IDENTIFYING GENETIC SEQUENCE EXPRESSION PROFILES ACCORDING TO CLASSIFICATION FEATURE SETS

Classifying genetic sequences by receiving genetic sequence data according to sequence features associated with gene expression, determining a genetic sequence feature set, determining a first classification for the genetic sequence feature set according to a machine learning model, defining a causal feature set associated with the first classification for the genetic sequence according to the machine learning model, altering the causal feature set for the genetic sequence, yielding an altered causal feature set, determining a second classification for the altered causal feature set according to the machine learning model, wherein the second classification differs from the first classification, and defining a set of target features, wherein the target features include causal features the altered causal feature set.

BACKGROUND

The disclosure relates generally to the detection and identification of genetic sequence expression profiles. The disclosure relates particularly to identifying genetic sequence features associated with genetic expression.

Understanding gene expression (also known as the transcriptome) is essential for understanding organism biological development and diseases. Machine learning (ML) has been used for the prediction of transcriptomic profiles using DNA base sequence and/or epigenetic data. DNA base sequence data typically encompasses transcription factor binding sites (TFBS) and/or enhancers. These attributes are thought to contribute to the control of gene expression and attributes such as DNA base sequence features can be identified from pre-existing resources that are widely and publicly available for many species. Current approaches utilize experimental genetic expression data and/or prior knowledge of genetic expression regulatory elements.

SUMMARY

Aspects of the invention disclose methods, systems and computer readable media associated with classifying genetic sequences according to sequence features associated with gene expression by receiving genetic sequence data, determining a genetic sequence feature set, determining a first classification for the genetic sequence feature set according to a machine learning model, defining a causal feature set associated with the first classification for the genetic sequence according to the machine learning model, altering the causal feature set for the genetic sequence, yielding an altered causal feature set, determining a second classification for the altered causal feature set according to the machine learning model, wherein the second classification differs from the first classification, and defining a set of target features, wherein the target features include causal features from the altered causal feature set.

DETAILED DESCRIPTION

In an embodiment, one or more components of the system can employ hardware and/or software to solve problems that are highly technical in nature (e.g., determining a genetic sequence feature set, determining a first classification for the genetic sequence feature set according to a machine learning model, defining a causal feature set for the genetic sequence according to the machine learning model, altering the causal feature set for the genetic sequence, yielding an altered causal feature set, determining a second classification for the altered causal feature set according to the machine learning model, wherein the second classification differs from the first classification, and defining a set of target features, etc.). These solutions are not abstract and cannot be performed as a set of mental acts by a human due to the processing capabilities needed to facilitate genetic sequence classification, for example. Further, some of the processes performed may be performed by a specialized computer for carrying out defined tasks related to classifying genetic sequences. For example, a specialized computer can be employed to carry out tasks related to the classification of genetic sequences, or the like.

Accurately classifying genetic sequences leads to understanding genetic sequence attributes which relate to patterns of gene expression. Identifying sequences associated with patterns of gene expression over the course of a day—circadian rhythms—enable the control and manipulation of such expression patterns through gene editing using tools such as Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR/Cas9). Applications include gene expression therapies and agricultural improvements. Disclosed embodiments enable the classification of genetic sequences associated with patterns of genetic expression.

In an embodiment, the method utilizes a trained machine learning (ML) model to classify genetic sequences. The method trains the model according to the nature of the desired classifications. As an example, for classification of gene sequences or gene promoter sequences associated as either circadian or non-circadian sequences, the method utilizes labeled data including genetic sequences know to be either circadian or non-circadian in their expression, as training and test data for developing the ML classification model.

The method evaluates time series transcriptome data for a set of genes and the set of associated gene promoters. In an embodiment, the method collects associated promoter sequences for input genes as the set of base pairs immediately upstream from the base pair sequence of the gene. For example, the method collects 1500 base pairs upstream from a gene as the promoter sequence for that gene. The transcriptome includes messenger RNA data associated with the activity of a gene/gene promoter. Time series transcriptome data provides data associated with changes in the messenger RNA for the gene/gene promoter over the observed time period. Changes in the transcriptome over time indicate changes in gene/promoter activity or gene/promoter expression over the observed time period.

