US20200042735A1 - Method and system for selective access of stored or transmitted bioinformatics data - Google Patents

Method and system for selective access of stored or transmitted bioinformatics data Download PDF

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US20200042735A1
US20200042735A1 US16/341,426 US201716341426A US2020042735A1 US 20200042735 A1 US20200042735 A1 US 20200042735A1 US 201716341426 A US201716341426 A US 201716341426A US 2020042735 A1 US2020042735 A1 US 2020042735A1
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genomic
data
type
reads
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Mohamed Khoso Baluch
Giorgio Zoia
Daniele Renzi
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Genomsys SA
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Genomsys SA
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Priority claimed from PCT/EP2016/074301 external-priority patent/WO2018068828A1/en
Priority claimed from PCT/EP2016/074307 external-priority patent/WO2018068829A1/en
Priority claimed from PCT/EP2016/074311 external-priority patent/WO2018068830A1/en
Priority claimed from PCT/EP2016/074297 external-priority patent/WO2018068827A1/en
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Assigned to GENOMSYS SA reassignment GENOMSYS SA ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BALUCH, Mohamed Khoso, RENZI, Daniele, ZOIA, GIORGIO
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Definitions

  • the present application provides new methods for the efficient storage, transmission and multiplexing of bioinformatics data, and in particular genomic sequencing data, in compressed form that enable efficient selective access and selective protection of the different data categories composing the genomic datasets.
  • genome sequencing data is fundamental to enable efficient processing, storage and transmission of genomic data to make possible and facilitate analysis applications such as genome variants calling and all analysis performed, with various purposes, by processing the sequencing data and metadata.
  • genome sequencing information is generated by High Throughput Sequencing (HTS) machines in the form of sequences of nucleotides (a. k. a. bases) represented by strings of letters from a defined vocabulary.
  • HTS High Throughput Sequencing
  • sequence reads do not read out an entire genomes or genes, but they produce short random fragments of nucleotide sequences known as sequence reads.
  • a quality score is associated to each nucleotide in a sequence read. Such number represents the confidence level given by the machine to the read of a specific nucleotide at a specific location in the nucleotide sequence.
  • This raw sequencing data generated by NGS machines are commonly stored in FASTQ files (see also FIG. 1 ).
  • the smallest vocabulary to represent sequences of nucleotides obtained by a sequencing process is composed by five symbols: ⁇ A, C, G, T, N ⁇ representing the four types of nucleotides present in DNA namely Adenine, Cytosine, Guanine, and Thymine plus the symbol N to indicate that the sequencing machine was not able to call any base with a sufficient level of confidence, so the type of base in such position remains undetermined in the reading process.
  • RNA Thymine is replaced by Uracil (U).
  • the nucleotides sequences produced by sequencing machines are called “reads”. In case of paired reads the term “template” is used to designate the original sequence from which the read pair has been extracted. Sequence reads can be composed by a number of nucleotides in a range from a few dozen up to several thousand. Some technologies produce sequence reads in pairs where each read can be originated from one of the two DNA strands.
  • the term “coverage” is used to express the level of redundancy of the sequence data with respect to a reference genome. For example, to reach a coverage of 30 ⁇ on a human genome (3.2 billion bases long) a sequencing machine shall produce a total of about 30 ⁇ 3.2 billion bases so that in average each position in the reference is “covered” 30 times.
  • the most used genome information representations of sequencing data are based on FASTQ and SAM file formats which are commonly made available in zipped form in the attempt of reducing the original size.
  • the traditional file formats respectively FASTQ and SAM for non-aligned and aligned sequencing data, are constituted by plain text characters and are thus compressed by using general purpose approaches such as LZ (from Lempel and Ziv) schemes (the well-known zip, gzip etc).
  • LZ from Lempel and Ziv
  • general purpose compressors such as gzip
  • the result of the compression is usually a single blob of binary data.
  • the information in such monolithic form results quite difficult to archive, transfer and elaborate particularly in the case of high throughput sequencing when the volumes of data are extremely large.
  • each stage of a genomic information processing pipeline produces data represented by a completely new data structure (file format) despite the fact that in reality only a small fraction of the generated data is new with respect to the previous stage.
  • FIG. 1 shows the main stages of a typical genomic information processing pipeline with the indication of the associated file format representation.
  • genomic data is slow and inefficient because the currently used data formats are organized into monolithic files of up to several hundred Gigabytes of size which need to be entirely transferred at the receiving end in order to be processed. This implies that the analysis of a small segment of the data requires the transfer of the entire file with significant costs in terms of consumed bandwidth and waiting time. Often online transfer is prohibitive for the large volumes of the data to be transferred, and the transport of the data is performed by physically moving storage media such as hard disk drives or storage servers from one location to another.
  • the present invention provides a solution to this need.
  • the invention aims at providing an appropriate genomic sequencing data and metadata representation by organizing and partitioning the data so that the compression of data and metadata is maximized and several functionality such as selective access and support for incremental updates are efficiently enabled.
  • a key aspect of the invention is a specific definition of classes of data and metadata to be represented by an appropriate source model, coded (i.e. compressed) separately by being structured in specific layers.
  • the present application discloses a method and system addressing the problem of efficient manipulation, storage and transmission of very large amounts of genomic sequencing data, by employing a structured access units approach combined with multiplexing techniques.
  • the present application overcomes all the limitations of the prior art approaches related to the functionality of genomic data accessibility, selective data protection, efficient processing of data subsets, transmission and streaming functionality combined with an efficient compression.
  • SAM Sequence Alignment Mapping
  • CRAM CRAM specification: https://samtools.github.io/hts-specs/CRAMv3.pdf.
  • CRAM provides a more efficient compression for the adoption of differential encoding with respect to an existing reference (it partially exploits the data source redundancy), but it still lacks features such as incremental updates, support for streaming and selective access to specific classes of compressed data.
  • CRAM relies on the concept of the CRAM record. Each CRAM record encodes a single mapped or unmapped reads by encoding all the elements necessary to reconstruct it.
  • Beside CRAM also the other approaches to genomic data compression and processing present strong limitations to most of the desired functionality and do not support features that are provided by this invention disclosure as described and specified in the following of the document.
  • the first two categories share the disadvantage of not exploiting the specific characteristics of the data source (genomic sequence reads) and process the genomic data as string of text to be compressed without taking into account the specific properties of such kind of information (e.g. redundancy among reads, reference to an existing sample).
  • Two of the most advanced toolkits for genomic data compression namely CRAM and Goby (“Compression of structured high-throughput sequencing data”, F. Campagne, K. C. Dorff, N. Chambwe, J. T. Robinson, J. P. Mesirov, T. D. Wu), make a poor use of arithmetic coding as they implicitly model data as independent and identically distributed by a Geometric distribution.
  • Goby is slightly more sophisticated since it converts all the fields to a list of integers and each list is encoded independently using arithmetic coding without using any context. In the most efficient mode of operation, Goby is able to perform some inter-list modeling over the integer lists to improve compression. These prior art solutions yield poor compression ratios and data structures that are difficult if not impossible to selectively access and manipulate once compressed. Downstream analysis stages can result to be inefficient and very slow due to the necessity of handling large and rigid data structures even to perform simple operation or to access selected regions of the genomic dataset.
  • FIG. 1 A simplified vision of the relation among the file formats used in genome processing pipelines is depicted in FIG. 1 .
  • file inclusion does not imply the existence of a nested file structure, but it only represents the type and amount of information that can be encoded for each format (i.e. SAM contains all information in FASTQ, but organized in a different file structure).
  • CRAM contains the same genomic information as SAM/BAM, but it has more flexibility in the type of compression that can be used, therefore it is represented as a superset of SAM/BAM.
  • Genomic Information Storage Format Geneomic File Format
  • Transport Mechanism that enable efficient compression, support selective access and protection functionality in the compressed domain, of local and remotely stored data and support the incremental addition of heterogeneous metadata in the compressed domain at all levels of the different stages of the genomic data processing.
  • the present invention provides a solution to the limitations of the state of the art by employing the method, devices and computer programs as claimed in the accompanying set of claims.
  • FIG. 1 shows the main steps of a typical genomic pipeline and the related file formats.
  • FIG. 2 shows the mutual relationship among the most used genomic file formats
  • FIG. 3 shows how genomic sequence reads are assembled in an entire or partial genome via de-novo assembly or reference based alignment.
