Patent Publication Number: US-11664090-B2

Title: Basecaller with dilated convolutional neural network

Description:
BACKGROUND 
     The present disclosure relates generally to systems, devices, and methods for basecalling, and more specifically to systems, devices, and methods for basecalling using deep learning for DNA sequencing analysis using capillary electrophoresis. 
     In capillary electrophoresis (CE), a biological sample, such as a nucleic acid sample, is injected at the inlet end of the capillary, into a denaturing separation medium in the capillary, and an electric field is applied to the capillary ends. The different nucleic acid components in a sample, e.g., a polymerase chain reaction (PCR) mixture or other sample, migrate to the detector point with different velocities due to differences in their electrophoretic properties. Consequently, they reach the light detector (usually a fluorescence detector operating in the visible light range or an ultraviolet (UV) absorbance detector) at different times. Results present as a series of detected peaks, where each peak represents ideally one nucleic acid component or species of the sample. 
     The magnitude of any given peak, including an artifact peak, is most often determined optically on the basis of either UV absorption by nucleic acids, e.g., DNA, or by fluorescence emission from one or more labelled dyes associated with the nucleic acid. UV and fluorescence detectors applicable to nucleic acid CE detection are well known in the art. 
     CE capillaries themselves are frequently quartz, although other materials known to those of skill in the art can be used. There are a number of CE systems available commercially, having both single and multiple-capillary capabilities. The methods described herein are applicable to any device or system for denaturing CE of nucleic acid samples. 
     Historically, Sanger sequencing with capillary electrophoresis (CE) genetic analyzers has been considered the gold-standard DNA sequencing technology. It provides a high degree of accuracy, long-read capabilities, and the flexibility to support a diverse range of applications in many research areas. The accuracies of basecalls and quality values (QVs) for Sanger sequencing on CE genetic analyzers are considered essential for successful sequencing projects. Legacy basecallers were previously developed to provide a complete and integrated basecalling solution to support sequencing platforms and applications and were originally engineered to basecall long plasmid clones (pure bases) and then extended later to basecall mixed base data to support variant identification. 
     However, obvious mixed bases are occasionally called as pure bases even with high predicted QVs, and false positives, in which pure bases are incorrectly called as mixed bases, also occur relatively frequently due to sequencing artefacts such as dye blobs, n−1 peaks due to polymerase slippage and primer impurities, mobility shifts, etc. Clearly, the basecalling and QV accuracy for mixed bases need to be improved to support sequencing applications for identifying variants such as Single Nucleotide Polymorphisms (SNPs) and heterozygous insertion deletion variants (het indels). The basecalling accuracy of legacy basecallers at 5′ and 3′ ends is also relatively low due to mobility shifts and low resolution at 5′ and 3′ ends. Legacy basecallers may also struggle to basecall amplicons shorter than 150 base pairs (bps) in length, particularly shorter than 100 bps, failing to estimate average peak spacing, average peak width, spacing curve, and/or width curve, sometimes resulting in increased error rate. 
     Therefore, improved basecalling accuracy for pure and mixed bases, especially at 5′ and 3′ ends is also very desirable so that basecalling algorithms can deliver higher fidelity of Sanger sequencing data, improve variant identification, increase read length, and also save sequencing costs for sequencing applications. 
     Recent basecallers frequently use recurrent neural network-based models to identify the basecalling sequence based on raw input data. With the recurrent structures, the recurrent neural networks can properly model the time-series data in basecalling, but as the computation of one time point must wait for the result of earlier time points, the speed of basecallers based on recurrent networks may be severely restricted, particularly when dealing with longer sequencing reads. 
     SUMMARY 
     Systems and methods are described for use in capillary electrophoresis deep learning based basecalling, such as in convolutional neural network-based basecalling systems utilizing capillary electrophoresis genetic analyzers based on microfluidic separations (in which separation is performed through micro-channels etched into or onto glass, silicon or other substrate), or separation through capillary electrophoresis using single or multiple cylindrical capillary tubes. 
     Convolutional architectures, such as dilated convolutional neural networks implemented in embodiments of the present invention described herein, may perform well in genetic sequence modeling tasks and outperform recurrent networks, and reach state-of-the-art accuracy in a broad range of sequence modeling tasks. The training and inference of convolutional neural networks is much faster than for recurrent networks such as long short term memory (LSTM) networks. Dilated convolutional neural networks in particular, may achieve an exponentially large receptive field with fewer parameters and fewer layers. 
     A method of automatically basecalling one or more DNA (deoxyribonucleic acid) molecules of a biological sample is described. The method comprises converting a plurality of fluorescent signals of the biological sample, wherein each of the plurality of fluorescent signals is measured by a capillary electrophoresis genetic analyzer, to at least one input trace comprising digital data corresponding to fluorescent values at a plurality of scans. Scan labelling probabilities for each of the plurality of scans are generated. The scan labelling probabilities are generated using a trained deep neural network comprising a plurality of layers including convolutional layers. A basecall sequence comprising a plurality of basecalls for the one or more DNA molecules based on the one or more scan labelling probabilities for each of the plurality of scans is determined. 
     In some embodiments, a basecall position for each basecall in a basecall sequence is also determined, where the basecall position corresponds to a scan position of a peak scan labelling probability associated with the basecall. In some embodiments, searching for the peak probability associated with a given basecall is made more efficient by first identifying a first and last scan in a scan range of scans corresponding to scan labelling probabilities associated with the given basecall and then only searching within that scan range. 
     In some embodiments a quality value for each basecall is determined using feature values derived from the scan probability values associated with that basecall rather than using values of the image trace associated with the basecall. In addition, in some embodiments of the present invention a neural network is trained to call mixed bases of 2 bases per basecall position. In some embodiments, a neural network may be trained to call mixed bases of greater than 2 bases per basecall position. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a capillary electrophoresis sequencing system in accordance with an embodiment of the present invention; 
         FIG.  2    illustrates an exemplary electropherogram that may be displayed in accordance with an embodiment of the present invention; 
         FIG.  3    illustrates a capillary electrophoresis genetic analysis process in accordance with some embodiments of the present invention; 
         FIG.  4    illustrates a diagram of exemplary input and output data that may be displayed in accordance with an embodiment of the present invention; 
         FIG.  5    illustrates a deep learning based basecalling workflow process in accordance with an embodiment of the present invention; 
         FIG.  6    illustrates a deep neural network architecture in accordance with an embodiment of the present invention; 
         FIG.  7    illustrates a residual block architecture in accordance with an embodiment of the present invention; 
         FIG.  8    illustrates a method for generating a basecall sequence in accordance with an embodiment of the present invention; 
         FIG.  9    illustrates a method for generating a scan range and scan position for one or more basecalls in a basecall sequence in accordance with an embodiment of the present invention; 
         FIG.  10    illustrates a scan labelling model training method in accordance with one embodiment of the present invention; 
         FIG.  11    illustrates a method for constructing a trained quality value lookup table in accordance with one embodiment of the present invention; and 
         FIG.  12    illustrates a block diagram of an exemplary computing device that may incorporate embodiments of the present invention. 
     
    
    
