Single Nucleotide Polymorphism - Science method

Hello,

I was using package "dartR" and I had to use "dartRverse".

I could solve the above issue by:

> Install(dartRverse)

> Library(dartRverse)

> t1 <-gl.read.dart(

"Filename.csv",

ind.metafile = "Filename_Metadata.csv",

recalc = TRUE,

mono.rm = FALSE,

nas = "-",

topskip = NULL,

# lastmetric = RepAvg,

# covfilename = "Filename_Metadata.csv",

probar = FALSE,

verbose = NULL

)

> t2 <- gl.impute(

x = t1,

method = "frequency",

fill.residual = TRUE,

parallel = FALSE,

verbose = NULL

)

> gl.write.csv(t2, outfile = "Outfile.csv", outpath = "C:/../../../", verbose = NULL)

Query kindly solved by Luis, the main dartR developer from Diversity Array Technologies.

M. Reza Pahlevi Loubna Youssar Amy Klocko You generally cannot reliably predict alternative splicing events solely from Sanger-generated DNA sequences, as you need transcript-level evidence. However, you can attempt in silico predictions using known transcriptomic data and computational tools for splice site recognition.

The file you are trying to liftover is not a .vcf.

You need to reorder columns to match the Variant call format, that is #chromosome, #position, #rsID, #refAllele, #altAllele, etc.

Hi Radwan,

You're right to be concerned about the current accessibility issues with PhenoScanner V2, as it is a useful tool for conducting Mendelian Randomization (MR) studies and identifying potential confounders associated with specific SNPs. I’ve faced similar difficulties due to the ongoing unavailability of PhenoScanner V2.

PhenoScanner V2, as described in its methodology, aggregates data from several large genetic association datasets, including the NHGRI-EBI GWAS Catalog and other resources, to provide genotype–phenotype associations (Kamat et al., 2019). The tool offers a Python-R interface that connects to a MySQL database, enabling the identification of relevant SNPs across various traits and conditions, with a comprehensive catalogue that includes billions of associations for diseases, traits, gene expression, protein levels, metabolite levels, and epigenetic markers. However, its data sources, such as the GWAS Catalog, remain accessible independently, even when PhenoScanner is down.

In your case, I would recommend using the GWAS Catalog directly as an alternative. The GWAS Catalog (https://www.ebi.ac.uk/gwas/home) contains a wealth of information on SNP-trait associations and would allow you to query SNPs related to your traits of interest. You can search for associations using SNP identifiers, genes, or traits, much like you would with PhenoScanner, albeit without the streamlined Python-R interface of PhenoScanner V2.

This approach should serve as a viable alternative and can provide similar insights for your study. I hope this helps, and please feel free to ask if you encounter further issues.

It's probably going to be cheaper and faster to PCR amplify and Sanger sequence to find SNPs.

Your assay is based on amplification from one primer (SNP) vs no amplification (other allele). And a second reaction where you get amplification from (other allele) and none from (SNP). Relying on negative results (e.g. not seeing amplification for homozygous for SNP with 2nd set of primers) is a risky design since it relies on negative data. PCR can fail for lots of reasons. If the SNP is rare, you can easily confused a failed amplification with presence of the SNP.

And it is common for the polymerase to be able to "read across" a mismatch. Yes, I know it's not supposed to, but certain mismatches are well-tolerated and easy to ignore.

RFLPs can work well, but can be really time consuming to optimize. And sometimes they just don't work reliably enough or the enzyme is expensive.

My vote is Sanger, back up the primers so they are at least 150 bp away from the SNP, make the product less than 1000 bp, and sequence from both directions.

try to introduce additional mismatch in position -1 to the 3` end for both primers

CRISPR-Cas9 corrects an SNP by creating a DSB near the SNP site, which is then repaired using a donor DNA template with the correct nucleotide sequence. The process relies on the cell's natural HDR mechanism to incorporate the correct sequence into the genome.

VCF file is similar to multiple sequence alignments. Your question is about filtering variants. You can always set filtering criteria by yourself. Filtering variants with AF<5% is not a criteria but filtering false variants is criteria. Once false variants are filtered then you can use any tool to detect Tajima's D.

VCF2Tajima can be helpful to run Tajima's D on VCF files.

And you'll be able to get more of the details from the figure caption & methods section.

The convention is that the boxes are exons & the thick solid line is the introns. In general, folks don't bother to look for SNPs in introns unless it's a splice-site acceptor.

In the best capacity of my knowledge, I would suggest the most commonly used MutationalTaster (omit if a spelled it incorrectly) which aids in efficient detection of any modifications incorporating prediction of changes based on data collected (can be changes in proteins or splicing of the genome). MutationTaster has never been a modality in my memory where I actually analysed it in-person.

Exomiser is one of the platforms which came in my Gene Manipulation and Genomics module during my Engineering course. Exomiser is one of the most trusted modality to depict and identify de-novo and recessive variants.

