In a simple English, could someone explain why is this good and what does it all mean?
Or, to make this more simple: Finding the complete DNA sequence of chromosomes is difficult. That's because some parts of the sequence are highly repetitive. Using a new type of lab machine, the scientists were able sequence the repetitive parts of the X chromosome. This gives a more complete picture of the X chromosome. And that can help scientists fight diseases and understand human biology better.
Of all the fields that I've worked in, genomics has been one of the most overhyped (virtual drug discovery is the other) and it takes a ton of training just to understand how messed up the field is.
The section "Background to the Human Genome Project" gives some color on why they did what they did (TL;DR there was an ostensibly competitive race between the public project and a private one).
I ended up providing some useful resources for helping uncover just how bad genomic assemblies were (at the comptuational level): most genomic assemblies using whole genome shotgun sequencing used a number of heuristics which were believed to be correct, but I suspected that the heuristics failed to deal with repetitive regions and short sequences well. So I built a computing system with >1M xeon cores (Google Exacycle) and we provided the system to Gene Myers (who did the original WGS assembly for Celera). he used the system to do an all-vs-all comparison of sequence pairs, which found numerous bugs and problems with the heuristics that were being used. It was a huge amount of compute but the result was that myers was able to use PacBio data to assembly a significantly better genome, faster, on a laptop: (https://www.yuzuki.org/favorite-talk-agbt-2014-gene-myers-ma...)
Ah, https://en.wikipedia.org/wiki/Clock_recovery !
Too bad DNA isn't a run-length limited code. (Wouldn't that be something.)
There are error detecting codes, in a way. Protein is encoded by 3 base codes, and if you insert or delete bases not in a multiple of 3 it will be misaligned, then eventually likely encode a stop code and cause the bad protein to be truncated and likely removed via nonsense mediated decay.
So despite having taken multiple photos of every square inch of land in your target area, there's no way you can assemble them into one big image just by matching up the overlaps. Without a source of larger-scale information about the region, like a satellite photograph or GPS coordinates for the photos, you have no way of knowing how wide that desert is. All you know is that it's wider than one or two photographs.
This is essentially same problem that current genome assemblies have: there are regions of repetitive sequence in the genome, so all the sequencing reads from those regions look identical to each other, just like the photographs of flat sandy desert, and there's no way to tell how they're supposed to overlap to form the full sequence. The only way to resolve these regions is with a technology that can read all the way through from one end to the other without stopping, producing a single contiguous sequence.
The link here describes the fruits of an effort using exactly those sorts of long-read technologies to fill in all the gaps in the X chromosome sequence, thus generating a single contiguous sequence from end to end, something that hasn't previously been possible for DNA sequences of this size.
As to why this is important, these repetitive sequences, despite being apparently featureless, still sometimes have important effects (not unlike the apparently dead and featureless desert in the analogy). In addition, sometimes there are "oases" of functionally important non-repetitive DNA sequence within the "desert" of repetition, and previous genome assembly methods would not be able to tell where these oases belonged. All of this is important because many functional DNA elements are cis-acting. That is, they exert effects on genes that are nearby on the genome. So if you don't know where they belong, then you don't know what they're doing.
If you can assemble one big chromosome sequence from end to end, all of the above problems go away, and you can finally get on with the analysis you wanted to do anyway and stop worrying about not being able to calculate meaningful distances between DNA elements.
Long sequences reads allowed them to map the highly repetitive chromosome. Most sequencing is done by high-throughput short reads.
The technology can theoretically be used to map other regions of the genome which are highly repetitive.