Methods and systems for sequence deconvolution

Abstract

In a first aspect, the disclosure provide methods of identifying polynucleotide molecules distributed in a plurality of partitions, the method including providing a plurality of sequencing read data; providing a plurality of reference sequences; comparing each of the plurality of sequencing read data to each of the plurality of reference sequences using an algorithm; generating, based on the comparison of each of the plurality of sequencing read data to each of the plurality of reference sequences, a score for each of the sequencing read data; and assigning to each of the plurality of sequencing read data, based on the score for each of the sequencing read data, a polynucleotide molecule, thereby identifying each polynucleotide molecule in each of the plurality of partitions.

Description

[0001] METHODS AND SYSTEMS FOR SEQUENCE DECONVOLUTION

[0002] TECHNICAL FIELD

[0003] This disclosure relates to the computational analysis of sequencing data generated from nucleic acid constructs encoding engineered proteins. More specifically, this disclosure relates to methods for deconvolution of sequencing data of a mixed sample and to related apparatuses and related computer readable medium storing processorexecutable instructions to provide the methods for deconvolution of sequencing data.

[0004] BACKGROUND

[0005] During protein engineering workflows, large design spaces are interrogated and explored. These discovery campaigns can involve high-throughput and bulk cloning technologies for the expression of engineered proteins, processes that generate large and diverse libraries of protein therapeutics. These techniques require unambiguous and efficient identification of nucleic acid constructs corresponding to engineered protein species.

[0006] Cloning processes can generate a large amount of data, for example, up to tens of thousands of nucleic constructs encoding engineered proteins. Manual identification and characterization of this number of nucleic acid constructs is not practical. Therefore, there exists a need for methods and systems for the parallelized analysis and deconvolution of sequencing data generated from large numbers of nucleic acid constructs encoding engineered proteins.

[0007] SUMMARY

[0008] The methods disclosed herein are based, at least in part, on a modified longest common substring (LCS) algorithm wherein additional thresholds and parameters have been introduced. The modified LCS algorithm disclosed herein is referred to as a maximum common subsequence (MCS) algorithm which is used to assign sequencing reads to reference sequences, thereby deconvoluting sequencing data generated from large numbers of nucleic acid constructs encoding engineered proteins. Compared to previously available methods, the methods disclosed herein have several advantages. First, the methods disclosed herein are faster and more computationally efficient than previously available methods, exhibiting a quantitative improvement in run time. Second, the methods disclosed herein reduce the number of false positives and false negatives and result in an increased number of unambiguous matches per analysis relative to previously available methods. A third advantage of the methods disclosed herein is the usage of a multithreading approach, z.e., single sequencing reads are distributed to different CPU cores leading to an increase in performance, especially if executed on a dedicated multicore workstation. One sequencing read can be compared via MCS with many references in parallel rather than sequentially

[0009] In a first aspect, the disclosure provides methods of identifying polynucleotide molecules distributed in a plurality of partitions, the methods including obtaining a plurality of sequencing read data; obtaining a plurality of reference sequences; comparing each of the plurality of sequencing read data to each of the plurality of reference sequences using an algorithm; generating, based on the comparison of each of the plurality of sequencing read data to each of the plurality of reference sequences, a score for each of the sequencing read data; and assigning to each of the plurality of sequencing read data, based on the score for each of the sequencing read data, a polynucleotide molecule, thereby identifying each polynucleotide molecule in each of the plurality of partitions. In some embodiments, each of the plurality of reference sequences corresponds to an expected identity of a polynucleotide molecule. In some embodiments, providing the plurality of sequencing read data comprises sequencing each of the polynucleotide molecules. In some embodiments, the plurality of partitions are wells in a multi-well plate.

[0010] In some embodiments, the polynucleotide molecules are DNA constructs. In some embodiments, each of the DNA constructs encodes a protein. In some embodiments, each of the DNA constructs is assembled in vitro. In some embodiments, each of the DNA constructs is assembled in vivo in an organism. In some embodiments, the organism is a bacterium. In some embodiments, the organism is a fungus.