I have read not witnessed a tool called as PolyPhen-2 (please ignore if I spelled it incorrectly). I came to know about this in the same module and to the best of my knowledge I think it is specifically meant for the identifying SNPs (Nonsynonymous)

Hi

From the figure file shared, it is clear that it is a false association. You need to do data cleaning by removing minor alleles and missing data, both genotype-wise and marker-wise, with certain thresholds. Then impute the missing data points. I think you should get true associations with cleaned data.

Best regards

@Frederic Lepretre, it's a good answer.

It is still useful to know if the NTC is amplifying but if we treat the 2 bands as real amplimers then I suggest running a dimethyl sulphoxide DMSO gradien. Set up 7 identical samples but in each tube the dmso concentration (final) should be 0%, 1%, 2%,4%,5%, 6%, 7% and 8% dmso. At some dmso concentration the lower band should disappear

UCSC is a powerful friend that can give you lot of tools around genomics and genetics. spending some times on its manipulation is not a waste, it's an investment

U're welcome.

fred

Almost certainly yes.

The base change itself may not create/delete an enzyme cut site for rflp but using dcaps

it will be seen that a second deliberately introduced base change in the amplifying primer will almost always create a restriction site specific to the base found in the template sequence.. This software designs primers to detect either/both of the bases in the snp position. You just have to be a bit careful that the cut site that you are creating does not occur naturally within the amplimer making analysis more difficult

Hi,

The Legume Information System hold information across many legume species. Try this under Taxa tab of home page.

That is the effect size, the "beta" of the regression.

This would be helpful.

Hi. I am having similar issues. Were u able to find any solution for this?

Hii,

Which algorithms is used your study?

It obviously will depend the melting temperature, sequence and length of your probe, but Taqman probes can tolerate a few bp difference (e.g., 1 base mismatch will likely be barely noticeable, but 3-4 mismatches will result in a decrease of amplification efficiency)

I think it can be done in several ways.

One simple way can be -

firstly you can copy the reference of your sequenced gene from NCBI. Then using mafft or other multiple sequence alignment tool you have to align all the sequences. And finally open the aligned fasta file using JalView you can find the mismatches. This "JalView" software provides an interactive platform.

u're welcome

fred

Hi Yasmin

if you look at NCBI or even better at UCSC you'll see that the human ST gene is on the minus strand of DNA.

that means that the sequence of the variant rs2304365 is given on the + strand, and its impact given in the coding sequence strand, the-.

in consequence, the variant is a T on the genomic hand, and A when you look the impact on the RNA and proteins. the reverse complement is therefore to be applicator for minus strand coding genes.

you can zoom in, but look at the arrow:

all the best

fred

Thank you Danial Hariz Zainal Abidin , will give it a try.

Hi Asmaa

since genetics positions differs from species to species, depending on the structure of the samples you're working on (on the genetics point of view of course), you can't do that.

the best is to get the list of your markers and search their physical positions by hand (on the UCSC for example), or by command lines if you got enough informations and all is structured as one.

in fact, as always in bioinformatics, all depends on what you got on start.

all the best

fred

Page 14 in https://www.acgs.uk.com/media/11631/uk-practice-guidelines-for-variant-classification-v4-01-2020.pdf

ChIP analysis

Hi Yasmin

it depends on what you got in your hands and labs, and what you""l do with this work, publishing or not...

go to this website and see what will be possible, power is the only way to get reliable results and to be published:

all the best

fred

Malik Olatunde Oduoye , it requires some level of abstraction to wrap your head around.

Consider this simple case: you have two groups in which you measured the value of some variable. Say you measured the weight of sample of male and of female bugs. You are interested to analyze the expected (mean) difference between the weights of males and females.

A statistical model tries to express the expected (mean) weight as a function of sex.

Now "sex" is a binary (categorical, nominal) variable. How to use this in a mathematical formula that operates on numbers? This is where we need to encode the different possible values of the variable "sex" by different values of a numerical variable that we can use in a formula.

There are no restrictions in how to encode names/groups/categories by numbers. However, some ways will give you results that are meaningful for your research question and that you can readily interpret. We will now first take a small detour to demonstrate this, before coming to the "usual solution".

You have two sexes, and you may think of using an indicator function for each of the sexes that take the values 1 or 0, depending on whether the sex is male or female. These indicator variables can be used to multiply the expected weights of the respective sex, and these products can be added to give the expected weight of the desired sex.

This is the mapping from sex to the values 0 or 1 based on two separate indicator functions:

I_male(sex) = {if sex = "male" then 1, otherwise 0}

I_female(sex) = {if sex = "female" then 1, otherwise 0}

The function of the expected value is then

µ(sex)= b_male*I_male(sex) + b_female*I_female(sex)

Hence, µ(male) gives b_male*1 + b_female*0 = b_male and µ(female) gives b_male*0 + b_female*1 = b_female

Although this function works, it is somewhat unwieldy. It is about two coefficients, b_male and b_female, and a statistical model based on this function can estimate their values along with standard errors, confidence intervals, and p-values, if you wish. The problem with this formula is that it does not give you direct access to what you actually wanted to investigate: the difference between males and females.