In an embodiment, transcriptomic analysis of individual genes/promoters of a set of genes/promoters occurred every two hours over a total observation period of 48 hours. The gene/promoter sequences used included known and publicly available gene/promoter sequences. Circadian genes exhibit regular periodic changes in expression—and accompanying changes in the transcriptomic data, over a 24-hour period. Non-circadian gene expression lacks such regular periodic changes in expression. This analysis yielded a training data set of 50,000 genes/promoters with 25,000 labeled as circadian due to transcriptomic data changes over the observed time period and a further 25,000 genes/promoters labeled as non-circadian based upon, the time-series transcriptomic data. The method labeled genes/promoters of the training set according to the expression data observed in the time-series transcriptomic data. Genes/promoters having time-series data including periodic patterns of expression over twenty-four periods labeled as circadian and genes/promoters lacking such periodic patterns of expression labeled as non-circadian. Similarly, the method may be adapted using time-series transcriptomic data for other complex expression patterns to categorize and label training data sets for those complex expression patterns. Once categorized and labeled the set of training genetic sequences need not be generated again.

After using time-series transcriptomic analysis of available gene sequences to generate the training data set, the method processes each gene of the 50,000 gene training data set. The method generates a set of genetic nucleotide subsequences, or k-mer. In an embodiment, the method utilizes k-mer 6 nucleotides in length. Other k-mer lengths, e.g., 4, 8, 10, 12, or more, may be selected and used. For the k-mer, the method generates the set of all possible combinations for nucleotide options of A, T, G, and C (Adenine, Thymine, Guanine, and Cytosine). A total of 4096 possible combinations exist for the 4 nucleotide bases in sets of 6 for the k-mer.

For each of the possible k-mer combinations, the method analyzes the training set of genes and determines the number of occurrences of the k-mer in each gene of the training data set. In an embodiment, the analysis yields a matrix indicating the number of occurrences of each k-mer in each of the genes. For each gene the matrix entries constitute the features of the gene.

In an embodiment, the method counts the number of feature occurrences across the base pair sequence of the gene and additionally counts the feature occurrences across the base pair sequences of the associated gene promoter. The matrix includes the distribution of feature count values for each of the gene and the gene promoter. For this embodiment, the total number of possible features doubles to 8192, 4096 possible features for the gene and 4096 possible features for the gene promoter.

In an embodiment, the method counts the feature occurrences across the combined sequence of the gene and gene promoter. In this embodiment the matrix includes feature count values for each of the 4096 possible features.

In an embodiment, the method reduces the number of features for each gene from the possible 4096 to a smaller number of features such as 100 features. As an example, the method may use a chi squared test to identify the most significant 100 features from the overall set of features in the matrix.

In an embodiment, the method utilizes a classification algorithm to predict classifications for the labeled data of the training set. Exemplary classification algorithms include Logistic Regression, Random Forest, XGBoost, Decision Tree, K-NN (K-nearest neighbors), Gaussian Process, LightGBM (gradient boosting method), and SVM (support vector machine). The method splits the training data set, using 80% of the data for training and 20% of the data for testing the developed algorithm. In this embodiment, the method utilizes a k-nearest neighbors algorithm and achieves an accuracy of 77% in classifying labelled training data utilizing a k value of 2. The method may utilize other k values depending upon the fit of the training data and the accuracy desired in the predictions. The developed model relies solely upon k mer distributions within the training set sequences, without the use of experimental data associated with the genetic sequences. For the example, the trained model classifies feature sets derived from input data sequences as either circadian or non-circadian. The classification dichotomy results from the nature of the training data set. By analogy, labelled training data associated with other complex gene expression patterns yields a model adapted to classify feature sets from input sequences as conforming or not conforming to the complex gene expression patterns.

In practice, the method receives genetic sequence data, processes the sequence data as described yielding a feature set of the sequence and passes the feature set to the classification model for analysis. The model returns a classification of the feature set and associated genetic sequence.

In an embodiment, a user interface, such as a graphical user interface (GUI), provides a user access to the disclosed methods. The method receives genetic sequence data from the user. The user may download, or otherwise provide, publicly available genomic (and epigenetic if available) resources for their species of interest, or else use private user defined datasets. In an embodiment, the method provides links to publicly available genomic databases using application program interfaces (API) associated with such databases. Provided genetic sequence resources will be in the form of genome sequence with gene annotations and/or DNA methylation and/or histone modifications etc.

The method processes the provided sequence data, analyzing the provided data to count the number of occurrences of each of 4096 possible k mer A-G-T-C, nucleotide combinations for k-mers having 6 bases. In an embodiment, the method utilizes epigenetic data to disregard known heavily methylated transcription factor binding sites (TFBS) from amongst the set of features captured in the feature matrix. Ignoring such sites reduces the number of matrix values and limits the matrix of features to features/attributes associated with sequence differences associated with expression differences. The TFBS serve a utilitarian function for expression rather than serving as a gene attribute. The method captures the respective feature counts as a matrix of values associated with each gene analyzed.