  • FIG. 4 shows how reads mapping positions on the reference sequence are calculated.
  • FIG. 5 shows how reads pairing distances are calculated.
  • FIG. 6 shows how pairing errors are calculated.
  • FIG. 7 shows how the pairing distance is encoded when a read mate pair is mapped on a different chromosome.
  • FIG. 8 shows how sequence reads can be generated from the first or second DNA strand of a genome.
  • FIG. 9 shows how a read mapped on strand 2 has a corresponding reverse complemented read on strand 1.
  • FIG. 10 shows the four possible combinations of reads composing a reads pair and the respective encoding in the rcomp layer.
  • FIG. 11 shows how “n type” mismatches are encoded in a nmis layer.
  • FIG. 12 shows an example of substitutions in a mapped read pair.
  • FIG. 13 shows how substitutions positions can be calculated either as absolute or differential values.
  • FIG. 14 shows how symbols encoding substitutions without IUPAC codes are calculated.
  • FIG. 15 shows how substitution types are encoded in the snpt layer.
  • FIG. 16 shows how symbols encoding substitutions with IUPAC codes are calculated.
  • FIG. 17 shows an alternative source model for substitution where only positions are encoded, but one layer per substitution type is used.
  • FIG. 18 shows how to encode substitutions, insertions and deletions in a reads pair of class I when IUPAC codes are not used.
  • FIG. 19 shows how to encode substitutions, insertions and deletions in a reads pair of class I when IUPAC codes are used.
  • FIG. 20 shows the structure of the Genomic Dataset Header of the genomic information data structure disclosed by this invention.
  • FIG. 21 shows how the Master Index Table contains the positions on the reference sequences of the first read in each Access Unit.
  • FIG. 22 shows an example of partial MIT showing the mapping positions of the first read in each pos AU of class P.
  • FIG. 23 shows how the Local Index Table in the layer header is a vector of pointers to the AUs in the payload.
  • FIG. 24 shows an example of Local Index Table.
  • FIG. 25 shows the functional relation between Master Index Table and Local Index Tables
  • FIG. 26 shows how Access Units are composed by blocks of data belonging to several layers. Layers are composed by Blocks subdivided in Packets.
  • FIG. 27 shows how a Genomic Access Unit of type 1 (containing positional, pairing, reverse complement and read length information) is packetized and encapsulated in a Genomic Data Multiplex.
  • FIG. 28 shows how Access Units are composed by a header and multiplexed blocks belonging to one or more layers of homogeneous data. Each block can be composed by one or more packets containing the actual descriptors of the genomic information.
  • FIG. 29 shows the structure of Access Units of type 0 which do not need to refer to any information coming from other access units to be accessed or decoded and accessed.
  • FIG. 30 shows the structure of Access Units of type 1.
  • FIG. 31 shows the structure of Access Units of type 2 which contain data that refer to an access unit of type 1. These are the positions of N bases in the encoded reads.
  • FIG. 32 shows the structure of Access Units of type 3 which contain data that refer to an access unit of type 1. These are the positions and types of mismatches in the encoded reads.
  • FIG. 33 shows the structure of Access Units of type 4 which contain data that refer to an access unit of type 1. These are the positions and types of mismatches in the encoded reads.
  • FIG. 34 shows the first five type of Access Units.
  • FIG. 35 shows that Access Units of type 1 refer to Access Units of type 0 to be decoded.
  • FIG. 36 shows that Access Units of type 2 refer to Access Units of type 0 and 1 to be decoded.
  • FIG. 37 shows that Access Units of type 3 refer to Access Units of type 0 and 1 to be decoded.
  • FIG. 38 shows that Access Units of type 4 refer to Access Units of type 0 and 1 to be decoded.
  • FIG. 39 shows the Access Units required to decode sequence reads with mismatches mapped on the second segment of the reference sequence (AU 0-2).
  • FIG. 40 shows how raw genomic sequence data that becomes available can be incrementally added to pre-encoded genomic data.
  • FIG. 41 shows how a data structure based on Access Units enables genomic data analysis to start before the sequencing process is completed.
  • FIG. 42 shows how new analysis performed on existing data can imply that reads are moved from AUs of type 4 to one of type 3.
  • FIG. 43 shows how newly generated analysis data are encapsulated in a new AU of type 8 and a corresponding index is created in the MIT.
  • FIG. 44 shows how to transcode data due to the publication of a new reference sequence (genome).
  • FIG. 45 shows how reads mapped to a new genomic region with better quality (e.g. no indels) are moved from AU of type 4 to AU of type 3
  • FIG. 46 shows how, in case new mapping location is found, (e.g. with less mismatches) the related reads can be moved from one AU to another of the same type.
  • FIG. 47 shows how selective encryption can be applied on Access Units of Type 4 only as they contain the sensible information to be protected.
  • FIG. 48 shows the data encapsulation in a genomic multiplex where one or more genomic datasets 482 - 483 contain Genomic streams 484 and streams of Genomic Datasets Mapping Table Lists 481 , Genomic Dataset Mapping Tables 485 , and Reference Identifiers Mapping Tables 487 .
  • Each genomic stream is composed by a Header 488 and Access Units 486 .
  • Access Units encapsulate Blocks 489 which are composed by Packets 4810 .
  • FIG. 49 shows how raw genomic sequence data ( 499 ) or aligned genomic data (produced by element 491 ) are processed to be encapsulated in a Genomic Multiplex.
  • the alignment ( 491 ) and reference genome construction ( 492 ) stages can be necessary to prepare the data for encoding.
  • Data classes ( 498 ) generated by a data classification unit ( 494 ) can be further classified with respect to one or more transformed reference generated by a reference transformation unit ( 4919 ).
  • the transformed classes ( 4918 ) are then sent to layers encoders ( 495 - 497 ).
  • the generated layers ( 4911 ) are encoded by entropy coders ( 4912 - 4914 ) which generate Genomic Streams of Access Units ( 4915 ) fed to the Genomic Multiplexer ( 4916 ).
  • FIG. 50 shows how a genomic demultiplexer ( 500 ) extracts Genomic Streams ( 501 ) from the Genomic Multiplex ( 5010 ), one decoder per AU type ( 502 - 504 ) extracts the genomic layers which are then decoded ( 506 - 507 ) into various data classes ( 5011 ) which are used by class decoders ( 509 ) to reconstruct genomic formats such as for example FASTQ and SAM/BAM.
  • a genomic stream containing one or more reference transformations is decoded by an entropy decoder ( 504 ) to produce reference transformation descriptors ( 5012 ).
  • Reference transformation descriptors are processed by a reference transformation unit ( 5013 ) to transform one or more “external” references to generate one or more transformed references ( 5014 ) to be used by the class decoders ( 509 ).
  • FIG. 51 shows the process of encoding sequence reads belonging to class U using a self-generated reference sequence using six layers of descriptors. Four layers are the same used for other classes P, N, M, I while two layers are specific to class U reads.
  • FIG. 52 shows how a label is built to aggregate genomic regions belonging to two different references.
  • FIG. 53 shows how an existing label can be updated in case new results of analysis require to add an additional region R4 to the existing ones (R1, R2 and R3).
  • FIG. 54 shows how the labeling mechanism can be used to implement access control and data protection on specific genomic regions or sub regions.
  • the simple case uses one access control rule (AC) and one protection mechanism (e. g. encryption) for all genomic regions identified by one label.
  • AC access control rule
  • protection mechanism e. g. encryption
  • FIG. 55 shows how the different genomic regions identified by the same label can be protected by several different access control rules (AC) and several different encryption keys.
  • AC access control rules
  • FIG. 56 shows how an alternative encoding of reads of class U where a signed POS descriptor is used to encode the mapping position of a read on the computed reference
  • FIG. 57 shows how half mapped read pairs can help in filling unknown regions of the reference sequence by assembling longer contigs with unmapped reads.
  • FIG. 58 shows the hierarchical structure of headers for genomic data stored following the structure described in this invention.
  • FIG. 59 shows how a device implementing the labeling mechanism described by this invention enables concurrent access to data related to several genomic regions when they are stored in different records of a database. This can happen either in presence of controlled access or not.
  • FIG. 60 shows how vectors of thresholds are used in encoders of classes N, M and I to generate separated subclasses of data
  • FIG. 61 provides an example of how reference transformations can change the class reads belong to when all or a subset of mismatches are removed (i.e. the read belonging to class M before transformation is assigned to class P after the transformation of the reference has been applied).