[0011] In some embodiments, each of the plurality of proteins is a wild-type protein or an engineered protein. In some embodiments, each of the plurality of references sequences are designed to correspond to each of the plurality of wild-type proteins or engineered proteins. In some embodiments, the algorithm is a longest common substring (LCS) algorithm. In some embodiments, the algorithm is a modified LCS algorithm. In some embodiments, the algorithm is a maximum common subsequence (MCS) algorithm. In some embodiments, the score for each of the sequencing read data is a longest match score. In some embodiments, a polynucleotide molecule is assigned to one of the plurality of sequencing read data if the score for the sequencing read data exceeds a predetermined threshold. In some embodiments, at least 10,000 polynucleotide molecules are identified in under five hours.

[0012] In another aspect, the disclosure provides systems for identifying polynucleotide molecules distributed in a plurality of partitions, the system including a data store configured to receive a plurality of sequencing read data and a plurality of reference sequences; a computing device communicatively connected to the data store, the computing device configured to: compare each of the plurality of sequencing read data to each of the plurality of reference sequences using an algorithm; generate, based on the comparison of each of the plurality of sequencing read data to each of the plurality of reference sequences, a score for each of the sequencing read data; and assign to each of the plurality of sequencing read data, based on the score for each of the sequencing read data, a polynucleotide molecule, thereby identifying each polynucleotide molecule in each of the plurality of partitions. In some embodiments, each of the plurality of reference sequences corresponds to an expected identity of a polynucleotide molecule. In some embodiments, providing the plurality of sequencing read data comprises sequencing each of the polynucleotide molecules. In some embodiments, the plurality of partitions are wells in a multi-well plate.

[0013] In some embodiments, the polynucleotide molecules are DNA constructs. In some embodiments, each of the DNA constructs encodes a protein. In some embodiments, each of the DNA constructs is assembled in vitro. In some embodiments, each of the DNA constructs is assembled in vivo in an organism. In some embodiments, the organism is a bacterium. In some embodiments, the organism is a fungus.

[0014] In some embodiments, each of the plurality of proteins is a wild-type protein or an engineered protein. In some embodiments, each of the plurality of references sequences are designed to correspond to each of the plurality of wild-type proteins or engineered proteins. In some embodiments, the algorithm is a longest common substring (LCS) algorithm. In some embodiments, the algorithm is a modified LCS algorithm. In some embodiments, the algorithm is a maximum common subsequence (MCS) algorithm. In some embodiments, the score for each of the sequencing read data is a longest match score. In some embodiments, a polynucleotide molecule is assigned to one of the plurality of sequencing read data if the score for the sequencing read data exceeds a predetermined threshold. In some embodiments, at least 10,000 polynucleotide molecules are identified in under five hours.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0016] Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

[0017] DESCRIPTION OF DRAWINGS

[0018] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0019] FIG. 1 shows an overview of the workflow of an embodiment of the disclosed methods.

[0020] FIG. 2A is a schematic of a plurality of multi-well plates that can be iteratively processed according to the methods and systems disclosed herein.

[0021] FIG. 2B is a schematic of a multi-well plate, wherein each well comprises about one unique nucleic acid construct.

[0022] FIG. 2C is a schematic of a comparison step of the methods and systems disclosed herein, wherein each sequencing read is compared to each reference sequence of a plurality of reference sequences according to a longest common substring (LCS) algorithm.

[0023] FIG. 3A is a schematic of a possible scenario of the method, wherein after applying the LCS algorithm, a given sequencing read may share a common substring of equal length with more than one reference sequence.

[0024] FIG. 3B is a schematic of a possible scenario of the method, wherein after applying the LCS algorithm, a first sequencing read may have a common substring with a first reference sequence that is identical to a common substring shared by a second sequencing read and a second reference sequence.

[0025] FIG. 3C is a schematic of a possible scenario of the method, wherein after applying the LCS algorithm, a sequencing read may not share a common substring with any reference sequence, or the longest common substring may be shorter than a predetermined threshold such that the hit is not informative.

[0026] FIG. 4A shows a flow chart and decision tree comprising the steps of the methods disclosed herein.

[0027] FIG. 4B shows a decision tree providing rules and guidelines for prioritizing results after applying the methods disclosed herein.

[0028] FIG. 5 is a diagram of computer system components that can be used to implement methods for deconvolution of sequencing data generated from large numbers of nucleic acid constructs encoding engineered proteins.