The method provides the matrix of features to the trained ML model for classification. The method may reduce the number of matrix values from the full 4096 to a lesser number such as 100 prior to passing the feature set to the ML model for classification. The ML model, such as k-nearest neighbor model, classifies each input feature set. The method provides an explanation for the classification in the form of feature vectors for the input feature set and the nearest neighbors leading to the classification. The method compares the input feature vector and nearest neighbor feature vectors, and the comparison leads to identifying members of a candidate causal feature set—those features of the input feature set most likely to be responsible for the classification of the input as the final classification assigned to it.

In an embodiment, the method ranks the features of the candidate causal feature set using data from the comparison of the input feature vector and the k nearest neighbor feature vectors.

In an embodiment, the method selectively evolves the input gene “in-silico”. For each feature of the candidate causal feature set, the method selectively edits the input genetic sequence, removing the candidate feature from the sequence and from the feature set of the sequence. The method then classifies the edited feature set. The method categorizes edited features which result in a change of classification—for example a feature which alters a sequence from circadian to non-circadian—as members of a target feature set. The method compiles a complete set of target features as all candidate causal features which resulted in a classification change after editing. The complete target feature set provides candidates for actual gene editing to alter the pattern of gene expression of the original input gene. Selectively removing a candidate target feature through a means such as CRISPR/Cas9, should change the expression pattern of the gene as indicated by the change of classification of the edited evolved sequence.

In an embodiment, the final set of target features provides a means of identifying genetic homologs to the input genetic sequence from a first species, in a related species. As an example, a user of the method may apply classification results associated with bread wheat,Triticum aestivum, to a related wheat species such asTriticum durum, or to related grain species such as barley or oat species. As another example, a user may apply gene expression classification results associated with the genome of a first subject to the genome of other subjects of the same species. Application of disclosed embodiments to human genetic sequences presumes that the human donors have consented to, or otherwise opted-in to the use of their genetic sequence data by users of the disclosed methods and systems.

In an embodiment, the method maintains candidate causal feature sets for each classification of the model. In this embodiment, the method selects features from the candidate causal feature set for a first classification for addition through in-silico evolution to input genetic sequences identified as a different classification by the model. Similarly, the method selects features from the candidate causal feature set for a classification for removal through in-silico evolution, from input genetic sequences identified with that classification by the model.

In an embodiment, the method begins the in-silico evolution of the input sequence using the candidate causal feature ranked highest and proceeds from this highest ranked candidate to the lowest ranked candidate. In this embodiment, the method ceases in-silico evolution of candidate causal features after a threshold number of successively ranked candidate causal features fail to result in a classification change; e.g., after 10 successively ranked candidates each fail to result in a classification change, the method ceases the in-silico evolution of the input genetic sequence using the candidate causal features.

FIG. 1provides a schematic illustration of exemplary network resources associated with practicing the disclosed inventions. The inventions may be practiced in the processors of any of the disclosed elements which process an instruction stream. As shown in the figure, a networked Client device110connects wirelessly to server sub-system102. Client device104connects wirelessly to server sub-system102via network114. Client devices104and110comprise genetic sequence classification program (not shown) together with sufficient computing resource (processor, memory, network communications hardware) to execute the program. Client devices104and110serve as user interface devices enabling a user to provide input genetic sequence and epigenetic data to the disclosed methods and system. The client devices104and110further serve as output devices for the disclosed embodiment to provide output data to the user.

As shown inFIG. 1, server sub-system102comprises a server computer150.FIG. 1depicts a block diagram of components of server computer150within a networked computer system1000, in accordance with an embodiment of the present invention. It should be appreciated thatFIG. 1provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made.

Server computer150can include processor(s)154, memory158, persistent storage170, communications unit152, input/output (I/O) interface(s)156and communications fabric140. Communications fabric140provides communications between cache162, memory158, persistent storage170, communications unit152, and input/output (I/O) interface(s)156. Communications fabric140can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric140can be implemented with one or more buses.

Memory158and persistent storage170are computer readable storage media. In this embodiment, memory158includes random access memory (RAM)160. In general, memory158can include any suitable volatile or non-volatile computer readable storage media. Cache162is a fast memory that enhances the performance of processor(s)154by holding recently accessed data, and data near recently accessed data, from memory158.

Program instructions and data used to practice embodiments of the present invention, e.g., the genetic sequence classification program175, are stored in persistent storage170for execution and/or access by one or more of the respective processor(s)154of server computer150via cache162. In this embodiment, persistent storage170includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage170can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage170may also be removable. For example, a removable hard drive may be used for persistent storage170. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage170.

