I will see about setting up a Google. Docs version and re- post here later. Signal Processing Sophocles J. 11.2 First-Order Lowpass and Highpass Filters, 566. The Scientist and Engineer's Guide to. 2 The Scientist and Engineer's Guide to Digital Signal Processing. Signal Processing First 1st Edition.

Bored to death seeing public releases of sequencing runs from E. Coli coming off desktop sequencers? Today, Life Technologies released through the Ion Community a sequencing run that wasn’t from E.

Thank goodness. Ion Torrent released a human shotgun sequencing run from (aka Venter published in ). Unfortunately, it was only two separate runs (C18-99 and C24-141) from a 318 chip so has bugger all in terms of coverage. I am extremely grateful for the release of the data set (kudos to Matt in Life Tech for the early access:D), though it would have been much nicer if they released results from a custom capture because at least it wouldn’t be totally useless for analysis.

However, all is not lost as the coverage from the mitochondrial genome was sufficient to do some analysis (Figure 1). Shows the coverage (determined by BEDtools) from using the two supplied BAMs.

The coverage output for bwa-sw aligned BAMs produces an almost identical coverage and produces a bit of a mess when it is plotted also. Mitochondrial variants What’s made the analysis of the mitochondrial genome (chrM) a bit annoying is that the two BAM files supplied don’t appear to be aligned to a hg19 chrM reference. According to the BAM header the chrM it was aligned to was 16569 bases in length (@SQ SN:chrM LN:16569). This is two bases less than the or obtained from UCSC Genome Browsers. Since I was missing this version of chrM I decided to create my own alignments using bwa-sw, in addition to using tmap and subsequent base calling performed by the The table below shows the results (tmap alignment of C24 is missing as it was still grinding away, while writing this 😕 Variants Called C18-99 (VCF supplied) 29 (including 3 INDELS) C24-141 (VCF supplied) 30 (including 3 INDELS) C18-99 (bwa-sw/GATK) 41 C24-141 (bwa-sw/GATK) 39 C18-99 (tmap/GATK) 35 It is hard to determine the overlap between the variants called by the supplied VCF (i.e.

By mpileup) and the ones called by GATK as the differences between the two reference chrM creates an off by 1-2 base difference in the coordinates. On inspection the majority of the mpileup calls are due to differences between the two references evident in what is marked as the ref or alt base. Below is the Venn diagram showing the variants called by GATK between the two runs. Using the, the variants outside the intersection had reads supporting it on the run which it was not called on. The only exception is the 10279C>A variant, which appears to have from each of the sequencing runs. Relationship between the variants called on chrM from the two runs (i.e.

C18 and C24). Ideally all variants should be in the intersection.

One noticeable difference is that GATK although the -glm BOTH option was turned on, did not call any insertions or deletions (INDELS) on chrM. Using Integrative Genomics Viewer (IGV), there does not appear to be enough reads supporting the In contrast, the deletion at position 9905 had However, there appears to be an unusual amount of areas surrounding it in the form of colored bars (i.e. Undercalls/overcalls) and black lines (deletions). For those that haven’t used IGV before, the bars/lines running horizontally are the reads which are mostly colored grey as they usually completely match with the reference. Systematic Biases?

A public release of data would not be complete unless it included an E. Coli data set. This release included 194X coverage PGM run from a 318 chip (C22-169). Despite the very high coverage, the supplied VCF file showed there were 36 INDELS, which were all deletions.

There seems be a bias in undercalling G or C bases as they account for 33/36, while 4/36 were A or T undercalls. There was a deletion that involved undercalling both a G and a T and hence the appearance that I can’t add. 😳 These variants were counted manually and without a calculator so there may be a mistake anyways 🙂 Using IGV, I had a look at the sequence context for the A/T undercalls. All three (,, ) have the exact same sequence context, that is AAAATTTT (click on each link to view IGV screen shot).

There is a possibility that errors in mapping to low complexity or repetitive regions may also explain some of these instances. Using the same methodology to identify the G/C undercalls, will help to identify the systematic biases that still remain in terms of base calling. This in combination with Torrent Scout and the wealth of Test Fragments data available would be a good avenue to pursue for the Accuracy challenge. I’ll insert some details on the methods a little later.

