Here are a few things to consider about the patient's lower back pain over time:
- Acute vs chronic: Determine if the pain is a new onset (acute) or has been present long-term (chronic). The duration can provide clues.
- Progression: Note if the pain has gotten better, worse or stayed the same over time. Progression may indicate a more serious problem.
- Radiation: Document if the pain radiates anywhere (e.g. legs). Radiating pain can suggest nerve root involvement.
- Relieving/aggravating factors: Identify what makes the pain better or worse (e.g. activity, rest, position). This can help determine the
The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.
The Application of the Human Phenotype Ontology mhaendel
Presented at the II International Summer School for Rare Disease and Orphan Drug Registries, September 15-19, 2014, Organized by the National Centre for Rare Diseases
Istituto Superiore di Sanità (ISS), Rome, Italy.
Note the extensive contribution by many consortium members and partners listed in the acknowledgements slide.
Patient-led deep phenotyping using a lay-friendly version of the Human Phenot...mhaendel
Presented at AMIA TBI CRI 2018.
Rare disease patients are expert in their medical history and these patients not only are some of the most engaged, but also they can themselves provision data for use in clinical evaluation. We therefore created a lay-person version of our clinical deep phenotyping instrument, the Human Phenotype Ontology. Here, we evaluate the diagnostic utility of this lay-HPO, and debut a new software tool for patient-led deep phenotyping.
Deep phenotyping to aid identification of coding & non-coding rare disease v...mhaendel
Whole-exome sequencing has revolutionized disease research, but many cases remain unsolved because ~100-1000 candidates remain after removing common or non-pathogenic variants. We present Genomiser to prioritize coding and non-coding variants by leveraging phenotype data encoded with the Human Phenotype Ontology and a curated database of non-coding Mendelian variants. Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes.
Why the world needs phenopacketeers, and how to be onemhaendel
Keynote presented at the the Ninth International Biocuration Conference Geneva, Switzerland, April 10-14, 2016
The health of an individual organism results from complex interplay between its genes and environment. Although great strides have been made in standardizing the representation of genetic information for exchange, there are no comparable standards to represent phenotypes (e.g. patient disease features, variation across biodiversity) or environmental factors that may influence such phenotypic outcomes. Phenotypic features of individual organisms are currently described in diverse places and in diverse formats: publications, databases, health records, registries, clinical trials, museum collections, and even social media. In these contexts, biocuration has been pivotal to obtaining a computable representation, but is still deeply challenged by the lack of standardization, accessibility, persistence, and computability among these contexts. How can we help all phenotype data creators contribute to this biocuration effort when the data is so distributed across so many communities, sources, and scales? How can we track contributions and provide proper attribution? How can we leverage phenotypic data from the model organism or biodiversity communities to help diagnose disease or determine evolutionary relatedness? Biocurators unite in a new community effort to address these challenges.
The Human Phenotype Ontology (HPO) was developed to describe phenotypic abnormalities, aka, “deep phenotyping”, whereby symptoms and characteristic phenotypic findings (a phenotypic profile) are captured. The HPO has been utilized to great success for assisting computational phenotype comparison against known diseases, other patients, and model organisms to support diagnosis of rare disease patients. Clinicians and geneticists create phenotypic profiles based on clinical evaluation, but this is time consuming and can miss important phenotypic features. Patients are sometimes the best source of information about their symptoms that might otherwise be missed in a clinical encounter. However, HPO primarily use medical terminology, which can be difficult for patients and their families to understand. To make the HPO accessible to patients, we systematically added non-expert terminology (i.e., layperson terms) synonyms. Using semantic similarity, patient-recorded phenotypic profiles can be evaluated against those created clinically for undiagnosed patients to determine the improvement gained from the patient-driven phenotyping, as well as how much the patient phenotyping narrows the diagnosis. This patient-centric HPO can be utilized by all: in patient-centered rare disease websites, in patient community platforms and registries, or even to post one’s hard-to-diagnosed phenotypic profile on the Web.
The Application of the Human Phenotype Ontology mhaendel
Presented at the II International Summer School for Rare Disease and Orphan Drug Registries, September 15-19, 2014, Organized by the National Centre for Rare Diseases
Istituto Superiore di Sanità (ISS), Rome, Italy.
Note the extensive contribution by many consortium members and partners listed in the acknowledgements slide.
Patient-led deep phenotyping using a lay-friendly version of the Human Phenot...mhaendel
Presented at AMIA TBI CRI 2018.
Rare disease patients are expert in their medical history and these patients not only are some of the most engaged, but also they can themselves provision data for use in clinical evaluation. We therefore created a lay-person version of our clinical deep phenotyping instrument, the Human Phenotype Ontology. Here, we evaluate the diagnostic utility of this lay-HPO, and debut a new software tool for patient-led deep phenotyping.
Deep phenotyping to aid identification of coding & non-coding rare disease v...mhaendel
Whole-exome sequencing has revolutionized disease research, but many cases remain unsolved because ~100-1000 candidates remain after removing common or non-pathogenic variants. We present Genomiser to prioritize coding and non-coding variants by leveraging phenotype data encoded with the Human Phenotype Ontology and a curated database of non-coding Mendelian variants. Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes.