[0029] DETAILED DESCRIPTION

[0030] Methods of assigning sequencing reads to a sequencing reaction

[0031] The present disclosure provides methods and systems for the analysis and deconvolution of sequencing data generated from nucleic acid constructs encoding engineered proteins. In some embodiments, the methods and systems provided herein include identifying polynucleotide molecules distributed in a plurality of partitions by comparison of each of a plurality of sequencing reads to each of a plurality of provided reference sequences. FIG 1 shows an overview of the workflow of an embodiment of the methods and systems disclosed herein. In an embodiment of the method 100, a plurality of sequencing read data is provided (102) and a plurality of reference sequences are provided (104). Each of the plurality of sequencing read data is compared to each of the plurality of reference sequences using an algorithm (106). In some embodiments, the algorithm is a modified approach of the longest common substring (LCS) algorithm hereinafter called maximum common subsequence (MCS). A score for each of the sequencing read data is generated based on the comparison of each of the plurality of sequencing read data to each of the plurality of reference sequences (108). Finally, a polynucleotide molecule is assigned to each of the plurality of sequencing read data, based on the score for each of the sequencing read data, thereby identifying each polynucleotide molecule in each of the plurality of partitions (110).

[0032] In some embodiments, each of a plurality nucleic acid constructs are distributed into a plurality of partitions. In some embodiments, there is about one nucleic acid construct per partition. In some embodiments, the partitions are wells of a multi-well plate. In some embodiments, the multi -well plate is a 96-well plate, a 384-well plate, or a 1,536-well plate. In some embodiments, the partitions are partitions in a microfluidic device. In some embodiments, each of the plurality of nucleic acid constructs encodes one of a plurality of proteins. In some embodiments, the plurality of nucleic acid constructs are designed to encode a plurality of engineered proteins. In some embodiments, the plurality of engineered proteins include variants of wild-type proteins, antibodies, fragments of antibodies, immunoglobulin single variable domains (ISVD), monovalent ISVD variants, multi-specific ISVD variants, ISVD-antibody fusions, ISVD- Fc fusions, antibody -based drugs, Fc fusion proteins, anticoagulants, blood factors, bone morphogenetic proteins, engineered protein scaffolds, enzymes, growth factors, hormones, interferons, interleukins, thrombolytics, cytokines, immunocytokines, among other engineered proteins. In some embodiments, the plurality of proteins is a plurality of engineered proteins or variants thereof.

[0033] In some embodiments, the nucleic acid constructs are produced by combinatorial cloning. In some embodiments, the nucleic acid constructs are produced by mass directed mutagenesis. In some embodiments, the nucleic acid constructs are transformed into cells. In some embodiments the cells are E. coli cells. In some embodiments, there is about 1 E. coli colony per well of a multi-well plate, wherein each E. coli colony harbors and expresses a single unique nucleic acid construct such there is about 1 single unique nucleic acid construct per well of the multi-well plate.

[0034] The methods and systems disclosed herein can be useful for determining which nucleic acid constructs are in which partition among a plurality of partitions, e.g., among a plurality of wells in a multi-well plate. The methods and systems disclosed herein can be useful for confirming that the expected nucleic acid constructs are in the expected partitions among a plurality of partitions, e.g., among a plurality of wells in a multi-well plate. In some embodiments, a portion of each of the plurality of nucleic acid constructs are sequenced. The plurality of nucleic acid constructs can be sequenced by any means known in the art. In some embodiments, the nucleic acid constructs are sequenced by next-generation sequencing. In some embodiments, the nucleic acid constructs are sequenced by Sanger sequencing. The term "next-generation sequencing (NGS)" as used herein refers to sequencing methods that allow for massively parallel sequencing of clonally amplified molecules and of single nucleic acid molecules. Non-limiting examples of NGS include sequencing-by-synthesis using reversible dye terminators, and sequencing-by-ligation. In some embodiments, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more sequencing reads are generated per each of a plurality of partitions, e.g., per each of a plurality of wells in a multi-well plate. The term "read" refers to a sequence obtained from a portion of a nucleic acid sample. Typically, though not necessarily, a read represents a short sequence of contiguous base pairs in the sample. The read may be represented symbolically by the base pair sequence (in A, T, C, or G) of the sample portion. It may be stored in a memory device and processed as appropriate to determine whether it matches a reference sequence or meets other criteria. A read may be obtained directly from a sequencing apparatus or indirectly from stored sequence information concerning the sample. In some cases, a read is a DNA sequence of sufficient length (e.g., at least about 25 bp) that can be used to identify a larger sequence or region, e.g., that can be aligned and specifically assigned to a nucleic acid construct.

