Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.

Sharing Genomic Data Market Outlook

The global sharing genomic data market size was valued at $5.2 billion in 2023 and is projected to reach $15.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. The surge in market size is driven by advancements in genomic research, widespread adoption of precision medicine, and increasing governmental and private sector investments in genomics.

One of the primary growth factors in the sharing genomic data market is the rapid advancement in genomic technologies. The cost of sequencing an entire genome has plummeted over the past decade, making it more accessible for researchers and healthcare providers. This democratization of genomic data has catalyzed numerous projects aimed at understanding genetic disorders, optimizing drug development, and personalizing medical treatments. Additionally, the development of robust bioinformatics tools for the analysis and interpretation of vast genomic datasets has further propelled the market forward.

Another significant growth factor is the increasing emphasis on precision medicine. Precision medicine aims to tailor medical treatment to the individual characteristics of each patient, and genomic data is a critical component in this approach. By understanding the genetic makeup of patients, healthcare providers can prescribe more effective treatments and interventions. Furthermore, governments and private institutions around the world are heavily investing in initiatives that support genomic research and data sharing, thereby boosting market growth. For instance, the National Institutes of Health (NIH) in the United States and the UK Biobank are exemplary projects that highlight the importance of genomic research.

The integration of artificial intelligence (AI) and machine learning (ML) with genomic data sharing platforms is another driving force. AI and ML algorithms are increasingly being used to identify patterns and correlations in genomic data that would be impossible for humans to discern. These technologies are enhancing the speed and accuracy of genomic data analysis, leading to quicker insights and more effective treatments. Furthermore, collaborations between tech companies and genomic research institutions are accelerating innovations in this field. These collaborations foster an ecosystem that supports rapid technological advancements and the efficient sharing of genomic data.

Regionally, North America holds the largest share in the sharing genomic data market, driven by the presence of leading genomic research institutions, substantial funding from government and private sectors, and favorable regulatory frameworks. Europe follows closely, with significant contributions from countries like the UK, Germany, and France. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate due to increasing investments in genomic research, growing healthcare infrastructure, and the rise of biotech startups. Latin America and the Middle East & Africa are also emerging markets, showing potential for substantial growth driven by healthcare reforms and investments in genomic research initiatives.

Data Type Analysis

The data type segment of the sharing genomic data market is categorized into whole genome sequencing, exome sequencing, and targeted sequencing. Whole genome sequencing (WGS) is the most comprehensive form of sequencing, providing a complete picture of an individual's genetic makeup. WGS is increasingly being adopted in various research projects and clinical settings due to its thoroughness and the declining costs associated with the technology. This method encompasses all coding and non-coding regions of the genome, offering invaluable insights into complex genetic disorders, cancer genomics, and population genetics.

Exome sequencing, which focuses on sequencing only the coding regions of the genome (or exons), is another crucial component of this market segment. Exome sequencing is less costly compared to WGS and is highly effective in identifying mutations that cause diseases. This method is particularly popular in clinical diagnostics and personalized medicine, where quick and accurate detection of genetic anomalies is imperative. Exome sequencing is also widely used in research applications, where the focus is on understanding the functional aspects of genes.

Targeted sequencing involves sequencing specific regions of the genome that are of interest. This approach is highly efficient and cost-effective, making it an attractive option for both research and c

N : Total number of genes incorporated into the integrated YeastNet version 2E : Total number of linkages incorporated into the integrated YeastNet version 2

A statistical framework for genomic data fusion is a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. Matlab code to center a kernel matrix and Matlab code for normalization are available.

Genomic Data Analysis Service Market Outlook

The global genomic data analysis service market size was valued at approximately $1.5 billion in 2023 and is projected to reach around $5.2 billion by 2032, growing at a CAGR of 15.2% during the forecast period. The market's robust growth is primarily driven by significant advancements in sequencing technologies, increased funding for genomics research, and the rising prevalence of genetic disorders and cancer, which necessitate precise and personalized medical interventions.

