Bioinformatics - Science topic

Thank you all!

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.

Thanks

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

Sanjay Nagar Mohammad Shahbaz Khan Sabine Strehl Sanjay, with the addition of Mohammad's suggestions it looks to me that you now have information to make the best manual ever. - Add my "thank you" to Mohammed & Sabine.

Yes, there is a standard orientation for chromosomes to be assembled in most genome sequencing and assembly projects. Chromosomes are typically assembled with a consistent orientation known as the "forward" or "+" orientation. In this orientation, the DNA sequence is aligned from the 5' end (start) to the 3' end (end) of the chromosome. This convention ensures consistency in the representation of genomic information across different studies and facilitates comparisons between different genomes.

The forward orientation is determined based on the directionality of DNA replication during genome sequencing and assembly processes. The DNA strands are typically sequenced in both directions, and the resulting reads are then aligned and assembled into contigs, which are further scaffolded to construct chromosome-level assemblies.

It's worth noting that in some cases, certain regions or genes within a chromosome may be inverted or have a reverse orientation due to specific biological features, such as gene rearrangements or evolutionary events. However, the majority of the chromosome is assembled and represented in the forward orientation to maintain consistency and standardization in genome research.

To extract a sequence from a larger genome file based on a specific location, you can use various command-line tools available in Bash. you can achieve this using the samtools and bedtools utilities, which can be installed via conda.

It is better to consider batch as a factor in the design formula. The tximport pipeline proposed by Michael Love himself offers the most useful solution. Please have a look.

Quite honestly if your protein isn't too large, i.e., to many amino acids for it I would just use AlphaFold or ESMFold and compare the best model with the resolved one by aligning on this region. I think the models (or variants of it that participated listed in the previous post all do have lower performance in the last CASP competitions then AF had. Although I haven't checked this ^^

RosettaFold would also be a good option.

Of course homology modeling can still work pretty well, but usually only if you have good templates and ideally many of them. But if you have regions that basically are missing in your templates and those are significant it usually doesn't really work that well.

If you are still encountering difficulties, you can try using alternative molecular visualization tools to view the SwissDock results. Examples of popular visualization software include PyMOL, VMD, and UCSF Chimera.

I believe Rob is correct.

Since you are using the intersect function, the numbers in your figure (e.g. 365 and 154) are the number of genes without any intersection.

The total genes of each set (e.g. OE21) will be the sum of all the numbers in each intersection + genes with no intersection. I couldn't do full sum for you as the core intersection number is missing.

Coursera is a good option:

Thumbs up Tuba Sena Ogurlu

if you are passionate about bioinformatics and its application in medical industry then there is a lot of research going on in molecular and functional genomics now a days. certainly, you will have diverse arena of research from research from metagenomics to single cell RNA sequencing. If you like you can also try to develop a computational pipeline to analyze publicly available cancer genomics data, such as The Cancer Genome Atlas (TCGA) dataset. Focus on identifying potential biomarkers, genetic variants, or gene expression patterns associated with specific types of cancer, aiming to contribute to personalized medicine and targeted therapies. you should read about this and need to have clear understanding.

Assessing diversity metrics in 16S data is an important step in analyzing microbial communities. Handling count data in this context can be challenging due to the compositional nature of the data, as you mentioned. While there is no one-size-fits-all approach, there are several techniques and considerations you can explore. Here are some suggestions:

Remember that there is ongoing research in the field, and best practices continue to evolve. It's important to critically evaluate the methods, consider the specific characteristics of your data, and consult with your advisor or peers with expertise in microbiome analysis to make informed decisions about data transformations and diversity metric assessment.

That's a good approach Susanta Roy I would add that once you are working with your multiple sequence alignment (MSA) in Jalview (https://www.jalview.org), you load an experimental 3D protein structure, or an AlphaFold model (all possible from Jalview, just right-click on a sequence label), and visualise the mutations and conservation scores on the structure too. Jalview makes this easy by colouring the structure by the sequence, so you can choose to colour by conservation and add features to represent your mutations and they will instantly be viewable on the structure.

The other thing I would add is that in addition to BLASTing the full-length protein, have a look at it on InterPro and see what domains it has. Then you can work with curated MSAs from the individual domains too.

Great question Muhammad Abrar Yousaf !

The CRISPRCasFinder helps with CRISPRs and cas genes finding in your MSA/fasta files - you can upload them on this website - https://crisprcas.i2bc.paris-saclay.fr/CrisprCasFinder/Index. Otherwise you may need to use another soft in case your files are too big and the first link does not work https://crisprcas.i2bc.paris-saclay.fr/Home/Download

Reference: David Couvin, Aude Bernheim, Claire Toffano-Nioche, Marie Touchon, Juraj Michalik, Bertrand Néron, Eduardo P C Rocha, Gilles Vergnaud, Daniel Gautheret, Christine Pourcel, CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins, Nucleic Acids Research, Volume 46, Issue W1, 2 July 2018, Pages W246–W251, https://doi.org/10.1093/nar/gky425

Hi, I am in

For what journal do you need reviewers?

It is possible to add other ions to the solvent the system in addition to NaCl and MgCl2 during a MD simulation. But, it is important to consider the effect of additional ions on the simulation results and the choose of the appropriate ion concentration are based on the strudied system.