  • FIG. 62 shows how reference transformations can be applied to remove mismatches (MMs) from reads.
  • reference transformations may generate new mismatches or change the type of mismatches found when referring to the reference before the transformation has been applied.
  • FIG. 63 The same reference transformation A0 can be used for all classes of data or different transformations AN, A M , A I are used for each class N, M, I
  • labels comprising: an identifier of a reference genomic sequence ( 521 ), an identifier of said genomic regions ( 522 ), and an identifier of the data class ( 523 ) of said genomic data
  • genomic data are sequences of genomic reads.
  • data classes can be of the following type or a subset of them:
  • genomic data are paired sequences of genomic reads.
  • said data class of paired reads can be of the following types or a subset of them:
  • said identifier of said genomic regions is comprised in a master index table.
  • genomic data and said labels are entropy coded.
  • said master index table ( 4812 ) is comprised in a genomic dataset header ( 4813 ).
  • said regions of genomic data are dispersed among separate Access Units ( 524 , 486 ).
  • the location of said regions of genomic data, in a file is indicated in a local index table ( 525 ).
  • said labels are user specified.
  • said regions are protected and/or encrypted in a separate manner, without encrypting the whole genomic file.
  • said labels are stored in a genomic label list (GLL)
  • the method further comprises encoding genomic data with selective access to regions of genomic data as previously defined.
  • the method further comprises decoding a stream or a file of genomic data with selective access to regions of genomic data as previously defined.
  • the present invention further provides an apparatus for encoding genomic data as previously defined.
  • the present invention further provides an apparatus for decoding genomic data as previously defined.
  • the present invention further provides a storing mean for storing genomic data encoded as previously defined.
  • the present invention further provides a computer-readable medium comprising instructions that when executed cause at least one processor to perform the encoding method previously defined.
  • the present invention further provides a computer-readable medium comprising instructions that when executed cause at least one processor to perform the decoding method previously defined.
  • the present invention describes a labelling mechanism providing selective access and selective access control to genomic regions or sub-regions or aggregations of regions or sub-regions of compressed genomic data stored in a file format and/or the relevant access units to be used to store, transport, access and process genomic or proteomic information in the form of sequences of symbols representing molecules.
  • nucleotides include, for example, nucleotides, amino acids and proteins.
  • amino acids include, for example, nucleotides, amino acids and proteins.
  • sequence of symbols One of the most important pieces of information represented as sequence of symbols are the data generated by high-throughput genome sequencing devices.
  • the genome of any living organism is usually represented as a string of symbols expressing the chain of nucleic acids (bases) characterizing that organism.
  • bases the chain of nucleic acids
  • Current state of the art genome sequencing technology is able to produce only a fragmented representation of the genome in the form of several (up to billions) strings of nucleic acids associated to metadata (identifiers, level of accuracy etc.). Such strings are usually called “sequence reads” or “reads”.
  • the typical steps of the genomic information life cycle comprise Sequence reads extraction, Mapping and Alignment, Variant detection, Variant annotation and Functional and Structural Analysis (see FIG. 1 ).
  • Sequence reads extraction is the process —performed by either a human operator or a machine—of representation of fragments of genetic information in the form of sequences of symbols representing the molecules composing a biological sample.
  • sequences of symbols representing the molecules composing a biological sample.
  • nucleic acids such molecules are called “nucleotides”.
  • sequences of symbols produced by the extraction are commonly referred to as “reads”.
  • This information is usually encoded in prior art as FASTA files including a textual header and a sequence of symbols representing the sequenced molecules.
  • the alphabet is composed by the symbols (A,C,G,T,N).
  • RNA of a living organism the alphabet is composed by the symbols (A,C,G,U,N).
  • the alphabet used for the symbols composing the reads are (A, C, G, T, U, W, S, M, K, R, Y, B, D, H, V, N or ⁇ ).
  • sequence of quality score can be associated to each sequence read.
  • prior art solutions encode the resulting information as a FASTQ file. Sequencing devices can introduce errors in the sequence reads such as:
  • Coverage is used in literature to quantify the extent to which a reference genome or part thereof can be covered by the available sequence reads. Coverage is said to be:
  • Sequence alignment refers to the process of arranging sequence reads by finding regions of similarity that may be a consequence of functional, structural, or evolutionary relationships among the sequences.
  • reference genome a pre-existing nucleotides sequence referred to as “reference genome”
  • Sequence alignment can also be performed without a pre-existing sequence (i.e. reference genome) in such cases the process is known in prior art as “de novo” alignment.
  • Prior art solutions store this information in SAM, BAM or CRAM files.
  • FIG. 3 The concept of aligning sequences to reconstruct a partial or complete genome is depicted in FIG. 3 .
  • Variant detection is the process of translating the aligned output of genome sequencing machines, (sequence reads generated by NGS devices and aligned), to a summary of the unique characteristics of the organism being sequenced that cannot be found in other pre-existing sequence or can be found in a few pre-existing sequences only. These characteristics are called “variants” because they are expressed as differences between the genome of the organism under study and a reference genome. Prior art solutions store this information in a specific file format called VCF file.
  • Variant annotation is the process of assigning functional information to the genomic variants identified by the process of variant calling. This implies the classification of variants according to their relationship to coding sequences in the genome and according to their impact on the coding sequence and the gene product. This is in prior art usually stored in a MAF file.
  • the invention disclosed in this document consists in the definition of a selective and controlled data access applied to a compressed data structure for representing, processing manipulating and transmitting genome sequencing data that differs from prior art solutions for at least the following aspects:
  • the key elements of the invention are:
  • the method described in this document aims at exploiting the available a-priori knowledge on genomic data to define an alphabet of syntax elements with reduced entropy.
  • genomics the available knowledge is represented by an existing genomic sequence usually —but not necessarily —of the same species as the one to be processed.
  • human genomes of different individuals differ only of a fraction of 1%.
  • such small amount of data contain enough information to enable early diagnosis, personalized medicine, customized drugs synthesis etc.
  • This invention aims at defining a genomic information representation format where the relevant information is efficiently accessible, access can be selectively controlled and data protected, the information is efficiently transportable and all such processing is performed handling compressed data structures.
  • the present invention application provides a specific data structure specification that implements appropriate data reordering into accessible units of homogeneous and/or semantically significant data enabling seamless access and processing required by state of the art genome data analysis applications.
  • the present invention adopts a data structure based on the concept of Access Unit, “Labels” and the multiplexing of the relevant data, concepts which are absent from all state of the art genomic data formats.
  • Genomic data are structured and encoded into different Access Units. Hereafter follows a description of the genomic data that are contained into different Access Units and can be identified by “Labels” associating genomic data to specific genomic regions or sub-regions or aggregations of regions or sub-regions versus reference genomes.
  • sequence reads generated by sequencing machines are classified by the disclosed invention into five different “classes” according to the matching results of the alignment with respect to one or more pre-existing reference sequences.
  • the classification specified in the previous section concerns single sequence reads.
  • sequencing technologies that generates read in pairs (i.e. Illumina Inc.) in which two reads are known to be separated by an unknown sequence of variable length, it is appropriate to consider the classification of the entire pair to a single data class.
  • a read that is coupled with another is said to be its “mate”.
  • the entire pair is assigned to the same class for any class (i.e. P, N, M, I, U). In the case the two reads belong to a different class, but none of them belongs to the “Class U”, then the entire pair is assigned to the class with the highest priority defined according to the following expression:
  • the table below summarizes the matching rules applied to reads in order to define the class of data each read belongs to.
  • the rules are defined in the first five columns of the table in terms of presence or absence of type of mismatches (n, s, d, i and c type mismatches).
  • the sixth column provides rules in terms of maximum threshold for each mismatch type and any function f(n,s) and w(n,s,d,i,c) of the possible mismatch types.
  • the data classes of type N, M and I as defined in the previous sections can be further decomposed into an arbitrary number of distinct sub-classes with different degrees of matching accuracy. Such option is an important technical advantage in providing a finer granularity and as consequence a much more efficient selective access to each data class.
  • Sub-Class N k it is necessary to define a vector with the corresponding components MAXN 1 , MAXN 2 , MAXN (k-1) , MAXN (k) , with the condition that MAXN 1 ⁇ MAXN 2 ⁇ . . .