     While the invention is described with reference to the above drawings, the drawings are intended to be illustrative, and other embodiments are consistent with the spirit, and within the scope, of the invention. 
     DETAILED DESCRIPTION 
     The various embodiments now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific examples of practicing the embodiments. This specification may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this specification will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Among other things, this specification may be embodied as methods or devices. Accordingly, any of the various embodiments herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. The following specification is, therefore, not to be taken in a limiting sense. 
     This patent application contains material related to PCT Application No. PCT/US2019/065540, filed on Dec. 10, 2019, with a priority date of Dec. 10, 2018, which is hereby incorporated by reference herein in its entirety. This and other technical publications, patent publications, scientific publications, and all other extrinsic materials discussed herein are incorporated by reference in their entirety. 
     Embodiments of the present invention discussed herein utilize principles of DNA replication used in Sanger dideoxy sequencing. This process takes advantage of the ability of DNA polymerase to incorporate 2′,3′-dideoxynucleotides—nucleotide base analogs that lack the 3′-hydroxyl group essential in phosphodiester bond formation. 
     Sanger dideoxy sequencing requires a DNA template, a sequencing primer, DNA polymerase, deoxynucleotides (dNTPs), dideoxynucleotides (ddNTPs), and reaction buffer. As Sanger dideoxy sequencing was originally designed, four separate reactions are set up, each containing radioactively labeled nucleotides and either ddA, ddC, ddG, or ddT. The annealing, labeling, and termination steps are performed on separate heat blocks. DNA synthesis is performed at 37° C., the temperature at which DNA polymerase has the optimal enzyme activity. DNA polymerase adds a deoxynucleotide or the corresponding 2′,3′-dideoxynucleotide at each step of chain extension. Whether a deoxynucleotide or a dideoxynucleotide is added depends on the relative concentration of both molecules. When a deoxynucleotide (A, C, G, or T) is added to the 3′ end, chain extension can continue. However, when a dideoxynucleotide (ddA, ddC, ddG, or ddT) is added to the 3′ end, chain extension terminates. Sanger dideoxy sequencing results in the formation of extension products of various lengths terminated with dideoxynucleotides at the 3′ end. 
     The extension products are then separated by electrophoresis. During electrophoresis, an electrical field is applied so that the negatively charged DNA fragments move toward the positive electrode. The speed at which a DNA fragment moves through the medium is inversely proportional to its molecular weight. This process of electrophoresis can separate the extension products by size at a resolution of one base. 
     As used in embodiments of the present invention, an automated DNA fluorescence-based cycle sequencing system manufactured and used by Applied Biosystems, Inc., is an extension and refinement of Sanger dideoxy sequencing. Applied Biosystems automated DNA sequencing generally follows the flow of DNA template preparation, cycle sequencing, purification after cycle sequencing, capillary electrophoresis, and data analysis. Exemplary fluorescence-based cycle sequencing systems that may be used in embodiments of the present invention are further described in “DNA Sequencing by Capillary Electrophoresis Chemistry Guide (3 rd  Edition, 2016) published by Thermo Fisher Scientific, Inc., which is incorporated by reference herein in its entirety. 
     Like Sanger sequencing, fluorescence-based cycle sequencing requires a DNA template, a sequencing primer, a thermal stable DNA polymerase, deoxynucleoside triphosphates/deoxynucleotides (dNTPs), dideoxynucleoside triphosphates/deoxynucleotides (ddNTPs), and buffer. But unlike Sanger&#39;s method, which uses radioactive material, cycle sequencing uses fluorescent dyes to label the extension products, and the components are combined in a reaction that is subjected to cycles of annealing, extension, and denaturation in a thermal cycler. Thermal cycling the sequencing reactions creates and amplifies extension products that are terminated by one of the four dideoxynucleotides. The ratio of deoxynucleotides to dideoxynucleotide is optimized to produce a balanced population of long and short extension products. 
     Automated cycle sequencing procedures used in some embodiments of the present invention incorporate fluorescent dye labels using dye-labeled dideoxynucleotide (dye terminators) using four different dyes. Because each dye emits a unique wavelength when excited by light, the fluorescent dye on the extension product identifies the 3′ terminal dideoxynucleotide as A, C, G, or T. 
     With dye terminator chemistry, each of the four dideoxynucleotide terminators is tagged with a different fluorescent dye. One reaction is performed, containing the enzyme, nucleotides, and all dye-labeled dideoxynucleotides. The products from this reaction are injected into one capillary. 
     In one embodiment of the invention, the cycle sequencing reaction is directed by highly modified, thermally stable DNA polymerases, selected to allow incorporation of dideoxynucleotides, to process through stretches of G-C-rich and other difficult sequences, and to produce peaks of varying heights. The modified DNA polymerases are also formulated with a pyrophosphatase to prevent reversal of the polymerization reaction (pyrophosphorolysis). 
     In one embodiment of the present invention, Applied Biosystems Cycle Sequencing Kits available for dye terminator chemistries include: BigDye Terminator v1.1 and v3.1 Cycle Sequencing Kits, dGTP BigDye Terminator v1.0 and v3.0 Cycle Sequencing Kits, and BigDye Direct Cycle Sequencing Kits. The fluorescent dyes used in BigDye terminators, BigDye primers, and BigDye Direct have narrower emission spectra and less spectral overlap than the rhodamine dyes used in previous sequencing kits. As a result, the dyes may tend to produce less noise. 
     Historically, DNA sequencing products were separated using polyacrylamide gels that were manually poured between two glass plates. Capillary electrophoresis using a denaturing flowable polymer has largely replaced the use of gel separation techniques due to significant gains in workflow, throughput, and ease of use. Fluorescently labeled DNA fragments are separated according to molecular weight. Because there is no need to pour gels with capillary electrophoresis, DNA sequence analysis using CE is automated more easily and can process more samples at once. 
     During capillary electrophoresis, the extension products of the cycle sequencing reaction enter the capillary as a result of electrokinetic injection. A high voltage charge applied to the buffered sequencing reaction forces the negatively charged fragments into the capillaries. The extension products are separated by size based on their total charge. The electrophoretic mobility of the sample can be affected by the run conditions: the buffer type, concentration, and pH, the run temperature, the amount of voltage applied, and the type of polymer used. 
     Shortly before reaching the positive electrode, the fluorescently labeled DNA fragments, separated by size, move across the path of a laser beam. The laser beam causes the dyes on the fragments to fluoresce. In one embodiment of the invention, an optical detection device on Applied Biosystems genetic analyzers and/or DNA analyzers detects the fluorescence. Data collection software used in one embodiment of the invention converts the fluorescent signal to digital data, then records the data in an AB1 (.ab1) file. Because each dye emits light at a different wavelength when excited by the laser, all four colors, and therefore all four bases, can be detected and distinguished in one capillary injection. 
       FIG.  1    illustrates System  100  in accordance with an exemplary embodiment of the present invention. System  100  comprises capillary electrophoresis (“CE”) instrument  101 , one or more computers  103 , and user device  107 . 
     Referencing  FIG.  1   , a CE instrument  101  in one embodiment comprises a source buffer  118  containing buffer and receiving a fluorescently labeled sample  120 , a capillary  122 , a destination buffer  126 , a power supply  128 , and a controller  112 . The source buffer  118  is in fluid communication with the destination buffer  126  by way of the capillary  122 . The power supply  128  applies voltage to the source buffer  118  and the destination buffer  126  generating a voltage bias through an anode  130  in the source buffer  118  and a cathode  132  in the destination buffer  126 . The voltage applied by the power supply  128  is configured by a controller  112  operated by the computing device  103 . The fluorescently labeled sample  120  near the source buffer  118  is pulled through the capillary  122  by the voltage gradient, and optically labeled nucleotides of the DNA fragments within the sample are detected as they pass through an optical sensor  124  on the way to destination buffer  126 . Differently sized DNA fragments within the fluorescently labeled sample  120  are pulled through the capillary at different times due to their size. 
     The optical sensor  124  detects the fluorescent labels on the nucleotides as an image signal and communicates the image signal to the computing device  103 . The computing device  103  aggregates the image signal as sample data and utilizes a basecaller computer program product  104  to operate a deep neural network  102  to transform the sample data into processed data, including a basecall sequence and quality values, and generate an electropherogram that may be shown on a display  108  of user device  107 . 
     Instructions for implementing deep neural network  102  reside on computing device  103  in computer program product  104  which is stored in storage  105  and those instructions are executable by processor  106 . When processor  106  is executing the instructions of computer program product  104 , the instructions, or a portion thereof, are typically loaded into working memory  109  from which the instructions are readily accessed by processor  106 . In one embodiment, computer program product  104  is stored in storage  105  or another non-transitory computer readable medium (which may include being distributed across media on different devices and different locations). In alternative embodiments, the storage medium is transitory. 
     In one embodiment, processor  106  in fact comprises multiple processors which may comprise additional working memories (additional processors and memories not individually illustrated) including a graphics processing unit (GPU) comprising at least thousands of arithmetic logic units supporting parallel computations on a large scale. GPUs are often utilized in deep learning applications because they can perform the relevant processing tasks more efficiently than can typical general-purpose processors (CPUs). Other embodiments comprise one or more specialized processing units comprising systolic arrays and/or other hardware arrangements that support efficient parallel processing. In some embodiments, such specialized hardware works in conjunction with a CPU and/or GPU to carry out the various processing described herein. In some embodiments, such specialized hardware comprises application specific integrated circuits and the like (which may refer to a portion of an integrated circuit that is application-specific), field programmable gate arrays and the like, or combinations thereof. In some embodiments, however, a processor such as processor  106  may be implemented as one or more general purpose processors (preferably having multiple cores) without necessarily departing from the spirit and scope of the present invention. 