Why the world needs phenopacketeers, and how to be onemhaendel
Keynote presented at the the Ninth International Biocuration Conference Geneva, Switzerland, April 10-14, 2016
The health of an individual organism results from complex interplay between its genes and environment. Although great strides have been made in standardizing the representation of genetic information for exchange, there are no comparable standards to represent phenotypes (e.g. patient disease features, variation across biodiversity) or environmental factors that may influence such phenotypic outcomes. Phenotypic features of individual organisms are currently described in diverse places and in diverse formats: publications, databases, health records, registries, clinical trials, museum collections, and even social media. In these contexts, biocuration has been pivotal to obtaining a computable representation, but is still deeply challenged by the lack of standardization, accessibility, persistence, and computability among these contexts. How can we help all phenotype data creators contribute to this biocuration effort when the data is so distributed across so many communities, sources, and scales? How can we track contributions and provide proper attribution? How can we leverage phenotypic data from the model organism or biodiversity communities to help diagnose disease or determine evolutionary relatedness? Biocurators unite in a new community effort to address these challenges.
Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanis...mhaendel
Presented at the IRDiRC 2017 conference in Paris, Feb 9th, 2017 (http://irdirc-conference.org/). This talk reviews use of the Human Phenotype Ontology for phenotype comparisons against other patients, known diseases, and animal models for diagnostic discovery. It also discusses the new Phenopackets Exchange mechanism for open phenotypic data sharing.
www.monarchinitiative.org
www.phenopackets.org
www.human-phenotype-ontology.org
Enhancing the Human Phenotype Ontology for Use by the LaypersonNicole Vasilevsky
Presentation at the International Conference on Biological Ontology & BioCreative, August 1-4, 2016, Corvallis, Oregon, USA.
Abstract
In rare or undiagnosed diseases, physicians rely upon genotype and phenotype information in order to compare abnormalities to other known cases and to inform diagnoses. Patients are often the best sources of information about their symptoms and phenotypes. The Human Phenotype Ontology (HPO) contains over 12,000 terms describing abnormal human phenotypes. However, the labels and synonyms in the HPO primarily use medical terminology, which can be difficult for patients and their families to understand. In order to make the HPO more accessible to non-medical experts, we systematically added new synonyms using non-expert terminology (i.e., layperson terms) to the existing HPO classes or tagged existing synonyms as layperson. As a result, the HPO contains over 6,000 classes with layperson synonyms.
Data Translator: an Open Science Data Platform for Mechanistic Disease Discoverymhaendel
Architecture of language and data translation that underlays the NCATS Biomedical Data Translator. Presented at the Fanconi Anemia Annual Meeting. http://fanconi.org/index.php/research/annual_symposium
Empowering patients by increasing accessibility to clinical terminologyNicole Vasilevsky
Flash talk at Medical Library Association Pacific Northwest Chapter meeting in Portland, OR on October 18, 2016.
http://pnc-mla.cloverpad.org/annual2016
Authors: Erin Foster, Mark Engelstad, Chris Mungall, Peter Robinson, Sebastian Kohler, Melissa Haendel and Nicole Vasilevsky
Enhancing Rare Disease Literature for Researchers and PatientsErin D. Foster
Objectives: In rare disease research, structured phenotype information is crucial to document in order to draw connections between other known cases and work towards diagnosis and treatment of disease. The Human Phenotype Ontology (HPO) is a standardized vocabularly that describes phenotypic abnormalities encountered in human diseases. To enable the increased identification of traits (i.e., phenotypes) associated with rare diseases, the HPO was expanded to include layperson synonyms to make the ontology more accessible and useful to patients. Additionally, the HPO was used to annotate phenotypes in a sample of rare disease case reports to provide structured annotations of rare disease phenotypes.
Methods: The HPO was systematically reviewed and 'layperson synonyms' were added to include terms used by patients and non-medical professionals. Subsequent work annotated phenotypic descriptions in a sample of rare disease case reports with HPO terms. The literature sample was identified by filtering 'case reports' in PubMed and excluding articles that were already included in the Online Mendelian Inheritance of Man (OMIM) database. The sample set was further restricted to articles from the European Journal of Human Genetics for the pilot set, which resulted in a final sample size of 143 articles. The papers were reviewed and annotated for the following information: disease name, associated gene(s), and corresponding phenotypes.
Results: The review of the HPO resulted in approximately half of the terms including layperson synonyms. A subset of the literature sample was annotated to determine the best curation workflow. Of that subset of papers (n=20), 353 total phenotypes were identified. 12% of these phenotypes were not included in the HPO and required new term and/or synonym requests. Some challenges encountered in this work included maintaining consistency in HPO term definitions and use, as well as annotation reliability.
Conclusion: This work contributes to knowledge of rare diseases by curating the existing literature to provide structured annotation of rare disease traits, which helps with information retrieval and data interoperability and reuse. Additionally, the expansion of the HPO to include layperson synonyms enables patients to 'self-phenotype' and contribute to the identification of rare disease traits. Following the completed annotation of the literature sample, future work will focus on incorporating the annotations into databases that collect rare disease phenotypic information. Further work is also being done to add additional layperson synonyms to the HPO through review of patient forums and medical message boards to continue to identify terminology used by actual patients.
