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8

We sequence and therefore typically report assemblies as DNA sequences, even if they're actually RNA.


5

I would just blast it. When blasting locally, you need to first make a database from your genome, so assuming you got the command-line version of blast installed you can do something like makeblastdb -in my_study_genome.fa -dbtype nucl blastn -max_target_seqs 10 -db my_study_genome.fa -evalue 1e-10 -outfmt 6 -query my_downloaded_gene_of_interest.fasta -out ...


4

In your sequence header you will see: [location=complement(940023..940580)] It's a reverse strand gene. Reverse complement the sequence, then search the genome sequence.


3

You could also get your genes from UCSC. Here's how:


3

Via Gencode and BEDOPS convert2bed: $ wget -qO- ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/gencode.v28.annotation.gff3.gz \ | gunzip --stdout - \ | awk '$3 == "gene"' - \ | convert2bed -i gff - \ > genes.bed Replace the FTP path with that needed for your genome of interest. Feel free to edit your question ...


2

Maximal Unique Matches To answer the question about MUMs, there are two important definitions: A "match" is maximal if it cannot be extended in either direction and still be a match i.e, for two strings $s,r$, a match $s_i,...,s_{i+k}$, $r_{j},...,r_{j+k}$ is maximal if $s_i,...,s_{i+k+1}$, $r_{j},...,r_{j+k+1}$ is not a match and $s_{i-1},...,s_{...


2

For example from Ensembl: https://www.ensembl.org/Rattus_norvegicus/Info/Index Can find other links via a search engine of choice.


2

Genomes are always assemblies. Assembly basically means making a consensus sequence from many partially overlapping readouts. As there is currently no sequencing technology that allows to sequence an entire genome or even an entire chromosome with a single super high fidelity read that is without errors we have to combine several reads to form the consensus ...


2

You can do this with the reutils package, which provides an API to NCBI's E-utilities. Here's an example for your specific question: install.packages("reutils") library(reutils) # Get universal identifier uid <- esearch("NP_000029", db = "gene") # Fetch summary sm <- esummary(uid, db = "gene") # Extract specific ...


2

Yes, that can be done. A common approach for these types of analysis is to carry out a transformation on the data first in order for it to have a normal distribution (the most general approach called an inverse normal transformation, among other names), then run the test on the transformed data. As an example, for data that are positive and zero-skewed, a ...


1

The current release of Gencode for hg38 is v38. This is one way you could get a list of Gencode gene annotations: $ wget -qO- ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/gencode.v38.annotation.gff3.gz \ | gunzip --stdout - \ | awk '$3 == "gene"' - \ | convert2bed -i gff - \ > genes.bed To get convert2bed, ...


1

The best way to approach it would be to take trimmed reads map them to the bins (i.e. with bowtie2) and estimate the abundance of each bin with samtools by getting the depth of coverage: https://www.metagenomics.wiki/tools/samtools/breadth-of-coverage or you can use QUANT_BIN module from the METAWRAP to do it for you. Follow this for the METAWRAP approach: ...


1

as mentioned in previous answers, transformations are frequently used. One commonly used method is quantile normal transformation. Basically you calculate the quantile from the original data and match it to a standard normal distribution. This transforms your data to a standard normal distribution. Even for traits that are naturally normal distributed like ...


1

For downloading lists of genes, together with associated features, I like using Ensembl Biomart: http://ensembl.org/biomart/martview/ In this case, you can "CHOOSE DATABASE -> Ensembl Genes", then "CHOOSE DATASET -> Human Genes" or "CHOOSE DATASET -> Chimpanzee genes" to get to a table selection. Clicking on "...


1

Maybe take a look at workflow management systems, like snakemake Nextflow Toil Cromwell Admitted, each of those adds their additional learning curve. However, especially snakemake is not so much more complicated than a standard bash workflow. Those systems do help a lot organize and orchestrate your workflows


1

Anyone can declare anything to be a "gold-standard", the term is meaningless. All if actually means is, "we think this is the test that everything else should be benchmarked against".


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