     User device  107  incudes a display  108  for displaying results of processing carried out by neural network  102 . In alternative embodiments, a neural network such as neural network  102 , or a portion of it, may be stored in storage devices and executed by one or more processors residing on CE instrument  101  and/or user device  107 . Such alternatives do not depart from the scope of the invention. 
       FIG.  2    illustrates an exemplary electropherogram  200  that may be displayed in accordance with an embodiment of the present invention. Electropherogram  200  includes a graph (with a y-axis of the relative fluorescence units (RFUs), and an x-axis of the scan), which displays the image signal of detected fluorescent labels on the nucleotides as a sequence of peaks, e.g.,  210 ,  211 ,  212  and  213 . The signals corresponding to the fluorescently labelled nucleotides may be displayed in four different colors, which may be represented in  FIG.  2   , and in other figures herein either in color, or in grayscale or as different variations of black and white hatched lines representing the various colors. Each color represents a base (e.g., in IUPAC-IUB notation T=red, C=blue, G=black, A=green, respectively) called for that peak. Two or more (e.g., 3 or 4) peaks may also occur in one position, in which case a mixed base may be called (e.g., mixed bases of 2 peaks may be expressed in IUPAC-IUB notation as follows: A+C=M, A+G=R, A+T=W, C+G=S, C+T=Y, G+T=K). 
     Referencing  FIG.  3   , a CE process  300  utilized in one embodiment of the present invention involves configuring capillary electrophoresis instrument operating parameters to sequence at least one fluorescently labeled sample (block  302 ). The configuration of the instrument may include creating or importing a plate setting for running a series of samples and assigning labels to the plate samples to assist in the processing of the collected imaging data. The process may also include communicating configuration controls to a controller to start applying voltage at a predetermined time. In block  304 , the CE process  300  loads the fluorescently labeled sample into the instrument. After the sample is loaded into the instrument, the instrument may transfer the sample from a plate well into the capillary tube and then position the capillary tube into the starting buffer at the beginning of the capillary electrophoresis process. In block  306 , the CE process  300  begins the instrument run after the sample has been loaded into the capillary by applying a voltage to the buffer solutions positioned at opposite ends of the capillary, forming an electrical gradient to transport DNA fragments of the fluorescently labeled sample from the starting buffer to a destination buffer and traversing an optical sensor. In block  308 , the CE process  300  detects the individual fluorescent signals on the nucleotides of the DNA fragments as they move towards the destination buffer through the optical sensor and communicates the image signal to the computing device. In block  310 , the CE process  300  aggregates the image signal at the computing device from the optical sensor, analyzes the aggregated image signals, and generates sample data that corresponds to the fluorescent intensity of the nucleotides of the DNA fragments. In block  312 , the CE process  300  processes the sample data through the utilization of both a deep learning neural network and sequence analysis algorithms to help identify the bases called in the DNA fragments at the particular time point (which will correspond to a particular scan number in a plurality of scans). In block  314 , the CE process  300  displays processed data as an analyzed trace displayed in an electropherogram and a basecall sequence on a display device. 
       FIG.  4    illustrates a diagram of exemplary input and output data  400  that may be produced and/or displayed in accordance with an embodiment of the present invention. Input data comprises an analyzed trace  410  produced using CE process  300  which may be displayed in an electropherogram similar to that shown in  FIG.  2   . Output data comprises a plurality of basecall positions  420 , a plurality of basecall labels  430 , and a plurality of quality values  440 .  FIG.  4    also shows intermediate data CTC scan labelling probabilities  450  corresponding to each basecall that comprises an output of the dilated convolutional neural network described below and implemented in embodiments of the present invention described herein. In particular embodiments, CTC scan labelling probabilities are generally not displayed in electropherogram  200  of  FIG.  2   , although the basecall positions  420 , basecall labels  430 , and quality values  440  are generally displayed in a typical embodiment. 
     In some embodiments of the invention, the user may select whether their input data contains pure bases only, or contains mixed bases. Basecalling is the interpretation of the dye data that is used to draw the electropherogram. This determines which nucleotide (represented by basecall label  430 ) belongs at which position (represented by basecall position  420 ). Each color shown in the input analyzed trace  410  and basecall label  430  represents a base (may be rendered here in grayscale and/or different/distinct dotted/dashed lines for each base instead of in the standard color notation. In  FIG.  4   , input analyzed trace  410  and basecall labels  430  are rendered in the colors T=red, C=blue, G=black, A=green, respectively called for each peak. As discussed above, a basecall could also be a mixture of two or more (e.g., three or four) nucleotides showing two or more peaks that are superimposed on each other or shifted slightly from each other, and possibly of different peak heights. 
     A quality value  440  in  FIG.  4    is also generated for each basecall in embodiments of the present invention. Quality value  440  is shown in  FIG.  4    output data  400  as a vertical bar of varying height for each called base  430  called depending on a computed estimated probability of error or quality value. A quality value computation implemented in embodiments of the present invention is described further in this specification. 
       FIG.  5    illustrates a diagram for a deep basecalling workflow process  500  in accordance with an embodiment of the present invention. Input data comprises an analyzed trace  510  produced using CE process  300  which may be displayed in an electropherogram similar to that shown in  FIG.  2   . The input trace  510  may be a sequence of dye RFUs collected from a capillary electrophoresis (CE) instrument, or raw spectrum data collected in the CE instrument directly. Input trace  510  may be divided into a number of windows, each comprising a plurality of scans. In one embodiment of the invention, a scan window size determines the number of scans to the scan labelling model  520 . 
     The scan labelling model  520  receives the input scan window and generates scan labelling probabilities for all scans in the scan window. The scan labelling model  520  may comprise one or more trained models. The models may be selected to be utilized to generate the scan labelling probabilities. A deep learning model comprising a neural network  520  is trained to learn an optimal mapping function from analyzed trace  510  to scan labelling probabilities  530  in one embodiment of the present invention. In one embodiment of the present invention, the neural network  520  comprises a dilated convolutional neural network trained to minimize a loss between a target sequence of bases and the corresponding predicted scan labelling probabilities  530  using a Connectionist Temporal Classification (CTC) loss function as described further herein in this specification below. The deep learning model may be trained in accordance with the process depicted in  FIG.  10   . 
     The decoder  540  receives the scan labelling probabilities for the assembled scan windows. The decoder  540  then decodes the scan labelling probabilities into basecalls for the input trace sequence. The decoder  540  may utilize a prefix beam search or other decoders on the assembled label probabilities to find the basecalls for the sequencing sample. 
     The CTC scan labelling probabilities  530  are decoded using CTC decoder and segmentation module  540 , which walks through scan labelling probabilities  530  for all scans to generate a sequence with a maximum labelling probability as a final result. CTC decoder and segmentation module  540  also finds a scan range and then the scan position of a peak labelling probability within the scan range for each called base to generate basecalls (labels) and basecall positions  550  for the sequence. Output data generated by CTC decoder and segmentation module  540  is then used by Basecall Quality Value (QV) Predictor  560  to calculate Quality Values (QVs)  570 , a quality score for each called base as described further herein in this specification below. Basecall QV Predictor  560  finds a quality score for each called base from a trained QV lookup table by using features calculated from CTC scan labelling probabilities as a key. 
     Dilated Convolutional Neural Network 
     Recent research indicates that convolutional neural network architectures can outperform recurrent neural networks and reach state-of-the-art accuracy in audio synthesis, word-level language modeling, and machine translation. For example, a generic temporal convolutional network (TCN) architecture as described in the following reference: Bai, Shaojie, Kolter, J. Zico and Koltun, Vladlen, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, arXiv:1803.01271v2 [cs.LG], 19 Apr. 2018 (“Bai et al.”) has been evaluated across a broad range of sequence modeling tasks other than Sanger sequencing using CE, such as polyphonic music modeling, word-level sequence modeling, and character-level sequence modeling. The results of Bai et al. indicate that TCN outperforms canonical recurrent networks such as LSTMs while demonstrating longer effective memory. 
     Embodiments of the present invention utilize a neural network architecture similar to TCN but, in the case of some embodiments, the neural network architecture utilized has some important distinctions. In one embodiment of the present invention, the network architecture differs from TCN in that 1D fully dilated convolutions were used instead of 1D fully dilated causal convolutions. TCN uses causal convolutions, where an output at time t is convolved only with elements from time t and earlier in the previous layer. However, in CE basecalling, since the entire input scan trace is available during basecalling, past, current and future scan data may be exploited. Some embodiments of the present invention utilize a one-dimensional non-causal fully dilated convolutional network, where an output at time t is convolved not only with elements from time t and earlier but also with later elements in the previous layer, and the length of subsequent layers is the same as the previous layers with zero padding added. In one embodiment of the invention, dilated convolutions are used to achieve an exponentially large receptive field with fewer parameters and fewer layers. 