Poster presentation at the Rare Disease Symposium at Oregon Health & Science University in Portland, Oregon, 2015.
http://openwetware.org/wiki/OHSU_Rare_Disease_Research_Consortium_Symposium_2015
Dr. Katherine Sims opens the Norrie Disease Association's first international conference with a warm welcome and historical overview of Norrie Disease. (NDA International Conference, 2009)
Global Phenotypic Data Sharing Standards to Maximize Diagnostics and Mechanis...mhaendel
Presented at the IRDiRC 2017 conference in Paris, Feb 9th, 2017 (http://irdirc-conference.org/). This talk reviews use of the Human Phenotype Ontology for phenotype comparisons against other patients, known diseases, and animal models for diagnostic discovery. It also discusses the new Phenopackets Exchange mechanism for open phenotypic data sharing.
www.monarchinitiative.org
www.phenopackets.org
www.human-phenotype-ontology.org
Enhancing the Human Phenotype Ontology for Use by the LaypersonNicole Vasilevsky
Presentation at the International Conference on Biological Ontology & BioCreative, August 1-4, 2016, Corvallis, Oregon, USA.
Abstract
In rare or undiagnosed diseases, physicians rely upon genotype and phenotype information in order to compare abnormalities to other known cases and to inform diagnoses. Patients are often the best sources of information about their symptoms and phenotypes. The Human Phenotype Ontology (HPO) contains over 12,000 terms describing abnormal human phenotypes. However, the labels and synonyms in the HPO primarily use medical terminology, which can be difficult for patients and their families to understand. In order to make the HPO more accessible to non-medical experts, we systematically added new synonyms using non-expert terminology (i.e., layperson terms) to the existing HPO classes or tagged existing synonyms as layperson. As a result, the HPO contains over 6,000 classes with layperson synonyms.
Data Translator: an Open Science Data Platform for Mechanistic Disease Discoverymhaendel
Architecture of language and data translation that underlays the NCATS Biomedical Data Translator. Presented at the Fanconi Anemia Annual Meeting. http://fanconi.org/index.php/research/annual_symposium
Empowering patients by increasing accessibility to clinical terminologyNicole Vasilevsky
Flash talk at Medical Library Association Pacific Northwest Chapter meeting in Portland, OR on October 18, 2016.
http://pnc-mla.cloverpad.org/annual2016
Authors: Erin Foster, Mark Engelstad, Chris Mungall, Peter Robinson, Sebastian Kohler, Melissa Haendel and Nicole Vasilevsky
Enhancing Rare Disease Literature for Researchers and PatientsErin D. Foster
Objectives: In rare disease research, structured phenotype information is crucial to document in order to draw connections between other known cases and work towards diagnosis and treatment of disease. The Human Phenotype Ontology (HPO) is a standardized vocabularly that describes phenotypic abnormalities encountered in human diseases. To enable the increased identification of traits (i.e., phenotypes) associated with rare diseases, the HPO was expanded to include layperson synonyms to make the ontology more accessible and useful to patients. Additionally, the HPO was used to annotate phenotypes in a sample of rare disease case reports to provide structured annotations of rare disease phenotypes.
Methods: The HPO was systematically reviewed and 'layperson synonyms' were added to include terms used by patients and non-medical professionals. Subsequent work annotated phenotypic descriptions in a sample of rare disease case reports with HPO terms. The literature sample was identified by filtering 'case reports' in PubMed and excluding articles that were already included in the Online Mendelian Inheritance of Man (OMIM) database. The sample set was further restricted to articles from the European Journal of Human Genetics for the pilot set, which resulted in a final sample size of 143 articles. The papers were reviewed and annotated for the following information: disease name, associated gene(s), and corresponding phenotypes.
Results: The review of the HPO resulted in approximately half of the terms including layperson synonyms. A subset of the literature sample was annotated to determine the best curation workflow. Of that subset of papers (n=20), 353 total phenotypes were identified. 12% of these phenotypes were not included in the HPO and required new term and/or synonym requests. Some challenges encountered in this work included maintaining consistency in HPO term definitions and use, as well as annotation reliability.
Conclusion: This work contributes to knowledge of rare diseases by curating the existing literature to provide structured annotation of rare disease traits, which helps with information retrieval and data interoperability and reuse. Additionally, the expansion of the HPO to include layperson synonyms enables patients to 'self-phenotype' and contribute to the identification of rare disease traits. Following the completed annotation of the literature sample, future work will focus on incorporating the annotations into databases that collect rare disease phenotypic information. Further work is also being done to add additional layperson synonyms to the HPO through review of patient forums and medical message boards to continue to identify terminology used by actual patients.