Next week I’ll post regarding the contributions signal processing and base calling make in regards to accuracy. Until then back to my PhD thesis and having no life 😥 materials and methods The hg19 reference file labelled ucsc.hg19.fasta was taken from the GATK 1.3 resource bundle directory. Back from my North East American trip and still jet lagged so I’ll return to the blog sphere with a non-technical post. The term “democratizing sequencing” is synonymous with the Ion Torrent. This probably doesn’t mean Life Technologies are pitching to a bunch of hippie scientist trying to relive the 70s but what does it mean instead? The definitions of “democracy” usually refers to a form of government so this general definition would be more suitable – “The practice or principles of social equality”. This post will cover the following components of social equality: Economical equality, Freedom of speech and Freedom of information.

This month has seen a massive effort introducing initiatives to emphasize these components. Economical equality This shows the positions of where all the next generation sequencers are in the world. This requires the facility to self report so is not entirely accurate but is close because people like to brag 🙂 There are two things you may notice on this map: • The richer countries tend to have more sequencers. Copilot Live Laptop 10 Keygen Idm. This is not surprising as they tend to have more of everything including obese people 😛 • Within each country it tends to be the richer Institutes and Universities that have these machines.

In the case of my home city, Sydney there are three sites with us way out in suburbia. Given the correlation between high impact publications and next generation sequencing, why aren’t there more in Sydney? Simple answer, it costs at least 1 million dollars to build the infrastructure and then there is on going costs. In Australia, this would require many investigators to get together to apply for a massive grant. Too many egos involved and that’s why it rarely happens. The other alternative is to sell 2 million dollars worth of This would require you to sell one chocolate to approximately every adult in Sydney. If this charity model is successful, we will have an even bigger type 2 diabetes problem 😥 What most researchers in Australia have to settle for is sending samples to Sequencing centres such as the Ramaciotti Centre and the Australian Genome Research Facility (AGRF), which provide a great service for Australian researchers.

Then why get a sequencer, most researchers ask? We got a sequencer as a way of controlling each step of the workflow and more importantly the time frames in which projects can be completed. Ever collaborated in Science before? Felt disappointed how long things take? Well you are not the only one!! Then you would understand why controlling time frames is SO important for scientist. Most have realized this but never have had the money to act upon it.

The Ion Torrent marketed at $USD 50 K is the first time a lab in Australia can seriously say lets get a sequencing machine. The Illumina MiSeq and Roche Junior are also competitively priced. A carefully planned strategy aligned with local sequencing facilities will now give everyone an equal opportunity to publish in good genetics journals as economics is no longer a barrier.

Freedom of speech The advent of the Internet has amplified the freedom of speech of everyone! Something we should not take for granted. In the past (i.e. Early 90s), if I wanted to communicate information I would use the following: • Publish a book, journal article, TV or radio • Local newspaper, public notice boards and town hall meetings • Letter box drops • Tell my mom! There would be no way a teenager would have the ability to use the first option of communication if all they wanted to say was that “they had an epic ” or a recording of them “owning a n00b on “. Unfortunately they now can, it’s called Twitter, Facebook and YouTube 😛 Life Technologies has embraced the Internet and freedom of speech through the. This site allows members to provide feedback and problems that they are having on the Ion Torrent.

The comments made by members is NOT censored in any way. This allows people like me to say absolutely whatever they want. Most of the time I alternate between skeptic hater and annoying bug. Many are still afraid to speak their minds or even contribute which is a shame. It is good to say stuff but is worthless if you can not get to your targeted audience. In other words, the reason why you complain is because you want something to be done.

From my experiences, Life Technologies are very fast to respond to comments and try their best to help. In addition, Ion Torrent is providing strong support to the blogging community. This takes on the form of early access to data and resources allowing bloggers to do what they do best review and complain 😀 The release of affordable sequencing technology has seen a massive explosion in technical blogging. I think there are few reasons for this: • First and foremost it’s affordable, therefore a lot of people want to know more about it and want the opinion of the wise Internet. No one nowadays goes to a restaurant, hotel or buys anything without reading a review on the Internet. Next generation sequencing is no different! • It may be Science but no one can wait for a suppressed report in a journal article which usually goes something like this “we suggest perhaps maybe the Ion Torrent would be good for X, however further research will be required”.