[0035] In some embodiments, the methods disclosed herein include providing a plurality of reference sequences. In some embodiments, the plurality of reference sequences are designed in silico. In some embodiments, the plurality of reference sequences are designed to correspond to a plurality of proteins. In some embodiments, the plurality of reference sequences are designed to correspond to a plurality of engineered proteins encoded by a nucleic acid construct. In some embodiments, the plurality of engineered proteins include variants of wild-type proteins, antibodies, fragments of antibodies, ISVDs, monovalent ISVD variants, multi-specific ISVD variants, ISVD-antibody fusions, ISVD-Fc fusions, antibody -based drugs, Fc fusion proteins, anticoagulants, blood factors, bone morphogenetic proteins, engineered protein scaffolds, enzymes, growth factors, hormones, interferons, interleukins, thrombolytics, cytokines, immunocytokines, among other engineered proteins.

[0036] In order to accommodate a range of primer lengths and sequencing read lengths while maximizing matches to reference sequences, the methods disclosed herein utilize a modified longest common substring (LCS) algorithm. Generally, for two given sequencesI and B, LCS finds the longest common substring C where |C I < | and |C | < |B|. Using dynamic programming the problem can be solved with costs of 0(|zl| X |S | ). In contrast to heuristic alignment-algorithms, LCS is a robust and precise algorithm, but can be resource intensive if and / or B are relatively long, which is often the case for DNA sequences.

[0037] In some embodiments of the methods disclosed herein, the methods utilize a modified LCS algorithm wherein a threshold-parameter y is introduced, which defines a minimal sequence length for sequencing reads and matches to the reference, i.e., the modified LCS algorithm holds that y < |C |. Based on empirical findings it was determined that y = 300 is an appropriate parameter for the methods disclosed herein, i.e., minimal read and match length is 300 base pairs (BP) long. As the LCS algorithm consists of two interlaced loops where the first iterates over 4 and the second over B, the inner loop can be decreased by | B | — y steps. The inner loop can start at the first occurrence of a minimal C or directly decline the current comparing operation if no C exists where [C | > y holds. The modified LCS algorithm disclosed herein is referred to as a maximum common subsequence (MCS) algorithm. In order to reduce computational run time, the set size of R per read is reduced.

[0038] For example, before initiating MCS for each read as many reference sequences as possible are declined and excluded rather than using the complete library of reference sequences. A fast sequence-trimming procedure can be used to equally trim the current read on both sides of the sequence, i.e., a predefined number of bases can be trimmed from at the ends of each read and determine if and which references match the trimmed sequence. If no references match, this trimming approach can be repeated to remove additional bases until one or more references are matched, or the length of the trimmed sequence gets smaller than threshold y. The resulting subset E R is used for the following MCS to identify unambiguous matches.

[0039] In some embodiments, at least 1,000, 2,000, 3,000, 4,000, 5,000, 10,000, 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 or more sequencing read data are identified by the methods disclosed herein. In some embodiments, the sequencing read data are identified by the methods disclosure herein in under 15 hours, under 14 hours, under 13 hours, under 12 hours, under 11 hours, under 10 hours, under 9 hours, under 8 hours, under 7 hours, under 6 hours, under 5 hours, under 4 hours, under 3 hours, under 2 hours, or under 1 hour. In some embodiments, at least 50,000 sequencing read data are identified in under 15 hours. In some embodiments, at least 10,000 sequencing read data are identified in under five hours. In some embodiments, at least 7,000 sequencing read data are identified in under 3.5 hours. In some embodiments, at least 2,000 sequencing read data are identified in under 10 minutes.