  • ⁇ MAXN (k-1) ⁇ MAXN and assign each read to the lowest ranked sub-class that satisfy the constrains specified in Table 1 when evaluated for each element of the vector.
  • a data classification unit 601 contains Class P, N, M, I U, HM encoder and encoders for annotations and metadata.
  • Class N encoder is configured with a vector of thresholds, MAXN 1 to MAXN k 602 which generates k subclasses of N data ( 606 ).
  • the same principle is applied by defining a vector with the same properties for MAXM and MAXTOT respectively and use each vector components as threshold for checking if the functions f(n,s) and w(n,s,d,i,c) satisfy the constraint.
  • the assignment is given to the lowest sub-class for which the constraint is satisfied.
  • the number of sub-classes for each class type is independent and any combination of subdivisions is admissible. This is shown in FIG.
  • a Class M encoder and a Class I encoder are configured respectively with a vector of thresholds MAXM 1 to MAXM j ( 603 ) and MAXTOT 1 to MAXTOT h ( 604 ).
  • the two encoders generate respectively j subclasses of M data ( 607 ) and h subclasses of I data ( 608 ). When two reads in a pair are classified in the same sub-class, then the pair belongs to the same sub-class.
  • N has the lowest priority and I has the highest priority.
  • the mismatches found for the reads classified in the classes N, M and I can be used to create “transformed references” to be used to compress more efficiently the read representation.
  • Reads classified as belonging to the Classes N, M or I (with respect to the pre-existing (i.e. “external”) reference sequence denoted as RS 0 ) can be coded with respect to the “transformed” reference sequence RS 1 according to the occurrence of the actual mismatches with the transformed reference.
  • FIG. 61 shows an example on how reads containing mismatches (belonging to Class M) with respect to reference sequence 1 (RS 1 ) can be transformed into perfectly matching reads with respect to the reference sequence 2 (RS 2 ) obtained from RS 1 by modifying the bases corresponding to the mismatch positions. They remain classified and they are coded together the other reads in the same data class access unit, but the coding is done using only the descriptors and descriptor values needed for a Class P read. This transformation can be denoted as:
  • FIG. 62 shows an example on how a reference transformation is applied to reduce the number of mismatches to be coded on the mapped reads.
  • FIG. 61 shows an example on how reads can change the type of coding from a data class to another by means of the appropriate set of descriptors (e.g. using the descriptors of a Class P to code a read from Class M) after a reference transformation is applied and the read is represented using the transformed reference.
  • the definition of the set of descriptors used for each class of data is provided in the following sections.
  • genomic data requires the storage of global parameters and metadata to be used by the decoding engine. These data are organized in the following structures: For file based storage:
  • FIG. 58 The hierarchical relationship among these headers is shown in FIG. 58 .
  • a dataset is defined as the ensemble of coding elements needed to reconstruct the genomic information related to a single genomic sequencing run and all the following analysis. If the same genomic sample is sequenced twice in two distinct runs, the obtained data will be encoded in two distinct datasets.
  • Master index table Byte array This is a Alignment positions of first read in each block (Access Unit). multidimensional l.e. smaller position of the first read on the reference genome array supporting per each block of the six classes random access to 1 per pos class (six) per reference Access Units.
  • Label List Byte array This is a list of Sub-part of the Genomic Dataset Header indicating (e.g. Labels, each one number of Labels integers) represented as a for each Label: multidimensional the Label ID array in order to the number of reference sequences concerned support selective by the label access to specific for each reference sequence genomic regions the reference identifier or sub-regions or the number of regions covered by the aggregations of label, regions or sub- for each region: regions.
  • the class ID the start position in the genomic range the end position in the genomic range Start position and end position can be replaced by “block numbers”, composing, together with reference sequence ID and class ID, a three dimensional vector addressing the coordinates of the Master Index Table. Parameters set Byte array Encoding parameters used to configure the encoding process and sent to the decoder.
  • Descriptors (a.k.a. syntax elements) are described in the following sections of this document and are the building blocks of the genomic information representation described by this invention. They are organized in layers (a.k.a. descriptors streams) of homogeneous elements partitioned according to the specific statistical properties of each descriptor. This has the advantage of reducing the entropy of each layer and improving compression efficiency.
  • Each layer is prepended by the Descriptors Layer Header described below.
  • Descriptors_Layer_Header ⁇
  • Descriptors_Layer_ID Descriptors layer ID, table specified in this specification Num_Of_Blocks Number of Blocks in the Descriptors Layer Label size Size of the human readable label Label (Human-Readable) Label Flag Flag used to interpret the following metadata Local Index Table
  • the Local Index Table structure as described in this invention Metadata Data structure carrying metadata to be used for application- specific processing such as data analysis and content protection. ⁇
  • Every Descriptors Layer is composed by one or multiple Genomic Data Blocks.
  • One or more Blocks from different Layers compose an Access Unit, depending on the Class of data.
  • An Access Unit is a set of Genomic Blocks that can be decoded either independently from other Access Units by using only globally available data (e.g. decoder configuration) or by using information contained in other Access Units.
  • Semantic Block_Header ⁇ Descriptors_Layer_ID Unambiguously identifies the descriptors stream. Same as Descriptors_Layer_ID in Descriptor Layer Header Block size (BS) Number of bytes composing Block, including this header and payload, and excluding padding (total Block size will be BS + padding size). ⁇
  • BS Layer Header Block size
  • further processing consists in defining a set of distinct syntax elements which represent the remaining information enabling the reconstruction of the DNA read sequence when represented as being mapped on a given reference sequence.
  • a sequence read (e.g. a DNA segment) referred to a given reference sequence can be fully expressed by:
  • This classification creates groups of descriptors (syntax elements) that can be used to univocally represent genome sequence reads.
  • syntax elements syntax elements that can be used to univocally represent genome sequence reads.
  • the table below summarizes the syntax elements needed for each class of reads aligned with “pre-existing” (i.e. “external”) or “constructed” (i.e. “internal”) references.
  • Reads belonging to class P are characterized and can be perfectly reconstructed by only a position, a reverse complement information and an offset between mates in case they have been obtained by a sequencing technology yielding mated pairs, some flags and a read length.
  • Class HM is applied to read pairs only and it is a special case where one read belongs to class P, N, M or I and the other to class U.
  • mapping position of the first encoded read is stored in the AU header as absolute position on the reference genome. All the other positions are expressed as a difference with respect to the previous position and are stored in a specific layer.
  • This modeling of the information source, defined by the sequence of read positions, is in general characterized by a reduced entropy particularly for sequencing processes generating high coverage results.
  • FIG. 4 shows how after encoding the starting position of the first alignment as position “10000” on the reference sequence, the position of the second read starting at position 10180 is coded as “180”. With high coverage data (>50 ⁇ ) most of the descriptors of the position vector will show very high occurrences of low values such as 0 and 1 and other small integers.
  • FIG. 10 shows how the positions of three read pairs are encoded in a pos Layer.
  • the same source model is used for the positions of reads belonging to classes N, M, P and I.
  • the positions of reads belonging to the four classes are encoded in separate layers as depicted in Table I.
  • Each read of the read pairs produced by sequencing technologies can be originated from either genome strands of the sequenced organic sample. However, only one of the two strands is used as reference sequence.
  • FIG. 8 shows how in a reads pair one read (read 1) can be originated from one strand and the other (read 2) can be originated from the other strand.
  • read 2 can be encoded as reverse complement of the corresponding fragment on strand 1. This is shown in FIG. 9 .
  • the reverse complement information of reads belonging to classes P, N, M, I are coded in different layers as depicted in Table 3.
  • the pairing descriptor is stored in the pair layer.
  • Such layer stores descriptors encoding the information needed to reconstruct the originating reads pairs, when the employed sequencing technology produces reads by pairs.
  • the vast majority of sequencing data is generated by using a technology generating paired reads, it is not the case of all technologies. This is the reason for which the presence of this layer is not necessary to reconstruct all sequencing data information if the sequencing technology of the genomic data considered does not generate paired reads information.
  • FIG. 5 shows how the pairing distance among read pairs is calculated.
  • the pair descriptor layer is the vector of pairing errors calculated as number of reads to be skipped to reach the mate pair of the first read of a pair with respect to the defined decoding pairing distance.