Communications unit152, in these examples, provides for communications with other data processing systems or devices, including resources of client computing devices104, and110. In these examples, communications unit152includes one or more network interface cards. Communications unit152may provide communications through the use of either or both physical and wireless communications links. Software distribution programs, and other programs and data used for implementation of the present invention, may be downloaded to persistent storage170of server computer150through communications unit152.

I/O interface(s)156allows for input and output of data with other devices that may be connected to server computer150. For example, I/O interface(s)156may provide a connection to external device(s)190such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and/or some other suitable input device. External device(s)190can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., genetic sequence classification program175on server computer150, can be stored on such portable computer readable storage media and can be loaded onto persistent storage170via I/O interface(s)156. I/O interface(s)156also connect to a display180.

Display180provides a mechanism to display data to a user and may be, for example, a computer monitor. Display180can also function as a touch screen, such as a display of a tablet computer.

FIG. 2provides a flowchart200, illustrating exemplary activities associated with the practice of the disclosure. After program start, a user provides the genetic sequence classification program175, with genetic sequence data acquired from public sources, private sources, or a combination of public and private sources. The input data includes genome sequence data214as well as gene annotations and DNA methylation and/or histone modification data. The input data may further include epigenetic data such as prior domain knowledge of the genome sequence e.g., heavily methylated TFBS sites of the sequence,218.

At220, the method of genetic sequence classification program175processes input genetic data214, yielding a matrix of sequence features for the input data. The sequence features include data relating the distribution of possible 6 base k mers within the genome sequence of the input data214.

At230the method of genetic sequence classification program175optionally utilizes epigenetic data218to reduce the number of entries in the feature matrix from220. The method removes features associated with known heavily methylated TFBS sites from the matrix or reduces the related matrix entry values to zero.

At240, the method of genetic sequence classification program175classifies or predicts a classification for the input genetic sequence feature set from either220or the feature set modified with epigenetic information from230. The method utilizes a machine learning model trained to classify genetic sequences using a training data set of labeled genetic sequence data related to the desired classifications. As an example, a machine learning model trained using labeled gene sequences associated with each of circadian and non-circadian genetic sequences provides a prediction of either circadian or non-circadian for the provided input feature set.

At250, the method of genetic sequence classification program175uses the classification model explanation for the classification to generate a candidate causal feature set. This set includes those sequence features of the input genetic sequence most likely to have resulted in the model's classification of that input sequence. In an embodiment, the method ranks the members of the candidate feature set from most likely to least likely.

At260, the method of genetic sequence classification program175selectively edits the input genetic sequence and associated input sequence feature set from either220or230. For each member of the candidate causal feature set, the method removes the feature from the input genetic sequence and associated input sequence feature set.

At270, the method of genetic sequence classification program175predicts or classified the edited input feature set using the trained machine learning model. The method passes input features whose removal alters the classification to a target feature set,280. The method returns to260and edits each candidate causal feature in turn, editing the input sequence and associated feature set by only a single candidate causal feature with each iteration.

In an embodiment, the method a general candidate causal feature set for each possible classification of the machine learning model. In this embodiment, at260, the method either removes a candidate causal feature from the input sequence and input feature from the general candidate causal feature set for the classification of the input sequence, or adds a candidate causal feature from the general candidate causal feature set for a different classification. AS an example, for an input sequence classified as circadian, the method either adds a candidate causal feature from the general candidate causal feature for non-circadian sequences, or removes a candidate casual feature from the candidate causal feature set for the input sequence and input feature set. In this embodiment, the method refines the target feature sets for each possible classification of the machine learning classification model. (Features added from a general causal feature set which result in a change of classification are added to associated target feature set for that classification; e.g., the method adds a feature from the general candidate causal feature set, added to a circadian sequence which results in a re-classification of that sequence to non-circadian, to the target feature set for non-circadian sequences.)

The method provides the sets of target features from280to the user via user interface210. The user may utilize the target features for selectively editing actual genetic sequences for genetic therapies associated with altering gene expression patterns, or to alter plant species genetic expression to enhance agricultural production.

In an embodiment, execution of disclosed methods requires computational resources exceeding those locally available to a user. In this embodiment, the user connects to networked resource including edge cloud and cloud resources to enable a timely execution of the methods.

Characteristics are as Follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG. 4, a set of functional abstraction layers provided by cloud computing environment50(FIG. 3) is shown. It should be understood in advance that the components, layers, and functions shown inFIG. 4are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The invention may be beneficially practiced in any system, single or parallel, which processes an instruction stream. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.