[0040] In some embodiments, each of the plurality of sequencing read data are compared to each of the provided reference sequences using an algorithm. In some embodiments, the algorithm is an alignment-free algorithm for comparing sequences. In some embodiments, the algorithm is a modified approach of the longest common substring (LCS) algorithm, hereinafter referred to as the maximum common subsequence (MCS). In the context of LCS, a longest common substring of two or more strings, e.g., of two or more polynucleotide sequences, is a longest string that is a substring of all of the two or more strings. As shown in FIG. 2A, the MCS algorithm can be applied iteratively to each multi-well plate, and as shown in FIG. 2B, the MCS algorithm can be applied iteratively to each well of each of the multi-well plates. As shown in FIG. 2C, each of the provided sequencing reads is compared to each of the provided reference sequences in an iterative fashion. In the example of FIG. 2C, reference sequence #3 is identified as having the longest common substring in common with the sequencing read being interrogated by MCS. In some embodiments, a sequencing read of the plurality of sequencing read data is compared to each of the provided reference sequences using the MCS algorithm until the reference sequence having the maximum length substring in common with the sequencing read is identified.

[0041] In some cases, in order to assign sequencing reads to a sequencing reaction, e.g., in order to assign a given sequencing read to a well in a multi-well plate, additional resolving steps are performed. As shown in FIG. 3A, after applying the MCS algorithm, a given sequencing read may share a common substring of equal length with more than one reference sequence. In the example of FIG. 3A, the sequencing read shares a common substring of equal length with reference sequence #2 and reference sequence #3. As shown in FIG. 3B, after applying the MCS algorithm, a first sequencing read may have a common substring with a first reference sequence that is identical to a common substring shared by a second sequencing read and a second reference sequence. In the example of FIG 3B, reference sequence #1 and sequencing read #1 share a common substring that is identical to the substring shared by reference sequence #2 and sequencing read #2. As shown in FIG. 3C, after applying the MCS algorithm, a sequencing read may not share a common substring with any reference sequence, or the longest common substring may be shorter than a predetermined threshold such that the hit is not informative.

[0042] In some embodiments, in order to resolve these potential problems, additional resolving steps are performed.

[0043] FIG. 4A shows a flow chart comprising the steps previously described including MCS search. Each read (of current well) becomes processed independently first. As a result of the initial MCS search each read is assigned to a new set of references comprising all references which match best with the current read. The algorithm does not only keep the reference with the longest match but also all references where the matches are nearly as long minus a defined number k of bases. The different subsets are combined to one intersection (f^) only consisting of references which have been found for each read.

[0044] FIG. 4B shows a decision tree providing rules and guidelines for prioritizing results after applying the MCS algorithm to the provided sequencing read data and reference sequences.

[0045] The intersection gained from the steps depicted in FIG 4A is either empty

[0046] (|Ri|=0), consists of more than one reference (|Ri|> 1), or comprises exactly one reference

[0047] (|Ri|=l):

[0048] • |Ri|= 1 : one common reference for all sets indicates likelihood of an unambiguous match. Here it only must be checked that all reads do overlap and no gap between the reads exist. A gap might point to introduction of insertions or deletions during the clone process, i.e., the molecule is not appropriate for subsequent analysis and cloning approaches.

[0049] • |Ri|=0: If there is no common reference in intersection, this indicates an error during cloning or sequencing and indicates rejection of the current clone. An exception will be made if only one of the sets have references and the other none. If exactly one best match exists within this set, then the method will keep this match and ignore that the other reads did not lead to any references. This is likely due to a sequencing error rather than an impaired clone.

[0050] • |Ri|> 1 : If there is more than one common reference, each of the references in is checked for consistency, and the intersection is reduced by declining erroneous references. First, order of the reads mapping to the current reference is checked to confirm that the reads correspond to sequencing primer direction. Second, reads are confirmed to overlap. If at least one or both of these conditions is not met, the reference is declined. After this iterative check typically one reference remains which indicates which clone likely to be in the current well.

[0051] Based on the decision rules depicted in FIGs. 4A-4B, it is possible to reduce the number of false positives and false negatives and to increase the number of unambiguous matches per analysis in contrast to previously available methods. Computer Implementation of the Methods

[0052] FIG. 5 is a diagram of computer system 500 components that can be used to implement methods and systems for the analysis and deconvolution of sequencing data generated from nucleic acid constructs encoding engineered proteins. The systems and methods disclosed herein are faster and less computationally intensive than previously available methods. In some embodiments, the methods disclosed herein are implemented on a RAM based system. In some embodiments, the methods disclosed herein are implemented on multiple CPUs in parallel. In some embodiments, references sequences are provided as FASTA format, e.g., as a .fasta file. In some embodiments, references sequences provided as .fasta files are loaded into the system from a harddrive in bulk at once. In some embodiments, sequencing read data are provided as FASTA format, e.g., as a .fasta file. In some embodiments, a control file is provided as TXT format, e.g., as a .txt file. In some embodiments, sequencing read data provided as .fasta files are compared to reference sequences using many CPUs in parallel using an algorithm. In some embodiments, the algorithm is a longest common substring algorithm. In some embodiments, the algorithm is a modified longest common substring algorithm.

