Category Archives: For Researchers

#nCoVStats: An Interactive Data Analysis Tool for COVID-19 (Coronavirus SARS-CoV-2 2019-nCoV)

Launch “#nCoVStats Data Analysis Tool by HPI”
password: 2019-nCoV

Latest News

#nCoVstats @HPI_DE Daily #COVID19 update: Worldwide #infections reached 177.7M, about 2.7M new cases reported within the past week.
Focus #India 🇮🇳, intl #2: Fir the past 3wks reported numbers stable 30M infections in contrast to the fast increase since Mar/Apr.


#nCoVstats @HPI_DE Daily #COVID19 update: Worldwide infections reached 177.3M, a plus of 443k new cases within the past 24hrs. A total of almost 3.9M (2%) people is reported as #deceased and 143.9M (81%) are reported as #recovered.


#nCoVStats @HPI_DE: Worldwide infections exceeded 176.8M, about 2.6M new cases in the past week. Midterm trend shows downwards for the past 7wks. 📉
Focus #WesternPacific: Overall infections exceeded 3M, a plus of almost 1M in the past two months mostly driven by Malaysia 🇲🇾.

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Global Health Challenge: 2019-nCoV Coronavirus outbreak

Just a couple of days ago, the year 2020 started when a new global health challenge approached: the outbreak of 2019-nCoV Coronavirus in China end of 2019. As seen in other pandemic situations, the global news coverage deals with the topic on a daily basis. With the provided data analysis tool, we want to contribute to an informed news coverage about the situational development.

Background: Graphical Data Exploration using In-Memory Database Technology

Screenshot of the HPI 2019-nCoV Data Analyzer ToolResearchers of the Hasso Plattner Institute designed an interactive graphical data exploration tool. It support unbiased and informed news coverage about the outbreak by exploring latest Coronavirus case reports provided by the Robert Koch Institute in Berlin and DXY in China. Therefore, you can use the tool to explore latest available case report data and to assess the developments of the past days in different graphical perspectives. The tool builds on the latest in-memory database technology. The same technology is the centerpiece of the cloud platform for precision medicine.


Dr. Matthieu-P. Schapranow
Group Leader and Scientific Manager Digital Health Innovations
Digital Health Center @ Hasso Plattner Institute
Phone: +49 331 5509 -1331
Mail: please use the contact form
14482 Potsdam, Germany

Archive (no longer updated)

  • Feb 20, 2020:

  • Feb 19, 2020:

  • Feb 18, 2020:

  • Feb 17, 2020:

  • Feb 16, 2020:

  • Feb 15, 2020:

  • Feb 14, 2020: Situation in Japan unchanged.

  • Feb 13, 2020: A spike in reported cases in Hubei region was reported. However, this might be the result of unconfirmed cases for days reported just now as confirmed and does not necessary indicate a super spreading event.

  • Feb 12, 2020:

  • Feb 11, 2020: As of today, we also included the reported number of cured cases to provide additional insights. Furthermore, WHO decided to label the lung disease as COVID-19 from today on.
  • Feb 10, 2020: We switched data source from RKI to DXY to have more accurate and up-to-date numbers.

  • Feb 9, 2020:

  • Feb 7, 2020


Cloud Services for Analysis of Genome Data

Alignment screenshotGenome data can be used to identify individual roots of certain diseases and to derive specific treatment decision. However, clinicians and medical experts only rarely incorporate genomic data due the required technical knowledge nowadays. We focus on providing tools and services for non-IT experts that enable them to process and analyze medical data, e.g. genome data, by themselves. Our services are provided as Software-as-a-Service (SaaS) cloud applications eliminating the need for local hardware resources. Test-drive our Cloud Services for Analysis of Genome Data today.


The user logs into the personal account, which protects all personal data. After submitting raw genome sequence data, e.g. as FASTQ file, the algorithm for alignment and the reference genome are configured. The high-throughput processing of data is performed asynchronously, i.e. multiple samples can be submitted in parallel. After processing, results can be explored interactively. Thus, medical results from international research databases are combined to identify relevant mutations and diseases. Identified mutation sites of individual study participants are listed and sorted accordingly to their relevance for certain diseases. In addition, each mutation site can be investigated in a detailed way on various levels, e.g. nucleotide or amino acid base, using our Genome Browser. The comparison of genomic data from multiple patients or samples, is supported by our Cohort Analysis.