• The release of publicly available data set and for the first time in the history of Biotech the exact data set used to generate the application notes and brochures! This is a gold mine for reviewing and complaining 😀 • The support of Life Technologies, Illumina and Roche. Some more than others. I think they have realized bloggers are like good global marketers, the only difference is we pay them absolutely nothing and people tend to believe them more! Lastly, the greatest display of freedom of speech is allowing me to present at the Ion User Group Meeting.

Putting everything in context, I am only a PhD student and quite unpredictable at times. I was given carte blanche so really could have said anything I felt like during the 10 minutes.

Saying “I was busting to take a piss” during my talk shows I had freedom of speech. Freedom of information Currently Biotech companies have two types of customers, their preferred ones and the rest of their customers. The preferred customers usually get access to technology and information that other customers will see on a later date. How do they pick these preferred customers?

But I know one thing that these customers are usually the richer ones that can afford to do field testing for them. Having this information early gives these preferred customers an unfair advantage in terms of producing preliminary data for grant applications.

These are usually the institutes that DO NOT require an advantage to compete for grants. This model is extremely non-democratic and not COOL:(, although makes economical sense to Biotech companies. There are two initiatives which Ion Torrent launched recently: • • In each of these initiatives, all customers are treated equally and therefore will be provided information whether they are a preferred customers or not. The main emphasis is on giving back to the community, in other words sharing what you have learned while having early access to the technology.

A huge difference to using it to benefit only yourself! This will definitely rock the boat amongst the preferred customers but is the only way democracy and freedom of information can be achieved. Illumina being more established in sequencing will have a very difficult time doing this assuming they actually care about democratizing sequencing.

You can put a pipette (noun) in the hand of the scientist but you can’t make them pipette (verb)! The paradigm shift in the business model implemented by Life Technologies is contingent upon Ion Torrent PGM purchases and the success of the Ion Community. In order to help with the steep learning curve required for sample preparation, Ion Torrent has an The emphasis again is to give back to the community what you have learned. This will greatly help small labs like ours to develop successful workflows in order for us to produce preliminary data so we can be competitive for large government grants. The grant program is a great incentive to buy a PGM over the MiSeq or Junior. The Ion Community like all online forums and communities in general suffer from the problem of participation. It’s in human nature to be more take than give.

Due to internet lurking, forums typically follow the or the 90:9:1 rule. 1% contribute, 9% edits/moderates, 90% just view. The Ion Community despite it’s steady increase in membership suffers from this same problem. It is no surprise the most active thread is the one where you get to boast how great your chip runs are with the possibility of winning a pack of chips. Thankfully, Ion Torrent has learned from this and have introduced an initiative called. A program which aims to reward regular contributors. This reward system was extremely successful in the Sun Java forum I used to frequent to complain on.

I nearly earned myself a free T-shirt 😦 Some people’s problems are just too difficult! Despite it’s extremely lame name, RecogitION will make for a more successful active community. Scientist have recognized Ion Torrent through Semi-conductors as revolutionizing sequencing. After everything is said and done, it may be recognized instead as the first Biotech to make a bold move in embracing the Internet culture and what it stands for DEMOCRACY.

Disclaimer: For the good of all mankind! This is purely my opinion and interpretations.

I dedicate this post to the in Rock Hall, Maryland. I try to send you bankrupt by eating all the crabs only got to number 6 😦. This is the second post of what is now to be a four part series looking at how Ion Torrent accuracy has improved over time. In this edition, I will show what a massive difference software can make with this technology.

The results presented here was only possible because the software is open source. In addition, Mike and Mel have given me early access to binaries (ion-Analysis v1.61) that will be released in Torrent Suite v1.5. That’s a huge thank you to Mel and Mike! There are three major areas that software can improve • Throughput – Identify more Ion Sphere Particles (ISPs) with library fragments therefore increasing the total number of reads.