       FIG.  6    illustrates a deep neural network architecture  600  in accordance with an embodiment of the present invention. In the embodiment shown in  FIG.  6   , deep neural network architecture  600  is trained to learn an optimal mapping function from an input analyzed trace  610  to output of scan labelling probabilities  670 . 
     Input analyzed trace  610  may comprise a plurality of scans which segment a plurality of fluorescent signals of input analyzed trace  610 . As the rate of DNA movement may be unstable and slower than the rate of measurement of the fluorescent signals, the base sequences may differ in length and be much shorter than the segments of fluorescent signal measurements. Thus, the main task of the model is to transform the scans of fluorescent signal measurements with fixed length T into base sequences with non-uniform length M (0&lt;M&lt;T). 
     Network architecture  600  comprises a plurality of hidden layers, included in each of four residual blocks, shown as blocks  620 ,  630 ,  640 , and  650 , where each residual block comprises one or more non-causal convolutional layers. In one embodiment of the invention, a filter size k=9 for all residual blocks  620 - 650  is used. The dilation factors for each residual block are given in one embodiment of the invention as d=2 (i−1)  where i is the depth of the residual block in the neural network, where i=1, 2, 3, and 4 for residual blocks  620 ,  630 ,  640 , and  650  respectively. The feature map sizes are given as w=32, 48, 64, and 128 for residual blocks  620 ,  630 ,  640 , and  650  respectively. The stacked residual blocks act as a feature extractor to map fluorescent signal measurements to feature space. As the dilated non-causal convolution is implemented in the time dimension in some embodiments of the present invention, the extracted features indicate the correlation of the fluorescent signal measurements at different time points. Subsequently, a 1×1 convolutional reduction layer  655  is added after the last residual block to reduce the number of extracted features to match the number of output labels, and a softmax function layer  660  is added after the 1×1 convolutional reduction layer. In one embodiment of the invention, softmax function  660  transforms the output of the 1×1 convolution reduction layer  655  into a matrix of probabilities, in which each matrix row indicates the probabilities of bases appearing at that time point to generate the plurality of scan labelling probabilities  670 . 
       FIG.  7    illustrates a residual block architecture in accordance with some embodiments of the present invention. 
     In some embodiments of the residual block architecture shown in  FIG.  7   , two one-dimensional (1D) fully dilated convolutional layers  702  and  708  of  FIG.  7   , are stacked inside a residual block. A layer normalization  704  and  710  in  FIG.  7   , and a spatial dropout  706  and  712  in  FIG.  7   , may also be added after each dilated convolution for effective training and regularization in some embodiments of the invention. 
     Non-linearity such as one or more rectified linear units (ReLUs), shown here as ReLUs  714 ,  716 , and  718  in  FIG.  7   , may also be included after dilated convolution too. Within a residual block, a skip connection may be used to add the input  701  of the block  700  in  FIG.  7    directly into the output  730  of the block, which is useful for deep network training. If the widths of input and output are different, an additional optional 1×1 convolution  720  in  FIG.  7    can be applied to the inputs to make it match the width of the output. A plurality of residual blocks may be stacked together as shown in an exemplary manner in  FIG.  6    to reach the desired receptive field by increasing dilation factor d exponentially with the depth of the network. 
     Connectionist Temporal Classification 
     For tasks like automated speech recognition (ASR), the process is often broken down into a series of subtasks such as speech segmentation, acoustic modelling, and language modelling. Each of these subtasks is then solved by separate, individually trained models. In 2006, Connectionist Temporal Classification (CTC) was introduced by Alex Graves (see Graves, Alex, Supervised Sequence Labeling with Recurrent Neural Networks, volume 385 of Studies in Computational Intelligence, Springer 2012), to allow training deep neural networks end-to-end for tasks such as ASR. 
     CTC is an objective function that allows a deep learning model to be trained for sequence-to-sequence tasks without requiring any prior alignment between the input and target sequences. More specifically here, CTC is used as a loss function to train the dilated convolutional neural network to minimize the loss between the target sequence of bases and the predicted scan labelling probabilities, which are the output of the network but normalized with the softmax function. 
     Besides the labels for bases (pure bases with a single nucleotide, A, C, G or T or mixed bases with two nucleotides), an additional ‘blank’ label is introduced for CTC. There are two important functions of the blank label: First, the blank label can separate bases, especially successive repeat bases such as AAAA. It makes it possible to label the scans which do not belong to any valid bases and to predict the sequence of bases with varying length. 
     Each input scan can be labelled as bases or blank. A CTC path is a sequence of all scan labels, either bases or blank. The probability of a CTC path is the product of the scan labelling probabilities of all scans in that CTC path. By collapsing the successive repeated labels and then removing the blanks, a CTC path is converted into a basecall sequence. Since many possible CTC paths can be converted into one basecall sequence, the total probability of a basecall sequence is the sum of all probabilities of all possible CTC path for that basecall sequence. For a given input scan sequence x and a target basecall sequence y*, if we write the probability of y* given x as Pr(y*|x), then the CTC loss function is defined as −log(Pr(y*|x)), the negative logarithm of the probability. The dilated convolutional neural network is trained to minimize the CTC loss. 
     CTC Decoder and Segmentation by Prefix Beam Search 
     Since many possible CTC paths can be converted into one basecall sequence, the probability of all possible paths yielding the same basecall sequence are calculated and then summed together to give the probability of a basecall sequence. By selecting the basecall sequence with the highest probability, the final basecalling result may be obtained. In embodiments of the present invention discussed herein, CTC Prefix beam search was used to decode CTC output efficiently (See Graves, Alex. Towards End-to-End Speech Recognition with Recurrent Neural Networks, Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&amp;CP volume 32). In one embodiment of the present invention, a CTC decoder algorithm is employed to decode scan labelling probabilities to generate the final basecall sequence, and then also extend this algorithm to find the scan range and then locate the scan position for each basecalls in the final sequence. 
       FIG.  8    illustrates a method  800  for generating a basecall sequence in accordance with an embodiment of the present invention. Prefix beam search starts with the empty basecall sequence as an initial candidate at step  810 . Method  800  then iterates over all scans in a window of the input trace to determine CTC scan labelling probabilities in step  820 . 
     In step  830 , at each scan in a scan window, all candidate sub-subsequences are extended with all possible labels (all possible options for bases (pure or mixed), or a blank label) and score them by incorporating the scan labelling probabilities of the extended label at that scan. In step  840 , the fixed sized subset B of extended candidates with the K highest scores are saved and then extended at the next scan. The candidates at each scan are normally referred to as prefixes and the number of candidates saved, K, is called as beam width. In step  850 , a separate candidate subset C is created to save all the top candidates at each scan during beam search. At scan t, the candidate with the highest score is saved to the subset C and the scan t is assigned to this candidate if it is different from the top candidate at previous scan t−1. After the last scan, the basecall sequence with the best score is returned as the final basecaller result, and the candidate subset of the top candidates saved at each scan during beam search is also returned in step  860 . 
       FIG.  9    illustrates a method  900  for generating a scan range and scan position for one or more basecalls in a basecall sequence in accordance with an embodiment of the present invention. In method  900 , a basecall sequence y, the most likely sequence found by the decoder in method  800 , is denoted as a length L sequence, and the basecall at the position i=1, . . . , L in the sequence is denoted as y i . Method  900  finds the scan position ti for a basecall in position i in sequence y, where i=1, . . . , L. 
     The method starts at step  910  with the first basecall of the final basecall sequence, where i=1. At iteration i, the sub-sequence y (1 . . . i)  with the first i basecalls in the sequence y is examined at step  920 . The method then iterates over all basecalls in the basecall sequence y as shown, by searching the sub-sequence y (1 . . . i)  in the candidate subset C in step  930 , until each basecall in the entire basecall sequence y has been examined. 
     If the examined sub-sequence is in the candidate subset C as determined in method  800 , the method  900  continues at step  940 , where the scan assigned to the sub-sequence y (1 . . . i)  is used as the begin scan for y i  and then extended by the prefixed scan number nt until the begin scan of next basecall to find the end scan for y i . Once the scan range for y i  is determined, the scan position with the peak scan labelling probability within the defined scan range can be selected as the scan position for the basecall y i  as shown in step  950 . At step  960 , the scan position and the begin and end scans for each basecall in the entire basecall sequence y is returned. 
     The pseudocode in Algorithm  1  describes the CTC decoder and segmentation procedure for a CTC network implemented in one embodiment of the present invention. The blank probability, Pb(y,t), is the probability of the output sequence y at a specific time t, originating from one or more CTC paths ending in the blank label. The non-blank probability, Pnb(y,t), is the probability of the output sequence y at a specific time t accounting for all CTC paths ending in a non-blank labels. The total probability, Pt(y,t), is the sum of Pb(y,t) and Pnb(y,t). 
     Given an input scan sequence x, the probability of emitting the label (or blank) with index k at time t is denoted as Pr(k,t|x). The extension probability Pr(k,y,t) of y by label k at time t is defined as: 
     
