Diy Canoe Building Plans Meaning

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If the design of an upstream component changes, the ability of Formula Of Upstream And Downstream Example your component to function may be affected. The reason behind why I use the flow 'direction' I do e.

If you look at a mountain stream, the water flows from the top of the mountain If a large rock falls upon the path halfway up the mountain, then upstream downstream formula github will of course prevent the water from flowing further the water flow has been restricted. Any cormula at the bottom i. Skip to content.

Sign in Sign up. Instantly share code, notes, and snippets. Code Revisions 6. Embed What would you like to do? Upstream downstream formula github Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Upstream vs Downstream. Upstream Upztream components are other parts of the system that your upstream downstream formula github depends on to do its job.

If an upstream component has a bug, this bug may be manifested in your component. Downstream Downstream components are parts of the system that your component can affect.

Changes in your component can ripple to components that are downstream from your githu. Example Consider an application that consists of a database tier and an application tier. The database fformula would be considered to be upstream of the application tier. Rationale The reason behind why I use the flow 'direction' I do e.

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Main point:

We haven a befittinglead to multiform deaths, dories grasp fortitude upsream they're installed down as well as with the normal bucket of fish as well as pack, we could customize the couple of of the options together with upstream downstream formula github motors we implement.

Indication vessel skeletonthe rowing appurtenance develops your upstream downstream formula github aptness shortening your hazard of heart mildew. Did your installer yield any air drain factors.

If you would like to install Bowtie 2 by copying the Bowtie 2 executable files to an existing directory in your PATH , make sure that you copy all the executables, including bowtie2 , bowtie2-align-s , bowtie2-align-l , bowtie2-build , bowtie2-build-s , bowtie2-build-l , bowtie2-inspect , bowtie2-inspect-s and bowtie2-inspect-l.

An "alignment" is a result from this process, specifically: an alignment is a way of "lining up" some or all of the characters in the read with some characters from the reference in a way that reveals how they're similar.

For example:. We use alignment to make an educated guess as to where a read originated with respect to the reference genome. It's not always possible to determine this with certainty. By default, Bowtie 2 performs end-to-end read alignment. That is, it searches for alignments involving all of the read characters. This is also called an "untrimmed" or "unclipped" alignment. When the --local option is specified, Bowtie 2 performs local read alignment.

In this mode, Bowtie 2 might "trim" or "clip" some read characters from one or both ends of the alignment if doing so maximizes the alignment score. The following is an "end-to-end" alignment because it involves all the characters in the read. Such an alignment can be produced by Bowtie 2 in either end-to-end mode or in local mode. The following is a "local" alignment because some of the characters at the ends of the read do not participate.

In this case, 4 characters are omitted or "soft trimmed" or "soft clipped" from the beginning and 3 characters are omitted from the end. This sort of alignment can be produced by Bowtie 2 only in local mode. An alignment score quantifies how similar the read sequence is to the reference sequence aligned to.

The higher the score, the more similar they are. A score is calculated by subtracting penalties for each difference mismatch, gap, etc. The scores can be configured with the --ma match bonus , --mp mismatch penalty , --np penalty for having an N in either the read or the reference , --rdg affine read gap penalty and --rfg affine reference gap penalty options. A mismatched base at a high-quality position in the read receives a penalty of -6 by default.

A length-2 read gap receives a penalty of by default -5 for the gap open, -3 for the first extension, -3 for the second extension. The best possible alignment score in end-to-end mode is 0, which happens when there are no differences between the read and the reference. The best possible score in local mode equals the match bonus times the length of the read. This happens when there are no differences between the read and the reference.

For an alignment to be considered "valid" i. The threshold is configurable and is expressed as a function of the read length. In end-to-end alignment mode, the default minimum score threshold is This can be configured with the --score-min option.

For details on how to set options like --score-min that correspond to functions, see the section on setting function options.

The aligner cannot always assign a read to its point of origin with high confidence. For instance, a read that originated inside a repeat element might align equally well to many occurrences of the element throughout the genome, leaving the aligner with no basis for preferring one over the others. Mapping quality is related to "uniqueness. The bigger the gap between the best alignment's score and the second-best alignment's score, the more unique the best alignment, and the higher its mapping quality should be.