One of the primary growth factors for the genomic data analysis service market is the rapid advancement in sequencing technologies, particularly Next-Generation Sequencing (NGS). This technology has drastically reduced the cost and time required for sequencing, thereby making it more accessible for various applications such as clinical diagnostics, drug discovery, and personalized medicine. The continuous innovations in bioinformatics tools and computational biology have further enhanced the accuracy and speed of genomic data analysis, contributing to the market's expansion.

Another significant driver is the increasing prevalence of genetic disorders and personalized medicine's rising importance. With the growing understanding of the human genome, healthcare providers are increasingly adopting genomic data analysis to develop tailored treatment plans based on individual genetic profiles. This personalized approach not only improves treatment efficacy but also minimizes adverse effects, thereby boosting the demand for genomic data analysis services in clinical settings.

Government initiatives and funding in genomics research also play a crucial role in propelling the market forward. Numerous countries are investing heavily in genomics projects to better understand and combat various diseases at the genetic level. For instance, initiatives like the Precision Medicine Initiative in the United States and the 100,000 Genomes Project in the United Kingdom are fostering the adoption of genomic data analysis services. Such programs not only enhance research capabilities but also drive the market by creating a substantial demand for genomic data interpretation services.

Bioinformatics Services play a pivotal role in the genomic data analysis service market by providing essential computational tools and platforms that facilitate the interpretation of complex genomic data. As sequencing technologies advance and generate vast amounts of data, the need for sophisticated bioinformatics solutions becomes increasingly critical. These services enable researchers and healthcare providers to efficiently analyze and interpret genomic sequences, leading to more accurate diagnostics and personalized treatment plans. The integration of bioinformatics services into genomic data analysis workflows enhances the precision and speed of data interpretation, thereby driving the market's growth and expanding its applications across various sectors.

The regional outlook for the genomic data analysis service market indicates a significant growth trajectory across various parts of the world. North America holds the largest market share due to its advanced healthcare infrastructure, high funding for genomics research, and the presence of leading market players. Europe follows closely, with substantial investments in genomics projects and favorable government policies supporting genomic research. The Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by increasing healthcare expenditure, rising awareness of personalized medicine, and significant investments in biotechnology sectors.

Service Type Analysis

The genomic data analysis service market can be segmented by service type into whole genome sequencing, exome sequencing, targeted sequencing, RNA sequencing, and others. Whole genome sequencing represents the comprehensive examination of an organism's entire genetic makeup, providing a complete map of all its genes. This service type is gaining traction due to its ability to offer extensive data that can be used for various applications, such as identifying genetic mutations linked to diseases, evolutionary studies, and population genetics. The decreasing costs of sequencing and the increasing speed and accuracy of sequencing technologies have further bolstered the adoption of whole genome sequencing services.

Exome sequencing, which focuses on sequenci

The Optical Genome Mapping (OGM) Instruments market is experiencing robust growth, driven by the increasing demand for accurate and high-throughput genomic analysis in various applications. The market's expansion is fueled by advancements in OGM technology, offering a faster and more cost-effective alternative to traditional methods like next-generation sequencing (NGS). This technology's ability to detect large structural variations, which are often missed by NGS, is a significant advantage, making it crucial for applications in oncology, inherited disease diagnosis, and infectious disease research. The market is further boosted by the rising prevalence of genetic disorders and the increasing focus on personalized medicine, requiring comprehensive genomic profiling. Key players like OpGen, Bionano Genomics, and Nabsys are actively contributing to market growth through technological innovations, strategic partnerships, and product launches. However, high initial investment costs and the need for specialized expertise can pose challenges to market penetration. Despite these challenges, the market is expected to maintain a strong Compound Annual Growth Rate (CAGR) throughout the forecast period (2025-2033). This growth is anticipated to be driven by continuous technological improvements leading to increased accuracy, reduced costs, and expanded applications. The development of user-friendly software and improved data analysis tools is also expected to broaden the adoption of OGM technology among clinical labs and research institutions. The market segmentation reveals a strong presence in North America and Europe, initially, with substantial growth potential in Asia-Pacific and other emerging markets. Future growth will depend on successfully addressing the challenges related to data interpretation and the integration of OGM data with other genomic data types. Regulatory approvals and reimbursements for OGM-based diagnostic tests are also critical factors for sustained market expansion.