There is, of course, a more convenient function to address this directly. This function needs only one indicator variable for one of the sexes and includes an intercept term that will represent the expected value of the other sex. You are free to chose which sex should be represented by the intercept term. This is the "reference". Let's say you decide using females as the reference, then the intercept is b_female and indicator function would be I_male which is multiplied with a coefficient d which will then represent the mean difference between females and males:

µ(sex)= b_female + d*I_male(sex)

Here, µ(female) gives b_female+d*0 = b_female and µ(male) gives b_female + d*1 = b_female+d, so

µ(male) - µ(female) = d

As is obvious here, the coefficient d represents the difference in the mean weight between males and females, and a statistical model will provide an estimate for this difference with corresponding standard error, confidence interval, and p-value.

Of course you can choose to let the intercept represent the mean weight of males as well. The indicator function must then be I_female and the coefficient d then represents the difference in the mean weight between females and males. You may convince yourself by actually calculating the results of the formula for the two possible sexes:

µ(female) gives b_male+d*1 = b_male + d and µ(male) gives b_male + d*0 = b_male, so µ(female) - µ(male) = d

This is it. Practically, statistical software will automatically encode categorical variables into so-called dummy variable by applying the respective indicator functions and use these dummy variables in the statistical models. But these are technicalities.

This interpretation of the coefficient d depends on what sex you have chosen as the reference. If you have chosen males as reference and d > 0, then female bugs are expected to be heavier than male bugs. If you have chosen females as reference and d > 0, then female bugs are expected to be lighter than male bugs.

Analysis of the information you provided:

1. Significance: The corrected p-value of 0.025 suggests that the missense/coding SNP is statistically significant. However, it's important to consider the multiple testing correction applied, as it can affect the threshold for significance.

2. Functional impact: The fact that other papers have shown this SNP to lead to a less functional protein adds further support to its potential role in the phenotype of interest. It suggests that the SNP might have a functional impact on the gene's protein product.

3. LD with other significant SNPs: As you mentioned, the missense/coding SNP is not in linkage disequilibrium (LD) with the other significant SNPs in the region. This suggests that it represents an independent genetic variant influencing the phenotype, rather than being in LD with another causal variant.

4. Rarity and false positive: The SNP's low minor allele frequency (MAF) of 0.005 indicates that it is quite rare in the population. Rare variants can pose challenges in terms of statistical power and increased probability of false positives. Given the population size of 400k and the number of cases (n=1100), it is possible that the rarity of the SNP could contribute to increased uncertainty in the association results.

To further validate the association and assess the potential false positive rate, it would be beneficial to consider additional evidence, such as functional studies or replication in independent cohorts. Additionally, performing appropriate statistical analyses, such as replication analysis, permutation testing, or assessing genomic control inflation factors, can help evaluate the robustness of the association and minimize potential false positives.

It is advisable to consult with experts in the field or bioinformatics/statistical geneticists who can provide more specialized guidance based on your specific dataset and research question.

Hope it helps:credit AI

Hi, did you get the Mac-compatible software for Illumina's GenomeStudio? I met the same problem and need some suggestions. Thanks!

Mojtaba Lotfi

Unfortunately I couldnt find any company to sell it online, which is a bit weird, but as you probably know, CEL I Endonuclease is naturally exists in Celery juice, which normally utilized for detection of heteroduplex and SNPs, specifically in confirmation of genome editing by CRISPR-Cas. In this link you will find a protocol which has been described an extraction method of CEL1 from Celery juice which could be done by your own.

Also, based on your target in working with such a Endonuclease, you may be able to substitute it by T7E1 which is more common than CEL1 and could be purchased from NEB.

Good luck

Probably refers to a coding sequence.

Take a look at this: https://www.biostars.org/p/80014/

People usually suggest excluding monomorphic loci from STRUCTURE analysis because there is no differentiation or any variation in these loci and they bring no information on ancestry. This means that using a dataset with some monomorphic loci versus excluding them would lead to the same results (or with negligible differences?). So, it just saves time.

However, if we are interested in the real state of the populations/localities, it seems reasonable not to exclude them because we have always only some random(?) proportion of a genome. I think it is the same as with sampling individuals from a population - different sets lead to different estimates of an average, for example, and the point is to maximise the sample sizes to improve the accuracy of estimates. Still, not sure...

As for polymerase selection, you may find this chart useful:

As for the rest, if you only have the promega mix available, try to optimize tmeperature/timing/salt cc

If it's a human SNP simply search for it in dbSNP and there you will find links to RefSeq protein and nucleotide sequences.

