Bioinformatics - Science topic

GenomicRanges is an R package for representing and manipulating genomic intervals. When used with the rtracklayer R package and it, dealing with the BED data format is really convenient.

Please see https://bioconductor.org/packages/GenomicRanges

San Diego Supercomputer Center (sdsc.edu)

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It is hypothetical since its existence is presumed based on the transcriptomic data rather than being directly detected by proteomics or WB. You can try based on some apriori knowledge about it as a lead molecule or entirely randomly. Nothing wrong with that. Also, 'polypeptide' suits better for an amino acid chain this short.

Histidin can be either delta or epsilon protonated, and it is user's responsibility to check the protonation state. Before runing the gmx pdb2gmx, you should have set this protonation state and change the residue name to HIE (epsilon protonated) or HID (delta protonated). Although, pdb2gmx can approximatly guess the protonation state which are fairley good.

Since, you ignore the Hydrogen atoms, pdb2gmx will rebuilt all those h-atoms based on the residue entery of the forcefield.

OXT seems to be the terminal atom. Is Val961 is the terminal residue? Do you have proper TER record in your pdb?

Hey, there's an important point to remember about ordination techniques like PCoA and NMDS. While they're great for visualizing complex data in 2D or 3D, they're just a simplified snapshot.

Look at the contribution % of each axes or the stress values (NMDS). It can some time help to try different ordination tech.

Another point to consider is the number of samples per condition A and B, maybe with more sample the cleavage will be more visible.

you can use cutadapt or prinseqlite in bash terminal, these are having quality trimming options.

Please let me know if anyone is interested to o

Dear Toheeb Jumah

Your first suggestion is an excellent suggestion:

Your second suggestion, though interesting it is of 1st january 2024 no longer free of costs:

Possibly intesteresting, but they state that they are free of charge in 2023 (not sure what is the situation now in 2024):

Best regards.

Thank you very much :)

Blast search the full ORF with the genome, you can then manually mine the promoter region, later check for the presence of regulatory elements associated with the gene function for validation

I hope someone finds this useful, I have myself been struggling with the same problem. My solution to this is looking at the chemical time and ns/day data, which is constantly updated.

To check this go to MONITOR -> double click running MD job -> check the last entry (status -running).

Double click the last entry for a 100ns default Simulation 100000 chemical time means 100ns this might give a rough idea of percentage job progress.

Going by the ns/day data can give you a rough idea of simulation speed like 11ns/day for 100ns simulation will finish on 10th day of the run applied.

Hii

Javeria Tanveer

before uploading your sequence, maybe concatenate your sequences

For the alignment step, I think it's important to mention pseudo alignment (Salmon, Sailfish, Kallisto) and RUM for hybrid alignment to both genome and transcriptome.

I think they remove the need for programming skills and make data analysis much easier to do quickly and efficiently! For the future, I look forward to considering adding more no-code functions to meet a wider range of research needs. Just like the no-code platforms used before, a lot of time will be spent on data processing and analysis, and with no-code tools It will make our work easier and easier

Anuradha Goswami Thank you for the information.

How to determine boundaries for the manual curation of pseudomolecules of chromosomes?

I created the pseudomolecules of chromosomes using HIC, Long reads assembly, HapHiC, 3d-DNA, and Juicebox. I have manually curated the pseudomolecules of chromosomes. Anyone let me Explain.

The broken yellow line with the distance indicator (6.2) looks like a simple distance monitor which you generate with a "measure" command, although I do not know how you generated the blue tubes around it. At 6.2Å, the Ca-Ca distance indicated by the broken line is far larger than the sum of the carbon Van der Waals radii (3.4Å). It is just about short enough that you might classify the contact as a solvent excluding contact (hydrophobic interaction)

Did you install mgltools before installing autodock ? It contains certain commands that support autodock visualizations. You should try installing MGLtools and then re-install autodock. with updated versions

Veryy nice I interesting

Hi Glauco,

Are you aware of the Xena browser (https://xenabrowser.net/)? There are at least gene expression, mutation and methylation data per patient for the TCGA cohorts. Unfortunately I am not sure about the TE data, since I am not working with that but I think that Xena would be your best bet.

Hello, Mr. Pedro Paulo Gattai,

I would suggest Mohsin Saleet Jafri from George Mason University.

