dna | Progressive Web Components | Web Framework library

I'm really struggling with this exercise about linked lists and node removal.

Basically, DNA sequences are said to be complementary when each base in one sequence has the corresponding complement in the other sequence at the same position. Base A bonds with T, and base C bonds with G. For example, the sequences ACGT and TGCA are complementary.

I have to implement a function which takes two linked lists: the first is the original DNA and the second is the edit sequence. The function must apply the edit method described earlier and return the new list.

For instance, we've got "A C G T A G A C G T T C T A" as the original DNA sequence and "G C A" as the bonding sequence. G matches with C, C matches with G and A matches with T (and vice-versa). Therefore, "C G T" is "G C A"'s reflex. Then we have to remove "C", "G" and T", following this exact order, from the original DNA sequence, which turns out to be the expected result stated below.

Also, two things: 1. I must ignore any subsequent matches generated as a result of any node removals. 2. I cannot use any headers besides stdlib.h and functions such as malloc() or calloc(). I am only allowed to use free().

Input:

I have API that generate pdf file after saving values into database. My customer needed to generate this pdf and then send it by mail. He sended my photo of how should that pdf look like. I recreated it, it looks same as in that picture but it is hard to read because there are missing vertical lines. I looked trought docs and also tried to google, bud I did not found anyithing. Here is how my PDF looks like:

As you can see, vertical lines are missing and because of that is harder to read.

Is there any possibility to add vertical lines?

Here is my code:

Here is a DNA string that I want to split and then combine in groups of 3

I have a vector with equal sized 0/1 elements, dna. And a similar vector with same size, flip. If the flip = 1, I want to flip the corresponding figure in the dna vector. So 0 would change to 1 and 1 would change to 0. And without looping to make it fast. My real dataset has a lot of data. Below is some sample data:

I'm trying to drop duplicates and keep the row with the maximum values. I can do this separately per strategy.

However, when trying to do this based on two separate conditionals based on the strategy, the dataframe tends to overwrite one another when trying to apply these.

This is needed given that one strategy contains values that one strategy has and another does not; note these do share one common column though.

I've got data with time (seconds) on the x axis and intensity (in relative fluorescent units, or rfu) on the y-axis. It's generated by watching fragments of DNA pass a camera - the bigger the DNA fragment the bigger the time. There are 23 fragments of known size (in DNA base pair units, bp), and therefore there should be 23 peaks. As I know the size of the DNA fragments in bp, I want to recalibrate the x-axis from time (seconds) to base pairs (bp) using a linear model.

Unfortunately there is quite a lot of noise in the data that produces spurious peaks. The only way to confidently tell the true ones from the false ones is that the false ones don't fit the expected pattern in DNA base pairs.

I've provided data from one sample at this link in a data frame called demo. Unfortunately it's too large to paste below.

https://1drv.ms/t/s!AvBi5ipmBYfrhf0v_kvWuN2foLyBgg?e=RWfdXZ

I can pick out all the peaks as follows.

Unambiguous DNA sequences consist only of the nucleobases adenine (A), cytosine (C), guanine (G), thymine (T). For human consumption, the bases may be represented by the corresponding char in either uppercase or lowercase: A, C, G, T, or a, c, g, t. This representation is inefficient, however, when long sequences need to be stored. Since only four symbols need to be stored, each symbol can be assigned a 2-bit code. The commonly used .2bit-format specified by UCSC does exactly that, using the following encoding: T = 0b00, C = 0b01, A = 0b10, G = 0b11.

The C code below shows a reference implementation written for clarity. Various open-source software that converts genomic sequences represented as a char sequence typically uses a 256-entry lookup table indexed by each char in sequence. This also isolates from the internal representation of char. However, memory access is energetically expensive, even if the access is to an on-chip cache, and generic table look-ups are difficult to SIMDize. It would therefore be advantageous if the conversion could be accomplished by simple integer arithmetic. Given that ASCII is the dominating char encoding, one can restrict to that.

What are efficient computational approaches to convert nucleobases given as ASCII characters to their .2bit-representation?

I have thousands of DNA sequences that look like this :).

I was attempting to complete this problem as a newbie, and I'm seeing extra elements within my returned array after my loop.