# Tag Info

30

The earliest mention of the 30x paradigm I could find is in the original Illumina whole-genome sequencing paper: Bentley, 2008. Specifically, in Figure 5, they show that most SNPs have been found, and that there are few uncovered/uncalled bases by the time you reach 30x: These days, 30x is still a common standard, but large-scale germline sequencing ...

26

What does this soft masking actually mean? A lot of the sequence in genomes are repetitive. Human genome, for example, has (at least) two-third repetitive elements.[1]. These repetitive elements are soft-masked by converting the upper case letters to lower case. An important use-case of these soft-masked bases will be in homology searches: An atatatatatat ...

21

All humans have some differences in their DNA, but there's far more that is shared. On average the difference between humans is only about one thousandth of their full DNA, which means we're about 99.9% the same. These differences aren't distributed fully randomly, but are often because of specific gene alternatives. (Random mutations do occur, but they are ...

19

In brief, the “genome” is the collection of all DNA present in the nucleus and the mitochondria of a somatic cell. The initial product of genome expression is the “transcriptome”, a collection of RNA molecules derived from those genes.

12

There's rarely a good reason to use a hard-masked genome (sometimes for blast, but that's it). For that reason, we use soft-masked genomes, which only have the benefit of showing roughly where repeats are (we never make use of this for our *-seq experiments, but it's there in case we ever want to). For primary vs. toplevel, very few aligners can properly ...

12

In any scenario where depth of coverage is an important factor, PCR duplicates erroneously inflate the coverage and, if not removed, can give the illusion of high confidence when it is not really there. For example, consider the following hypothetical scenario. * ...

11

The use of lower/upper case letters and N/n letters in genomes sequences is not completely standardised and you should always check the specification of the resource you are using. Lower case letters are most commonly used to represent “soft-masked sequences”, a convention popularised by RepeatMasker, where interspersed repeats (which covers transposons, ...

10

There are two types of INDELs: short indels and long indels. Some put the threshold at 50bp; others choose 1000bp. Short and long indels are called differently. <50bp short indels are called from read alignment directly. Modern indel callers essentially build a multi-alignment of reads and call an indel with enough read supports. Short indels may break ...

9

Whole genome aliment can be done using Progressive Mauve, LAST or Mummer. For bacteria I used Mauve since it has also very nice visualisation engine. A very new tool is Minimap2, a super fast mapper that supposed beside read mapping be able to handle reference vs reference. However, I do not know how performance of it compares to the tools mentioned above. ...

9

Insertions and deletions (indels) are one type among many different types of genetic variation, such as single nucleotide variants (SNVs), copy number variants (CNVs), and structural variants (SVs). I'll assume here that you know how indels are defined, but are simple trying to understand the importance of discovering and analyzing them. The goal of indel ...

8

Generally, you should use the soft-masked or unmasked primary assembly. Cross-species whole-genome aligners, especially older ones, do need to know soft-masked regions; otherwise they can be impractically slow for mammalian genomes. Modern read aligners are designed to work with repeats efficiently and therefore they don't need to see the soft mask. For ...

8

using a http request. if there is a DAS server, you can always use this protocol to download the xml -> fasta. see https://www.biostars.org/p/56/ $curl -s "http://genome.ucsc.edu/cgi-bin/das/hg19/dna?segment=chrM:100,200" | xmllint --xpath '/DASDNA/SEQUENCE/DNA/text()' - | tr -d '\n' ... 8 DNA is only the substance that makes up the genome. DNA can come with many forms. For example, the fragments you get by PCR in the lab are DNA. A bacterium also has DNA molecules in it. You may also find DNA fragments in many other places, like in our blood, or at a crime scene. Genome, however, is a very specific term. It means all the heritable ... 8 We sequence and therefore typically report assemblies as DNA sequences, even if they're actually RNA. 7 I wrote a command-line k-mer counter called kmer-counter that will output results in a form that your Python script can consume: https://github.com/alexpreynolds/kmer-counter You can grab, build and install it like so:$ git clone https://github.com/alexpreynolds/kmer-counter.git $cd kmer-counter$ make $cp kmer-counter /usr/local/bin Once the binary is ... 7 Solexa Inc. sequenced NA12878 chrX to ~30x in early 2007, which later became part of Bentley (2008). This, I believe, was the first time that 30x showed up. I don't recall they had a particular reason for that. Figure 5 in the published paper was just aftermath. It does not really explain why not 25x or 35x, given that the curves between 25x and 35x in that ... 7 PCR polymerases introduce errors. When an error arises in the first few cycles of amplifications, it will appear in a reasonably high fraction of DNA fragments in the library. After sequencing, you may see the same error occur to multiple reads. If you remove PCR duplicates when calling variants, all errors are reduced down to one read. For high-coverage ... 7 The question is a bit confusing to me, at it's core I understand you want non-overlapping windows across chromosomes. One way to achieve is to use GenomicRanges::tileGenome function, which needs the chromosome lengths as input, e.g. library(Biostrings) mygenome <- readDNAStringSet(list.files(mypath,"mygenome.fa$",full=TRUE)) chrSizes <- width(...