If it's a pathogenic SNP you may also search in https://www.ncbi.nlm.nih.gov/clinvar/

Hi

just type the rs id in UCSC (https://genome.ucsc.edu) in the genome browser, go to the location and click on the rs.

if there is some information on population frequencies, the next page will give you the informations.

otherwise, you can also Dig in dbsnp (https://www.ncbi.nlm.nih.gov/snp/) from NCBI and see what next.

fred

Although the number of individuals is small to conduct a statistical test and draw strong conclusions, you can still use the Chi-square test with null and alternative hypotheses. For more details, you may search any statistics homepage and apply the test in R or Python.

Hope it helps.

Best

in human there are 2 copies of chromosomes...

fred

Un haplotipo en genética es una combinación de alelos de diferentes loci de un cromosoma que son transmitidos juntos. Un haplotipo puede ser un locus, varios loci, o un cromosoma entero dependiendo del número de eventos de recombinación que han ocurrido entre un conjunto dado de loci. Si tu haplotipo es un locus lugar que ocupa un gen y sabes el lugar o la banda, porque la amplificaste entonces debe limpiar y secuenciar esa banda, así tendrás tu haplotipo y tu secuencia de nucleotidos. Luego los informaticos tienen programas especiales que dan la posición.

You'll have to provide some more background.

What genotypes are you expecting? In what proportion? What is the sample population (mutagenized? inbred? wild? etc.)

You can't involve probability unless you have expected numbers to start with.

Also, do you have controls to show that AA genotype is detectable with your assay?

Hi Hokmabadi,

May I know if you mean to find SNP on known region? If yes, I have the similar experience. I use the NCBI in which all SNPs in specific gene can be shown.

1. Open the home page for NCBI. In the drop-down list, choose "Gene" and enter the gene name like CYP2D6 in the field and search.

2 Please choose the suitable option for human gene in the first result.

3 Please scroll down to find the button "GenBank" and click it. Then you will skip to new page and please enter the target region position on right column and click "update view".

4 Finally, please click the button "Graphics" at the top of the page. Then you can see all SNPs with rs number and red line in target region.

Hope this is wanted guideline.

Abstract

The decreasing cost along with rapid progress in next-generation sequencing and related bioinformatics computing resources has facilitated large-scale discovery of SNPs in various model and nonmodel plant species. Large numbers and genome-wide availability of SNPs make them the marker of choice in partially or completely sequenced genomes. Although excellent reviews have been published on next-generation sequencing, its associated bioinformatics challenges, and the applications of SNPs in genetic studies, a comprehensive review connecting these three intertwined research areas is needed. This paper touches upon various aspects of SNP discovery, highlighting key points in availability and selection of appropriate sequencing platforms, bioinformatics pipelines, SNP filtering criteria, and applications of SNPs in genetic analyses. The use of next-generation sequencing methodologies in many non-model crops leading to discovery and implementation of SNPs in various genetic studies is discussed. Development and improvement of bioinformatics software that are open source and freely available have accelerated the SNP discovery while reducing the associated cost. Key considerations for SNP filtering and associated pipelines are discussed in specific topics. A list of commonly used software and their sources is compiled for easy access and reference.

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1. Introduction

Molecular markers are widely used in plant genetic research and breeding. Single Nucleotide Polymorphisms (SNPs) are currently the marker of choice due to their large numbers in virtually all populations of individuals. The applications of SNP markers have clearly been demonstrated in human genomics where complete sequencing of the human genome led to the discovery of several million SNPs [1] and technologies to analyze large sets of SNPs (up to 1 million) have been developed. SNPs have been applied in areas as diverse as human forensics [2] and diagnostics [3], aquaculture [4], marker assisted-breeding of dairy cattle [5], crop improvement [6], conservation [7], and resource management in fisheries [8]. Functional genomic studies have capitalized upon SNPs located within regulatory genes, transcripts, and Expressed Sequence Tags (ESTs) [9, 10]. Until recently large scale SNP discovery in plants was limited to maize, Arabidopsis, and rice [11–15]. Genetic applications such as linkage mapping, population structure, association studies, map-based cloning, marker-assisted plant breeding, and functional genomics continue to be enabled by access to large collections of SNPs. Arabidopsis thaliana was the first plant genome sequenced [16] followed soon after by rice [17, 18]. In the year 2011 alone, the number of plant genomes sequenced doubled as compared to the number sequenced in the previous decade, resulting in currently, 31 and counting, publicly released sequenced plant genomes (http://www.phytozome.net/). With the ever increasing throughput of next-generation sequencing (NGS), de novo and reference-based SNP discovery and application are now feasible for numerous plant species.

Sequencing refers to the identification of the nucleotides in a polymer of nucleic acids, whether DNA or RNA. Since its inception in 1977, sequencing has brought about the field of genomics and increased our understanding of the organization and composition of plant genomes. Tremendous improvements in sequencing have led to the generation of large amounts of DNA information in a very short period of time [19]. The analyses of large volumes of data generated through various NGS platforms require powerful computers and complex algorithms and have led to a recent expansion of the bioinformatics field of research. This book chapter focuses on the a priori discovery of SNPs through NGS, bioinformatics tools and resources, and the various downstream applications of SNPs.