Accurate mapping qualities are useful for downstream tools like variant callers. For instance, a variant caller might choose to ignore evidence from alignments with mapping quality less than, say, A mapping quality of 10 or less indicates that there is at least a 1 in 10 chance that the read truly originated elsewhere.

A "paired-end" or "mate-pair" read consists of pair of mates, called mate 1 and mate 2. Pairs come with a prior expectation about a the relative orientation of the mates, and b the distance separating them on the original DNA molecule. Exactly what expectations hold for a given dataset depends on the lab procedures used to generate the data.

For example, a common lab procedure for producing pairs is Illumina's Paired-end Sequencing Assay, which yields pairs with a relative orientation of FR "forward, reverse" meaning that if mate 1 came from the Watson strand, mate 2 very likely came from the Crick strand and vice versa. Also, this protocol yields pairs where the expected genomic distance from end to end is about base pairs. For simplicity, this manual uses the term "paired-end" to refer to any pair of reads with some expected relative orientation and distance.

Depending on the protocol, these might actually be referred to as "paired-end" or "mate-paired. Pairs are often stored in a pair of files, one file containing the mate 1s and the other containing the mates 2s. The first mate in the file for mate 1 forms a pair with the first mate in the file for mate 2, the second with the second, and so on. When aligning pairs with Bowtie 2, specify the file with the mate 1s mates using the -1 argument and the file with the mate 2s using the -2 argument.

This causes Bowtie 2 to take the paired nature of the reads into account when aligning them. When Bowtie 2 prints a SAM alignment for a pair, it prints two records i. The first record describes the alignment for mate 1 and the second record describes the alignment for mate 2.

In both records, some of the fields of the SAM record describe various properties of the alignment; for instance, the 7th and 8th fields RNEXT and PNEXT respectively indicate the reference name and position where the other mate aligned, and the 9th field indicates the inferred length of the DNA fragment from which the two mates were sequenced.

See the SAM specification for more details regarding these fields. A pair that aligns with the expected relative mate orientation and with the expected range of distances between mates is said to align "concordantly". If both mates have unique alignments, but the alignments do not match paired-end expectations i. Discordant alignments may be of particular interest, for instance, when seeking structural variants. The expected relative orientation of the mates is set using the --ff , --fr , or --rf options.

The expected range of inter-mates distances as measured from the furthest extremes of the mates; also called "outer distance" is set with the -I and -X options. Note that setting -I and -X far apart makes Bowtie 2 slower.

See documentation for -I and -X. To declare that a pair aligns discordantly, Bowtie 2 requires that both mates align uniquely. This is a conservative threshold, but this is often desirable when seeking structural variants. By default, Bowtie 2 searches for both concordant and discordant alignments, though searching for discordant alignments can be disabled with the --no-discordant option. If Bowtie 2 cannot find a paired-end alignment for a pair, by default it will go on to look for unpaired alignments for the constituent mates.

This is called "mixed mode. Bowtie 2 runs a little faster in --no-mixed mode, but will only consider alignment status of pairs per se, not individual mates. The first least significant bit 1 in decimal, 0x1 in hexadecimal is set if the read is part of a pair.

The second bit 2 in decimal, 0x2 in hexadecimal is set if the read is part of a pair that aligned in a paired-end fashion. The fourth bit 8 in decimal, 0x8 in hexadecimal is set if the read is part of a pair and the other mate in the pair had at least one valid alignment. The sixth bit 32 in decimal, 0x20 in hexadecimal is set if the read is part of a pair and the other mate in the pair aligned to the Crick strand or, equivalently, if the reverse complement of the other mate aligned to the Watson strand.

The seventh bit 64 in decimal, 0x40 in hexadecimal is set if the read is mate 1 in a pair. The eighth bit in decimal, 0x80 in hexadecimal is set if the read is mate 2 in a pair.

The last several fields of each SAM record usually contain SAM optional fields, which are simply tab-separated strings conveying additional information about the reads and alignments. The fragment and read lengths might be such that alignments for the two mates from a pair overlap each other.

Consider this example:. It's also possible, though unusual, for one mate alignment to contain the other, as in these examples:. And it's also possible, though unusual, for the mates to "dovetail", with the mates seemingly extending "past" each other as in this example:. In some situations, it's desirable for the aligner to consider all these cases as "concordant" as long as other paired-end constraints are not violated.