Poster presentation at the Rare Disease Symposium at Oregon Health & Science University in Portland, Oregon, 2015.
http://openwetware.org/wiki/OHSU_Rare_Disease_Research_Consortium_Symposium_2015
Dr. Katherine Sims opens the Norrie Disease Association's first international conference with a warm welcome and historical overview of Norrie Disease. (NDA International Conference, 2009)
Unified Medical Language System & MetaMapOsama Jomaa
UMLS is a metathesaurus that facilitates the development of computer systems that behave as if they "understand"
the meaning of the language of biomedicine
and health. It comprises a controlled vocabulary, semantic network and specialist lexicon and lexical tools. MetaMap is a tool for recognizing UMLS concepts in text
This is used for brief talk about AI and its recent application in Machine Learning and Deep Learning field.
I'd like to ask your understanding about any missing references.
I appreciate you would comment about it.
I will immediately update the slides.
Supporting Genomics in the Practice of Medicine by Heidi RehmKnome_Inc
View the webinar at http://www.knome.com/webinar-supporting-genomics-practice-medicine. In this presentation, Dr. Heidi Rehm, Chief Laboratory Director of the Laboratory for Molecular Medicine at Partners Healthcare and one of the Principal Investigators on ClinGen, elucidates the challenges of genomics in medicine and outlined the path to integrating large scale sequencing into clinical practice.
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
Nipple discharge is an important problem in young females. As FOGSI has embraced Breast and Breast Committee has been formed, it is imperative that obstetrician and gynecologist have basic knowledge of nipple discharge. This is the most simplistic representation of discharges and how they can be differentiated.
Similar to Semantic phenotyping for disease diagnosis and discovery (20)
The Software and Data Licensing Solution: Not Your Dad’s UBMTA mhaendel
Presented at the Association of University Technology Managers (AUTM) Annual Conference 2018
Moderator: Arvin Paranjpe, Oregon Health & Science University
Speakers: Frank Curci, Ater Wynne LLP
Melissa Haendel, Oregon Health & Science University
Charles Williams, University of Oregon
Big data is an open frontier, and it’s quickly expanding. However, transaction costs and legal barriers stand squarely in the way of meaningful, far-reaching data integration. We’ll grapple with the issues regarding a large-scale data integration project across humans, model and non-model organisms. Without pointing fingers, we’ll also share a few highlights from the (Re)usable Data Project, which outlined a five-part rubric to evaluate data licenses with respect to clarity and the reuse and redistribution of data. In addition, the topic raises the question: How well-suited are off-the-shelf software and data licenses for universities? Data scientists and software programmers are all too quick to pick one when they release their technology on GitHub. What should technology transfer professionals
recommend? We’ll discuss the usefulness and attributes of a uniform software and data license for university researchers and software programmers.
Equivalence is in the (ID) of the beholdermhaendel
Presented at PIDapalooza 2018. https://pidapalooza.org/
Determining identifier equivalency is key to data integration and to realizing the scientific discoveries that can only be made by collating our vast disconnected data stores.
There are two key problems in determining equivalency - conceptual and syntactic alignment. Conceptual alignment often relies on Xrefs and string-matching against synonyms. There is indeed a better way! Algorithmic determination of identifier equivalency across different sources can use a combination of Xrefs, priors rules, existing semantic relations, and synonyms to create equivalency cliques than can highlight the discrepancies in conceptual definitions for manual review. This is especially useful for data sources annotated with concept drift and differences, such as diseases. Syntactic issues are that there are so many variations of the same identifier, making data joins difficult. We present a framework to reconcile and provide authoritative and integration-ready prefixed identifiers (CURIES), to capture and consolidate prefixes and to build links across key resource registries. The combination of JSON-LD context technology with a prefix metadata repository provides the basis for the infrastructure to handle identifiers in a consistent fashion. Finally, this architecture also allows resources to be self describing "beacons" with respect to their identifiers.
Building (and traveling) the data-brick road: A report from the front lines ...mhaendel
The NIH Data Commons must treat the data it will contain not unlike the mortar and stones of a road. To help our fellow scientists travelers use the road, we must engineer for heavy traffic and diverse destinations. There are many steps to architecting a robust and persistent road. First, the data must be sourced and manipulated into common data models. This requires versioned access to the data, equivalency determination of identifiers within the data or minting of new ones for the data and/or within it, manipulating the data according to common data models (e.g. a genotype-to-pehnotype association in one source may relate a variant to a disease, where in another it may be a set of alleles associated with a set of phenotypes, each source models the data differently). Inclusion of the data in the Commons must meet all licensing restrictions, which are varied and usually poorly declared, as well as security, HIPAA, and ethics requirements. Software tools are needed to perform the Enhance-Transform-Load (ETL) process on a regular cycle to keep the data current, and to assess changes and quality assurance over time. For records that disappear, there needs to be a way to keep an archive of them. Once in the Commons, the data requires a map to navigate the roads: where do you want to go? Indexing and search across the data requires having the data be self-reporting - loading ontologies used in the data for indexing and providing faceted query over these and other attributes, sophisticated text mining tools, relevance ranking, and equivalency and similarity determination from amongst different providers. Once found, the users need vehicles to drive upon the road. These are their workspaces, the place where they design and implement the operations they need in order to get where they want to go. Unimaginable scientific emeralds are to be found at the end of the road, as the sum of all the data, if well integrated and made computationally reusable, has proven to be well beyond the sum of its parts in getting us where we want to go.