  • FIG. 6 shows an example of how pairing errors are calculated, both as absolute value and as differential vector (characterized by lower entropy for high coverages).
  • the same descriptors are used for the pairing information of reads belonging to classes N, M, P and I.
  • the pairing information of reads belonging to the four classes are encoded in different layer as depicted in.
  • mapping sequence reads on a reference sequence it is not uncommon to have the first read in a pair mapped on one reference (e.g. chromosome 1) and the second on a different reference (e.g. chromosome 4).
  • the pairing information described above has to be integrated by additional information related to the reference sequence used to map one of the reads. This is achieved by coding
  • a reserved value indicating that the pair is mapped on two different sequences (different values indicate if read1 or read2 are mapped on the sequence that is not currently encoded)
  • a unique reference identifier referring to the reference identifiers encoded in the Genomic Dataset Header structure as described in Table 2.
  • FIG. 7 provides an example of this scenario.
  • the third element contains the mapping information on the concerned reference ( 170 ).
  • Class N includes all reads in which only “n type” mismatches are present, at the place of an A, C, G or T base a N is found as called base. All other bases of the read perfectly match the reference sequence.
  • FIG. 11 shows how:
  • a substitution is defined as the presence, in a mapped read, of a different nucleotide with respect to the one that is present in the reference sequence at the same position (see FIG. 12 ).
  • a substitution position is calculated as for the values of the nmis layer, i.e.: In read 1 substitutions are encoded
  • mismatches are coded by an index (moving from right to left) from the actual symbol present in the reference to the corresponding substitution symbol present in the read ⁇ A, C, G, T, N, Z ⁇ .
  • the mismatch index will be denoted as “4”.
  • the decoding process reads the encoded syntax element, the nucleotide at the given position on the reference and moves from left to right to retrieve the decoded symbol. E.g. a “2” received for a position where a G is present in the reference will be decoded as “N”.
  • FIG. 14 shows all the possible substitutions and the respective encoding symbols when IUPAC ambiguity codes are not used and
  • FIG. 15 provides an example of encoding of substitutions types in the snpt layer.
  • substitution indexes change as shown in FIG. 16 .
  • an alternative method of substitution encoding consists in storing only the mismatches positions in separate layers, one per nucleotide, as depicted in FIG. 17 .
  • mismatches and deletions are coded by an indexes (moving from right to left) from the actual symbol present in the reference to the corresponding substitution symbol present in the read: ⁇ A, C, G, T, N, Z ⁇ .
  • the mismatch index will be “4”.
  • the coded symbol will be “5”.
  • the decoding process reads the coded syntax element, the nucleotide at the given position on the reference and moves from left to right to retrieve the decoded symbol. E.g. a “3” received for a position where a G is present in the reference will be decoded as “Z” which indicates the presence of a deletion in the sequence read.
  • Inserts are coded as 6, 7, 8, 9, 10 respectively for inserted A, C, G, T, N.
  • FIG. 18 and FIG. 19 show examples of how to encode substitutions, inserts and deletions in a reads pair of class I.
  • syntax elements that can be of the following types:
  • FIG. 51 provides an example of such encoding procedure.
  • FIG. 56 shows an alternative encoding of unmapped reads on the internal reference where pos+pair syntax elements are replaced by a signed pos.
  • pos would express the distance —in terms of positions on the reference sequence —of the left most nucleotide position of read n with respect of the position of the left most nucleotide of read n ⁇ 1.
  • This coding approach can be extended to support N start positions per read so that reads can be split over two or more reference positions. This can be particularly useful to encode reads generated by those sequencing technology (e.g. from Pacific Bioscience) producing very long reads (50K+bases) which usually present repeated patterns generated by loops in the sequencing methodology. The same approach can be used as well to encode chimeric sequence reads defined as reads that align to two distinct portions of the genome with little or no overlap.
  • MIT Master Index Table
  • the MIT contains one section per each class of data (P, N, M, I, U and HM) and per each reference sequence.
  • the MIT is contained in the Genomic Dataset Header of the encoded data.
  • FIG. 20 shows the structure of the Genomic Dataset Header
  • FIG. 21 shows a generic visual representation of the MIT
  • FIG. 22 shows an example of MIT for the class P of encoded reads.
  • the values contained in the MIT depicted in FIG. 22 are used to directly access the region of interest (and the corresponding AU) in the compressed domain.
  • a decoding application would skip to the second reference in the MIT and would look for the two values k1 and k2 so that k1 ⁇ 150,000 and k2>250,000.
  • k1 and k2 are 2 indexes read from the MIT. In the example of FIG. 22 this would result in positions 3 and 4 of the second vector of the MIT.
  • the MIT can be uses as an index of additional metadata and/or annotations added to the genomic data during its life cycle.
  • Each data layer described above is prefixed with a data structure referred to as local header.
  • the local header contains a unique identifier of the layer, a vector of Access Units counters per each reference sequence, a Local Index Table (LIT) and optionally some layer specific metadata.
  • the LIT is a vector of pointers to the physical position of the data belonging to each AU in the layer payload.
  • FIG. 23 depicts the generic layer header and payload where the LIT is used to access specific regions of the encoded data in a non-sequential way.
  • the decoding application in order to access region 150,000 to 250,000 of reads aligned on the reference sequence no. 2, the decoding application retrieved positions 3 and 4 from the MIT. These values shall be used by the decoding process to access the 3 rd and 4 th elements of the corresponding section of the LIT.
  • the Total Access Units counters contained in the layer header are used to skip the LIT indexes related to AUs related to reference 1 (5 in the example).
  • the indexes containing the physical positions of the requested AUs in the encoded stream are therefore calculated as:
  • the blocks of data retrieved using the indexing mechanism called Local Index Table, are part of the Access Units requested.
  • FIG. 26 shows how the data blocks retrieved using the MIT and the LIT compose one or more Access Units.
  • Access Units The genomic data classified in data classes and structured in compressed or uncompressed layers are organized into different Access Units.
  • Genomic Access Units are defined as sections of genome data (in a compressed or uncompressed form) that reconstructs nucleotide sequences and/or the relevant metadata, and/or sequence of DNA/RNA (e.g. the virtual reference) and/or annotation data generated by a genome sequencing machine and/or a genomic processing device or analysis application.
  • An example of Access Unit is provided in FIG. 26 .
  • An Access Unit is a block of data that can be decoded either independently from other Access Units by using only globally available data (e.g. decoder configuration) or by using information contained in other Access Units.
  • Access Units are differentiated by:
  • Access units of any type can be further classified into different “categories”.
  • Access Units of type 0 are ordered (e.g. numbered), but they do not need to be stored and/or transmitted in an ordered manner (technical advantage: parallel processing/parallel streaming, multiplexing)
  • Access Units of type 1, 2, 3, 4, 5 and 6 do not need to be ordered and do not need to be stored and/or transmitted in an ordered manner (technical advantage: parallel processing/parallel streaming).
  • FIG. 26 shows how Access Units are composed by a header and one or more layers of homogeneous data.
  • Each layer can be composed by one or more blocks.
  • Each block contains several packets and the packets are a structured sequence of the descriptors introduced above to represent e.g. reads positions, pairing information, reverse complement information, mismatches positions and types etc.
  • Each Access unit can have a different number of packets in each block, but within an Access Unit all blocks have the same number of packets.
  • Each data packet can be identified by the combination of 3 identifiers X Y Z where:
  • FIG. 28 shows an example of Access Units and packets labelling where AU T N is an access unit of type T with identifier N which may or may not imply a notion of order according to the Access Unit Type. Identifiers are used to uniquely associate Access Units of one type with those of other types required to completely decode the carried genomic data.
  • Access Units of any type can be further classified and labelled in different “categories” according to different sequencing processes. For example, but not as a limitation, classification and labelling can take place when
  • the access units of type 1, 2, 3, 4, 5 and 6 are built according to the result of a matching function applied on genome sequence fragments (a.k.a. reads) with respect to the reference sequence encoded in Access Units of type 0 they refer to.
  • access units (AUs) of type 1 may contain the positions and the reverse complement flags of those reads which result in a perfect match (or maximum possible score corresponding to the selected matching function) when a matching function is applied to specific regions of the reference sequence encoded in AUs of type 0. Together with the data contained in AUs of type 0, such matching function information is sufficient to completely reconstruct all genome sequence reads represented by the data set carried by the access units of type 1.