I had this issue - I used 7-zip to unpack teh folder instead and then it was fine

Are there any updates on the book chapter?. We have not received any update for the abstract that we have sent

Machine learning algorithms have been shown to enhance the accuracy of protein structure prediction in bioinformatics. Traditional methods for protein structure prediction rely on energy minimization and molecular dynamics simulations, which can be computationally expensive and time-consuming. Machine learning algorithms can be used to predict protein structure more efficiently and accurately by learning from large datasets of known protein structures and their corresponding sequences

Machine learning algorithms can be used to predict protein structure by analyzing the relationships between amino acid sequences and protein structures. These algorithms can identify patterns in the data and use them to predict the structure of unknown proteins. Machine learning algorithms can also be used to predict the stability of protein structures and to identify potential drug targets

First up, blockchain. It's like the trusty sidekick ensuring data integrity. By nature, it's transparent and tamper-proof. So, when you have a bunch of IoT devices communicating, blockchain can help keep that data exchange secure and verifiable. It's like having a digital ledger that says, "Yep, this data is legit and hasn't been messed with."

Then, enter machine learning. ML is the brains of the operation, constantly learning and adapting. It can analyze data patterns from IoT devices to spot anything unusual. Think of it as a detective that's always on the lookout for anomalies or suspicious activities.

And finally, there's NLP. It's a bit like the communicator of the group. In this context, NLP can be used to sift through tons of textual data from IoT devices or networks, helping to identify potential security threats or unusual patterns that might not be obvious at first glance.

Put them all together, and you've got a powerful team. Blockchain keeps the data trustworthy, ML hunts down anomalies, and NLP digs deeper into the data narrative. This combo can seriously level up cybersecurity in IoT, making it harder for bad actors to sneak in and cause havoc. Cool, right?

Debora Santos, here is a link for Gblock, I just opened this

Phylogeny.fr: Gblocks (lirmm.fr)

Combining image-trained neural networks, bioinformatics breaches, and quantum-resistant backdoors has major limitations.

Moving from image-trained neural networks to bioinformatics data requires significant domain transfer, which is not straightforward due to the distinct nature of these data types and tasks.

Secure IoT medical devices are designed with robust security features in mind and deployed. Successful attacks requires exploiting a specific vulnerability in the implementation of security measures, rather than the reliance on neural network capabilities.

Deliberately inserting backdoors and to the extent, even quantum-resistant ones, poses ethical and legal questions that would go against norms and standards of cybersecurity practitioners. The actions would violate privacy rights on the federal level, ethical standards and codes of conduct and pose severe legal consequences. Those would be the domestic ones; assuming we're keeping the products in the US.

Quantum computers with sufficient power to break current cryptographic systems are not yet available. Developing quantum-resistant backdoors knowingly anticipates a future scenario to be truth that is still today largely theoretical, without being proven or true.

Dear Loubna Youssar , there are several cases, and they are not hard to replica as I have shown on my preprint.

It is similar to the self-driving car: it is no longer a technology limitation, rather a question of time.

Best regards,

Jorge

Here you can see better the format. If you want you can send me your code to check it.

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Some recommendations to explore:

1. Online Courses and Tutorials:

- Coursera (https://www.coursera.org/): Coursera offers a wide range of bioinformatics courses, including introductory courses and specialized topics. Some popular courses include "Bioinformatics Specialization" by the University of California, San Diego and "Genomic Data Science" by Johns Hopkins University.

- edX (https://www.edx.org/): edX provides bioinformatics courses from renowned universities and institutions. "Bioinformatics: Introduction and Methods" by the University of Toronto and "Introduction to Bioinformatics" by the University of California, San Diego are highly recommended.

- Rosalind (http://rosalind.info/): Rosalind is an online platform that offers bioinformatics problems and challenges with interactive tutorials in various programming languages. It's a great resource to practice and apply bioinformatics concepts.

2. Bioinformatics Websites and Databases:

- National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/): NCBI provides a vast collection of biological databases, tools, and resources. It includes the GenBank database, PubMed, BLAST, and more.

- Ensembl (https://www.ensembl.org/): Ensembl offers genome annotation and analysis tools for various species. It provides access to genomic data, gene annotations, and comparative genomics.

- UniProt (https://www.uniprot.org/): UniProt is a comprehensive resource for protein sequence and functional information. It includes protein databases, tools for sequence analysis, and protein annotation.

3. Online Communities and Forums:

- BioStars (https://www.biostars.org/): BioStars is a popular Q&A platform for bioinformatics and computational biology. It's a great place to ask questions, find solutions, and learn from the community.

- SeqAnswers (https://www.seqanswers.com/): SeqAnswers is a forum dedicated to next-generation sequencing (NGS) technologies and data analysis. It covers a wide range of topics related to NGS and bioinformatics.

4. Books and Textbooks:

- "Bioinformatics Data Skills" by Vince Buffalo: This book provides practical guidance on bioinformatics data analysis using command-line tools and scripting.

