Next – Generation Sequencing

Structural integrity is one of the key factor needs to be confirmed. In this factor, the deoxyribonucleic acid may mixed with native contaminants, phenol, fermentation alcohol and salts through the deoxyribonucleic acid extraction procedure. Incomplete removal of phenol, or not doing with recent phenol can damage deoxyribonucleic acid i.e. introducing nicks can create the DNA a lot of fragile; it may also impair enzymes employed in downstream procedures, as well as incompletely removed fermentation alcohol. High salt concentrations (i.e EDTA carry-over) will also cause lower effectivity in any downstream reactions.

A second vital issue is that the deoxyribonucleic acid structural integrity, that is particularly vital for long-read sequencing technologies. deoxyribonucleic acid will become fragile due to the nicking introduced throughout deoxyribonucleic acid extraction, or utilizing the storage buffer with inappropriate pH. Prolonged deoxyribonucleic acid storage in water and on top of -20°C isn’t recommended; it will increase the deoxyribonucleic acid degradation risk due to reaction. High molecular weight deoxyribonucleic acids are fragile; thus mild handling needed i.e. vortexing at nominal speed. Then, pipetting with wide-bore measuring device tips, transportation in a solid frozen stage is also suggested. It’s also best to keep the amount of freeze-thaw cycles to a minimum, since ice-crystals will automatically damage the deoxyribonucleic acid. For a similar reason, one ought to avoid deoxyribonucleic acid extraction protocols involving harsh bead-beating treatment through tissue homogenisation. It should be conjointly noticed that RNA contamination of deoxyribonucleic acid samples should be avoided. Most NGS deoxyribonucleic acid library preps will solely with efficiency utilize double-stranded deoxyribonucleic acid. Having RNA contamination within the sample can overestimate the library NA molecules concentration. that’s very true for PacBio and 10X chromium libraries technology( Del Angel et al., 2018)

Other concerns
• Pooling of individual samples – for a few organisms it is tough to extract a enough quantity of deoxyribonucleic acid, and in these cases it would be tempting to pool many samples before extraction. Note that this can increase the genetic variability of the extraction, and might result in a a lot of fragmented assembly, a bit like high levels of heterozygosity state would. generally pooling ought to be avoided, however if it’s done, it is better to use closely connected and/or inbred species is usually recommended.

• Presence of different or other organisms – Contamination is usually a high risk once operating with deoxyribonucleic acid. Contamination is usually introduced within the laboratory at the deoxyribonucleic acid extraction stage, or alternative organisms is presented within the tissue used, e.g. contaminants and/or symbionts. Care ought to be taken to form certain that the deoxyribonucleic acid of alternative organisms doesn’t occur in higher concentrations than the deoxyribonucleic acid of interest, as several reads can then be from the foreign sample instead of the genome of the studied organism. little amounts of contamination are rarely a drag as these scans is filtered out at the read internal control step or once assembly, unless the contaminants are extremely like the studied organism.

• organelle deoxyribonucleic acid – It is well noted that certain tissues are thus wealthy in mitochondria or chloroplasts that the organelle deoxyribonucleic acid happens in higher concentrations than the nuclear deoxyribonucleic acid. this could result in lower coverage of the nuclear genome in your sequences. If one have got an alternative option for a tissue better to go for nuclear sample with low organelle deoxyribonucleic acid (Del Angel et al., 2018).

Reproducibility and repeatability
Reproducibility and repeatability are reportable as a serious scientific issue once it involves massive scale information analysis. For genetics or genomics to fulfil its complete scientific and social potential, in silico analysis should be easily repeatable, duplicatable and traceable. Repeatability refers to the recomputation of an existing result with the initial information and therefore the original code. for example, the authors report numerical instability arising from a mere modification of UNIX operating system platforms, even once adopted precisely the same version of the genomic analysis tools. as luck would have it, solutions exist and beside their report of numerical instability, the authors did show that repeatability can be achieved through the efficient combination of containers technology and advancement tools. Containers are delineated as a brand new generation of light-weight virtual machines whose preparation has restricted impact on performances. ( Munafò et al., 2017)