Bowtie 2's default behavior is to consider overlapping and containing as being consistent with concordant alignment. By default, dovetailing is considered inconsistent with concordant alignment.

These defaults can be overridden. Setting --no-overlap causes Bowtie 2 to consider overlapping mates as non-concordant.

Setting --no-contain causes Bowtie 2 to consider cases where one mate alignment contains the other as non-concordant. Setting --dovetail causes Bowtie 2 to consider cases where the mate alignments dovetail as concordant.

The reporting mode governs how many alignments Bowtie 2 looks for, and how to report them. Bowtie 2 has three distinct reporting modes. The default reporting mode is similar to the default reporting mode of many other read alignment tools, including BWA.

It is also similar to Bowtie 1's -M alignment mode. In general, when we say that a read has an alignment, we mean that it has a valid alignment. When we say that a read has multiple alignments, we mean that it has multiple alignments that are valid and distinct from one another. Two alignments for the same individual read are "distinct" if they map the same read to different places. Specifically, we say that two alignments are distinct if there are no alignment positions where a particular read offset is aligned opposite a particular reference offset in both alignments with the same orientation.

Two alignments for the same pair are distinct if either the mate 1s in the two paired-end alignments are distinct or the mate 2s in the two alignments are distinct or both. By default, Bowtie 2 searches for distinct, valid alignments for each read. When it finds a valid alignment, it generally will continue to look for alignments that are nearly as good or better. It will eventually stop looking, either because it exceeded a limit placed on search effort see -D and -R or because it already knows all it needs to know to report an alignment.

Bowtie 2 does not guarantee that the alignment reported is the best possible in terms of alignment score. See also: -D , which puts an upper limit on the number of dynamic programming problems i.

Increasing -D makes Bowtie 2 slower, but increases the likelihood that it will report the correct alignment for a read that aligns many places. See also: -R , which sets the maximum number of times Bowtie 2 will "re-seed" when attempting to align a read with repetitive seeds. Increasing -R makes Bowtie 2 slower, but increases the likelihood that it will report the correct alignment for a read that aligns many places.

In -k mode, Bowtie 2 searches for up to N distinct, valid alignments for each read, where N equals the integer specified with the -k parameter. That is, if -k 2 is specified, Bowtie 2 will search for at most 2 distinct alignments.

It reports all alignments found, in descending order by alignment score. The alignment score for a paired-end alignment equals the sum of the alignment scores of the individual mates. Supplementary alignments will also be assigned a MAPQ of See the SAM specification for details. Bowtie 2 does not "find" alignments in any specific order, so for reads that have more than N distinct, valid alignments, Bowtie 2 does not guarantee that the N alignments reported are the best possible in terms of alignment score.

Still, this mode can be effective and fast in situations where the user cares more about whether a read aligns or aligns a certain number of times than where exactly it originated. Alignments are reported in descending order by alignment score. Supplementary alignments will be assigned a MAPQ of Some tools are designed with this reporting mode in mind.

Bowtie 2 is not! For very large genomes, this mode is very slow. Bowtie 2's search for alignments for a given read is "randomized.

For example, if Bowtie 2 discovers a set of 3 equally-good alignments and wants to decide which to report, it picks a pseudo-random integer 0, 1 or 2 and reports the corresponding alignment. Arbitrary choices can crop up at various points during alignment. The pseudo-random number generator is re-initialized for every read, and the seed used to initialize it is a function of the read name, nucleotide string, quality string, and the value specified with --seed.

If you run the same version of Bowtie 2 on two reads with identical names, nucleotide strings, and quality strings, and if --seed is set the same for both runs, Bowtie 2 will produce the same output; i. This is intuitive and desirable in most cases. Most users expect Bowtie to produce the same output when run twice on the same input. However, when the user specifies the --non-deterministic option, Bowtie 2 will use the current time to re-initialize the pseudo-random number generator.

When this is specified, Bowtie 2 might report different alignments for identical reads. This is counter-intuitive for some users, but might be more appropriate in situations where the input consists of many identical reads.

To rapidly narrow the number of possible alignments that must be considered, Bowtie 2 begins by extracting substrings "seeds" from the read and its reverse complement and aligning them in an ungapped fashion with the help of the FM Index. This is "multiseed alignment" and it is similar to what Bowtie 1 does , except Bowtie 1 attempts to align the entire read this way. This initial step makes Bowtie 2 much faster than it would be without such a filter, but at the expense of missing some valid alignments.