- "Bioinformatics for Dummies" by Jean-Michel Claverie and Cedric Notredame: This beginner-friendly book covers the basics of bioinformatics and provides an introduction to various tools and techniques.

5. Research Internships and Projects:

- Reach out to professors or researchers at your university who are involved in bioinformatics or computational biology research. Inquire about opportunities to join their labs as an undergraduate research assistant or intern. This can provide hands-on experience and exposure to real-world bioinformatics projects.

Good luck: partial credit AI

Hello. In order to dock a protein whose 3D structure is not available on the uniprot site, you must model its 3D structure, for this there are many servers such as Itasser, Quark, and Robetta, and after evaluating and measuring your 3D structure, you can do the docking by Servers like Cluspro, Zdock or Hdock

it's important to consider specific proteins that play crucial roles in the survival and reproduction of microorganisms. Enzymes involved in cell wall synthesis: Proteins like penicillin-binding proteins (PBPs) are crucial for bacterial cell wall formation.

DNA gyrase and topoisomerases: Involved in DNA replication and repair, these are essential targets for antimicrobial compounds.

Ribosomal proteins: Targeting bacterial ribosomes can disrupt protein synthesis. Utilize databases like the Protein Data Bank (PDB) to find crystal structures of your selected proteins. Molecular docking predictions should be validated through in vitro and in vivo experiments.for in vitro evaluation you can use microorganisms directly.

Albert Lee : the issue with doing this is it makes the fold changes entirely arbitrary. Imagine I have a protein I detect in my test samples at "arbitrary value 10" but do not detect in my control samples at all.

If I call the ctrl value 0.5, then 0.5 vs 10.5 = 20 fold increase.

If I call the ctrl value 0.1, then 0.1 vs 10.1 = 100 fold increase.

If I call the ctrl value 0.0001, then 0.0001 vs 10.0001 = 100,000 fold increase.

In reality, the increase is effectively "infinite fold", but what this is really highlighting is that fold changes are not an appropriate metric here.

A lot (most) of statistical analysis is predicated on the measurement of change in values, not "present/absent" scenarios.

For disease biomarkers, for example, something that is present/absent is of use as a diagnostic biomarker, but not as a monitoring biomarker: you can say "if you see this marker at all, you have the disease", but you cannot really use it to track therapeutic efficacy, because all values of this marker other than "N/A" are indicative of disease.

For monitoring biomarkers you really want "healthy" and "diseased" values such that you can track the shift from one to the other.

David Genisys: I agree with Jochen Wilhelm , and would not plot my data in this manner.

A lot will depend on the kind of reviewers you get, and the type of paper you're trying to produce, but it would be more appropriate to note that these markers are entirely absent in one group, and then to comment on the robustness of their detection in the other. You wouldn't run stats necessarily, because as noted, stats are horrible for yes/no markers, but you could use the combination of presence/absence and actual level of the former to make inferences as to biological effect. If a marker goes from "not detected" to "detected but barely", then it might be indicative of dysregulated, aberrant expression behaviour, or perhaps stochastic low-level damage. Interesting, but perhaps not of biological import or diagnostic utility. If instead if goes from "not detected" to "readily detected, at high levels", then it's probably very useful as a diagnostic biomarker, and also indicative of some active biological process, be it widespread damage/release, or active expression of novel targets.

In either case you can make biological inferences without resorting to making up numbers so you can stick them on a volcano plot (and to be honest, if you get the kind of reviewers that demand volcano plots, you can always use the trick Albert suggests).

Volcano plots are primarily a way to take BIG DATA and present it in a manner that allows you to highlight the most interesting targets that have changed between groups: if you have whole swathes of genes that are instead present/absent, then those could be presented as a table, perhaps sorted by GO terms or something (if it looks like there are shared ontological categories you could use to infer underlying biology).

Hey,

You need to consider a few things:

Hi Harshita

lot of possibilities, but the main ones are to go to the NCBI or UCSC database (for instance, just type NCBI XXXX YYYY UTR region, where XXXX is your bacteria and YYYY your gene) in google.

or just give the species and target in research gate...maybe someone could answer ;)

all the best

fred

To gain insights from your proteomic data in the context of pathways:

1. Protein-protein interaction networks: Construct protein-protein interaction (PPI) networks using available databases or tools. These networks represent the physical interactions between proteins and can provide insights into functional relationships and pathway associations. Analyze the network topology, identify highly connected proteins (hubs), and explore protein clusters or modules that may represent enriched pathways.