Reusable data for biomedicine: A data licensing odysseymhaendel
Biomedical data integrators grapple with a fundamental blocker in research today: licensing for data use and redistribution. Complex licensing and data reuse restrictions hinder most publicly-funded, seemingly “open” biomedical data from being put to its full potential. Such issues include missing licenses, non-standard licenses, and restrictive provisions. The sheer diversity of licenses are particularly thorny for those that aim to redistribute data. Redistributors are often required to contact each sub-source to obtain permissions, and this is complicated by the fact that on each side of the agreement there may be multiple legal entities involved and some sub-sources may themselves already be aggregating data from other sub-sources. Furthermore, interpreting legal compliance with source data licensing and use agreements is complicated, as data is often manipulated, shared, and redistributed by many types of research groups and users in various and subtle ways. Here, we debut a new effort, the (Re)usable Data Project, where we have created a five-part rubric to evaluate biomedical data sources and their licensing information to determine the degree to which unnegotiated and unrestricted reuse and redistribution are provided. We have tested the (Re)usable Data rubric against various biomedical data sources, ranking each source on a scale of zero to five stars, and have found that approximately half of the resources rank poorly, getting 2.5 stars or less. Our goal is to help biomedical informaticians and other users navigate the plethora of issues in reusing and redistributing biomedical data. The (Re)usable Data project aims to promote standardization and ease of reuse licensing practices by data providers.
How open is open? An evaluation rubric for public knowledgebasesmhaendel
Presented at the 2017 International Biocuration Conference.
Data relevant to any given scientific investigation is highly decentralized across thousands of specialized databases. Within the Biocuration community, we recognize that the value of open scientific knowledge bases is that they make scientific knowledge easier to find and compute, thereby maximizing impact and minimizing waste. The ever-increasing number of databases makes us necessarily question what are our priorities with respect to maintaining them, developing new ones, or senescing/subsuming ones that have completed in their mission. Therefore, open biomedical data repositories should be carefully evaluated according to quality, accessibility, and value of the database resources over time and across the translational divide.
Traditional citation count and publication impact factors as a measure of success or value are known to be inadequate to assess the usefulness of a resource. This is especially true for integrative resources. For example, almost everyone in biomedicine relies on PubMed, but almost no one ever cites or mentions it in their publications. While the Nucleic Acids Research Database issues have increased citation of some databases, many still go unpublished or uncited; even novel derivations of methodology, applications, and workflows from biomedical knowledge bases are often “adapted” but never cited. There is a lack of citation best practices for widely used biomedical database resources (e.g. should a paper be cited? A URL? Is mention of the name and access date sufficient?).
We have developed a draft evaluation rubric for evaluating open science databases according to the commonly cited FAIR principles -- Findable, Accessible, Interoperable, and Reusable, but with three additional principles: Traceable, Licensed, and Connected. These additions are largely overlooked and underappreciated, yet are critical to reuse of the knowledge contained within any given database. It is worth noting that FAIR principles apply not only to the resource as a whole, but also to their key components; this “fractal FAIRness” means that even the license, identifiers, vocabularies, APIs themselves must be Findable, Accessible, Interoperable, Reusable, etc. Here we report on initial testing of our evaluation rubric on the recent NIH/Wellcome Trust Open Science projects and seek community input for how to further advance this rubric as a Biocuration community resource.
Credit where credit is due: acknowledging all types of contributionsmhaendel
This is an update for COASP (http://oaspa.org/conference/) on the representation of attribution beyond authorship of a publication. Publications are proxies for the projects and people that area actually engaged in the work, and represent the dissemination aspect. How can we better understand the individual contributions and their impact? The openRIF, openVIVO and FORCE11 Attribution WG efforts aim to represent scholarship in a computationally tractable manner so as to enable credit and evaluation of all types of scholarly contributions.
On the frontier of genotype-2-phenotype data integrationmhaendel
Presented at AMIA TBI 2016 BD2K Panel. A description of the Monarch Initiative's efforts to perform deep phenotyping data integration across species, facilitate exchange, and build computable G2P evidence modesl to aid variant interpretation.
Envisioning a world where everyone helps solve diseasemhaendel
Keynote presented at the Semantic Web for Life Sciences conference in Cambridge, UK, December 9th, 2015
http://www.swat4ls.org/
The talk focuses on the use of ontologies for data integration to support rare disease diagnostics, and how so very many people unbeknownst to the patient or even to the researchers creating the data are involved in a diagnosis.
The Monarch Initiative: From Model Organism to Precision Medicinemhaendel
NIH BD2K all-hands meeting poster November 12, 2015.