       
         
           
             
               Pr 
               ⁡ 
               
                 ( 
                 
                   k 
                   , 
                   y 
                   , 
                   t 
                 
                 ) 
               
             
             = 
             
               
                 Pr 
                 ⁡ 
                 
                   ( 
                   
                     k 
                     , 
                     
                       t 
                       | 
                       x 
                     
                   
                   ) 
                 
               
               ⁢ 
               
                 { 
                 
                   
                     
                       
                         
                           
                             
                               Pb 
                               ⁡ 
                               
                                 ( 
                                 
                                   y 
                                   , 
                                   
                                     t 
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                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             if 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             
                               y 
                               e 
                             
                           
                           = 
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                           Pt 
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                             ( 
                             
                               y 
                               , 
                               
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                         otherwise 
                       
                     
                   
                 
               
             
           
         
       
     
     Where y e  is the final label in y. Also define y ←  as the prefix of y with the last label removed, y 1, . . . , i  is the sub-sequence of y with the first i labels only, and ∅ as the empty sequence. The algorithm returns the most probable candidate as the final basecall sequence and the begin and end scans, t b  and t e , for each basecall in the final sequence. 
     
       
         
           
               
             
               
                   
               
               
                 Algorithm 1 CTC Decoder and Segmentation 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 Initialize: B ← {Ø}; Pb(Ø, 0) ← 1; Pnb(Ø, 0) ← 0; Pt(Ø, 0) ← 1 
               
               
                        C ← { }; ŷ max  ← Ø 
               
               
                 for t = 1...T do 
               
               
                   {circumflex over (B)} ← the K most probable candidates in B 
               
               
                   B ← { } 
               
               
                   for y ∈ {circumflex over (B)} do 
               
               
                    if y ≠ Ø then 
               
               
                      Pnb(y, t) ← Pnb(y,t − 1)Pr(y e , t|x) 
               
               
                      if      ∈ {circumflex over (B)} then 
               
               
                       Pnb(y, t) ← Pnb(y, t) + Pr(y e ,     , t) 
               
               
                    Pb(y,t) ← Pt(y, t − 1)Pr(−, t|x); Pt(y,t) ← Pnb(y,t) + Pb(y,t) 
               
               
                    Add y to B 
               
               
                    for k = 1...K do 
               
               
                     Pb(y + k,t) ← 0 
               
               
                     Pnb(y + k, t) ← Pnb(y + k, t) + Pr(k,y, t) 
               
               
                     Pt(y + k,t) ← Pnb(y + k,t) + Pb(y + k,t) 
               
               
                     Add (y + k) to B 
               
               
                   y max  ← max yϵB Pt(y,t) the most probable candidate in B 
               
               
                   If y max  ≠ ŷ max  then 
               
               
                    ŷ max  ← y max  ; r(y max ) ← t 
               
               
                    Add y max     o C 
               
               
                 L ← the length of y max  ← max yϵB Pt(y,T) 
               
               
                 for i = 1...L do 
               
               
                  if y max   1...i  ∈ C then 
               
               
                   t b  (i) ← r(y max   1...i ) 
               
               
                   if i &lt; L then t e  (i) ← min    (y max   1...i ) + nt,r(y max   1...i+1 ) − 1) 
               
               
                   else t e (i) ← min    (y max   1...i ) + nt, T) 
               
               
                 Return: y max , t b  and t e   
               
               
                   
               
            
           
         
       
     
     Referring to  FIG.  10   , a scan labelling model training method  1000  in one embodiment of the present invention receives sequencing datasets (block  1002 ). The datasets may include pure base datasets and mixed base datasets. In some embodiments of the invention, the data in these datasets has been annotated and manually reviewed to have the correct basecall sequence (“ground truth”) written into each data file (such as a .ab1 data file). In one embodiment, representative data files compiled from data generated using a large variety of CE genetic analyzer and CE DNA analyzer instruments and instrument configurations (e.g., voltage, temperature, chemistry type, capillary array length, etc.) may be used in the sequencing datasets. 
     For example, in one embodiment of the invention, the pure base dataset may comprise ˜49M basecalls and the mixed base dataset may comprise ˜13.4M basecalls. The mixed base data set may be composed primarily of pure bases with occasional mixed bases. For each sample in the dataset, the entire trace is divided into scan windows or segments (block  1004 ). Each scan window may have 500 scan segments. The trace may be a sequence of preprocessed or processed dye RFUs. Additionally, the scan windows for each sample can be shifted by 250 scans to minimize the bias of the scan position on training. The annotated basecalls are listed for each scan window (block  1006 ). These are utilized as the target sequence during the training. The training samples are then constructed (block  1008 ). Each of them may comprise the scan window with 500 scans and the respective annotated basecalls. A CNN is initialized (block  1010 ). In one embodiment of the present invention, the CNN may comprise one or more residual blocks and one 1×1 convolutional reduction layer as shown in  FIG.  7   . A Softmax layer may be utilized as the output layer of the CNN, which outputs the scan labelling probabilities for all scans in the input trace. 
     A mini-batch of training samples is then selected (block  1011 ). The mini-batch may be selected randomly from the training dataset at each training step. The mini-batch of training samples are then applied to the CNN (block  1012 ). The scan labelling probabilities for all scans in the input scan windows are output (block  1014 ). The loss between the output scan labelling probabilities and the target annotated basecalls are calculated. 
     A Connectionist Temporal Classification (CTC) loss function may be utilized to calculate the loss between the output scan labelling probabilities and the target annotated basecalls. The weights of the networks are updated to minimize the CTC loss against the mini-batch of training samples (block  1020 ). An Adam optimizer or other gradient descent optimizer may be utilized to update the weights. The networks are then saved as a model (block  1022 ). In some embodiments, the model is saved during specific training steps. The saved model is evaluated utilizing a validation dataset, an independent subset of samples, which are not included in the training process. The scan labelling model training method  1000  then determines whether the validation loss and error rate have stopped decreasing, or a predetermined number of training steps has been reached, whichever comes first (decision block  1024 ). If not, the scan labelling modelling training method  1000  is re-performed from block  1012  utilizing the network with the updated weights (i.e., the next iteration of the network). Once the validation loss and error rate have stopped decreasing, or a predetermined number of training steps are performed, the saved models are evaluated (block  1026 ). The best trained models are then selected based on minimum validation loss or error rate from the trained models. These model(s) may then be utilized by the CTC decoder and segmentation basecalling system  540 . 
     In some embodiments, two scan labelling models/neural networks may be generated using scan labelling model training method  1000 : one model for a pure base category of data, and a second model for a mixed base category of data. 
     Embodiments of the present invention can also be trained to call mixed bases, e.g. basecalls of 2, 3, or 4 bases in one position. However, training data from diploid organisms such as human samples, having mixed bases with 2 bases per position, are generally more common than training data from samples with &gt;2 bases per position, such as from some bacterial samples. Mixed basecalling is more challenging than pure basecalling because the peaks of a mixed base position, e.g., two bases in one position, often do not line up exactly superimposed one on the other. Typically, the two peaks may be shifted slightly from each other. Furthermore, in Sanger sequencing, peak heights are often not uniform, and hence the two peaks may be of different peak heights, sometimes even significantly different peak heights. 
     In some embodiments, data augmentation techniques such as adding noise, spikes, dye blobs or other data artefacts or simulated sequencing trace may be utilized to improve the robustness of the models. Also, during training, other techniques, such as drop-out or weight decay, may be used to improve the generality of the models. Generative Adversarial Nets (GANs) may be utilized to implement these techniques. 
     Transfer Learning for Customized or Application Specific Models 
     Transfer Learning has been successfully used to reuse existing neural models for image classification, object recognition, translation, speech synthesis, and many other domains By using transfer learning, the network already trained for general pure or mixed basecalling can be re-trained for customized and application specific models in some embodiments of the present invention. General models learned from existing training datasets may be reused, and just the final 1×1 convolutional reduction layer may be retrained with additional customer or application data to generate specific models for different customers and applications. Since the trained features saved in earlier layers will be reused and only the weights in final layer will be updated, far less customer or application training data is required for training. Transfer learning as used in some embodiments of the present invention may allow customers to leverage their annotated data to optimize the general deep basecalling neural network for better basecalling performance for their specific applications. 
     Since only the weights of the last 1×1 convolutional reduction layer needed to be re-trained with customer datasets and the number of weights in the last layer ranges from several hundred to several thousand, a customer or application specific dataset of several thousand samples could be enough, which far less than the number of samples used for baseline model training, which can number in the hundreds of thousands. 
     The process performed by the users to retrain the model is similar. First, select annotated training, validation and test datasets. Then, train the model by using training dataset and monitor the training. Next, select the best trained model by using the validation dataset and test the selected model by the test dataset. However, since the training starts from the general trained model instead of starting from scratch, the number of samples needed are much less and the training time (maybe just a few minutes) is far less compared against the training time needed for baseline model training. 
     Base Call Quality Values (QV) 
     The quality value model  560  of  FIG.  5    receives the scan labelling probabilities for the assembled scan windows, the basecalls, and the peak scan labelling probability. The quality value model  560  then generates an estimated basecalling error probability. The estimated basecalling error probability may be translated to Phred-style quality scores by the following equation:
 