7

Yes, that's a short low complexity region wedged between a SINE and an snRNA. More importantly, your alignments have MAPQ of 0 (that's why they're filled with white in IGV), which will happen if they map equally well to multiple locations. Without looking at the sequence one can use that alone to determine that these are not trustworthy mappings.

7

To follow up on Devon Ryan's answer, I thought it would be a little fun to write a Python script that demonstrates using a bit array to maintain a presence/absence table. Note: I wrote a C++ port that includes a custom bitset implementation that can be sized at runtime. This and the Python script are available on Github: https://github.com/alexpreynolds/...

7

1–4% is from an evolution point of view. 99.7% is from a sequence point of view. Because they are measuring different things, they can be compatible with each other. The correct interpretation is: 1–4% of a non-African genome is inherited from Neanderthals and the sequence of this 1–4% differs from modern human sequence by 0.3%. PS: so, for each non-African, ...

7

It seems to be explained right there in the image you posted: So, the three strains were classified based on three specific variants: Strain S, variant ORF8-L84S: a variant in the gene "ORF8" which changes the leucine (L) residue at position 84 of the gene's protein product to a serine (S). Strain G, variant S-D614G: a variant in the gene "S&...

6

They are two very different things. Your genome is a large section of about 3 billion DNA nucleotide bases. It has no concept of exon and introns. Transcriptome is a study of transcriptions. You have introns and exons. We can now talk about alternative splicing and gene expression. You can think your genome is like a cooking recipe. While it's good to have ...

6

With a k-mer size of 28 it shouldn't be finding that many matches. And the prokka results are suspicious as well. Maybe you have multiple contigs (none larger than 100kb) in that file? What is the result of grep ^'>' fasta_file | wc -l ? This would show how many contigs you have in the file.

6

At the moment, the standard reference genomes (e.g. hg19, hg38) are haploid genomes. We know that the human genome is diploid. Naturally, the latter would be the respectively correct representation of the human genome. The premise of the OP's question is false. The natural reference representation of the human genome is not diploid. Think of a reference ...

6

30 times coverage is not unique to this problem, but number 30 has its empirical role in statistics: In statistical analysis, the rule of three states that if a certain event did not occur in a sample with n subjects, the interval from 0 to 3/n is a 95% confidence interval for the rate of occurrences in the population. When n is greater than 30, this is a ...

6

Yes, this is a low complexity region. Regions are considered low complexity (or having a simple sequence) when they contain an abundance of a single base, or an abundance of short tandem repeats. The simplest is a tandem repeat of a single base (e.g. AAAAAAAAAAAAAAAA), also called a homopolymer. In your case there is a 3-unit tandem repeat of TAAAAA, and ...

6

I think that my calculations must be wrong. Otherwise, how could programs count kmers in RAM? Hash table based k-mer counters only keep k-mers seen in data. For $16<k\le32$, you need 64-bit integers to store a k-mer. Given $n$ distinct k-mers, a naive implementation with open addressing hash table would roughly take $2n\cdot 64/8=16n$ bytes. We assume ...

6

For simple variants like SNPs it would not really be a problem to use the current genome assembly for other ethnic groups. But for more complex variants this could be indeed problematic, however not only for ethnic groups but also for individuals within the same population. Think of very complex regions such as HLA or KIR. In studies where they compare ...

6

Found a solution, using D-Genies, worked great. Some examples from their website: Thanks to @user172818.

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