knowing⁰¹ Product Information

You are working on Version 1.7 released August 2026. See what's new.

This page is a practical guide to working in our app. If a question is not answered here, just drop us a mail at any time.

Short guide

The knowing⁰¹ app assists you in exploring and connecting your experimental data quickly and independently.

Getting started:

• Upload Studies, Datasets, and Hooks with a few clicks: Studies Upload, Datasets and Hooks Upload
• Set significance cutoffs: choose two cutoffs for each Dataset or Hook in Dataset details
• Explore your data and learn how your data relates to each other
• Contextualize your data and use Search One to query a single gene, protein or genomic position or Search Many to use the top 20 observations of a selected dataset or hook as your query
• Download or explore results for further analysis in the Results view or the Download center, or return to the lab for follow-up experiments
• Learn more about file processing, the Cellmap and components in the Background section

About the Cellmap: The major ingredient of the knowing⁰¹ app is our proprietary Cellmap, a knowledge-graph that enables automatic translation between your data at unprecedented speed. To leverage the full information from the Cellmap, we automatically link your data to it directly after file upload.

Get in touch: If you are stuck or something is unclear, we want to know, help and do better. We would love to hear from you at any time.

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Product feature guide

Data center Dashboard

The Data center Dashboard provides a structured overview of your studies, datasets and hooks in a single view. Key metrics, status indicators and tag-based breakdowns let you see the state of your data foundation and spot any datasets that are linked with issues at a glance.

Studies Upload

Each study groups one or more datasets under a shared scientific context. While only minimal information is required to create a study, the more detail you provide, the better the app can support downstream analyses, grouping, and interpretation.

Study metadata:

The name will be used to represent the study throughout the entire app. We recommend choosing a concise label, for example the first author, publication year, and journal (e.g. Li et al. (2025, Nature)).

The relevance and methodological quality fields allow you to rate the applicability and scientific rigor of the study for your project. This helps prioritize studies during analysis. Rating is based on a three-tier scale: low, mid, and high, where high indicates strong relevance or excellent quality, mid indicates moderate relevance or acceptable quality, and low indicates limited relevance or methodological concerns.

Provide the original title, authors, year, journal and a link to connect the study to its primary publication. This information is displayed in the study view and can be used to trace results back to the source literature.

Use the summary field to briefly describe the study's scientific background, key findings, and relevance to your research question. This may include the disease context, patient cohort, experimental design, or key conclusions.

Figure

Upload a figure formatted as JPG, PNG, or similar image format. Include the figure legend to provide context and support interpretation.

Study numbers

The study numbers table captures quantitative information about participants, samples, assays, control groups, disease groups, and cell/nuclei counts. To enter study numbers:

1. Specify each assay used in the study
2. Enter the number of participants involved in that assay
3. Enter the final sample number for the assay
4. Select a condition (e.g., disease/control samples or cell/nuclei count)
5. Specify the exact number within that condition
6. Optionally, add details such as the specific disease (e.g., Breast cancer)

Study tags

Add study tags by typing and pressing Enter. Use tags to label studies according to disease area, species (e.g., human, mouse, rat), technology, or research theme. Tags facilitate grouping across studies in later analyses and should be true for all downstream linked datasets.

Comments

Use the comments field for internal notes or curation remarks that do not belong in the summary.

Datasets

Existing datasets can be linked to a study under Datasets. This connection allows the app to provide study-level context for all associated data.

Studies View

Once you complete the study creation, it becomes available to all your project members in the knowing⁰¹ app. In the left-hand navigation of the Data center, you see all your studies and linked datasets or hooks with their respective processing status. Clicking on a study displays its details. The Studies view presents the key metadata you entered, including name, authors, year, journal, summary, uploaded figure, study numbers table, and linked datasets. Studies can be marked as favourites and sorted.

Datasets and Hooks Upload

Each file uploaded is transformed into one dataset or hook. Datasets contain your own experimental data, while Hooks are personally created lists or results from the Contextualize and Explore results. While only minimal information is required for the transformation, the more information you provide, the better the app can leverage the potential of your data.

File and Information

You can upload any text file in tabular format. Common file endings of such files are .CSV, .TSV, or .TXT. We automatically detect headers in your file and recognize common field delimiters, such as tabs or commas.