  • the Access Units of type 1 described above would contain information related to genomic sequence reads of class P (perfect matches).
  • the matching functions applied with respect to access units of type 1 to classify the content of AU for the type 2, 3 and 4 can provide results such as:
  • Access units of type 0 are ordered (e.g. numbered), but they do not need to be stored and/or transmitted in an ordered manner (technical advantage: parallel processing/parallel streaming, multiplexing)
  • Access units of type 1, 2, 3, 4, 5 and 6 do not need to be ordered and do not need to be stored and/or transmitted in an ordered manner (technical advantage: parallel processing/parallel streaming).
  • An additional mechanism is provided by the disclosed invention enabling user-defined selective access to data classes referring to specific genomic regions or sub-regions or aggregations of regions or sub-regions.
  • a “Label” is an identifier which is assigned to a specific genomic region or sub-region or aggregations of regions or sub-regions. Labels identify genomic regions by specifying: the reference sequence id (“Ref ids”), the index of the MIT corresponding to the desired region of the reference sequence, and the data classes. An example is provided in FIG. 52 .
  • a single, a subset, or all data classes can be referenced by a Label, enabling selective access to only a sub-set of the data associated to a specific genomic region or sub-regions or aggregations of regions or sub-regions.
  • a Label list should be created by a Genomic Labels Generator ( 4917 FIG. 49 ), in a storage scenario, and/or in a streaming scenario to make available the available Labels to the analysis applications applying a selective access to the stored or streamed data.
  • one or more Access Units can be identified using a specific “Label” by means of a Block Header field (“Label ID”), which serves as an identifier for the “Label” in the “Label List” which the current block belongs to.
  • Label ID serves as an identifier for the “Label” in the “Label List” which the current block belongs to.
  • Label ID serves as an identifier for the “Label” in the “Label List” which the current block belongs to.
  • start_pos and “end_pos” fields can be replaced by the block numbers referring to all “blocks” belonging to a specific “Label”, as follows:
  • the “Label List” is created by a Genomic Labels Generator ( 4917 ) and sent to the genomic multiplexer (see also FIG. 49 ).
  • the demultiplexer parses the Label List syntax and exposes the available Labels to the data access application, which according to the specific data access required selects the Access Units corresponding to the subset of “Labels”.
  • Generic random access can be achieved by specifying a three dimensional vector determining the MIT and LIT coordinates of interest (reference id, position range and classes) and ignoring the information carried by the Label List.
  • FIG. 51 shows how labels are used to aggregate and uniquely identify several genomic regions by using indexes contained in the MIT.
  • FIG. 59 shows how a device ( 592 ) implementing the labelling mechanism disclosed by this invention can enable concurrent access to several records of data ( 596 ) stored in a database ( 595 ). Selective protection of one or more regions identified by the same label is supported as well by means of a dedicated module ( 591 ) in charge of parsing the query ( 591 ) and dispatching the required metadata to the security module ( 594 ) in charge of enforcing access control.
  • the labels decoder ( 593 ) is in charge of translating the label syntax into object identifiers that can be protected (and therefore access is controlled by the security module 594 ) or not.
  • FIG. 39 shows how the access to the genomic information mapped on the second segment of the reference sequence (AU 0-2) with mismatches only requires the decoding of AUs 0-2, 1-2 and 3-2 only.
  • This is an example of selective access according to both a criteria related to a mapping region (i.e. position on the reference sequence) and a criteria related to the matching function applied to the encoded sequence reads with respect to the reference sequence (e.g. mismatches only in this example).
  • a further technical advantage is that the querying on the data is much more efficient in terms of data accessibility and execution speed because it can be based on accessing and decoding only selected “categories”, specific regions of longer genomic sequences and only specific layers for access units of type 1, 2, 3, 4 that match the criteria of the applied queries and any combination thereof.
  • FIG. 52 shows how the access to the genomic information associated only to specific genomic regions or sub-regions or aggregations of regions or sub-regions associated to user defined “Labels”.
  • the syntax of a label is based on a three coordinates system where each region or sub-region associated to a label can be uniquely identified by:
  • a further technical advantage is that the querying on the data results to be much more efficient in terms of data accessibility and execution speed because it can be based on accessing and decoding only selected “categories”, of the labelled specific regions and only specific layers for access units of type 1, 2, 3, 4 that corresponds to the “Labels” of the applied queries and any combination thereof.
  • Another technical advantage of this labelling mechanism is the possibility of efficiently retrieving encoded genomic information that has been scattered among several Access Units due to its characteristics such as position on the reference genome, type of mismatches with respect to the reference ( 524 ).
  • Filtering genomic data according to the characteristics of the mapped reads (e.g. perfectly matching, substitutions only, etc.) today can take hours when using the traditional formats such as BAM and CRAM. This is due to the fact that the data are sparse within the compressed format and require decompression and filtering using pipelines of commands.
  • the present invention describes a data structure that enables data filtering in a matter of seconds. Memory usage can be as well reduced by a factor that is proportional with the file size (from 10 ⁇ to 100 ⁇ ) since the present invention does not require the decoding (i.e. memory allocation) of the entire file.
  • the Label parameter “Label_lenght_in_blocks” and for each block the parameters: “ref_num”, “class_ID”, “block_num” are determined by the multiplexer based on the position on the reference of the “GeneXY” and “GeneWZ” regions and the class of data for which the selective access is desired. The complete syntax is reported in Table 5.
  • the Label parameters “ref ID”, “class_ID”, “start_pos” and “end_pos” are determined by the multiplexer based on the position on the reference of the “GeneXY” and “GeneWZ” regions and the class of data for which the selective access is desired. The complete syntax is reported in Table 4.
  • the access units of type 7 and 8 allow for easy insertion of annotations without the need of depacketizing/decoding/decompressing the whole file thereby adding to the efficient handling of the file which is a limitation of prior art approaches.
  • Existing compression solutions may have to access and process a large amount of compressed data before the desired genomic data can be accessed. This will cause inefficient RAM bandwidth utilization and more power consumption also in hardware implementations. Power consumption and memory access issues may be alleviated by using the approach based on Access Units described here.
  • the data indexing mechanism described in the Master Index Table together with the utilization of Access Unites and the possibility of identifying Access Units with user-defined “Labels” associated to specific genomic regions or sub-regions or aggregations of regions or sub-regions enables incremental update of the encoded content as described below. This mechanism is shown with an example in FIG. 53 .
  • New genomic information can be periodically added to existing genomic data for several reasons. For example when:
  • This mechanism is illustrated in FIG. 40 where pre-existing data encoded in 3 AUs of type 1 and 4 AUs per each type from 2 to 4 are updated with 3 AUs per type with encoding data coming for example from a new sequence run for the same individual.
  • FIG. 52 and FIG. 53 The mechanism of creating or updating “Labels” and the “Label List” are illustrated in FIG. 52 and FIG. 53 .
  • the incremental update of a pre-existing data set may be useful when analyzing data as soon as they are generated by a sequencing machine and before the actual sequencing is completed.
  • An encoding engine (compressor) can assemble several AUs in parallel by “clustering” sequence reads that map on the same region of the selected reference sequence. Once the first AU contains a number of reads above a pre-configured threshold/parameter, the AU is ready to be sent to the analysis application. Together with the newly encoded Access Unit, the encoding engine (the compressor) shall make sure that all Access Units the new AU depends on have already been sent to the receiving end or is sent together with it. For example an AU of type 3 will require the appropriate AU of type 0 and type 1 to be present at the receiving end in order to be properly decoded.
  • a receiving variant calling application would be able to start calling variants on the AU received before the sequencing process has been completed at the transmitting side.
  • a schematic of this process is depicted in FIG. 41 .
  • Compressed genomic data can require transcoding, for example, in the following situations:
  • prior art compression solutions may have to access and process a large amount of compressed data before the desired genomic data can be accessed. This will cause inefficient RAM bandwidth utilization and more power consumption and in hardware implementations. Power consumption and memory access issues may be alleviated by using the approach based on Access Units described here.
  • a further advantage of the adoption of the genomic access units described here is the facilitation of parallel processing and suitability for hardware implementations.
  • Current solutions such as SAM/BAM and CRAM are conceived for single-threaded software implementation.
  • a person skilled in the art knows that the majority of genomic information related to an organism's genetic profile relies in the differences (variants) with respect to a known sequence (e.g. a reference genome or a population of genomes).