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2. History and Evolution of Sequencing Technologies

2.1. Invention of Sequencing

In 1977, two sequencing methods were developed and published. The Sanger method is a sequencing-by-synthesis (SBS) method that relies on a combination of deoxy- and dideoxy-labeled chain terminator nucleotides [20]. The first complete genome sequencing, that of bacteriophage phi X174, was achieved that same year using this pioneering method [21]. The chemical modification followed by cleavage at specific sites method also published in 1977 [22] quickly became the less favored of the two methods because of its technical complexities, use of hazardous chemicals, and inherent difficulty in scale-up. In contrast, the Sanger method, for which Frederick Sanger was awarded his second Nobel Prize in chemistry in 1980, was quickly adopted by the biotechnology industry which implemented it using a broad array of chemistries and detection methods [19].

2.2. Sequencing Technologies

In the last decade, new sequencing technologies have outperformed Sanger-based sequencing in throughput and overall cost, if not quite in sequence length and error rate [23]. This section will focus on the three main NGS platforms as well as the two main third-generation sequencing (TGS) platforms, their throughput and relative cost. We made every effort to ensure the accuracy of the data at the time of submission. However, the cost and throughput of these sequencing platforms change rapidly and, as such, our analysis only represents a snapshot in time. The flux of innovation in this field imposes a need for constant assessment of the technologies' potentials and realignment of research goals.

2.2.1. Roche (454) Sequencing

Pyrosequencing was the first of the new highly parallel sequencing technologies to reach the market [24]. It is commonly referred to as 454 sequencing after the name of the company that first commercialized it. It is an SBS method where single fragments of DNA are hybridized to a capture bead array and the beads are emulsified with regents necessary to PCR amplifying the individually bound template. Each bead in the emulsion acts as an independent PCR where millions of copies of the original template are produced and bound to the capture beads which then serve as the templates for the subsequent sequencing reaction. The individual beads are deposited into a picotiter plate along with DNA polymerase, primers, and the enzymes necessary to create fluorescence through the consumption of inorganic phosphate produced during sequencing. The instrument washes the picotiter plate with each of the DNA bases in turn. As template-specific incorporation of a base by DNA polymerase occurs, a pyrophosphate (PPi) is produced. This pyrophosphate is detected by an enzymatic luminometric inorganic pyrophosphate detection assay (ELIDA) through the generation of a light signal following the conversion of PPi into ATP [25]. Thus, the wells in which the current nucleotides are being incorporated by the sequencing reaction occurring on the bead emit a light signal proportional to the number of nucleotides incorporated, whereas wells in which the nucleotides are not being incorporated do not. The instrument repeats the sequential nucleotide wash cycle hundreds of times to lengthen the sequences. The 454 GS FLX Titanium XL+ platform currently generates up to 700 MB of raw 750 bp reads in a 23 hour run. The technology has difficulty quantifying homopolymers resulting in insertions/deletions and has an overall error rate of approximately 1%. Reagent costs are approximately $6,200 per run [26].

2.2.2. Illumina Sequencing

Illumina technology, acquired by Illumina from Solexa, followed the release of 454 sequencing. With this sequencing approach, fragments of DNA are hybridized to a solid substrate called a flow cell. In a process called bridge amplification, the bound DNA template fragments are amplified in an isothermal reaction where copies of the template are created in close proximity to the original. This results in clusters of DNA fragments on the flow cell creating a “lawn” of bound single strand DNA molecules. The molecules are sequenced by flooding the flow cell with a new class of cleavable fluorescent nucleotides and the reagents necessary for DNA polymerization [27]. A complementary strand of each template is synthesized one base at a time using fluorescently labeled nucleotides. The fluorescent molecule is excited by a laser and emits light, the colour of which is different for each of the four bases. The fluorescent label is then cleaved off and a new round of polymerization occurs. Unlike 454 sequencing, all four bases are present for the polymerization step and only a single molecule is incorporated per cycle. The flagship HiSeq2500 sequencing instrument from Illumina can generate up to 600 GB per run with a read length of 100 nt and 0.1% error rate. The Illumina technique can generate sequence from opposite ends of a DNA fragment, so called paired-end (PE) reads. Reagent costs are approximately $23,500 per run [26].