[0053] Computing device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device 550 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices. Additionally, computing device 500 or 550 can include Universal Serial Bus (USB) flash drives. The USB flash drives can store operating systems and other applications. The USB flash drives can include input / output components, such as a wireless transmitter or USB connector that can be inserted into a USB port of another computing device. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the methods and compositions described and / or claimed in this document.

[0054] Computing device 500 includes a processor 502, memory 504, a storage device 506, a high-speed interface 508 connecting to memory 504 and high-speed expansion ports 510, and a low speed interface 512 connecting to low speed bus 514 and storage device 506. Each of the components 502, 504, 506, 508, 510, and 512, are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. The processor 502 can process instructions for execution within the computing device 500, including instructions stored in the memory 504 or on the storage device 506 to display graphical information for a GUI on an external input / output device, such as display 516 coupled to high speed interface 508. In other implementations, multiple processors and / or multiple buses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 500 can be connected, with each device providing portions of the necessary operations, e.g., as a server bank, a group of blade servers, or a multi-processor system.

[0055] The memory 504 stores information within the computing device 500. In one implementation, the memory 504 is a volatile memory unit or units. In another implementation, the memory 504 is a non-volatile memory unit or units. The memory 504 can also be another form of computer-readable medium, such as a magnetic or optical disk.

[0056] The storage device 506 is capable of providing mass storage for the computing device 500. In one implementation, the storage device 506 can be or contain a computer- readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid-state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product can also contain instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 504, the storage device 506, or memory on processor 502.

[0057] The high-speed controller 508 manages bandwidth-intensive operations for the computing device 500, while the low speed controller 512 manages lower bandwidth intensive operations. Such allocation of functions is only an example. In one implementation, the high-speed controller 508 is coupled to memory 504, display 516, e.g., through a graphics processor or accelerator, and to high-speed expansion ports 510, which can accept various expansion cards (not shown). In the implementation, low-speed controller 512 is coupled to storage device 506 and low-speed expansion port 514. The low-speed expansion port, which can include various communication ports, e.g., USB, Bluetooth, Ethernet, wireless Ethernet can be coupled to one or more input / output devices, such as a keyboard, a pointing device, microphone / speaker pair, a scanner, or a networking device such as a switch or router, e.g., through a network adapter. The computing device 500 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 520, or multiple times in a group of such servers. It can also be implemented as part of a rack server system 524. In addition, it can be implemented in a personal computer such as a laptop computer 522. Alternatively, components from computing device 500 can be combined with other components in a mobile device (not shown), such as device 550. Each of such devices can contain one or more of computing device 500, 550, and an entire system can be made up of multiple computing devices 500, 550 communicating with each other.

[0058] The computing device 500 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 520, or multiple times in a group of such servers. It can also be implemented as part of a rack server system 524. In addition, it can be implemented in a personal computer such as a laptop computer 522. Alternatively, components from computing device 500 can be combined with other components in a mobile device (not shown), such as device 550. Each of such devices can contain one or more of computing device 500, 550, and an entire system can be made up of multiple computing devices 500, 550 communicating with each other.

[0059] Computing device 550 includes a processor 552, memory 564, and an input / output device such as a display 554, a communication interface 566, and a transceiver 568, among other components. The device 550 can also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the components 550, 552, 564, 554, 566, and 568, are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.

[0060] The processor 552 can execute instructions within the computing device 550, including instructions stored in the memory 564. The processor can be implemented as a chipset of chips that include separate and multiple analog and digital processors. Additionally, the processor can be implemented using any of a number of architectures. For example, the processor 510 can be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor. The processor can provide, for example, for coordination of the other components of the device 550, such as control of user interfaces, applications run by device 550, and wireless communication by device 550.