The global optical genome mapping market is experiencing robust growth, projected to reach a significant size by 2033, driven by a compound annual growth rate (CAGR) of 18.50% from 2025 to 2033. This expansion is fueled by several key factors. Advancements in optical genome mapping technology offer higher resolution and accuracy compared to traditional methods, enabling detailed analysis of complex genomes. This increased precision is particularly valuable in oncology, where identifying subtle genomic variations is crucial for accurate diagnosis and personalized treatment. Furthermore, the rising prevalence of genetic disorders and the growing demand for faster, more efficient genomic sequencing are significantly boosting market adoption. The increasing adoption of next-generation sequencing (NGS) technologies further complements optical genome mapping, creating synergistic opportunities for integrated genomic analysis solutions. Major market players are continuously investing in research and development, leading to innovative product launches and improved workflow solutions, further propelling market growth. The market is segmented by product type (instruments and consumables) and end-user (biotechnology & pharmaceutical companies, research & academic institutions, and others). North America currently holds a substantial market share, owing to robust research infrastructure and early adoption of advanced technologies. However, Asia Pacific is poised for significant growth in the coming years due to increasing healthcare spending and expanding genomic research initiatives in countries like China and India. While the market enjoys considerable momentum, challenges such as high initial investment costs associated with optical genome mapping instruments and the need for specialized expertise to operate and analyze the data present certain restraints. However, these challenges are expected to be mitigated by ongoing technological advancements, decreasing instrument costs, and the development of user-friendly software solutions. The overall market outlook remains highly positive, with continued expansion driven by technological advancements, increased demand for precise genomic analysis, and growing collaborations between technology providers and research institutions. The market is expected to witness considerable consolidation as leading players expand their product portfolios and geographic reach. Recent developments include: In February 2022, Arima Genomics launched two new product offerings named Arima-HiC+ FFPE kit in which scientists will be able to find and discover structural variants in their 3D genomic data with this tool. By combining structural variant detection with 3D genome orientation data, researchers will gain a better understanding of how variants affect gene and cellular function. Finally, these findings will benefit scientists in the discovery of novel disease mechanisms and therapeutic targets., In October 2021 Bionano Genomics announced the acquisition of BioDiscovery, leading software company with high-class solutions for the analysis, interpretation, and reporting of genomics data. The Bionano and BioDiscovery teams will collaborate to create a version of NxClinical that includes optical genome mapping data alongside existing next-generation sequencing and microarray data types. Future goals include RNA expression profiling, epigenetics including methylation, and maybe proteomics in the future.. Key drivers for this market are: Rapidly Increasing Bio-Pharmaceutical Advances in Drug Development Coupled with Government Funding, Cost-effectiveness and Accuracy. Potential restraints include: Rapidly Increasing Bio-Pharmaceutical Advances in Drug Development Coupled with Government Funding, Cost-effectiveness and Accuracy. Notable trends are: Optical Genome Mapping Instruments are Expected to Witness a Positive Growth Over the Forecast Period.

SpatioTemporal Asset Catalog (STAC) Item - CEG5Z7KQPR in no-ML collection

Historical NCI Genomic Data Commons data (v09-14-2017). Clinical ('phenotype') and gene expression (HTSeq FPKM-UQ).