Container types such as docker tech and Singularity, create it doable to compile and deploy a code in a very given atmosphere, and to later re-deploy that very same code within the same original atmosphere whereas being hosted on a distinct host atmosphere. Once encapsulated this manner, analysis pipelines were shown to become entirely repeatable across platforms. many advancement management systems, like Nextflow, and Galaxy, have recently been reportable as having the capability to use and deploy containers. These tools all share a similar philosophy: they create it comparatively simple to outline and implement new pipelines, and that they offer a lot of or less intensive support for the massively parallel preparation of those pipelines across high performance process (HPC) infrastructures or over the cloud. Containerization conjointly provides a really powerful approach of distributing tools in production mode. This makes it an integral part of the continuing effort to standardise genome analysis tools. The wide availableness of public code repositories, like GitHub or docker Hub provides a context during which the implementation of existing standards bring immediate advantages to the analysis, each in terms of prices, repeatability and dissemination across a large kind of environments.
The choice of a work flow manager and the correct integration of the chosen pipelines through a well thought containerization strategy will therefore be an effective associate degree and an integral part of the genome annotation method, particularly if one expects annotation to being updated over time. This makes the adoption of good practices just like the one represented here a vital milestone for genomic analysis to become compliant with the new information paradigm. so as to hold this out, the primary pointers to form information “findable, accessible, practical and re-usable” (FAIR) was revealed in 2016. Even the principles were originally centered on information, they’re sufficiently general thus these high level ideas will be applied to any Digital Object like code or pipelines (Helliwell et al., 2019)

Repeatability is just the foremost technical aspect of reliability. reliability may be a broader construct that encompasses any call and accounting procedure that might compromise the reliability of a longtime scientific result. For this reason the implementation of the honest principle conjointly impacts higher level aspect of the genome annotation strategy and for a genomic project to be FAIR compliant, these smart practices ought to be applied to each information, meta-data and code ( Helliwell et al., 2019)

Every assembly or annotation project is completely different. Distinctive properties of the genome can measure the most reason behind this. to urge a concept of the complexness of associated genome assembly or annotation project, it’s important to look into these properties before beginning. Here, we are going to discuss some genome properties, and the way they influence the type and quantity of data required, further because the complexness of analyses ( Del Angel et al., 2018).

Genome size
To assemble a genome, a particular quantity of sequences (reads) is required. as an example, for Illumina sequencing, a range of >60x sequence depth is commonly mentioned. this implies that the quantity of total nucleotides within the reads got to be a minimum of sixty times the quantity of nucleotides within the genome. From this it follows that the larger the genome size, the additional information is required. One should urge genome size estimation before ordering sequence information, maybe from flow cytometry studies, or if no higher information exists, by investigation what’s the genome size of closely connected or related species and already assembled species. this can be a crucial step to connect with the sequencing facility, because the genome size can greatly influence the number of reads opr data that has to be ordered.

Repeats
Repeats are regions of thegenome that can occur in multiple copies, doubtless at completely different locations within the genome. quantity and distribution of repeats in an exceedingly genome vastly influences the genome assembly results, just because reads from these completely different repeats are terribly similar, and also the assembly tools cannot distinguish between them. this may cause mis-assemblies, wherever regions that are distant within the genome are assembled along, or an incorrect estimate of the scale or range of copies of the repeats themselves. fairly often a high repeat content results in a fragmented assembly, because the assembly tools cannot confirm the right assembly of those regions and easily stop extending the contigs at the border of the repeats . To resolve the assembly of repeats, reads got to be long enough to conjointly embody the distinctive sequences flanking the repeats. It will so be a better plan to order data from a long-read technology, if you recognize that you just are operating with a genome with a high content in repeats. ( Phillippy et al., 2008 and Chaisson et al., 2015)

Heterozygosity
Assembly programs usually attempt to collapse issue variations into one agreement sequence, that the final assembly that is reportable is haploid. If the genome ordering is extraordinarily heterozygous, sequence reads from homologous alleles is just too fully completely different to be assembled on and these alleles will then be assembled singly. this suggests that heterozygous regions may perhaps be reportable as diploid organisms, whereas less variable regions will only be reportable once, or that the assembly simply fails at these variable regions. extraordinarily heterozygous genomes can lead to further fragmented assemblies, or one can doubt relating to the similarity of the contigs. large population sizes tend to steer to high heterostate levels. as an example, marine organisms generally have high heterozygosity state levels and are generally problematic to assemble. it’s recommended to sequence inbred species, if possible. ( Pryszcz and Gabaldón ., 2016)