Biotype

The biotype guides the linking of the dataset to our Cellmap. The app supports three top-level biotypes (Molecular Layer): DNA, RNA, and Protein. Within each, you select a specific second-level biotype (Entity Type) such as Variant, Genomic Range, Gene, Transcript, Protein or Gene Symbol. Read more on the current scope of our Cellmap to learn about how the biotype information influences your analysis results.

• For the top-level biotype DNA and second-level biotypes Variant and Genomic Range, you need to specify the genome assembly version. If you are unsure, please request this information from your data provider. Commonly used assembly versions of human genomes are provided.
• For the top-level biotype RNA and second-level biotype Gene Symbol, you need to specify the organism. Especially for rodents, gene names may be identical, so the taxonomy cannot be directly inferred from data alone.

Column roles

We ask you to assign a role to the columns of your file. Information from all assigned columns will be displayed during analysis, so we recommend assigning roles to as many columns as possible.

• Assign columns containing significance and effect size (e.g., fold changes) values to differentiate between relevant (e.g., highly significant) and irrelevant (e.g., non-significant) observations after the dataset is fully integrated. Specify the direction of effect size in the metadata below.
• Assign the "Link Identifier" role to the column containing the molecular/reference database identifier (e.g., UniProt ID or accession, ENSEMBL gene ID, Gene Symbol, dbSNP ID, etc.) (for further details see Processing). This is essential to ensure meaningful linking of the observations to the Cellmap.
• Additional column roles that are available:
  • Score: A score is a positive value where higher numbers imply greater relevance. When a score is used, the dataset or hook may be classified into stringent and lax cutoffs by using the top 10% and top 20% percentiles of this column. Please contact support for assistance with this handling.
  • Data Slice: It may occur that the uploaded file contains several types of comparisons in a long format, e.g., all marker genes per cell type or pathway names of a gene-to-pathway master table. Use the Data Slice role for these typically categorical columns.
  • For Single Cell Datasets: The PC1 and PC2 columns are often available. These refer to the fraction of cells in which the feature is found within the cluster and outside the cluster, respectively.
  • Use our custom roles 1–5 for columns that are of relevance during your analysis.

Note that in every export you will receive the full file information, including unassigned columns. We are not losing your data, but try to reduce complexity with and for you during exploration.

Dataset and Hook metadata

The dataset title will be used to represent the dataset or hook throughout the entire app. We recommend providing a crisp and concise title, including assay type, biosample, and result type (e.g., PWAS - T2D - Plasma Proteins or ATAC peaks - T2D islets).

Parent Study you can connect the dataset or hook to an existing study. This connection allows the app to provide study-level context for the data.

Use the description to provide a brief summary of the data in the file, including measurement methods or technologies.

Provide the effect size definition to clarify what the effect size values represent in your dataset based on the effect size column assigned above. It is important to specify the direction: whether higher values (e.g. fold changes) indicate increased risk in cases or controls, as this can vary across different studies.

Add analysis model information to describe the underlying sample sizes, quality control filters, bioinformatic pre-processing, and statistical methods used.

Add dataset tags by typing and pressing Enter. Use tags to label datasets according to experiment type, species, technology, or indication group. This will facilitate dataset grouping in later analyses. Tags should be specific to the dataset and not overlapping with study tags.

Add the data source (e.g., Supplementary Table 1) to trace back to the original data location.

Use the comments field for internal notes or curation remarks that do not belong in the description.

Datasets and Hooks Views

Once upload is finished and the app processes the file and transforms it into a dataset or hook. Afterwards it is also available to all your project members in the knowing⁰¹ app. In the left hand navigation of the Data center, you see all your datasets or hooks with their respective processing status. Clicking on an integrated dataset, its details are displayed.

The top of the detail view covers the key metadata captured during upload, such as title, description, original filename, as well as the selected biotype and analysis model. Furthermore, it contains basic statistics on the number of observations extracted from the file and how many of those observations were linked into the Cellmap and can thus be translated for analyses across biological data types.

If significance or score values are assigned, they determine which observations are considered relevant in Explore and Contextualize. Each dataset has two cutoffs: Lax and Stringent, which you can adjust at any time by clicking edit next to either. For significance-based datasets, choose from standard thresholds (< 0.05, < 0.01, < 0.0001 or < 5e-8). For score-based datasets, choose from top percentile thresholds (Top 20% for Lax, Top 10% for Stringent). The observation count and percentage meeting each threshold are shown to guide your choice.