  • An individual genetic profile to be protected from unauthorized access will therefore be encoded in Access Units of type 3 and 4 as described in this document.
  • the implementation of controlled access to the most sensible genomic information produced by a sequencing and analysis process can therefore be realized by encrypting only the payload of AUs of type 3 and 4 (see FIG. 47 for an example). This will generate significant savings in terms of both processing power and bandwidth since the resources consuming encryption process shall be applied on a subset of data only.
  • the labelling mechanism enables different mechanisms of data protection and access control.
  • FIG. 54 shows how one protection mechanism (e.g. encryption) and one access control rule (AC) can be applied to several genomic regions identified by the same label.
  • data protection can be implemented by applying a different access control rule and a different protection mechanism (encryption) to each region identified by a label. This is shown in FIG. 55 .
  • genomic regions or sub-regions or aggregations of regions or sub-regions identified by different “Labels” can be easily implemented by applying encryption only to compressed data corresponding to a “Label” for both file and streamed scenarios.
  • two genomic regions labelled as “GeneXY” and “GeneWZ” like in the example of section “Selective Access to Specific Genomic Regions identified by User Specified “Labels” in “storage” and “streaming” scenarios” can be differentiated by only encrypting data labelled by “GeneXY” and leaving in clear the compressed data labelled as “GeneWZ”.
  • Encryption rules can be carried by the metadata fields (in both storage and streaming scenarios) and associated to each element of the “Label List”
  • Genomic Access Units can be transported over a communication network within a Genomic Data Multiplex.
  • a Genomic Data Multiplex is defined as a sequence of packetized genomic data and metadata represented according to the data classification disclosed as part of this invention, transmitted in network environments where errors, such as packet losses, may occur.
  • Genomic Data Multiplex is conceived to ease and render more efficient the transport of genomic coded data over different environments (typically network environments) and has the following advantages not present in state of the art solutions:
  • FIG. 49 An Example of Genomic Data Multiplexing is Shown in FIG. 49 .
  • Genomic Dataset is defined as a structured set of Genomic Data including, for example, genomic data of a living organism, one or more sequences and metadata generated by several steps of genomic data processing, or the result of the genomic sequencing of a living organism.
  • One Genomic Data Multiplex may include multiple Genomic Datasets (as in a multi-channel scenario) where each dataset refers to a different organism.
  • the multiplexing mechanism of the several datasets into a single Genomic Data Multiplex is governed by information contained in data structures called Genomic Datasets List (GDL), Genomic Dataset Mapping Tables List (GDMTL) and Genomic Dataset Mapping Table (GDMT).
  • GDL Genomic Datasets List
  • GDMTL Genomic Dataset Mapping Tables List
  • GDMT Genomic Dataset Mapping Table
  • Genomic Dataset List is defined as a data structure listing all Genomic Datasets available in a Genomic Data Multiplex. Each of the listed Genomic Datasets is identified by a unique value called Genomic Dataset ID (GID).
  • Each Genomic Dataset listed in the GDL is associated to:
  • the GDL is sent as payload of a single Transport Packet at the beginning of a Genomic Data Stream transmission; it can then be periodically re-transmitted in order to enable random access to the Stream.
  • the syntax of the GDL data structure is provided in the table below with an indication of the data type associated to each syntax element.
  • section_length bitstring field specifying the number of bytes composing the section, starting immediately following the section_length field, and including the CRC.
  • multiplex_id bitstring field which serves as a label to identify this multiplexed stream from any other multiplex within a network.
  • version_number bitstring field indicating the version number of the whole Genomic Dataset List Section. The version number shall be incremented by 1 whenever the definition of the Genomic Dataset Mapping Table changes. When the applicable_section_flag is set to ‘1’, then the version_number shall be that of the currently applicable Genomic Dataset List. When the applicable_section_flag is set to ‘0’, then the version_number shall be that of the next applicable Genomic Dataset List.
  • applicable_section_flag A 1 bit indicator, which when set to ‘1’ indicates that the Genomic Dataset Mapping Table sent is currently applicable. When the bit is set to ‘0’, it indicates that the table sent is not yet applicable and shall be the next table to become valid.
  • list_ID This is a bitstring field identifying the current genomic dataset list.
  • genomic_dataset_ID genomic_dataset_ID is a bitstring field which specifies the genomic dataset to which the genomic_dataset_map_SID is applicable. This field shall not take any single value more than once within one version of the Genomic Dataset Mapping Table.
  • genomic_dataset_map_SID genomic_dataset_map_SID is a bitstring field identifying the Genomic Data Stream carrying the Genomic Dataset Mapping Table (GDMT) associated to this Genomic Dataset. No genomic_dataset_ID shall have more than one genomic_dataset_map_SID associated. The value of the genomic_dataset_map_SID is defined by the user.
  • reference_id_map_SID reference_id_map_SID is a bitstring field identifying the Genomic Data Stream carrying the Reference ID Mapping Table (RIDMT) associated to this Genomic Dataset. No genomic_dataset_ID shall have more than one reference_id_map_SID associated. The value of the reference_id_map_SID is defined by the user.
  • genomic_Label_list_SID genomic_Label_list_SID is a bitstring field identifying the Genomic Data Stream carrying the Genomic Label List (GLL) associated to this Genomic Dataset. No genomic_dataset_ID shall have more than one genomic_Label_list_SID associated. The value of the genomic_Label_list_SID is defined by the user. Chacksum This is a bitstring field that contains an integrity check value for the entire GDL.
  • One typical algorithm used for this purpose function is the CRC32 algorithm producing a 32 bit value other algorithms include the hashing functions MD5 and SHA-256.
  • the Genomic Dataset Mapping Table (GDMT) is produced and transmitted at the beginning of a streaming process (and possibly periodically re-transmitted, updated or identical in order to enable the update of correspondence points and the relevant dependencies in the streamed data).
  • the GDMT is carried by a single Packet following the Genomic Dataset List and lists the SIDs identifying the Genomic Data Streams composing one Genomic Dataset.
  • the GDMT is the complete collection of all identifiers of Genomic Data Streams (e.g., the genomic sequence, reference genome, metadata, etc) composing one Genomic Dataset carried by a Genomic Multiplex.
  • a genomic dataset mapping table is instrumental in enabling random access to genomic sequences by providing the identifier of the stream of genomic data associated to each genomic dataset.
  • the syntax of the GDMT data structure is provided in the table below with an indication of the data type associated to each syntax element.
  • the syntax elements composing the GDMT described above have the following meaning and function.
  • Genomic_dataset_ID bitstring field identifying a Genomic Dataset mapping_table_ID bitstring bit field identifying the current Genomic Dataset Mapping Table genomic_dataset_ef_length bitstring field specifying the number of bytes of the optional extension_field associated with this Genomic Dataset data_type bitstring field specifying the type of genomic data carried by the packets identified by the genomic_data_SID.
  • genomic_data_SID bitstring bit field specifying the Stream ID of the packets carrying the encoded genomic data associated with one component of this Genomic Dataset (e.g. read p positions, read p pairing information etc. as defined in this invention)
  • gd_component_ef_length bitstring field specifying the number of bytes of the optional extension_field associated with the genomic Stream identified by genomic_data_SID.
  • Checksum This is a bitstring field that contains an integrity check value for the entire GDMT.
  • One typical algorithm used for this purpose function is the CRC32 algorithm producing a 32 bit value or hashing functions such as MD5 and SHA-256.
  • extension_fields are optional descriptors that might be used to further describe either a Genomic Dataset or one Genomic Dataset component.
  • the data_type field can have the following values
  • This structure carries information about all the datasets mapping tables related to a Genomic Datasets Multiplex.
  • the Reference ID Mapping Table (RIDMT) is produced and transmitted at the beginning of a streaming process.
  • the RIDMT is carried by a single Packet following the Genomic Dataset List.
  • the RIDMT specifies a mapping between the numeric identifiers of reference sequences (REFID) contained in the Block header of an access unit and the (typically literal) reference identifiers contained in the Genomic Dataset Header specified in Table 2.
  • the RIDMT can be periodically re-transmitted in order to:
  • the syntax of the RIDMT data structure is provided in the table below with an indication of the data type associated to each syntax element.