2. Functional enrichment analysis: Perform functional enrichment analysis using tools such as DAVID, Enrichr, or g:Profiler. These tools allow you to input a list of proteins and assess enrichment of Gene Ontology (GO) terms, biological pathways, or other functional annotations. This analysis can help identify overrepresented functions or pathways in your protein dataset.

3. Cross-referencing with gene-level data: If available, consider integrating your proteomic data with gene-level or transcriptomic data from the same samples or a related study. By mapping proteins to corresponding genes, you can leverage gene-level pathway analysis methods and identify pathways enriched with differentially expressed genes associated with the proteins of interest.

4. Literature-based analysis: Conduct a literature search to explore existing knowledge and studies related to the proteins identified in your proteomic dataset. Look for studies that have investigated the functions, interactions, or pathways associated with these proteins. This qualitative analysis can provide valuable insights into the potential involvement of specific pathways in your disease sample.

5. Pathway databases: Explore curated pathway databases such as Reactome, KEGG, or WikiPathways. These databases provide well-annotated pathways and can serve as a reference to investigate potential connections between your identified proteins and known pathways. Look for proteins within your dataset that are annotated to specific pathways of interest.

Remember that pathway analysis based solely on proteomic data has limitations.

Hope it helps:credit AI

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.

there are few R packages as Vegan and Phyloseq which you can use for your intial analysis and beside this ANCOM-BC and QIIME2 is also available which basically based on linux environment.

You can also give it a try with the MEGA Platform.

Home (megasoftware.net)

You can start by taking courses in bioinformatics from Coursera.

I think the most relevant avenue to be pursued is eliminating the term 'informatic' that constraints the filed to a purely technical and ancillary role of 'software development' while what is needed is to develop a new 'biological statistical mechanics' allowing to face the complexity of biological systems after the total failure of deterministic 'gene-centric' era.

See for example:

so, what is your experience when you use a functional enrichment analysis application? do you think its result satisfy your expectation or you think it still has a room for improvement?

I think you can do correlation analysis between lncRNA expression, For each gene within the pathway, calculate the correlation coefficients between its expression and the expression of all known lncRNAs (list can be obtained from LNCipedia etc). then using the approriate threshold can narrowdown the list.

or the other method,

As we know the pathway then get the gene list for that pathways and can Search for lncRNAs that are located near the genes in the pathway and may act as cis-regulatory elements/ trans by using tools that predict the potential of lncRNAs to act as trans-regulatory elements by interacting with genes at the transcriptional or post-transcriptional level like LncTar (https://www.cuilab.cn/lnctar)

hope that helps

Hi,

check the ISSN of the journal in the master journal list (https://mjl.clarivate.com/home) or in the scientific journal ranking (SJR) (https://www.scimagojr.com/journalrank.php) site to see its IF and Q ranking.

If it is a valid journal, you should find its information on these sites.

BW

I guess it would depend on the context, but generally "frame" refers to a sequences that encodes a peptide/protein. "Query" is the user input, (ie sequences you enter) and "subject" refers to the reference sequence

Now coming to your table p-value in the right column of the forest plot is the p-value for the overall test of the treatment effect across all subgroups. It is calculated by combining the results of the individual studies in the meta-analysis. In this case, the p-value is 0.56, which is not statistically significant.

The p-value for the test for effect in the subgroup is the p-value for the test of the null hypothesis that the treatment effect in the subgroup is equal to zero. It is calculated using only the data from the studies in the subgroup. In this case, the p-value for the test for effect in the subgroup is 0.094035, which is statistically significant.

The two p-values are different because of the heterogeneity between the studies in the meta-analysis. The heterogeneity statistic (0.5) is very high, which indicates that there is a lot of variability in the treatment effects across studies. This variability could be due to a number of factors, such as different study designs, different populations of patients, and different treatment regimens.

When there is heterogeneity in the treatment effects across studies, it is more difficult to detect a significant overall treatment effect. This is because the variability in the treatment effects across studies can mask the true effect of the treatment.

In this case, the p-value for the overall test of the treatment effect is not statistically significant, but the p-value for the test for effect in the subgroup is statistically significant. This suggests that the treatment may be effective in the subgroup, but it is not possible to draw a definitive conclusion without further research.

It is important to note that a statistically significant p-value for the test for effect in a subgroup does not necessarily mean that the treatment is clinically effective in that subgroup. It is possible that the difference in the treatment effect is small or that it is not clinically meaningful.

To determine whether the treatment is clinically effective in a subgroup, it is important to consider the magnitude of the difference in the treatment effect and the clinical implications of that difference

Hongzhuang Chen I am afraid that you can lose a lot of information from such comparison. But, it can be applied (and very useful) to illustrate the differences supported statistically by analysis of the original data (sequences).