Attempts at correlating phenotypic aspects of disease with causal genetic influences are often confounded by the challenges of interpreting diverse data distributed across numerous resources. New approaches to data modeling, integration, tooling, and community practices are needed to make efficient use of these data. The Monarch Initiative is an international consortium working on the development of shared data, tools, and standards to enable direct translation of integrated genotype, phenotype, and environmental data from human and model organisms to enhance our understanding of human disease. We utilize sophisticated semantic mapping techniques across a diverse set of standardized ontologies to deeply integrate data across species, sources, and modalities. Using phenotype similarity matching algorithms across these data enables disorder prediction, variant prioritization, and patient matching against known diseases and model organisms. These similarity algorithms form the core of several innovative tools. The Exomiser, which enables exome variant prioritization by combining pathogenicity, frequency, inheritance, protein interaction, and cross-species phenotype data. Our Phenotype Sufficiency tool provides clinicians the ability to compare patient phenotypic profiles using the Human Phenotype Ontology to determine uniqueness and specificity in support of variant prioritization. The PhenoGrid visualization widget illustrates phenotype similarity between patients, known diseases, and model organisms. Monarch develops models in collaboration with the community in support of the burgeoning genotype-phenotype disease research community. We have successfully used Exomiser to solve a number of undiagnosed patient cases in collaboration with the NIH Undiagnosed Disease Program. Ongoing development in coordination with the Global Alliance for Genetic Health (GA4GH) and other groups will catalyze the realization of our goal of a vital translational community focused on the collaborative application of integrated genotype, phenotype, and environmental data to human disease.
Force11: Enabling transparency and efficiency in the research landscapemhaendel
Presented at the Feb 2015, NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
http://www.niso.org/news/events/2015/virtual_conferences/sci_data_management/
Dataset description using the W3C HCLS standardmhaendel
This talk was presented at the BioCaddie http://biocaddie.org/ workshop at the Force15 conference (https://www.force11.org/meetings/force2015) on changing the future of scholarly communication. The goal was to increase awareness of why a Semantic Web-compliant standard was needed for describing data, where current standards fall short, and how this new emerging standard that extends prior efforts can aid data discovery and integration. This work is being lead by Michel Dumontier, Alasdair Gray, Joachim Baran, and M. Scott Marshall; participants and end-user testers are welcome, see: http://tiny.cc/hcls-datadesc-ed
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
2. TODAY’S TALK
The computable phenotypic profile
Exome analysis for disease diagnosis
Crossing the species divide
What is GOOD phenotyping?
Chronological considerations
6. 6% OF THE GENERAL POPULATION SUFFERS FROM
A RARE DISORDER
6% of patients contacting the NIH Office of
Rare Disorders do not have a diagnosis
7. THE YET-TO-BE DIAGNOSED PATIENT
Known disorders not recognized during
prior evaluations?
Atypical presentation of known
disorders?
Combinations of several disorders?
Novel, unreported disorder?
8. THE CHALLENGE: INTERPRETATION OF
DISEASE CANDIDATES
?
What’s in the box?
How are
candidates
identified?
How do they
compare?
Prioritized
Candidates,
functional validation
C1
C2
C3
C4
...
Phenotypes
P1
P2
P3
…
Genotype
G1
G2
G3
G4
…
Pathogenicity, frequency,
protein interactions, gene
expression, gene
networks, epigenomics,
metabolomics….
Environments
E1, E2, E3, E4 …
9. MATCHING PATIENTS TO DISEASES
Patient
Disease X
Differential diagnosis with similar but non-matching phenotypes is difficult
Flat back of head Hypotonia
Abnormal skull morphology Decreased muscle mass
10. SEARCHING FOR PHENOTYPES USING
TEXT ALONE IS INSUFFICIENT
OMIM Query # Records
“large bone” 785
“enlarged bone” 156
“big bone” 16
“huge bones” 4
“massive bones” 28
“hyperplastic bones” 12
“hyperplastic bone” 40
“bone hyperplasia” 134
“increased bone growth” 612
13. DISEASE X IS A COLLECTION OF NODES
Each disease is associated with different phenotype nodes in the graph
Disease X
14. EACH DISEASE IS ANNOTATED WITH A
PHENOTYPIC PROFILE
Chromosome 21 Trisomy
Failure
to thrive
Umbilical
hernia
Broad
hands
Abnormal
ears
Flat
head
Down’s
Syndrome
15. PHENOTYPE “BLAST”: WHICH PHENTOYPIC
PROFILE IS GRAPHICALLY MOST SIMILAR?
Disease X
Patient
Disease Y
17. THE HUMAN PHENOTYPE ONTOLOGY
Used to annotate:
• Patients
• Disorders/Diseases
• Genotypes
• Genes
• Sequence variants
In human
Reduced pancreatic
beta cells
Abnormality of
pancreatic islet
cells
Abnormality of endocrine
pancreas physiology
Pancreatic islet
cell adenoma
Pancreatic islet cell
adenoma
Insulinoma
Multiple pancreatic
beta-cell adenomas
Abnormality of exocrine
pancreas physiology
Köhler et al. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.
18. WHY DO WE NEED THE HUMAN
PHENOTYPE ONTOLOGY?
Winnenburg and Bodenreider, ISMB PhenoDay, 2014
How does HPO relate to other clinical vocabularies?