  • table_length, genomic_dataset_ID These elements have the same meaning as for the version_number, applicable_section_flag GDMT reference_id_mapping_table_ID bitstring field identifying the current Reference ID Mapping Table ref_string_length bitstring field specifying the number of characters (bytes) composing ref_string, excluding the end of string (‘ ⁇ 0’) character.
  • ref_string[i] byte field encoding each character of the string representation of a reference sequence (e.g. “chr1” for chromosome 1).
  • the end of string (‘ ⁇ 0’) character is not necessary, as it is implicitly inferred from the ref_string_length field REFID This is a bitstring field uniquely identifying a reference sequence.
  • Checksum This is a bitstring field that contains an integrity check value for the entire RIDMT.
  • One typical algorithm used for this purpose function is the CRC32 algorithm producing a 32 bit value or any hash function producing longer strings of bits.
  • a label is an identifier which is assigned to a specific genomic regions or sub-regions or aggregations of regions or sub-regions.
  • Labels identify genomic regions by specifying the reference sequence id, the position range with respect to the reference sequence and the data classes that they identify.
  • the Genomic Label List (GLL) is created during the packetization process by the multiplexer and transmitted.
  • the depacketizer of the demultiplexer parses the GLL syntax and exposes the available “Labels” to the data access application, which has the possibility to select and access the desired sub-set of data.
  • the GLL is (optionally) produced and transmittedat the beginning of a stream and typically transmitted periodically in order to enable multiple synchronization points ( 4811 ), and provides the list of “Labels” associated to the Multiplex and Dataset identified by the multiplex_id and dataset_id fields.
  • the syntax of the GLL data structure is provided in the table below with an indication of the data type associated to each syntax element.
  • table_length Bitstring field specifying the number of bytes composing the list, starting after the table_length field, and including the Checksum field multiplex_ID Byte which serves as a label to identify the Genomic Multiplex from any other multiplex within a network dataset_ID Byte which serves as a label to identify the Genomic Dataset from any other dataset within the multiplex identified by multiplex_id num_Labels Bitstring representing the total number of Labels in this GLL Label_id Bitstring identifying the i-th Label num_ref Bitstring identifying the number of references concerned by the current label ref_id Bitstring identifying the j-th reference sequence the i-th Label refers to num_regions Bistring identifying the number of regions conveyed by the i-th Label class_id Bitstring identifying the class of the k-th region in the j-th reference in the i-th Label start_pos Bitstring indicating the position in the j-th reference sequence of the first read of
  • a Genomic Data Multiplex contains one or several Genomic Data Streams where each stream can transport
  • a Genomic Data Stream containing genomic data is essentially a packetized version of a Genomic Data Layer where each packet is prepended with a header describing the packet content and how it is related to other elements of the Multiplex.
  • Genomic Data Stream format described in this document and the File Format described in this document are mutually convertible. Whereas a full file format can be reconstructed in full only after all data have been received, in case of streaming a decoding tool can reconstruct and access, and start processing the partial data at any time.
  • a Genomic Data Stream is composed by several Genomic Data Blocks each containing one or more Genomic Data Packets.
  • Genomic Data Blocks are containers of genomic information composing one genomic AU. GDB can be split into several Genomic Data Packets, according to the communication channel requirements.
  • Genomic access units are composed by one or more Genomic Data Blocks belonging to different Genomic Data Streams.
  • Genomic Data Packets are transmission units composing one GDB. Packet size is typically set according to the communication channel requirements.
  • FIG. 27 shows the relationship among Genomic Multiplex, Streams, Access Units, Blocks and Packets when encoding data belonging to the P class as defined in this invention.
  • three Genomic Streams encapsulate information on position, pairing and reverse complement of sequence reads.
  • Genomic Data Blocks are composed by a header, a payload of compressed data and padding information.
  • the table below provides an example of implementation of a GDB header with a description of each field and a typical data type.
  • AUID Unambiguous ID, linearly increasing (not necessarily by 1, even bitstring though recommended). Needed to implement proper random access, as described in the Master Index Table defined in this invention.
  • Label ID Unambiguous ID, linearly increasing by 1, identifying the bitstring genomic region/classes (Label) this block belongs to. It corresponds to the i-th index in the main for loop in the Genomic Label List described above.
  • Optional Reference Unambiguous ID, identifying the reference sequence the AU bitstring ID (REFID) containing this block refers to. This is needed, along with POS field, to have proper random access, as described in the Master Index Table.
  • POS POS
  • bitstring Position on the reference sequence of the bitstring first read in the block.
  • Additional optional fields presence signaled by BS.
  • bytestring Optional
  • Padding Optional, presence signaled by PDF Fixed bitstring value that bitstring can be inserted in order to meet the channel requirements. If present, the first byte indicates how many bytes compose the padding. It is discarded by the decoder.
  • AUID Master Index Table
  • LIT Local Index Table
  • AUID and BS enable the receiving end to dynamically re-create a LIT locally, without the need to send extra-data.
  • AUID, BS and POS will enable to recreate a MIT locally without the need to send additional data.
  • a Genomic Data Block can be split into one or more Genomic Data Packets, depending on network layer constraints such as maximum packet size, packet loss rate, etc.
  • a Genomic Data Packet is composed by a header and a payload of encoded or encrypted genomic data as described in the table below.
  • Genomic Data Packet syntax elements Data type Description Data size Stream ID (SID) Unambiguously identifies data type carried by this bitstring packet. A Genomic Dataset Mapping Table is needed at the beginning of the stream in order to map Stream IDs to data types. Used also for updating correspondence points and relevant dependencies. Access Unit Marker Bit Set for the last packet of the access unit. Allows to bit (MB) identify the last packet of an AU. Packet Counter Counter associated to each Stream ID linearly increasing bitstring Number (SN) by 1. Needed to identify gaps/packet losses. Wrap around at 255. Packet Size (PS) Number of bytes composing the packet, including bitstring header, optional fields and payload. Extension Flag (EF) Set if extension fields are present. Bit Extension Fields Optional fields, presence signaled by PS. bytestring Payload Block data (entire block or fragment) bytestring
  • the Genomic Multiplex can be properly decoded only when at least one Genomic Dataset List, one Genomic Dataset Mapping Table and one Reference ID Mapping Table have been received, allowing to map every packet to a specific Genomic Dataset component.
  • Every Genomic Data Block may be split in fragments, which may be transmitted in the payload of Genomic Data Packets, depending on channel requirements, such as packet loss rate, protocol maximum packet size, etc.
  • a Genomic Data Packet is defined as follows.
  • Packet_header( ) ⁇ Layer ID (LID) Unambiguously identifies data type carried by this Packet. Unique for each sub-stream/data type. Mapping Table needed at beginning of stream in order to map Layer IDs to data types. Reserved To maintain byte-alignment Access Unit Marker Bit (MB) Set for the last Packet of the Access Unit. Allows identifying the end of an AU as a set of Blocks. Sequence Number (SN) Packet counter, linearly increasing by 1. Needed to identify packet losses as gaps in SNs for each individual sub-stream. Associated to LID, i.e., different SN for every LID. Packet Size (PS) Number of bytes composing Packet, including header, optional fields and payload. Extension Flag (EF) Set if extension field is present. [optional] Extension field Optional field, present if EF is set. ⁇
  • FIG. 49 shows how before being transformed in the data structures presented in this invention, raw genomic sequence data need to be mapped ( 491 ) on one or more reference sequence known a-priori ( 4920 ).
  • a reference sequence is not available a “constructed” reference can be built from the raw sequence data ( 492 ).
  • Already aligned data can be re-aligned in order to reduce the information entropy.
  • a genomic classifier 494 ) creates the data classes according to the matching functions described in Table land separates metadata (e.g. quality values) and annotation data from the genomic sequences.
  • a reference transformation ( 4919 ) can be applied on the external reference ( 4920 ) in order to further reduce the entropy of the generated classes of data ( 498 ).
  • the transformed data classes ( 4918 ) are fed to layers encoders ( 495 - 497 ) to produce genomic layers ( 491 ) which are then encoded by entropy encoders ( 4912 - 4914 ).
  • the genomic streams generated by the entropy encoders are then sent to Genomic Multiplexer ( 4916 ) which generates the Genomic Multiplex.
  • Genomic labels generated by a Genomic Labels Generator ( 4917 ) can be associated to the genomic streams ( 4915 ) by the Multiplexer ( 4916 ).

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