• Quality – More and longer reads aligning back to the reference sequence. • Speed – Reduce the computational time to analyze the data. The way I am going to present this data is to keep the data set the same (i.e. Input DAT files) BUT perform the analysis using the different versions of the software, i.e. The ion-Analysis binary/software is responsible for ISP finding, signal processing and base calling. I have discussed signal processing and base calling in my previous blog posts. I have also briefly touched on bead finding ISPs but will go into more detail in my Signal Processing blog series.

The three versions I have used are: • ion-Analysis v1.40 (from Torrent Suite v1.3) REL: 20110414 • ion-Analysis v1.52 (from Torrent Suite v1.4) REL: 20110712 • ion-Analysis v1.61 ( pre-release Torrent Suite v1.5) DATED: 20110914 Method // The datadir contains a 314 Run of the DH10B library. In this stand alone blog post, I will attempt to detail the predicted quality value (phred scoring) algorithm that the Ion Torrent is currently using. As the quality values is one of the battlegrounds the Next Generation Sequencing wars (Clone Wars is way cooler!) are currently being fought, it would be good to explain the difficulty in using this as a benchmark. Illumina has fought this battle on the predicted quality values. This is a good ground to have a fight on considering Illumina’s prediction algorithm is mature and is quite good at predicting the empirical quality (Figure 1). Illumina has pointed this out in their A good prediction algorithm is good if there is no reference sequence to compare your target against (aka de novo sequecing). In addition, “the point of predicted accuracy is that many tools use this in their calculations.

The more accurate these estimates, the happier those tools are. Of course, you can always go and recalibrate everything, but that’s an extra step one would rather avoid.” (Thanks Keith for the input taken from the ).

Ion Torrent has fought on the empirical quality battleground. Their argument is who cares what the predicted values are, actual values are more important. This is a great point, given Economist spend most of their time explaining why things they predicted yesterday didn’t happen today. On the rare occasion when they get it right their ego expands faster than the rate the Universe expanded slightly after the big bang!

😀 The reason why Ion Torrent has fought this battle on the empirical battleground is mainly due to the current weakness in their quality prediction algorithms (Figure 2). Illumina phred score prediction is closer to the empirically derived values. This is read 1 from the Figure 2. The Ion Torrent prediction algorithm under predicts quality by approximately 10 phred points. Since Ion Torrent have released the source code, I am able to interpret how per base quality values have been calculated. These quality values are determined after carry forward, incomplete extension and droop correction (aka CAFIE or Phase correction).

The quality values are recorded with the corrected signal incorporation in the SFF file. Please note all equations are MY INTERPRETATION of the source code and since I didn’t write the code, I am probably incorrect sometimes. Big thanks to Eugene (see comments below) from Life Technologies for correcting and providing an example for Predictors 4 and 5.

There are six metrics that are used to predict the per base quality values: • Residue (float)- distance the corrected incorporation value is from the nearest integer. • Local noise (float) – maximum residue amongst the previous, current and next corrected incorporation value. Radius of 3 bases. • Global noise (float) – Calculated from the mean and standard deviation of all zero-mer and 1mer signals for this well/read. • “The homopolymer length, but it is assigned to the last base in the homopolymer (since there is a much higher chance of being off by 1 in the homopolymer length than by 2 or more).” All other bases in the homopolymer are assigned the value 1. • “The homopolymer length the last time this nucleotide was incorporated – this basically a penalty for incomplete incorporation.” • Local noise (float) – Calculated similar to (2) but with a radius of 10 bases.

An example of predictors 4 and 5 is detailed below: A A A A T A C C C 1 1 1 4 1 1 1 1 3 (Predictor 4) 0 0 0 0 0 4 0 0 0 (Predictor 5) Note: Predictor 5 is dependent on flow order so in the above case it depends where in the 32 redundant flow cycle these bases were called. Once these six metrics have been calculated for this flow/base call these values are compared to a empirically derived phred table (Note: each flow produces a base call, many just have a value of zero). There is currently two versions for this phred lookup table. The comparison is made from the top of the table (i.e. Phred score 33) and works it’s way down until the six metrics are below the minimum criteria for that phred score. The maximum phred score is 33, while the minimum is 7 and 5 for phred table versions 1 and 2, respectively. As the Ion Torrent is quite new, it is understandable that the phred scoring algorithm still needs more calibration.