The significance of gene mutation burden in endometrial cancer data collated from different studies can be assessed using statistical methods such as Fisher’s exact test and logistic regression.

With some sequencing technologies up to the first 50 bases read tend to be unreliable so do not pass quality control. This means that often your primers are already cropped from the 5' end. I find it best to just align the forward and reverse sequences and see how much overlap you are getting.

Waqas Abbasi, Hi- No Rosetta-Membrane did not work well at all for this task and I would not waste your time attempting to do so, unless you have preliminary signs indicating positively for your protein (or your membrane anchored protein has some specific other attributes meaning it may work better). I would suggest firstly you thoroughly reading Anne Marie Honegger's post above and the link/paper they kindly provided there though.

I would strongly suggest you instead just try and use the new generation methods RosettaFold and AlphaFold2 as they seem to be able to position single/double TMHs away from the membrane-associated globular regions/domains of the chains better, albeit still fa from perfect. However, there does seem to be some signs in the near-ish future similar methods may be released better for such instances of TMH-anchored membrane proteins, but it remains to be seen.

Best of luck,

David

As an update to this discussion, I have decided to reduce my sample size and incorporate a pooled reference channel. Mostly to open up the possibility of integrating additional samples and conditions in the future.

Sam

Dear Omar A. Soliman I reckon that the following articles and books would help you.

1.

2.

3.

4.

5.

All the best Omar A. Soliman

RAM= 32 GB or higher

Processor= Intel core i7 or higher

High-end GPU instead of CPU

Linux OS

I would suggest using a workstation instead of a laptop.

Thank you for this...

try adding the -len flag for the mean distance you are expecting and add the -binw flag for the bin width so you have less bins. It seems like it only makes so many bins and then last bin will have 100% probability if all the prior bins are unfilled. So for my example i had a distance of 5.1nm and i set the average to 3 and the binw to 0.1 like this -len 3 -binw 0.1

Hello Piyush. I am not able to completely understand your problem. Did you download a protein with an ion "nd" that you want to re-dock with using pyrx? Or did you separately downloaded the ion file and want to perform docking with the unbound protein?

thank you maryam ,

i have used this finder some times but not aware of these settings. ll use it soon. thanks

is there any other option available for the same

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!

Thankyou so much Susanta for your reply ,

can you suggest way of network analysis on this data or any good resource to read relevant to this

About 2min of google search would have found this review describing the sequences of the various vaccines.

Including a link to the publicly available sequences of the Pfizer and Moderna mRNA vaccines.

I don't think you can predict host range using bioinformatics tools There are so many subtleties that impact host range, both in terms of gene expression, chaperons that influence folding of proteins, and most importantly the interactions with the host receptor for phage binding and injection. We don't yet know enough to predict except in a very few well studies examples.

This is not the complete list ... where are all the Human Resource Management journals, for example?

I am a bioinformatician, let me know what specific query you have. Bioinformatics has many subfields like genomics, proteomics etc.

Mohammad Shahbaz Khan Certainly! Although the data is currently presented in Gene Ontology (GO) format, I want to create a comprehensive graph that visualizes the entire information. Further, I intend to annotate each gene with its corresponding GO term, including all parent terms associated with each gene.

Feel free to add more details on your perspective!

Thank you, ChatGPT.

The hierarchical structure observed in the Gene Ontology (GO) OBO-basic file is primarily based on the "is a" relationship, which represents the parent-child relationship between terms. The "is a" relationship defines a broader term (parent term) and a more specific term (child term), indicating a hierarchical structure.

However, the GO also incorporates other types of relationships beyond "is a" to capture additional aspects of gene function. Two such relationships are "has part" and "regulates":

Although the "has part" and "regulates" relationships do not exhibit a hierarchical nature like the "is a" relationship, they can still be informative for understanding the functional relationships between terms. However, the propagation of these relationships to the root of the hierarchy is not as straightforward as it is with the "is a" relationship. The propagation of relationships to the root may require additional analysis and considerations based on the specific research context or requirements.

The majority of bioinformatics tools offer sample data. Instead, you may use the data. For instance, test data may be found at https://github.com/FelixKrueger/Bismark.

Susanta Roy Very many thanks for answering my query. I just have one more query. Is there any possibility to check for synergism between 2 ligands when docked with a protein? That is multiple ligand dockings? Any protocols

do you know for that?

1. http://scratch.proteomics.ics.uci.edu/

1a. http://scratch.proteomics.ics.uci.edu/explanation.html#DIpro