I'll return with more info.

But it doesn't contain data about all pathogenic bacteria. As I'm searching toxins of Gardrenella vaginalis and couldn't get that.

Thank you for your answer. If you mean the university library, I have no access to it currently due to the pandemic, Hidetoshi Shimizu

Bharat Gupta how you become able to write your umbrella sampling input disang file (as mentioned in the amber tutorial) using the LCOD method. Can you tell me, please?

Matthew Edward Thornton

Is there any method to work with NONCODE in R?

I was looking for the definition of the cell-free system and thanks to you I found what I need.

Thank you.

All of the suggesttions can be not feasible for John. I guess Raman microscope is what he has had and he needs to obtain Raman peaks out of the strong fluorescence background.

John, may I ask what is the model of your equipment? In general, you could try different configuration to enhace Raman peaks and reducing fluorescence background: objective lens, generally the larger magnifier the better; the shorter exposure time with larger acquisition numbers; adjusting sampling focal point, the best Raman response obained not always from the focused sample; sampling on larger particles/or particulates, the larger particles the smaller surface area and weaker fluorescence.

I believe the answer to this question is covered here:

You can use GelJ software, it is java-based, free, and user friendly, which can draw different dendrogram with different analysis.

You can first renumber the chains using the alter command (https://pymolwiki.org/index.php?title=Alter&redirect=no) in such a way that each residue has a unique residue number,

e.g.

alter (chain B),resi=str(int(resi)+100)

alter (chainC),resi=str(int(resi)+200)

to give chains B and C an offset of 100 and 200, respectively.

then again use the alter command to change the chain label

e.g.

alter (all), chain='A'

Molecular docking

Dear Apu, Bioinformatics is a mean not an end in itself and the confusion of a mean with an end is the reason of the drmatic crisis science (not technology) is experiencing in these days (see for example https://www.pnas.org/content/113/34/9384.short).

Thus, first of all, you must aquire a 'quantitative sensibility' for biological problems that means: in the face of a biological problem how to restate the issue in order to have a simple recognition of which are the statistical units, the variables of interest , the most interesting scale where to look and if I can provide a suitable metrics preserving the original biological meaining.

Then the informatics will come by alone, tis means you must learn statistics (with a special emphasis on multidimensional descriptive methods like PCA, Cluster Analysis, MDS..), complex networks analysis, non-linear dynamics fundamentals (what an attractor is, what is a transition) and fundamentals of probability.

Attached you will find a sketchy representation of the quantitative needs for facing biological problems.

Dear Mr. Kapoor,

you will have to email Dr. Ehab Mostafa Bakr the registration number of your Ldp line software to get the key number. the registration number can be found on the registration window when you open ldp line on trial verson. his email address is [email protected]. I got mine for free.

You might try Webina too: https://durrantlab.pitt.edu/webina/ . It runs AutoDock Vina in your browser, without having to install anything.

Dear William Ford ,

As indicated by Farhana Rumzum Bhuiyan you most likely have to look at program packages like GROMACS (requires quite some efforts to understand the way it works).

It sounds to me that you need to enter the field of molecular docking:

A variety of programs (and approaches like molecular dynamics as used by GROMACS and Monte Carlo etc.) can be used:

Hope this helps you further.

Best regards.

PS. The suggestions like PSIPRED are excellent ways to get a pretty reliable prediction of the protein (itself) but does not allow to combine this with a prediction of the same protein in the presence of some kind of ligand.

Yes, you can use these datasets for research work equivalent to a PhD thesis. As a reference, you can check the publications by TCGA and other groups which utilized TCGA data. A series of these publications have been published by Cell Press as TCGA-Pan Cancer Atlas.

You can see, just in silico work published in the Cell Press journals. But, before thinking of that extend i.e., to entirely rely on these datasets, think what novel question you can address. If you have a highly relevant question, you can go for it. Otherwise, a simple and safe plan can be using hypothesis generation by datasets followed by validation using in vitro studies or vice-versa. This type of combinational work is regularly published and will be more acceptable to most universities and individuals. All the best.

You can check our papers also where we have used simple tools to analyze TCGA data.

Receptors are good targets, because the drug does not have to enter the cell. Proteins expressed in large quantity are poor targets, because you need a lot of drug. Proteins with fewer variants are better targets, because it may be harder for the pathogen to evolve resistance.

Dear Anahita Khammari

After all that time, unfortunately, it still unaccessible.

Kindly see the attached file and the link below:

http://bidd.nus.edu.sg/group/softwares/invdock.htm

Regards

Hello Fatemeh Razavi !

In case you are still facing the problem, can you please post your param example file?