The assigned column role section allows you to review the roles provided during upload, which determines how the dataset is used in the knowing⁰¹ app. In case any column role needs to be updated, please use the File Details Editor (in edit mode).

Contextualize

Choose between two options: Search One and Search Many.

Search One: Search for a human, murine, or rat gene, protein or genomic position and retrieve all observations across all your available datasets. The app auto-translates the search term to all matching biotypes.

Search Many: Search for a Dataset and automatically use the top 20 observations as query for the human, murine, or rat gene, protein, or genomic position observations that are to be found across all your available datasets. The app auto-translates the search term to all matching biotypes.

How it works: The query will first be linked to all matching Cellmap nodes. Especially for Gene Symbols this might result in multiple hits. Second, the Cellmap hits are auto-translated before, finally, being searched for across your available datasets. Search results will show all matching Cellmap nodes, which may be also de-selected by you in order to filter the search results.

All results are consolidated in a single, comparative view, allowing for intuitive comparisons of data distribution and quality across datasets at a glance. The view is interactive, enabling users to filter results by adjusting rows and columns, as well as modifying cutoff criteria to explore the data in greater detail.

The search results are summarized in a visualization that provides a detailed, comparative view of observational data across matched datasets within the same group. Each dot represents the number of observations, with dot sizes indicating relative quantity. The dot pie charts further break down these observations, showing the proportion that meets specific cutoff criteria for each dataset group and match.

The Search results are all observations that were found in your available datasets. You may subset the results further by whether they match to each dataset's stringent or lax cutoff criteria.

Use the export button to download your results. You can export the full result or filter first by query match, dataset group or observation cutoff and download only the filtered selection. Note that only the top 20 query matches can be explored directly in the app. To export all query matches, click Deselect all in the query match filter and then download - the dataset group and cutoff filters still apply. All exports are generated automatically and become available in the Download center.

Explore

Explore lets you combine datasets to identify joint or unique components across your data foundation. Build your full selection first, then run the analysis in one go.

1. Type to search and add your first dataset. Its total observation count appears to the left. Where significance values are available, set a cutoff (All, Lax or Stringent) to focus on relevant observations.
2. Add a second dataset and choose a set operation:

   • INTERSECT: returns components found in both the current selection and this dataset.

   • SUBTRACT: returns components in the current selection that are not found in this dataset.

     Set a cutoff per dataset where significance values are available.

3. Continue adding datasets, choosing a set operation and cutoff for each. Remove any dataset at any time with the trash icon.
4. Click Analyze to run the full selection at once. Use Reset to clear the selection and start over.

Results

The Results view is shared across Contextualize and Explore. Components are ranked by significance and grouped by biological comparability according to the current Cellmap version, including homology components across human, mouse and rat, variant or SNP proxy components using linkage disequilibrium information, and genomic range or variant components.

Figure 1. The number of components shown according to component size.

The component size histogram gives an overview of how results are distributed. Filter components by concordance of effect size: up-regulated, down-regulated or mixed, to focus on directionally consistent hits.

Use the Download results button to export your results. All exports become available in the Download center.

Download center

All your export requests are sent to the Download center. Large files may take a little time to process and become available for download once ready.

If a dataset is re-uploaded or re-linked after an export was generated, the affected downloads are marked as invalid rather than removed. Your full download history stays visible, so you always know which exports reflect the current state of your data foundation and which ones need to be re-run.

Background

File processing

After uploading a file to your project repository, it is processed in two steps before becoming available as a dataset or hook. The progress is shown in the data status field.

1. During import, the file is processed on the basis of the provided biotype, dataset tags and the significance cutoffs. Errors may occur if the file format is unexpected or field values are not recognised.
2. Special Case: Significance and Adjusted Significance values are checked for log10 transformation during import. Values outside the (0,1) range are assumed to be -log10 or log10 transformed and are automatically back-transformed, so cutoff filters behave as intended.
3. During linking, connections to the Cellmap are established.

The information on biotype plus the tagged columns is used to link the entries/rows of a file (lets call them * observations*) to the knowing⁰¹ universe. Imagine that each observation is going to be plugged into our universe according to your tagged information (Figure 2). So, as of today, the more and comprehensive your tags are, the better we can work with the data. We are continuously learning in order to automate this in the future. Check out this figure to better understand the idea of tagging the correct column with what we call "Link Identifier".