Yes, your reading is correct. The article implies that there is potential for future nucleotide variations within the conserved restriction sites that are located in variable regions of the 16S rDNA gene.

The possible mechanisms for such variations are mutations, insertions, deletions, or recombinations, which can occur spontaneously or as a result of exposure to environmental factors, such as UV radiation, chemicals, or antibiotics. These changes can accumulate over time and result in differences in the sequence and/or length of the conserved restriction sites, leading to the generation of different band patterns upon restriction digestion.

The time frame for such variations can vary depending on the bacterial species, its population size, its growth rate, and the selective pressures it faces. Some bacterial species have high mutation rates and/or frequent horizontal gene transfer events, which can result in rapid evolution and diversification. Others have lower mutation rates and/or stable environments, which can lead to slower evolution and conservation of certain traits. However, even slow evolution can accumulate changes over time, and it is difficult to predict the exact time frame for future nucleotide variations within conserved restriction sites.

Regarding your edited question, the frequency of new 16S rRNA gene variants in bacterial species can also vary depending on the factors mentioned above. Some bacterial species have high genetic diversity and high rates of recombination and horizontal gene transfer, leading to frequent emergence of new variants. Others have low genetic diversity and low rates of recombination and horizontal gene transfer, resulting in slower emergence of new variants. However, the 16S rRNA gene is generally considered to be a stable and conserved marker for bacterial identification and classification, and many conserved regions within this gene are used as targets for PCR amplification and sequencing.

These video playlists might be helpful to you:

Dear Shopnil Akash ,

Network pharmacology, a systematic analytical method, can analyze the interaction network of multiple factors such as drugs, protein target, diseases, and genes.

Regards,

Shafagat

There are several databases and repositories available that provide genomic data and tools for the study of non-cancer diseases. Here are a few examples:

These are just a few examples of the many databases and repositories available for the study of non-cancer diseases. It is important to choose the appropriate resource based on your research question and to make sure that the data and tools are reliable and validated.

Luke V Schneider Thank You...

I highly recommend you to focus on your education and understanding the basics and fundamentals and not to spam here by posting questions and answering yourself.

Further, since you don't have the proper education to understand what software can be used for what it is totally illogical to talk about bioinformatics tools.

Yes, there are several bioinformatics tools available to predict nanobody sequences based on antigen sequences. One such tool is called "AbDesign," which is a web-based server that predicts the sequence and structure of nanobodies based on the input antigen sequence.

AbDesign uses a computational algorithm to predict the amino acid sequence of nanobodies that can bind to the input antigen sequence. The algorithm takes into account the physicochemical properties of the antigen and the CDRs (complementarity-determining regions) of the nanobody.

Other bioinformatics tools that can be used for nanobody sequence prediction include "Nanobody Mapper," "VHHDB," and "Nanobodies.org." These tools use a variety of algorithms and techniques to predict nanobody sequences, and some also provide additional features, such as database searches and visualization tools.

It's important to note that while bioinformatics tools can be useful for predicting nanobody sequences, experimental validation is still necessary to confirm the predicted sequence and determine its binding properties.

Hi , in selection of mismatches (SNPs) it easily works. Coupling flourcent dyes to such primers can convert PCR to RT PCR .

Hi, a crude measure is to use -ignh during pdb2gmx, it will rebuild all the H-atoms based on the force field you are using. Most of the time, it is a reasonable choice (though not always), as the H-atoms are mostly absent in crystallographic structure (as it is difficult to resolve h-atom positions).

Histidine is unique in the sense that its side chain offers multiple h-bonds at physiological pH. The better procedure is to check which heavy atom of His side chain is forming H-bond in the protein (either it is delta or epsilon), and rename your His residues accordingly (HIS, HIE, HID, please check your FF how these residues are named there).

"2. I do not know if I should add the atom HD1 in the HSE entry in the rtp file (I tried this and got several warnings)." > Try not to mess with rtp entry at this stage (as you are very new), and if you like to play around, just make a backup of FF directory and do as you like.

Dear Dr Abhijeet Singh, thank you so much.

Dear Kian,

have you tried improve your purity by FACS? It´s fairly easy to choose markers to distinguish MSC & macrophages and sort highly pure populations.

Systematic reviews and meta-analyses are proliferating, as they are an important building block to inform evidence-based guidelines and decision-making. Enforcement of best practice in clinical trials is firmly on the research agenda of good clinical practice, but there is less clarity as to how evidence syntheses that combine these studies can be influenced by bad practice. Our aim was to conduct a living systematic review of articles that highlight flaws in published systematic reviews to formally document and understand these problems...

Many hundreds of articles highlight that there are many flaws in the conduct, methods and reporting of published systematic reviews, despite the existence and frequent application of guidelines. Considering the pivotal role that systematic reviews have in medical decision-making due to having apparently transparent, objective and replicable processes, a failure to appreciate and regulate problems with these highly cited research designs is a threat to credible science...

You must be knowing the flowcell and kit used to generate the sequence data/fast5 files. Printing workflow would not help much if you dont know what specifics were practically used.

You might some help from here