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Drug Response Analysis

Drug Response Analysis (Poster)


Medical doctors can chose from a variety of available medical drugs for specific types of cancer. However, the American Society of Cancer published in 2012 that three out of four applied chemotherapies do not work as expected. The challenge for medical experts in course of personalized medicine is to select the combination of medical drugs, which works best for an individual patient.

To predict the drug’s effect for a concrete tumor and patient, it is possible to extract the tumor and test various therapies in parallel in laboratories and document the outcome. Today, this is a time-consuming process requiring excessive wet-lab work and time-consuming manual data analysis.

With the help of our in-memory technology, we were able to improve the analysis process from weeks of manual data analysis to minutes of interactive data exploration. With a growing library of experiment results for a drugs applied to a certain type of cancer, we are now able to predict the drug response for new tumors minimizing the drugs to test. Furthermore, we enable researchers to discover correlations between genetic variants and drug response interactively. Thus, researchers are able to verify hypothesis in a couple of minutes for the first time and deriving new indicators to select a concrete drug and therapy combination per patient.

You can find a detailed overview of the process and our findings in the poster attached to this webpage and selected application screenshots in the gallery below.

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Medical Knowledge Cockpit App

The Medical Knowledge Cockpit shows how in-memory database technology combines international research data with patient specifics to find most relevant details for the treatment of individuals in course of personalized medicine in real-time. The video provides you with a brief walk through of an interactive use of the Medical Knowledge Cockpit.

Experience our Medical Knowledge Cockpit yourself and experience the advantages of in-memory technology interactively.

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Interactive Genome Browser

Interactive Genome Browser

The genome browser enables you to interactively explore arbitrary genome locations on base pair level. It supports the comparison of selected genomes with each others. For example, you can compare a certain genome with a reference, e.g. diseased vs. normal tissue, or you can compare any number of genomes with each other, e.g. during patient cohort analysis.

Your exploration tasks is supported by a number of assistant information. The latest content of international annotation databases is automatically checked for relevant annotations. For example, by clicking on a single nucleotide polymorphism location the in-memory database systems scans all for relevant data, such as associated diseases, translocation partners, and known tumor types. Since links to external databases are provided, you can navigate there easily to acquire additional details information about the source data.

Furthermore, you can navigate through the genome on different levels of detail using the genome browser. For example, you can switch between base-pair-, amino-acid-, and gene-level. Thus, you can zoom-in and -out of a certain genome in accordance to your requirements.

The windowing system helps you to work on multiple cases at the same time without loosing your information. Thus, you can keep results from multiple analyses and compare them by switching between windows.

Real-Time Cohort Analysis

Real-time Cohort AnalysisIf you consider a set of genomes, you are typically interested in comparison to identify similarities and differences between individual genomes. Consider a cohort of patients where you observed that a certain therapy works for 80% of them. The question arises, how does the genotype of the 20% differ from the 80% and what do they have in common.

We support various clustering algorithms, such as k-means or hierarchical clustering, to group individual genome data. If you want to verify your hypotheses, you can set the gene and locus coordinates to build the clusters. If you do not know, why these cohorts differ, you can start an automatic discovery. Location permutations are calculated on the fly and ranked by relevance.

Thus, the cohort analysis helps to discovery new coordinates to form clusters. It supports you to obtain new insights and to build new hypotheses. The results of the clustering are visualized as interactive diagrams.

Oncolyzer Mobile App

The Oncolyzer project is an interdisciplinary cooperation with Charité — Universitätsmedizin Berlin and SAP started in 2011. It combines individual competences in software engineering, IT systems, and medicine and defines a major key in our strategy to provide real-time data analysis cloud services for clinicians and researchers. The objective of the Oncolyzer project is to improve the treatment process of patients suffering from cancer diseases by leveraging optimized IT-aided software components for clinicians and researchers. As a result, doctors are enabled to select best available cancer therapies much faster by having all relevant information at hand.