Significance tags determine which observations are extracted as relevant during initial upload, according to the following defaults:

1. If Adjusted Significance is tagged: extract observations with Adjusted Significance < 0.0001.
2. Else if Significance is tagged: extract observations with Significance < 0.05.
3. Else: all observations are considered relevant.

These defaults can be changed at any time in the dataset detail view. Files are processed sequentially through an internal queue; large files or simultaneous uploads may take longer.

Figure 2. Minimally required information per file. Left: the significance column splits data into relevant and non-relevant observations. Right: the Link Identifier is used to connect observations to the knowing⁰¹ universe across biotypes.

Cellmap

Current Version: 6.0

During Explore and Contextualize, data is auto-translated according to the current scope of the Cellmap, allowing comparisons across biotypes.

Biotypes

Version 6.0 encompasses the following biotypes, assembled from public reference databases.

• Proteins are defined as proteins and their isoforms from UniProtKB, covering verified proteins (SwissProt) and automatically translated mRNA sequences (trEMBL). For example, the protein of human gene TBC1D9B has UniProtKB identifier Q66K14.
• Gene Symbols are short gene names such as TP53. Symbols are subject to change over time and synonyms are common, for example PU.1 is now referred to as SPI1. The exact symbol used depends on when a dataset was generated.
• Genes are defined as in major reference databases such as ENSEMBL, identified by stable identifiers such as ENSG00000066336. From a bioinformatic point of view, these identifiers are unique, easy to find, and work with in a non-ambiguous manner.
• Variants are defined as 1 base pair (bp) positions on a chromosome of a reference genome.
• Genomic Ranges are defined as ranges (with start and stop position) on a chromosome of a reference genome.

Translations

The following translations between biotypes are supported. Figure 3 illustrates the Cellmap knowledge graph in a graph model.

• Proteins are linked to ENSEMBL genes, while isoforms are linked to ENSEMBL transcripts, according to the data provided by UniProtKB.
• Gene Symbols and all synonyms are obtained from genome organisation consortia. More specifically HGNC for human, MGI for mouse and RGC/RGNC for rat.
• Gene files with ENSEMBL identifier that corresponds to human, murine (mouse) or rat. This homology information is obtained from ENSEMBL database and directly reflects efforts of the COMPARA initiative. ENSEMBL Version 101 is used. Links between Gene Symbol and Genes are included, too.
• Variant files with Linkage disequilibrium information, with r>0.2, GRCh37, 1000Genomes, Phase 3 V5, and European ancestry. Note that assembly number GRCh37 is identical to hg19.
• Genomic Ranges are linking to each other whenever there exists an overlap of 1 base. For examples a range from chr1: 1-10 overlaps the range chr1:10-900. The ranges on assembly hg19 are readily linked to variants.

Figure 3. The Cellmap 6.0 knowledge graph covers Gene Symbols and synonyms from human, mouse and rat; genes and their homologs across the three species; proteins and isoforms linked to transcripts and genes; linkage disequilibrium for hg19 variants; and genomic ranges linked to co-localised variants.

Components

When one or more datasets are selected in Explore or Contextualize, we are grouping the observations of the dataset(s) into so called components, whenever they are comparable biologically speaking (see Cellmap for scope). Components are currently given random names for identifiability while we work on biologically meaningful labels.

Example: Variant Components

Six genome-wide significant variants on chromosome 21 (positions 34,593,673–34,626,654) from a GWAS dataset may all be in high linkage disequilibrium (LD), as measured by r2. In Explore, these six variants are grouped into a single component (Figure 4), making it easy to identify independent loci. The same logic applies when intersecting two datasets: a single component is returned when each dataset hits the same LD block.

Figure 4. Variant components explained. Left: dataset A has six significant variants on chromosome 21. Middle: all six are in high LD. Right: Explore groups them into one component.

Example: Homology

Let us investigate the component as explore results in the situation of homology. Imagine we have been analysis disease tissue expression with a blood group effect. To that end we analyzed gene expression in three species, namely human, mouse, and rat (Figure 5). We aim to know the overlap of the three datasets with few clicks. In our example the alpha 1-3-N-acetylgalactosaminyltransferase and alpha 1-3-galactosyltransferase gene is significantly regulated in all three species. From our Cellmap we know about the homology although genes are named differently in the different species.