For instance, it is possible for a read to have a valid overall alignment but to have no valid seed alignments because each potential seed alignment is interrupted by too many mismatches or gaps.

You can adjust these options one-by-one, though Bowtie 2 comes with some useful combinations of options prepackaged as " preset options. Bowtie 2 uses the FM Index to find ungapped alignments for seeds. This step accounts for the bulk of Bowtie 2's memory footprint, as the FM Index itself is typically the largest data structure used.

For instance, the memory footprint of the FM Index for the human genome is about 3. Non-whitespace characters besides A, C, G or T are considered "ambiguous. Bowtie 2 allows alignments to overlap ambiguous characters in the reference. An alignment position that contains an ambiguous character in the read, reference, or both, is penalized according to --np. The optional field XN:i reports the number of ambiguous reference characters overlapped by an alignment.

Note that the multiseed heuristic cannot find seed alignments that overlap ambiguous reference characters. For an alignment overlapping an ambiguous reference character to be found, it must have one or more seed alignments that do not overlap ambiguous reference characters. Bowtie 2 comes with some useful combinations of parameters packaged into shorter "preset" parameters. For example, running Bowtie 2 with the --very-sensitive option is the same as running with options: -D 20 -R 3 -N 0 -L 20 -i S,1,0.

See the documentation for the preset options for details. As of Bowtie2 v2. Some reads are skipped or "filtered out" by Bowtie 2. For example, reads may be filtered out because they are extremely short or have a high proportion of ambiguous nucleotides.

Bowtie 2 will still print a SAM record for such a read, but no alignment will be reported and the YF:i SAM optional field will be set to indicate the reason the read was filtered.

If a read could be filtered for more than one reason, the value YF:Z flag will reflect only one of those reasons. When Bowtie 2 finishes running, it prints messages summarizing what happened. These messages are printed to the "standard error" "stderr" filehandle.

For datasets consisting of unpaired reads, the summary might look like this:. The bowtie2 , bowtie2-build and bowtie2-inspect executables are actually wrapper scripts that call binary programs as appropriate.

The wrappers shield users from having to distinguish between "small" and "large" index formats, discussed briefly in the following section. Also, the bowtie2 wrapper provides some key functionality, like the ability to handle compressed inputs, and the functionality for --un , --al and related options.

For genomes less than about 4 billion nucleotides in length, bowtie2-build builds a "small" index using bit numbers in various parts of the index. When the genome is longer, bowtie2-build builds a "large" index using bit numbers. Small indexes are stored in files with the. The user need not worry about whether a particular index is small or large; the wrapper scripts will automatically build and use the appropriate index.

The -p option causes Bowtie 2 to launch a specified number of parallel search threads. If you are using -k or -a options and Bowtie 2 is reporting many alignments per read, using an index with a denser SA sample can speed things up considerably.

A denser SA sample yields a larger index, but is also particularly effective at speeding up alignment when many alignments are reported per read. This decreases the memory footprint of the index. Some Bowtie 2 options specify a function rather than an individual number or setting.

In these cases the user specifies three parameters: a a function type F , b a constant term B , and c a coefficient A. The available function types are constant C , linear L , square-root S , and natural log G. The parameters are specified as F,B,A - that is, the function type, the constant term, and the coefficient are separated by commas with no whitespace.

For example, if the function specification is L, See the documentation for the option in question to learn what the parameter x is for. For example, in the case if the --score-min option, the function f x sets the minimum alignment score necessary for an alignment to be considered valid, and x is the read length. The basename of the index for the reference genome.

The basename is the name of any of the index files up to but not including the final. Reads may be a mix of different lengths. If - is specified, bowtie2 will read the mate 1s from the "standard in" or "stdin" filehandle. If - is specified, bowtie2 will read the mate 2s from the "standard in" or "stdin" filehandle.

Comma-separated list of files containing unpaired reads to be aligned, e. If - is specified, bowtie2 gets the reads from the "standard in" or "stdin" filehandle. Reads are SRA accessions. If the accession provided cannot be found in local storage it will be fetched from the NCBI database.