2.2.3. Applied Biosystems (SOLiD) Sequencing

The SOLiD system was jointly developed by the Harvard Medical School and the Howard Hughes Medical Institute [28]. The library preparation in SOLiD is very similar to Roche/454 in which clonal bead populations are prepared in microreactors containing DNA template, beads, primers, and PCR components. Beads that contain PCR products amplified by emulsion PCR are enriched by a proprietary process. The DNA templates on the beads are modified at their 3′ end to allow attachment to glass slides. A primer is annealed to an adapter on the DNA template and a mixture of fluorescently tagged oligonucleotides is pumped into the flow cell. When the oligonucleotide matches the template sequence, it is ligated onto the primer and the unincorporated nucleotides are washed away. A charged couple device (CCD) camera captures the different colours attached to the primer. Each fluorescence wavelength corresponds to a particular dinucleotide combination. After image capture, the fluorescent tag is removed and new set of oligonucleotides are injected into the flow cell to begin the next round of DNA ligation [19]. This sequencing-by-ligation method in SOLiD-5500x1 platform generates up to 1,410 million PE reads of 75 + 35 nt each with an error rate of 0.01% and reagent cost of approximately $10,500 per run [26].

Although widely accepted and used, the NGS platforms suffer from amplification biases introduced by PCR and dephasing due to varying extension of templates. The TGS technologies use single molecule sequencing which eliminates the need for prior amplification of DNA thus overcoming the limitations imposed by NGS. The advantages offered by TGS technology are (i) lower cost, (ii) high throughput, (iii) faster turnaround, and (iv) longer reads [19, 29]. The TGS can broadly be classified into three different categories: (i) SBS where individual nucleotides are observed as they incorporate (Pacific Biosciences single molecule real time (SMART), Heliscope true single molecule sequencing (tSMS), and Life Technologies/Starlight and Ion Torrent), (ii) nanopore sequencing where single nucleotides are detected as they pass through a nanopore (Oxford/Nanopore), and (iii) direct imaging of individual molecules (IBM).

2.2.4. Helicos Biosciences Corporation (Heliscope) Sequencing

Heliscope sequencing involves DNA library preparation and DNA shearing followed by addition of a poly-A tail to the sheared DNA fragments. These poly-A tailed DNA fragments are attached to flow cells through poly-T anchors. The sequencing proceeds by DNA extension with one out of 4 fluorescent tagged nucleotides incorporated followed by detection by the Heliscope sequencer. The fluorescent tag on the incorporated nucleotide is then chemically cleaved to allow subsequent elongation of DNA [30]. Heliscope sequencers can generate up to 28 GB of sequence data per run (50 channels) with maximum read length of 55 bp at ~99% accuracy [31]. The cost per run per channel is approximately $360.

2.2.5. Pacific Biosciences SMART Sequencing

The Pacific Biosciences sequencer uses glass anchored DNA polymerases which are housed at the bottom of a zero-mode waveguide (ZMW). DNA fragments are added into the ZMW chamber with the anchored DNA polymerase and nucleotides, each labeled with a different colour fluorophore, and are diffused from above the ZMW. As the nucleotides circulate through the ZMW, only the incorporated nucleotides remain at the bottom of the ZMW while unincorporated nucleotides diffuse back above the ZMW. A laser placed below the ZMW excites only the fluorophores of the incorporated nucleotides as the ZMW entraps the light and does not allow it to reach the unincorporated nucleotides above [32]. The Pacific Biosciences sequencers can generate up to 140 MB of sequences per run (per smart cell) with reads of 2.5 Kbp at ~85% accuracy. The cost per run per smart cell is approximately $600.

Among the TGS technologies, Pacific Biosciences SMART and Heliscope tSMS have been used in characterizing bacterial genomes and in human-disease-related studies [31]; however, TGS has yet to be capitalized upon in plant genomes. The Heliscope generates short reads (55 bp) which may cause ambiguous read mapping due to the presence of paralogous sequences and repetitive elements in plant genomes. The Pacific Biosciences reads have high error rates which limit their direct use in SNP discovery. However, their long reads offer a definite advantage to fill gaps in genomic sequences and, at least in bacterial genomes, NGS reads have proven capable of “correcting” the base call errors of this TGS technology [33–36]. Hybrid assemblies incorporating short (Illumina, SOLiD), medium (454/Roche), and long reads (Pac-Bio) have the potential to yield better quality reference genomes and, as such, would provide an improved tool for SNP discovery.

The choice of a sequencing strategy must take into account the research goals, ability to store and analyze data, the ongoing changes in performance parameters, and the cost of NGS/TGS platforms. Some key considerations include cost per raw base, cost per consensus base, raw and consensus accuracy of bases, read length, cost per read, and availability of PE or single end reads. The pre- and postprocessing protocols such as library construction [37] and pipeline development and implementation for data analysis [38] are also important.

2.3. RNA and ChIP Sequencing

Genome-wide analyses of RNA sequences and their qualitative and quantitative measurements provide insights into the complex nature of regulatory networks. RNA sequencing has been performed on a number of plant species including Arabidopsis [39], soybean [40], rice [41], and maize [42] for transcript profiling and detection of splice variants. RNA sequencing has been used in de novo assemblies followed by SNP discovery performed in nonmodel plants such as Eucalyptus grandis [43], Brassica napus [44], and Medicago sativa [45].