Let us select the three datasets in the explore module and ask to intersect them. As one result hit, the ABO/Abo component is returned, finding and grouping all homologous genes into one group of biologically comparable observations.

Figure 5. Homology components explained. Left: one or more observations of the gene are significant in all three datasets under different ENSEMBL identifiers and symbols. Middle: the Cellmap knows all five human/mouse/rat genes are pairwise homologs. Right: the intersect returns one component with all five genes.

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Version history

In the following section, we share the main changes of the different versions of both, the knowing⁰¹ SaaS application and the underlying knowledge graph, the Cellmap.

Application

Main updates in V1.7:

• Improved Data center and Dashboard: Status consistency

  The integration status of datasets and hooks is now displayed consistently across both the Dashboard and the Data center, making it easier to spot datasets that linked with issues at a glance. Linking of newly added entities from the Dashboard has also been fixed and aligns with the Data center display.

Main updates in V1.6:

• Explore and Contextualize: Faster component loading

  Component data in Explore and Contextualize now loads significantly faster. Backend query handling has been optimized across dataset groups and component item fetching is more efficient for analyses involving multiple datasets.

Main updates in V1.5:

• Improved Download center

  If a dataset is re-uploaded or re-linked after an export was generated, the affected downloads are now marked as invalid rather than silently removed. Your full download history stays visible in the Download center, so you always know which exports reflect the current state of your data foundation and which ones need to be re-run.

Main updates in V1.4:

• New Feature: Filtered Contextualize Export

  You can now export exactly what you see. After narrowing Contextualize results by query match, dataset group or observation cutoff, use the export button to download only the filtered selection (not the full result). This keeps your output files focused on the observations relevant to your current question and reduces the cleanup work downstream.

Main updates in V1.3:

• New Feature: Data center Dashboard

  The Data center Dashboard provides a structured overview of your studies, datasets and hooks. Key metrics, status indicators and tag-based breakdowns are consolidated in a single view, giving you immediate insight into the state and composition of your data foundation.

Main updates in V1.2:

• New Feature: Studies

  You can now add studies and link them directly to available datasets in your data center. The Studies section is accessible alongside the existing Dataset and Hook views.

• New Visualization: Studies View

  The Studies View provides a structured summary of individual publications, consolidating key findings, a primary figure, and study numbers in a single view. Each study can be ranked by relevance and methodological quality and displays a overview of its linked datasets.

Main updates in V0.17:

• New Feature Update: Contextualize

  Now, in addition to searching one query at a time, you can enrich the top 20 observations of each dataset with contextual information—regardless of dataset biotype.

• New Visualization

  The Contextualize feature enables multi-query searches and summarizes results in a comparative view. This top-level overview displays search matches, the number of matching observations per dataset group, and details on cutoff types.

Main updates in V0.16:

• The latest feature update, Search, now includes an export option. The search results in the app provide a preview for molecules with large amounts of evidence. The export option contains the full scope.
• Datasets may now contain sets of meta tags that help users with numerous datasets introduce an additional layer of clarity alongside the dataset title. Meta tags can be added during upload and separated by a semicolon ";" if multiple tags are used.
• We introduced new column tags to facilitate more precise data descriptions that are later required for improved analysis: "Score" for classifying datasets based on percentile rankings, "PC1 and PC2" for mapping feature distribution in single cell datasets, and "Data Slice" for tagging categorical columns in datasets with multiple types of comparisons. These tags are intended to provide users with enhanced capabilities for organizing and analyzing their data effectively.

Main updates in V0.15:

• New analysis feature Search. With search, you are able to query your data across all datasets that are linked to the Cellmap. The search term will matched to its Cellmap node(s), e.g. a Gene Symbol, before being translated to all possible biotypes. Resulting hits are dataset observations reachable through the Cellmap links to your search term.
• Explore gets a summary plot for component sizes. Read more in Explore Section.