19. EXOME ANALYSIS
Recessive, de novo filters
Remove off-target, common variants,
and variants not in known disease
causing genes
http://compbio.charite.de/PhenIX/
Target panel of 2741 known
Mendelian disease genes
Compare
phenotype
profiles using
data from:
HGMD, Clinvar,
OMIM, Orphanet
Zemojtel et al. Sci Transl Med 3 September 2014: Vol. 6,
Issue 252, p.252ra123
20. CONTROL PATIENTS WITH KNOWN
MUTATIONS
Inheritance Gene Average
Rank
AD ACVR1, ATL1, BRCA1, BRCA2, CHD7 (4),
CLCN7, COL1A1, COL2A1, EXT1, FGFR2 (2),
FGFR3, GDF5, KCNQ1, MLH1 (2), MLL2/KMT2D,
MSH2, MSH6, MYBPC3, NF1 (6), P63, PTCH1,
PTH1R (2), PTPN11 (2), SCN1A, SOS1, TRPS1,
TSC1, WNT10A
1.7
AR ATM, ATP6V0A2, CLCN1 (2), LRP5, PYCR1,
SLC39A4
5
X EFNB1, MECP2 (2), DMD, PHF6 1.8
52 patients with diagnosed rare diseases
21. PHENIX HELPED DIAGNOSE 11/40 PATIENTS
global developmental delay (HP:0001263)
delayed speech and language development (HP:0000750)
motor delay (HP:0001270)
proportionate short stature (HP:0003508)
microcephaly (HP:0000252)
feeding difficulties (HP:0011968)
congenital megaloureter (HP:0008676)
cone-shaped epiphysis of the phalanges of the hand (HP:0010230)
sacral dimple (HP:0000960)
hyperpigmentated/hypopigmentated macules (HP:0007441)
hypertelorism (HP:0000316)
abnormality of the midface (HP:0000309)
flat nose (HP:0000457)
thick lower lip vermilion (HP:0000179)
thick upper lip vermilion (HP:0000215)
full cheeks (HP:0000293)
short neck (HP:0000470)
22. WHAT ABOUT THE PATIENTS WE CAN’T
SOLVE?
HOW DO WE UNDERSTAND RARE
DISEASE ETIOLOGY AND DISCOVER
TREATMENTS?
30. lung
lung
lobular organ
parenchymatous
organ
solid organ
pleural sac
thoracic
cavity organ
thoracic
cavity
abnormal lung
morphology
abnormal respiratory
system morphology
Mammalian Phenotype
Mouse Anatomy
FMA
abnormal pulmonary
acinus morphology
abnormal pulmonary
alveolus morphology
lung
alveolus
organ system
respiratory
system
Lower
respiratory
tract
alveolar sac
pulmonary
acinus
organ system
respiratory
system
Human development
lung
lung bud
respiratory
primordium
pharyngeal region
PROBLEM: EACH ORGANISM USES
DIFFERENT VOCABULARIES
develops_from
part_of
is_a (SubClassOf)
surrounded_by
31. SOLUTION: BRIDGING SEMANTICS
Mungall et al. (2012). Genome Biology, 13(1), R5. doi:10.1186/gb-2012-13-1-r5
anatomical
structure
endoderm of
forgut
lung bud
lung
respiration organ
organ
foregut
alveolus
alveolus of lung
organ part
FMA:lung
MA:lung
endoderm
GO: respiratory
gaseous exchange
MA:lung
alveolus
FMA:
pulmonary
alveolus
is_a (taxon equivalent)
develops_from
part_of
is_a (SubClassOf)
capable_of
NCBITaxon: Mammalia
EHDAA:
lung bud
only_in_taxon
pulmonary acinus
alveolar sac
lung primordium
swim bladder
respiratory
primordium
NCBITaxon:
Actinopterygii
Köhler et al. (2014) F1000Research 2:30
Haendel et al. (2014) JBMS 5:21 doi:10.1186/2041-1480-5-21
32. => Web application for model phenotyping and G2P validation
PROBLEM: EACH SPECIES MAKES DIFFERENT
G2P ASSOCIATIONS
33. INTEGRATED GENTOYPE-2-
PHENOTYPE DATA IN MONARCH
Also in the system: Rat; IMPC; GO annotations; Coriell cell lines; OMIA; MPD; Yeast; CTD; GWAS;
Panther, Homologene orthologs; BioGrid interactions; Drugbank; AutDB; Allen Brain …157 sources
Coming soon: Animal QTLs for pig, cattle, chicken, sheep, trout, dog, horse
Species Data
source
Genes Genotypes Variants Phenotype
annotations
Diseases
mouse MGI 13,433 59,087 34,895 271,621
fish ZFIN 7,612 25,588 17,244 81,406
fly Flybase 27,951 91,096 108,348 267,900
worm Wormbase 23,379 15,796 10,944 543,874
human HPOA 112,602 7,401
human OMIM 2,970 4,437 3,651
human ClinVar 19,694 111,294 252,838 4,056
human KEGG 2,509 3,927 1,159
human ORPHANET 3,113 5,690 3,064
human CTD 7,414 23,320 4,912
34. EXOMISER: DIAGNOSING UDP_930 USING
A PHENOTYPICALLY SIMILAR MOUSE
Chronic acidosis
Neonatal
hypoglycemia
Ostopenia
Short stature
decreased
circulating
potassium level
Decreased
circulating
glucose level
Decreased bone
mineral density
decreased body
length
abnormal ion
homeostasis
Decreased
circulating
glucose level
Decreased
bone mineral
density
Short stature
UDP_930/29
phenotypes
Sms
tm1a(EUCOMM)Wtsi
Robinson et al. (2013). Genome Res, doi:10.1101/gr.160325.113
35. EXOMISER: COMBINING PHENOTYPIC
SIMILARITY WITH OTHER DATA
MED21
MAU2
MED8
MED26
Recurrent otitis
media
Spasticity
Esotropia
Cerebral palsy
Conductive
hearing
impairment
Limitation of joint
mobility
Strabismus
Hypertonia
Abnormality of
the middle ear
Abnormal joint
mobility
Strabismus
Abnormality of
central motor
function
UDP_2146/56
phenotypes
Brachmann-de
Lange syndrome
NIPBL
MED23
?
