5

You could use the pubmed API to query it directly. Typically I use Biopython for my pubmed queries. There are some good examples in the cookbook and on other SO posts. This example from the cookbook link above has the guts of what you need. You will just have to iterate over your list. Depending on the number of queries you want to do you might have ...


4

If what you want is to split the main VCF file into 1 file per sample, you could use bcftools query and view commands. A similar question was asked on biostars, adapting Jorge Amigo's solution to your situation, you might use: for sample in $(bcftools query -l $combined_vcf) do bcftools view -c1 -Ov -s $sample -o ${$sample.vcf} $file done


4

A mutation is most often likely to be either neutral (no effect) or destabiling (protein misfolds). Changes in activity require changes at the active site or the entranceway to it —which are often only a handful of residues, so are very uncommon. So the answer to your question is most mutations keep the activity, although a large fraction break it. While to ...


3

Alright, so there are a number of problematic patterns in your code - as far as I understand what you are trying to do. Next time, try to post a reproducible example that people can use and more people will be willing to help. L18-21: combination_labels = [] combination_counts = [] for lineage in lineages: Declaring these two lists before the loop, then ...


3

Given that TP53 is the most significant and is already known to have driver mutations in cancer it would seem to be the logical choice.


3

from Bio import SeqIO for normal, treated in zip(SeqIO.parse("/data/statistic/normal_samples", "fasta"), SeqIO.parse("/data/statistic/with_treatment", "fasta")): ... do stuff... That's generally how you zip iterators together in python.


3

If you only have one gene and you only need to do this once then the simplest possible workflow is to generate the aliment using STAR (optimally with the two pass method) and open the two resultant .bam files in IGV, the coverage column at the stop above your alignment track should clearly show the counts of reference and non-reference supporting reads. The ...


3

For counting reads I use mpileup, e.g. samtools mpileup --reference hg38.fa -r Chr10:18000-45500 input.bam, which will give base-resolution coverage for a BAM file. I've written my own script to process mpileup output and make it easier to understand. By default it reports read coverage as a proportion of total coverage, but this can be modified by using ...


2

Without control data from your subjects, I don't think there's really no way to distinguish somatic mutations from germ-line mutations. The best you can do is to screen out common variants, which are germ-line mutations that are shared by large numbers of individuals using the population frequencies from something like the Exome Aggregation Consortium: ...


2

Question I suspect this question is about the phylogenetics of methylation and the approach the investigator is proposing would be the last approach to use. Summary The approaches to assess the phylogenetics of methylation in order of preference are: dN/dS between CpG sites and non-CpG sites, An explicit molecular clock between CpG sites and non-CpG sites ...


2

You can't know which is the driver and which is the carrier. At most you can say that a specific gene deviate more of the expected underlying hypothesis. See also other resources online. You also seem to ignore other genes that deviate more of your null hypothesis. I recommend to plot the histogram of the p-values to see if the distribution is uniform


2

Per Atom You are correct. Rosetta scorefunction does not store any per atom data. The scoring operates at the per residue level. Whereas each atom has its coordinates and properties in full atom mode, in Pyrosetta it is clear that an atom is a just part of a residue and every operation is applied at the residue level. It's a team effort: the functional group ...


1

Related question: Access base aligned to particular reference position After an initial effort posted here, I ended up rewriting this and testing it a little, and posted the script here. It still has some drawbacks (handling indels intelligently) but now it more or less works.


1

To Answer my own question, one can use variation viewer to get all the information :) DisGeNet is another resource.


1

If it's just a comparison of two sequences that you're looking for, then diffseq from the emboss toolkit should be okay: https://www.bioinformatics.nl/cgi-bin/emboss/diffseq I think this tool requires the sequences to be oriented in the same direction. If you discover a lot more variants than you expect, try reverse-complementing one of the sequences.


1

I'm not sure how to generate the additional mutations, but I would say that HGMD is not the way to find all the pathogenic variants. I would probably filter this table by either Clinical significance or Evidence->Phenotype association.


1

What is fed into ComplexHeatmap is what it outputs! So if one of your genes has a Missense_Mutation as well as a Frame_Shift_Del and you would like to plot its value, of course you will have a category and color that is different than those of Missense_Mutation or Frame_Shift_Del, that is only natural. What you can do is to create additional variables like ...


1

You can still align a FASTA file with a tool like bwa mem ( if they are short reads ) or minimap2 for long reads and run it through a variant caller like freebayes. Alternatively, if you have sufficient reads to create an assembly of all of the genomes, or if they already are you can create a MSA of them using a tool like Cactus (as it is built for genomes) ...


1

There seems to be a bioconductor package named synmut which does just that. Apparently, it can take codon usage into account when generating synonymous mutations. Copy pasting an example that matches your use case from the official doc: library("SynMut") filepath.fasta <- system.file("extdata", "example.fasta", package = "SynMut") filepath.csv <- ...


1

I am having trouble finding anything that directly addresses the issue of failed collaborations leading to scooping, but here are some examples of similar behavior: it is sort of implicit in the discussion here. This question on academia SE is related but not exactly right. There are some related stories here. Here is an article about plagiarism among ...


1

VarSome allows you to annotate any variant, including non coding ones. While ACMG classification will not be available if the variant isn't in a gene's transcript (the ACMG criteria are only applicable to variants in transcribed regions), non-coding variants in transcripts will be fully annotated. For example, if you look up variant chr17-41197559-G-T which ...


1

A circos plot is most likely not the most appropriate solution here. What I would suggest is a confusion matrix, of which you can find an example here: For every variant in your vcf you'll add a number in this matrix. One sample is the columns, the other is the lines. If your variant is homozygous in both, then you add in that square +1 (the cell with 5845 ...


1

This looks to be a pretty good Q-Q plot. My recollection of doing Q-Q plots is that a good Q-Q requires a "more or less" linear relationship for the model to be considered okay. Again this model looks good, its 1:1, albeit it does deviate a little (for the gene of interest). TP15 falls outside the 1:1. @Devon Ryan states TP15 has good biological credentials,...


1

For working with a relatively small dataset in tabular format, I would recommend dplyr. You can read in a excel file using readxl library(readxl) library(dplyr) df <- read_excel(FILENAME) To get the number of mutations per gene: df %>% group_by(`Gene name`) %>% summarise(num_mutations=n()) If you want to know if there are mutations in a ...


1

Those will have to be treated as separate analysis. You can use Mutect or VarScan for the matched samples and the tools listed in the other question for the unmatched samples however that means that you won't be able to compare the results easily. If the inter-sample comparison is important you will have to run all samples as unmatched. Personally I have ...


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