The ‘heart’ of this innovation builds the in-memory computing platform, which supports the combined processing of structured and unstructured medical data in real-time. Thus, it is enables the integration of heterogeneous data sources within the clinical environment without the need for long-running and complex Extract Transform Load (ETL) processes to unify data.  The organizational changes in the healthcare sector require increasing support by proper IT-aided tools and processes. Data needs to be instantaneously available at any location a doctor requires the data — even worldwide. The immense increase of knowledge about cancer requires the detailed analysis of biological and genetic details acquired during diagnosis of cancer cells to address only harmful cells as the target of future treatments while keeping side effects at a minimum. Until recently, common therapies that were not individually targeted were applied during the treatment of cancer. Meanwhile, treatments for specific genetic mutations are available, which enable treatment of cancer based on individual genetic dispositions. Nowadays, data processing and analysis becomes a time-consuming challenge due to improved and more detailed diagnostic approaches, such as next-generation sequencing of tumor DNA.

In the near future, the tumor’s DNA of all cancer patients will be sequenced to support individualized patient-specific cancer therapies, which result in diagnostic medical data in amount of multiple terabytes. The analysis of these data required optimized software tools that enable graphical exploration of data, their real-time analysis, and the identification of therapy-relevant details to support clinical decision taking.

Features of the Hana Oncolyzer iPad Application

The Oncolyzer iPad application provides clinicians and researchers access to relevant patient data while in the secured network of the clinic’s campus. The in-memory technology builds the backend of the application performing relevant data processing and analyses. Selected  features of the Oncolyzer application are described in the following.

Combined Search in Structured and Unstructured Data

Oncolyzer: Search in structured and unstructured data medical data

Oncolyzer: Search in structured and unstructured data medical data

The Oncolyzer combines data from various data sources — structured and unstructured.

Structured data are, for example, biopsy results, size of tumor or tissue regions, blood concentration, etc. They are stored in a relational database format and can be accessed via defined attributes. Unstructured data are, for example, text documents, diagnosis, notes, etc. They are stored in text file and they neither consist of a predefined structure nor use standardized text paragraphs. As a result, unstructured data has the following drawback: typos, usage of pseudonyms, abbreviations, etc. However, the majority of clinical medical data is unstructured. Thus, it is important to analyze them in a systematic way and to extract relevant details in real-time. Further details about combined processing of structured and unstructured data can be found on the corresponding feature page.

Visualization of Patient Details for Personalized Medicine

Oncolyzer: Summary of patient's anamnesis

Oncolyzer: Summary of patient’s anamnesis

All patient related information need to be available for decision taking by medical doctors. Individual specifics of patients need to be analyzed and evaluated to for personalized medicine. The Oncolyzer combines current as well as historic data of a selected patient on a single screen and performs automatically analysis of the available data, e.g. to highlight patient specifics compared to patients with similar diagnosis or anamnesis. Thus, characteristics and important differences are highlighted, i.e. medical doctors can use these additional information as indicator assessment of the individual reason for the disease and impact of the selected treatment. All information are visualized on an interactive time line, which enables clinicians to move back and forward through the patient’s anamnesis while having access to all relevant data with a single click.

Analytical Exploration of Patient Cohorts

Oncolyzer: Real-time analysis of individual patient cohorts using freely definable criteria

Oncolyzer: Real-time analysis of individual patient cohorts using freely definable criteria

Analysis data of all patients or a patient cohort is a time-consuming and often manual task. The Oncolyzer app enables analysis of patient data on mobile devices, i.e. there is no longer a need for a desktop PC to perform the analysis. Furthermore, freely definable filters can be applied to explore patient details. Thus, the Oncolyzer app enables identification of individual patients, e.g. for participation in specific clinical studies to provide individualized treatment. The use of always up-to-date data bridges information gaps and supports fast analysis and evaluation of patient cohorts. Further details about analysis of patient cohorts can be found in the corresponding app description.

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