QV=−10×log(Probability of Error).
 
     In another example, Phred (Ewing &amp; Green, 1998) proposed in their Phred Basecaller to use a function of certain parameters computed from the trace data to estimate a probability of error or quality value, negative log-transformed error probability, for each basecall (See Brent Ewing and Phil Green, Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities, Genome Res. 1998 8: 186-194). 
     A similar strategy has also been applied in genetic sequencing analysis software, such as KB Basecaller manufactured by the Applied Biosystems unit of Thermo Fisher Scientific, Inc. to calculate QV for each basecall (See Labrenz, James, Sorenson, Jon M. and Gehman, Curtis. Methods and systems for the analysis of biological sequence data, WO2004113557A2, 2004 Dec. 29). However, different parameters computed from the trace data are used for QV calculation in KB Basecaller relative to the original Phred basecaller. Similarly, the deep learning basecaller described herein as embodiments of the present invention also calculates quality value to provide an estimation of the confidence of all called bases. Unlike the Phred Basecaller described above and KB Basecaller, the parameters or features used for QV calculation in embodiments of the present invention are based on the CTC scan labelling probabilities instead of trace data. Specifically, a feature vector with four parameters or features listed below are computed from local window of CTC scan labelling probabilities around the basecall scan position for each basecall: 
     (1) CTC scan labelling probability: CTC scan labelling probability of called base at basecall scan position 
     (2) Noise-to-signal ratio: the ratio of the maximum scan labelling probability from uncalled bases or noise scan labelling probabilities within local windows to the scan labelling probability of called base at basecall scan position 
     (3) Basecall spacing ratio: the ratio of the base spacing between a basecall and their neighboring basecalls 
     (4) Resolution: the ratio of local base spacing to the width of the scan labelling probability peak for the called base 
       FIG.  11    illustrates a method  1100  for constructing a trained quality value lookup table in accordance with one embodiment of the present invention. A large annotated data set is required in QV training to produce one or more QV lookup tables as shown in step  1110 . 
     In one embodiment of the present invention, at step  1120 , a convolutional neural network trained using a method as shown in  FIG.  6   , a decoder using a method as shown in  FIG.  8   , and a basecall position finder using a method as shown in  FIG.  9    are used in to compute CTC scan labelling probabilities, basecalls, and basecall scan positions for each basecall in the training dataset. Whether the basecall in this QV training data set is considered correct or not depends on an alignment between the called and annotated sequences for each sample. All basecalls in the training dataset can be assigned in step  1130  into one of two categories: correct basecalls and incorrect basecalls. A basecall can be characterized by a feature vector, p, with the four features listed above. A feature vector for each basecall is computed in step  1140 . All features must be positively and monotonically related to probability of error. In step  1150 , the basecalls used for QV training are grouped into many cuts, which equalize histograms for each feature. The empirical error rate is also computed for each cut in step  1150 . In step  1160 , a lookup table is constructed. The cut with the lowest error rate is added to lookup table first. A new line is added in the lookup table for that cut with a feature vector defining the cut and the QV corresponding to the error rate of that cut (p i , q i ). Once a cut is added to lookup table, the calls contained in that cut are also removed from all remaining cuts. This process is repeated until all cuts are added to QV lookup table. The QV lookup table is then complete. 
     A plurality of trained QV lookup tables can be then used to assign a QV for each basecall. Embodiments of the present invention utilize three separate trained QV tables: one for pure bases in a pure base data category, one for pure bases in a mixed base data category (i.e., a sample that is almost entirely pure bases with occasional mixed bases), and one for mixed bases in a mixed base data category. In some embodiments of the present invention, the QV lookup table training may be done twice: once using the pure base dataset to create the pure base data category QV table, and a second time using the mixed base dataset to create the pure base in a mixed base data category QV table, as well as the mixed base in a mixed base category QV table. 
     For a called base, the feature vector, p, for that basecall is calculated. The feature vector is then used as a query key to search the lookup table line by line, in order, until a line with all feature values larger or equal to the corresponding features for that basecall. The QV associated to that line is then assigned to that basecall. The basecalls with no lines found are assigned to QV=0. 
     Exemplary Computing Device Embodiment 
       FIG.  12    is an example block diagram of a computing device  1200  that may incorporate embodiments of the present invention.  FIG.  12    is merely illustrative of a machine system to carry out aspects of the technical processes described herein, and does not limit the scope of the claims One of ordinary skill in the art would recognize other variations, modifications, and alternatives. In one embodiment, the computing device  1200  typically includes a monitor or graphical user interface  1202 , a data processing system  1220 , a communication network interface  1212 , input device(s)  1208 , output device(s)  1206 , and the like. 
     As depicted in  FIG.  12   , the data processing system  1220  may include one or more processor(s)  1204  that communicate with a number of peripheral devices via a bus subsystem  1218 . These peripheral devices may include input device(s)  1208 , output device(s)  1206 , communication network interface  1212 , and a storage subsystem, such as a volatile memory  1210  and a nonvolatile memory  1214 . The volatile memory  1210  and/or the nonvolatile memory  1214  may store computer-executable instructions and thus forming logic  1222  that when applied to and executed by the processor(s)  1204  implement embodiments of the processes disclosed herein. 
     The input device(s)  1208  include devices and mechanisms for inputting information to the data processing system  1220 . These may include a keyboard, a keypad, a touch screen incorporated into the monitor or graphical user interface  1202 , audio input devices such as voice recognition systems, microphones, and other types of input devices. In various embodiments, the input device(s)  1208  may be embodied as a computer mouse, a trackball, a track pad, a joystick, wireless remote, drawing tablet, voice command system, eye tracking system, and the like. The input device(s)  1208  typically allow a user to select objects, icons, control areas, text and the like that appear on the monitor or graphical user interface  1202  via a command such as a click of a button or the like. 
     The output device(s)  1206  include devices and mechanisms for outputting information from the data processing system  1220 . These may include the monitor or graphical user interface  1202 , speakers, printers, infrared LEDs, and so on as well understood in the art. 
     The communication network interface  1212  provides an interface to communication networks (e.g., communication network  1216 ) and devices external to the data processing system  1220 . The communication network interface  1212  may serve as an interface for receiving data from and transmitting data to other systems. Embodiments of the communication network interface  1212  may include an Ethernet interface, a modem (telephone, satellite, cable, ISDN), (asynchronous) digital subscriber line (DSL), FireWire, USB, a wireless communication interface such as Bluetooth or WiFi, a near field communication wireless interface, a cellular interface, and the like. The communication network interface  1212  may be coupled to the communication network  1216  via an antenna, a cable, or the like. In some embodiments, the communication network interface  1212  may be physically integrated on a circuit board of the data processing system  1220 , or in some cases may be implemented in software or firmware, such as “soft modems”, or the like. The computing device  1200  may include logic that enables communications over a network using protocols such as HTTP, TCP/IP, RTP/RTSP, IPX, UDP and the like. 