RNA deep-sequencing technologies such as digital gene expression [46] and Illumina RNASeq [47] are both qualitative and quantitative in nature and permit the identification of rare transcripts and splice variants [48]. RNA sequencing may be performed following its conversion into cDNA that can then be sequenced as such. This method is, however, prone to error due to (i) the inefficient nature of reverse transcriptases (RTs) [49], (ii) DNA-dependent DNA polymerase activity of RT causing spurious second strand DNA [50], and (iii) artifactual cDNA synthesis due to template switching [51]. Direct RNA sequencing (DRS) developed by Helicos Biosciences Corporation is a high throughput and cost-effective method which eliminates the need for cDNA synthesis and ligation/amplification leading to improved accuracy [52].

Chromatin immunoprecipitation (ChIP) is a specialized sequencing method that was specifically designed to identify DNA sequences involved in in vivo protein DNA interaction [53]. ChIP-sequencing (ChIP-Seq) is used to map the binding sites of transcription factors and other DNA binding sites for proteins such as histones. As such, ChIP-Seq does not aid SNP discovery, but the availability of SNP data along with ChIP-Seq allows the study of allele-specific states of chromatin organization. Deep sequence coverage leading to dense SNP maps permits the identification of transcription factor binding sites and histone-mediated epigenetic modifications [54]. ChIP-Seq can be performed on serial analysis of gene expression (SAGE) tags or PE using Sanger, 454, and Illumina platforms [55, 56].

The DNA, RNA, and ChIP-Seq data is analysed using a reference sequence if available or, in the absence of such reference, it requires de novo assembly, all of which is performed using specialized software, algorithms, pipelines, and hardware.

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3. Computing Resources for Sequence Assembly

The next-generation platforms generate a considerable amount of data and the impact of this with respect to data storage and processing time can be overlooked when designing an experiment. Bioinformatics research is constantly developing new software and algorithms, data storage approaches, and even new computer architectures to better meet the computation requirements for projects incorporating NGS. This chapter describes the state-of-the-art with respect to software for NGS alignment and analysis at the time of writing.

3.1. Software for Sequence Analysis

Both commercial and noncommercial sequence analysis software are available for Windows, Macintosh, and Linux operating systems. NGS companies offer proprietary software such as consensus assessment of sequence and variation (Cassava) for Illumina data and Newbler for 454 data. Such software tend to be optimized for their respective platform but have limited cross applicability to the others. Web-based portals such as Galaxy [57] are tailored to a multitude of analyses, but the requirement to transfer multigigabyte sequence files across the internet can limit its usability to smaller datasets. Commercially available software such as CLC-Bio (http://www.clcbio.com/) and SeqMan NGen (http://www.dnastar.com/t-sub-products-genomics-seqman-ngen.aspx) provide a friendly user interface, are compatible with different operating systems, require minimal computing knowledge, and are capable of performing multiple downstream analyses. However, they tend to be relatively expensive, have narrow customizability, and require locally available high computing power. A recent review by Wang et al. [58] recommends Linux-based programs because they are often free, not specific to any sequencing platform, and less computing power hungry and, as a consequence, tend to perform faster. Flexibility in the parameter's choice for read assembly is another major advantage. However, most biologists are unfamiliar with Linux operating systems, its structure and command lines, thereby imposing a steep learning curve for adoption. Linux-based software such as Bowtie [59], BWA [60], and SOAP2/3 [61] have been used widely for the analysis of NGS data. Other software may not have gained broad acceptance but may have unique features worth noting. For reviews on NGS software, see Li and Homer [62], Wang et al. [58], and Treangen and Salzberg [63]. Characteristics of the most common NGS software and their attributes are listed in Table 1, and their download information can be found in Table 4.

Hi Jialiang Lin

amplicons panels need to be sure samples are in good shape and designed amplicons not too long. dis you test quality of your samples (DIN or RIN) and how long are your amplicons?

fred

You are correct in your understanding. When you calculate pairwise FST estimates and derive confidence intervals (CIs) using the bootstrapping technique, you estimate parameters rather than conduct hypothesis tests. Therefore, there is no need to correct the CIs for multiple testing.

Multiple testing correction is typically applied when conducting hypothesis tests that involve multiple comparisons (e.g., testing for significant differences between groups). The purpose of correction methods, such as the Bonferroni correction or false discovery rate (FDR) control, is to control the overall false positive rate when testing multiple hypotheses simultaneously.

In your case, you are estimating parameters (pairwise FST values) and constructing confidence intervals around these estimates to quantify the uncertainty. The CIs provide a range of plausible values for the parameters, and they reflect the precision of your estimates based on the bootstrapping procedure. These intervals indicate how reliable your estimates are and allow you to draw appropriate conclusions from your data.

In summary, since you are not conducting hypothesis tests and focusing on parameter estimation instead, there is no need to correct the confidence intervals for multiple testing.