[0061] Processor 552 can communicate with a user through control interface 558 and display interface 556 coupled to a display 554. The display 554 can be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 556 can comprise appropriate circuitry for driving the display 554 to present graphical and other information to a user. The control interface 558 can receive commands from a user and convert them for submission to the processor 552. In addition, an external interface 562 can be provided in communication with processor 552, so as to enable near area communication of device 550 with other devices. External interface 562 can provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces can also be used.

[0062] The memory 564 stores information within the computing device 550. The memory 564 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 574 can also be provided and connected to device 550 through expansion interface 572, which can include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 574 can provide extra storage space for device 550, or can also store applications or other information for device 550. Specifically, expansion memory 574 can include instructions to carry out or supplement the processes described above, and can also include secure information. Thus, for example, expansion memory 574 can be provided as a security module for device 550, and can be programmed with instructions that permit secure use of device 550. In addition, secure applications can be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

[0063] The memory can include, for example, flash memory and / or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 564, expansion memory 574, or memory on processor 552 that can be received, for example, over transceiver 568 or external interface 562.

[0064] Device 550 can communicate wirelessly through communication interface 566, which can include digital signal processing circuitry where necessary. Communication interface 566 can provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication can occur, for example, through radio-frequency transceiver 568. In addition, short-range communication can occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 570 can provide additional navigation- and location-related wireless data to device 550, which can be used as appropriate by applications running on device 550.

[0065] Device 550 can also communicate audibly using audio codec 560, which can receive spoken information from a user and convert it to usable digital information. Audio codec 560 can likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 550. Such sound can include sound from voice telephone calls, can include recorded sound, e.g., voice messages, music files, etc. and can also include sound generated by applications operating on device 550.

[0066] The computing device 550 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a cellular telephone 580. It can also be implemented as part of a smartphone 582, personal digital assistant, or other similar mobile device.

[0067] Various implementations of the systems and methods described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations of such implementations. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0068] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms "machine-readable medium" "computer-readable medium" refers to any computer program product, apparatus and / or device, e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0069] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0070] The systems and methods described here can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), and the Internet.

[0071] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0072] A number of embodiments have been described. Nevertheless, it will be understood that various modifications can be made without departing from the spirit and scope of the invention. In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps can be provided, or steps can be eliminated, from the described flows, and other components can be added to, or removed from, the described systems. Accordingly, other embodiments are within the scope of the following claims.

[0073] Embodiments of the disclosure and all of the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the methods and compositions can be implemented as one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus.

[0074] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0075] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0076] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a tablet computer, a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0077] To provide for interaction with a user, embodiments of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0078] Embodiments of the disclosure can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the methods, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

[0079] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of the invention. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

[0080] Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0081] In each instance where an HTML file is mentioned, other file types or formats may be substituted. For instance, an HTML file may be replaced by an XML, JSON, plain text, or other types of files. Moreover, where a table or hash table is mentioned, other data structures (such as spreadsheets, relational databases, or structured files) may be used.

[0082] EXAMPLES

[0083] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims. EXAMPLE 1: Comparative Performance of the MCS Method

[0084] In order to demonstrate the improved performance of the methods and systems disclosed herein relative to previously available methods, the methods were performed as a test case and compared to performance of previously available methods. In a first experiment, 73 plates, each including 96 wells with two sequencing reads per well (5'-to- 3' and 3'-to-5') were processed using the method as described herein. The method was performed on a multi-core workstation using up to 48 CPUs in parallel. Approximately 14,000 sequencing reads were processed. Each of the sequencing reads was compared to each of 4,489 provided reference sequences that were designed in silico to correspond to nucleic acid constructs assigned to wells of the 73 plates. Processing all 14,000 reads according to the methods disclosed herein took approximately 3.5 hours. Previously available methods took 9 hours.

[0085] In addition to the quantitative improvement in run time, the disclosed method led to a qualitative improvement due to the decision tree and rules scheme. The method resulted in fewer false positives and fewer false negatives relative to previously available methods. The previously available method led to 65 error messages stating that no unambiguous clone could be identified for the corresponding wells / clones. In contrast, the disclosed method led to only 17 ambiguous clones. Additionally, the disclosed method provides more information relating to the identification of the single clones. For 13,469 clones, the equality-score (measurement of the length of similar bases between the reference sequence and the corresponding read) was higher than with the previously available method. In a second experiment, 37 plates, each including 96 wells with two sequencing reads per well were processed using the method as described herein. Approximately 7,100 sequencing reads were processed. Each of the sequencing reads was compared to each of 1,797 provided reference sequences that were designed in silico to correspond to nucleic acid constructs assigned to wells of the 37 plates. Processing all 7,100 reads according to the methods disclosed herein took approximately 1.5 hours. Previously available methods took 5.5 hours.