Therefore, it is quite unfair to compare Illumina predicted QVs against the Ion Torrent one. Disclaimer: For the good of all mankind! This is purely my opinion and interpretations. This is an independent analysis using Novoalign kept simple so others can reproduce the results.

This is the first part of a planned three part blog series on Ion Torrent signal processing. In this first part I will discuss the important aspects of the background and foreground model using key mathematical equations and pseudo code. In the second part, I will outline the high level process of signal processing which includes the key parameters that must be fitted.

In the final part, I will discuss the major assumptions and where the model breaks down. The goal of Ion Torrent signal processing is summarize time series data (Figure 1) into just ONE value which is then stored in the 1.wells file. The 454 equivalent is the.cwf files (thanks flxlex), however the difference is that Life Technologies has made their signal processing OPEN through the release of their source code. Without the source code, I would just be speculating in this blog series. So yay to available source code and kudos goes to Ion Community contributors Mel, Mike and particularly Simon for answering all my questions in great detail. In my opinion signal processing is the root cause of two problems: • Reads that must be filtered out due to poor signal profile*.

This can account up to 30% of the reads as observed in the long read data set that was released. • The resulting base call particular towards the end of the reads. There is only so much signal normalization and correction (covered in Fundamentals of Base Calling series) that can be performed. Therefore, improvements made will have the biggest effect on improving accuracy and increase the amount of reads. In other words, if you improve on this you can have ONE million dollars.

Ion Torrent – Signal Challenge The major challenge of signal processing is that the foreground signal is not much bigger than the background signal. This is like trying to have a conversation with someone in a crowded noisy bar with loud music. This is very difficult but not impossible. Two reasons why it is possible: • You start getting used to the background sound and learn to ignore it. • You know how your friend sounds like and focus on only the key words in the sentence. In reality though I refuse to try and instead nod my head away pretending to listen 😛 However, the Ion Torrent signal processing works on a similar principle. Uncorrected signal from the first 100 flows from a live well.

This was from a 4 flow cycle (Q1 2001) and thus 25 flows per nucleotide. If you look hard enough there are small bumps between 1500-2000 ms that represent nucleotide incorporation. A typical baseline corrected measurement from an occupied well (red) and an adjacent empty well (black). The tiny red bump between 1500-2000 ms represent a nucleotide incorporation. Background Model The background model aims to approximate how the signal will look like for a given flow if there was NO nucleotide incorporation. The problem is what to use as a point of reference.

The best and intuitive source is a zero-mer signal from the well itself as this would encapsulate all the well specific variance and parameters. A known zero-mer signal can be taken from the key flows (i.e. First 7 flows). The only draw back is that each well is a dynamic system which changes over time due to slight variance in flow parameters and changing state of the system.

Another possibility is to re-estimate the zero-mer signal every N flows. The problem with this approach is that later on there will be no TRUE zero-mer signal as there will be contributions from lagging strands. The surrounding empty wells are the only candidate left. The loading of a chip wells with Ion Sphere Particles is a probabalistic event and not all particles fall into wells. Due to the size of the particles and wells, it is physically impossible to fit two particles in a well. Therefore, a well should either be empty or have one particle in it.

The way the Ion Torrent detects whether a well is empty or not is by washing NaOH and measuring the signal delay compared to its neighboring wells (Figure 2). An empty well has less buffering capacity and therefore should respond earlier than its occupied neighbors with particles. There is sometimes a grey area in between and the Ion Torrent analysis uses clustering to best deal with this grey area.

The voltage response from the NaOH wash at the start to detect occupied and empty wells. I’ll explain in more detail in next blog post. The putative empty wells (colored black) respond earlier and much faster than occupied wells (rainbow colored). The well represented as a red dotted line lies in the “grey zone”, i.e. Hard to classify as either empty or occupied.