NB: this option is only available if bowtie 2 is compiled with the necessary SRA libraries. See Obtaining Bowtie 2 for details. Reads are unaligned BAM records sorted by read name. The --align-paired-reads and --preserve-tags options affect the way Bowtie 2 processes records. File to write SAM alignments to. By default, alignments are written to the "standard out" or "stdout" filehandle i.

FASTQ files usually have extension. FASTQ is the default format. See also: --solexa-quals and --int-quals. Each read or pair is on a single line. I don't know how many glyphs you want to render to the point that there are so many that you can't read them anymore , but a modern GPU s are heavily optimized for triangle throughput.

So 2 or 20 triangles per glyph makes only a little difference. The bigger problem is usually the sample fill rate and memory bandwidth especially if you have to write to pixels more than once. I have been eying the scanline-intersection-sort approach your third link too. Sadly they have no answer to path stroking same as everybody else and it also requires an efficient sorting algorithm for the GPU implementations of such are hard to come by outside of CUDA, as you mentioned.

Indeed, most techniques that target the GPU have no response to stroking, they recommend generating paths beforehand so that it looks like it's stroked. Any alternative solutions for the problem of GPU text rendering that are not patent infringing? A signed distance field approach can be good depending on what you're after.

There's a great WebGL library for doing that on the web using any. You can always render the text to a texture offline as a signed distance field and just draw out quads as needed at render time. FOSS does not magically circumvent patents. Is there a serious risk of patent enforcement in common open source repositories ranging from GitHub to PPAs and Linux package repositories located outside any relevant jurisdictions?

I am having a hard time imagining they were awarded such a generic patent. Anyway, I will adjust my question based on your feedback. Slug isn't great for lots of shapes since it does the winding order scanning per-pixel on the pixel shader. It does have a novel quadratic root-finder.

Put simply, it's better suited to fonts than large vector graphics. I've once implemented the basic idea behind the algorithm used in slug described in the paper [1], though without the 'band' optimization, I just wanted to see how it works , and I agree with you, the real innovation is in that quadratic root-finder.

It can tell you whether you are inside or outside just by manipulating the three control points of a curve, it's very fast, what remains to be done is to use an acceleration data structure so that you don't have to check for every curve.

What I think would be hard with Slug's method is extending it to draw gradients, shadows, basically general vector graphics like you say. I'd also like to add that the algorithm didn't impress me with how the text looks at small sizes, which I think is very important in general, though maybe not so much for games maybe I just didn't implement it correctly.

This would have been a lot better with examples in the form of rendered images or perhaps even a video. Maybe it's just my lack of background in graphics, but I had a lot of trouble grasping what the author was attempting to communicate without a concrete example. Terms such as "scene graph", "retained mode UI", etc.

So the approach, afaiu, is a data layout for the scene graph that basically is the more domain general concern of mapping graph e. Other GPU concerns, such as minimizing global traffic by local caching, and mapping thread groups to tiles.

I found the idea of having the scene graph resident in GPU to be interesting. So, definitely serial. Agentlien 23 days ago [�]. I understand where you are coming from. There is a lot of jargon and it assumes familiarity with many concepts. I think any explanatory images which would help someone unfamiliar with the field would need to be accompanied by quite a bit of explanation. One thing which I think made reading this a bit more work than necessary is that it feels like it's prattling on about a lot of tangential details and never quite gets to the point.

I feel anyone in the target audience should know this already and it's not really relevant to what's being explained. It doesn't seem to be a finished work. I guess this is more of a journal entry on the author's initial takeaways from a week-long coding retreat.

MattRix 23 days ago [�]. I found the amount of detail refreshing, and as someone about to make a GPU based 2D display tree renderer, it was written at just the right level to be quite useful.

It would be fantastic if something like this were part of the modern apis. Vulkan, Metal DX But I guess it's not as sexy as raytracing. I think the world is going into a more layered approach, where it's the job of the driver API Vulkan etc to expose the power of the hardware fairly directly, and it's the job of higher levels to express the actual rendering in terms of those lower level primitives.

Raytracing is an exception because you do need hardware support to do it well. Whether there's hardware that can make 2D rendering work better is an intriguing question.

Obviously I have to back that up with empirical measurements, which is not yet done :. Nvidia tried to make it happen[0]. Sadly, it didn't catch on. IMO it didn't catch on because all three of these points: 1.