[0086] The previously available method led to 82 ambiguous clones, i.e., 82 clones that were not assigned to reference sequences and thereby identified. The methods disclosed herein led to only 14 ambiguous clones, i.e., 14 clones that were not assigned to reference sequences and thereby identified. In addition, the disclosed method provides higher equality-scores than the previously available method for 6,342 clones. These results demonstrate that the methods disclosed herein are a quantitative and qualitative improvement over previously available methods.

[0087] OTHER EMBODIMENTS

[0088] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method of identifying polynucleotide molecules distributed in a plurality of partitions, the method comprising: obtaining a plurality of sequencing read data; obtaining a plurality of reference sequences; comparing each of the plurality of sequencing read data to each of the plurality of reference sequences using an algorithm; generating, based on the comparison of each of the plurality of sequencing read data to each of the plurality of reference sequences, a score for each of the sequencing read data; and assigning to each of the plurality of sequencing read data, based on the score for each of the sequencing read data, a polynucleotide molecule, thereby identifying each polynucleotide molecule in each of the plurality of partitions.

2. A system for identifying polynucleotide molecules distributed in a plurality of partitions, the system comprising: a data store configured to receive a plurality of sequencing read data and a plurality of reference sequences; a computing device communicatively connected to the data store, the computing device configured to: compare each of the plurality of sequencing read data to each of the plurality of reference sequences using an algorithm; generate, based on the comparison of each of the plurality of sequencing read data to each of the plurality of reference sequences, a score for each of the sequencing read data; and assign to each of the plurality of sequencing read data, based on the score for each of the sequencing read data, a polynucleotide molecule, thereby identifying each polynucleotide molecule in each of the plurality of partitions.

3. The method of claim 1 or the system of claim 2, wherein each of the plurality of reference sequences corresponds to an expected identity of a polynucleotide molecule.

4. The method of claim 1 or 3 or the system of claim 2 or 3, wherein providing the plurality of sequencing read data comprises sequencing each of the polynucleotide molecules.

5. The method of any one of claims 1 and 3-4 or the system of any one of claims 2-4, wherein the plurality of partitions are wells in a multi-well plate.

6. The method of any one of claims 1 and 3-5 or the system of any one of claims 2-5, wherein the polynucleotide molecules are DNA constructs.

7. The method or the system of claim 6, wherein each of the DNA constructs encodes a protein of a plurality of proteins.

8. The method or the system of any one of claims 6-7, wherein each of the DNA constructs is assembled in vitro.

9. The method or the system of any one of claims 6-8, wherein each of the DNA constructs is assembled in vivo in an organism, optionally wherein the organism is a bacterium, or optionally wherein the organism is a fungus.

10. The method of any one of claims 1 and 3-9 or the system of any one of claims 2-9, wherein each protein of the plurality of proteins is a wild-type protein or an engineered protein.

11. The method of any one of claims 1 and 3-10 or the system of any one of claims 2-10, wherein each of the plurality of references sequences are designed to correspond to each of a plurality of wild-type proteins or engineered proteins.

12. The method of any one of claims 1 and 3-11 or the system of any one of claims 2-11, wherein the algorithm is a longest common substring (LCS) algorithm, optionally wherein the algorithm is a modified LCS algorithm, further optionally wherein the algorithm is a maximum common subsequence (MCS) algorithm.

13. The method of any one of claims 1 and 3-12 or the system of any one of claims 2-12, wherein the score for each of the sequencing read data is a longest match score.

14. The method of any one of claims 1 and 3-13 or the system of any one of claims 2-13, wherein a polynucleotide molecule is assigned to one of the plurality of sequencing read data if the score for the sequencing read data exceeds a predetermined threshold.

15. The method of any one of claims 1 and 3-14 or the system of any one of claims 2-14, wherein at least 10,000 polynucleotide molecules are identified in under five hours.

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