Background Signal There are three major contributors to the background signal • Measured average signal from neighboring empty wells ( ve). This signal must be time shifted as it will be subtracted to leave foreground signal.

• Dynamic voltage change ( delta v). Can’t explain it beyond that 😦 • Crosstalk flux ( xtflux) I will let the mathematics do all the talking below 🙂 This is a screen capture of a latex document I produced a few months a go so I don’t remember much 😥 Please note all equations are MY INTERPRETATION of the source code and since I didn’t write the code, I am probably incorrect sometimes. Foreground Signal – Nucleotide Incorporation Model The Foreground signal is calculated by subtracting the background signal away from the measured signal for an occupied well. By using this model, we can determine the value A which represents the nucleotide incorporation value (aka uncorrected signal) that gets stored in the 1.wells file. During each nucleotide flow, the polymerase adds nucleotides in a relatively synchronous manner and therefore produces a combined signal for the well observed as a detectable voltage change.

What I mean by “relatively” is that most nucleotides are incorporated soon after the nucleotide is flowed in, while some take a little longer to incorporate which is usually the case with the homopolymers. This looks like a sudden spike followed by an exponential decay (Figure 3). This foreground nucleotide incorporation is modeled as a Poisson distribution using empirically derived nucleotide base (A,C,T,G) specific parameters such as Km values (plagiarized from myself:lol:). Signal produced by subtracting an empty well from an occupied live well, (i.e. Subtracting the dotted black line from the red line in Figure 1).

The peak of ~60. The average key flow peak in a typical Ion Torrent report is calculated in a similar way. This is from Q1 2011 DAT file so is not sampled at a more desired rate. Nucleotide Specific Parameters Nucleotide Incorporation Simulation The goal is to find A that best reduces the error.

I will let the mathematics below speak for itself. In the next blog post for this series, I will list the major parameters used in signal processing. These are the mysterious unaccounted variables in all the above equations and also high level description on how parameter fitting is performed. Disclaimer: For the good of all mankind! This is purely my opinion and interpretations. I have tried my best to keep all analyses correct. The mathematical interpretation was done some time ago when I was in my “happy place”.

Now I’m not in that “happy place” so don’t remember a thing! The periodic public release of data sets by Life Technologies and others in the scientific community has allowed me to perform a “longitudinal study” of the improvements made on the Ion Torrent. In fact, the last few months has been quite exciting with Ion Torrent engaging the community through public data release along with source code. This has made the whole scientific community feel in some way as being part of the action. In this three part series which will run in parallel with the Signal Processing series, I will look at three major developmental themes: • Improvements in Accuracy • Homopolymer problem – can’t call it improvements because I haven’t analyzed the data yet 😛 • Changes in the ion-Analysis source code. This binary is largely responsible for all the data analysis, that is going from raw voltages (DAT files) to corrected incorporation signal (SFF files). Subsequent base calling from SFF files is quite trival 🙂 The analysis was performed using Novoalign in the (v2.07.12) according to the instructions detailed on their The plots were produced using the Rscripts provided in the package with slight modifications to change the look and feel.

I used the fastq files as input and did not do any pre-processing to ensure the reproducibility of the data. The same command line options was used and is noted at the bottom of each plot. The only exception is the last plot for the long range data set where the “-n 300” option was used to inspect quality past the default 150 bases. Kudos to Nick Loman for the help (see Comments below).

I quite like the package and the fast support provided on the user forum (kudos to Colin). There is a nice gallery of figures provided on their From the quality plots there are two very obvious things.

First, the predicted estimation is overly conservative and they are underselling themselves by an average of 10 Phred points. This was noted also on the, and blog posts. Second, the predicted quality along reads from the 316 data set (Figure below) used by the is an unfair and incorrect representation of what is happening. Raw accuracy: Q10 = 90% accuracy Q20 = 99% accuracy Q23 = 99.5% accuracy Q30 = 99.9% accuracy In my opinion, actual observed accuracy is more important than predicted. For example, I predicted network marketing was going to make me a fortune and I would be financially free by now. Unfortunately, my friends didn’t want to buy my stuff 😥 Their loss!! The plots from the long read data set shows the massive improvements made in just a few months.