     The volatile memory  1210  and the nonvolatile memory  1214  are examples of tangible media configured to store computer readable data and instructions forming logic to implement aspects of the processes described herein. Other types of tangible media include removable memory (e.g., pluggable USB memory devices, mobile device SIM cards), optical storage media such as CD-ROMS, DVDs, semiconductor memories such as flash memories, non-transitory read-only-memories (ROMS), battery-backed volatile memories, networked storage devices, and the like. The volatile memory  1210  and the nonvolatile memory  1214  may be configured to store the basic programming and data constructs that provide the functionality of the disclosed processes and other embodiments thereof that fall within the scope of the present invention. Logic  1222  that implements embodiments of the present invention may be formed by the volatile memory  1210  and/or the nonvolatile memory  1214  storing computer readable instructions. Said instructions may be read from the volatile memory  1210  and/or nonvolatile memory  1214  and executed by the processor(s)  1204 . The volatile memory  1210  and the nonvolatile memory  1214  may also provide a repository for storing data used by the logic  1222 . The volatile memory  1210  and the nonvolatile memory  1214  may include a number of memories including a main random access memory (RAM) for storage of instructions and data during program execution and a read only memory (ROM) in which read-only non-transitory instructions are stored. The volatile memory  1210  and the nonvolatile memory  1214  may include a file storage subsystem providing persistent (non-volatile) storage for program and data files. The volatile memory  1210  and the nonvolatile memory  1214  may include removable storage systems, such as removable flash memory. 
     The bus subsystem  1218  provides a mechanism for enabling the various components and subsystems of data processing system  1220  communicate with each other as intended. Although the communication network interface  1212  is depicted schematically as a single bus, some embodiments of the bus subsystem  1218  may utilize multiple distinct busses. 
     It will be readily apparent to one of ordinary skill in the art that the computing device  1200  may be a device such as a smartphone, a desktop computer, a laptop computer, a rack-mounted computer system, a computer server, or a tablet computer device. As commonly known in the art, the computing device  1200  may be implemented as a collection of multiple networked computing devices. Further, the computing device  1200  will typically include operating system logic (not illustrated) the types and nature of which are well known in the art. 
     One embodiment of the present invention includes systems, methods, and a non-transitory computer readable storage medium or media tangibly storing computer program logic capable of being executed by a computer processor. 
     Those skilled in the art will appreciate that computer system  1200  illustrates just one example of a system in which a computer program product in accordance with an embodiment of the present invention may be implemented. To cite but one example of an alternative embodiment, execution of instructions contained in a computer program product in accordance with an embodiment of the present invention may be distributed over multiple computers, such as, for example, over the computers of a distributed computing network. 
     While the present invention has been particularly described with respect to the illustrated embodiments, it will be appreciated that various alterations, modifications and adaptations may be made based on the present disclosure and are intended to be within the scope of the present invention. While the invention has been described in connection with what are presently considered to be the most practical and preferred embodiments, it is to be understood that the present invention is not limited to the disclosed embodiments but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the underlying principles of the invention as described by the various embodiments referenced above and below. 
     Terminology 
     Terminology used herein with reference to embodiments of the present invention disclosed in this document should be accorded its ordinary meaning according to those of ordinary skill in the art unless otherwise indicated expressly or by context. 
     “Quality values” in this context refers to an estimate (or prediction) of the likelihood that a given basecall is in error. Typically, the quality value is scaled following the convention established by the Phred program: QV=−10 log 10(Pe), where Pe stands for the estimated probability that the call is in error. See Brent Ewing and Phil Green, Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities, Genome Res. 1998 8: 186-194. Quality values are a measure of the certainty of the basecalling and consensus-calling algorithms. Higher values correspond to lower chance of algorithm error. Sample quality values refer to the per base quality values for a sample, and consensus quality values are per-consensus quality values. 
     “Sigmoid function” in this context refers to a function of the form f(x)=1/(exp(−x)). The sigmoid function is used as an activation function in artificial neural networks. It has the property of mapping a wide range of input values to the range 0-1, or sometimes −1 to 1. 
     “Capillary electrophoresis genetic analyzer” or “capillary electrophoresis DNA analyzer” in this context refers to instrument that applies an electrical field to a capillary loaded with a biological sample so that the negatively charged DNA fragments move toward the positive electrode. The speed at which a DNA fragment moves through the medium is inversely proportional to its molecular weight. This process of electrophoresis can separate the extension products by size at a resolution of one base. 
     “Image signal” in this context refers to an intensity reading of fluorescence from one of the dyes used to identify bases during a data run. In one embodiment of the present invention, signal strength numbers are shown in an Annotation view of the sample file. 
     “Exemplary commercial CE devices” in this context may refer to and include, but are not limited to, the following: the Applied Biosystems, Inc. (ABI) genetic analyzer models 310 (single capillary), 3130 (4 capillary), 3130xL (16 capillary), 3500 (8 capillary), 3500xL (24 capillary), and the SeqStudio genetic analyzer models; DNA analyzer models 3730 (48 capillary), and 3730xL (96 capillary); as well as the Agilent 7100 device, Prince Technologies, Inc.&#39;s PrinCE™ Capillary Electrophoresis System, Lumex, Inc.&#39;s Capel-105™ CE system, and Beckman Coulter&#39;s P/ACE™ MDQ systems, among others. 
     “Base pair” in this context refers to complementary nucleotides in a DNA sequence. Thymine (T) is complementary to adenine (A) and guanine (G) is complementary to cytosine (C). 
     “ReLU” in this context refers to a rectified linear activation function unit, a piecewise linear function that will output the input directly if it is positive; otherwise, it will output zero. It is also known as a ramp function and is analogous to half-wave rectification in electrical signal theory. ReLU is a popular activation function in deep neural networks. 
     “Heterozygous insertion deletion variant” (or “het indel”) in this context refers to a polymorphism in which one copy of a DNA sequence has an insertion or deletion relative to the other copy being sequenced together simultaneously. The result of sequencing a het indel is that downstream of the heterozygous insertion or deletion there are two peaks (also known as mixed bases) at the majority of positions. 
     “Mobility shift” in this context refers to electrophoretic mobility differences imposed by the presence of different fluorescent dye molecules associated with differently labeled reaction extension products. 
     “Variant” in this context refers to bases where the consensus sequence differs from the reference sequence that is provided. 
     “Polymerase slippage” in this context results in the presence of minor peaks 3′ to a homopolymer. The polymerase can “slip” when sequencing a long homopolymer stretch, skipping one or more bases within the homopolymer, thereby creating shortened products that differ in length by one to a few bases that appear as minor peaks within and downstream of the homopolymer. 
     “Amplicon” in this context refers to the product of a PCR reaction. Typically, an amplicon is a short piece of DNA. 
     “Basecall” in this context refers to assigning a nucleotide base to each peak (IUPAC-IUB notation: A, C, G, T) of the fluorescent signal. Basecalls can also be mixed, with 2 peaks in one position (IUPAC-IUB notation: R=A and G, Y=C and T, S=G and C, W=A and T, K=G and T, and M=A and C), or 3 peaks in one position (IUPAC-IUB notation: B=C and G and T, D=A and G and T, H=A and C and T, V=A and C and G). 
     “Raw data” or “input analyzed trace” in this context refers to a multicolor graph displaying the fluorescence intensity (signal) collected for each of the four fluorescent dyes, and/or data that is used to populate or create such a graph. 
     “Base spacing” in this context refers to the number of data points from one peak to the next. A negative spacing value or a spacing value shown in red indicates that the basecaller used a default spacing value rather than one calculated based on the current data. 
     “Separation or sieving media” in this context refers to non-gel liquid polymers such as linear polyacrylamide, hydroxyalkyl cellulose (HEC), agarose, and cellulose acetate, and the like can be used. Other separation media that can be used for capillary electrophoresis include, but are not limited to, water soluble polymers such as poly(N,N′-dimethyl acrylamide)(PDMA), polyethylene glycol (PEG), poly(vinylpyrrolidone) (PVP), polyethylene oxide, polysaccharides and pluronic polyols; various polyvinyl alcohol (PVAL)-related polymers, polyether-water mixture, lyotropic polymer liquid crystals, among others. 