TCGA-COAD.GDC_phenotype.tsv

dataset: phenotype - Phenotype

cohortGDC TCGA Colon Cancer (COAD)
dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv
downloadhttps://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv.gz; Full metadata
samples570
version11-27-2017
hubhttps://gdc.xenahubs.net
type of dataphenotype
authorGenomic Data Commons
raw datahttps://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-90
raw datahttps://api.gdc.cancer.gov/data/
input data formatROWs (samples) x COLUMNs (identifiers) (i.e. clinicalMatrix)
570 samples X 151 identifiersAll IdentifiersAll Samples

TCGA-COAD.htseq_fpkm-uq.tsv

dataset: gene expression RNAseq - HTSeq - FPKM-UQ

cohortGDC TCGA Colon Cancer (COAD)
dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv
downloadhttps://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv.gz; Full metadata
samples512
version09-14-2017
hubhttps://gdc.xenahubs.net
type of datagene expression RNAseq
unitlog2(fpkm-uq+1)
platformIllumina
ID/Gene Mappinghttps://gdc.xenahubs.net/download/probeMaps/gencode.v22.annotation.gene.probeMap.gz; Full metadata
authorGenomic Data Commons
raw datahttps://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-80
raw datahttps://api.gdc.cancer.gov/data/
wranglingData from the same sample but from different vials/portions/analytes/aliquotes is averaged; data from different samples is combined into genomicMatrix; all data is then log2(x+1) transformed.
input data formatROWs (identifiers) x COLUMNs (samples) (i.e. genomicMatrix)
60,484 identifiers X 512 samples

Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in CyVerse. Data types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development. Resources in this dataset:Resource Title: CyVerse Genomes To Fields 2016 dataset download. File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018 Dataset (csv) and metadata (BibTex, Endnote) data downloads. See _readme.txt for file contents.

Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in CyVerse. Data types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development. Resources in this dataset:Resource Title: CyVerse Genomes To Fields 2014 dataset download. File Name: Web Page, url: http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3 Dataset (csv, h5, gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.

Genomics Software Market Outlook

The global genomics software market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach around USD 6.5 billion by 2032, growing at a robust CAGR of 15.2%. The growth of the genomics software market is driven by the increasing demand for personalized medicine, advancements in genomic data analysis technologies, and the growing prevalence of chronic diseases.

One of the primary growth factors driving the genomics software market is the surge in personalized medicine. Personalized medicine tailors healthcare treatments to individual genetic profiles, thereby enhancing treatment efficacy and minimizing adverse effects. This approach necessitates sophisticated genomics software to analyze vast amounts of genomic data quickly and accurately. As healthcare providers increasingly adopt personalized medicine, the demand for genomics software is expected to rise significantly. Additionally, the integration of Artificial Intelligence (AI) and machine learning in genomics software is improving the speed and accuracy of genomic data analysis, which further fuels market growth.

Another significant factor contributing to the market expansion is the progress in genomic data analysis technologies. Innovations such as next-generation sequencing (NGS) and bioinformatics tools have revolutionized genomic research by enabling high-throughput and cost-effective genomic sequencing. These technologies generate large volumes of data that require advanced software solutions for storage, management, and interpretation. The continuous development of these technologies is anticipated to drive the demand for genomics software, as they provide researchers and clinicians with powerful tools to decipher complex genomic information.

The increasing prevalence of chronic diseases, including cancer, diabetes, and cardiovascular diseases, is also bolstering the genomics software market. These diseases often have genetic components, making genomic analysis crucial for understanding disease mechanisms and developing targeted therapies. As the incidence of chronic diseases rises, there is a growing need for genomics software to support research and clinical diagnostics, thereby driving market growth. Additionally, government initiatives and funding for genomic research are providing a significant boost to the market, enabling more healthcare institutions and research centers to adopt advanced genomics software solutions.