Main updates in V0.14:

• New Cellmap Version 6.0. With the new cellmap version, the auto-translation mechanism will translate in longer-range depths: like from Protein to hg38 Variant and Genomic Range information, from Human Protein to Mouse/Rat Gene Symbols. Of note, the hg19 genome assembly information is not yet coupled to the remaining biotypes.
• The Download center was added beside Explore data Feature and the Data Center. Export requests ( explore and variant proxy exporter) become ready for download once generated. Please note, that knowing⁰¹ Support team is running our quality checks subsequently, and update the request status to "approved" after successful checks. The request status shows the current status of your request.

Main updates in V0.13.1:

• Major backend update regarding user data import and linking, which required an reimport the entire user data (starting 13. Dec. 2021). This was an essential step to take in terms of scaling and being able to process even more data in short amounts if time. Processing tasks are now fed through a queueing system also improving on data sanity checks.
• Import errors are getting fed back from the queueing system to the user, for improved user guidance. Additionally this information improves our support response times that are related to data upload and (failed) processing.
• Fixing variant-range data integrity with additional quality control steps, finally bringing full capacity of a correct and comprehensive link set to the app.
• Patch 0.13.1. further refined and scaled the queueing system.

Main updates in V0.12:

• Introducing Cellmap V004 introducing full potential of all possible variant - genomic range links. This mainly affects human genome assembly GRCh38/hg38
• Patch 0.12.1 fixes an overlap error that occurred before when combining large variant/genomic range datasets.

Main updates in V0.11:

• Reworked explore analysis algorithm on the backend to increase speed as well as expand overlap hits for sparsely connected components. Especially for huge variant files, with hundreds of variants per component, V0.11 eases the search burden.
• Small bug fix on Genomic Range linker to Cellmap. All Genomic Range files have been relinked.

Main updates in V0.10:

• Introducing proteins to the app, thus releasing Cellmap V003 and protein-gene components in Explore. Uniprot protein and isoform identifier (IDs) and accessions are used to link to proteins in the Cellmap, which are in turn connected to genes and transcripts from ENSEMBL.
• Updating component view to lower the data burden for the browser. Use load more and collapse buttons to show more hits if required.

Main updates in V0.9:

• Introducing genomic ranges to the app, as part of the Cellmap V002 release and range components in Explore. Data with genomic range data, for example BED files, may be incorporated now. Links to human hg19 variants were added, too. Note that integration of genomic ranges may take longer to integrate.
• To guide the cutoff choice in the explore view, the number of observations that adhere to the respective criterion are shown. Note that, you are may adapt the thresholds for both cutoffs in the data center view of each dataset.

Main updates in V0.8:

• Redesign of the dataset selection flow in Explore.
• Improved Explore analysis with respect to speed and information content: In shorter time, you will additionally learn if the Cellmap was employed for building a component – or not.
• Export requests are handled directly in the app. Although we like getting emails from you, the new way reduces complexity for both of us.
• Information on Cellmap linkage status of each dataset is provided in the Data center.

Main updates in V0.6:

• Every explore result is now available as export upon request. Find a export request button above every result preview.
• We are introducing two significance cutoffs, with the names stringent(default) and lax. You may now choose separate cutoff values for dataset in the data center.

Main updates in V0.5:

• Release of Cellmap Version 001. The first release encompasses: human/mouse/rat homology for ENSEMBL genes according to COMPARA consortium, gene symbols with primary and synonyms for human/mouse/rat, gene symbol to gene links, as well as variants with their linkage disequilibrium (LD) from EUR ancestry for human genome assembly hg19.
• We are introducing components, the concept to group biologically comparable observations across datasets, like grouping observations with current gene symbols and all their synonyms.

Main updates in V0.2:

• Explore with translations: When comparing data in Explore, variant datasets are translated using linkage disequilibrium as well as gene datasets from human, mouse, and rat.
• Progress information during processing: The two-step process of after uploading data to the app, importing, and linking to the Cellmap, are now made visible with progress information.

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Cellmap

• V005 is an optimized version of its content-wise equivalent V004. Few special cases are now included. For example only one rat Gene Symbol actually matches two ENSEMBL Genes instead of one.
• For V004 all possible overlaps between variants and genomic ranges were established, mainly introducing overlaps also on human GRCh38/hg38 assembly.
• For V003 the protein biotype was added, modelled by proteins and their known isoforms.
• For V002 Genomic Ranges were added.
• V001 includes (ENSEMBL) Genes, Gene Homology and Gene Symbols from human, mouse, and rat as well as variants and their LD information for hg19 human genome version.