     “Adam optimizer” in this context refers to an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iteratively based on training data. Stochastic gradient descent maintains a single learning rate (termed alpha) for all weight updates and the learning rate does not change during training. A learning rate is maintained for each network weight (parameter) and separately adapted as learning unfolds. Adam optimizers combine the advantages of two other extensions of stochastic gradient descent: specifically, Adaptive Gradient Algorithm (AdaGrad) that maintains a per-parameter learning rate that improves performance on problems with sparse gradients (e.g. natural language and computer vision problems), and Root Mean Square Propagation (RMSProp) that also maintains per-parameter learning rates that are adapted based on the average of recent magnitudes of the gradients for the weight (e.g. how quickly it is changing). This means the algorithm does well on online and non-stationary problems. Adam realizes the benefits of both AdaGrad and RMSProp. Instead of adapting the parameter learning rates based on the average of the first moments (the means) as in RMSProp, Adam also makes use of the average of the second moments of the gradients (the uncentered variances). Specifically, the algorithm calculates an exponential moving average of the gradient and the squared gradient, and the parameters beta1 and beta2 control the decay rates of these moving averages. The initial value of the moving averages and beta1 and beta2 values close to 1.0 (recommended) result in a bias of moment estimates towards zero. This bias is overcome by first calculating the biased estimates before then calculating bias-corrected estimates. 
     “Hyperbolic tangent function” in this context refers to a function of the form tanh(x)=sin h(x)/cos h(x). The tanh function is a popular activation function in artificial neural networks. Like the sigmoid, the tanh function is also sigmoidal (“s”-shaped), but instead outputs values that range (−1, 1). Thus, strongly negative inputs to the tanh will map to negative outputs. Additionally, only zero-valued inputs are mapped to near-zero outputs. These properties make the network less likely to get “stuck” during training. 
     “Relative fluorescence unit” in this context refers to measurements in electrophoresis methods, such as capillary electrophoresis methods for DNA sequencing analysis. A “relative fluorescence unit” is a unit of measurement used in analysis which employs fluorescence detection. 
     “CTC loss function” in this context refers to connectionist temporal classification, a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks, temporal convolutional networks (TCNs), or dilated causal or non-causal convolution networks to tackle sequence problems where the timing is variable. A CTC network has a continuous output (e.g. Softmax), which is fitted through training to model the probability of a label. CTC does not attempt to learn boundaries and timings: Label sequences are considered equivalent if they differ only in alignment, ignoring blanks Equivalent label sequences can occur in many ways—which makes scoring a non-trivial task. Fortunately, scoring of equivalent label sequences may be completed using the Forward-Backward algorithm. CTC scores can then be used with the back-propagation algorithm to update the neural network weights. Alternative approaches to a CTC-fitted neural network include a hidden Markov model (HMM). 
     “Polymerase” in this context refers to an enzyme that catalyzes polymerization. DNA and RNA polymerases build single-stranded DNA or RNA (respectively) from free nucleotides, using another single-stranded DNA or RNA as the template. 
     “Sample data” in this context refers to the output of a single lane or capillary on a sequencing instrument. Sample data can be entered into Sequencing Analysis, SeqScape, and other sequencing analysis software manufactured by Applied Biosystems, Inc. and other manufacturers. 
     “Plasmid” in this context refers to a genetic structure in a cell that can replicate independently of the chromosomes, typically a small circular DNA molecule in the cytoplasm of a bacterium or protozoan. Plasmids are often used in the laboratory manipulation of genes. 
     “Beam search” in this context refers to a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is an optimization of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states) according to some heuristic. But in beam search, only a predetermined number of best partial solutions are kept as candidates. It is thus a greedy algorithm. Beam search uses breadth-first search to build its search tree. At each level of the tree, it generates all successors of the states at the current level, sorting them in increasing order of heuristic cost. However, it only stores a predetermined number, K, of best states at each level (called the beam width). Only those states are expanded next. The greater the beam width, the fewer states are pruned. With an infinite beam width, no states are pruned and beam search is identical to breadth-first search. The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists). Beam search is not optimal (that is, there is no guarantee that it will find the best solution). In general, beam search returns the first solution found. Beam search for machine translation is a different case: once reaching the configured maximum search depth (i.e. translation length), the algorithm will evaluate the solutions found during search at various depths and return the best one (the one with the highest probability). The beam width can either be fixed or variable. One approach that uses a variable beam width starts with the width at a minimum. If no solution is found, the beam is widened and the procedure is repeated. 
     “Sanger Sequencing” in this context refers to a DNA sequencing process that takes advantage of the ability of DNA polymerase to incorporate 2′,3′-dideoxynucleotides—nucleotide base analogs that lack the 3′-hydroxyl group essential in phosphodiester bond formation. As originally designed, Sanger dideoxy sequencing required a DNA template, a sequencing primer, DNA polymerase, deoxynucleotides (dNTPs), dideoxynucleotides (ddNTPs), and reaction buffer. Four separate reactions are set up, each containing radioactively labeled nucleotides and either ddA, ddC, ddG, or ddT. The annealing, labeling, and termination steps are performed on separate heat blocks. DNA synthesis is performed at 37° C., the temperature at which DNA polymerase has the optimal enzyme activity. DNA polymerase adds a deoxynucleotide or the corresponding 2′,3′-dideoxynucleotide at each step of chain extension. Whether a deoxynucleotide or a dideoxynucleotide is added depends on the relative concentration of both molecules. When a deoxynucleotide (A, C, G, or T) is added to the 3′ end, chain extension can continue. However, when a dideoxynucleotide (ddA, ddC, ddG, or ddT) is added to the 3′ end, chain extension terminates. Sanger dideoxy sequencing results in the formation of extension products of various lengths terminated with dideoxynucleotides at the 3′ end. 
     “Single nucleotide polymorphism” in this context refers to a variation in a single base pair in a DNA sequence. 
     “Mixed base” in this context refers to one-base positions that contain 2, 3, or 4 bases. These bases are assigned the appropriate IUB code. 
     “Softmax function” in this context refers to a function of the form f(xi)=exp(xi)/sum(exp(x)) where the sum is taken over a set of x. Softmax is used at different layers (often at the output layer) of artificial neural networks to predict classifications for inputs to those layers. The Softmax function calculates the probabilities distribution of the event xi over ‘n’ different events. In general sense, this function calculates the probabilities of each target class over all possible target classes. The calculated probabilities are helpful for predicting that the target class is represented in the inputs. The main advantage of using Softmax is the output probabilities range. The range will be from 0 to 1, and the sum of all the probabilities will be equal to one. If the softmax function used for multi-classification model it returns the probabilities of each class and the target class will have the high probability. The formula computes the exponential (e-power) of the given input value and the sum of exponential values of all the values in the inputs. Then the ratio of the exponential of the input value and the sum of exponential values is the output of the softmax function. 
     “Noise” in this context refers to average background fluorescent intensity for each dye. 
     “Backpropagation” in this context refers to an algorithm used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network. It is commonly used to train deep neural networks, a term referring to neural networks with more than one hidden layer. For backpropagation, the loss function calculates the difference between the network output and its expected output, after a case propagates through the network. 
     “Dequeue max finder” in this context refers to an algorithm utilizing a double-ended queue to determine a maximum value. 
     “Pure base” in this context refers to one-base positions that contain only one base or nucleotide (A, C, G, and T). These bases are assigned the appropriate IUPAC-IUB code. 
     “Primer” in this context refers to a short single strand of DNA that serves as the priming site for DNA polymerase in a PCR reaction. 
     “Loss function” (sometime referred to as a cost function or error function) in this context refers to, is a function that maps values of one or more variables onto a real number intuitively representing some “cost” associated with those values.