CCNC
Contractures of
the joints of the
lower limbs
Hypertonicity
CDK8
36. UDP CASES ANALYZED WITH
EXOMISER
=> Use of genotype, phenotype, PPI, and inheritance
together provide best prioritization
37. ANALYSIS OF UNSOLVED UDP CASES
4 families now have a diagnosis including, one novel
disease-gene association discovered: York Platelet
syndrome and STIM1
Strong candidates identified for 19 families that are
now undergoing functional validation through mouse
and zebrafish modeling
Several hundred UDP cases now being analyzed
using Exomiser and cross-species phenotype data
38. HOW DOES THE CLINICIAN KNOW THEY’VE
PROVIDED ENOUGH PHENOTYPING?
How many annotations…?
How many different categories?
How many within each?
39. Image credit: Viljoen and Beighton, J Med Genet. 1992
Schwartz-jampel Syndrome, Type I
Schwartz-jampel Syndrome,
Type I
Caused by Hspg2 mutation, a
proteoglycan
~100 phenotype annotations
40. EVALUATION METHOD
Create a variety of “derived” diseases
More general (depth)
Remove subset(s) (breadth)
Introduce noise
Assess the change in similarity between the derived
disease and it’s parent.
Ask questions:
Is the derived disease considered similar to
original?
…or more similar to a different disease?
Is it distinguishable beyond random?
Are there any specific factors that influence
similarity?
41. FINDING THE PHENOTYPE GRAPH IN
COMMON
The most specific phenotypic profile in common
42. METHOD: DERIVE BY CATEGORY
REMOVAL
Remove annotations that are subclasses of a
single high-level node
Repeat for each 1° subclass
46. SEMANTIC SIMILARITY ALGORITHMS ARE ROBUST
IN THE FACE OF MISSING INFORMATION
(avg) 92% of derived diseases are most-similar to
original disease
Severity of impact follows proportion of
phenotype
Similarity of Derived Disease to Original Derived Disease Profile Rank
47. METHOD: DERIVE BY LIFTING
Iteratively map each class to their direct
superclass(es)
Keep only leaf nodes
48. SEMANTIC SIMILARITY ALGORITHMS ARE
SENSITIVE TO SPECIFICITY OF INFORMATION
Severity of impact increases with more-general
phenotypes
Similarity of Derived Disease to Original Derived Disease Profile Rank
59. CONCLUSIONS
Phenotypic data can be represented using
ontologies to support improved comparisons
within and across species
For known disease-gene associations comparison
to human phenotype data is effective at variant
prioritization.
For unknown disease-gene associations the
expansion of phenotypic coverage using model
organisms greatly improves variant prioritization.
Phenotype breadth is recommended to buffer lack
of information, ALSO very specific phenotypes are
necessary to ensure quality matches
60. FUTURE WORK
Add additional variables to semantic similarity
algorithm – e.g. negation, environment, chronology
Validate existing animal models for recapitulation
of disease
Further characterization of organism-specific
phenotypes
Adding many more non-model organisms to the
analysis
61. ACKNOWLEDGMENTS
NIH-UDP
William Bone
Murat Sincan
David Adams
Amanda Links
Joie Davis
Neal Boerkoel
Cyndi Tifft
Bill Gahl
OHSU
Nicole Vasilesky
Matt Brush
Bryan Laraway
Shahim Essaid
Kent Shefchek
Garvan
Tudor Groza
Lawrence Berkeley
Nicole Washington
Suzanna Lewis
Chris Mungall
UCSD
Jeff Grethe
Chris Condit
Anita Bandrowski
Maryann Martone
U of Pitt
Chuck Boromeo
Vincent Agresti
Becky Boes
Harry Hochheiser
Sanger
Anika Oehlrich
Jules Jacobson
Damian Smedley
Toronto
Marta Girdea
Sergiu Dumitriu
Heather Trang
Bailey Gallinger
Orion Buske
Mike Brudno
JAX
Cynthia Smith
Charité
Sebastian Kohler
Sandra Doelken
Sebastian Bauer
Peter Robinson
Funding:
NIH Office of Director: 1R24OD011883
NIH-UDP: HHSN268201300036C, HHSN268201400093P