Bioinformatics plays a pivotal role in the genomics software market, serving as the backbone for data analysis and interpretation. As the volume of genomic data continues to grow exponentially, bioinformatics tools are essential for managing, analyzing, and interpreting this data efficiently. These tools enable researchers to identify genetic variations, understand complex biological processes, and develop targeted therapies. The integration of bioinformatics in genomics software enhances the ability to process large datasets, facilitating breakthroughs in personalized medicine and precision healthcare. As the demand for sophisticated data analysis increases, the role of bioinformatics in genomics software is becoming increasingly critical, driving innovation and market growth.

Regionally, North America holds the largest share of the genomics software market, driven by a well-established healthcare infrastructure, high adoption rates of advanced technologies, and substantial government funding for genomic research. The region's strong presence of key market players and ongoing research and development activities further contribute to its dominance. Europe is also a significant market, with growing investments in precision medicine and genomic research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare expenditures, rising awareness about personalized medicine, and expanding research activities in countries like China, Japan, and India.

Product Type Analysis

The genomics software market can be segmented by product type into data analysis and interpretation tools, data management and storage solutions, visualization tools, and others. Data analysis and interpretation tools form a critical segment, as they are essential for deriving meaningful insights from raw genomic data. These tools employ advanced algorithms and bioinformatics to identify genetic variations and their potential implications for health and disease. With t

The sharing of genomic data is a rapidly growing market, driven by the increasing availability of genomic data and the decreasing cost of sequencing. The market is expected to reach $XXX billion by 2033, growing at a CAGR of XX% from 2025 to 2033. The primary drivers of this growth include the increasing use of genomic data in research, the development of new genomic technologies, and the growing awareness of the importance of genomic data sharing. The market is segmented by type (cloud-based and on-premise), application (hospital, clinic, laboratory, and other), and company. The cloud-based segment is expected to grow at a faster rate than the on-premise segment, due to the increasing popularity of cloud-based solutions. The hospital segment is expected to be the largest segment, followed by the clinic segment. The major companies in the market include DNAstack, LifeLabs, Microsoft, Merck, BC Genome, Molecular You, Deloitte, and others.

Manually curated database of all conditions with known genetic causes, focusing on medically significant genetic data with available interventions. Includes gene symbol, conditions, allelic conditions, inheritance, age in which interventions are indicated, clinical categorization, and general description of interventions/rationale. Contents are intended to describe types of interventions that might be considered. Includes only single gene alterations and does not include genetic associations or susceptibility factors related to more complex diseases.

Portal for providing data and tools to promote understanding and treatment of type 1 diabetes and its complications.Enables browsing, searching, and analysis of human genetic information linked to type 1 diabetes and related traits, while protecting integrity and confidentiality of underlying data.Represents effort to coordinate collection and deposition of genomic and epigenomic data related to type 1 diabetes and its complications.

Average number of copy number gains, losses and percent genome altered (PGA) across TCGA cancer types and the three exemplar tumors selected for our study.

Data views that are common to both the rare disease and the cancer domains. This data pertains to sample handling, genome sequencing, and participant data.

Data Relating to Participants:

• participant: Data on each individual participant in the 100,000 Genomes Project, e.g. personal information (such as relatives or self-reported ethnicity); points of contact with the Project (e.g. handling Genomic Medicine Centre or Trust); and a record of the status of their clinical review.
• death_details: Data on participant deaths submitted by GMCs, likely less complete than the data collected by ONS and NHSE.

Data Relating to Samples:

• clinic_sample: Data describing the taking and handling of participant samples at the Genomic Medicine Centres, i.e. in the clinic, as well as the type of samples obtained. Because of the complexities of handling and managing tumour tissues samples in a clinical setting, there are many fields that are cancer-specific.
• clinic_sample_quality_check_result: Data describing the quality control of obtaining and handling participant samples at the Genomic Medicine Centres, i.e. in the clinic.
• laboratory_sample: Data describing the handling of samples at the biorepository and in preparation for sequencing, as well as the type of sample.
• plated_sample: Data describing the handling and QC of samples at Illumina (the sequencing provider).
• laboratory_sample_omics_availability: Availability of samples collected from participants in the 100,000 Genomes Project for the purpose of omics research. Data includes: Participant ID, Sample Type (e.g. Serum, RNA Blood), the number of aliquots of that sample type for that participant, and the availability status - whether the sample has already been used for a research project. Research proposals for the use of these samples can be submitted, via the GECIP team, to the Scientific Advisory Committee and Access Review Committee.