1000 Bull Genomes Project aimed to sequence the genomes of a large number of cattle from various breeds and populations. The project provides a database of genetic variants, including validated SNPs, which you can access to find relevant variants for your study.

check dbSNP ,u may find the proportion for each group of population in RIO.

Dear Gregor,

With the bigger genome size (in humans, animals, or plants), you need more and more positions to detect significantly associated variants. It depends on your pre-specified distance through the genome for selecting the SNPs. But the thing is, you or the machine that generated the data should evenly pick up the positions through the genome. This condition, evenly distributed, is more important than the number of SNPs.

So, If the number of SNPs in your research is low, you will expect to find fewer variants that are significant signals or even new! In other words, the chances of winning will decrease.

Hopefully helpful.

Best,

Last time I ran GAPIT using BLINK, I got a file, 'GAPIT.Sex.PVE_by_Association_Markers', and I had a student run it a few weeks ago and still got the file, are you not getting that?

Which R version, GAPIT version and algorithm are you running? Feel free to reach out to me

Thank you.

I use Tassel to analyse SNPs panel. It can provide the similar result compared with Plink

One limitation is that you can't "phase" the SNPs using Sanger because they all show as a double-peak.

If you have C/T at one site and G/C at another, there is no way to know if the correct pairings are:

Site 1 C with Site 2 G

or

Site 1 C with Site 2 C

Long-read, single molecule sequencing would answer that question. Or the "old-school" method of cloning and sequencing.

Good luck!

You can also use other tools provided by NCBI, such as the Variation Viewer, to download SNPs.

Hello,

Yes this is the best description and I already assumed that, but still not sure! or I am just complicating the whole subject!

Thanks a lot

As someone that gets given data for similar studies (fingerprinting clonal crops) from lots of different providers I think it's best to just reach out to the companies for quotes.

DArT's new technology DArTag seems to be very popular right now rather than DArTseq.

LGC is offering FlexSeq, CaptureSeq and SNPseq (disappearing soon, but the technology is also offered as Allegro by Tecan if you look up SPET). FlexSeq may actually be the best for you since you will be able to construct haplotypes that then could be more powerful than microsats. LGC also offer KASP assays if you just want SNPs (if you bring this in house and use PACE it can be very cheap). LGC also have European and US centers whereas I'm not sure if DArT have centers exclusively in Australia (I doubt it considering the amount of studies using that platform).

Good luck! Happy to give more details if needed

Thank you for your answer,

But I've read the manual several times and it presents only six formats that Haploview can accept. The manual doesn't talk about what to do if you have DNA sequences aligned in fasta format.

When I import the sequences I have saved in phased format with the DnaSP software it gives me an error.

It is possible to convert PL values to genotype probabilities using the phred-scaled likelihoods. The process involves applying the phred-scaled likelihoods to the Hardy-Weinberg equilibrium equation, which is a mathematical model used to describe the distribution of genotypes in a population. The equation is as follows:

p^2 = p(AA) 2pq = p(Aa) q^2 = p(aa)

where p is the frequency of the A allele and q is the frequency of the non-A allele. The genotype probabilities can be obtained by solving this equation for p(AA), p(Aa), and p(aa) using the PL values as input.

One way to do this is to use the package "VCFR" which provide functions to read and manipulate VCF files in R.

Another way is to use the package "GenotypeIO" which provides utility functions for reading and writing genotype data in R.

You can also use the package "SNPRelate" which has a function "gtl2gprob()" that converts genotype likelihoods to genotype probabilities.

be aware of organising the files for each package. you may need to adjust accordingly.

Hi Jose

when you go to the UCSC website (http://genome.ucsc.edu) and search for your genes of interest in its genome browser, there is an option for viewing the SNP from the database you want.

you can also get those informations by digging in the table browser as xls files.

all the best

fred

go To snpdb and put in the name ( eg rs17102287) or sequence and search. Then select genomic position and you will get a lot of sequence either side of your snp but if you know the gene and want its accession number then search " accession number for (gene name)" and select gene cards and scroll down for identifiers

for the second part of your problem, you can try vcf2gwas pipeline, that is very easy to run as a docker image

Thanks Puneetpal for clearing my doubts

Hi

you can see this one:

all the best

fred

You may parse SNPs from the NGS sequencing reads using scikit-allel.

Hi Kay

some changes have been done during evolutions of the human genomes assemblies. in the last genome all sequences are now published and therefore some position have changed.

just go to the UCSC genome browser (http://genome.ucsc.edu), search for your genes or region of interest, choose the CHM13 version of the genome and open the optional track named "cactus alignment" to get an idea on what happened.

maybe it's just a problem of positions, you must use the rsid prior to their positions.

all the best

fred

Hi Danila

you can dig in the dbSNP tool from NCBI (https://www.ncbi.nlm.nih.gov/snp/rs28360521) and pick some data (the link shows you an example of one snp).

all the best

fred