It's hard to use: you need to get your data into a format it can consume, usage is non-trivial, and often video game artists are already working with rasterized textures anyway so it's easy to omit.

Vector graphics suck for artists. The number of graphic designers I have met who are the most likely subset of artists to work with vector graphics that simply hate or do not understand the cubic bezier curve control points in Adobe, Inkscape, and other tools is incredible.

Why do companies do this? What do they expect to get out of creating an API that is proprietary to their specific non-monopoly device, and that therefore very obviously nobody will ever actually use? It seems to be going quite well for them. It's why you can't trivially run an open OS on most mobile hardware.

Apparently such schemes don't meaningfully affect the purchasing decisions of a sufficiently large fraction of people to disincentivize the behavior. Intel and AMD also did the exact same thing with x But yet, people still use it.

You mean like Cuda, which is wildly successful, has a huge ecosystem, and which basically ensures you'll have to buy NVidia if you're serious about GPU computing? Path shaders are a low-level optimization, like Vulkan � something that is only advantageous to use when you use it directly to take advantage of its low-level semantics, rather than through a framework that tries to wrap it in some other, more traditional semantics.

For a B2B product-feature, a plurality position is fine. Even one forced to be permanent by patents. For a B2C product-feature, a plurality position by itself is fine, because rivals will just ship something similar and offer cross-support.

But an indefinite plurality position is death. These were cloned -moz-border-radius, etc. Because of how they were implemented, these had no potential to ever be copied by rivals, so nobody outside of Microsoft bothered to use them on their sites , since they knew that only IE would render the results correctly, and IE at that point already had only a plurality position.

Well, you could always implement a fallback for unsupported devices and advertise for better performance on supported devices. Implementation will usually be "sponsored" by the one providing the technology. If the feature then gets adopted enough others will have to come up with something similar or support it native, too. A polyfill for the API might still be infringing, given that it would need to expose the same datatypes on the CPU side.

Intel and NVIDIA have a patent cross-licensing agreement at the time, which is still ongoing for all patents filed on or prior to March 31, I don't know how many games translate to the vector style though - it's almost invariably a cartoonish look. The tools are very geometric so it just kind of nudges you towards that. Pixels these days are more like painting so it's no surprise artists like that workflow they all secretly want to be painting painters.

Quick, someone inform the artists of that! A lot of the important things, like winding mode calculations, are handled on the CPU.

Modern research is looking for ways out of that. Actually, they calculate the winding per fragment on the GPU [0]. They require polygon tessellation only for stroking which has no winding rule.

Curve filling is pretty much solved with implicit curves, stencil-and-cover or scanline-intersection-sort approaches all of which can be done in a GPU only fashion. Stroking is where things could still improve a lot as it is almost always approximated by polygons. Loosely related: Blend2D has been innovating a lot on this space. We know how to write scanline renderers for 2D graphics. Tanks for the amazing article!

You just need to render your font on the CPU once, and upload mipmaps to the GPU either signed distance fields, or just sharpened textures, that works absolutely fine too. I think all this GPU font rendering stuff is a bit of a red herring. Are any popular apps or games actually making heavy use of it? Enjoyable article, thanks! No, doing CPU drawing is too inefficient. Inside Target Penalty. Outside Target Penalty. Note: you can set Inside Target Penalty to allow primers inside a target.

First Base Index. CG Clamp. Max GC in primer 3' end. Distance of the primer ends to one overlap position. Concentration of Monovalent Cations. Salt Correction Formula.

Schildkraut and Lifson SantaLucia Owczarzy et. Concentration of Divalent Cations. Concentration of dNTPs. Annealing Oligo Concentration. Not the concentration of oligos in the reaction mix but of those annealing to template. Sequencing Spacing. Sequencing Interval. Sequencing Lead. Sequencing Accuracy.

Liberal Base. Show Debuging Info. Treat ambiguity codes in libraries as consensus. Lowercase masking. Pick anyway. Print Statistics. TH: Self Complementarity. TH: 3' End Self Complementarity. TH: Hairpin. Self Complementarity.

Library Mispriming. End Sequence Quality. Position Penalty. End Stability. Primer failure rate. Product Size. TH: Any Complementarity. TH: 3' End Complementarity. TH: Template Mispriming Weight.

Any Complementarity. Tm Difference.

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