Genetic data analysis software market size was evaluated at $214 million in 2023 and is slated to hit $347 million by the end of 2032

Context

Cannabis is a genus of flowering plants in the family Cannabaceae.

Source: https://en.wikipedia.org/wiki/Cannabis

Content

In October 2016, Phylos Bioscience released a genomic open dataset of approximately 850 strains of Cannabis via the Open Cannabis Project. In combination with other genomics datasets made available by Courtagen Life Sciences, Michigan State University, NCBI, Sunrise Medicinal, University of Calgary, University of Toronto, and Yunnan Academy of Agricultural Sciences, the total amount of publicly available data exceeds 1,000 samples taken from nearly as many unique strains.

https://medium.com/google-cloud/dna-sequencing-of-1000-cannabis-strains-publicly-available-in-google-bigquery-a33430d63998

These data were retrieved from the National Center for Biotechnology Information’s Sequence Read Archive (NCBI SRA), processed using the BWA aligner and FreeBayes variant caller, indexed with the Google Genomics API, and exported to BigQuery for analysis. Data are available directly from Google Cloud Storage at gs://gcs-public-data--genomics/cannabis, as well as via the Google Genomics API as dataset ID 918853309083001239, and an additional duplicated subset of only transcriptome data as dataset ID 94241232795910911, as well as in the BigQuery dataset bigquery-public-data:genomics_cannabis.

All tables in the Cannabis Genomes Project dataset have a suffix like _201703. The suffix is referred to as [BUILD_DATE] in the descriptions below. The dataset is updated frequently as new releases become available.

The following tables are included in the Cannabis Genomes Project dataset:

Sample_info contains fields extracted for each SRA sample, including the SRA sample ID and other data that give indications about the type of sample. Sample types include: strain, library prep methods, and sequencing technology. See SRP008673 for an example of upstream sample data. SRP008673 is the University of Toronto sequencing of Cannabis Sativa subspecies Purple Kush.

MNPR01_reference_[BUILD_DATE] contains reference sequence names and lengths for the draft assembly of Cannabis Sativa subspecies Cannatonic produced by Phylos Bioscience. This table contains contig identifiers and their lengths.

MNPR01_[BUILD_DATE] contains variant calls for all included samples and types (genomic, transcriptomic) aligned to the MNPR01_reference_[BUILD_DATE] table. Samples can be found in the sample_info table. The MNPR01_[BUILD_DATE] table is exported using the Google Genomics BigQuery variants schema. This table is useful for general analysis of the Cannabis genome.

MNPR01_transcriptome_[BUILD_DATE] is similar to the MNPR01_[BUILD_DATE] table, but it includes only the subset transcriptomic samples. This table is useful for transcribed gene-level analysis of the Cannabis genome.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: http://opencannabisproject.org/ Category: Genomics Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://www.ncbi.nlm.nih.gov/home/about/policies.shtml - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. Update frequency: As additional data are released to GenBank View in BigQuery: https://bigquery.cloud.google.com/dataset/bigquery-public-data:genomics_cannabis View in Google Cloud Storage: gs://gcs-public-data--genomics/cannabis

Banner Photo by Rick Proctor from Unplash.

Inspiration

Which Cannabis samples are included in the variants table?

Which contigs in the MNPR01_reference_[BUILD_DATE] table have the highest density of variants?

How many variants does each sample have at the THC Synthase gene (THCA1) locus?